[ { "path": ".dockerignore", "content": "# Python\n__pycache__/\n*.py[cod]\n*$py.class\n*.so\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\n*.egg-info/\n.installed.cfg\n*.egg\n\n# Virtual environments\n.venv/\nvenv/\nENV/\nenv/\n\n# Testing\n.pytest_cache/\n.coverage\nhtmlcov/\n.tox/\n\n# IDEs\n.vscode/\n.idea/\n*.swp\n*.swo\n*~\n\n# Git\n.git/\n.gitignore\n\n# Docker development files (not part of the SDK package)\nDockerfile\ndocker-compose.yml\n.dockerignore\nMakefile\n\n# OS\n.DS_Store\nThumbs.db\n\n# Documentation\ndocs/_build/\n\n# Local dev files\n*.log\n.env\n.env.*\n!.env.example\n" }, { "path": ".github/ISSUE_TEMPLATE/bug_report.yml", "content": "name: Bug Report\ndescription: Create a report to help us improve Memori\ntitle: \"[Bug] \"\nlabels: [\"bug\"]\nbody:\n - type: markdown\n attributes:\n value: |\n Thanks for taking the time to fill out this bug report! Please provide as much detail as possible.\n\n - type: checkboxes\n id: checks\n attributes:\n label: First Check\n description: Please ensure you have done the following before submitting your issue.\n options:\n - label: I added a very descriptive title to this issue.\n required: true\n - label: I searched existing issues and documentation.\n required: true\n\n - type: input\n id: version\n attributes:\n label: Memori Version\n description: What version of the sdk are you using?\n placeholder: e.g., 3.0.0\n validations:\n required: true\n\n - type: input\n id: environment\n attributes:\n label: OS / Python Version\n description: e.g., Windows 10 / Python 3.10, or Ubuntu 22.04 / Python 3.11\n placeholder: macOS / Python 3.12\n validations:\n required: true\n\n - type: dropdown\n id: llm_provider\n attributes:\n label: LLM Provider\n description: Which LLM provider are you using?\n options:\n - OpenAI\n - Anthropic\n - Gemini\n - Bedrock\n - Grok (xAI)\n - Ollama / Local\n - Other\n validations:\n required: true\n\n - type: input\n id: llm_details\n attributes:\n label: LLM Model & Version\n description: Please specify the model name/version.\n placeholder: e.g., gpt-4o or claude-3-5-sonnet\n validations:\n required: true\n\n - type: dropdown\n id: database\n attributes:\n label: Database\n description: Which database integration are you using?\n options:\n - Postgres\n - MySQL\n - MongoDB\n - Oracle\n - SQLite\n - Neon\n - Supabase\n - Other\n validations:\n required: true\n\n - type: textarea\n id: description\n attributes:\n label: Description\n description: Please describe the bug clearly. What happened? What did you expect to happen?\n validations:\n required: true\n\n - type: textarea\n id: reproduction\n attributes:\n label: Minimal Reproducible Example\n description: Please provide a code snippet that reproduces the issue.\n render: python\n placeholder: |\n from memori import Memori\n # Your code here...\n\n - type: textarea\n id: logs\n attributes:\n label: Log Output / Stack Trace\n description: Please copy and paste any relevant log output or error messages here.\n render: shell\n\n - type: checkboxes\n id: terms\n attributes:\n label: Participation\n options:\n - label: I am willing to submit a pull request for this issue.\n" }, { "path": ".github/ISSUE_TEMPLATE/config.yml", "content": "blank_issues_enabled: false\n" }, { "path": ".github/ISSUE_TEMPLATE/feature_request.yml", "content": "name: Feature Request\ndescription: Suggest an idea or improvement for Memori\ntitle: \"[Feature] \"\nlabels: [\"enhancement\"]\nbody:\n - type: markdown\n attributes:\n value: |\n Thanks for your interest in improving Memori! Please describe your idea in detail.\n\n - type: textarea\n id: problem\n attributes:\n label: Is your feature request related to a problem?\n description: A clear and concise description of what the problem is.\n placeholder: Ex. I'm always frustrated when I try to integrate with...\n validations:\n required: true\n\n - type: textarea\n id: solution\n attributes:\n label: The Solution\n description: Describe the solution you'd like.\n placeholder: |\n I would like a new method `memori.recall_batch()` that accepts...\n validations:\n required: true\n\n - type: textarea\n id: alternatives\n attributes:\n label: Alternatives Considered\n description: Describe any alternative solutions or features you've considered.\n placeholder: I thought about using a loop, but it's too slow for my use case...\n\n - type: dropdown\n id: context\n attributes:\n label: Affected Components\n description: Which parts of Memori would this feature touch?\n multiple: true\n options:\n - LLM Provider / Adapter\n - Vector Store / Memory\n - Database Schema\n - CLI\n - Documentation\n - Other\n\n - type: checkboxes\n id: help\n attributes:\n label: Participation\n description: Would you be willing to help implement this?\n options:\n - label: I am willing to submit a pull request for this feature.\n" }, { "path": ".github/workflows/ci.yml", "content": "name: CI\n\non:\n push:\n branches: [main]\n paths:\n - 'memori/**' \n pull_request:\n branches: [main]\n paths:\n - 'memori/**' \n\njobs:\n lint:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v5\n\n - name: Install uv\n uses: astral-sh/setup-uv@v7\n\n - name: Set up Python\n run: uv python install 3.12\n\n - name: Install dependencies\n run: uv sync --dev\n\n - name: Run ruff linting\n run: uv run ruff check .\n\n - name: Run ruff format check\n run: uv run ruff format --check .\n\n security:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v5\n\n - name: Install uv\n uses: astral-sh/setup-uv@v7\n\n - name: Set up Python\n run: uv python install 3.12\n\n - name: Install dependencies\n run: uv sync --dev\n\n - name: Run Bandit security checks\n run: uv run bandit -r memori -ll -ii\n\n - name: Run pip-audit for dependency vulnerabilities\n run: uv run pip-audit --require-hashes --disable-pip\n continue-on-error: true\n\n type-check:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v5\n\n - name: Install uv\n uses: astral-sh/setup-uv@v7\n\n - name: Set up Python\n run: uv python install 3.12\n\n - name: Install dependencies\n run: uv sync --dev\n\n - name: Run type checking with ty\n run: uvx ty check --exclude 'tests/llm/clients/**/*.py' --exclude 'tests/integration/**/*.py' --exclude 'benchmarks/**/*.py' --exclude 'examples/**/*.py'\n\n test:\n runs-on: ubuntu-latest\n strategy:\n matrix:\n python-version: [\"3.10\", \"3.11\", \"3.12\", \"3.13\", \"3.14\"]\n\n steps:\n - uses: actions/checkout@v5\n\n - name: Install uv\n uses: astral-sh/setup-uv@v7\n\n - name: Set up Python ${{ matrix.python-version }}\n run: uv python install ${{ matrix.python-version }}\n\n - name: Install dependencies\n run: uv sync --dev\n\n - name: Run pytest with coverage\n run: uv run pytest --ignore=tests/benchmarks\n\n - name: Upload coverage to Codecov\n if: matrix.python-version == '3.12'\n uses: codecov/codecov-action@v5\n with:\n files: ./coverage.xml\n fail_ci_if_error: false\n" }, { "path": ".github/workflows/integration.yml", "content": "name: Integration Tests\n\non:\n workflow_dispatch:\n workflow_call:\n secrets:\n OPENAI_API_KEY:\n required: true\n ANTHROPIC_API_KEY:\n required: false\n GOOGLE_API_KEY:\n required: false\n XAI_API_KEY:\n required: false\n AWS_ACCESS_KEY_ID:\n required: false\n AWS_SECRET_ACCESS_KEY:\n required: false\n MEMORI_API_KEY:\n required: false\n\npermissions:\n contents: read\n\njobs:\n integration-tests:\n runs-on: ubuntu-latest\n timeout-minutes: 30\n\n services:\n postgres:\n image: postgres:15\n env:\n POSTGRES_USER: memori\n POSTGRES_PASSWORD: memori\n POSTGRES_DB: memori_test\n ports:\n - 5432:5432\n options: >-\n --health-cmd pg_isready\n --health-interval 10s\n --health-timeout 5s\n --health-retries 5\n\n mysql:\n image: mysql:8\n env:\n MYSQL_ROOT_PASSWORD: memori\n MYSQL_DATABASE: memori_test\n MYSQL_USER: memori\n MYSQL_PASSWORD: memori\n ports:\n - 3306:3306\n options: >-\n --health-cmd \"mysqladmin ping\"\n --health-interval 10s\n --health-timeout 5s\n --health-retries 5\n\n mongodb:\n image: mongo:6\n env:\n MONGO_INITDB_ROOT_USERNAME: memori\n MONGO_INITDB_ROOT_PASSWORD: memori\n ports:\n - 27017:27017\n\n env:\n MEMORI_TEST_MODE: \"1\"\n OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}\n ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}\n GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}\n XAI_API_KEY: ${{ secrets.XAI_API_KEY }}\n AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}\n AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}\n AWS_REGION: us-east-1\n MEMORI_API_KEY: ${{ secrets.MEMORI_API_KEY }}\n SQLITE_DATABASE_URL: sqlite:///test_memori.db\n POSTGRES_DATABASE_URL: postgresql://memori:memori@localhost:5432/memori_test\n MYSQL_DATABASE_URL: mysql+pymysql://memori:memori@localhost:3306/memori_test\n MONGODB_URL: mongodb://memori:memori@localhost:27017/memori_test?authSource=admin\n\n steps:\n - uses: actions/checkout@v5\n\n - name: Install uv\n uses: astral-sh/setup-uv@v7\n with:\n enable-cache: true\n\n - name: Set up Python\n run: uv python install 3.12\n\n - name: Install dependencies\n run: uv sync --dev\n\n - name: Run AA Payload Unit Tests (no API keys needed)\n run: |\n echo \"Running AA payload unit tests...\"\n uv run pytest tests/memory/augmentation/test_aa_payload_unit.py -v --tb=short\n\n - name: Run AA Integration Tests (staging API)\n if: env.OPENAI_API_KEY\n run: |\n echo \"Running AA integration tests against staging...\"\n uv run pytest tests/integration/test_aa_payload.py -v -m integration --tb=short\n\n - name: Run OpenAI Integration Tests\n if: env.OPENAI_API_KEY\n run: |\n echo \"Running OpenAI integration tests...\"\n uv run pytest tests/integration/providers/test_openai.py -v -m integration --tb=short\n\n - name: Run Anthropic Integration Tests\n if: env.ANTHROPIC_API_KEY\n run: |\n echo \"Running Anthropic integration tests...\"\n uv run pytest tests/integration/providers/test_anthropic.py -v -m integration --tb=short\n\n - name: Run Google Integration Tests\n if: env.GOOGLE_API_KEY\n run: |\n echo \"Running Google integration tests...\"\n uv run pytest tests/integration/providers/test_google.py -v -m integration --tb=short\n\n - name: Run xAI Integration Tests\n if: env.XAI_API_KEY\n run: |\n echo \"Running xAI integration tests...\"\n uv run pytest tests/integration/providers/test_xai.py -v -m integration --tb=short\n\n - name: Run Bedrock Integration Tests\n if: env.AWS_ACCESS_KEY_ID && env.AWS_SECRET_ACCESS_KEY\n run: |\n echo \"Running Bedrock integration tests...\"\n uv run pytest tests/integration/providers/test_bedrock.py -v -m integration --tb=short\n\n - name: Run cloud OpenAI Integration Tests\n if: env.OPENAI_API_KEY && env.MEMORI_API_KEY\n run: |\n echo \"Running cloud OpenAI integration tests...\"\n uv run pytest tests/integration/cloud/test_cloud_openai.py -v -m integration --tb=short\n\n - name: Run cloud Anthropic Integration Tests\n if: env.ANTHROPIC_API_KEY && env.MEMORI_API_KEY\n run: |\n echo \"Running cloud Anthropic integration tests...\"\n uv run pytest tests/integration/cloud/test_cloud_anthropic.py -v -m integration --tb=short\n\n - name: Run cloud Google Integration Tests\n if: env.GOOGLE_API_KEY && env.MEMORI_API_KEY\n run: |\n echo \"Running cloud Google integration tests...\"\n uv run pytest tests/integration/cloud/test_cloud_gemini.py -v -m integration --tb=short\n\n - name: Run cloud xAI Integration Tests\n if: env.XAI_API_KEY && env.MEMORI_API_KEY\n run: |\n echo \"Running cloud xAI integration tests...\"\n uv run pytest tests/integration/cloud/test_cloud_xai.py -v -m integration --tb=short\n\n - name: Run cloud Bedrock Integration Tests\n if: env.AWS_ACCESS_KEY_ID && env.AWS_SECRET_ACCESS_KEY && env.MEMORI_API_KEY\n run: |\n echo \"Running cloud Bedrock integration tests...\"\n uv run pytest tests/integration/cloud/test_cloud_bedrock.py -v -m integration --tb=short\n\n - name: Run Database Integration Tests\n if: env.OPENAI_API_KEY\n run: |\n echo \"Running database integration tests...\"\n uv run pytest tests/integration/databases/ -v -m integration --tb=short\n\n - name: Integration Test Summary\n if: always()\n run: |\n echo \"==========================================\"\n echo \"Integration Test Summary\"\n echo \"==========================================\"\n echo \"MEMORI_TEST_MODE: $MEMORI_TEST_MODE (staging)\"\n echo \"\"\n echo \"Tests run:\"\n echo \" - AA Payload Unit Tests: Yes (no API keys needed)\"\n if [ \"$OPENAI_API_KEY\" ]; then echo \" - AA Integration Tests: Yes\"; else echo \" - AA Integration Tests: Skipped (no key)\"; fi\n if [ \"$OPENAI_API_KEY\" ]; then echo \" - Database Integration Tests: Yes\"; else echo \" - Database Integration Tests: Skipped (no key)\"; fi\n echo \"\"\n echo \"Databases tested:\"\n echo \" - SQLite: Yes\"\n echo \" - PostgreSQL: Yes\"\n echo \" - MySQL: Yes\"\n echo \" - MongoDB: Yes\"\n echo \"\"\n echo \"Providers tested:\"\n if [ \"$OPENAI_API_KEY\" ]; then echo \" - OpenAI: Yes\"; else echo \" - OpenAI: Skipped (no key)\"; fi\n if [ \"$ANTHROPIC_API_KEY\" ]; then echo \" - Anthropic: Yes\"; else echo \" - Anthropic: Skipped (no key)\"; fi\n if [ \"$GOOGLE_API_KEY\" ]; then echo \" - Google: Yes\"; else echo \" - Google: Skipped (no key)\"; fi\n if [ \"$XAI_API_KEY\" ]; then echo \" - xAI: Yes\"; else echo \" - xAI: Skipped (no key)\"; fi\n if [ \"$AWS_ACCESS_KEY_ID\" ]; then echo \" - Bedrock: Yes\"; else echo \" - Bedrock: Skipped (no key)\"; fi\n echo \"\"\n echo \"Cloud (requires MEMORI_API_KEY) tests:\"\n if [ \"$MEMORI_API_KEY\" ] && [ \"$OPENAI_API_KEY\" ]; then echo \" - Cloud OpenAI: Yes\"; else echo \" - Cloud OpenAI: Skipped (no key)\"; fi\n if [ \"$MEMORI_API_KEY\" ] && [ \"$ANTHROPIC_API_KEY\" ]; then echo \" - Cloud Anthropic: Yes\"; else echo \" - Cloud Anthropic: Skipped (no key)\"; fi\n if [ \"$MEMORI_API_KEY\" ] && [ \"$GOOGLE_API_KEY\" ]; then echo \" - Cloud Google: Yes\"; else echo \" - Cloud Google: Skipped (no key)\"; fi\n if [ \"$MEMORI_API_KEY\" ] && [ \"$XAI_API_KEY\" ]; then echo \" - Cloud xAI: Yes\"; else echo \" - Cloud xAI: Skipped (no key)\"; fi\n if [ \"$MEMORI_API_KEY\" ] && [ \"$AWS_ACCESS_KEY_ID\" ]; then echo \" - Cloud Bedrock: Yes\"; else echo \" - Cloud Bedrock: Skipped (no key)\"; fi\n echo \"==========================================\"\n" }, { "path": ".github/workflows/publish-npm.yml", "content": "name: Publish to npm (TS)\n\non:\n workflow_dispatch: \n\npermissions:\n contents: read\n id-token: write\n\njobs:\n publish:\n runs-on: ubuntu-latest \n \n defaults:\n run:\n working-directory: ./memori-ts\n\n steps:\n - name: Checkout repository\n uses: actions/checkout@v5\n\n - name: Setup Node.js\n uses: actions/setup-node@v6\n with:\n node-version: \"24\"\n registry-url: \"https://registry.npmjs.org\"\n\n - name: Install dependencies\n run: npm ci\n\n - name: Generate version.ts\n run: |\n VERSION=$(node -p \"require('./package.json').version\")\n echo \"export const SDK_VERSION = '${VERSION}';\" > src/version.ts\n\n - name: Build project\n run: npm run build --if-present\n \n - name: Run tests\n run: npm test --if-present\n\n - name: Publish Package\n run: npm publish --access public --tag latest" }, { "path": ".github/workflows/publish-openclaw-plugin.yml", "content": "name: Publish OpenClaw to NPM\n\non:\n workflow_dispatch: \n\npermissions:\n contents: read\n id-token: write\n\njobs:\n publish:\n runs-on: ubuntu-latest \n \n defaults:\n run:\n working-directory: ./integrations/openclaw\n\n steps:\n - name: Checkout repository\n uses: actions/checkout@v5\n\n - name: Setup Node.js\n uses: actions/setup-node@v6\n with:\n node-version: \"24\"\n registry-url: \"https://registry.npmjs.org\"\n\n - name: Install dependencies\n run: npm ci\n\n - name: Generate version.ts\n run: |\n VERSION=$(node -p \"require('./package.json').version\")\n echo \"export const SDK_VERSION = '${VERSION}';\" > src/version.ts\n\n - name: Build project\n run: npm run build --if-present\n\n - name: Publish Package\n run: npm publish --access public --tag latest" }, { "path": ".github/workflows/publish.yml", "content": "name: Publish to PyPI\n\non:\n release:\n types: [published]\n workflow_dispatch:\n\npermissions:\n contents: read\n id-token: write\n\nconcurrency:\n group: publish-pypi-${{ github.ref }}\n cancel-in-progress: false\n\njobs:\n # Run integration tests before publishing\n integration-tests:\n uses: ./.github/workflows/integration.yml\n secrets:\n OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}\n ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}\n GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}\n XAI_API_KEY: ${{ secrets.XAI_API_KEY }}\n AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}\n AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}\n\n deploy:\n needs: integration-tests\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v5\n\n - name: Set up Python\n uses: actions/setup-python@v5\n with:\n python-version: '3.x'\n cache: 'pip'\n\n - name: Install build tools\n run: pip install --upgrade pip build twine toml\n\n - name: Build memori package\n run: python -m build --outdir dist/memori\n\n - name: Verify memori distribution\n run: twine check dist/memori/*\n\n - name: Publish memori to PyPI\n uses: pypa/gh-action-pypi-publish@release/v1\n with:\n packages-dir: dist/memori/\n user: __token__\n password: ${{ secrets.PYPI_API_TOKEN }}\n\n - name: Update package name to memorisdk\n run: |\n python -c \"\n import toml\n with open('pyproject.toml', 'r') as f:\n config = toml.load(f)\n config['project']['name'] = 'memorisdk'\n with open('pyproject.toml', 'w') as f:\n toml.dump(config, f)\n \"\n\n - name: Build memorisdk package\n run: python -m build --outdir dist/memorisdk\n\n - name: Verify memorisdk distribution\n run: twine check dist/memorisdk/*\n\n - name: Publish memorisdk to PyPI\n uses: pypa/gh-action-pypi-publish@release/v1\n with:\n packages-dir: dist/memorisdk/\n user: __token__\n password: ${{ secrets.PYPI_SDK_API_TOKEN }}\n" }, { "path": ".github/workflows/ts-ci.yml", "content": "name: CI Code Check (TS)\n\non:\n push:\n branches: [ \"main\" ]\n paths:\n - 'memori-ts/**' \n pull_request:\n branches: [ \"main\" ]\n paths:\n - 'memori-ts/**'\n\njobs:\n build-and-test:\n runs-on: ubuntu-latest\n\n strategy:\n matrix:\n node-version: [18, 20, 22, 24]\n \n defaults:\n run:\n working-directory: ./memori-ts \n\n steps:\n - name: Checkout repository\n uses: actions/checkout@v5\n\n - name: Setup Node.js ${{ matrix.node-version }}\n uses: actions/setup-node@v6\n with:\n node-version: ${{ matrix.node-version }}\n\n - name: Install dependencies\n run: npm ci\n\n - name: Generate version.ts\n run: |\n VERSION=$(node -p \"require('./package.json').version\")\n echo \"export const SDK_VERSION = '${VERSION}';\" > src/version.ts\n\n - name: Run Linter\n run: npm run lint\n\n - name: Run Tests\n run: npm test\n\n - name: Verify Build\n run: npm run build" }, { "path": ".github/workflows/ts-openclaw-ci.yml", "content": "name: CI Code Check (TS)\n\non:\n push:\n branches: [ \"main\" ]\n paths:\n - 'integrations/openclaw/**' \n pull_request:\n branches: [ \"main\" ]\n paths:\n - 'integrations/openclaw/**'\n\njobs:\n build-and-test:\n runs-on: ubuntu-latest\n\n strategy:\n matrix:\n node-version: [22, 24]\n \n defaults:\n run:\n working-directory: ./integrations/openclaw\n\n steps:\n - name: Checkout repository\n uses: actions/checkout@v5\n\n - name: Setup Node.js ${{ matrix.node-version }}\n uses: actions/setup-node@v6\n with:\n node-version: ${{ matrix.node-version }}\n\n - name: Install dependencies\n run: npm ci\n\n - name: Generate version.ts\n run: |\n VERSION=$(node -p \"require('./package.json').version\")\n echo \"export const SDK_VERSION = '${VERSION}';\" > src/version.ts\n\n - name: Run Linter\n run: npm run lint\n\n - name: Verify Build\n run: npm run build" }, { "path": ".gitignore", "content": "# Python\n*.pyc\n*.pyo\n*.pyd\n__pycache__\n*.so\n*.egg\n*.egg-info\ndist/\n/build/\n*.swp\n\n# Testing\n.pytest_cache\n.coverage\ncoverage.xml\nhtmlcov/\n.tox\n\n# IDEs\n.DS_Store\n.idea\n.vscode\n*.swo\n\n# Virtual environments\nvenv/\nenv/\n.venv/\n\n# Environment variables\n.env\n\n# SQLite databases\n*.db\n*.db-journal\n\n# UV\nuv.lock\n\n# Python packaging\n*.egg-info/\ndist/\n/build/\n\n# Sensitive files (do not commit)\ngoogle-credentials.json\n*.pem\n*.key\n\nAGENTS.md\n\ntests/examples/*\n\n# Benchmarking results\ntests/benchmarks/results/\nresults/\n*.json\n*.csv\n!pyproject.toml\n!package.json\n!composer.json\n\n# Node / TypeScript\nnode_modules/\ndist/\n*.tsbuildinfo\ncoverage\n!package-lock.json\n!tsconfig.json\n!tsconfig.build.json\n!.prettierrc.json\n!openclaw.plugin.json" }, { "path": ".pre-commit-config.yaml", "content": "repos:\n- repo: https://github.com/pre-commit/pre-commit-hooks\n rev: v4.5.0\n hooks:\n - id: check-yaml\n - id: end-of-file-fixer\n - id: trailing-whitespace\n - id: debug-statements\n\n- repo: https://github.com/astral-sh/ruff-pre-commit\n rev: v0.15.1\n hooks:\n - id: ruff-check\n args: [--fix]\n - id: ruff-format\n\n- repo: local\n hooks:\n # - id: ty\n # name: ty type checker\n # entry: uvx ty check\n # language: system\n # types: [python]\n # pass_filenames: false\n # always_run: true\n\n - id: pytest\n name: pytest\n entry: uv run pytest --ignore=tests/benchmarks --ignore=tests/integration\n language: system\n pass_filenames: false\n always_run: true\n\n - id: ts-format\n name: format (memori-ts)\n entry: bash -c 'cd memori-ts && npm run format'\n language: system\n pass_filenames: false\n files: ^memori-ts/\n\n - id: ts-lint\n name: lint (memori-ts)\n entry: bash -c 'cd memori-ts && npm run lint:fix'\n language: system\n pass_filenames: false\n files: ^memori-ts/\n\n - id: ts-typecheck\n name: typecheck (memori-ts)\n entry: bash -c 'cd memori-ts && npm run typecheck'\n language: system\n pass_filenames: false\n files: ^memori-ts/\n\n - id: ts-test\n name: test (memori-ts)\n entry: bash -c 'cd memori-ts && npm run test'\n language: system\n pass_filenames: false\n files: ^memori-ts/" }, { "path": "CHANGELOG.md", "content": "# Changelog\n\nAll notable changes to the Memori Python SDK will be documented in this file.\n\nThe format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),\nand this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).\n\n## [Unreleased]\n\n### Fixed\n\n- Fixed multi-turn conversation ingestion for AzureOpenAI and OpenAI clients. Previously, only the first conversation turn was being recorded. Now `conversation_id` is resolved early in the request lifecycle, ensuring all conversation turns are properly ingested into the same conversation. (Fixes #83)\n\n[3.0.0]: https://github.com/MemoriLabs/Memori/releases/tag/v3.0.0\n" }, { "path": "CONTRIBUTING.md", "content": "[![Memori Labs](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/banner.png)](https://memorilabs.ai/)\n\n# Contributing to Memori Python SDK\n\nThank you for your interest in contributing to Memori!\n\n## Development Setup\n\nWe use `uv` for fast dependency management and Docker for integration testing. You can develop locally or use our Docker environment.\n\n### Prerequisites\n\n- Python 3.10+ (3.12 recommended)\n- [uv](https://github.com/astral-sh/uv) - Fast Python package installer\n- Docker and Docker Compose (for integration tests)\n- Make\n\n### Quick Start (Local Development)\n\n```bash\n# Install uv if you haven't already\ncurl -LsSf https://astral.sh/uv/install.sh | sh\n\n# Clone the repository\ngit clone https://github.com/MemoriLabs/Memori.git\ncd Memori\n\n# Install dependencies\nuv sync\n\n# Install pre-commit hooks\nuv run pre-commit install\n\n# Run unit tests\nuv run pytest\n```\n\n### Quick Start (Docker)\n\n```bash\n# Copy the example environment file\ncp .env.example .env\n\n# Edit .env and add your API keys (optional for unit tests)\n# Required for integration tests: OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY\n\n# Start the environment\nmake dev-up\n```\n\nThis will:\n- Build the Docker container with Python 3.12\n- Install all dependencies with uv\n- Start PostgreSQL, MySQL, and MongoDB for integration tests\n- Start Mongo Express (web UI for MongoDB at http://localhost:8081)\n\n### Development Commands\n\n#### Local Development\n```bash\n# Run unit tests\nuv run pytest\n\n# Format code\nuv run ruff format .\n\n# Check linting\nuv run ruff check .\n\n# Run with coverage\nuv run pytest --cov=memori\n\n# Run security scans\nuv run bandit -r memori -ll -ii\nuv run pip-audit --require-hashes --disable-pip || true\n```\n\n#### Docker Development\n```bash\n# Enter the development container\nmake dev-shell\n\n# Run unit tests (fast, no external dependencies)\nmake test\n\n# Initialize database schemas\nmake init-postgres # PostgreSQL\nmake init-mysql # MySQL\nmake init-oceanbase # OceanBase\nmake init-mongodb # MongoDB\nmake init-sqlite # SQLite\n\n# Run a specific integration test script\nmake run-integration FILE=tests/llm/clients/oss/openai/async.py\n\n# Format code\nmake format\n\n# Check linting\nmake lint\n\n# Run security scans\nmake security\n\n# Stop the environment\nmake dev-down\n\n# Clean up everything (containers, volumes, cache)\nmake clean\n```\n\n## Testing\n\nWe use `pytest` with coverage reporting and `pytest-mock` for mocking.\n\n### Unit Tests\nUnit tests use mocks and run without external dependencies:\n```bash\n# Local\nuv run pytest\n\n# Docker\nmake test\n```\n\n### Integration Tests\nIntegration tests require:\n- Database instances (PostgreSQL, MySQL, MongoDB, or SQLite)\n- LLM API keys (OpenAI, Anthropic, Google)\n\n```bash\n# Set API keys in .env first\n# OPENAI_API_KEY=sk-...\n# ANTHROPIC_API_KEY=sk-ant-...\n# GOOGLE_API_KEY=...\n\n# Initialize database schema\nmake init-postgres # or init-mysql, init-oceanbase, init-mongodb, init-sqlite\n\n# Run integration test scripts\nmake run-integration FILE=tests/llm/clients/oss/openai/sync.py\n```\n\n### Test Coverage\n\nWe maintain high test coverage. Coverage reports are generated automatically:\n- Terminal output (summary)\n- HTML report in `htmlcov/`\n- XML report in `coverage.xml`\n\nView HTML coverage:\n```bash\nopen htmlcov/index.html # macOS\nxdg-open htmlcov/index.html # Linux\n```\n\n## Project Structure\n\n```\nmemori/ # SDK source code\n llm/ # LLM provider integrations (OpenAI, Anthropic, Google, etc.)\n memory/ # Memory system and augmentation\n storage/ # Storage adapters (PostgreSQL, MySQL, MongoDB, SQLite, etc.)\n api/ # API client for Memori Advanced Augmentation\n __init__.py # Main Memori class and public API\n py.typed # PEP 561 type hint marker\ntests/ # Test files\n build/ # Database initialization scripts\n llm/ # LLM provider tests (unit & integration)\n memory/ # Memory system tests\n storage/ # Storage adapter tests\nconftest.py # Pytest fixtures\npyproject.toml # Project metadata and dependencies\nuv.lock # Locked dependency versions\nCHANGELOG.md # Version history\n```\n\n## Code Quality\n\nWe use [Ruff](https://docs.astral.sh/ruff/) for linting and formatting (configured in `pyproject.toml`):\n\n```bash\n# Format code\nuv run ruff format . # or: make format\n\n# Check linting\nuv run ruff check . # or: make lint\n\n# Auto-fix issues\nuv run ruff check --fix .\n\n# Run security scans (Bandit + pip-audit)\nuv run bandit -r memori -ll -ii\nuv run pip-audit --require-hashes --disable-pip || true\n```\n\n### Pre-commit Hooks\n\nWe use pre-commit to automatically format and lint code:\n\n```bash\n# Install hooks (one-time setup)\nuv run pre-commit install\n\n# Run manually\nuv run pre-commit run --all-files\n```\n\n### Code Standards\n\n- Follow PEP 8 standards\n- Line length: 88 characters (Black-compatible)\n- Python 3.10+ syntax (use modern type hints)\n- All public APIs must have type hints\n- Lean, simple code preferred over complex solutions (KISS, YAGNI)\n- Minimize unnecessary comments - code should be self-documenting\n\n## Pull Request Guidelines\n\n1. **Fork and branch**: Create a feature branch from `main`\n2. **Write tests**: Add/update tests for your changes\n3. **Pass all checks**: Ensure tests, linting, and formatting pass\n4. **Update docs**: Update README or docs if adding features\n5. **Changelog**: Add entry to CHANGELOG.md under \"Unreleased\"\n6. **Atomic commits**: Keep commits focused and well-described\n\n## Supported Integrations\n\n### LLM Providers\n- OpenAI (sync/async, streaming)\n- Anthropic Claude (sync/async, streaming)\n- Google Gemini (sync/async, streaming)\n- AWS Bedrock\n\n### Frameworks\n- Agno\n- LangChain\n\n### Database Adapters\n- PostgreSQL (via psycopg2, psycopg3)\n- MySQL / MariaDB (via pymysql)\n- MongoDB (via pymongo)\n- Oracle (via cx_Oracle, python-oracledb)\n- SQLite (stdlib)\n- CockroachDB\n- Neon, Supabase (PostgreSQL-compatible)\n- Django ORM\n- DB-API 2.0 compatible connections\n\n## CLI Commands\n\nMemori provides CLI commands for managing your account and quota:\n\n```bash\n# Check your API quota\npython3 -m memori quota\n\n# Sign up for Memori Advanced Augmentation\npython3 -m memori sign-up \n```\n\nThese commands help you:\n- Monitor your memory quota and usage\n- Sign up for increased limits (always free for developers)\n- Obtain API keys for Advanced Augmentation features\n\n## Development Notes\n\n- Docker files (Dockerfile, docker-compose.yml, Makefile) are for development only\n- They are NOT included in the PyPI package\n- The SDK has minimal runtime dependencies - fully self-contained\n- Development dependencies (LLM clients, database drivers) are in `[dependency-groups]`\n" }, { "path": "Dockerfile", "content": "# Use Python 3.12 as base image\nFROM python:3.12-slim\n\n# Set working directory\nWORKDIR /app\n\n# Install system dependencies\nRUN apt-get update && apt-get install -y \\\n git \\\n curl \\\n build-essential \\\n && rm -rf /var/lib/apt/lists/*\n\n# Install uv for faster dependency management\nRUN pip install uv\n\n# Copy dependency files\nCOPY pyproject.toml uv.lock* ./\n\n# Install dependencies (including dev dependencies)\nRUN uv sync --all-extras\n\n# Copy the rest of the application\nCOPY . .\n\n# Install pre-commit hooks\nRUN pip install pre-commit && pre-commit install || true\n\n# Add venv to PATH so all tools are available\nENV PATH=\"/app/.venv/bin:$PATH\"\n\n# Default command opens a bash shell\nCMD [\"/bin/bash\"]\n" }, { "path": "LICENSE", "content": "Apache License\nVersion 2.0, January 2004\nhttp://www.apache.org/licenses/\n\nTERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n1. 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However, in\naccepting such obligations, You may act only on Your own behalf and on\nYour sole responsibility, not on behalf of any other Contributor, and\nonly if You agree to indemnify, defend, and hold each Contributor\nharmless for any liability incurred by, or claims asserted against,\nsuch Contributor by reason of your accepting any such warranty or support.\n\nEND OF TERMS AND CONDITIONS\n\nAPPENDIX: How to apply the Apache License to your work.\n\nTo apply the Apache License to your work, attach the following\nboilerplate notice, with the fields enclosed by brackets \"[]\"\nreplaced with your own identifying information. (Don't include\nthe brackets!) The text should be enclosed in the appropriate\ncomment syntax for the file format. We also recommend that a\nfile or class name and description of purpose be included on the\nsame \"printed page\" as the copyright notice for easier\nidentification within third-party archives.\n\nCopyright [2025] [Memori Team]\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n" }, { "path": "MANIFEST.in", "content": "# Include essential files\ninclude README.md\ninclude LICENSE\n\n# Exclude all development and Docker files\nexclude Dockerfile\nexclude docker-compose.yml\nexclude .dockerignore\nexclude Makefile\nexclude .env.example\nexclude conftest.py\nexclude CONTRIBUTING.md\nexclude .pre-commit-config.yaml\nexclude uv.lock\nexclude google-credentials.json\nexclude .coverage\nexclude coverage.xml\nexclude CHANGELOG.md\nexclude insert_facts.py\nexclude memori_test.db\n\n# Exclude test directories\nprune tests\n\n# Exclude documentation build artifacts\nprune docs\nprune htmlcov\n\n# Exclude TypeScript SDK (not shipped to PyPI)\nprune memori-ts\nprune integrations\n\n# Exclude benchmarks (not shipped with the SDK)\nprune benchmarks\n\n# Exclude version control and CI\nprune .git\nprune .github\nexclude .gitignore\n\n# Exclude IDE and editor files\nprune .vscode\nprune .venv\nprune .pytest_cache\nprune .ruff_cache\nglobal-exclude .DS_Store\nglobal-exclude *.pyc\nglobal-exclude *.pyo\nglobal-exclude __pycache__\n" }, { "path": "Makefile", "content": ".PHONY: help dev-up dev-down dev-shell dev-build dev-clean test lint format clean run-unit run-integration run-integration-provider run-integration-cloud\n\nhelp: ## Show this help message\n\t@echo 'Usage: make [target]'\n\t@echo ''\n\t@echo 'Available targets:'\n\t@awk 'BEGIN {FS = \":.*?## \"} /^[a-zA-Z_-]+:.*?## / {printf \" %-20s %s\\n\", $$1, $$2}' $(MAKEFILE_LIST)\n\ndev-up: ## Start development environment (builds and runs containers)\n\tdocker compose up -d --build\n\t@echo \"\"\n\t@echo \"✓ Development environment is ready!\"\n\t@echo \" Run 'make dev-shell' to enter the development container\"\n\t@echo \" Run 'make test' to run tests\"\n\ndev-down: ## Stop development environment\n\tdocker compose down\n\ndev-shell: ## Open a shell in the development container\n\tdocker compose exec dev /bin/bash\n\ninit-db: ## Initialize database schema for integration tests\n\tdocker compose exec -e PYTHONPATH=/app dev python tests/database/init_db.py\n\ninit-postgres: ## Initialize PostgreSQL schema\n\tdocker compose exec -e PYTHONPATH=/app dev python tests/build/postgresql.py\n\ninit-mysql: ## Initialize MySQL schema\n\tdocker compose exec -e PYTHONPATH=/app dev python tests/build/mysql.py\n\ninit-oceanbase: ## Initialize OceanBase schema\n\tdocker compose exec -e PYTHONPATH=/app dev python tests/build/oceanbase.py\n\ninit-oracle: ## Initialize Oracle schema\n\tdocker compose exec -e PYTHONPATH=/app dev python tests/build/oracle.py\n\ninit-mongodb: ## Initialize MongoDB schema\n\tdocker compose exec -e PYTHONPATH=/app dev python tests/build/mongodb.py\n\ninit-sqlite: ## Initialize SQLite schema\n\tdocker compose exec -e PYTHONPATH=/app dev python tests/build/sqlite.py\n\ndev-build: ## Rebuild the development container\n\tdocker compose build --no-cache\n\ndev-clean: ## Complete teardown: stop containers, remove images, prune build cache\n\tdocker compose down -v\n\tdocker builder prune -f\n\tdocker compose rm -f\n\t@echo \"✓ Docker environment cleaned (containers, volumes, and build cache removed)\"\n\ntest: ## Run tests in the container\n\tdocker compose exec dev pytest\n\nrun-unit: ## Run unit tests (no API keys needed)\n\t@echo \"Running unit tests...\"\n\tuv run pytest tests/ --ignore=tests/integration --ignore=tests/benchmarks -v --tb=short\n\nrun-integration: ## Run all integration tests (requires API keys)\n\t@echo \"Running all integration tests with MEMORI_TEST_MODE=1...\"\n\tMEMORI_TEST_MODE=1 uv run pytest tests/integration/ -v -m integration --tb=short\n\nrun-integration-provider: ## Run specific provider tests (e.g., make run-integration-provider P=openai)\n\t@echo \"Running $(P) integration tests...\"\n\tMEMORI_TEST_MODE=1 uv run pytest tests/integration/providers/test_$(P).py -v -m integration --tb=short\n\nrun-integration-cloud: ## Run cloud integration tests (production API, requires MEMORI_API_KEY)\n\t@echo \"Running cloud integration tests...\"\n\tuv run pytest tests/integration/cloud/ -v -m integration --tb=short\n\nlint: ## Run linting (format check)\n\tdocker compose exec dev uv run ruff check .\n\nsecurity: ## Run security scans (Bandit + pip-audit)\n\tdocker compose exec dev uv run bandit -r memori -ll -ii\n\tdocker compose exec dev uv run pip-audit --require-hashes --disable-pip || true\n\nformat: ## Format code\n\tdocker compose exec dev uv run ruff format .\n\nclean: ## Clean up containers, volumes, and Python cache files\n\tdocker compose down -v\n\tfind . -type d -name __pycache__ -exec rm -rf {} + 2>/dev/null || true\n\tfind . -type d -name .pytest_cache -exec rm -rf {} + 2>/dev/null || true\n\tfind . -type d -name \"*.egg-info\" -exec rm -rf {} + 2>/dev/null || true\n" }, { "path": "README.md", "content": "[![Memori Labs](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/banner.png)](https://memorilabs.ai/)\n\n

\n The memory fabric for enterprise AI\n

\n\n

\n Memori plugs into the software and infrastructure you already use. It is LLM, datastore and framework agnostic and seamlessly integrates into the architecture you've already designed.\n

\n\n

\n → Memori Cloud — Zero config. Get an API key and start building in minutes.\n

\n

\n \n \"Memori%2fLabs%2FMemori\n \n

\n\n

\n \n \"PyPI\n \n \n \"NPM\n \n \n \"Downloads\"\n \n \n \"License\"\n \n \n \"Discord\"\n \n

\n\n

\n \n \"Give\n \n

\n\n---\n\n## Getting Started\n\n### Installation\n\n
\nTypeScript SDK\n\n```bash\nnpm install @memorilabs/memori\n```\n
\n\n
\nPython SDK\n\n```bash\npip install memori\n```\n
\n\n### Quickstart\n\nSign up at [app.memorilabs.ai](https://app.memorilabs.ai), get a Memori API key, and start building. Full docs: [memorilabs.ai/docs/memori-cloud/](https://memorilabs.ai/docs/memori-cloud/).\n\nSet `MEMORI_API_KEY` and your LLM API key (e.g. `OPENAI_API_KEY`), then:\n\n
\nTypeScript SDK\n\n```typescript\nimport { OpenAI } from 'openai';\nimport { Memori } from '@memorilabs/memori';\n\n// Requires MEMORI_API_KEY and OPENAI_API_KEY in your environment\nconst client = new OpenAI();\nconst mem = new Memori().llm\n .register(client)\n .attribution('user_123', 'support_agent');\n\nasync function main() {\n await client.chat.completions.create({\n model: 'gpt-4o-mini',\n messages: [{ role: 'user', content: 'My favorite color is blue.' }],\n });\n // Conversations are persisted and recalled automatically in the background.\n\n const response = await client.chat.completions.create({\n model: 'gpt-4o-mini',\n messages: [{ role: 'user', content: \"What's my favorite color?\" }],\n });\n // Memori recalls that your favorite color is blue.\n}\n```\n
\n\n
\nPython SDK\n\n```python\nfrom memori import Memori\nfrom openai import OpenAI\n\n# Requires MEMORI_API_KEY and OPENAI_API_KEY in your environment\nclient = OpenAI()\nmem = Memori().llm.register(client)\n\nmem.attribution(entity_id=\"user_123\", process_id=\"support_agent\")\n\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"My favorite color is blue.\"}]\n)\n# Conversations are persisted and recalled automatically.\n\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"What's my favorite color?\"}]\n)\n# Memori recalls that your favorite color is blue.\n```\n
\n\n## Explore the Memories\n\nUse the [Dashboard](https://app.memorilabs.ai) — Memories, Analytics, Playground, and API Keys.\n\n> [!TIP]\n> Want to use your own database? Check out docs for Memori BYODB here:\n> [https://memorilabs.ai/docs/memori-byodb/](https://memorilabs.ai/docs/memori-byodb/).\n\n## LoCoMo Benchmark\n\nMemori was evaluated on the LoCoMo benchmark for long-conversation memory and achieved **81.95% overall accuracy** while using an average of **1,294 tokens per query**. That is just **4.97% of the full-context footprint**, showing that structured memory can preserve reasoning quality without forcing large prompts into every request.\n\nCompared with other retrieval-based memory systems, Memori outperformed Zep, LangMem, and Mem0 while reducing prompt size by roughly **67% vs. Zep** and lowering context cost by more than **20x vs. full-context prompting**.\n\nRead the [benchmark overview](docs/memori-cloud/benchmark/overview.mdx), see the [results](docs/memori-cloud/benchmark/results.mdx), or download the [paper](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/memori-locomo-benchmark.pdf).\n\n![\"Memori's average accuracy along with the standard deviation\"](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/memori-locomo-benchmark.webp)\n\n## OpenClaw (Persistent Memory for Your Gateway)\n\nBy default, OpenClaw agents forget everything between sessions. The Memori plugin fixes that. It captures durable facts and preferences after each conversation, then injects the most relevant context back into future prompts automatically.\n\nNo changes to your agent code or prompts are required. The plugin hooks into OpenClaw's lifecycle, so you get structured memory, Intelligent Recall, and Advanced Augmentation with a drop-in plugin.\n\n```bash\nopenclaw plugins install @memorilabs/openclaw-memori\nopenclaw plugins enable openclaw-memori\n\nopenclaw config set plugins.entries.openclaw-memori.config.apiKey \"YOUR_MEMORI_API_KEY\"\nopenclaw config set plugins.entries.openclaw-memori.config.entityId \"your-app-user-id\"\n\nopenclaw gateway restart\n```\n\nFor setup and configuration, see the [OpenClaw Quickstart](docs/memori-cloud/openclaw/quickstart.mdx). For architecture and lifecycle details, see the [OpenClaw Overview](docs/memori-cloud/openclaw/overview.mdx).\n\n## MCP (Connect Your Agent in One Command)\n\nYour agent forgets everything between sessions. Memori fixes that. It remembers your stack, your conventions, and how you like things done so you stop repeating yourself.\n\nWorks for solo developers and teams. Your agent learns coding patterns, reviewer preferences, and project conventions over time. For teams, that means shared context that new engineers pick up on day one instead of absorbing tribal knowledge over months.\n\nIf you use Claude Code, Cursor, Codex, Warp, or Antigravity, you can connect Memori with no SDK integration needed:\n\n```bash\nclaude mcp add --transport http memori https://api.memorilabs.ai/mcp/ \\\n --header \"X-Memori-API-Key: ${MEMORI_API_KEY}\" \\\n --header \"X-Memori-Entity-Id: your_username\" \\\n --header \"X-Memori-Process-Id: claude-code\"\n```\n\nFor Cursor, Codex, Warp, and other clients, see the [MCP client setup guide](docs/memori-cloud/mcp/client-setup.mdx).\n\n## Attribution\n\nTo get the most out of Memori, you want to attribute your LLM interactions to an entity (think person, place or thing; like a user) and a process (think your agent, LLM interaction or program).\n\nIf you do not provide any attribution, Memori cannot make memories for you.\n\n
\nTypeScript SDK\n\n```typescript\nmem.attribution(\"12345\", \"my-ai-bot\");\n```\n
\n\n
\nPython SDK\n\n```python\nmem.attribution(entity_id=\"12345\", process_id=\"my-ai-bot\")\n```\n
\n\n## Session Management\n\nMemori uses sessions to group your LLM interactions together. For example, if you have an agent that executes multiple steps you want those to be recorded in a single session.\n\nBy default, Memori handles setting the session for you but you can start a new session or override the session by executing the following:\n\n
\nTypeScript SDK\n\n```typescript\nmem.resetSession();\n// or\nmem.setSession(sessionId);\n```\n
\n\n
\nPython SDK\n\n```python\nmem.new_session()\n# or\nmem.set_session(session_id)\n```\n
\n\n## Supported LLMs\n\n- Anthropic\n- Bedrock\n- DeepSeek\n- Gemini\n- Grok (xAI)\n- OpenAI (Chat Completions & Responses API)\n\n_(unstreamed, streamed, synchronous and asynchronous)_\n\n## Supported Frameworks\n\n- Agno\n- LangChain\n- Pydantic AI\n\n## Supported Platforms\n\n- DeepSeek\n- Nebius AI Studio\n\n## Examples\n\nFor more examples and demos, check out the [Memori Cookbook](https://github.com/MemoriLabs/memori-cookbook).\n\n## Memori Advanced Augmentation\n\nMemories are tracked at several different levels:\n\n- **entity**: think person, place, or thing; like a user\n- **process**: think your agent, LLM interaction or program\n- **session**: the current interactions between the entity, process and the LLM\n\n[Memori's Advanced Augmentation](docs/memori-cloud/concepts/advanced-augmentation.mdx) enhances memories at each of these levels with:\n\n- attributes\n- events\n- facts\n- people\n- preferences\n- relationships\n- rules\n- skills\n\nMemori knows who your user is, what tasks your agent handles and creates unparalleled context between the two. Augmentation occurs in the background incurring no latency.\n\nBy default, Memori Advanced Augmentation is available without an account but rate limited. When you need increased limits, [sign up for Memori Advanced Augmentation](https://app.memorilabs.ai/signup) or use the Memori CLI:\n\n```bash\n# Install the CLI via pip to manage your account\npython -m memori sign-up \n```\n\nMemori Advanced Augmentation is always free for developers!\n\nOnce you've obtained an API key, set the following environment variable (used by both Python and TypeScript SDKs):\n\n```bash\nexport MEMORI_API_KEY=[api_key]\n```\n\n## Managing Your Quota\n\nAt any time, you can check your quota using the Memori CLI (works for both SDKs):\n\n```bash\npython -m memori quota\n```\n\nOr by checking your account at [https://app.memorilabs.ai/](https://app.memorilabs.ai/). If you have reached your IP address quota, sign up and get an API key for increased limits.\n\nIf your API key exceeds its quota limits we will email you and let you know.\n\n## Command Line Interface (CLI)\n\nThe Memori CLI is the unified tool for managing your account, keys, and quotas across all SDKs. To use it, execute the following from the command line:\n\n```bash\n# Requires Python installed\npython -m memori\n```\n\nThis will display a menu of the available options. For more information about what you can do with the Memori CLI, please reference [Command Line Interface](docs/memori-byodb/concepts/cli-quickstart.mdx).\n\n## Contributing\n\nWe welcome contributions from the community! Please see our [Contributing Guidelines](https://github.com/MemoriLabs/Memori/blob/main/CONTRIBUTING.md) for details on:\n\n- Setting up your development environment\n- Code style and standards\n- Submitting pull requests\n- Reporting issues\n\n---\n\n## Support\n\n- **Memori Cloud Documentation**: [memorilabs.ai/docs/memori-cloud/](https://memorilabs.ai/docs/memori-cloud/)\n- **Memori BYODB Documentation**: [https://memorilabs.ai/docs/memori-byodb/](https://memorilabs.ai/docs/memori-byodb/)\n- **Discord**: [https://discord.gg/abD4eGym6v](https://discord.gg/abD4eGym6v)\n- **Issues**: [GitHub Issues](https://github.com/MemoriLabs/Memori/issues)\n\n---\n\n## License\n\nApache 2.0 - see [LICENSE](https://github.com/MemoriLabs/Memori/blob/main/LICENSE)\n" }, { "path": "SECURITY.md", "content": "[![Memori Labs](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/banner.png)](https://memorilabs.ai/)\n\n## Security Policy\n\nIf you believe you have discovered a security vulnerability in Memori, please **do not** open a public GitHub issue.\n\nInstead, responsibly disclose it by:\n- Going to the **Security** tab of this repository\n- Clicking **\"Report a vulnerability\"**\n\nPlease provide as much detail as possible in your private report, including:\n- A clear description of the issue\n- Steps to reproduce (a minimal reproducible example is greatly appreciated)\n- Potential impact and any proof-of-concept if available\n\nThis allows us to investigate and address the issue quickly and safely before public disclosure.\n\nThank you for helping keep Memori secure!\n" }, { "path": "benchmarks/README.md", "content": "## Benchmarks\n\nThis directory contains **benchmark harnesses** that are intentionally **not** part of the\ndefault `pytest` unit test suite.\n\n### Performance / latency (pytest-benchmark)\n\nPerformance benchmarks (including **end-to-end recall latency**) live in `benchmarks/perf/`.\n\nRun locally (example):\n\n```bash\nuv run pytest -m benchmark --benchmark-only benchmarks/perf/test_recall_benchmarks.py -v\n```\n\nFor EC2 / VPC-adjacent database benchmarking, see `benchmarks/perf/README.md` and the helper\nscripts in `benchmarks/perf/`.\n\n### LoCoMo (retrieval evaluation)\n\nLoCoMo is a benchmark dataset by Snap Research for long conversation memory.\n\nIn Memori, we treat LoCoMo as a **retrieval evaluation** problem: given a question, does\nMemori retrieve the right supporting context (evidence)?\n\n#### Dataset\n\nLoCoMo is not vendored in this repo. Download the dataset JSON locally, then point the\nharness at that file path.\n\nUpstream: `https://github.com/snap-research/locomo`\n\n#### Preprocess (recommended for Memori)\n\nThe upstream LoCoMo format is a **third-person** dialogue between two speakers, and some\nconversations include multimodal fields (e.g., image URLs + captions) that Memori does not\ncurrently handle well.\n\nTo make evaluation more representative of Memori usage, we provide a small preprocessing step\nthat:\n\n- Skips any conversation that contains multimodal turn fields (`img_url`, `blip_caption`, `query`)\n- Rewrites speakers so `conversation.speaker_b` becomes `assistant` and the other speaker becomes `user`\n\nRun:\n\n```bash\nuv run python benchmarks/locomo/preprocess.py \\\n --in benchmarks/locomo10.json \\\n --out benchmarks/locomo10_memori.json\n```\n\n#### What gets written (artifacts)\n\nEach run writes:\n\n- `predictions.jsonl`: one row per QA question (retrieved top-k + hit@k/MRR metrics)\n- `summary.json`: aggregated metrics (overall + by category)\n- `locomo.sqlite`: SQLite DB used by Memori storage during the run\n- `locomo_provenance.sqlite`: (AA mode only) benchmark-only mapping of `fact_id → dia_id` for scoring\n\n#### Modes (ingestion)\n\nLoCoMo ingestion always uses **Advanced Augmentation**:\n\n- Stores turns as `conversation_message`s and runs Memori **Advanced Augmentation** to produce\n derived `entity_fact`s (closest to real usage).\n- Because LoCoMo evidence is turn-level, we write a **benchmark-only provenance DB**\n (`locomo_provenance.sqlite`) that maps each derived fact back to the LoCoMo `dia_id` turn(s),\n then score hit@k/MRR against evidence.\n- **Requires**: `MEMORI_API_KEY`.\n- **Note**: may be non-deterministic (API + model changes).\n\n#### Quickstart (advanced_augmentation, seeds + scores)\n\nPrerequisite:\n\n- `MEMORI_API_KEY` set (Advanced Augmentation API access)\n- LoCoMo harness forces staging routing (`MEMORI_TEST_MODE=1`)\n\nRun:\n\n```bash\nexport MEMORI_API_KEY=\"...\"\n# Optional: increase AA request timeout (default is 30s)\nexport MEMORI_AUGMENTATION_TIMEOUT_SECONDS=120\n\nuv run python benchmarks/locomo/run.py \\\n --dataset benchmarks/locomo10.json \\\n --out results/locomo/aa_run \\\n --aa-batch per_pair\n```\n\n#### Score-only (reuse an existing DB, no AA calls)\n\nIf you already seeded a SQLite DB (and, for AA runs, a provenance DB), you can skip ingestion and\nrun retrieval+scoring directly from the existing DB:\n\n```bash\nuv run python benchmarks/locomo/run.py \\\n --dataset benchmarks/locomo10.json \\\n --out results/locomo/score_only \\\n --sqlite-db results/locomo/aa_run/locomo.sqlite \\\n --provenance-db results/locomo/aa_run/locomo_provenance.sqlite \\\n --reuse-db\n```\n\nIf the DB contains multiple prior LoCoMo runs, pass `--run-id` to choose which one to score.\n\n#### Useful knobs (AA mode)\n\n- **Batching**:\n - `--aa-batch per_pair` (one AA request per user+assistant pair)\n\n- **Dry-run** (inspect payload; no network call):\n - `--aa-dry-run` writes `aa_payload_preview.json` and prints the payload + URL.\n\n- **Metadata** (only if your AA endpoint requires it; defaults are provided):\n - `--meta-llm-provider`\n - `--meta-llm-version`\n - `--meta-llm-sdk-version`\n - `--meta-framework-provider`\n - `--meta-platform-provider`\n\n- **Timeout**:\n - AA HTTP timeout is configured via `MEMORI_AUGMENTATION_TIMEOUT_SECONDS`.\n" }, { "path": "benchmarks/locomo/_run_impl.py", "content": "from __future__ import annotations\n\nimport datetime\nimport json\nimport os\nimport sqlite3\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom uuid import uuid4\n\nfrom benchmarks.locomo._types import LoCoMoSample\nfrom benchmarks.locomo.loader import load_locomo_json\nfrom benchmarks.locomo.provenance import (\n FactAttribution,\n ProvenanceStore,\n attribute_facts_to_turn_ids,\n)\nfrom benchmarks.locomo.retrieval import (\n build_turn_facts,\n evidence_to_turn_ids,\n)\nfrom benchmarks.locomo.scoring import hit_at_k_groups, mrr_groups\nfrom memori import Memori\nfrom memori.embeddings import embed_texts\nfrom memori.memory.augmentation.input import AugmentationInput\nfrom memori.memory.recall import Recall\n\nCATEGORY_LABELS: dict[str, str] = {\n # LoCoMo category IDs are numeric in the dataset JSON.\n # Mapping here matches the upstream LoCoMo taxonomy:\n # 1=multi-hop, 2=temporal, 3=open-domain knowledge, 4=single-hop, 5=adversarial.\n \"1\": \"multi-hop\",\n \"2\": \"temporal\",\n \"3\": \"open-domain\",\n \"4\": \"single-hop\",\n \"5\": \"adversarial\",\n \"unknown\": \"unknown\",\n}\n\n\n@dataclass(frozen=True, slots=True)\nclass RunConfig:\n dataset: str\n out: str\n sqlite_db: str = \"\"\n provenance_db: str = \"\"\n reuse_db: bool = False\n run_id: str = \"\"\n k: int = 5\n aa_timeout: float = 180.0\n aa_batch: str = \"per_pair\"\n aa_dry_run: bool = False\n aa_max_requests: int = 0\n meta_llm_provider: str = \"openai\"\n meta_llm_version: str = \"gpt-4.1-mini\"\n meta_llm_sdk_version: str = \"unknown\"\n meta_framework_provider: str = \"memori\"\n meta_platform_provider: str = \"benchmark\"\n aa_provenance_top_n: int = 1\n aa_provenance_min_score: float = 0.25\n aa_provenance_mode: str = \"similarity\"\n rebuild_provenance: bool = False\n allow_prod_aa: bool = False\n max_samples: int = 0\n max_questions: int = 0\n only_sample_id: str = \"\"\n max_sessions: int = 0\n verbose: bool = False\n log_every_questions: int = 0\n seed_only: bool = False\n\n\n@dataclass(frozen=True, slots=True)\nclass PairRequest:\n messages: list[dict[str, str]]\n pair_turn_ids: tuple[str, str]\n\n\ndef run_locomo(cfg: RunConfig) -> dict:\n out_dir = Path(cfg.out)\n out_dir.mkdir(parents=True, exist_ok=True)\n predictions_path = out_dir / \"predictions.jsonl\"\n summary_path = out_dir / \"summary.json\"\n\n sqlite_path = (\n Path(cfg.sqlite_db).expanduser()\n if cfg.sqlite_db\n else (out_dir / \"locomo.sqlite\")\n )\n provenance_path = (\n Path(cfg.provenance_db).expanduser()\n if cfg.provenance_db\n else (out_dir / \"locomo_provenance.sqlite\")\n )\n\n samples = load_locomo_json(cfg.dataset)\n if cfg.only_sample_id:\n samples = [s for s in samples if s.sample_id == cfg.only_sample_id]\n if cfg.max_samples and cfg.max_samples > 0:\n samples = samples[: cfg.max_samples]\n\n run_id = _resolve_run_id(\n cfg=cfg,\n out_dir=out_dir,\n sqlite_path=sqlite_path,\n provenance_path=provenance_path,\n )\n ts = datetime.datetime.now(datetime.timezone.utc).isoformat()\n\n mem = _init_memori(sqlite_path)\n recall = Recall(mem.config)\n prov_store = ProvenanceStore(provenance_path)\n\n if (\n not cfg.reuse_db\n and not cfg.aa_dry_run\n and not cfg.allow_prod_aa\n and os.environ.get(\"MEMORI_TEST_MODE\") != \"1\"\n ):\n raise RuntimeError(\n \"Advanced Augmentation LoCoMo runs must target staging. \"\n \"Set MEMORI_TEST_MODE=1 (recommended) or pass --allow-prod-aa to override.\"\n )\n\n if cfg.verbose:\n from memori._network import Api\n\n url = Api(mem.config).url(\"sdk/augmentation\")\n print(\n f\"[locomo][aa] resolved_api_url={url} MEMORI_TEST_MODE={os.environ.get('MEMORI_TEST_MODE')!r}\"\n )\n\n totals = _Totals()\n\n with predictions_path.open(\"w\", encoding=\"utf-8\") as f:\n if cfg.verbose:\n print(\n f\"[locomo] start ingest=advanced_augmentation aa_batch={cfg.aa_batch} \"\n f\"samples={len(samples)} k={cfg.k} seed_only={cfg.seed_only}\"\n )\n for sample in samples:\n if cfg.max_sessions and cfg.max_sessions > 0:\n sample = LoCoMoSample(\n sample_id=sample.sample_id,\n sessions=sample.sessions[: cfg.max_sessions],\n qa=sample.qa,\n )\n entity_external_id = f\"locomo:{run_id}:{sample.sample_id}\"\n if cfg.reuse_db:\n entity_db_id = _get_entity_id_sqlite(\n sqlite_path=sqlite_path,\n entity_external_id=entity_external_id,\n )\n if entity_db_id is None:\n raise RuntimeError(\n f\"--reuse-db was set but entity was not found in sqlite DB: external_id={entity_external_id}. \"\n \"Run ingestion first (omit --reuse-db) or pass the correct --run-id.\"\n )\n else:\n mem.attribution(\n entity_id=entity_external_id, process_id=\"locomo-benchmark\"\n )\n entity_db_id = mem.config.storage.driver.entity.create(\n entity_external_id\n )\n\n turn_facts, turn_index = build_turn_facts(sample)\n\n if cfg.reuse_db:\n fact_count = _count_entity_facts_sqlite(\n sqlite_path=sqlite_path, entity_db_id=entity_db_id\n )\n if fact_count <= 0:\n raise RuntimeError(\n f\"--reuse-db was set but no facts were found for entity external_id={entity_external_id}. \"\n \"Run ingestion first (omit --reuse-db) or pass the correct --run-id.\"\n )\n mem.config.recall_embeddings_limit = fact_count\n if cfg.rebuild_provenance and not cfg.seed_only:\n _build_aa_provenance(\n mem=mem,\n prov_store=prov_store,\n run_id=run_id,\n sample_id=sample.sample_id,\n entity_db_id=entity_db_id,\n turn_facts=turn_facts,\n top_n=cfg.aa_provenance_top_n,\n min_score=cfg.aa_provenance_min_score,\n )\n elif not cfg.seed_only and not prov_store.has_any(\n run_id=run_id, sample_id=sample.sample_id\n ):\n raise RuntimeError(\n f\"--reuse-db was set but no provenance rows were found for run_id={run_id} sample_id={sample.sample_id}. \"\n \"Seed AA ingestion once (omit --reuse-db) to generate locomo_provenance.sqlite, \"\n \"or point --provenance-db at an existing provenance DB, \"\n \"or pass --rebuild-provenance to recompute provenance offline.\"\n )\n if cfg.verbose:\n print(\n f\"[locomo][reuse] sample={sample.sample_id} facts={fact_count}\"\n )\n else:\n _configure_aa_meta(mem, cfg)\n if cfg.aa_dry_run:\n _write_aa_payload_preview(\n out_dir=out_dir,\n mem=mem,\n sample=sample,\n cfg=cfg,\n entity_external_id=entity_external_id,\n process_id=\"locomo-benchmark\",\n )\n # Exit before any network call.\n return {\n \"run_id\": run_id,\n \"timestamp_utc\": ts,\n \"dataset_path\": str(Path(cfg.dataset).resolve()),\n \"sqlite_db_path\": str(sqlite_path),\n \"provenance_db_path\": str(provenance_path),\n \"ingest\": \"advanced_augmentation\",\n \"sample_count\": len(samples),\n \"question_count\": 0,\n \"questions_by_category\": {},\n \"metrics_overall\": {},\n \"metrics_by_category\": {},\n \"phase\": 2,\n \"note\": \"AA dry-run: printed/wrote payload preview and exited before network calls.\",\n }\n conv_id = _ingest_with_advanced_augmentation(\n mem=mem,\n prov_store=prov_store,\n run_id=run_id,\n entity_external_id=entity_external_id,\n entity_db_id=entity_db_id,\n sample=sample,\n cfg=cfg,\n )\n if conv_id is not None:\n summary_val = _read_conversation_summary(mem, conv_id) or \"\"\n print(\n f\"[locomo][aa] sample={sample.sample_id} \"\n f\"conversation_id={conv_id} summary_is_set={bool(summary_val)}\"\n )\n if summary_val:\n print(f\"[locomo][aa] summary={summary_val}\")\n\n if cfg.seed_only:\n continue\n\n if cfg.verbose:\n fact_count = len(\n mem.config.storage.driver.entity_fact.get_embeddings(\n entity_db_id, limit=100000\n )\n )\n print(\n f\"[locomo][seed] sample={sample.sample_id} mode=advanced_augmentation \"\n f\"turns={len(turn_facts)} facts_written={fact_count}\"\n )\n if cfg.aa_provenance_mode == \"similarity\":\n _build_aa_provenance(\n mem=mem,\n prov_store=prov_store,\n run_id=run_id,\n sample_id=sample.sample_id,\n entity_db_id=entity_db_id,\n turn_facts=turn_facts,\n top_n=cfg.aa_provenance_top_n,\n min_score=cfg.aa_provenance_min_score,\n )\n # Keep recall bounded to the facts created for this entity.\n entity_fact_driver = mem.config.storage.driver.entity_fact\n mem.config.recall_embeddings_limit = (\n len(entity_fact_driver.get_embeddings(entity_db_id, limit=100000))\n or 1\n )\n\n qa = list(sample.qa)\n if cfg.max_questions and cfg.max_questions > 0:\n qa = qa[: cfg.max_questions]\n\n available_turn_ids = {t.turn_id for t in turn_facts}\n\n for q in qa:\n if (\n cfg.verbose\n and cfg.log_every_questions\n and totals.question_count % cfg.log_every_questions == 0\n ):\n print(\n f\"[locomo][score] questions={totals.question_count} \"\n f\"sample={sample.sample_id}\"\n )\n\n relevant = evidence_to_turn_ids(q.evidence, turn_index=turn_index)\n if not relevant:\n if cfg.verbose:\n print(\n f\"[locomo][skip] sample={sample.sample_id} \"\n f\"question_id={q.question_id} reason=no_evidence\"\n )\n continue\n missing = sorted(\n tid for tid in relevant if tid not in available_turn_ids\n )\n if missing:\n if cfg.verbose:\n preview = \", \".join(missing[:5])\n more = (\n \"\" if len(missing) <= 5 else f\" (+{len(missing) - 5} more)\"\n )\n print(\n f\"[locomo][skip] sample={sample.sample_id} \"\n f\"question_id={q.question_id} reason=missing_evidence \"\n f\"missing={preview}{more}\"\n )\n continue\n\n totals.count_question(q.category)\n results = recall.search_facts(\n query=q.question,\n limit=max(cfg.k, 1),\n entity_id=entity_db_id,\n )\n\n # Scoring in AA mode needs a mapping from fact_id -> dia_id (LoCoMo turn id).\n # A single fact can plausibly map to multiple turns; we score using any-match\n # semantics per retrieved rank.\n retrieved_ids: list[str] = []\n retrieved_groups: list[set[str]] = []\n top_k = _format_top_k(\n results=results[: max(cfg.k, 1)],\n prov_store=prov_store,\n run_id=run_id,\n sample_id=sample.sample_id,\n retrieved_ids_out=retrieved_ids,\n retrieved_groups_out=retrieved_groups,\n provenance_limit=max(int(cfg.aa_provenance_top_n), 1),\n )\n\n metrics = {\n \"hit@1\": hit_at_k_groups(relevant, retrieved_groups, 1),\n \"hit@3\": hit_at_k_groups(relevant, retrieved_groups, 3),\n \"hit@5\": hit_at_k_groups(relevant, retrieved_groups, 5),\n \"hit@10\": hit_at_k_groups(relevant, retrieved_groups, 10),\n \"hit@20\": hit_at_k_groups(relevant, retrieved_groups, 20),\n \"hit@30\": hit_at_k_groups(relevant, retrieved_groups, 30),\n \"mrr\": mrr_groups(relevant, retrieved_groups),\n }\n totals.add_metrics(category=q.category or \"unknown\", metrics=metrics)\n\n row = {\n \"run_id\": run_id,\n \"timestamp_utc\": ts,\n \"sample_id\": sample.sample_id,\n \"question_id\": q.question_id,\n \"category\": q.category,\n \"question\": q.question,\n \"answers\": list(q.answers),\n \"evidence\": q.evidence,\n \"retrieval\": {\n \"status\": \"ok\",\n \"ingest\": \"advanced_augmentation\",\n \"k\": cfg.k,\n \"relevant_turn_ids\": sorted(relevant),\n \"top_k\": top_k,\n \"metrics\": metrics,\n },\n }\n f.write(json.dumps(row, ensure_ascii=False) + \"\\n\")\n\n summary = totals.to_summary(\n run_id=run_id,\n timestamp_utc=ts,\n dataset_path=str(Path(cfg.dataset).resolve()),\n sqlite_db_path=str(sqlite_path),\n provenance_db_path=str(provenance_path),\n ingest=\"advanced_augmentation\",\n sample_count=len(samples),\n )\n summary_path.write_text(json.dumps(summary, indent=2), encoding=\"utf-8\")\n return summary\n\n\ndef _resolve_run_id(\n *, cfg: RunConfig, out_dir: Path, sqlite_path: Path, provenance_path: Path\n) -> str:\n if cfg.run_id:\n return cfg.run_id\n if not cfg.reuse_db:\n return str(uuid4())\n\n summary_path = out_dir / \"summary.json\"\n if summary_path.exists():\n try:\n obj = json.loads(summary_path.read_text(encoding=\"utf-8\"))\n rid = obj.get(\"run_id\")\n if isinstance(rid, str) and rid:\n return rid\n except Exception:\n pass\n\n run_ids: set[str] = set()\n if provenance_path.exists():\n run_ids |= _distinct_run_ids_from_provenance_sqlite(provenance_path)\n\n if sqlite_path.exists():\n run_ids |= _distinct_run_ids_from_memori_sqlite(sqlite_path)\n\n if len(run_ids) == 1:\n return next(iter(run_ids))\n if not run_ids:\n raise RuntimeError(\n \"--reuse-db was set but no prior LoCoMo run_id was found in the DB(s). \"\n \"Pass --run-id or run ingestion once without --reuse-db.\"\n )\n raise RuntimeError(\n \"--reuse-db was set but multiple run_ids were found in the DB(s). \"\n f\"Pass --run-id to choose one. found={sorted(run_ids)}\"\n )\n\n\ndef _distinct_run_ids_from_provenance_sqlite(path: Path) -> set[str]:\n with sqlite3.connect(str(path), check_same_thread=False) as conn:\n try:\n cur = conn.execute(\n \"SELECT DISTINCT run_id FROM bench_locomo_fact_provenance\"\n )\n except sqlite3.OperationalError:\n return set()\n return {r[0] for r in cur.fetchall() if r and isinstance(r[0], str) and r[0]}\n\n\ndef _distinct_run_ids_from_memori_sqlite(path: Path) -> set[str]:\n with sqlite3.connect(str(path), check_same_thread=False) as conn:\n try:\n cur = conn.execute(\n \"SELECT external_id FROM memori_entity WHERE external_id LIKE 'locomo:%'\"\n )\n except sqlite3.OperationalError:\n return set()\n out: set[str] = set()\n for row in cur.fetchall():\n if not row:\n continue\n external_id = row[0]\n if not isinstance(external_id, str) or not external_id.startswith(\n \"locomo:\"\n ):\n continue\n parts = external_id.split(\":\")\n if len(parts) >= 3 and parts[1]:\n out.add(parts[1])\n return out\n\n\ndef _get_entity_id_sqlite(*, sqlite_path: Path, entity_external_id: str) -> int | None:\n with sqlite3.connect(str(sqlite_path), check_same_thread=False) as conn:\n cur = conn.execute(\n \"SELECT id FROM memori_entity WHERE external_id = ?\",\n (entity_external_id,),\n )\n row = cur.fetchone()\n if not row:\n return None\n try:\n return int(row[0])\n except (TypeError, ValueError):\n return None\n\n\ndef _count_entity_facts_sqlite(*, sqlite_path: Path, entity_db_id: int) -> int:\n with sqlite3.connect(str(sqlite_path), check_same_thread=False) as conn:\n cur = conn.execute(\n \"SELECT COUNT(*) FROM memori_entity_fact WHERE entity_id = ?\",\n (int(entity_db_id),),\n )\n row = cur.fetchone()\n if not row:\n return 0\n try:\n return int(row[0])\n except (TypeError, ValueError):\n return 0\n\n\ndef _init_memori(sqlite_path: Path) -> Memori:\n def _conn():\n return sqlite3.connect(str(sqlite_path), check_same_thread=False)\n\n mem = Memori(conn=_conn)\n mem.config.storage.build()\n return mem\n\n\ndef _configure_aa_meta(mem: Memori, cfg: RunConfig) -> None:\n mem.config.framework.provider = cfg.meta_framework_provider\n mem.config.platform.provider = cfg.meta_platform_provider\n mem.config.llm.provider = cfg.meta_llm_provider\n mem.config.llm.version = cfg.meta_llm_version\n mem.config.llm.provider_sdk_version = cfg.meta_llm_sdk_version\n\n\ndef _ingest_with_advanced_augmentation(\n *,\n mem: Memori,\n prov_store: ProvenanceStore,\n run_id: str,\n entity_external_id: str,\n entity_db_id: int,\n sample,\n cfg: RunConfig,\n) -> int | None:\n if cfg.aa_batch == \"per_pair\":\n return _aa_enqueue_pairs_sequential(\n mem=mem,\n prov_store=prov_store,\n run_id=run_id,\n entity_external_id=entity_external_id,\n entity_db_id=entity_db_id,\n sample_id=sample.sample_id,\n sample=sample,\n timeout=cfg.aa_timeout,\n max_requests=int(cfg.aa_max_requests or 0),\n verbose=bool(cfg.verbose),\n )\n\n raise ValueError(f\"unknown aa batch: {cfg.aa_batch}\")\n\n\ndef _aa_enqueue_pairs_sequential(\n *,\n mem: Memori,\n prov_store: ProvenanceStore,\n run_id: str,\n entity_external_id: str,\n entity_db_id: int,\n sample_id: str,\n sample,\n timeout: float,\n max_requests: int = 0,\n verbose: bool = False,\n) -> int | None:\n all_msgs, all_turn_ids = _build_aa_messages_and_turn_ids_for_sample(sample)\n conv_id = _create_conversation_and_persist_messages(mem, all_msgs)\n\n reqs = _build_per_pair_requests(all_msgs, all_turn_ids)\n max_n = int(max_requests or 0)\n if max_n > 0:\n reqs = reqs[:max_n]\n\n entity_fact_driver = mem.config.storage.driver.entity_fact\n known_fact_ids = {\n int(r[\"id\"])\n for r in entity_fact_driver.get_embeddings(int(entity_db_id), limit=100000)\n }\n\n for idx, req in enumerate(reqs):\n _enqueue_aa(mem, conv_id, entity_external_id, req.messages)\n mem.augmentation.wait(timeout=timeout)\n\n after = {\n int(r[\"id\"])\n for r in entity_fact_driver.get_embeddings(int(entity_db_id), limit=100000)\n }\n new_fact_ids = sorted(after - known_fact_ids)\n known_fact_ids = after\n\n if new_fact_ids:\n rows: list[FactAttribution] = []\n for fid in new_fact_ids:\n for dia_id in req.pair_turn_ids:\n if dia_id:\n rows.append(\n FactAttribution(fact_id=fid, dia_id=dia_id, score=1.0)\n )\n prov_store.upsert_many(rows, run_id=run_id, sample_id=sample_id)\n\n if verbose:\n summary_val = _read_conversation_summary(mem, int(conv_id)) or \"\"\n print(\n f\"[locomo][aa][pair] idx={idx} new_facts={len(new_fact_ids)} \"\n f\"pair_turn_ids={req.pair_turn_ids} summary_is_set={bool(summary_val)}\"\n )\n\n return int(conv_id)\n\n\ndef _read_conversation_summary(mem: Memori, conversation_id: int) -> str | None:\n try:\n obj = mem.config.storage.driver.conversation.read(int(conversation_id))\n except Exception:\n return None\n if not isinstance(obj, dict):\n return None\n summary = obj.get(\"summary\")\n return summary if isinstance(summary, str) and summary.strip() else None\n\n\ndef _create_conversation_and_persist_messages(\n mem: Memori, msgs: list[dict[str, str]]\n) -> int:\n mem.new_session()\n conv_id = mem.config.storage.driver.conversation.create(\n str(mem.config.session_id), mem.config.session_timeout_minutes\n )\n for m in msgs:\n mem.config.storage.driver.conversation.message.create(\n conv_id, m[\"role\"], \"text\", m[\"content\"]\n )\n if mem.config.storage.adapter is not None:\n mem.config.storage.adapter.commit()\n return int(conv_id)\n\n\ndef _enqueue_aa(\n mem: Memori, conv_id: int, entity_external_id: str, msgs: list[dict[str, str]]\n) -> None:\n from memori.memory.augmentation._message import ConversationMessage\n\n mem.augmentation.enqueue(\n AugmentationInput(\n conversation_id=str(conv_id),\n entity_id=entity_external_id,\n process_id=\"locomo-benchmark\",\n conversation_messages=[\n ConversationMessage(role=m[\"role\"], content=m[\"content\"]) for m in msgs\n ],\n system_prompt=None,\n )\n )\n\n\ndef _build_aa_messages_for_sample(sample) -> list[dict[str, str]]:\n msgs, _turn_ids = _build_aa_messages_and_turn_ids_for_sample(sample)\n return msgs\n\n\ndef _build_aa_messages_for_session(sample, session) -> list[dict[str, str]]:\n speaker_to_role = _speaker_to_role(sample)\n msgs, _turn_ids = _build_aa_messages_and_turn_ids_for_turns(\n sample, session, speaker_to_role\n )\n return msgs\n\n\ndef _build_aa_messages_for_turns(\n sample, session, speaker_to_role: dict[str, str]\n) -> list[dict[str, str]]:\n msgs, _turn_ids = _build_aa_messages_and_turn_ids_for_turns(\n sample, session, speaker_to_role\n )\n return msgs\n\n\ndef _build_aa_messages_and_turn_ids_for_sample(\n sample,\n) -> tuple[list[dict[str, str]], list[str]]:\n speaker_to_role = _speaker_to_role(sample)\n msgs: list[dict[str, str]] = []\n turn_ids: list[str] = []\n for session in sample.sessions:\n m, t = _build_aa_messages_and_turn_ids_for_turns(\n sample, session, speaker_to_role\n )\n msgs.extend(m)\n turn_ids.extend(t)\n return msgs, turn_ids\n\n\ndef _build_aa_messages_and_turn_ids_for_turns(\n sample, session, speaker_to_role: dict[str, str]\n) -> tuple[list[dict[str, str]], list[str]]:\n msgs: list[dict[str, str]] = []\n turn_ids: list[str] = []\n session_id = session.session_id or \"\"\n for t_idx, turn in enumerate(session.turns):\n speaker = (turn.speaker or \"\").strip()\n role = speaker_to_role.get(speaker, \"assistant\")\n turn_id = (\n turn.turn_id or \"\"\n ).strip() or f\"{sample.sample_id}:{session_id}:{t_idx}\"\n content = _format_turn_content(\n turn_id=turn_id,\n speaker=speaker,\n text=turn.text,\n session_date_time=session.date_time,\n )\n msgs.append({\"role\": role, \"content\": content})\n turn_ids.append(turn_id)\n return msgs, turn_ids\n\n\ndef _format_turn_content(\n *, turn_id: str, speaker: str, text: str, session_date_time: str | None\n) -> str:\n # Mirror real-world Memori payloads: content is the plain message text only.\n # Keep turn_id/speaker/session_time out of the message body (benchmark-only metadata).\n return str(text).strip()\n\n\ndef _speaker_to_role(sample) -> dict[str, str]:\n \"\"\"\n Heuristic mapping: first unique speaker => user, second => assistant, others => assistant.\n \"\"\"\n out: dict[str, str] = {}\n ordered: list[str] = []\n for session in sample.sessions:\n for turn in session.turns:\n speaker = (turn.speaker or \"\").strip()\n if not speaker or speaker in out:\n continue\n ordered.append(speaker)\n out[speaker] = \"user\" if len(ordered) == 1 else \"assistant\"\n return out\n\n\ndef _build_aa_provenance(\n *,\n mem: Memori,\n prov_store: ProvenanceStore,\n run_id: str,\n sample_id: str,\n entity_db_id: int,\n turn_facts,\n top_n: int,\n min_score: float,\n) -> None:\n turn_ids = [t.turn_id for t in turn_facts]\n turn_texts = [t.content for t in turn_facts]\n turn_embs = embed_texts(\n turn_texts,\n model=mem.config.embeddings.model,\n )\n\n entity_fact_driver = mem.config.storage.driver.entity_fact\n rows = entity_fact_driver.get_embeddings(entity_db_id, limit=100000)\n fact_ids = [int(r[\"id\"]) for r in rows]\n facts = entity_fact_driver.get_facts_by_ids(fact_ids)\n content_by_id = {int(r[\"id\"]): r[\"content\"] for r in facts}\n\n fact_ids_aligned = [i for i in fact_ids if i in content_by_id]\n fact_texts = [content_by_id[i] for i in fact_ids_aligned]\n fact_embs = embed_texts(\n fact_texts,\n model=mem.config.embeddings.model,\n )\n\n # Clear any prior mappings for this run/sample to avoid mixing old/new strategies.\n prov_store.delete_sample(run_id=run_id, sample_id=sample_id)\n\n mapping = attribute_facts_to_turn_ids(\n turn_ids=turn_ids,\n turn_embeddings=turn_embs,\n turn_texts=turn_texts,\n fact_ids=fact_ids_aligned,\n fact_embeddings=fact_embs,\n fact_texts=fact_texts,\n top_n=top_n,\n min_score=min_score,\n )\n\n prov_rows: list[FactAttribution] = []\n for fid, mapped in mapping.items():\n for dia_id, score in mapped:\n prov_rows.append(FactAttribution(fact_id=fid, dia_id=dia_id, score=score))\n prov_store.upsert_many(prov_rows, run_id=run_id, sample_id=sample_id)\n\n\ndef _write_aa_payload_preview(\n *,\n out_dir: Path,\n mem: Memori,\n sample,\n cfg: RunConfig,\n entity_external_id: str,\n process_id: str,\n) -> None:\n \"\"\"\n Build and print the exact AA request payload (no network call).\n\n This is useful for debugging staging/prod routing, payload structure, and metadata.\n \"\"\"\n import hashlib\n\n from memori._network import Api\n from memori.memory.augmentation.augmentations.memori._augmentation import (\n AdvancedAugmentation,\n )\n\n dialect = (\n mem.config.storage.adapter.get_dialect()\n if mem.config.storage and mem.config.storage.adapter\n else \"unknown\"\n )\n\n aug = AdvancedAugmentation(config=mem.config, enabled=True)\n url = Api(mem.config).url(\"sdk/augmentation\")\n\n all_msgs, all_turn_ids = _build_aa_messages_and_turn_ids_for_sample(sample)\n requests: list[dict[str, object]] = []\n if cfg.aa_batch != \"per_pair\":\n raise ValueError(f\"unknown aa batch: {cfg.aa_batch}\")\n for i, req in enumerate(_build_per_pair_requests(all_msgs, all_turn_ids)):\n payload = aug._build_api_payload( # noqa: SLF001 - benchmark-only debug path\n req.messages,\n \"\",\n None,\n dialect,\n entity_external_id,\n process_id,\n )\n requests.append(\n {\n \"request_index\": i,\n \"pair_turn_ids\": list(req.pair_turn_ids),\n \"messages\": req.messages,\n \"payload\": payload,\n }\n )\n\n api_key = mem.config.api_key or \"\"\n api_key_preview = \"\"\n api_key_sha256 = \"\"\n if api_key:\n api_key_preview = (\n f\"{api_key[:4]}…{api_key[-4:]}\" if len(api_key) >= 8 else \"set\"\n )\n api_key_sha256 = hashlib.sha256(api_key.encode(\"utf-8\")).hexdigest()\n\n preview = {\n \"url\": url,\n \"ingest\": \"advanced_augmentation\",\n \"aa_batch\": cfg.aa_batch,\n \"entity_id\": entity_external_id,\n \"process_id\": process_id,\n \"memori_api_key\": {\n \"is_set\": bool(api_key),\n \"length\": len(api_key),\n \"preview\": api_key_preview,\n \"sha256\": api_key_sha256,\n },\n \"requests\": requests,\n }\n\n out_path = out_dir / \"aa_payload_preview.json\"\n out_path.write_text(json.dumps(preview, indent=2), encoding=\"utf-8\")\n\n print(f\"[locomo][aa-dry-run] url={url}\")\n print(f\"[locomo][aa-dry-run] wrote={out_path}\")\n # Avoid flooding stdout for large datasets.\n max_print = 5\n preview_print = dict(preview)\n preview_print[\"requests_total\"] = len(requests)\n preview_print[\"requests_printed\"] = min(max_print, len(requests))\n preview_print[\"requests\"] = requests[:max_print]\n print(json.dumps(preview_print, indent=2))\n\n\ndef _build_per_pair_requests(\n msgs: list[dict[str, str]],\n turn_ids: list[str],\n) -> list[PairRequest]:\n \"\"\"\n Build per-pair AA requests that mirror SDK behavior.\n\n Each request includes *all* prior messages (oldest -> newest) plus the next\n strict user->assistant pair at the end.\n\n Any messages that can't be paired as a strict consecutive user+assistant pair\n are skipped as pair boundaries, but still remain in the context for later requests.\n \"\"\"\n if len(msgs) != len(turn_ids):\n raise ValueError(\"msgs and turn_ids must be aligned\")\n\n out: list[PairRequest] = []\n i = 0\n while i < len(msgs) - 1:\n role0 = (msgs[i].get(\"role\") or \"\").strip()\n role1 = (msgs[i + 1].get(\"role\") or \"\").strip()\n if role0 == \"user\" and role1 == \"assistant\":\n tid0 = (turn_ids[i] or \"\").strip()\n tid1 = (turn_ids[i + 1] or \"\").strip()\n out.append(\n PairRequest(messages=list(msgs[: i + 2]), pair_turn_ids=(tid0, tid1))\n )\n i += 2\n continue\n i += 1\n return out\n\n\ndef _format_top_k(\n *,\n results: list,\n prov_store: ProvenanceStore,\n run_id: str,\n sample_id: str,\n retrieved_ids_out: list[str],\n retrieved_groups_out: list[set[str]],\n provenance_limit: int = 1,\n) -> list[dict[str, object]]:\n out: list[dict[str, object]] = []\n for r in results:\n if isinstance(r, dict):\n content = r.get(\"content\", \"\")\n fact_id = r.get(\"id\")\n similarity = r.get(\"similarity\")\n else:\n # Assume FactSearchResult or similar object with attributes\n content = getattr(r, \"content\", \"\")\n fact_id = getattr(r, \"id\", None)\n similarity = getattr(r, \"similarity\", None)\n\n turn_ids: list[str] = []\n if isinstance(fact_id, int):\n dia_ids = prov_store.best_dia_ids_for_fact(\n run_id=run_id,\n sample_id=sample_id,\n fact_id=fact_id,\n limit=max(int(provenance_limit), 1),\n )\n turn_ids = [d for d in dia_ids if d]\n\n # For backward compatibility, keep a single primary turn_id field (first).\n turn_id = turn_ids[0] if turn_ids else \"\"\n if turn_id:\n retrieved_ids_out.append(turn_id)\n\n retrieved_groups_out.append(set(turn_ids))\n\n out.append(\n {\n \"turn_id\": turn_id,\n \"turn_ids\": turn_ids,\n \"fact_id\": fact_id,\n \"similarity\": similarity,\n \"content\": content,\n }\n )\n return out\n\n\nclass _Totals:\n def __init__(self) -> None:\n self.question_count = 0\n self.questions_by_category: dict[str, int] = {}\n self.sums = {\n \"hit@1\": 0.0,\n \"hit@3\": 0.0,\n \"hit@5\": 0.0,\n \"hit@10\": 0.0,\n \"hit@20\": 0.0,\n \"hit@30\": 0.0,\n \"mrr\": 0.0,\n }\n self.sums_by_cat: dict[str, dict[str, float]] = {}\n self.counts_by_cat: dict[str, int] = {}\n\n def count_question(self, category: str | None) -> None:\n self.question_count += 1\n cat = category or \"unknown\"\n self.questions_by_category[cat] = self.questions_by_category.get(cat, 0) + 1\n\n def add_metrics(self, *, category: str, metrics: dict[str, float]) -> None:\n for key in self.sums:\n self.sums[key] += float(metrics[key])\n self.sums_by_cat.setdefault(\n category,\n {\n \"hit@1\": 0.0,\n \"hit@3\": 0.0,\n \"hit@5\": 0.0,\n \"hit@10\": 0.0,\n \"hit@20\": 0.0,\n \"hit@30\": 0.0,\n \"mrr\": 0.0,\n },\n )\n for key in self.sums_by_cat[category]:\n self.sums_by_cat[category][key] += float(metrics[key])\n self.counts_by_cat[category] = self.counts_by_cat.get(category, 0) + 1\n\n def to_summary(\n self,\n *,\n run_id: str,\n timestamp_utc: str,\n dataset_path: str,\n sqlite_db_path: str,\n provenance_db_path: str,\n ingest: str,\n sample_count: int,\n ) -> dict:\n denom = float(self.question_count) if self.question_count else 1.0\n metrics_overall = {k: (self.sums[k] / denom) for k in self.sums}\n\n metrics_by_category: dict[str, dict[str, float]] = {}\n for cat, sums_cat in self.sums_by_cat.items():\n denom_cat = float(self.counts_by_cat.get(cat, 0)) or 1.0\n metrics_by_category[cat] = {k: (sums_cat[k] / denom_cat) for k in sums_cat}\n\n questions_by_category_labeled = {\n CATEGORY_LABELS.get(cat, cat): count\n for cat, count in self.questions_by_category.items()\n }\n metrics_by_category_labeled = {\n CATEGORY_LABELS.get(cat, cat): vals\n for cat, vals in metrics_by_category.items()\n }\n\n return {\n \"run_id\": run_id,\n \"timestamp_utc\": timestamp_utc,\n \"dataset_path\": dataset_path,\n \"sqlite_db_path\": sqlite_db_path,\n \"provenance_db_path\": provenance_db_path,\n \"ingest\": ingest,\n \"sample_count\": sample_count,\n \"question_count\": self.question_count,\n \"category_labels\": dict(CATEGORY_LABELS),\n \"questions_by_category\": dict(\n sorted(self.questions_by_category.items(), key=lambda kv: kv[0])\n ),\n \"questions_by_category_labeled\": dict(\n sorted(questions_by_category_labeled.items(), key=lambda kv: kv[0])\n ),\n \"metrics_overall\": metrics_overall,\n \"metrics_by_category\": dict(\n sorted(metrics_by_category.items(), key=lambda kv: kv[0])\n ),\n \"metrics_by_category_labeled\": dict(\n sorted(metrics_by_category_labeled.items(), key=lambda kv: kv[0])\n ),\n \"phase\": 2,\n }\n" }, { "path": "benchmarks/locomo/_types.py", "content": "from __future__ import annotations\n\nfrom dataclasses import dataclass\nfrom typing import Any\n\n\n@dataclass(frozen=True, slots=True)\nclass LoCoMoTurn:\n speaker: str\n text: str\n turn_id: str | None = None\n timestamp: str | None = None\n\n\n@dataclass(frozen=True, slots=True)\nclass LoCoMoSession:\n session_id: str | None\n turns: tuple[LoCoMoTurn, ...]\n date_time: str | None = None\n\n\n@dataclass(frozen=True, slots=True)\nclass LoCoMoQA:\n question_id: str\n question: str\n answers: tuple[str, ...]\n category: str | None = None\n evidence: Any | None = None\n\n\n@dataclass(frozen=True, slots=True)\nclass LoCoMoSample:\n sample_id: str\n sessions: tuple[LoCoMoSession, ...]\n qa: tuple[LoCoMoQA, ...]\n" }, { "path": "benchmarks/locomo/loader.py", "content": "from __future__ import annotations\n\nimport json\nimport re\nfrom pathlib import Path\nfrom typing import Any\n\nfrom benchmarks.locomo._types import LoCoMoQA, LoCoMoSample, LoCoMoSession, LoCoMoTurn\n\n\ndef load_locomo_json(path: str | Path) -> list[LoCoMoSample]:\n raw = Path(path).read_text(encoding=\"utf-8\")\n data = json.loads(raw)\n\n if not isinstance(data, list):\n raise ValueError(\"LoCoMo JSON must be a list of samples\")\n\n samples: list[LoCoMoSample] = []\n for i, item in enumerate(data):\n if not isinstance(item, dict):\n raise ValueError(f\"Sample at index {i} must be an object\")\n\n sample_id = _coerce_str(item.get(\"sample_id\")) or _coerce_str(item.get(\"id\"))\n if not sample_id:\n raise ValueError(f\"Sample at index {i} is missing 'sample_id'\")\n\n sessions = _parse_conversation(item.get(\"conversation\"))\n qa = _parse_qa(item.get(\"qa\"))\n samples.append(LoCoMoSample(sample_id=sample_id, sessions=sessions, qa=qa))\n\n return samples\n\n\ndef _parse_conversation(value: Any) -> tuple[LoCoMoSession, ...]:\n if value is None:\n return ()\n if isinstance(value, dict):\n return _parse_conversation_dict(value)\n if not isinstance(value, list):\n raise ValueError(\"'conversation' must be a list of sessions or an object\")\n\n out: list[LoCoMoSession] = []\n for idx, session in enumerate(value):\n if not isinstance(session, dict):\n raise ValueError(f\"conversation[{idx}] must be an object\")\n\n session_id = _coerce_str(session.get(\"session_id\")) or _coerce_str(\n session.get(\"id\")\n )\n turns_raw = (\n session.get(\"dialogue\") or session.get(\"turns\") or session.get(\"messages\")\n )\n turns = _parse_turns(turns_raw, context=f\"conversation[{idx}]\")\n out.append(LoCoMoSession(session_id=session_id, turns=turns, date_time=None))\n\n return tuple(out)\n\n\n_SESSION_KEY_RE = re.compile(r\"^session_(?P\\d+)$\")\n\n\ndef _parse_conversation_dict(value: dict[str, Any]) -> tuple[LoCoMoSession, ...]:\n sessions: list[tuple[int, str]] = []\n for key, v in value.items():\n if not isinstance(key, str):\n continue\n m = _SESSION_KEY_RE.match(key)\n if not m:\n continue\n if not isinstance(v, list):\n continue\n sessions.append((int(m.group(\"n\")), key))\n\n sessions.sort(key=lambda t: t[0])\n\n out: list[LoCoMoSession] = []\n for n, key in sessions:\n date_time = _coerce_str(value.get(f\"session_{n}_date_time\"))\n turns = _parse_turns(value.get(key), context=f\"conversation[{key}]\")\n out.append(LoCoMoSession(session_id=key, turns=turns, date_time=date_time))\n return tuple(out)\n\n\ndef _parse_turns(value: Any, *, context: str) -> tuple[LoCoMoTurn, ...]:\n if value is None:\n return ()\n if not isinstance(value, list):\n raise ValueError(f\"{context}.dialogue must be a list of turns\")\n\n out: list[LoCoMoTurn] = []\n for idx, turn in enumerate(value):\n if not isinstance(turn, dict):\n raise ValueError(f\"{context}.dialogue[{idx}] must be an object\")\n\n turn_id = _coerce_str(turn.get(\"dia_id\")) or _coerce_str(turn.get(\"turn_id\"))\n speaker = (\n _coerce_str(turn.get(\"speaker\")) or _coerce_str(turn.get(\"role\")) or \"\"\n )\n text = _coerce_str(turn.get(\"text\")) or _coerce_str(turn.get(\"content\")) or \"\"\n if not text:\n continue\n\n timestamp = _coerce_str(turn.get(\"timestamp\"))\n out.append(\n LoCoMoTurn(turn_id=turn_id, speaker=speaker, text=text, timestamp=timestamp)\n )\n\n return tuple(out)\n\n\ndef _parse_qa(value: Any) -> tuple[LoCoMoQA, ...]:\n if value is None:\n return ()\n if not isinstance(value, list):\n raise ValueError(\"'qa' must be a list\")\n\n out: list[LoCoMoQA] = []\n for idx, qa in enumerate(value):\n if not isinstance(qa, dict):\n raise ValueError(f\"qa[{idx}] must be an object\")\n\n qid = (\n _coerce_str(qa.get(\"question_id\")) or _coerce_str(qa.get(\"id\")) or f\"q{idx}\"\n )\n question = _coerce_str(qa.get(\"question\")) or \"\"\n if not question:\n continue\n\n answers_raw = qa.get(\"answer\") if \"answer\" in qa else qa.get(\"answers\")\n answers = _coerce_answers(answers_raw)\n category = _coerce_str(qa.get(\"category\")) or _coerce_str(qa.get(\"type\"))\n evidence = (\n qa.get(\"evidence\") if \"evidence\" in qa else qa.get(\"supporting_facts\")\n )\n out.append(\n LoCoMoQA(\n question_id=qid,\n question=question,\n answers=answers,\n category=category,\n evidence=evidence,\n )\n )\n\n return tuple(out)\n\n\ndef _coerce_str(value: Any) -> str | None:\n if value is None:\n return None\n if isinstance(value, str):\n v = value.strip()\n return v or None\n return str(value).strip() or None\n\n\ndef _coerce_answers(value: Any) -> tuple[str, ...]:\n if value is None:\n return ()\n if isinstance(value, str):\n v = value.strip()\n return (v,) if v else ()\n if isinstance(value, list):\n out: list[str] = []\n for item in value:\n s = _coerce_str(item)\n if s:\n out.append(s)\n return tuple(out)\n s = _coerce_str(value)\n return (s,) if s else ()\n" }, { "path": "benchmarks/locomo/preprocess.py", "content": "from __future__ import annotations\n\nimport argparse\nimport json\nimport re\nfrom collections import Counter\nfrom pathlib import Path\nfrom typing import Any\n\n_SESSION_KEY_RE = re.compile(r\"^session_(?P\\d+)$\")\n_MULTIMODAL_KEYS = (\"img_url\", \"blip_caption\", \"query\")\n\n\ndef preprocess_locomo_json(in_path: str | Path, out_path: str | Path) -> dict[str, int]:\n \"\"\"\n Preprocess LoCoMo into a Memori-friendly role format.\n\n - Removes multimodal turn fields (img_url, blip_caption, query).\n - Rewrites turn speakers so conversation.speaker_b becomes \"assistant\" and all others \"user\".\n If conversation.speaker_b is missing, the sample is kept and left unchanged.\n \"\"\"\n src = Path(in_path)\n dst = Path(out_path)\n\n data = json.loads(src.read_text(encoding=\"utf-8\"))\n if not isinstance(data, list):\n raise ValueError(\"LoCoMo JSON must be a list of samples\")\n\n kept: list[dict[str, Any]] = []\n dropped_reasons: Counter[str] = Counter()\n removed_multimodal_turns = 0\n\n for _idx, sample in enumerate(data):\n if not isinstance(sample, dict):\n dropped_reasons[\"invalid_sample\"] += 1\n continue\n\n conv = sample.get(\"conversation\")\n removed_multimodal_turns += _strip_multimodal_fields_inplace(conv)\n\n speaker_b = None\n if isinstance(conv, dict):\n speaker_b = _coerce_str(conv.get(\"speaker_b\"))\n\n if speaker_b:\n _rewrite_speakers_inplace(conv, assistant_speaker=speaker_b)\n else:\n # Keep unchanged when we can't confidently map assistant roles.\n dropped_reasons[\"missing_speaker_b\"] += 0\n\n kept.append(sample)\n\n dst.parent.mkdir(parents=True, exist_ok=True)\n dst.write_text(json.dumps(kept, ensure_ascii=False), encoding=\"utf-8\")\n\n return {\n \"samples_in\": len(data),\n \"samples_out\": len(kept),\n \"removed_multimodal_turns\": int(removed_multimodal_turns),\n \"dropped_invalid_sample\": int(dropped_reasons.get(\"invalid_sample\", 0)),\n }\n\n\ndef _strip_multimodal_fields_inplace(conversation: Any) -> int:\n removed = 0\n for turn in _iter_turn_dicts(conversation):\n if not isinstance(turn, dict):\n continue\n before = len(turn)\n for k in _MULTIMODAL_KEYS:\n turn.pop(k, None)\n removed += int(len(turn) != before)\n return removed\n\n\ndef _rewrite_speakers_inplace(conversation: Any, *, assistant_speaker: str) -> None:\n for turn in _iter_turn_dicts(conversation):\n speaker = _coerce_str(turn.get(\"speaker\")) or \"\"\n turn[\"speaker\"] = \"assistant\" if speaker == assistant_speaker else \"user\"\n\n\ndef _iter_turn_dicts(conversation: Any):\n if conversation is None:\n return\n\n if isinstance(conversation, list):\n for session in conversation:\n if not isinstance(session, dict):\n continue\n turns = (\n session.get(\"dialogue\")\n or session.get(\"turns\")\n or session.get(\"messages\")\n )\n if not isinstance(turns, list):\n continue\n for t in turns:\n if isinstance(t, dict):\n yield t\n return\n\n if isinstance(conversation, dict):\n sessions: list[tuple[int, str]] = []\n for key, v in conversation.items():\n if not isinstance(key, str):\n continue\n m = _SESSION_KEY_RE.match(key)\n if not m:\n continue\n if isinstance(v, list):\n sessions.append((int(m.group(\"n\")), key))\n sessions.sort(key=lambda x: x[0])\n\n for _, key in sessions:\n v = conversation.get(key)\n if not isinstance(v, list):\n continue\n for t in v:\n if isinstance(t, dict):\n yield t\n return\n\n\ndef _coerce_str(value: Any) -> str | None:\n if value is None:\n return None\n if isinstance(value, str):\n v = value.strip()\n return v or None\n return str(value).strip() or None\n\n\ndef main(argv: list[str] | None = None) -> int:\n parser = argparse.ArgumentParser(\n description=\"Preprocess LoCoMo: strip multimodal fields and map speaker_b -> assistant.\"\n )\n parser.add_argument(\n \"--in\", dest=\"in_path\", required=True, help=\"Input LoCoMo JSON path.\"\n )\n parser.add_argument(\n \"--out\", dest=\"out_path\", required=True, help=\"Output JSON path.\"\n )\n args = parser.parse_args(argv)\n\n stats = preprocess_locomo_json(args.in_path, args.out_path)\n print(\n \"[locomo][preprocess] \"\n f\"samples_in={stats['samples_in']} samples_out={stats['samples_out']} \"\n f\"removed_multimodal_turns={stats['removed_multimodal_turns']} \"\n f\"dropped_invalid_sample={stats['dropped_invalid_sample']}\"\n )\n return 0\n\n\nif __name__ == \"__main__\":\n raise SystemExit(main())\n" }, { "path": "benchmarks/locomo/provenance.py", "content": "from __future__ import annotations\n\nimport math\nimport re\nimport sqlite3\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom memori.search import find_similar_embeddings\n\nif TYPE_CHECKING:\n from memori.search._types import FactId\n\n\n@dataclass(frozen=True, slots=True)\nclass FactAttribution:\n fact_id: int\n dia_id: str\n score: float\n\n\nclass ProvenanceStore:\n def __init__(self, path: str | Path) -> None:\n self.path = Path(path)\n self.path.parent.mkdir(parents=True, exist_ok=True)\n self._ensure_schema()\n\n def _connect(self) -> sqlite3.Connection:\n return sqlite3.connect(str(self.path), check_same_thread=False)\n\n def _ensure_schema(self) -> None:\n with self._connect() as conn:\n conn.execute(\n \"\"\"\n CREATE TABLE IF NOT EXISTS bench_locomo_fact_provenance(\n run_id TEXT NOT NULL,\n sample_id TEXT NOT NULL,\n fact_id INTEGER NOT NULL,\n dia_id TEXT NOT NULL,\n score REAL NOT NULL,\n PRIMARY KEY (run_id, sample_id, fact_id, dia_id)\n )\n \"\"\"\n )\n conn.execute(\n \"\"\"\n CREATE INDEX IF NOT EXISTS idx_bench_locomo_fact_prov_lookup\n ON bench_locomo_fact_provenance(run_id, sample_id, fact_id, score DESC)\n \"\"\"\n )\n conn.commit()\n\n def upsert_many(\n self, rows: list[FactAttribution], *, run_id: str, sample_id: str\n ) -> None:\n if not rows:\n return\n with self._connect() as conn:\n conn.executemany(\n \"\"\"\n INSERT OR REPLACE INTO bench_locomo_fact_provenance(\n run_id, sample_id, fact_id, dia_id, score\n ) VALUES (?, ?, ?, ?, ?)\n \"\"\",\n [\n (run_id, sample_id, r.fact_id, r.dia_id, float(r.score))\n for r in rows\n ],\n )\n conn.commit()\n\n def best_dia_ids_for_fact(\n self, *, run_id: str, sample_id: str, fact_id: int, limit: int = 1\n ) -> list[str]:\n if limit <= 0:\n return []\n with self._connect() as conn:\n cur = conn.execute(\n \"\"\"\n SELECT dia_id\n FROM bench_locomo_fact_provenance\n WHERE run_id = ? AND sample_id = ? AND fact_id = ?\n ORDER BY score DESC\n LIMIT ?\n \"\"\",\n (run_id, sample_id, fact_id, limit),\n )\n return [r[0] for r in cur.fetchall() if r and r[0]]\n\n def has_any(self, *, run_id: str, sample_id: str) -> bool:\n with self._connect() as conn:\n cur = conn.execute(\n \"\"\"\n SELECT 1\n FROM bench_locomo_fact_provenance\n WHERE run_id = ? AND sample_id = ?\n LIMIT 1\n \"\"\",\n (run_id, sample_id),\n )\n return cur.fetchone() is not None\n\n def delete_sample(self, *, run_id: str, sample_id: str) -> None:\n with self._connect() as conn:\n conn.execute(\n \"\"\"\n DELETE FROM bench_locomo_fact_provenance\n WHERE run_id = ? AND sample_id = ?\n \"\"\",\n (run_id, sample_id),\n )\n conn.commit()\n\n\ndef attribute_facts_to_turn_ids(\n *,\n turn_ids: list[str],\n turn_embeddings: list[list[float]],\n turn_texts: list[str] | None = None,\n fact_ids: list[int],\n fact_embeddings: list[list[float]],\n fact_texts: list[str] | None = None,\n top_n: int = 1,\n min_score: float | None = None,\n) -> dict[int, list[tuple[str, float]]]:\n \"\"\"\n Map each fact to the most similar LoCoMo turn_id(s).\n\n This is intentionally heuristic: it enables benchmark-only provenance without\n changing Memori's product schema.\n \"\"\"\n if top_n <= 0:\n return {}\n if len(turn_ids) != len(turn_embeddings):\n raise ValueError(\"turn_ids and turn_embeddings must be the same length\")\n if len(fact_ids) != len(fact_embeddings):\n raise ValueError(\"fact_ids and fact_embeddings must be the same length\")\n if turn_texts is not None and len(turn_texts) != len(turn_ids):\n raise ValueError(\"turn_texts and turn_ids must be the same length\")\n if fact_texts is not None and len(fact_texts) != len(fact_ids):\n raise ValueError(\"fact_texts and fact_ids must be the same length\")\n\n embeddings: list[tuple[FactId, Any]] = cast(\n \"list[tuple[FactId, Any]]\", list(enumerate(turn_embeddings))\n )\n out: dict[int, list[tuple[str, float]]] = {}\n for i, (fact_id, qemb) in enumerate(zip(fact_ids, fact_embeddings, strict=True)):\n # Use a larger semantic pool before reranking to improve coverage.\n semantic_pool = max(top_n, min(len(turn_ids), max(top_n * 10, 50)))\n similar = find_similar_embeddings(embeddings, qemb, limit=semantic_pool)\n\n fact_text = (fact_texts[i] if fact_texts is not None else \"\") or \"\"\n lexical_scores = (\n _lexical_scores(query_text=fact_text, docs=turn_texts)\n if turn_texts is not None\n else None\n )\n\n mapped: list[tuple[str, float]] = []\n scored: list[tuple[int, float]] = []\n for raw_idx, score in similar:\n # idx is always int here (from enumerate), cast for type checker\n idx = cast(int, raw_idx)\n if idx < 0 or idx >= len(turn_ids):\n continue\n lex = float(lexical_scores[idx]) if lexical_scores is not None else 0.0\n combined = (0.8 * float(score)) + (0.2 * lex)\n scored.append((idx, combined))\n\n scored.sort(key=lambda t: t[1], reverse=True)\n for idx, score in scored[:top_n]:\n if min_score is not None and score < min_score:\n continue\n mapped.append((turn_ids[idx], float(score)))\n\n # If the threshold filtered everything, prefer some attribution over none.\n if not mapped and scored:\n idx_best, score_best = scored[0]\n mapped.append((turn_ids[idx_best], float(score_best)))\n\n out[int(fact_id)] = mapped\n return out\n\n\n_TOKEN_RE = re.compile(r\"[a-z0-9]+\")\n_STOPWORDS = {\n \"a\",\n \"an\",\n \"and\",\n \"are\",\n \"as\",\n \"at\",\n \"be\",\n \"by\",\n \"did\",\n \"do\",\n \"does\",\n \"for\",\n \"from\",\n \"had\",\n \"has\",\n \"have\",\n \"how\",\n \"i\",\n \"in\",\n \"is\",\n \"it\",\n \"of\",\n \"on\",\n \"or\",\n \"that\",\n \"the\",\n \"their\",\n \"then\",\n \"there\",\n \"to\",\n \"was\",\n \"were\",\n \"what\",\n \"when\",\n \"where\",\n \"which\",\n \"who\",\n \"why\",\n \"with\",\n \"you\",\n \"your\",\n}\n\n\ndef _tokenize(text: str) -> list[str]:\n tokens = [t for t in _TOKEN_RE.findall((text or \"\").lower()) if t]\n return [t for t in tokens if t not in _STOPWORDS]\n\n\ndef _lexical_scores(*, query_text: str, docs: list[str]) -> list[float]:\n q_tokens = _tokenize(query_text)\n if not q_tokens or not docs:\n return [0.0 for _ in docs]\n\n doc_tokens = [set(_tokenize(d)) for d in docs]\n n = float(len(docs)) or 1.0\n df: dict[str, int] = {}\n for t in set(q_tokens):\n df[t] = sum(1 for toks in doc_tokens if t in toks)\n idf = {t: (math.log((n + 1.0) / (float(df[t]) + 1.0)) + 1.0) for t in df}\n denom = sum(idf.get(t, 0.0) for t in q_tokens) or 1.0\n\n out: list[float] = []\n for toks in doc_tokens:\n num = sum(idf.get(t, 0.0) for t in q_tokens if t in toks)\n out.append(float(num / denom))\n return out\n" }, { "path": "benchmarks/locomo/report.py", "content": "from __future__ import annotations\n\nimport argparse\nimport json\nfrom pathlib import Path\nfrom typing import Any\n\nfrom benchmarks.locomo._run_impl import CATEGORY_LABELS\n\n\ndef main(argv: list[str] | None = None) -> int:\n parser = argparse.ArgumentParser(\n description=\"Aggregate LoCoMo predictions.jsonl into summary.json\"\n )\n parser.add_argument(\n \"--predictions\",\n required=True,\n help=\"Path to predictions.jsonl created by benchmarks/locomo/run.py\",\n )\n parser.add_argument(\n \"--out\",\n required=True,\n help=\"Path to write summary.json\",\n )\n args = parser.parse_args(argv)\n\n categories: dict[str, int] = {}\n sample_ids: set[str] = set()\n total = 0\n\n run_id: str | None = None\n timestamp_utc: str | None = None\n sums = {\n \"hit@1\": 0.0,\n \"hit@3\": 0.0,\n \"hit@5\": 0.0,\n \"hit@10\": 0.0,\n \"hit@20\": 0.0,\n \"hit@30\": 0.0,\n \"mrr\": 0.0,\n }\n sums_by_cat: dict[str, dict[str, float]] = {}\n counts_by_cat: dict[str, int] = {}\n\n for row in _read_jsonl(Path(args.predictions)):\n total += 1\n sample_id = str(row.get(\"sample_id\", \"\")).strip()\n if sample_id:\n sample_ids.add(sample_id)\n\n cat = str(row.get(\"category\") or \"unknown\")\n categories[cat] = categories.get(cat, 0) + 1\n\n metrics = (\n (row.get(\"retrieval\") or {}).get(\"metrics\")\n if isinstance(row.get(\"retrieval\"), dict)\n else None\n )\n if isinstance(metrics, dict):\n for key in sums:\n if key in metrics:\n sums[key] += float(metrics[key])\n sums_by_cat.setdefault(\n cat,\n {\n \"hit@1\": 0.0,\n \"hit@3\": 0.0,\n \"hit@5\": 0.0,\n \"hit@10\": 0.0,\n \"hit@20\": 0.0,\n \"hit@30\": 0.0,\n \"mrr\": 0.0,\n },\n )\n for key in sums_by_cat[cat]:\n if key in metrics:\n sums_by_cat[cat][key] += float(metrics[key])\n counts_by_cat[cat] = counts_by_cat.get(cat, 0) + 1\n\n run_id = run_id or _coerce_str(row.get(\"run_id\"))\n timestamp_utc = timestamp_utc or _coerce_str(row.get(\"timestamp_utc\"))\n\n denom = float(total) if total else 1.0\n metrics_overall = {k: (sums[k] / denom) for k in sums}\n metrics_by_category: dict[str, dict[str, float]] = {}\n for cat, sums_cat in sums_by_cat.items():\n denom_cat = float(counts_by_cat.get(cat, 0)) or 1.0\n metrics_by_category[cat] = {k: (sums_cat[k] / denom_cat) for k in sums_cat}\n\n questions_by_category_labeled = {\n CATEGORY_LABELS.get(cat, cat): count for cat, count in categories.items()\n }\n metrics_by_category_labeled = {\n CATEGORY_LABELS.get(cat, cat): vals for cat, vals in metrics_by_category.items()\n }\n\n out = {\n \"run_id\": run_id,\n \"timestamp_utc\": timestamp_utc,\n \"sample_count\": len(sample_ids),\n \"question_count\": total,\n \"category_labels\": dict(CATEGORY_LABELS),\n \"questions_by_category\": dict(sorted(categories.items(), key=lambda kv: kv[0])),\n \"questions_by_category_labeled\": dict(\n sorted(questions_by_category_labeled.items(), key=lambda kv: kv[0])\n ),\n \"metrics_overall\": metrics_overall,\n \"metrics_by_category\": dict(\n sorted(metrics_by_category.items(), key=lambda kv: kv[0])\n ),\n \"metrics_by_category_labeled\": dict(\n sorted(metrics_by_category_labeled.items(), key=lambda kv: kv[0])\n ),\n }\n Path(args.out).write_text(json.dumps(out, indent=2), encoding=\"utf-8\")\n return 0\n\n\ndef _read_jsonl(path: Path) -> list[dict[str, Any]]:\n rows: list[dict[str, Any]] = []\n for line in path.read_text(encoding=\"utf-8\").splitlines():\n if not line.strip():\n continue\n rows.append(json.loads(line))\n return rows\n\n\ndef _coerce_str(value: Any) -> str | None:\n if value is None:\n return None\n if isinstance(value, str):\n v = value.strip()\n return v or None\n return str(value).strip() or None\n\n\nif __name__ == \"__main__\":\n raise SystemExit(main())\n" }, { "path": "benchmarks/locomo/retrieval.py", "content": "from __future__ import annotations\n\nimport re\nfrom dataclasses import dataclass\nfrom typing import Any\n\nfrom benchmarks.locomo._types import LoCoMoSample\n\n\n@dataclass(frozen=True, slots=True)\nclass TurnFact:\n turn_id: str\n content: str\n\n\n_TURN_ID_RE = re.compile(r\"^\\[(?P[^\\]]+)\\]\\s*\")\n\n\ndef build_turn_facts(\n sample: LoCoMoSample,\n) -> tuple[tuple[TurnFact, ...], dict[tuple[str, int], str]]:\n facts: list[TurnFact] = []\n index: dict[tuple[str, int], str] = {}\n\n for s_idx, session in enumerate(sample.sessions):\n session_id = session.session_id or f\"session-{s_idx}\"\n for t_idx, turn in enumerate(session.turns):\n turn_id = turn.turn_id or f\"{sample.sample_id}:{session_id}:{t_idx}\"\n index[(session_id, t_idx)] = turn_id\n\n speaker = (turn.speaker or \"\").strip()\n prefix = f\"[{turn_id}]\"\n body = f\"{speaker}: {turn.text}\" if speaker else turn.text\n if session.date_time:\n body = f\"{body} (session_time: {session.date_time})\"\n if turn.timestamp:\n body = f\"{body} (ts: {turn.timestamp})\"\n facts.append(TurnFact(turn_id=turn_id, content=f\"{prefix} {body}\".strip()))\n\n return tuple(facts), index\n\n\ndef extract_turn_id_from_content(content: str) -> str | None:\n m = _TURN_ID_RE.match(content or \"\")\n if not m:\n return None\n v = m.group(\"turn_id\").strip()\n return v or None\n\n\ndef evidence_to_turn_ids(\n evidence: Any, *, turn_index: dict[tuple[str, int], str]\n) -> set[str]:\n if evidence is None:\n return set()\n\n out: set[str] = set()\n\n if isinstance(evidence, dict):\n _add_evidence_obj(evidence, out=out, turn_index=turn_index)\n return out\n\n if isinstance(evidence, list):\n for item in evidence:\n if isinstance(item, dict):\n _add_evidence_obj(item, out=out, turn_index=turn_index)\n elif isinstance(item, int):\n # If evidence is a bare turn index, only safe when there's a single session.\n _add_evidence_turn_index(item, out=out, turn_index=turn_index)\n elif isinstance(item, str):\n if item:\n out.add(item)\n return out\n\n if isinstance(evidence, int):\n _add_evidence_turn_index(evidence, out=out, turn_index=turn_index)\n return out\n\n if isinstance(evidence, str) and evidence:\n out.add(evidence)\n\n return out\n\n\ndef _add_evidence_obj(\n obj: dict[str, Any], *, out: set[str], turn_index: dict[tuple[str, int], str]\n) -> None:\n session_id = _coerce_str(obj.get(\"session_id\")) or _coerce_str(obj.get(\"session\"))\n turn_idx = _coerce_int(obj.get(\"turn_index\"))\n if session_id is not None and turn_idx is not None:\n tid = turn_index.get((session_id, turn_idx))\n if tid:\n out.add(tid)\n return\n\n turn_id = _coerce_str(obj.get(\"turn_id\")) or _coerce_str(obj.get(\"evidence_id\"))\n if turn_id:\n out.add(turn_id)\n\n\ndef _add_evidence_turn_index(\n idx: int, *, out: set[str], turn_index: dict[tuple[str, int], str]\n) -> None:\n sessions = {session_id for (session_id, _), _tid in turn_index.items()}\n if len(sessions) != 1:\n return\n (session_id,) = sessions\n tid = turn_index.get((session_id, idx))\n if tid:\n out.add(tid)\n\n\ndef _coerce_str(value: Any) -> str | None:\n if value is None:\n return None\n if isinstance(value, str):\n v = value.strip()\n return v or None\n return str(value).strip() or None\n\n\ndef _coerce_int(value: Any) -> int | None:\n if value is None:\n return None\n if isinstance(value, int):\n return value\n try:\n return int(str(value).strip())\n except Exception:\n return None\n" }, { "path": "benchmarks/locomo/run.py", "content": "import argparse\nimport os\n\nfrom benchmarks.locomo._run_impl import RunConfig, run_locomo\n\n\ndef main(argv: list[str] | None = None) -> int:\n parser = argparse.ArgumentParser(description=\"LoCoMo benchmark harness (Phase 2)\")\n parser.add_argument(\n \"--allow-prod-aa\",\n action=\"store_true\",\n help=\"(Deprecated) Allow Advanced Augmentation calls without MEMORI_TEST_MODE=1 (production).\",\n )\n parser.add_argument(\n \"--dataset\",\n required=True,\n help=\"Path to a LoCoMo JSON file (downloaded locally).\",\n )\n parser.add_argument(\n \"--out\",\n required=True,\n help=\"Output directory for artifacts (predictions.jsonl, summary.json).\",\n )\n parser.add_argument(\n \"--sqlite-db\",\n default=\"\",\n help=\"SQLite DB file path used for the run (default: /locomo.sqlite).\",\n )\n parser.add_argument(\n \"--provenance-db\",\n default=\"\",\n help=\"Benchmark-only provenance DB (default: /locomo_provenance.sqlite).\",\n )\n parser.add_argument(\n \"--reuse-db\",\n action=\"store_true\",\n help=\"Skip ingestion and reuse the existing SQLite/provenance DB for retrieval+scoring.\",\n )\n parser.add_argument(\n \"--run-id\",\n default=\"\",\n help=\"Run namespace used for entity external IDs/provenance (required when --reuse-db and multiple runs exist in the DB).\",\n )\n parser.add_argument(\n \"--k\",\n type=int,\n default=5,\n help=\"Retrieval top-k to store and score (default: 5).\",\n )\n parser.add_argument(\n \"--aa-timeout\",\n type=float,\n default=180.0,\n help=\"Timeout (seconds) to wait for Advanced Augmentation to finish per sample.\",\n )\n parser.add_argument(\n \"--aa-batch\",\n choices=[\"per_pair\"],\n default=\"per_pair\",\n help=\"How to batch messages when calling Advanced Augmentation (default: per_pair).\",\n )\n parser.add_argument(\n \"--aa-dry-run\",\n action=\"store_true\",\n help=\"Print/write AA request payload and exit before making any network calls.\",\n )\n parser.add_argument(\n \"--aa-max-requests\",\n type=int,\n default=0,\n help=\"Limit the number of AA requests to enqueue during ingestion (0 = no limit).\",\n )\n parser.add_argument(\n \"--seed-only\",\n action=\"store_true\",\n help=\"Run ingestion only (no retrieval/scoring). Useful for AA payload/summary validation.\",\n )\n parser.add_argument(\n \"--meta-llm-provider\",\n default=\"openai\",\n help=\"Metadata only: LLM provider to report to Advanced Augmentation (default: openai).\",\n )\n parser.add_argument(\n \"--meta-llm-version\",\n default=\"gpt-4.1-mini\",\n help=\"Metadata only: LLM model version to report (default: gpt-4.1-mini).\",\n )\n parser.add_argument(\n \"--meta-llm-sdk-version\",\n default=\"unknown\",\n help=\"Metadata only: LLM SDK version to report (default: unknown).\",\n )\n parser.add_argument(\n \"--meta-framework-provider\",\n default=\"memori\",\n help=\"Metadata only: framework provider to report (default: memori).\",\n )\n parser.add_argument(\n \"--meta-platform-provider\",\n default=\"benchmark\",\n help=\"Metadata only: platform provider to report (default: benchmark).\",\n )\n parser.add_argument(\n \"--aa-provenance-top-n\",\n type=int,\n default=1,\n help=\"How many turn_ids to attribute to each augmented fact (default: 1).\",\n )\n parser.add_argument(\n \"--aa-provenance-min-score\",\n type=float,\n default=0.25,\n help=\"Min cosine similarity to accept a fact->turn attribution (default: 0.25).\",\n )\n parser.add_argument(\n \"--aa-provenance-mode\",\n choices=[\"similarity\"],\n default=\"similarity\",\n help=(\n \"How to attribute AA facts back to LoCoMo turn IDs for scoring. \"\n \"'similarity' maps facts to turns post-hoc using embedding/text similarity (default). \"\n ),\n )\n parser.add_argument(\n \"--rebuild-provenance\",\n action=\"store_true\",\n help=\"When --reuse-db: recompute provenance offline from the SQLite DB (no AA calls).\",\n )\n parser.add_argument(\n \"--max-samples\",\n type=int,\n default=0,\n help=\"Limit number of samples (0 = no limit).\",\n )\n parser.add_argument(\n \"--only-sample-id\",\n default=\"\",\n help=\"Run only a single sample_id (exact match).\",\n )\n parser.add_argument(\n \"--max-sessions\",\n type=int,\n default=0,\n help=\"Limit number of sessions per sample (0 = no limit).\",\n )\n parser.add_argument(\n \"--max-questions\",\n type=int,\n default=0,\n help=\"Limit questions per sample (0 = no limit).\",\n )\n parser.add_argument(\n \"--verbose\",\n action=\"store_true\",\n help=\"Print progress/logging about seeding and scoring.\",\n )\n parser.add_argument(\n \"--log-every-questions\",\n type=int,\n default=0,\n help=\"When --verbose, log progress every N questions (0 = disabled).\",\n )\n args = parser.parse_args(argv)\n # LoCoMo benchmarks should never hit production AA. Force staging routing.\n os.environ[\"MEMORI_TEST_MODE\"] = \"1\"\n run_locomo(\n RunConfig(\n dataset=args.dataset,\n out=args.out,\n sqlite_db=args.sqlite_db,\n provenance_db=args.provenance_db,\n reuse_db=args.reuse_db,\n run_id=args.run_id,\n k=args.k,\n aa_timeout=args.aa_timeout,\n aa_batch=args.aa_batch,\n aa_dry_run=args.aa_dry_run,\n aa_max_requests=args.aa_max_requests,\n meta_llm_provider=args.meta_llm_provider,\n meta_llm_version=args.meta_llm_version,\n meta_llm_sdk_version=args.meta_llm_sdk_version,\n meta_framework_provider=args.meta_framework_provider,\n meta_platform_provider=args.meta_platform_provider,\n aa_provenance_top_n=args.aa_provenance_top_n,\n aa_provenance_min_score=args.aa_provenance_min_score,\n aa_provenance_mode=args.aa_provenance_mode,\n rebuild_provenance=args.rebuild_provenance,\n allow_prod_aa=args.allow_prod_aa,\n max_samples=args.max_samples,\n only_sample_id=args.only_sample_id,\n max_sessions=args.max_sessions,\n max_questions=args.max_questions,\n seed_only=args.seed_only,\n verbose=args.verbose,\n log_every_questions=args.log_every_questions,\n )\n )\n return 0\n\n\nif __name__ == \"__main__\":\n raise SystemExit(main())\n" }, { "path": "benchmarks/locomo/scoring.py", "content": "from __future__ import annotations\n\n\ndef hit_at_k(relevant: set[str], retrieved: list[str], k: int) -> float:\n if not relevant or not retrieved or k <= 0:\n return 0.0\n top = retrieved[:k]\n return 1.0 if any(item in relevant for item in top) else 0.0\n\n\ndef mrr(relevant: set[str], retrieved: list[str]) -> float:\n if not relevant or not retrieved:\n return 0.0\n for i, item in enumerate(retrieved, start=1):\n if item in relevant:\n return 1.0 / float(i)\n return 0.0\n\n\ndef hit_at_k_groups(relevant: set[str], retrieved: list[set[str]], k: int) -> float:\n \"\"\"\n Variant of hit@k where each retrieved rank can map to multiple IDs.\n\n This is useful when a retrieved item (e.g., an AA fact) may plausibly originate\n from multiple LoCoMo turns (dia_id).\n \"\"\"\n if not relevant or not retrieved or k <= 0:\n return 0.0\n for group in retrieved[:k]:\n if group and (group & relevant):\n return 1.0\n return 0.0\n\n\ndef mrr_groups(relevant: set[str], retrieved: list[set[str]]) -> float:\n if not relevant or not retrieved:\n return 0.0\n for i, group in enumerate(retrieved, start=1):\n if group and (group & relevant):\n return 1.0 / float(i)\n return 0.0\n" }, { "path": "benchmarks/perf/README.md", "content": "# Performance / latency benchmarks\n\nThis folder contains **performance benchmarks** for Memori recall, powered by `pytest-benchmark`.\n\n## Run locally\n\n```bash\nuv run pytest -m benchmark --benchmark-only benchmarks/perf/test_recall_benchmarks.py -v\n```\n\n## Run on EC2 (recommended for realistic DB latency)\n\nUse an EC2 instance in the same VPC as your database (RDS Postgres/MySQL).\n\n- Setup:\n\n```bash\nchmod +x benchmarks/perf/setup_ec2_benchmarks.sh\n./benchmarks/perf/setup_ec2_benchmarks.sh\n```\n\n- Run:\n\n```bash\nexport BENCHMARK_POSTGRES_URL=\"CHANGEME\"\nDB_TYPE=postgres TEST_TYPE=all ./benchmarks/perf/run_benchmarks_ec2.sh\n```\n\n### Environment variables\n\n- `DB_TYPE`: `postgres` (default) or `mysql`\n- `TEST_TYPE`: `all` (default), `end_to_end`, `db_retrieval`, `semantic_search`, `embedding`\n- `BENCHMARK_POSTGRES_URL`: Postgres connection string\n- `BENCHMARK_MYSQL_URL`: MySQL connection string\n\n## Outputs\n\nResults are saved to `./results`:\n\n- JSON: `results_{db}_{type}_{timestamp}.json`\n- CSV: `report_{db}_{type}_{timestamp}.csv`\n" }, { "path": "benchmarks/perf/_results.py", "content": "from __future__ import annotations\n\nimport csv\nfrom pathlib import Path\nfrom typing import Any\n\n\ndef repo_root() -> Path:\n return Path(__file__).resolve().parents[2]\n\n\ndef results_dir() -> Path:\n path = repo_root() / \"results\"\n path.mkdir(parents=True, exist_ok=True)\n return path\n\n\ndef append_csv_row(path: str | Path, *, header: list[str], row: dict[str, Any]) -> None:\n out_path = Path(path)\n out_path.parent.mkdir(parents=True, exist_ok=True)\n file_exists = out_path.exists()\n with out_path.open(\"a\", newline=\"\") as f:\n writer = csv.DictWriter(f, fieldnames=header)\n if not file_exists:\n writer.writeheader()\n writer.writerow(row)\n" }, { "path": "benchmarks/perf/conftest.py", "content": "\"\"\"Pytest fixtures for performance benchmarks.\"\"\"\n\nimport os\n\nimport pytest\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nfrom benchmarks.perf.fixtures.sample_data import (\n generate_facts_with_size,\n generate_sample_queries,\n)\nfrom memori import Memori\nfrom memori.embeddings import embed_texts\n\n\n@pytest.fixture\ndef postgres_db_connection():\n \"\"\"Create a PostgreSQL database connection factory for benchmarking (via AWS/Docker).\"\"\"\n postgres_uri = os.environ.get(\n \"BENCHMARK_POSTGRES_URL\",\n # Matches docker-compose.yml default DB name\n \"postgresql://memori:memori@localhost:5432/memori_test\",\n )\n\n from sqlalchemy import text\n\n # Support SSL root certificate via environment variable (for AWS RDS)\n connect_args = {}\n sslrootcert = os.environ.get(\"BENCHMARK_POSTGRES_SSLROOTCERT\")\n if sslrootcert:\n connect_args[\"sslrootcert\"] = sslrootcert\n # Ensure sslmode is set if using SSL cert\n if \"sslmode\" not in postgres_uri:\n # Add sslmode=require if not already in URI\n separator = \"&\" if \"?\" in postgres_uri else \"?\"\n postgres_uri = f\"{postgres_uri}{separator}sslmode=require\"\n\n engine = create_engine(\n postgres_uri,\n pool_pre_ping=True,\n pool_recycle=300,\n connect_args=connect_args,\n )\n\n try:\n with engine.connect() as conn:\n conn.execute(text(\"SELECT 1\"))\n except Exception as e:\n pytest.skip(\n f\"PostgreSQL not available at {postgres_uri}: {e}. \"\n \"Set BENCHMARK_POSTGRES_URL to a database that exists.\"\n )\n\n Session = sessionmaker(autocommit=False, autoflush=False, bind=engine)\n\n yield Session\n engine.dispose()\n\n\n@pytest.fixture\ndef mysql_db_connection():\n \"\"\"Create a MySQL database connection factory for benchmarking (via AWS/Docker).\"\"\"\n mysql_uri = os.environ.get(\n \"BENCHMARK_MYSQL_URL\",\n \"mysql+pymysql://memori:memori@localhost:3306/memori_test\",\n )\n\n from sqlalchemy import text\n\n engine = create_engine(\n mysql_uri,\n pool_pre_ping=True,\n pool_recycle=300,\n )\n\n try:\n with engine.connect() as conn:\n conn.execute(text(\"SELECT 1\"))\n except Exception as e:\n pytest.skip(f\"MySQL not available at {mysql_uri}: {e}\")\n\n Session = sessionmaker(autocommit=False, autoflush=False, bind=engine)\n\n yield Session\n engine.dispose()\n\n\n@pytest.fixture(\n params=[\"postgres\", \"mysql\"],\n ids=[\"postgres\", \"mysql\"],\n)\ndef db_connection(request):\n \"\"\"Parameterized fixture for realistic database types (no SQLite).\"\"\"\n db_type = request.param\n\n if db_type == \"postgres\":\n return request.getfixturevalue(\"postgres_db_connection\")\n elif db_type == \"mysql\":\n return request.getfixturevalue(\"mysql_db_connection\")\n\n pytest.skip(f\"Unsupported benchmark database type: {db_type}\")\n\n\n@pytest.fixture\ndef memori_instance(db_connection, request):\n \"\"\"Create a Memori instance with the specified database for benchmarking.\"\"\"\n mem = Memori(conn=db_connection)\n mem.config.storage.build()\n\n db_type_param = None\n for marker in request.node.iter_markers(\"parametrize\"):\n if \"db_connection\" in marker.args[0]:\n db_type_param = marker.args[1][0] if marker.args[1] else None\n break\n\n # Try to infer from connection\n if not db_type_param:\n try:\n # SQLAlchemy sessionmaker is callable, so detect it first by presence of a bind.\n bind = getattr(db_connection, \"kw\", {}).get(\"bind\", None)\n if bind is not None:\n db_type_param = bind.dialect.name\n else:\n db_type_param = \"unknown\"\n except Exception:\n db_type_param = \"unknown\"\n\n mem._benchmark_db_type = db_type_param\n return mem\n\n\n@pytest.fixture\ndef sample_queries():\n \"\"\"Provide sample queries of varying lengths.\"\"\"\n return generate_sample_queries()\n\n\n@pytest.fixture\ndef fact_content_size():\n \"\"\"Fixture for fact content size.\n\n Note: Embeddings are always 768 dimensions (3072 bytes binary) regardless of text size.\n \"\"\"\n return \"small\"\n\n\n@pytest.fixture(\n params=[5, 50, 100, 300, 600, 1000],\n ids=lambda x: f\"n{x}\",\n)\ndef entity_with_n_facts(memori_instance, fact_content_size, request):\n \"\"\"Create an entity with N facts for benchmarking database retrieval.\"\"\"\n fact_count = request.param\n entity_id = f\"benchmark-entity-{fact_count}-{fact_content_size}\"\n memori_instance.attribution(entity_id=entity_id, process_id=\"benchmark-process\")\n\n facts = generate_facts_with_size(fact_count, fact_content_size)\n fact_embeddings = embed_texts(\n facts,\n model=memori_instance.config.embeddings.model,\n )\n\n entity_db_id = memori_instance.config.storage.driver.entity.create(entity_id)\n memori_instance.config.storage.driver.entity_fact.create(\n entity_db_id, facts, fact_embeddings\n )\n\n db_type = getattr(memori_instance, \"_benchmark_db_type\", \"unknown\")\n\n return {\n \"entity_id\": entity_id,\n \"entity_db_id\": entity_db_id,\n \"fact_count\": fact_count,\n \"content_size\": fact_content_size,\n \"db_type\": db_type,\n \"facts\": facts,\n }\n" }, { "path": "benchmarks/perf/fixtures/sample_data.py", "content": "\"\"\"Helper functions for generating sample test data for benchmarks.\"\"\"\n\nimport random\nimport string\n\nrandom.seed(42)\n\n\ndef generate_random_string(length: int = 10) -> str:\n \"\"\"Generate a random string of specified length.\"\"\"\n return \"\".join(random.choices(string.ascii_letters + string.digits, k=length))\n\n\ndef generate_sample_fact() -> str:\n \"\"\"Generate a realistic sample fact for testing.\"\"\"\n templates = [\n \"User likes {item}\",\n \"User lives in {location}\",\n \"User works at {company}\",\n \"User's favorite color is {color}\",\n \"User prefers {preference}\",\n \"User has {count} {item}\",\n \"User enjoys {activity}\",\n \"User's birthday is {date}\",\n ]\n\n items = [\"pizza\", \"coffee\", \"books\", \"music\", \"movies\", \"travel\", \"coding\"]\n locations = [\"New York\", \"San Francisco\", \"London\", \"Tokyo\", \"Paris\"]\n companies = [\"Tech Corp\", \"Startup Inc\", \"Big Company\", \"Small Business\"]\n colors = [\"blue\", \"red\", \"green\", \"purple\", \"yellow\"]\n preferences = [\"dark mode\", \"light mode\", \"minimalist design\", \"detailed UI\"]\n activities = [\"reading\", \"hiking\", \"cooking\", \"gaming\", \"photography\"]\n dates = [\"January 1st\", \"March 15th\", \"June 30th\", \"December 25th\"]\n\n template = random.choice(templates)\n fact = template.format(\n item=random.choice(items),\n location=random.choice(locations),\n company=random.choice(companies),\n color=random.choice(colors),\n preference=random.choice(preferences),\n count=random.randint(1, 10),\n activity=random.choice(activities),\n date=random.choice(dates),\n )\n\n return fact\n\n\ndef generate_sample_queries() -> dict[str, list[str]]:\n \"\"\"Generate sample queries of varying lengths for benchmarking.\"\"\"\n return {\n \"short\": [\n \"What do I like?\",\n \"Where do I live?\",\n \"My preferences?\",\n \"Favorite color?\",\n \"Birthday?\",\n ],\n \"medium\": [\n \"What are my favorite things?\",\n \"Tell me about where I live\",\n \"What are my preferences for software?\",\n \"What is my favorite color and why?\",\n \"When is my birthday and how do I celebrate?\",\n ],\n \"long\": [\n \"Can you tell me about all the things I like and enjoy doing?\",\n \"I want to know more about where I currently live and work\",\n \"What are all my preferences when it comes to software and design?\",\n \"Please provide details about my favorite color and any related memories\",\n \"I'd like to know when my birthday is and how I typically celebrate it\",\n ],\n }\n\n\ndef generate_facts(count: int) -> list[str]:\n \"\"\"Generate a list of unique sample facts.\"\"\"\n facts = []\n seen = set()\n\n while len(facts) < count:\n fact = generate_sample_fact()\n # Add unique identifier to ensure no duplicates\n unique_fact = f\"{fact} (id: {len(facts)})\"\n\n # Double-check uniqueness (shouldn't be needed with id, but safe)\n if unique_fact not in seen:\n facts.append(unique_fact)\n seen.add(unique_fact)\n\n return facts\n\n\ndef generate_facts_with_size(count: int, size: str = \"small\") -> list[str]:\n \"\"\"Generate facts for benchmarking.\n\n Args:\n count: Number of facts to generate\n size: Content size\n\n Returns:\n List of unique facts\n \"\"\"\n base_facts = generate_facts(count)\n\n def with_id_suffix(text: str, idx: int, max_len: int) -> str:\n suffix = f\" (id: {idx})\"\n if max_len <= len(suffix):\n return suffix[-max_len:]\n return text[: max_len - len(suffix)] + suffix\n\n return [with_id_suffix(fact, i, 60) for i, fact in enumerate(base_facts)]\n" }, { "path": "benchmarks/perf/fixtures/sample_facts.py", "content": "# 1,000 synthetic user facts as semantic triples (subject, predicate, object)\n# Copy/paste into your project. Produces: user_data = {\"user\": \"John\", \"facts\": [(s,p,o), ...]}\n\n\ndef build_user_data() -> dict:\n facts: list[tuple[str, str, str]] = []\n\n def add(s: str, p: str, o: str) -> None:\n facts.append((s, p, o))\n\n # -----------------------------\n # Core profile facts (diverse)\n # -----------------------------\n add(\"John\", \"type\", \"Person\")\n add(\"John\", \"has_given_name\", \"John\")\n add(\"John\", \"prefers_language\", \"English\")\n add(\"John\", \"prefers_time_format\", \"12-hour\")\n add(\"John\", \"prefers_temperature_unit\", \"Fahrenheit\")\n add(\"John\", \"prefers_distance_unit\", \"miles\")\n add(\"John\", \"has_home_city\", \"St_Louis\")\n add(\"John\", \"has_home_state\", \"Missouri\")\n add(\"John\", \"has_home_country\", \"United_States\")\n add(\"John\", \"has_role\", \"Software_Engineer\")\n add(\"John\", \"works_in\", \"Technology\")\n add(\"John\", \"interested_in\", \"AI\")\n add(\"John\", \"interested_in\", \"cloud_infrastructure\")\n add(\"John\", \"interested_in\", \"personal_finance\")\n add(\"John\", \"interested_in\", \"fitness\")\n add(\"John\", \"interested_in\", \"cooking\")\n add(\"John\", \"interested_in\", \"travel\")\n add(\"John\", \"interested_in\", \"productivity\")\n add(\"John\", \"interested_in\", \"home_improvement\")\n add(\"John\", \"uses_llm_for\", \"writing_assistance\")\n add(\"John\", \"uses_llm_for\", \"coding_help\")\n add(\"John\", \"uses_llm_for\", \"travel_planning\")\n add(\"John\", \"uses_llm_for\", \"learning_new_topics\")\n add(\"John\", \"uses_llm_for\", \"recipe_ideas\")\n add(\"John\", \"uses_llm_for\", \"career_advice\")\n add(\"John\", \"uses_llm_for\", \"data_analysis\")\n add(\"John\", \"uses_llm_for\", \"brainstorming\")\n add(\"John\", \"prefers_response_style\", \"structured\")\n add(\"John\", \"prefers_response_style\", \"actionable\")\n add(\"John\", \"concerned_about\", \"latency\")\n add(\"John\", \"concerned_about\", \"cost\")\n add(\"John\", \"concerned_about\", \"privacy\")\n add(\"John\", \"primary_os\", \"macOS\")\n add(\"John\", \"primary_browser\", \"Chrome\")\n add(\"John\", \"primary_editor\", \"VS_Code\")\n add(\"John\", \"primary_shell\", \"zsh\")\n add(\"John\", \"primary_email_provider\", \"Gmail\")\n add(\"John\", \"primary_calendar\", \"Google_Calendar\")\n add(\"John\", \"primary_messaging_app\", \"Slack\")\n add(\"John\", \"primary_code_host\", \"GitHub\")\n add(\"John\", \"prefers_document_format\", \"Markdown\")\n add(\"John\", \"prefers_spreadsheet_tool\", \"Google_Sheets\")\n add(\"John\", \"uses_password_manager\", \"1Password\")\n add(\"John\", \"uses_cloud_storage\", \"Google_Drive\")\n add(\"John\", \"uses_issue_tracker\", \"GitHub_Issues\")\n add(\"John\", \"uses_ci_cd\", \"GitHub_Actions\")\n add(\"John\", \"prefers_container_runtime\", \"Docker\")\n add(\"John\", \"prefers_database\", \"PostgreSQL\")\n add(\"John\", \"prefers_cache\", \"Redis\")\n add(\"John\", \"prefers_language_for_backend\", \"Python\")\n add(\"John\", \"knows_language\", \"JavaScript\")\n add(\"John\", \"knows_language\", \"SQL\")\n add(\"John\", \"knows_language\", \"Bash\")\n add(\"John\", \"learning_language\", \"Rust\")\n add(\"John\", \"prefers_testing_framework\", \"pytest\")\n add(\"John\", \"prefers_package_manager\", \"uv\")\n add(\"John\", \"uses_llm_provider\", \"OpenAI\")\n add(\"John\", \"uses_llm_provider\", \"Anthropic\")\n add(\"John\", \"uses_model_family\", \"GPT\")\n add(\"John\", \"uses_model_family\", \"Claude\")\n add(\"John\", \"has_hobby\", \"fantasy_football\")\n add(\"John\", \"follows_sport\", \"NFL\")\n add(\"John\", \"follows_sport\", \"NHL\")\n add(\"John\", \"prefers_coffee_drink\", \"latte\")\n add(\"John\", \"prefers_breakfast\", \"oatmeal\")\n add(\"John\", \"prefers_lunch\", \"salad\")\n add(\"John\", \"prefers_dinner\", \"grilled_chicken\")\n add(\"John\", \"commutes_by\", \"car\")\n add(\"John\", \"prefers_meeting_platform\", \"Zoom\")\n\n # -----------------------------\n # Family / household facts\n # -----------------------------\n family = [\n (\"Spouse_01\", \"spouse\"),\n (\"Child_01\", \"child\"),\n (\"Child_02\", \"child\"),\n (\"Parent_01\", \"parent\"),\n (\"Parent_02\", \"parent\"),\n (\"Sibling_01\", \"sibling\"),\n (\"Sibling_02\", \"sibling\"),\n (\"Pet_01\", \"pet\"),\n ]\n for i, (entity, rel) in enumerate(family, start=1):\n add(entity, \"type\", \"Person\" if \"Pet\" not in entity else \"Animal\")\n add(\"John\", f\"has_{rel}\", entity)\n add(entity, \"has_first_name\", entity.split(\"_\")[0])\n add(entity, \"located_in\", f\"City_{(i % 25) + 1:03d}\")\n add(\n entity,\n \"preferred_contact_method\",\n [\"text\", \"call\", \"email\"][i % 3] if \"Pet\" not in entity else \"n/a\",\n )\n\n # -----------------------------\n # Friends (varied interests)\n # -----------------------------\n friend_interests = [\n \"music\",\n \"travel\",\n \"tech\",\n \"food\",\n \"sports\",\n \"finance\",\n \"fitness\",\n \"gaming\",\n \"books\",\n \"photography\",\n ]\n contact_methods = [\"email\", \"text\", \"slack\", \"signal\", \"call\"]\n for i in range(1, 61): # 60 friends\n f = f\"Friend_{i:03d}\"\n add(f, \"type\", \"Person\")\n add(\"John\", \"has_friend\", f)\n add(f, \"located_in\", f\"City_{(i % 80) + 1:03d}\")\n add(f, \"interested_in\", friend_interests[i % len(friend_interests)])\n add(f, \"preferred_contact_method\", contact_methods[i % len(contact_methods)])\n\n # -----------------------------\n # Coworkers (teams, roles, tools)\n # -----------------------------\n roles = [\n \"Backend_Engineer\",\n \"Frontend_Engineer\",\n \"DevOps_Engineer\",\n \"Product_Manager\",\n \"Designer\",\n \"Data_Engineer\",\n ]\n teams = [\"Platform\", \"Infra\", \"Product\", \"Data\", \"Security\", \"Growth\"]\n tools = [\"Jira\", \"Linear\", \"Confluence\", \"Notion\", \"Figma\", \"Datadog\", \"Grafana\"]\n for i in range(1, 41): # 40 coworkers\n c = f\"Coworker_{i:03d}\"\n add(c, \"type\", \"Person\")\n add(\"John\", \"works_with\", c)\n add(c, \"has_role\", roles[i % len(roles)])\n add(c, \"member_of_team\", teams[i % len(teams)])\n add(c, \"uses_tool\", tools[i % len(tools)])\n\n # -----------------------------\n # Places: cities, venues, travel\n # -----------------------------\n for i in range(1, 81): # 80 cities\n city = f\"City_{i:03d}\"\n add(city, \"type\", \"City\")\n add(city, \"in_country\", \"United_States\" if i <= 60 else \"International\")\n add(\"John\", \"has_visited\", city if i <= 45 else f\"Planned_{city}\")\n add(\n city,\n \"has_timezone\",\n \"America/Chicago\"\n if i % 3 == 0\n else \"America/New_York\"\n if i % 3 == 1\n else \"America/Los_Angeles\",\n )\n\n venue_types = [\n \"Restaurant\",\n \"Coffee_Shop\",\n \"Gym\",\n \"Airport\",\n \"Hotel\",\n \"Park\",\n \"Museum\",\n \"Stadium\",\n ]\n for i in range(1, 81): # 80 venues\n v = f\"Venue_{i:03d}\"\n add(v, \"type\", venue_types[i % len(venue_types)])\n add(v, \"located_in\", f\"City_{(i % 80) + 1:03d}\")\n add(\"John\", \"likes_place\", v if i % 4 != 0 else f\"Neutral_{v}\")\n add(v, \"has_price_tier\", [\"$\", \"$$\", \"$$$\"][i % 3])\n\n # -----------------------------\n # Devices, apps, services, accounts\n # -----------------------------\n devices = [\n \"MacBook_Pro\",\n \"iPhone\",\n \"iPad\",\n \"AirPods\",\n \"Smart_TV\",\n \"Router\",\n \"NAS\",\n \"Mechanical_Keyboard\",\n \"Gaming_PC\",\n \"Monitor_34inch\",\n \"Standing_Desk\",\n \"Ergonomic_Chair\",\n \"Fitness_Tracker\",\n \"Smart_Speaker\",\n \"Kindle\",\n \"External_SSD\",\n \"Webcam\",\n \"Microphone\",\n \"Printer\",\n \"Smart_Thermostat\",\n ]\n for d in devices:\n add(d, \"type\", \"Device\")\n add(\"John\", \"owns_device\", d)\n add(d, \"used_for\", \"work\" if \"MacBook\" in d or \"Monitor\" in d else \"personal\")\n add(d, \"has_status\", \"active\")\n\n apps = [\n \"Notion\",\n \"Slack\",\n \"Gmail\",\n \"Google_Calendar\",\n \"Spotify\",\n \"YouTube\",\n \"Netflix\",\n \"Amazon\",\n \"GitHub\",\n \"Figma\",\n \"Jira\",\n \"Linear\",\n \"Zoom\",\n \"1Password\",\n \"Google_Drive\",\n ]\n for i, app in enumerate(apps, start=1):\n add(app, \"type\", \"App\")\n add(\"John\", \"uses_app\", app)\n add(app, \"has_version\", f\"{(i % 5) + 1}.{(i % 10)}.{(i % 20)}\")\n\n return {\"user\": \"John\", \"facts\": facts}\n" }, { "path": "benchmarks/perf/generate_percentile_report.py", "content": "\"\"\"Generate percentile report (p50/p95/p99) from benchmark JSON results.\"\"\"\n\nimport json\nimport sys\nfrom pathlib import Path\n\n\ndef calculate_percentile(data, percentile):\n \"\"\"Calculate percentile from sorted data.\"\"\"\n if not data:\n return None\n sorted_data = sorted(data)\n index = (len(sorted_data) - 1) * percentile / 100\n lower = int(index)\n upper = lower + 1\n weight = index - lower\n\n if upper >= len(sorted_data):\n return sorted_data[lower]\n\n return sorted_data[lower] * (1 - weight) + sorted_data[upper] * weight\n\n\ndef extract_n_from_test_name(test_name):\n \"\"\"Extract N (number of records) from test name.\"\"\"\n import re\n\n # Our parametrized ids use \"n{N}\" (e.g. \"[n1000-postgres-small]\")\n match = re.search(r\"n(\\d+)\", test_name)\n if match:\n return int(match.group(1))\n\n # Backwards-compatible: plain numeric parameter (e.g. \"[1000]\")\n match = re.search(r\"\\[(\\d+)\\]\", test_name)\n return int(match.group(1)) if match else None\n\n\ndef extract_db_type_from_test_name(test_name):\n \"\"\"Extract database type from test name.\"\"\"\n import re\n\n # Look for database type in test name (postgres, mysql)\n match = re.search(r\"\\[(postgres|mysql)[-\\]]\", test_name)\n if not match:\n match = re.search(r\"-(postgres|mysql)[-\\]]\", test_name)\n if not match:\n match = re.search(r\"(postgres|mysql)\", test_name)\n return match.group(1) if match else \"unknown\"\n\n\ndef extract_content_size_from_test_name(test_name):\n \"\"\"Extract content size from test name.\"\"\"\n import re\n\n match = re.search(r\"\\[small[-\\]]\", test_name)\n if not match:\n match = re.search(r\"-small[-\\]]\", test_name)\n if not match:\n match = re.search(r\"small\", test_name)\n return match.group(0) if match else \"small\"\n\n\ndef extract_benchmark_id_from_test_name(test_name):\n \"\"\"\n Extract a stable benchmark identifier from pytest-benchmark's name field.\n Examples:\n \"test_benchmark_end_to_end_recall[n1000-sqlite-small]\" -> \"test_benchmark_end_to_end_recall\"\n \"test_benchmark_query_embedding_short\" -> \"test_benchmark_query_embedding_short\"\n \"\"\"\n import re\n\n match = re.match(r\"([^\\[]+)\", test_name)\n return match.group(1) if match else test_name\n\n\ndef generate_percentile_report(json_file_path, max_n=None):\n \"\"\"Generate p50/p95/p99 report from benchmark JSON.\n\n Args:\n json_file_path: Path to pytest-benchmark JSON output\n max_n: Optional maximum N value to include (filters out tests with N > max_n)\n \"\"\"\n with open(json_file_path) as f:\n data = json.load(f)\n\n benchmarks = {}\n\n for benchmark in data.get(\"benchmarks\", []):\n test_name = benchmark.get(\"name\", \"\")\n benchmark_id = extract_benchmark_id_from_test_name(test_name)\n n = extract_n_from_test_name(test_name)\n\n # Skip if N is None (couldn't extract) or exceeds max_n filter\n if n is None:\n continue\n if max_n is not None and n > max_n:\n continue\n\n db_type = extract_db_type_from_test_name(test_name)\n content_size = extract_content_size_from_test_name(test_name)\n extra_info = benchmark.get(\"extra_info\", {}) or {}\n\n stats = benchmark.get(\"stats\", {})\n times = stats.get(\"data\", [])\n\n if not times:\n continue\n\n peak_rss_bytes = extra_info.get(\"peak_rss_bytes\")\n\n p50 = calculate_percentile(times, 50)\n p95 = calculate_percentile(times, 95)\n p99 = calculate_percentile(times, 99)\n\n # Create composite key. We include benchmark_id so different benchmark types\n # don't overwrite each other (e.g. end-to-end vs db fetch for same N/db/size).\n key = (benchmark_id, n, db_type, content_size)\n\n benchmarks[key] = {\n \"benchmark_id\": benchmark_id,\n \"n\": n,\n \"db_type\": db_type,\n \"content_size\": content_size,\n \"p50\": p50,\n \"p95\": p95,\n \"p99\": p99,\n \"mean\": stats.get(\"mean\", 0),\n \"min\": stats.get(\"min\", 0),\n \"max\": stats.get(\"max\", 0),\n \"peak_rss_bytes\": peak_rss_bytes,\n }\n\n return benchmarks\n\n\ndef generate_report(benchmarks, output_format=\"table\"):\n \"\"\"Generate percentile report in specified format as string.\"\"\"\n lines = []\n\n if output_format == \"table\":\n lines.append(\"\\n\" + \"=\" * 100)\n lines.append(\n \"PERCENTILE REPORT (p50/p95/p99) per N, Database Type, and Content Size\"\n )\n lines.append(\"=\" * 100)\n lines.append(\n f\"{'Benchmark':<34} {'N':<8} {'DB':<12} {'Size':<8} {'p50 (ms)':<12} {'p95 (ms)':<12} \"\n f\"{'p99 (ms)':<12} {'Mean (ms)':<12} {'Peak RSS (MB)':<14}\"\n )\n lines.append(\"-\" * 100)\n\n for key in sorted(benchmarks.keys()):\n stats = benchmarks[key]\n peak_rss_mb = (\n (stats[\"peak_rss_bytes\"] / (1024 * 1024))\n if stats.get(\"peak_rss_bytes\") is not None\n else None\n )\n lines.append(\n f\"{stats['benchmark_id']:<34} \"\n f\"{stats['n']:<8} \"\n f\"{stats['db_type']:<12} \"\n f\"{stats['content_size']:<8} \"\n f\"{stats['p50'] * 1000:<12.4f} \"\n f\"{stats['p95'] * 1000:<12.4f} \"\n f\"{stats['p99'] * 1000:<12.4f} \"\n f\"{stats['mean'] * 1000:<12.4f} \"\n f\"{(f'{peak_rss_mb:.1f}' if peak_rss_mb is not None else ''):<14}\"\n )\n lines.append(\"=\" * 100)\n return \"\\n\".join(lines)\n\n if output_format == \"csv\":\n lines.append(\n \"benchmark_id,N,db_type,content_size,p50_ms,p95_ms,p99_ms,mean_ms,min_ms,max_ms,peak_rss_mb\"\n )\n for key in sorted(benchmarks.keys()):\n stats = benchmarks[key]\n peak_rss_mb = (\n (stats[\"peak_rss_bytes\"] / (1024 * 1024))\n if stats.get(\"peak_rss_bytes\") is not None\n else None\n )\n lines.append(\n f\"{stats['benchmark_id']},\"\n f\"{stats['n']},\"\n f\"{stats['db_type']},\"\n f\"{stats['content_size']},\"\n f\"{stats['p50'] * 1000:.4f},\"\n f\"{stats['p95'] * 1000:.4f},\"\n f\"{stats['p99'] * 1000:.4f},\"\n f\"{stats['mean'] * 1000:.4f},\"\n f\"{stats['min'] * 1000:.4f},\"\n f\"{stats['max'] * 1000:.4f},\"\n f\"{(f'{peak_rss_mb:.4f}' if peak_rss_mb is not None else '')}\"\n )\n return \"\\n\".join(lines)\n\n if output_format == \"json\":\n output = {}\n for key in sorted(benchmarks.keys()):\n stats = benchmarks[key]\n output_key = f\"{stats['benchmark_id']}_{stats['n']}_{stats['db_type']}_{stats['content_size']}\"\n peak_rss_mb = (\n (stats[\"peak_rss_bytes\"] / (1024 * 1024))\n if stats.get(\"peak_rss_bytes\") is not None\n else None\n )\n output[output_key] = {\n \"benchmark_id\": stats[\"benchmark_id\"],\n \"n\": stats[\"n\"],\n \"db_type\": stats[\"db_type\"],\n \"content_size\": stats[\"content_size\"],\n \"p50_ms\": stats[\"p50\"] * 1000,\n \"p95_ms\": stats[\"p95\"] * 1000,\n \"p99_ms\": stats[\"p99\"] * 1000,\n \"mean_ms\": stats[\"mean\"] * 1000,\n \"min_ms\": stats[\"min\"] * 1000,\n \"max_ms\": stats[\"max\"] * 1000,\n \"peak_rss_mb\": peak_rss_mb,\n }\n return json.dumps(output, indent=2)\n\n return \"\"\n\n\ndef main():\n if len(sys.argv) < 2:\n print(\n \"Usage: python generate_percentile_report.py [format] [output_file] [max_n]\"\n )\n print(\" format: table (default), csv, or json\")\n print(\" output_file: optional file path to write report (default: stdout)\")\n print(\n \" max_n: optional maximum N value to include (filters out tests with N > max_n)\"\n )\n sys.exit(1)\n\n json_file = Path(sys.argv[1])\n if not json_file.exists():\n print(f\"Error: File not found: {json_file}\")\n sys.exit(1)\n\n output_format = sys.argv[2] if len(sys.argv) > 2 else \"table\"\n\n if output_format not in [\"table\", \"csv\", \"json\"]:\n print(f\"Error: Invalid format '{output_format}'. Use: table, csv, or json\")\n sys.exit(1)\n\n output_file = sys.argv[3] if len(sys.argv) > 3 else None\n max_n = int(sys.argv[4]) if len(sys.argv) > 4 else None\n\n benchmarks = generate_percentile_report(json_file, max_n=max_n)\n\n if not benchmarks:\n print(\"No benchmark data found.\")\n sys.exit(1)\n\n report = generate_report(benchmarks, output_format)\n\n if output_file:\n Path(output_file).write_text(report, encoding=\"utf-8\")\n print(f\"Report written to: {output_file}\")\n else:\n print(report)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "benchmarks/perf/memory_utils.py", "content": "import threading\nimport time\nfrom collections.abc import Callable\nfrom typing import TypeVar\n\nT = TypeVar(\"T\")\n\n\ndef measure_peak_rss_bytes(\n fn: Callable[[], T], *, sample_interval_seconds: float = 0.005\n) -> tuple[T, int | None]:\n \"\"\"\n Measure approximate peak RSS (process resident set size) while running fn.\n\n Returns (result, peak_rss_bytes). If psutil isn't available, peak is None.\n \"\"\"\n try:\n import psutil\n except Exception:\n return fn(), None\n\n proc = psutil.Process()\n peak = {\"rss\": proc.memory_info().rss}\n stop = threading.Event()\n\n def _sampler() -> None:\n while not stop.is_set():\n try:\n rss = proc.memory_info().rss\n if rss > peak[\"rss\"]:\n peak[\"rss\"] = rss\n except Exception:\n pass\n time.sleep(sample_interval_seconds)\n\n t = threading.Thread(target=_sampler, daemon=True)\n t.start()\n try:\n result = fn()\n finally:\n stop.set()\n t.join(timeout=1.0)\n\n return result, int(peak[\"rss\"])\n" }, { "path": "benchmarks/perf/run_benchmarks_ec2.sh", "content": "#!/bin/bash\n# Shared benchmark execution functions for AWS EC2 environment\n\nset -e\n\n# Get script location to handle relative paths correctly\nBENCHMARK_DIR=\"$(cd \"$(dirname \"${BASH_SOURCE[0]}\")\" && pwd)\"\nREPO_ROOT=\"$(cd \"$BENCHMARK_DIR/../..\" && pwd)\"\n\n# Default settings\nDB_TYPE=${DB_TYPE:-\"postgres\"}\nTEST_TYPE=${TEST_TYPE:-\"all\"} # options: all, end_to_end, db_retrieval, semantic_search, embedding\nOUTPUT_DIR=${OUTPUT_DIR:-\"$REPO_ROOT/results\"}\n\nmkdir -p \"$OUTPUT_DIR\"\n\nrun_benchmarks() {\n local db=$1\n local type=$2\n local timestamp=$(date +%Y%m%d_%H%M%S)\n local output_json=\"$OUTPUT_DIR/results_${db}_${type}_${timestamp}.json\"\n local output_csv=\"$OUTPUT_DIR/report_${db}_${type}_${timestamp}.csv\"\n\n echo \"====================================================\"\n echo \"Running benchmarks for: DB=$db, Type=$type\"\n echo \"Output JSON: $output_json\"\n echo \"Output CSV: $output_csv\"\n echo \"====================================================\"\n\n # Determine pytest filter (-k) based on test type\n local filter=\"\"\n case $type in\n \"end_to_end\") filter=\"TestEndToEndRecallBenchmarks\" ;;\n \"db_retrieval\") filter=\"DatabaseEmbeddingRetrievalBenchmarks or DatabaseFactContentRetrievalBenchmarks\" ;;\n \"semantic_search\") filter=\"TestSemanticSearchBenchmarks\" ;;\n \"embedding\") filter=\"TestQueryEmbeddingBenchmarks\" ;;\n \"all\") filter=\"\" ;;\n *) echo \"Unknown test type: $type\"; exit 1 ;;\n esac\n\n # Add database filter\n if [[ -n \"$filter\" ]]; then\n filter=\"($filter) and $db\"\n else\n filter=\"$db\"\n fi\n\n # Run benchmarks from repo root\n (\n cd \"$REPO_ROOT\"\n uv run pytest -m benchmark \\\n --benchmark-only \\\n benchmarks/perf/test_recall_benchmarks.py \\\n -k \"$filter\" \\\n -v \\\n --benchmark-json=\"$output_json\"\n\n # Automatically convert to CSV\n if [[ -f \"$output_json\" ]]; then\n echo \"Converting results to CSV...\"\n uv run python benchmarks/perf/generate_percentile_report.py \\\n \"$output_json\" \\\n csv \\\n \"$output_csv\"\n echo \"CSV Report generated: $output_csv\"\n else\n echo \"Warning: JSON results file not found, skipping CSV generation.\"\n fi\n )\n}\n\n# If script is executed directly (not sourced), run based on env vars\nif [[ \"${BASH_SOURCE[0]}\" == \"${0}\" ]]; then\n # Print usage info if help requested\n if [[ \"$1\" == \"--help\" || \"$1\" == \"-h\" ]]; then\n echo \"Usage: DB_TYPE=[postgres|mysql] TEST_TYPE=[all|end_to_end|db_retrieval|semantic_search|embedding] $0\"\n exit 0\n fi\n\n run_benchmarks \"$DB_TYPE\" \"$TEST_TYPE\"\n\n echo \"====================================================\"\n echo \"Benchmark Run Complete\"\n echo \"====================================================\"\nfi\n" }, { "path": "benchmarks/perf/setup_ec2_benchmarks.sh", "content": "#!/bin/bash\n# Setup script for running benchmarks on AWS EC2\n\nset -e\n\necho \"Setting up Memori benchmarks on EC2...\"\n\n# Install system dependencies\nsudo apt-get update\nsudo apt-get install -y \\\n python3.12 \\\n python3.12-venv \\\n git \\\n curl \\\n build-essential \\\n postgresql-client \\\n default-mysql-client\n\n# Install uv\ncurl -LsSf https://astral.sh/uv/install.sh | sh\nsource $HOME/.cargo/env\n\n# Clone repository (replace with your repo URL if not already cloned)\n# git clone https://github.com/MemoriLabs/Memori.git\n# cd Memori\n\n# Sync dependencies\nuv sync --all-extras\n\n# Source the runner to get run_benchmarks function\nSCRIPT_DIR=\"$(cd \"$(dirname \"${BASH_SOURCE[0]}\")\" && pwd)\"\nsource \"$SCRIPT_DIR/run_benchmarks_ec2.sh\"\n\necho \"Setup complete!\"\necho \"\"\necho \"To run all benchmarks for Postgres:\"\necho \" DB_TYPE=postgres TEST_TYPE=all ./benchmarks/perf/run_benchmarks_ec2.sh\"\necho \"\"\necho \"To run end-to-end benchmarks for MySQL:\"\necho \" DB_TYPE=mysql TEST_TYPE=end_to_end ./benchmarks/perf/run_benchmarks_ec2.sh\"\necho \"\"\necho \"Results will be automatically saved to the ./results directory as CSV.\"\n" }, { "path": "benchmarks/perf/test_cloud_recall_benchmarks.py", "content": "\"\"\"Cloud (Memori API) performance / latency benchmarks.\n\nThese benchmarks exercise the cloud recall path (network + API latency) rather than\nlocal DB-backed recall.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport os\nfrom uuid import uuid4\n\nimport pytest\n\nfrom memori._config import Config\nfrom memori.llm._base import BaseInvoke\nfrom memori.llm._constants import OPENAI_LLM_PROVIDER\nfrom memori.memory._manager import Manager\nfrom memori.memory.recall import Recall\n\n\ndef _make_cloud_cfg(*, entity_id: str, process_id: str) -> Config:\n cfg = Config()\n cfg.cloud = True\n cfg.entity_id = entity_id\n cfg.process_id = process_id\n if cfg.session_id is None:\n cfg.session_id = uuid4()\n # Pick a provider so injection rules are deterministic.\n cfg.llm.provider = OPENAI_LLM_PROVIDER\n cfg.framework.provider = \"bench\"\n return cfg\n\n\ndef _seed_cloud_messages(\n cfg: Config, *, n_messages: int, entity_id: str, process_id: str\n) -> None:\n cfg.cloud = True\n cfg.entity_id = entity_id\n cfg.process_id = process_id\n if cfg.session_id is None:\n cfg.session_id = uuid4()\n\n messages: list[dict[str, object]] = []\n for i in range(n_messages):\n messages.append({\"role\": \"user\", \"type\": None, \"text\": f\"I like item_{i}.\"})\n messages.append({\"role\": \"assistant\", \"type\": None, \"text\": \"Ok.\"})\n\n Manager(cfg).execute(\n {\n \"attribution\": {\n \"entity\": {\"id\": str(entity_id)},\n \"process\": {\"id\": str(process_id)},\n },\n \"messages\": messages,\n \"session\": {\"id\": str(cfg.session_id)},\n }\n )\n\n\n@pytest.mark.benchmark\n@pytest.mark.parametrize(\"n_messages\", [20, 100], ids=[\"n20\", \"n100\"])\ndef test_benchmark_cloud_recall_latency(benchmark, n_messages: int) -> None:\n \"\"\"Benchmark end-to-end cloud recall latency.\n\n Includes: HTTP request + cloud service time. Excludes: local DB retrieval.\n \"\"\"\n os.environ[\"MEMORI_TEST_MODE\"] = \"1\"\n api_key = os.environ.get(\"MEMORI_API_KEY\")\n if not api_key:\n pytest.skip(\"Set MEMORI_API_KEY to benchmark cloud recall.\")\n\n cfg = Config()\n\n entity_id = os.environ.get(\"BENCHMARK_CLOUD_ENTITY_ID\", \"bench-cloud-entity\")\n process_id = os.environ.get(\"BENCHMARK_CLOUD_PROCESS_ID\", \"bench-cloud-process\")\n\n _seed_cloud_messages(\n cfg,\n n_messages=n_messages,\n entity_id=entity_id,\n process_id=process_id,\n )\n\n recall = Recall(cfg)\n\n def _call():\n return recall.search_facts(query=\"What do I like?\", limit=5)\n\n result = benchmark(_call)\n assert isinstance(result, list)\n\n\n@pytest.mark.benchmark\n@pytest.mark.parametrize(\"n_history_pairs\", [20, 100], ids=[\"n20\", \"n100\"])\ndef test_benchmark_cloud_pre_llm_overhead(benchmark, n_history_pairs: int) -> None:\n \"\"\"Benchmark Memori overhead up to the LLM call (cloud mode).\n\n This measures the \"added latency\" before calling an LLM:\n - (optional) fetch cloud conversation history\n - cloud recall request (server-side embeddings + retrieval)\n - prompt/message injection logic\n\n It intentionally does NOT call an LLM provider.\n \"\"\"\n if not os.environ.get(\"MEMORI_API_KEY\"):\n pytest.skip(\"Set MEMORI_API_KEY to benchmark cloud pre-LLM overhead.\")\n\n entity_id = os.environ.get(\"BENCHMARK_CLOUD_ENTITY_ID\", \"bench-cloud-entity\")\n process_id = os.environ.get(\"BENCHMARK_CLOUD_PROCESS_ID\", \"bench-cloud-process\")\n cfg = _make_cloud_cfg(entity_id=entity_id, process_id=process_id)\n\n # Seed a stable session for cloud history fetch.\n _seed_cloud_messages(\n cfg,\n n_messages=n_history_pairs,\n entity_id=entity_id,\n process_id=process_id,\n )\n\n query = \"What do I like?\"\n\n def _prepare():\n invoke = BaseInvoke(cfg, lambda **_kwargs: None)\n kwargs = {\"messages\": [{\"role\": \"user\", \"content\": query}]}\n kwargs = invoke.inject_conversation_messages(kwargs)\n kwargs = invoke.inject_recalled_facts(kwargs)\n return kwargs\n\n result = benchmark(_prepare)\n assert isinstance(result, dict)\n\n\n@pytest.mark.benchmark\n@pytest.mark.parametrize(\"n_history_pairs\", [20, 100], ids=[\"n20\", \"n100\"])\ndef test_benchmark_cloud_network_history_get(benchmark, n_history_pairs: int) -> None:\n \"\"\"cloud conversation history fetched via recall (POST), no injection.\"\"\"\n if not os.environ.get(\"MEMORI_API_KEY\"):\n pytest.skip(\"Set MEMORI_API_KEY to benchmark cloud history GET.\")\n\n entity_id = os.environ.get(\"BENCHMARK_CLOUD_ENTITY_ID\", \"bench-cloud-entity\")\n process_id = os.environ.get(\"BENCHMARK_CLOUD_PROCESS_ID\", \"bench-cloud-process\")\n cfg = _make_cloud_cfg(entity_id=entity_id, process_id=process_id)\n\n _seed_cloud_messages(\n cfg,\n n_messages=n_history_pairs,\n entity_id=entity_id,\n process_id=process_id,\n )\n\n def _call():\n recall = Recall(cfg)\n data = recall._cloud_recall(query=\"History fetch benchmark\")\n _facts, messages = recall._parse_cloud_recall_response(data)\n return messages\n\n result = benchmark(_call)\n assert isinstance(result, list)\n\n\n@pytest.mark.benchmark\n@pytest.mark.parametrize(\"n_history_pairs\", [20, 100], ids=[\"n20\", \"n100\"])\ndef test_benchmark_cloud_network_recall_post(benchmark, n_history_pairs: int) -> None:\n \"\"\"ONLY cloud recall call (POST /recall), no injection.\n\n We seed N history pairs first so cloud has a consistent amount of data.\n \"\"\"\n if not os.environ.get(\"MEMORI_API_KEY\"):\n pytest.skip(\"Set MEMORI_API_KEY to benchmark cloud recall POST.\")\n\n entity_id = os.environ.get(\"BENCHMARK_CLOUD_ENTITY_ID\", \"bench-cloud-entity\")\n process_id = os.environ.get(\"BENCHMARK_cloud_PROCESS_ID\", \"bench-cloud-process\")\n cfg = _make_cloud_cfg(entity_id=entity_id, process_id=process_id)\n\n _seed_cloud_messages(\n cfg,\n n_messages=n_history_pairs,\n entity_id=entity_id,\n process_id=process_id,\n )\n\n recall = Recall(cfg)\n\n def _call():\n return recall.search_facts(query=\"What do I like?\", limit=5)\n\n result = benchmark(_call)\n assert isinstance(result, list)\n\n\n@pytest.mark.benchmark\n@pytest.mark.parametrize(\"n_history_pairs\", [20, 100], ids=[\"n20\", \"n100\"])\ndef test_benchmark_cloud_network_only_history_plus_recall(\n benchmark, n_history_pairs: int\n) -> None:\n \"\"\"ONLY the cloud recall call (history + facts), no injection.\"\"\"\n if not os.environ.get(\"MEMORI_API_KEY\"):\n pytest.skip(\"Set MEMORI_API_KEY to benchmark cloud network-only overhead.\")\n\n entity_id = os.environ.get(\"BENCHMARK_cloud_ENTITY_ID\", \"bench-cloud-entity\")\n process_id = os.environ.get(\"BENCHMARK_CLOUD_PROCESS_ID\", \"bench-cloud-process\")\n cfg = _make_cloud_cfg(entity_id=entity_id, process_id=process_id)\n\n _seed_cloud_messages(\n cfg,\n n_messages=n_history_pairs,\n entity_id=entity_id,\n process_id=process_id,\n )\n\n recall = Recall(cfg)\n\n def _call():\n data = recall._cloud_recall(query=\"What do I like?\")\n facts, _messages = recall._parse_cloud_recall_response(data)\n return facts\n\n result = benchmark(_call)\n assert isinstance(result, list)\n\n\ndef _make_invoke_with_stubbed_history(\n cfg: Config, *, n_history_pairs: int\n) -> BaseInvoke:\n history: list[dict[str, str]] = []\n for i in range(n_history_pairs):\n history.append({\"role\": \"user\", \"content\": f\"I like item_{i}.\"})\n history.append({\"role\": \"assistant\", \"content\": \"Ok.\"})\n invoke = BaseInvoke(cfg, lambda **_kwargs: None)\n invoke._cloud_conversation_messages = history\n return invoke\n\n\n@pytest.mark.benchmark\n@pytest.mark.parametrize(\"n_history_pairs\", [20, 100], ids=[\"n20\", \"n100\"])\ndef test_benchmark_cloud_injection_only_history(\n benchmark, n_history_pairs: int\n) -> None:\n \"\"\"ONLY Python-side history injection (no network).\"\"\"\n entity_id = os.environ.get(\"BENCHMARK_CLOUD_ENTITY_ID\", \"bench-cloud-entity\")\n process_id = os.environ.get(\"BENCHMARK_CLOUD_PROCESS_ID\", \"bench-cloud-process\")\n cfg = _make_cloud_cfg(entity_id=entity_id, process_id=process_id)\n\n query = \"What do I like?\"\n invoke = _make_invoke_with_stubbed_history(cfg, n_history_pairs=n_history_pairs)\n\n def _call():\n kwargs = {\"messages\": [{\"role\": \"user\", \"content\": query}]}\n return invoke.inject_conversation_messages(kwargs)\n\n result = benchmark(_call)\n assert isinstance(result, dict)\n\n\n@pytest.mark.benchmark\n@pytest.mark.parametrize(\"n_recalled_facts\", [5, 20], ids=[\"n5\", \"n20\"])\ndef test_benchmark_cloud_injection_only_recalled_facts(\n benchmark, n_recalled_facts: int, mocker\n) -> None:\n \"\"\"ONLY Python-side recalled-facts injection (no network).\"\"\"\n entity_id = os.environ.get(\"BENCHMARK_CLOUD_ENTITY_ID\", \"bench-cloud-entity\")\n process_id = os.environ.get(\"BENCHMARK_CLOUD_PROCESS_ID\", \"bench-cloud-process\")\n cfg = _make_cloud_cfg(entity_id=entity_id, process_id=process_id)\n\n fake_facts: list[dict[str, object]] = [\n {\"content\": f\"User likes item_{i}\", \"rank_score\": 1.0, \"date_created\": None}\n for i in range(n_recalled_facts)\n ]\n mocker.patch(\"memori.memory.recall.Recall.search_facts\", return_value=fake_facts)\n\n invoke = BaseInvoke(cfg, lambda **_kwargs: None)\n query = \"What do I like?\"\n\n def _call():\n kwargs = {\"messages\": [{\"role\": \"user\", \"content\": query}]}\n return invoke.inject_recalled_facts(kwargs)\n\n result = benchmark(_call)\n assert isinstance(result, dict)\n" }, { "path": "benchmarks/perf/test_hosted_recall_benchmarks.py", "content": "\"\"\"Cloud (Memori API) performance / latency benchmarks.\n\nThese benchmarks exercise the cloud recall path (network + API latency) rather than\nlocal DB-backed recall.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport os\nfrom uuid import uuid4\n\nimport pytest\n\nfrom memori._config import Config\nfrom memori.llm._base import BaseInvoke\nfrom memori.llm._constants import OPENAI_LLM_PROVIDER\nfrom memori.memory._manager import Manager\nfrom memori.memory.recall import Recall\n\n\ndef _make_cloud_cfg(*, entity_id: str, process_id: str) -> Config:\n cfg = Config()\n cfg.cloud = True\n cfg.entity_id = entity_id\n cfg.process_id = process_id\n if cfg.session_id is None:\n cfg.session_id = uuid4()\n # Pick a provider so injection rules are deterministic.\n cfg.llm.provider = OPENAI_LLM_PROVIDER\n cfg.framework.provider = \"bench\"\n return cfg\n\n\ndef _seed_cloud_messages(\n cfg: Config, *, n_messages: int, entity_id: str, process_id: str\n) -> None:\n cfg.cloud = True\n cfg.entity_id = entity_id\n cfg.process_id = process_id\n if cfg.session_id is None:\n cfg.session_id = uuid4()\n\n messages: list[dict[str, object]] = []\n for i in range(n_messages):\n messages.append({\"role\": \"user\", \"type\": None, \"text\": f\"I like item_{i}.\"})\n messages.append({\"role\": \"assistant\", \"type\": None, \"text\": \"Ok.\"})\n\n Manager(cfg).execute(\n {\n \"attribution\": {\n \"entity\": {\"id\": str(entity_id)},\n \"process\": {\"id\": str(process_id)},\n },\n \"messages\": messages,\n \"session\": {\"id\": str(cfg.session_id)},\n }\n )\n\n\n@pytest.mark.benchmark\n@pytest.mark.parametrize(\"n_messages\", [20, 100], ids=[\"n20\", \"n100\"])\ndef test_benchmark_cloud_recall_latency(benchmark, n_messages: int) -> None:\n \"\"\"Benchmark end-to-end cloud recall latency.\n\n Includes: HTTP request + cloud service time. Excludes: local DB retrieval.\n \"\"\"\n os.environ[\"MEMORI_TEST_MODE\"] = \"1\"\n api_key = os.environ.get(\"MEMORI_API_KEY\")\n if not api_key:\n pytest.skip(\"Set MEMORI_API_KEY to benchmark cloud recall.\")\n\n cfg = Config()\n\n entity_id = os.environ.get(\"BENCHMARK_CLOUD_ENTITY_ID\", \"bench-cloud-entity\")\n process_id = os.environ.get(\"BENCHMARK_CLOUD_PROCESS_ID\", \"bench-cloud-process\")\n\n _seed_cloud_messages(\n cfg,\n n_messages=n_messages,\n entity_id=entity_id,\n process_id=process_id,\n )\n\n recall = Recall(cfg)\n\n def _call():\n return recall.search_facts(query=\"What do I like?\", limit=5)\n\n result = benchmark(_call)\n assert isinstance(result, list)\n\n\n@pytest.mark.benchmark\n@pytest.mark.parametrize(\"n_history_pairs\", [20, 100], ids=[\"n20\", \"n100\"])\ndef test_benchmark_cloud_pre_llm_overhead(benchmark, n_history_pairs: int) -> None:\n \"\"\"Benchmark Memori overhead up to the LLM call (cloud mode).\n\n This measures the \"added latency\" before calling an LLM:\n - (optional) fetch cloud conversation history\n - cloud recall request (server-side embeddings + retrieval)\n - prompt/message injection logic\n\n It intentionally does NOT call an LLM provider.\n \"\"\"\n if not os.environ.get(\"MEMORI_API_KEY\"):\n pytest.skip(\"Set MEMORI_API_KEY to benchmark cloud pre-LLM overhead.\")\n\n entity_id = os.environ.get(\"BENCHMARK_CLOUD_ENTITY_ID\", \"bench-cloud-entity\")\n process_id = os.environ.get(\"BENCHMARK_CLOUD_PROCESS_ID\", \"bench-cloud-process\")\n cfg = _make_cloud_cfg(entity_id=entity_id, process_id=process_id)\n\n # Seed a stable session for cloud history fetch.\n _seed_cloud_messages(\n cfg,\n n_messages=n_history_pairs,\n entity_id=entity_id,\n process_id=process_id,\n )\n\n query = \"What do I like?\"\n\n def _prepare():\n invoke = BaseInvoke(cfg, lambda **_kwargs: None)\n kwargs = {\"messages\": [{\"role\": \"user\", \"content\": query}]}\n kwargs = invoke.inject_conversation_messages(kwargs)\n kwargs = invoke.inject_recalled_facts(kwargs)\n return kwargs\n\n result = benchmark(_prepare)\n assert isinstance(result, dict)\n\n\n@pytest.mark.benchmark\n@pytest.mark.parametrize(\"n_history_pairs\", [20, 100], ids=[\"n20\", \"n100\"])\ndef test_benchmark_cloud_network_history_get(benchmark, n_history_pairs: int) -> None:\n \"\"\"cloud conversation history fetched via recall (POST), no injection.\"\"\"\n if not os.environ.get(\"MEMORI_API_KEY\"):\n pytest.skip(\"Set MEMORI_API_KEY to benchmark cloud history GET.\")\n\n entity_id = os.environ.get(\"BENCHMARK_CLOUD_ENTITY_ID\", \"bench-cloud-entity\")\n process_id = os.environ.get(\"BENCHMARK_CLOUD_PROCESS_ID\", \"bench-cloud-process\")\n cfg = _make_cloud_cfg(entity_id=entity_id, process_id=process_id)\n\n _seed_cloud_messages(\n cfg,\n n_messages=n_history_pairs,\n entity_id=entity_id,\n process_id=process_id,\n )\n\n def _call():\n recall = Recall(cfg)\n data = recall._cloud_recall(query=\"History fetch benchmark\")\n _facts, messages = recall._parse_cloud_recall_response(data)\n return messages\n\n result = benchmark(_call)\n assert isinstance(result, list)\n\n\n@pytest.mark.benchmark\n@pytest.mark.parametrize(\"n_history_pairs\", [20, 100], ids=[\"n20\", \"n100\"])\ndef test_benchmark_cloud_network_recall_post(benchmark, n_history_pairs: int) -> None:\n \"\"\"ONLY cloud recall call (POST /recall), no injection.\n\n We seed N history pairs first so cloud has a consistent amount of data.\n \"\"\"\n if not os.environ.get(\"MEMORI_API_KEY\"):\n pytest.skip(\"Set MEMORI_API_KEY to benchmark cloud recall POST.\")\n\n entity_id = os.environ.get(\"BENCHMARK_CLOUD_ENTITY_ID\", \"bench-cloud-entity\")\n process_id = os.environ.get(\"BENCHMARK_cloud_PROCESS_ID\", \"bench-cloud-process\")\n cfg = _make_cloud_cfg(entity_id=entity_id, process_id=process_id)\n\n _seed_cloud_messages(\n cfg,\n n_messages=n_history_pairs,\n entity_id=entity_id,\n process_id=process_id,\n )\n\n recall = Recall(cfg)\n\n def _call():\n return recall.search_facts(query=\"What do I like?\", limit=5)\n\n result = benchmark(_call)\n assert isinstance(result, list)\n\n\n@pytest.mark.benchmark\n@pytest.mark.parametrize(\"n_history_pairs\", [20, 100], ids=[\"n20\", \"n100\"])\ndef test_benchmark_cloud_network_only_history_plus_recall(\n benchmark, n_history_pairs: int\n) -> None:\n \"\"\"ONLY the cloud recall call (history + facts), no injection.\"\"\"\n if not os.environ.get(\"MEMORI_API_KEY\"):\n pytest.skip(\"Set MEMORI_API_KEY to benchmark cloud network-only overhead.\")\n\n entity_id = os.environ.get(\"BENCHMARK_cloud_ENTITY_ID\", \"bench-cloud-entity\")\n process_id = os.environ.get(\"BENCHMARK_CLOUD_PROCESS_ID\", \"bench-cloud-process\")\n cfg = _make_cloud_cfg(entity_id=entity_id, process_id=process_id)\n\n _seed_cloud_messages(\n cfg,\n n_messages=n_history_pairs,\n entity_id=entity_id,\n process_id=process_id,\n )\n\n recall = Recall(cfg)\n\n def _call():\n data = recall._cloud_recall(query=\"What do I like?\")\n facts, _messages = recall._parse_cloud_recall_response(data)\n return facts\n\n result = benchmark(_call)\n assert isinstance(result, list)\n\n\ndef _make_invoke_with_stubbed_history(\n cfg: Config, *, n_history_pairs: int\n) -> BaseInvoke:\n history: list[dict[str, str]] = []\n for i in range(n_history_pairs):\n history.append({\"role\": \"user\", \"content\": f\"I like item_{i}.\"})\n history.append({\"role\": \"assistant\", \"content\": \"Ok.\"})\n invoke = BaseInvoke(cfg, lambda **_kwargs: None)\n invoke._cloud_conversation_messages = history\n return invoke\n\n\n@pytest.mark.benchmark\n@pytest.mark.parametrize(\"n_history_pairs\", [20, 100], ids=[\"n20\", \"n100\"])\ndef test_benchmark_cloud_injection_only_history(\n benchmark, n_history_pairs: int\n) -> None:\n \"\"\"ONLY Python-side history injection (no network).\"\"\"\n entity_id = os.environ.get(\"BENCHMARK_CLOUD_ENTITY_ID\", \"bench-cloud-entity\")\n process_id = os.environ.get(\"BENCHMARK_CLOUD_PROCESS_ID\", \"bench-cloud-process\")\n cfg = _make_cloud_cfg(entity_id=entity_id, process_id=process_id)\n\n query = \"What do I like?\"\n invoke = _make_invoke_with_stubbed_history(cfg, n_history_pairs=n_history_pairs)\n\n def _call():\n kwargs = {\"messages\": [{\"role\": \"user\", \"content\": query}]}\n return invoke.inject_conversation_messages(kwargs)\n\n result = benchmark(_call)\n assert isinstance(result, dict)\n\n\n@pytest.mark.benchmark\n@pytest.mark.parametrize(\"n_recalled_facts\", [5, 20], ids=[\"n5\", \"n20\"])\ndef test_benchmark_cloud_injection_only_recalled_facts(\n benchmark, n_recalled_facts: int, mocker\n) -> None:\n \"\"\"ONLY Python-side recalled-facts injection (no network).\"\"\"\n entity_id = os.environ.get(\"BENCHMARK_CLOUD_ENTITY_ID\", \"bench-cloud-entity\")\n process_id = os.environ.get(\"BENCHMARK_CLOUD_PROCESS_ID\", \"bench-cloud-process\")\n cfg = _make_cloud_cfg(entity_id=entity_id, process_id=process_id)\n\n fake_facts: list[dict[str, object]] = [\n {\"content\": f\"User likes item_{i}\", \"rank_score\": 1.0, \"date_created\": None}\n for i in range(n_recalled_facts)\n ]\n mocker.patch(\"memori.memory.recall.Recall.search_facts\", return_value=fake_facts)\n\n invoke = BaseInvoke(cfg, lambda **_kwargs: None)\n query = \"What do I like?\"\n\n def _call():\n kwargs = {\"messages\": [{\"role\": \"user\", \"content\": query}]}\n return invoke.inject_recalled_facts(kwargs)\n\n result = benchmark(_call)\n assert isinstance(result, dict)\n" }, { "path": "benchmarks/perf/test_recall_benchmarks.py", "content": "\"\"\"Performance benchmarks for Memori recall functionality.\"\"\"\n\nimport datetime\nimport os\nfrom time import perf_counter\nfrom typing import cast\n\nimport pytest\n\nfrom benchmarks.perf._results import append_csv_row, results_dir\nfrom benchmarks.perf.memory_utils import measure_peak_rss_bytes\nfrom memori._config import Config\nfrom memori.embeddings import embed_texts\nfrom memori.memory.recall import Recall\nfrom memori.search import find_similar_embeddings\nfrom memori.search._lexical import lexical_scores_for_ids # noqa: PLC2701\nfrom memori.search._types import FactId\n\n\ndef _default_benchmark_csv_path() -> str:\n return str(results_dir() / \"recall_benchmarks.csv\")\n\n\ndef _write_benchmark_row(*, benchmark, row: dict[str, object]) -> None:\n csv_path = (\n os.environ.get(\"BENCHMARK_RESULTS_CSV_PATH\") or _default_benchmark_csv_path()\n )\n stats = getattr(benchmark, \"stats\", None)\n row_out: dict[str, object] = dict(row)\n row_out[\"timestamp_utc\"] = datetime.datetime.now(datetime.timezone.utc).isoformat()\n\n for key in (\n \"mean\",\n \"stddev\",\n \"median\",\n \"min\",\n \"max\",\n \"rounds\",\n \"iterations\",\n \"ops\",\n ):\n value = getattr(stats, key, None) if stats is not None else None\n if value is not None:\n row_out[key] = value\n\n header = [\n \"timestamp_utc\",\n \"test\",\n \"db\",\n \"fact_count\",\n \"query_size\",\n \"retrieval_limit\",\n \"one_shot_seconds\",\n \"peak_rss_bytes\",\n \"mean\",\n \"stddev\",\n \"median\",\n \"min\",\n \"max\",\n \"rounds\",\n \"iterations\",\n \"ops\",\n ]\n append_csv_row(csv_path, header=header, row=row_out)\n\n\n@pytest.mark.benchmark\nclass TestQueryEmbeddingBenchmarks:\n \"\"\"Benchmarks for query embedding generation.\"\"\"\n\n def test_benchmark_query_embedding_short(self, benchmark, sample_queries):\n \"\"\"Benchmark embedding generation for short queries.\"\"\"\n query = sample_queries[\"short\"][0]\n cfg = Config()\n\n def _embed():\n return embed_texts(\n query,\n model=cfg.embeddings.model,\n )\n\n start = perf_counter()\n result = benchmark(_embed)\n one_shot_seconds = perf_counter() - start\n assert len(result) > 0\n assert len(result[0]) > 0\n _write_benchmark_row(\n benchmark=benchmark,\n row={\n \"test\": \"query_embedding_short\",\n \"db\": \"\",\n \"fact_count\": \"\",\n \"query_size\": \"short\",\n \"retrieval_limit\": \"\",\n \"one_shot_seconds\": one_shot_seconds,\n \"peak_rss_bytes\": benchmark.extra_info.get(\"peak_rss_bytes\", \"\"),\n },\n )\n\n def test_benchmark_query_embedding_medium(self, benchmark, sample_queries):\n \"\"\"Benchmark embedding generation for medium-length queries.\"\"\"\n query = sample_queries[\"medium\"][0]\n cfg = Config()\n\n def _embed():\n return embed_texts(\n query,\n model=cfg.embeddings.model,\n )\n\n start = perf_counter()\n result = benchmark(_embed)\n one_shot_seconds = perf_counter() - start\n assert len(result) > 0\n assert len(result[0]) > 0\n _write_benchmark_row(\n benchmark=benchmark,\n row={\n \"test\": \"query_embedding_medium\",\n \"db\": \"\",\n \"fact_count\": \"\",\n \"query_size\": \"medium\",\n \"retrieval_limit\": \"\",\n \"one_shot_seconds\": one_shot_seconds,\n \"peak_rss_bytes\": benchmark.extra_info.get(\"peak_rss_bytes\", \"\"),\n },\n )\n\n def test_benchmark_query_embedding_long(self, benchmark, sample_queries):\n \"\"\"Benchmark embedding generation for long queries.\"\"\"\n query = sample_queries[\"long\"][0]\n cfg = Config()\n\n def _embed():\n return embed_texts(\n query,\n model=cfg.embeddings.model,\n )\n\n start = perf_counter()\n result = benchmark(_embed)\n one_shot_seconds = perf_counter() - start\n assert len(result) > 0\n assert len(result[0]) > 0\n _write_benchmark_row(\n benchmark=benchmark,\n row={\n \"test\": \"query_embedding_long\",\n \"db\": \"\",\n \"fact_count\": \"\",\n \"query_size\": \"long\",\n \"retrieval_limit\": \"\",\n \"one_shot_seconds\": one_shot_seconds,\n \"peak_rss_bytes\": benchmark.extra_info.get(\"peak_rss_bytes\", \"\"),\n },\n )\n\n def test_benchmark_query_embedding_batch(self, benchmark, sample_queries):\n \"\"\"Benchmark embedding generation for multiple queries at once.\"\"\"\n queries = sample_queries[\"short\"][:5]\n cfg = Config()\n\n def _embed():\n return embed_texts(\n queries,\n model=cfg.embeddings.model,\n )\n\n start = perf_counter()\n result = benchmark(_embed)\n one_shot_seconds = perf_counter() - start\n assert len(result) == len(queries)\n assert all(len(emb) > 0 for emb in result)\n _write_benchmark_row(\n benchmark=benchmark,\n row={\n \"test\": \"query_embedding_batch\",\n \"db\": \"\",\n \"fact_count\": \"\",\n \"query_size\": \"batch\",\n \"retrieval_limit\": \"\",\n \"one_shot_seconds\": one_shot_seconds,\n \"peak_rss_bytes\": benchmark.extra_info.get(\"peak_rss_bytes\", \"\"),\n },\n )\n\n\n@pytest.mark.benchmark\nclass TestDatabaseEmbeddingRetrievalBenchmarks:\n \"\"\"Benchmarks for database embedding retrieval.\"\"\"\n\n def test_benchmark_db_embedding_retrieval(\n self, benchmark, memori_instance, entity_with_n_facts\n ):\n \"\"\"Benchmark retrieving embeddings from database for different fact counts.\"\"\"\n entity_db_id = entity_with_n_facts[\"entity_db_id\"]\n fact_count = entity_with_n_facts[\"fact_count\"]\n entity_fact_driver = memori_instance.config.storage.driver.entity_fact\n\n def _retrieve():\n return entity_fact_driver.get_embeddings(entity_db_id, limit=fact_count)\n\n _, peak_rss = measure_peak_rss_bytes(_retrieve)\n if peak_rss is not None:\n benchmark.extra_info[\"peak_rss_bytes\"] = peak_rss\n\n result = benchmark(_retrieve)\n assert len(result) == fact_count\n assert all(\"id\" in row and \"content_embedding\" in row for row in result)\n _write_benchmark_row(\n benchmark=benchmark,\n row={\n \"test\": \"db_embedding_retrieval\",\n \"db\": entity_with_n_facts[\"db_type\"],\n \"fact_count\": fact_count,\n \"query_size\": \"\",\n \"retrieval_limit\": \"\",\n \"one_shot_seconds\": \"\",\n \"peak_rss_bytes\": benchmark.extra_info.get(\"peak_rss_bytes\", \"\"),\n },\n )\n\n\n@pytest.mark.benchmark\nclass TestDatabaseFactContentRetrievalBenchmarks:\n \"\"\"Benchmarks for fetching fact content by ids (final recall DB step).\n\n This benchmarks the final step after semantic search has already identified\n the top-k most similar embeddings. We only retrieve content for those top results\n (typically 5-10 facts), not all facts in the database.\n \"\"\"\n\n @pytest.mark.parametrize(\"retrieval_limit\", [5, 10], ids=[\"limit5\", \"limit10\"])\n def test_benchmark_db_fact_content_retrieval(\n self, benchmark, memori_instance, entity_with_n_facts, retrieval_limit\n ):\n \"\"\"Benchmark retrieving content for top-k facts after semantic search.\n\n Args:\n retrieval_limit: Number of fact IDs to retrieve content for (after semantic\n search has already filtered to top results). This should be small (5-10).\n \"\"\"\n entity_db_id = entity_with_n_facts[\"entity_db_id\"]\n entity_fact_driver = memori_instance.config.storage.driver.entity_fact\n\n # Simulate semantic search returning top-k IDs (outside benchmark timing)\n # In real flow: get_embeddings(embeddings_limit=1000) -> FAISS search -> top-k IDs\n seed_rows = entity_fact_driver.get_embeddings(\n entity_db_id, limit=retrieval_limit\n )\n fact_ids = [row[\"id\"] for row in seed_rows]\n\n def _retrieve():\n return entity_fact_driver.get_facts_by_ids(fact_ids)\n\n _, peak_rss = measure_peak_rss_bytes(_retrieve)\n if peak_rss is not None:\n benchmark.extra_info[\"peak_rss_bytes\"] = peak_rss\n\n result = benchmark(_retrieve)\n assert len(result) == len(fact_ids)\n assert all(\"id\" in row and \"content\" in row for row in result)\n _write_benchmark_row(\n benchmark=benchmark,\n row={\n \"test\": \"db_fact_content_retrieval\",\n \"db\": entity_with_n_facts[\"db_type\"],\n \"fact_count\": entity_with_n_facts[\"fact_count\"],\n \"query_size\": \"\",\n \"retrieval_limit\": retrieval_limit,\n \"one_shot_seconds\": \"\",\n \"peak_rss_bytes\": benchmark.extra_info.get(\"peak_rss_bytes\", \"\"),\n },\n )\n\n\n@pytest.mark.benchmark\nclass TestSemanticSearchBenchmarks:\n \"\"\"Benchmarks for semantic search (FAISS similarity search).\"\"\"\n\n def test_benchmark_semantic_search(\n self, benchmark, memori_instance, entity_with_n_facts, sample_queries\n ):\n \"\"\"Benchmark FAISS similarity search for different embedding counts.\"\"\"\n entity_db_id = entity_with_n_facts[\"entity_db_id\"]\n fact_count = entity_with_n_facts[\"fact_count\"]\n entity_fact_driver = memori_instance.config.storage.driver.entity_fact\n\n db_results = entity_fact_driver.get_embeddings(entity_db_id, limit=fact_count)\n embeddings = [(row[\"id\"], row[\"content_embedding\"]) for row in db_results]\n\n query = sample_queries[\"short\"][0]\n query_embedding = embed_texts(\n query,\n model=memori_instance.config.embeddings.model,\n )[0]\n\n def _search():\n return find_similar_embeddings(embeddings, query_embedding, limit=5)\n\n _, peak_rss = measure_peak_rss_bytes(_search)\n if peak_rss is not None:\n benchmark.extra_info[\"peak_rss_bytes\"] = peak_rss\n\n result = benchmark(_search)\n assert len(result) > 0\n assert all(isinstance(item, tuple) and len(item) == 2 for item in result)\n assert all(\n isinstance(item[0], int) and isinstance(item[1], float) for item in result\n )\n _write_benchmark_row(\n benchmark=benchmark,\n row={\n \"test\": \"semantic_search_faiss\",\n \"db\": entity_with_n_facts[\"db_type\"],\n \"fact_count\": fact_count,\n \"query_size\": \"short\",\n \"retrieval_limit\": \"\",\n \"one_shot_seconds\": \"\",\n \"peak_rss_bytes\": benchmark.extra_info.get(\"peak_rss_bytes\", \"\"),\n },\n )\n\n\n@pytest.mark.benchmark\nclass TestLexicalBenchmarks:\n \"\"\"Benchmarks for BM25 scoring.\"\"\"\n\n @pytest.mark.parametrize(\"fact_count\", [200, 1000], ids=[\"facts200\", \"facts1000\"])\n def test_benchmark_bm25_scoring(self, benchmark, fact_count):\n ids = list(range(1, fact_count + 1))\n content_map = {i: f\"fact {i} user likes blue pizza coffee {i % 7}\" for i in ids}\n query_text = \"blue pizza\"\n\n def _score():\n return lexical_scores_for_ids(\n query_text=query_text,\n ids=cast(list[FactId], ids),\n content_map=cast(dict[FactId, str], content_map),\n )\n\n _, peak_rss = measure_peak_rss_bytes(_score)\n if peak_rss is not None:\n benchmark.extra_info[\"peak_rss_bytes\"] = peak_rss\n\n result = benchmark(_score)\n assert isinstance(result, dict)\n assert len(result) == fact_count\n _write_benchmark_row(\n benchmark=benchmark,\n row={\n \"test\": \"bm25_scoring\",\n \"db\": \"\",\n \"fact_count\": fact_count,\n \"query_size\": \"short\",\n \"retrieval_limit\": \"\",\n \"one_shot_seconds\": \"\",\n \"peak_rss_bytes\": benchmark.extra_info.get(\"peak_rss_bytes\", \"\"),\n },\n )\n\n\n@pytest.mark.benchmark\nclass TestEndToEndRecallBenchmarks:\n \"\"\"Benchmarks for end-to-end recall (embed query + DB + FAISS + content fetch).\"\"\"\n\n @pytest.mark.parametrize(\n \"query_size\",\n [\"short\", \"medium\", \"long\"],\n ids=[\"short_query\", \"medium_query\", \"long_query\"],\n )\n def test_benchmark_end_to_end_recall(\n self,\n benchmark,\n memori_instance,\n entity_with_n_facts,\n sample_queries,\n query_size,\n ):\n entity_db_id = entity_with_n_facts[\"entity_db_id\"]\n query = sample_queries[query_size][0]\n\n recall = Recall(memori_instance.config)\n\n def _recall():\n return recall.search_facts(query=query, limit=5, entity_id=entity_db_id)\n\n _, peak_rss = measure_peak_rss_bytes(_recall)\n if peak_rss is not None:\n benchmark.extra_info[\"peak_rss_bytes\"] = peak_rss\n\n start = perf_counter()\n result = benchmark(_recall)\n one_shot_seconds = perf_counter() - start\n assert isinstance(result, list)\n assert len(result) <= 5\n _write_benchmark_row(\n benchmark=benchmark,\n row={\n \"test\": \"end_to_end_recall\",\n \"db\": entity_with_n_facts[\"db_type\"],\n \"fact_count\": entity_with_n_facts[\"fact_count\"],\n \"query_size\": query_size,\n \"retrieval_limit\": \"\",\n \"one_shot_seconds\": one_shot_seconds,\n \"peak_rss_bytes\": benchmark.extra_info.get(\"peak_rss_bytes\", \"\"),\n },\n )\n" }, { "path": "benchmarks/scripts/fetch_locomo.py", "content": "from __future__ import annotations\n\nimport argparse\nimport hashlib\nimport shutil\nimport sys\nfrom pathlib import Path\n\n\ndef main(argv: list[str] | None = None) -> int:\n parser = argparse.ArgumentParser(\n description=\"Fetch/prepare LoCoMo dataset locally (does not commit data to git).\"\n )\n parser.add_argument(\n \"--source\",\n default=\"\",\n help=\"Path to an existing LoCoMo JSON file to copy into the cache.\",\n )\n parser.add_argument(\n \"--dest\",\n default=\"\",\n help=\"Destination file path (defaults to ~/.cache/memori/benchmarks/locomo/locomo.json).\",\n )\n parser.add_argument(\n \"--print-dest\",\n action=\"store_true\",\n help=\"Print resolved destination path and exit.\",\n )\n args = parser.parse_args(argv)\n\n dest = _default_dest() if not args.dest else Path(args.dest).expanduser()\n dest = dest.resolve()\n\n if args.print_dest:\n print(str(dest))\n return 0\n\n if not args.source:\n print(\n \"No --source provided.\\n\\n\"\n \"Download LoCoMo from Snap Research and pass the JSON path here.\\n\"\n \"Upstream repo: https://github.com/snap-research/locomo\\n\",\n file=sys.stderr,\n )\n return 2\n\n src = Path(args.source).expanduser().resolve()\n if not src.exists():\n print(f\"Source file not found: {src}\", file=sys.stderr)\n return 2\n\n dest.parent.mkdir(parents=True, exist_ok=True)\n shutil.copyfile(src, dest)\n\n digest = _sha256(dest)\n print(f\"Wrote: {dest}\")\n print(f\"sha256: {digest}\")\n return 0\n\n\ndef _default_dest() -> Path:\n return Path(\"~/.cache/memori/benchmarks/locomo/locomo.json\").expanduser()\n\n\ndef _sha256(path: Path) -> str:\n h = hashlib.sha256()\n with path.open(\"rb\") as f:\n for chunk in iter(lambda: f.read(1024 * 1024), b\"\"):\n h.update(chunk)\n return h.hexdigest()\n\n\nif __name__ == \"__main__\":\n raise SystemExit(main())\n" }, { "path": "conftest.py", "content": "import pytest\n\nfrom memori._config import Config\nfrom memori.storage import Manager as StorageManager\n\n\n@pytest.fixture\ndef mock_mysql_session(mocker):\n session = mocker.MagicMock()\n session.get_bind.return_value.dialect.name = \"mysql\"\n type(session).__module__ = \"sqlalchemy.orm.session\"\n\n mock_result = mocker.MagicMock()\n mock_result.mappings.return_value.fetchone.return_value = {\"1\": 1}\n mock_result.mappings.return_value.fetchall.return_value = []\n session.connection.return_value.exec_driver_sql.return_value = mock_result\n\n return session\n\n\n@pytest.fixture\ndef mock_postgres_session(mocker):\n session = mocker.MagicMock()\n session.get_bind.return_value.dialect.name = \"postgresql\"\n type(session).__module__ = \"sqlalchemy.orm.session\"\n\n mock_result = mocker.MagicMock()\n mock_result.mappings.return_value.fetchone.return_value = {\"one\": 1}\n mock_result.mappings.return_value.fetchall.return_value = []\n session.connection.return_value.exec_driver_sql.return_value = mock_result\n\n return session\n\n\n@pytest.fixture\ndef session(mock_mysql_session):\n return mock_mysql_session\n\n\n@pytest.fixture\ndef postgres_session(mock_postgres_session):\n return mock_postgres_session\n\n\n@pytest.fixture\ndef config(mocker, session):\n config = Config()\n config.storage = StorageManager(config)\n config.storage.adapter = mocker.MagicMock()\n config.storage.driver = mocker.MagicMock()\n return config\n" }, { "path": "docker-compose.yml", "content": "services:\n dev:\n build:\n context: .\n dockerfile: Dockerfile\n volumes:\n - .:/app\n - /app/.venv\n - /app/__pycache__\n - ./google-credentials.json:/app/google-credentials.json:ro\n working_dir: /app\n stdin_open: true\n tty: true\n env_file:\n - .env\n environment:\n - PYTHONUNBUFFERED=1\n - DATABASE_URL=postgresql://memori:memori@postgres:5432/memori_test\n - MONGODB_URL=mongodb://memori:memori@mongodb:27017/memori_test?authSource=admin\n - MYSQL_DATABASE_URL=mysql+pymysql://memori:memori@mysql:3306/memori_test\n - OCEANBASE_DATABASE_URL=mysql+oceanbase://root:@oceanbase:2881/memori_test?charset=utf8mb4\n - ORACLE_DATABASE_URL=oracle+oracledb://system:memori@oracle:1521/?service_name=FREEPDB1\n - GOOGLE_APPLICATION_CREDENTIALS=/app/google-credentials.json\n command: /bin/bash\n depends_on:\n postgres:\n condition: service_healthy\n mongodb:\n condition: service_healthy\n mysql:\n condition: service_healthy\n oceanbase:\n condition: service_started\n oracle:\n condition: service_healthy\n networks:\n - memori-network\n\n postgres:\n image: postgres:16\n environment:\n POSTGRES_USER: memori\n POSTGRES_PASSWORD: memori\n POSTGRES_DB: memori_test\n ports:\n - \"5432:5432\"\n volumes:\n - postgres_data:/var/lib/postgresql/data\n healthcheck:\n test: [\"CMD-SHELL\", \"pg_isready -U memori\"]\n interval: 5s\n timeout: 5s\n retries: 5\n networks:\n - memori-network\n\n mongodb:\n image: mongo:7.0\n environment:\n MONGO_INITDB_ROOT_USERNAME: memori\n MONGO_INITDB_ROOT_PASSWORD: memori\n MONGO_INITDB_DATABASE: memori_test\n ports:\n - \"27017:27017\"\n volumes:\n - mongodb_data:/data/db\n healthcheck:\n test: [\"CMD\", \"mongosh\", \"--eval\", \"db.adminCommand('ping')\"]\n interval: 5s\n timeout: 5s\n retries: 5\n networks:\n - memori-network\n\n mongo-express:\n image: mongo-express:1.0.0\n restart: unless-stopped\n ports:\n - \"8081:8081\"\n environment:\n ME_CONFIG_MONGODB_ADMINUSERNAME: memori\n ME_CONFIG_MONGODB_ADMINPASSWORD: memori\n ME_CONFIG_MONGODB_URL: mongodb://memori:memori@mongodb:27017/\n ME_CONFIG_BASICAUTH_USERNAME: admin\n ME_CONFIG_BASICAUTH_PASSWORD: pass\n depends_on:\n - mongodb\n networks:\n - memori-network\n\n mysql:\n image: mysql:8\n environment:\n MYSQL_ROOT_PASSWORD: memori\n MYSQL_DATABASE: memori_test\n MYSQL_USER: memori\n MYSQL_PASSWORD: memori\n ports:\n - \"3307:3306\"\n volumes:\n - mysql_data:/var/lib/mysql\n healthcheck:\n test: [\"CMD\", \"mysqladmin\", \"ping\", \"-h\", \"localhost\", \"-u\", \"memori\", \"-pmemori\"]\n interval: 5s\n timeout: 5s\n retries: 5\n networks:\n - memori-network\n\n oceanbase:\n image: quay.io/oceanbase/seekdb:latest\n ports:\n - \"2882:2881\"\n networks:\n - memori-network\n\n oracle:\n image: container-registry.oracle.com/database/free:latest-lite\n environment:\n ORACLE_PWD: memori\n ORACLE_CHARACTERSET: AL32UTF8\n ports:\n - \"1521:1521\"\n volumes:\n - oracle_data:/opt/oracle/oradata\n shm_size: 2gb\n healthcheck:\n test: [\"CMD-SHELL\", \"echo 'SELECT 1 FROM DUAL;' | sqlplus -s system/memori@FREEPDB1 || exit 1\"]\n interval: 15s\n timeout: 10s\n retries: 20\n start_period: 90s\n networks:\n - memori-network\n\nvolumes:\n postgres_data:\n mongodb_data:\n mysql_data:\n oracle_data:\n\nnetworks:\n memori-network:\n driver: bridge\n" }, { "path": "docs/memori-byodb/concepts/advanced-augmentation.mdx", "content": "---\ntitle: Advanced Augmentation\ndescription: How Memori's Advanced Augmentation engine extracts structured facts, preferences, and knowledge from your AI conversations — all stored in your own database.\n---\n\n# Advanced Augmentation\n\nAdvanced Augmentation is the AI engine inside Memori that turns raw conversations into structured, searchable memories. It runs asynchronously in the background to minimize impact on your response path. All extracted data is stored directly in your database.\n\n## What It Does\n\nWhen your application has a conversation through a Memori-wrapped LLM client, the augmentation engine:\n\n1. Reads the full conversation (user messages and AI responses)\n2. Identifies facts, preferences, skills, attributes, and events\n3. Extracts semantic triples (subject-predicate-object relationships)\n4. Generates vector embeddings for semantic search\n5. Stores everything in your database\n\nNo extra code required — just initialize Memori with your database connection and set attribution.\n\n## How It Works\n\nThe augmentation flow is fully asynchronous and designed to avoid blocking your main request path.\n\n1. Your app makes an LLM call through the wrapped client\n2. Memori returns the response immediately\n3. In the background, the conversation is queued for processing\n4. The augmentation engine extracts structured memories\n5. Memories are stored in your database for future recall\n\nIn short-lived scripts, call `mem.augmentation.wait()` to ensure processing completes before exit.\n\n```python\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom memori import Memori\nfrom openai import OpenAI\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = OpenAI()\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"my_agent\")\nmem.config.storage.build()\n\n# This returns immediately — no augmentation delay\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[\n {\"role\": \"user\", \"content\": \"I love hiking in the mountains.\"}\n ]\n)\nprint(response.choices[0].message.content)\n\n# Only needed in short-lived scripts\nmem.augmentation.wait()\n```\n\n## Extraction Types\n\n| Type | What it captures | Scope |\n| ---------------------- | ------------------------------------------------------- | ------------------------------------ |\n| **Facts** | Objective information with vector embeddings | Per entity — shared across processes |\n| **Preferences** | User choices, opinions, and tastes | Per entity |\n| **Skills & Knowledge** | Abilities and expertise levels | Per entity |\n| **Attributes** | Process-level information about what your agent handles | Per process |\n\n**Database tables:**\n\n| Table | Purpose |\n| ----------------------------- | -------------------------------------------- |\n| `memori_conversation` | Stores conversations |\n| `memori_conversation_message` | Individual messages within conversations |\n| `memori_session` | Groups related LLM interactions |\n| `memori_entity` | Entities (users, organizations, etc.) |\n| `memori_process` | Processes (agents, bots, workflows) |\n| `memori_entity_fact` | Extracted facts with vector embeddings |\n| `memori_process_attribute` | Process-level attributes and characteristics |\n\n## Semantic Triples\n\nAdvanced Augmentation uses named-entity recognition to extract semantic triples (subject, predicate, object). These form the building blocks of the [Knowledge Graph](/docs/memori-byodb/concepts/knowledge-graph).\n\n**Example** — from _\"My favorite database is PostgreSQL and I use it with FastAPI\"_:\n\n| Subject | Predicate | Object |\n| ------- | ----------------- | -------------------- |\n| user | favorite_database | PostgreSQL |\n| user | uses | FastAPI |\n| user | uses_with | PostgreSQL + FastAPI |\n\nMemori automatically deduplicates triples — if the same fact is mentioned multiple times, it increments the mention count and updates the timestamp.\n\n**Database tables:** `memori_subject`, `memori_predicate`, `memori_object`, `memori_knowledge_graph`\n\n## Embeddings\n\nVector embeddings are created using the **all-mpnet-base-v2** sentence transformer with **768 dimensions**. These embeddings power the semantic search used for context recall via cosine similarity.\n\n## Context Recall\n\nWhen a query is sent to an LLM through a wrapped client, Memori automatically:\n\n1. Intercepts the outbound LLM call\n2. Uses semantic search to find entity facts matching the query\n3. Passes vector embeddings to FAISS for similarity ranking\n4. Injects the top-N most relevant facts into the system prompt\n5. Forwards the enriched request to the LLM provider\n\n## Schema ERD\n\n![Memori Schema ERD](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/erd.png)\n\n## When to Use It\n\n- Chatbots or AI assistants with returning users\n- Use cases that need to remember user preferences across sessions\n- You want personalized AI interactions\n- With multi-step workflows or agentic systems\n- For building relationships between entities\n\n\n For Memori to provide all Advanced Augmentation capabilities, attribution must\n be set before making LLM calls. Without attribution, Memori cannot create or\n recall memories.\n\n" }, { "path": "docs/memori-byodb/concepts/architecture.mdx", "content": "---\ntitle: Architecture\ndescription: Understand how Memori's open-source architecture works — from your app to your own database, with local storage, augmentation, and recall.\n---\n\n# Architecture\n\nMemori is a modular memory layer for AI applications. You connect your LLM client, set attribution, point Memori at your database, and it handles everything else — storage, augmentation, knowledge graph construction, and recall. All data stays on your infrastructure.\n\n## System Overview\n\n![\"Memori BYODB System Overview\"](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/memori-byodb-architecture-detail.webp)\n\n## Core Components\n\n**Memori Core** — The central coordinator between your application and your database. Manages attribution, coordinates storage and augmentation, provides LLM wrappers, and exposes the Recall API.\n\n**LLM Provider Wrappers** — Wraps your existing LLM client transparently. Intercepts calls, captures messages and responses, persists conversation data to your database. Supports sync, async, and streaming.\n\n**Attribution System** — Tags every memory with who created it and in what context. Tracks three dimensions: entity (the user), process (the agent), and session (the conversation thread).\n\n**Storage System** — Stores all data in your database with no external dependencies. Supports SQLAlchemy `sessionmaker`, DB-API 2.0 connections, Django ORM, and MongoDB. Works with SQLite, PostgreSQL, MySQL, MariaDB, Oracle, CockroachDB, and OceanBase, including providers like Neon, Supabase, and AWS RDS/Aurora.\n\n**Advanced Augmentation** — Turns raw conversations into structured memories. Extracts facts, preferences, and skills, generates vector embeddings locally, and builds a knowledge graph. Runs asynchronously with zero latency impact.\n\n## Configuration\n\nSetting up Memori requires a database connection and attribution:\n\n```python\nimport sqlite3\nfrom memori import Memori\nfrom openai import OpenAI\n\ndef get_connection():\n return sqlite3.connect(\"memori.db\")\n\nclient = OpenAI()\nmem = Memori(conn=get_connection).llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"my_agent\")\nmem.config.storage.build()\n```\n\n## Data Flow\n\n1. **Conversation Capture** — Every LLM call through the wrapped client is captured and stored in your database. Your app gets the response immediately.\n\n2. **Attribution Tracking** — Attribution links every conversation to a specific entity and process so memories are properly scoped and indexed.\n\n3. **Augmentation** — After a conversation completes, Memori processes it asynchronously — extracts facts, generates embeddings locally, and builds knowledge graph triples.\n\n4. **Recall** — On the next LLM call, Memori surfaces the right memories at the right time: semantic search, intelligent ranking and decay, and seamless injection of the most relevant context into the system prompt so your AI stays contextually aware.\n" }, { "path": "docs/memori-byodb/concepts/async-patterns.mdx", "content": "---\ntitle: Async Patterns\ndescription: Best practices for using Memori with async/await — AsyncOpenAI, AsyncAnthropic, FastAPI, and thread safety.\n---\n\n# Async Patterns\n\nMemori works with Python's async/await out of the box. Use `AsyncOpenAI` or `AsyncAnthropic` instead of their sync counterparts — everything else stays the same.\n\n## When to Use Async\n\n| Scenario | Async? | Why |\n| ------------------------ | ------ | --------------------------- |\n| Web servers (FastAPI) | Yes | Concurrent request handling |\n| Chatbots with many users | Yes | Non-blocking I/O |\n| CLI scripts | No | Sync is simpler |\n| Jupyter notebooks | No | Event loop already running |\n\n## Basic Async Setup\n\n```python\nimport os\nimport asyncio\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom memori import Memori\nfrom openai import AsyncOpenAI\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nasync def main():\n client = AsyncOpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\n mem = Memori(conn=SessionLocal).llm.register(client)\n mem.attribution(entity_id=\"user_123\", process_id=\"async_agent\")\n mem.config.storage.build()\n\n response = await client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"I prefer async Python.\"}]\n )\n print(response.choices[0].message.content)\n mem.augmentation.wait()\n\nasyncio.run(main())\n```\n\nWorks identically with `AsyncAnthropic` — just swap the client.\n\n## FastAPI Example\n\n```python\nimport os\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom memori import Memori\nfrom openai import AsyncOpenAI\n\napp = FastAPI()\nengine = create_engine(\"sqlite:///memori.db\", connect_args={\"check_same_thread\": False})\nSessionLocal = sessionmaker(bind=engine)\nMemori(conn=SessionLocal).config.storage.build()\n\nclass ChatRequest(BaseModel):\n message: str\n\n@app.post(\"/chat/{user_id}\")\nasync def chat(user_id: str, req: ChatRequest):\n client = AsyncOpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\n mem = Memori(conn=SessionLocal).llm.register(client)\n mem.attribution(entity_id=user_id, process_id=\"fastapi_async\")\n\n response = await client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": req.message}]\n )\n return {\"response\": response.choices[0].message.content}\n```\n\n## Thread Safety\n\n| Pattern | Safe? | Why |\n| ------------------------------- | ----- | ------------------------- |\n| `conn=SessionLocal` (factory) | Yes | New session per operation |\n| `conn=lambda: existing_session` | No | Shares one session |\n\nFor production async apps, use PostgreSQL with larger pools:\n\n```python\nengine = create_engine(\n \"postgresql+psycopg://user:pass@host/db\",\n pool_pre_ping=True,\n pool_size=20,\n max_overflow=40,\n pool_recycle=300\n)\n```\n" }, { "path": "docs/memori-byodb/concepts/cli-quickstart.mdx", "content": "---\ntitle: CLI Quickstart\ndescription: Get started with the Memori command line interface for setup, diagnostics, and management.\n---\n\n# CLI Quickstart\n\nThe Memori CLI lets you manage your environment, check quotas, and perform setup tasks from the terminal. It is included with the `memori` package.\n\n## View Available Commands\n\n```bash\npython -m memori\n```\n\n## Essential Setup\n\nPre-download the embedding model that Memori uses for semantic search. Without this, the model downloads automatically on first use (slower).\n\n```bash\npython -m memori setup\n```\n\n## Command Reference\n\n| Command | Parameters | Description |\n| ---------------------------- | ---------- | ---------------------------------------------- |\n| `setup` | None | Prepare environment (download embedding model) |\n| `quota` | None | Display available augmentation quota |\n| `sign-up` | `` | Sign up for an API key |\n| `cockroachdb cluster start` | None | Start a new CockroachDB cluster |\n| `cockroachdb cluster claim` | None | Get cluster claim URL |\n| `cockroachdb cluster delete` | None | Delete cluster (destructive) |\n\n\n\n```bash {{ title: 'Setup' }}\n# Cache the embedding model for faster runtime\npython -m memori setup\n```\n\n```bash {{ title: 'Check Quota' }}\n# View augmentation usage and limits\npython -m memori quota\n```\n\n```bash {{ title: 'Sign Up' }}\n# Get an API key for higher augmentation limits\npython -m memori sign-up your-email@example.com\n```\n\n```bash {{ title: 'CockroachDB' }}\n# Start a new serverless database cluster\npython -m memori cockroachdb cluster start\n\n# Get the cluster claim URL\npython -m memori cockroachdb cluster claim\n\n# Delete the cluster (destructive!)\npython -m memori cockroachdb cluster delete\n```\n\n\n\n\n The `cockroachdb cluster delete` command permanently removes your cluster\n including all data. This cannot be undone.\n\n" }, { "path": "docs/memori-byodb/concepts/how-memory-works.mdx", "content": "---\ntitle: How Memori Works\ndescription: Understand the core concepts behind Memori — entities, processes, sessions, memory types, attribution, and how recall brings it all together.\n---\n\n# How Memori Works\n\nMemori gives your AI application long-term memory. Instead of forgetting everything after each conversation, your AI can remember facts, preferences, and context across sessions and across different applications — all stored in your own database.\n\n## Attribution\n\nEvery memory in Memori is tagged with three pieces of information: **who**, **what**, and **when**.\n\n- **Entity (`entity_id`)** — The person, place, or thing generating memories. Typically a user ID (e.g., `\"user_alice\"`, `\"company_acme\"`).\n- **Process (`process_id`)** — The agent, program, or workflow creating memories (e.g., `\"support_bot\"`, `\"code_review_agent\"`).\n- **Session (`session_id`)** — Groups related LLM interactions into a conversation thread. Auto-generated as a UUID by default.\n\nThe combination of `entity_id` + `process_id` + `session_id` creates a unique memory scope — different users have isolated memories, the same user can have different context in different applications, and each conversation is tracked separately.\n\n```python\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom memori import Memori\nfrom openai import OpenAI\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = OpenAI()\nmem = Memori(conn=SessionLocal).llm.register(client)\n\n# Set attribution before any LLM calls\nmem.attribution(\n entity_id=\"user_alice\",\n process_id=\"support_bot\"\n)\n# session_id is auto-generated\n\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[\n {\"role\": \"user\", \"content\": \"I prefer dark mode.\"}\n ]\n)\n```\n\n## Memory Types\n\nWhen you have a conversation through a Memori-wrapped LLM client, Advanced Augmentation extracts structured memories in the background:\n\n| Type | What it captures | Example |\n| --------------- | -------------------------- | ----------------------------------------------- |\n| **Facts** | Objective information | \"User uses PostgreSQL for production databases\" |\n| **Preferences** | Choices, opinions, tastes | \"Prefers concise answers\" |\n| **Skills** | Abilities and expertise | \"Experienced with React (5 years)\" |\n| **Rules** | Constraints and principles | \"Follows test-driven development\" |\n| **Events** | Milestones and occurrences | \"Product launched recently\" |\n\n## How Recall Works\n\nRecall brings stored memories back into your AI conversations. There are two modes.\n\n### Automatic Recall (Default)\n\nOn every LLM call, Memori automatically:\n\n1. Intercepts the outbound request\n2. Uses semantic search to find relevant facts for the current entity\n3. Injects the most relevant memories into the system prompt\n4. Forwards the enriched request to the LLM\n\nNo extra code required — it happens transparently.\n\n### Manual Recall\n\nUse `mem.recall()` when you want to retrieve memories explicitly — for custom prompts, displaying memories in a UI, or debugging.\n\n```python\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom memori import Memori\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nmem = Memori(conn=SessionLocal)\nmem.attribution(entity_id=\"user_alice\", process_id=\"support_bot\")\n\nfacts = mem.recall(\"coding preferences\", limit=5)\n\nfor fact in facts:\n print(f\"Fact: {fact.content}\")\n print(f\"Score: {fact.similarity:.4f}\")\n```\n\nEach returned fact includes `id`, `content`, `similarity` (0–1 relevance score), `rank_score`, and `date_created`.\n\n### Recall Configuration\n\nMemori uses the **all-mpnet-base-v2** sentence transformer with cosine similarity for semantic search. You can tune recall behavior with these configuration options:\n\n| Option | Default | Description |\n| --------------------------------------- | ------- | -------------------------------------------------- |\n| `mem.config.recall_relevance_threshold` | `0.1` | Minimum similarity score for a fact to be included |\n| `mem.config.recall_embeddings_limit` | `1000` | Maximum number of embeddings to compare against |\n\n```python\n# Example: tune recall for broader or narrower results\nmem.config.recall_relevance_threshold = 0.05 # Lower = more results\nmem.config.recall_embeddings_limit = 500 # Reduce for lower memory usage\n```\n\n## Memory Lifecycle\n\n![\"Memori Lifecycle\"](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/memori-lifecycle.webp)\n\n1. **Conversation** — Your user talks to your AI through the wrapped LLM client\n2. **Capture** — Memori intercepts and stores the raw conversation in your database\n3. **Augmentation** — Advanced Augmentation processes the conversation asynchronously\n4. **Extraction** — Facts, preferences, skills, rules, and events are identified\n5. **Storage** — Extracted memories are stored in your database with vector embeddings\n6. **Recall** — On the next LLM call, relevant memories are retrieved and injected into context\n" }, { "path": "docs/memori-byodb/concepts/knowledge-graph.mdx", "content": "---\ntitle: Knowledge Graph\ndescription: How Memori automatically builds a knowledge graph from your AI conversations using semantic triples, stored in your own database where you can query it directly.\n---\n\n# Knowledge Graph\n\nMemori automatically builds a knowledge graph from your AI conversations. Every time Advanced Augmentation processes a conversation, it extracts structured relationships — semantic triples — and connects them into a graph. Because you own the database, you can query the knowledge graph directly using SQL.\n\n## How It Works\n\n1. **Conversation captured** — Your user talks to your AI through the Memori-wrapped LLM client\n2. **Augmentation processes** — Memori analyzes the conversation in the background\n3. **NER extraction** — Named-entity recognition identifies key entities and relationships\n4. **Triple creation** — Relationships are expressed as subject-predicate-object triples\n5. **Graph storage** — Triples are stored and deduplicated in your database\n6. **Recall ready** — The graph is available for semantic search on the next LLM call\n\n## Semantic Triples\n\nEvery fact in the knowledge graph is a semantic triple — a three-part statement: **[Subject]** **[Predicate]** **[Object]**.\n\n- \"Alice\" \"prefers\" \"dark mode\"\n- \"PostgreSQL\" \"is\" \"a relational database\"\n- \"The project\" \"uses\" \"FastAPI\"\n\n### Example Extraction\n\nFrom _\"My favorite database is PostgreSQL and I use it with FastAPI for our REST APIs. I've been using Python for about 8 years\"_:\n\n| Subject | Predicate | Object |\n| ------- | ----------------- | -------------------- |\n| user | favorite_database | PostgreSQL |\n| user | uses | FastAPI |\n| user | uses_for | REST APIs |\n| user | uses_with | PostgreSQL + FastAPI |\n| user | experience_years | Python (8 years) |\n\nMemori automatically deduplicates triples — frequently mentioned facts get a higher mention count and updated timestamp.\n\n## Database Tables\n\n| Table | Purpose |\n| ------------------------ | ---------------------------------------------------- |\n| `memori_subject` | Stores unique subjects |\n| `memori_predicate` | Stores unique predicates |\n| `memori_object` | Stores unique objects |\n| `memori_knowledge_graph` | Links subjects, predicates, and objects into triples |\n| `memori_entity_fact` | Stores facts with vector embeddings for recall |\n\n## Querying\n\n### Via Recall API\n\n```python\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom memori import Memori\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nmem = Memori(conn=SessionLocal)\nmem.attribution(entity_id=\"user_alice\", process_id=\"my_agent\")\n\nfacts = mem.recall(\"database preferences\", limit=5)\n\nfor fact in facts:\n print(f\"Fact: {fact.content}\")\n print(f\"Similarity: {fact.similarity:.4f}\")\n```\n\n### Via Direct SQL\n\nSince the knowledge graph lives in your database, you can query it directly for debugging, dashboards, or exploration.\n\n\n\n```sql {{ title: 'View All Triples' }}\nSELECT\n s.name AS subject,\n p.name AS predicate,\n o.name AS object\nFROM memori_knowledge_graph kg\nJOIN memori_subject s ON kg.subject_id = s.id\nJOIN memori_predicate p ON kg.predicate_id = p.id\nJOIN memori_object o ON kg.object_id = o.id;\n```\n\n```sql {{ title: 'Triples for an Entity' }}\nSELECT\n s.name AS subject,\n p.name AS predicate,\n o.name AS object,\n kg.mention_count,\n kg.last_mentioned_at\nFROM memori_knowledge_graph kg\nJOIN memori_subject s ON kg.subject_id = s.id\nJOIN memori_predicate p ON kg.predicate_id = p.id\nJOIN memori_object o ON kg.object_id = o.id\nWHERE kg.entity_id = 'user_alice'\nORDER BY kg.mention_count DESC;\n```\n\n```sql {{ title: 'View Facts' }}\nSELECT content, created_at\nFROM memori_entity_fact\nWHERE entity_id = 'user_alice'\nORDER BY created_at DESC;\n```\n\n\n\n## Scope\n\n| Aspect | Scope |\n| -------------- | --------------------------------------------------------------- |\n| **Triples** | Per entity — shared across all processes |\n| **Visibility** | All processes for an entity can see and use the graph |\n| **Growth** | Conversations from any process contribute to the entity's graph |\n\nIf Alice tells your support bot about PostgreSQL, your code assistant also knows she uses PostgreSQL.\n" }, { "path": "docs/memori-byodb/concepts/multi-user-support.mdx", "content": "---\ntitle: Multi-User Support\ndescription: How Memori isolates memories across users, applications, and sessions so each user gets a personalized experience — all in your own database.\n---\n\n# Multi-User Support\n\nMemori provides built-in multi-user and multi-process isolation through its attribution system. Each combination of entity, process, and session creates an isolated memory space — User A never sees User B's memories, and your support bot has different context than your sales bot.\n\n## Isolation Model\n\n![\"Memori - Multi User Support\"](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/memori-multi-user-support.webp)\n\n## What's Shared vs Isolated\n\n| Data | Scope |\n| ------------------- | ---------------------------------------- |\n| **Facts** | Per entity — shared across all processes |\n| **Preferences** | Per entity |\n| **Skills** | Per entity |\n| **Attributes** | Per process |\n| **Conversations** | Per entity + process + session |\n| **Sessions** | Per entity + process |\n| **Knowledge Graph** | Per entity |\n\n## Examples\n\n\n\n```python {{ title: 'Per-User Isolation' }}\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom memori import Memori\nfrom openai import OpenAI\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = OpenAI()\nmem = Memori(conn=SessionLocal).llm.register(client)\n\n# User A's conversations\nmem.attribution(entity_id=\"user_alice\", process_id=\"support_bot\")\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"I prefer dark mode\"}]\n)\n\n# User B's conversations — completely isolated\nmem.attribution(entity_id=\"user_bob\", process_id=\"support_bot\")\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"What are my preferences?\"}]\n)\n# Bob will NOT see Alice's preferences\n```\n\n```python {{ title: 'Multi-Process' }}\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom memori import Memori\nfrom openai import OpenAI\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = OpenAI()\nmem = Memori(conn=SessionLocal).llm.register(client)\n\n# Same user, different processes\nmem.attribution(entity_id=\"user_alice\", process_id=\"support_bot\")\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[\n {\"role\": \"user\", \"content\": \"I use PostgreSQL for my databases\"}\n ]\n)\n\n# Switch to a different process for the same user\nmem.attribution(entity_id=\"user_alice\", process_id=\"sales_bot\")\n# The sales bot can recall Alice's facts (like \"uses PostgreSQL\")\n# because facts are shared across processes for the same entity.\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[\n {\"role\": \"user\", \"content\": \"What databases do I use?\"}\n ]\n)\n```\n\n```python {{ title: 'Session Management' }}\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom memori import Memori\nfrom openai import OpenAI\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = OpenAI()\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.attribution(entity_id=\"user_alice\", process_id=\"support_bot\")\n\n# Get the current session ID\ncurrent_session = mem.config.session_id\n\n# Start a new conversation group\nmem.new_session()\n\n# Or restore a previous session\nmem.set_session(current_session)\n```\n\n\n\n## Common Patterns\n\n### Web Application\n\nSet the entity ID from the authenticated user's session. Works with Flask, FastAPI, Django, or any framework.\n\n```python\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom memori import Memori\nfrom openai import OpenAI\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\ndef handle_chat(user_id: str, message: str):\n client = OpenAI()\n mem = Memori(conn=SessionLocal).llm.register(client)\n mem.attribution(entity_id=user_id, process_id=\"web_assistant\")\n\n response = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": message}]\n )\n return response.choices[0].message.content\n```\n\n### Multi-Agent System\n\nGive each agent a unique process ID. Facts are shared across agents for the same user, but each maintains its own conversation history.\n\n```python\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom memori import Memori\nfrom openai import OpenAI\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\ndef create_agent(user_id: str, agent_name: str):\n client = OpenAI()\n mem = Memori(conn=SessionLocal).llm.register(client)\n mem.attribution(entity_id=user_id, process_id=agent_name)\n return client\n\n# Three agents, one user, shared facts\nsupport = create_agent(\"user_alice\", \"support_agent\")\nsales = create_agent(\"user_alice\", \"sales_agent\")\nonboard = create_agent(\"user_alice\", \"onboarding_agent\")\n```\n" }, { "path": "docs/memori-byodb/contribute/development-setup.mdx", "content": "---\ntitle: Development Setup\ndescription: Set up your local development environment for contributing to the Memori open-source project.\n---\n\n# Development Setup\n\nThis guide walks you through setting up a local development environment for working on the Memori codebase. Follow these steps to clone the repository, install dependencies, and run the test suite.\n\n\n Want a zero-setup option? The Memori Cloud at\n [app.memorilabs.ai](https://app.memorilabs.ai).\n\n\n## Prerequisites\n\nBefore you begin, make sure you have the following installed on your system:\n\n- **Python 3.10 or higher** — Memori requires Python 3.10+. Check your version with `python --version`.\n- **git** — For cloning the repository and managing branches.\n- **pip** — Python's package manager (usually included with Python).\n\n## Clone the Repository\n\nStart by forking the repository on GitHub, then clone your fork locally:\n\n```bash\ngit clone https://github.com/YOUR_USERNAME/Memori.git\ncd Memori\n```\n\nIf you plan to submit a pull request, make sure to keep your fork synced with the upstream repository:\n\n```bash\ngit remote add upstream https://github.com/MemoriLabs/Memori.git\ngit fetch upstream\n```\n\n## Install Dependencies\n\nIt is recommended to use a virtual environment to avoid conflicts with other Python projects:\n\n\n\n```bash {{ title: 'venv (Built-in)' }}\npython -m venv .venv\nsource .venv/bin/activate # macOS/Linux\n# .venv\\Scripts\\activate # Windows\n```\n\n```bash {{ title: 'conda' }}\nconda create -n memori python=3.12\nconda activate memori\n```\n\n\n\nOnce your virtual environment is active, install Memori with development dependencies:\n\n```bash\npip install -e \".[dev]\"\n```\n\nThis installs Memori in editable mode (`-e`) so that changes you make to the source code are immediately reflected without reinstalling. The `[dev]` extra includes testing and linting tools.\n\nIf the project uses a `requirements-dev.txt` file instead, install with:\n\n```bash\npip install -e .\npip install -r requirements-dev.txt\n```\n\n## Running Tests\n\nMemori uses pytest for its test suite. Run all tests from the project root:\n\n```bash\npytest\n```\n\nTo run tests with verbose output:\n\n```bash\npytest -v\n```\n\nTo run a specific test file or test function:\n\n```bash\n# Run all tests in a specific file\npytest tests/test_openai.py\n\n# Run a specific test function\npytest tests/test_openai.py::test_sync_completion\n```\n\nTo run tests with coverage reporting:\n\n```bash\npytest --cov=memori --cov-report=term-missing\n```\n\n## Local Development Workflow\n\nHere is a typical workflow for making changes to Memori:\n\n1. **Create a branch** for your feature or fix:\n\n ```bash\n git checkout -b feat/my-new-feature\n ```\n\n2. **Make your changes** — Edit the source code in the `memori/` directory.\n\n3. **Write tests** — Add or update tests in the `tests/` directory to cover your changes.\n\n4. **Run the tests** to make sure nothing is broken:\n\n ```bash\n pytest\n ```\n\n5. **Check code style** — The project uses Ruff for linting and formatting:\n\n ```bash\n # Lint\n ruff check .\n\n # Format\n ruff format --check .\n ```\n\n6. **Commit your changes** with a descriptive message:\n\n ```bash\n git add .\n git commit -m \"feat: add support for Redis storage backend\"\n ```\n\n7. **Push to your fork** and open a pull request:\n ```bash\n git push origin feat/my-new-feature\n ```\n\n## Project Structure\n\nHere is a high-level overview of the Memori repository structure:\n\n| Directory | Purpose |\n| ----------- | ------------------------------- |\n| `memori/` | Core library source code |\n| `tests/` | Test suite (pytest) |\n| `docs/` | Documentation source files |\n| `examples/` | Example scripts and usage demos |\n\nThe core integration logic for each LLM provider typically lives in separate modules within the `memori/` directory. When adding support for a new provider, look at existing integrations (such as OpenAI or Anthropic) as templates.\n" }, { "path": "docs/memori-byodb/contribute/overview.mdx", "content": "---\ntitle: Contributing to Memori\ndescription: How to contribute to the Memori open-source project — code, documentation, issues, and discussions.\n---\n\n# Contributing to Memori\n\nThank you for your interest in contributing to Memori. Whether you are fixing a bug, adding a feature, improving documentation, or just asking a question, every contribution helps make Memori better for everyone.\n\n\n Want a zero-setup option? The Memori Cloud at\n [app.memorilabs.ai](https://app.memorilabs.ai).\n\n\nThe Memori source code lives on GitHub at [github.com/MemoriLabs/Memori](https://github.com/MemoriLabs/Memori).\n\n## Ways to Contribute\n\nThere are many ways to contribute to Memori, and not all of them involve writing code:\n\n### Report Bugs\n\nFound a bug? Open an issue on [GitHub](https://github.com/MemoriLabs/Memori/issues) with:\n\n- A clear title describing the problem\n- Steps to reproduce the issue\n- What you expected to happen versus what actually happened\n- Your Python version, OS, and Memori version (`pip show memori`)\n\n### Suggest Features\n\nHave an idea for a new feature or improvement? Open a [GitHub Discussion](https://github.com/MemoriLabs/Memori/discussions) or an issue tagged with `enhancement`. Describe the use case and why it would be valuable.\n\n### Improve Documentation\n\nDocumentation improvements are always welcome. This includes:\n\n- Fixing typos or unclear explanations\n- Adding new examples or use cases\n- Improving the guides for specific LLM providers or databases\n- Translating documentation\n\n### Contribute Code\n\nReady to write some code? Here is the process:\n\n1. **Fork the repository** — Create your own fork of [MemoriLabs/Memori](https://github.com/MemoriLabs/Memori) on GitHub\n2. **Create a branch** — Branch off `main` with a descriptive name (for example, `fix/streaming-bug` or `feat/redis-support`)\n3. **Make your changes** — Write your code, add tests, and update documentation if needed\n4. **Run the tests** — Make sure all existing tests pass and your new tests cover the changes\n5. **Submit a pull request** — Open a PR back to the `main` branch with a clear description of what you changed and why\n\nSee the [Development Setup](/docs/memori-byodb/contribute/development-setup) guide for instructions on setting up your local environment.\n\n## Pull Request Guidelines\n\nWhen submitting a pull request:\n\n- **Keep PRs focused** — One feature or fix per PR. This makes review faster and easier.\n- **Write descriptive commit messages** — Explain what changed and why, not just what files were modified.\n- **Include tests** — New features should include tests. Bug fixes should include a test that reproduces the bug.\n- **Update documentation** — If your change affects the public API or user-facing behavior, update the relevant docs.\n- **Follow existing patterns** — Look at how existing code is structured and follow the same conventions.\n\n## Code of Conduct\n\nWe are committed to providing a welcoming and inclusive experience for everyone. All contributors are expected to be respectful and constructive in their interactions. Harassment, discrimination, and toxic behavior are not tolerated.\n\nPlease review the project's Code of Conduct on the [GitHub repository](https://github.com/MemoriLabs/Memori) before contributing.\n\n## Community\n\nJoin the Memori community:\n\n- **GitHub Issues** — For bug reports and feature requests: [github.com/MemoriLabs/Memori/issues](https://github.com/MemoriLabs/Memori/issues)\n- **GitHub Discussions** — For questions, ideas, and general conversation: [github.com/MemoriLabs/Memori/discussions](https://github.com/MemoriLabs/Memori/discussions)\n" }, { "path": "docs/memori-byodb/dashboard/api-keys.mdx", "content": "---\ntitle: API Keys\ndescription: Create and manage Memori API keys from the dashboard.\n---\n\n# API Keys\n\nUse the **API keys** page in [app.memorilabs.ai](https://app.memorilabs.ai)\nto create and manage keys for your organization.\n\n## Overview\n\nThe table includes:\n\n- **Name**\n- **Key** (masked)\n- **Created at**\n- **Last used**\n\nIf no keys are connected yet, the table shows **No connected APIs.**\n\n## Create a New API Key\n\n1. Open **API keys** in the dashboard sidebar.\n2. Click **Create API key**.\n3. In **New API key**, enter a value in **Name**.\n4. Click **Create key**.\n\n![API keys page with empty table state and New API key modal](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/new-api-key-modal.webp)\n\n## Copy the Key (Shown Once)\n\nAfter creating a key, the **Your API key** modal appears with the full key\nvalue and a **Copy** button.\n\n\n The full key value is shown only once. Copy it and store it securely before\n clicking **Continue**.\n\n\n1. Click **Copy**.\n2. Click **Continue** to return to the API keys table.\n\n![API keys page with table row and Your API key modal for one-time copy](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/your-api-key-modal.webp)\n\n## Security Best Practices\n\n\n Never commit API keys to version control. Treat API keys like passwords.\n\n\n- Store keys in environment variables or a secure secrets manager\n- Rotate keys immediately if you suspect exposure\n- Use separate keys for development, staging, and production\n" }, { "path": "docs/memori-byodb/databases/cockroachdb.mdx", "content": "---\ntitle: CockroachDB\ndescription: Set up Memori with CockroachDB — distributed SQL database with PostgreSQL compatibility, automatic scaling, and strong consistency.\n---\n\n# CockroachDB\n\nCockroachDB is a distributed SQL database that uses the PostgreSQL wire protocol. It works with Memori through the same `psycopg` driver as PostgreSQL — no special adapter needed.\n\n## Install\n\n```bash\npip install memori psycopg2-binary\n```\n\n## Quick Start\n\n\n\n```python {{ title: 'Basic' }}\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\n \"cockroachdb+psycopg2://user:password@localhost:26257/memori_db\",\n pool_pre_ping=True\n)\nSessionLocal = sessionmaker(bind=engine)\n\nmem = Memori(conn=SessionLocal)\nmem.config.storage.build()\n```\n\n```python {{ title: 'CockroachDB Cloud' }}\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\n \"cockroachdb+psycopg2://user:password@free-tier.gcp-us-central1.cockroachlabs.cloud:26257/memori_db\"\n \"?sslmode=verify-full\",\n pool_pre_ping=True,\n pool_size=10,\n max_overflow=20\n)\nSessionLocal = sessionmaker(bind=engine)\n\nmem = Memori(conn=SessionLocal)\nmem.config.storage.build()\n```\n\n\n\n## Connection Strings\n\n| Environment | Connection String |\n| --------------------- | -------------------------------------------------------------------------------------------------- |\n| **Local** | `cockroachdb+psycopg2://root@localhost:26257/memori_db` |\n| **With Auth** | `cockroachdb+psycopg2://user:pass@host:26257/memori_db` |\n| **CockroachDB Cloud** | `cockroachdb+psycopg2://user:pass@cluster.cockroachlabs.cloud:26257/memori_db?sslmode=verify-full` |\n| **PostgreSQL scheme** | `postgresql+psycopg2://user:pass@host:26257/memori_db` |\n\n## Complete Example\n\n```python\nimport os\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom memori import Memori\nfrom openai import OpenAI\n\nengine = create_engine(\n os.getenv(\"COCKROACHDB_URL\"),\n pool_pre_ping=True,\n pool_size=10,\n max_overflow=20,\n pool_recycle=300\n)\nSessionLocal = sessionmaker(bind=engine)\n\nclient = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"my_agent\")\nmem.config.storage.build()\n\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"I manage a distributed systems team.\"}]\n)\nprint(response.choices[0].message.content)\n\nmem.augmentation.wait()\nfacts = mem.recall(\"job role\")\nprint(facts)\n```\n" }, { "path": "docs/memori-byodb/databases/mongodb.mdx", "content": "---\ntitle: MongoDB\ndescription: Set up Memori with MongoDB — document-oriented AI memory using PyMongo.\n---\n\n# MongoDB\n\nMongoDB stores data as flexible JSON-like documents. Memori integrates through PyMongo, giving you a NoSQL option for AI agent memory.\n\n## Install\n\n```bash\npip install memori pymongo\n```\n\n## Quick Start\n\n```python\nfrom memori import Memori\nfrom pymongo import MongoClient\n\nclient = MongoClient(\"mongodb://localhost:27017\")\n\ndef get_db():\n return client[\"memori_db\"]\n\nmem = Memori(conn=get_db)\nmem.config.storage.build()\n```\n\n## Connection Strings\n\n| Environment | Connection String |\n| --------------- | ---------------------------------------------------- |\n| **Local** | `mongodb://localhost:27017` |\n| **With Auth** | `mongodb://user:password@localhost:27017` |\n| **Atlas (SRV)** | `mongodb+srv://user:password@cluster.mongodb.net` |\n| **Replica Set** | `mongodb://host1:27017,host2:27017/?replicaSet=myRS` |\n\n## Complete Example\n\n```python\nimport os\nfrom pymongo import MongoClient\nfrom memori import Memori\nfrom openai import OpenAI\n\nmongo_client = MongoClient(\"mongodb://localhost:27017\")\n\ndef get_db():\n return mongo_client[\"memori_db\"]\n\nclient = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\nmem = Memori(conn=get_db).llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"my_agent\")\nmem.config.storage.build()\n\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"I love hiking in the Rockies.\"}]\n)\nprint(response.choices[0].message.content)\n\nmem.augmentation.wait()\nfacts = mem.recall(\"hobbies and outdoor activities\")\nprint(facts)\n```\n\n## MongoDB Atlas\n\n```python\nfrom pymongo import MongoClient\n\nmongo_client = MongoClient(\n \"mongodb+srv://user:password@cluster.mongodb.net\"\n \"/?retryWrites=true&w=majority\"\n)\n\ndef get_db():\n return mongo_client[\"memori_db\"]\n```\n\n## Connection Pooling\n\nPyMongo manages its own connection pool:\n\n```python\nmongo_client = MongoClient(\n \"mongodb://localhost:27017\",\n maxPoolSize=50,\n minPoolSize=5,\n maxIdleTimeMS=30000\n)\n```\n" }, { "path": "docs/memori-byodb/databases/mysql.mdx", "content": "---\ntitle: MySQL\ndescription: Set up Memori with MySQL — use your existing MySQL infrastructure for AI agent memory.\n---\n\n# MySQL\n\nIf your infrastructure already runs MySQL, you can use it directly with Memori without setting up a separate database.\n\n## Install\n\n\n\n```bash {{ title: 'PyMySQL (Pure Python)' }}\npip install memori pymysql\n```\n\n```bash {{ title: 'mysqlclient (C Extension)' }}\npip install memori mysqlclient\n```\n\n\n\n## Quick Start\n\n\n\n```python {{ title: 'PyMySQL' }}\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\n \"mysql+pymysql://user:password@localhost:3306/memori_db\",\n pool_pre_ping=True\n)\nSessionLocal = sessionmaker(bind=engine)\n\nmem = Memori(conn=SessionLocal)\nmem.config.storage.build()\n```\n\n```python {{ title: 'mysqlclient' }}\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\n \"mysql+mysqldb://user:password@localhost:3306/memori_db\",\n pool_pre_ping=True\n)\nSessionLocal = sessionmaker(bind=engine)\n\nmem = Memori(conn=SessionLocal)\nmem.config.storage.build()\n```\n\n\n\n## Connection Strings\n\n| Driver | Connection String |\n| ---------------- | --------------------------------------------------------------------- |\n| **PyMySQL** | `mysql+pymysql://user:pass@host:3306/database` |\n| **mysqlclient** | `mysql+mysqldb://user:pass@host:3306/database` |\n| **With charset** | `mysql+pymysql://user:pass@host:3306/database?charset=utf8mb4` |\n| **With SSL** | `mysql+pymysql://user:pass@host:3306/database?ssl_ca=/path/to/ca.pem` |\n\n## Complete Example\n\n```python\nimport os\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom memori import Memori\nfrom openai import OpenAI\n\nengine = create_engine(\n \"mysql+pymysql://user:password@localhost:3306/memori_db\"\n \"?charset=utf8mb4\",\n pool_pre_ping=True,\n pool_size=5,\n max_overflow=10,\n pool_recycle=1800\n)\nSessionLocal = sessionmaker(bind=engine)\n\nclient = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"my_agent\")\nmem.config.storage.build()\n\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"I work at Acme Corp as a designer.\"}]\n)\nprint(response.choices[0].message.content)\n\nmem.augmentation.wait()\nfacts = mem.recall(\"workplace\")\nprint(facts)\n```\n" }, { "path": "docs/memori-byodb/databases/oracle.mdx", "content": "---\ntitle: Oracle\ndescription: Set up Memori with Oracle Database — enterprise-grade AI memory for Oracle infrastructure.\n---\n\n# Oracle\n\nOracle Database integrates with Memori through SQLAlchemy, so you can add AI agent memory to existing Oracle infrastructure.\n\n## Install\n\n\n\n```bash {{ title: 'oracledb (Recommended)' }}\npip install memori oracledb\n```\n\n```bash {{ title: 'cx_Oracle (Legacy)' }}\npip install memori cx_Oracle\n```\n\n\n\n## Quick Start\n\n\n\n```python {{ title: 'oracledb (Modern)' }}\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\n \"oracle+oracledb://user:password@hostname:1521/service_name\"\n)\nSessionLocal = sessionmaker(bind=engine)\n\nmem = Memori(conn=SessionLocal)\nmem.config.storage.build()\n```\n\n```python {{ title: 'cx_Oracle (Legacy)' }}\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\n \"oracle+cx_oracle://user:password@hostname:1521/service_name\"\n)\nSessionLocal = sessionmaker(bind=engine)\n\nmem = Memori(conn=SessionLocal)\nmem.config.storage.build()\n```\n\n\n\n## Connection Strings\n\n| Format | Connection String |\n| ---------------- | ----------------------------------------------------------------------- |\n| **oracledb** | `oracle+oracledb://user:pass@host:1521/service_name` |\n| **cx_Oracle** | `oracle+cx_oracle://user:pass@host:1521/service_name` |\n| **TNS Name** | `oracle+oracledb://user:pass@tns_alias` |\n| **Oracle Cloud** | `oracle+oracledb://user:pass@host:1522/service?ssl_server_dn_match=yes` |\n\n## Complete Example\n\n```python\nimport os\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom memori import Memori\nfrom openai import OpenAI\n\nengine = create_engine(\n \"oracle+oracledb://memori_user:password@oracle-host:1521/ORCL\",\n pool_pre_ping=True,\n pool_size=5,\n max_overflow=10,\n pool_recycle=300\n)\nSessionLocal = sessionmaker(bind=engine)\n\nclient = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"my_agent\")\nmem.config.storage.build()\n\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"Our quarterly review is on March 15.\"}]\n)\nprint(response.choices[0].message.content)\n\nmem.augmentation.wait()\nfacts = mem.recall(\"quarterly review date\")\nprint(facts)\n```\n\n## Oracle Cloud\n\nFor Oracle Autonomous Database, use the wallet-based connection:\n\n```python\nimport oracledb\n\noracledb.init_oracle_client(config_dir=\"/path/to/wallet\")\n\nengine = create_engine(\n \"oracle+oracledb://ADMIN:password@db_alias\"\n \"?config_dir=/path/to/wallet\"\n \"&wallet_location=/path/to/wallet\"\n \"&wallet_password=wallet_password\"\n)\n```\n" }, { "path": "docs/memori-byodb/databases/overview.mdx", "content": "---\ntitle: Databases Overview\ndescription: Memori supports SQLite, PostgreSQL, MySQL, MariaDB, Oracle, MongoDB, CockroachDB, and OceanBase, plus providers like Neon, Supabase, and AWS RDS/Aurora.\n---\n\n# Databases Overview\n\nMemori is database-agnostic. With the BYODB (Bring Your Own Database) approach, your data stays on your infrastructure — you choose the database, you own the data.\n\n\n Want a zero-setup option? Try Memori Cloud at\n [app.memorilabs.ai](https://app.memorilabs.ai).\n\n\n## Supported Databases\n\n| Database | Best For | Driver | Connection String |\n| ----------------------------------------------------------- | ---------------------------------- | -------------------------- | --------------------------------------------------- |\n| **[SQLite](/docs/memori-byodb/databases/sqlite)** | Development, prototyping | Built-in (`sqlite3`) | `sqlite:///memori.db` |\n| **[PostgreSQL](/docs/memori-byodb/databases/postgres)** | Production, high concurrency | `psycopg2-binary` | `postgresql://user:pass@host/db` |\n| **[MySQL](/docs/memori-byodb/databases/mysql)** | Existing MySQL infrastructure | `pymysql` or `mysqlclient` | `mysql+pymysql://user:pass@host/db` |\n| **MariaDB** | MySQL-compatible alternative | `pymysql` or `mysqlclient` | `mysql+pymysql://user:pass@host/db` |\n| **[Oracle](/docs/memori-byodb/databases/oracle)** | Enterprise environments | `oracledb` or `cx_Oracle` | `oracle+oracledb://user:pass@host/service` |\n| **[MongoDB](/docs/memori-byodb/databases/mongodb)** | Document-oriented, flexible schema | `pymongo` | `mongodb://host:27017` |\n| **[CockroachDB](/docs/memori-byodb/databases/cockroachdb)** | Distributed SQL, global scale | `psycopg2-binary` | `cockroachdb+psycopg2://user:pass@host/db` |\n| **OceanBase** | Distributed HTAP workloads | `pyobvector` | `mysql+pyobvector://user:pass@host:2881/db` |\n| **Neon** | Serverless PostgreSQL | `psycopg2-binary` | `postgresql://user:pass@ep-xxx.neon.tech/db` |\n| **Supabase** | Hosted PostgreSQL with extras | `psycopg2-binary` | `postgresql://user:pass@db.xxx.supabase.co:5432/db` |\n\nMariaDB uses the same drivers and connection strings as MySQL. Neon and Supabase are PostgreSQL-compatible — see the [PostgreSQL](/docs/memori-byodb/databases/postgres) page for full setup details.\n\n## Managed Providers\n\nMemori also works with hosted services that expose standard PostgreSQL/MySQL endpoints:\n\n| Provider | Engine Compatibility | Typical Host Pattern |\n| -------------- | -------------------- | --------------------- |\n| **Neon** | PostgreSQL | `*.neon.tech` |\n| **Supabase** | PostgreSQL | `*.supabase.co` |\n| **AWS RDS** | PostgreSQL/MySQL | `*.rds.amazonaws.com` |\n| **AWS Aurora** | PostgreSQL/MySQL | `*.rds.amazonaws.com` |\n\n## Quick Start Code\n\n\n\n```python {{ title: 'SQLite (DB API 2.0)' }}\nimport sqlite3\nfrom memori import Memori\n\ndef get_connection():\n return sqlite3.connect(\"memori.db\")\n\nmem = Memori(conn=get_connection)\nmem.config.storage.build()\n```\n\n```python {{ title: 'PostgreSQL' }}\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\n \"postgresql+psycopg://user:password@localhost:5432/memori_db\",\n pool_pre_ping=True\n)\nSessionLocal = sessionmaker(bind=engine)\n\nmem = Memori(conn=SessionLocal)\nmem.config.storage.build()\n```\n\n```python {{ title: 'MySQL' }}\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\n \"mysql+pymysql://user:password@localhost:3306/memori_db\",\n pool_pre_ping=True\n)\nSessionLocal = sessionmaker(bind=engine)\n\nmem = Memori(conn=SessionLocal)\nmem.config.storage.build()\n```\n\n```python {{ title: 'Oracle' }}\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\n \"oracle+oracledb://user:password@localhost:1521/service_name\"\n)\nSessionLocal = sessionmaker(bind=engine)\n\nmem = Memori(conn=SessionLocal)\nmem.config.storage.build()\n```\n\n```python {{ title: 'MongoDB' }}\nfrom memori import Memori\nfrom pymongo import MongoClient\n\nclient = MongoClient(\"mongodb://localhost:27017\")\n\ndef get_db():\n return client[\"memori_db\"]\n\nmem = Memori(conn=get_db)\nmem.config.storage.build()\n```\n\n```python {{ title: 'CockroachDB' }}\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\n \"cockroachdb+psycopg2://user:password@localhost:26257/memori_db\",\n pool_pre_ping=True\n)\nSessionLocal = sessionmaker(bind=engine)\n\nmem = Memori(conn=SessionLocal)\nmem.config.storage.build()\n```\n\n```python {{ title: 'OceanBase' }}\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\n \"mysql+pyobvector://user:password@localhost:2881/memori_db\"\n)\nSessionLocal = sessionmaker(bind=engine)\n\nmem = Memori(conn=SessionLocal)\nmem.config.storage.build()\n```\n\n\n\n## Connection Methods\n\nMemori supports four connection methods depending on your stack:\n\n| Method | Description | Use Case |\n| -------------- | ----------------------------------------- | ----------------------------------------- |\n| **SQLAlchemy** | Industry-standard ORM with `sessionmaker` | Production applications, connection pools |\n| **DB API 2.0** | Direct Python database drivers (PEP 249) | Lightweight, minimal dependencies |\n| **Django ORM** | Native Django ORM integration | Django applications |\n| **MongoDB** | Function returning a database object | Document databases via `pymongo` |\n\nMemori accepts a `conn` parameter — a callable that returns a new connection each time it is called.\n\n- **SQLAlchemy databases** (SQLite, PostgreSQL, MySQL, MariaDB, Oracle, CockroachDB, OceanBase, plus providers like Neon/Supabase/RDS/Aurora): pass a `sessionmaker`\n- **DB API 2.0**: pass a function that returns a PEP 249 connection object\n- **Django ORM**: use Django's native ORM integration\n- **MongoDB**: pass a function returning a database instance\n" }, { "path": "docs/memori-byodb/databases/postgres.mdx", "content": "---\ntitle: PostgreSQL\ndescription: Set up Memori with PostgreSQL — recommended for production with connection pooling and high concurrency.\n---\n\n# PostgreSQL\n\nPostgreSQL is the recommended database for production Memori deployments. Full concurrent write support, connection pooling, and cloud-ready.\n\n## Install\n\n```bash\npip install memori psycopg2-binary\n```\n\n## Quick Start\n\n\n\n```python {{ title: 'Basic' }}\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\n \"postgresql+psycopg://user:password@localhost:5432/memori_db\",\n pool_pre_ping=True\n)\nSessionLocal = sessionmaker(bind=engine)\n\nmem = Memori(conn=SessionLocal)\nmem.config.storage.build()\n```\n\n```python {{ title: 'With Connection Pool' }}\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\n \"postgresql+psycopg://user:password@localhost:5432/memori_db\",\n pool_pre_ping=True,\n pool_size=10,\n max_overflow=20,\n pool_recycle=300\n)\nSessionLocal = sessionmaker(bind=engine)\n\nmem = Memori(conn=SessionLocal)\nmem.config.storage.build()\n```\n\n\n\n## Complete Example\n\n```python\nimport os\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom memori import Memori\nfrom openai import OpenAI\n\nengine = create_engine(\n os.getenv(\"DATABASE_URL\"),\n pool_pre_ping=True,\n pool_size=10,\n max_overflow=20,\n pool_recycle=300\n)\nSessionLocal = sessionmaker(bind=engine)\n\nclient = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"my_agent\")\nmem.config.storage.build()\n\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"I'm a senior engineer at Google.\"}]\n)\nprint(response.choices[0].message.content)\n\nmem.augmentation.wait()\nfacts = mem.recall(\"job title and company\")\nprint(facts)\n```\n\n## Cloud Providers\n\n| Provider | Connection Format |\n| -------------------- | ------------------------------------------------------------ |\n| **Neon** | `postgresql+psycopg://...@*.neon.tech/...` |\n| **Supabase** | `postgresql+psycopg://...@*.supabase.co/...` |\n| **AWS RDS** | `postgresql+psycopg://...@*.rds.amazonaws.com/...` |\n| **AWS Aurora** | `postgresql+psycopg://...@*.rds.amazonaws.com/...` |\n| **Google Cloud SQL** | `postgresql+psycopg://...@*.cloudsql/...` |\n| **Azure Database** | `postgresql+psycopg://...@*.postgres.database.azure.com/...` |\n\n## SSL Connections\n\nFor cloud-hosted PostgreSQL, use SSL:\n\n```python\nengine = create_engine(\n \"postgresql+psycopg://user:password@host:5432/memori_db\"\n \"?sslmode=require\",\n pool_pre_ping=True\n)\n```\n\n| Mode | Description |\n| ------------- | ----------------------------------------- |\n| `require` | SSL required, no certificate verification |\n| `verify-ca` | SSL + verify server certificate |\n| `verify-full` | SSL + verify certificate + hostname |\n" }, { "path": "docs/memori-byodb/databases/sqlite.mdx", "content": "---\ntitle: SQLite\ndescription: Set up Memori with SQLite — zero install, file-based storage perfect for development and prototyping.\n---\n\n# SQLite\n\nSQLite requires no server setup and stores data in a local file. It is built into Python, so it works out of the box.\n\n## Install\n\nNo extra packages needed — SQLite is built into Python.\n\n```bash\npip install memori openai\n```\n\n## Quick Start\n\n\n\n```python {{ title: 'SQLAlchemy (Recommended)' }}\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nmem = Memori(conn=SessionLocal)\nmem.config.storage.build()\n```\n\n```python {{ title: 'DB API 2.0' }}\nimport sqlite3\nfrom memori import Memori\n\ndef get_connection():\n return sqlite3.connect(\"memori.db\")\n\nmem = Memori(conn=get_connection)\nmem.config.storage.build()\n```\n\n\n\n## Connection Strings\n\n| Path Type | Connection String | Description |\n| ------------- | ------------------------------------- | ---------------------------- |\n| **Relative** | `sqlite:///memori.db` | File in current directory |\n| **Absolute** | `sqlite:////home/user/data/memori.db` | Absolute path (four slashes) |\n| **In-Memory** | `sqlite:///:memory:` | Temporary, lost on exit |\n\n## Complete Example\n\n```python\nimport os\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom memori import Memori\nfrom openai import OpenAI\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"my_agent\")\nmem.config.storage.build()\n\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"My favorite language is Python.\"}]\n)\nprint(response.choices[0].message.content)\n\nmem.augmentation.wait()\nfacts = mem.recall(\"favorite programming language\")\nprint(facts)\n```\n\n## Multi-Threading\n\nFor web servers or multi-threaded apps, disable the same-thread check:\n\n```python\nengine = create_engine(\n \"sqlite:///memori.db\",\n connect_args={\"check_same_thread\": False}\n)\n```\n" }, { "path": "docs/memori-byodb/getting-started/installation.mdx", "content": "---\ntitle: Installation\ndescription: Install Memori and set up your database for the Memori BYODB.\n---\n\n# Installation\n\nGet Memori installed and connected to your own database in a few steps.\n\n\n Memori BYODB supports the Python SDK only. For TypeScript, use\n [Memori Cloud](/docs/memori-cloud).\n\n\n## Install Memori\n\n\n\n```bash {{ title: 'pip' }}\npip install memori\n```\n\n```bash {{ title: 'poetry' }}\npoetry add memori\n```\n\n```bash {{ title: 'uv' }}\nuv add memori\n```\n\n\n\n## Install Your Database Driver\n\nMemori supports SQLite, PostgreSQL, MySQL, MariaDB, Oracle, MongoDB, CockroachDB, and OceanBase. Managed services like Neon, Supabase, and AWS RDS/Aurora work through their compatible PostgreSQL/MySQL engines. Install the driver for your preferred database:\n\n\n\n```bash {{ title: 'SQLite (built-in)' }}\n# No extra install needed!\n# SQLite support is included with Python.\n```\n\n```bash {{ title: 'PostgreSQL' }}\npip install psycopg2-binary\n# Or for async: pip install asyncpg\n```\n\n```bash {{ title: 'MySQL' }}\npip install pymysql\n# Or: pip install mysqlclient\n```\n\n```bash {{ title: 'MariaDB' }}\npip install pymysql\n# Or: pip install mysqlclient\n```\n\n```bash {{ title: 'Oracle' }}\npip install oracledb\n```\n\n```bash {{ title: 'MongoDB' }}\npip install pymongo\n```\n\n```bash {{ title: 'CockroachDB' }}\npip install psycopg2-binary\n```\n\n```bash {{ title: 'OceanBase' }}\npip install pyobvector\n```\n\n\n\nNeon, Supabase, and AWS RDS/Aurora use standard PostgreSQL drivers (`psycopg2-binary` or `psycopg`).\n\n## Connection Patterns\n\n| Pattern | What to pass to `conn` | Works With |\n| ---------- | ----------------------------------------------- | --------------------------------------------------------------------- |\n| SQLAlchemy | `sessionmaker` | SQLite, PostgreSQL, MySQL, MariaDB, Oracle, CockroachDB, OceanBase |\n| DB API 2.0 | Function that returns a PEP 249 connection | SQLite and SQL drivers (`sqlite3`, `psycopg2`, `pymysql`, `oracledb`) |\n| Django ORM | Django connection callable | Django applications |\n| MongoDB | Function that returns a MongoDB database object | MongoDB via `pymongo` |\n\n\n\n```python {{ title: 'SQLite (DB API 2.0)' }}\nimport sqlite3\n\ndef get_connection():\n return sqlite3.connect(\"memori.db\")\n\nfrom memori import Memori\nmem = Memori(conn=get_connection)\n```\n\n```python {{ title: 'PostgreSQL (SQLAlchemy)' }}\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\n \"postgresql+psycopg2://user:password@localhost:5432/mydb\"\n)\nSessionLocal = sessionmaker(bind=engine)\n\nfrom memori import Memori\nmem = Memori(conn=SessionLocal)\n```\n\n```python {{ title: 'PostgreSQL (DB API 2.0)' }}\nimport psycopg2\nfrom memori import Memori\n\ndef get_connection():\n return psycopg2.connect(\n dbname=\"mydb\",\n user=\"user\",\n password=\"password\",\n host=\"localhost\",\n port=5432,\n )\n\nmem = Memori(conn=get_connection)\n```\n\n```python {{ title: 'MySQL / MariaDB (SQLAlchemy)' }}\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\n \"mysql+pymysql://user:password@localhost:3306/mydb\"\n)\nSessionLocal = sessionmaker(bind=engine)\n\nfrom memori import Memori\nmem = Memori(conn=SessionLocal)\n```\n\n```python {{ title: 'MongoDB' }}\nfrom pymongo import MongoClient\nfrom memori import Memori\n\nclient = MongoClient(\"mongodb://localhost:27017\")\n\ndef get_db():\n return client[\"memori_db\"]\n\nmem = Memori(conn=get_db)\n```\n\n\n\n## Create the Schema\n\nAfter setting up your connection, run `build()` once to create the Memori tables in your database. This only needs to be done the first time, or when you upgrade Memori.\n\n```python\nimport sqlite3\nfrom memori import Memori\n\ndef get_connection():\n return sqlite3.connect(\"memori.db\")\n\nmem = Memori(conn=get_connection)\nmem.config.storage.build() # Creates all required tables\n```\n\n## Install Your LLM Provider\n\nInstall the SDK for your preferred LLM provider:\n\n\n\n```bash {{ title: 'OpenAI' }}\npip install openai\n```\n\n```bash {{ title: 'Anthropic' }}\npip install anthropic\n```\n\n```bash {{ title: 'Google Gemini' }}\npip install google-genai\n```\n\n```bash {{ title: 'LangChain' }}\npip install langchain-openai\n```\n\n\n\n## Set Up Your LLM Provider Key\n\nYou will need an API key for your LLM provider:\n\n```bash\n# OpenAI\nexport OPENAI_API_KEY=\"your-openai-key\"\n\n# Anthropic\nexport ANTHROPIC_API_KEY=\"your-anthropic-key\"\n\n# Google Gemini\nexport GOOGLE_API_KEY=\"your-google-key\"\n```\n\n## Set Up Your Memori API Key (Optional)\n\nA Memori API key unlocks higher augmentation quotas (5,000/month vs 100 without a key). You can sign up directly from the CLI:\n\n```bash\npython -m memori sign-up your-email@example.com\n```\n\nThen set the key as an environment variable:\n\n```bash\nexport MEMORI_API_KEY=\"your-api-key-here\"\n```\n\nOr add it to a `.env` file in your project root:\n\n```\nMEMORI_API_KEY=your-api-key-here\n```\n\nCheck your current quota anytime:\n\n```bash\npython -m memori quota\n```\n\n## Pre-download the Embedding Model\n\nMemori uses a local embedding model for semantic search. On first run, it downloads the model automatically, which can take a moment. To pre-download it:\n\n```bash\npython -m memori setup\n```\n\n## Verify Installation\n\nRun `pip show memori` in your terminal to confirm the package is installed.\n" }, { "path": "docs/memori-byodb/getting-started/python-quickstart.mdx", "content": "---\ntitle: Python SDK Quickstart\ndescription: Get started with Memori BYODB in under 3 minutes using SQLite and OpenAI.\n---\n\n# Python SDK Quickstart\n\nGet started with Memori in under 3 minutes. Since Memori BYODB is open source, you bring your own database — and for this quick start, we will use SQLite so there is nothing extra to install.\n\n\n Want a zero-setup option? Try Memori Cloud at\n [app.memorilabs.ai](https://app.memorilabs.ai).\n\n\nIn this example, we will use Memori with OpenAI and SQLite. Check out the [LLM providers](/docs/memori-byodb/llm/overview) and [database](/docs/memori-byodb/databases/overview) guides for other integrations.\n\n## Prerequisites\n\n- Python 3.10 or higher\n- An OpenAI API key\n\n## Step 1: Install Libraries\n\nInstall Memori and the OpenAI SDK:\n\n\n\n```bash {{ title: 'pip' }}\npip install memori openai\n```\n\n```bash {{ title: 'poetry' }}\npoetry add memori openai\n```\n\n```bash {{ title: 'uv' }}\nuv add memori openai\n```\n\n\n\n## Step 2: Set Environment Variables\n\nSet your OpenAI API key as an environment variable:\n\n```bash\nexport OPENAI_API_KEY=\"your-openai-api-key\"\n```\n\n## Step 3: Run Your First Memori Application\n\nCreate a new Python file `quickstart.py` and add the following code:\n\n### Setup & Configuration\n\nImport libraries, set up a SQLite database with Python's built-in `sqlite3`, and initialize Memori with your OpenAI client.\n\n- `conn` accepts a connection factory (SQLAlchemy, DB-API 2.0, Django ORM, or MongoDB callable)\n- `llm.register()` wraps your LLM client for automatic memory capture\n- `attribution()` links memories to a specific user and process\n- `build()` creates the Memori schema tables in your database\n\n```python\nimport os\nimport sqlite3\nfrom memori import Memori\nfrom openai import OpenAI\n\ndef get_sqlite_connection():\n return sqlite3.connect(\"memori.db\")\n\nclient = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\nmem = Memori(conn=get_sqlite_connection).llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"test-ai-agent\")\nmem.config.storage.build()\n```\n\nIf your app already uses SQLAlchemy, you can pass a `sessionmaker` instead:\n\n```python\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom memori import Memori\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nmem = Memori(conn=SessionLocal)\n```\n\n### First Conversation\n\nTell the LLM a fact about yourself. Memori automatically captures the\nconversation and processes it through Advanced Augmentation.\n\nSince augmentation runs asynchronously, call `augmentation.wait()` in\nshort-lived scripts to ensure memories are fully processed before continuing.\n\n```python\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[\n {\"role\": \"user\", \"content\": \"My favorite color is blue.\"}\n ]\n)\nprint(response.choices[0].message.content + \"\\n\")\n\n# Wait for background augmentation to finish\nmem.augmentation.wait()\n```\n\n### Memory Recall\n\nCreate a completely new client and Memori instance — no prior context\ncarried over. When you ask the LLM what it remembers, Memori\nautomatically injects the relevant facts via semantic search.\n\nThe second response should correctly recall your favorite color, proving\nmemory persistence works across sessions.\n\n```python\nclient = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\n\nmem = Memori(conn=get_sqlite_connection).llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"test-ai-agent\")\n\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[\n {\"role\": \"user\", \"content\": \"What's my favorite color?\"}\n ]\n)\nprint(response.choices[0].message.content + \"\\n\")\n```\n\n## Step 4: Run the Application\n\nExecute your Python file:\n\n```bash\npython quickstart.py\n```\n\nYou should see the AI respond to both questions, with the second response correctly recalling that your favorite color is blue!\n\n## Step 5: Inspect Your Memories\n\nSince you own the database, you can inspect what Memori stored directly:\n\n\n\n```bash {{ title: 'Messages' }}\nsqlite3 memori.db \"SELECT * FROM memori_conversation_message;\"\n```\n\n```bash {{ title: 'Facts' }}\nsqlite3 memori.db \"SELECT * FROM memori_entity_fact;\"\n```\n\n```bash {{ title: 'Knowledge Graph' }}\nsqlite3 memori.db \"SELECT * FROM memori_knowledge_graph;\"\n```\n\n```bash {{ title: 'Sessions' }}\nsqlite3 memori.db \"SELECT * FROM memori_session;\"\n```\n\n\n" }, { "path": "docs/memori-byodb/getting-started/use-cases.mdx", "content": "---\ntitle: Use Cases\ndescription: Common use cases and applications for Memori open source.\n---\n\n# Use Cases\n\nMemori is designed for any application where AI agents need to remember context across conversations. Here are the most common use cases — all running with your own database.\n\n\n Want a zero-setup option? Try Memori Cloud at\n [app.memorilabs.ai](https://app.memorilabs.ai).\n\n\n## Customer Support Chatbots\n\nBuild support bots that remember customer history, preferences, and previous issues. No more \"Can you repeat your account number?\" — Memori recalls everything automatically.\n\n**Benefits:**\n\n- Remember customer preferences and history\n- Recall previous support tickets and resolutions\n- Personalize responses based on past interactions\n- Track issues across multiple sessions\n- All data stays in your database for compliance\n\n```python\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom memori import Memori\nfrom openai import OpenAI\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = OpenAI()\nmem = Memori(conn=SessionLocal).llm.register(client)\n\n# Each customer gets their own memory space\nmem.attribution(\n entity_id=\"customer_456\",\n process_id=\"support_bot\"\n)\n\n# Memori automatically recalls relevant context\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\n \"role\": \"user\",\n \"content\": \"I'm having that issue again\"\n }]\n)\n# Memori injects: \"Customer previously reported\n# login timeout issues on 2024-01-15\"\n```\n\n## Personalized AI Assistants\n\nCreate AI assistants that learn and adapt to each user over time. Memori builds a profile of preferences, skills, and context that makes every interaction more relevant.\n\n**Benefits:**\n\n- Learn coding preferences and tech stack\n- Remember project context across sessions\n- Adapt communication style to user preferences\n- Build long-term user profiles automatically\n\n```python\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom memori import Memori\nfrom anthropic import Anthropic\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = Anthropic()\nmem = Memori(conn=SessionLocal).llm.register(client)\n\nmem.attribution(\n entity_id=\"developer_789\",\n process_id=\"code_assistant\"\n)\n\n# Over time, Memori learns:\n# - \"Uses Python 3.12 with FastAPI\"\n# - \"Prefers type hints and dataclasses\"\n# - \"Works on e-commerce platform\"\nresponse = client.messages.create(\n model=\"claude-sonnet-4-5-20250929\",\n max_tokens=1024,\n messages=[{\n \"role\": \"user\",\n \"content\": \"How should I structure this endpoint?\"\n }]\n)\n```\n\n## Multi-Agent Workflows\n\nCoordinate multiple AI agents that share context through Memori. Each agent contributes to a shared memory space while maintaining its own process identity.\n\n**Benefits:**\n\n- Share context between specialized agents\n- Track which agent contributed what information\n- Maintain conversation continuity across handoffs\n- Build collective knowledge graphs\n\n```python\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom memori import Memori\nfrom openai import OpenAI\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = OpenAI()\nmem = Memori(conn=SessionLocal).llm.register(client)\n\n# Research agent gathers information\nmem.attribution(\n entity_id=\"project_alpha\",\n process_id=\"research_agent\"\n)\nclient.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\n \"role\": \"user\",\n \"content\": \"Research competitor pricing\"\n }]\n)\n\n# Analysis agent recalls research findings\nmem.attribution(\n entity_id=\"project_alpha\",\n process_id=\"analysis_agent\"\n)\n# Memori shares context across agents\n# for the same entity\n```\n" }, { "path": "docs/memori-byodb/index.mdx", "content": "---\ntitle: Introduction\ndescription: Memori is an open-source, structured memory layer for AI agents — own your data, choose your database, keep full control.\n---\n\n# What is Memori?\n\n__Memori__ is a memory layer for LLM applications, agents, and copilots. It continuously captures interactions, extracts structured knowledge, and intelligently ranks, decays, and retrieves the relevant memories. So your AI remembers the right things at the right time across every session.\n\n\n Memori BYODB supports the Python SDK only. For TypeScript, use\n [Memori Cloud](/docs/memori-cloud).\n\nMemori uses [Advanced\nAugmentation](/docs/memori-byodb/concepts/advanced-augmentation) to turn raw\nconversations into structured, searchable memories. It runs asynchronously in\nthe background to minimize impact on your response path\n\n## Why Memori BYODB?\n\n### Database Freedom\n\nUse SQLite, PostgreSQL, MySQL, MariaDB, Oracle, MongoDB, CockroachDB, or\nOceanBase. Managed providers like Neon, Supabase, and AWS RDS/Aurora are also\nsupported through their compatible engines.\n\n### Full Data Ownership\n\nYour data stays in your database, on your infrastructure. Full compliance and\nregulatory control with no third-party storage.\n\n### LLM Provider Support\n\nOpenAI, Anthropic, Gemini, and Grok (xAI) via direct SDK wrappers. Bedrock is\nsupported via LangChain `ChatBedrock`. OpenAI-compatible providers (Nebius,\nDeepseek, NVIDIA NIM, Azure OpenAI, and more) work through OpenAI's `base_url`\nparameter. Supports sync, async, streamed, and unstreamed modes, plus LangChain\n, Agno, and Pydantic AI.\n\n### Intelligent Recall\n\nIntelligent Recall surfaces the right memories at the right time. Memories are\nranked by relevance and importance, with intelligent decay so older or less\nrelevant facts recede — so your AI stays contextually aware without clutter.\nRecall any memory later with semantic search; use manual recall for custom\nprompts, UIs, or debugging. See [How Memori\nWorks](/docs/memori-byodb/concepts/how-memory-works#how-recall-works) for\nautomatic vs manual recall and tuning.\n\n## Quick Example\n\nGet started with a database connection and your favorite LLM:\n\n- **One-line setup** — Connect your DB and LLM; memory capture, augmentation, and recall work without extra config.\n- **Semantic recall** — Queries like “what does this user prefer?” pull the right memories automatically.\n- **Dashboard** — Use [app.memorilabs.ai](https://app.memorilabs.ai) for API keys, usage, and (with Memori Cloud) the Graph Explorer and Playground.\n- **Your data, your rules** — Store everything in your DB; compliance, backups, and custom analytics stay under your control.\n- **Roadmap** — Knowledge-graph APIs, configurable decay, and richer dashboarding are on the way.\n\n```python\nimport os\nimport sqlite3\nfrom memori import Memori\nfrom openai import OpenAI\n\n# Requires OPENAI_API_KEY in your environment\ndef get_sqlite_connection():\n return sqlite3.connect(\"memori.db\")\n\nclient = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\nmem = Memori(conn=get_sqlite_connection).llm.register(client)\n\n# Track conversations by user and process\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"support_agent\")\n\n# All conversations automatically persisted and recalled\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"My favorite color is blue.\"}]\n)\n```\n\n## OpenAI-Compatible Providers\n\nMemori supports any model that uses OpenAI's client interface via the `base_url` parameter — including Nebius, Deepseek, NVIDIA NIM, and more.\n\n```python\nimport os\nimport sqlite3\nfrom memori import Memori\nfrom openai import OpenAI\n\ndef get_sqlite_connection():\n return sqlite3.connect(\"memori.db\")\n\nclient = OpenAI(\n base_url=\"https://api.studio.nebius.com/v1/\",\n api_key=os.getenv(\"NEBIUS_API_KEY\"),\n)\n\nmem = Memori(conn=get_sqlite_connection).llm.register(client)\n```\n\n## Core Concepts\n\n| Concept | Description | Example |\n| ---------------- | ----------------------------------------------------- | ---------------------------------------- |\n| **Entity** | Person, place, or thing (like a user) | `entity_id=\"user_123\"` |\n| **Process** | Your agent, LLM interaction, or program | `process_id=\"support_agent\"` |\n| **Session** | Groups LLM interactions together | Auto-generated UUID, manually manageable |\n| **Augmentation** | Background AI enhancement of memories | Auto-runs after wrapped LLM calls |\n| **Recall** | Retrieve relevant memories from previous interactions | Auto-injects recalled memories |\n\n## Architecture Overview\n\nThe diagram has three lanes: your app, the Memori SDK, and your own database. Your app calls the LLM normally, Memori intercepts the call, and the synchronous response path continues with zero added latency.\n\nSynchronous capture: conversation messages are stored in `memori_conversation_message` while your normal LLM flow continues.\n\nRecall injection: relevant memories are pulled from `memori_entity_fact` and injected into later prompts.\n\nAsync augmentation: background processing extracts facts, preferences, rules, events, and relationships from conversations.\n\nOwn-your-data storage: structured memory records are written to your database, including `memori_entity_fact`, `memori_process_attribute`, and `memori_knowledge_graph`.\n\n![\"Memori BYODB architecture\"](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/memori-byodb-architecture.webp)\n" }, { "path": "docs/memori-byodb/llm/agno.mdx", "content": "---\ntitle: Agno\ndescription: Using Memori with Agno agents and Memori BYODB.\n---\n\n# Agno\n\nMemori integrates with Agno at the model layer. Register your Agno model with `llm.register(...)` and Memori captures `run()`, `arun()`, and streamed responses automatically.\n\n\n Want a zero-setup option? The Memori Cloud at\n [app.memorilabs.ai](https://app.memorilabs.ai).\n\n\n## Quick Start\n\n\n\n```python {{ title: 'Sync' }}\nfrom agno.agent import Agent\nfrom agno.models.openai import OpenAIChat\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nmodel = OpenAIChat(id=\"gpt-4o-mini\")\n\nmem = Memori(conn=SessionLocal).llm.register(openai_chat=model)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"agno_agent\")\n\nagent = Agent(\n model=model,\n instructions=[\"Be helpful and concise\"],\n markdown=True,\n)\n\nresponse = agent.run(\"Hello!\", session_id=\"support-session\")\nprint(response.content)\n```\n\n```python {{ title: 'Async' }}\nimport asyncio\nfrom agno.agent import Agent\nfrom agno.models.openai import OpenAIChat\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nmodel = OpenAIChat(id=\"gpt-4o-mini\")\n\nmem = Memori(conn=SessionLocal).llm.register(openai_chat=model)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"agno_agent\")\n\nagent = Agent(\n model=model,\n instructions=[\"Be helpful and concise\"],\n markdown=True,\n)\n\nasync def main():\n response = await agent.arun(\"Hello!\", session_id=\"support-session\")\n print(response.content)\n\nasyncio.run(main())\n```\n\n```python {{ title: 'Streaming' }}\nfrom agno.agent import Agent\nfrom agno.models.openai import OpenAIChat\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nmodel = OpenAIChat(id=\"gpt-4o-mini\")\n\nmem = Memori(conn=SessionLocal).llm.register(openai_chat=model)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"agno_agent\")\n\nagent = Agent(\n model=model,\n instructions=[\"Be helpful and concise\"],\n markdown=True,\n)\n\nstream = agent.run(\"Hello!\", session_id=\"support-session\", stream=True)\nfor chunk in stream:\n if hasattr(chunk, \"content\") and chunk.content:\n print(chunk.content, end=\"\")\n```\n\n\n\n## Different Providers\n\nAgno supports multiple model families. Use the matching registration keyword in Memori.\n\n| Package | Model Class | Registration Keyword |\n| ----------------------- | ------------ | -------------------- |\n| `agno.models.openai` | `OpenAIChat` | `openai_chat=model` |\n| `agno.models.anthropic` | `Claude` | `claude=model` |\n| `agno.models.google` | `Gemini` | `gemini=model` |\n| `agno.models.xai` | `xAI` | `xai=model` |\n\n\n\n```python {{ title: 'OpenAI' }}\nfrom agno.models.openai import OpenAIChat\n\nmodel = OpenAIChat(id=\"gpt-4o-mini\")\nmem = Memori(conn=SessionLocal).llm.register(openai_chat=model)\n```\n\n```python {{ title: 'Anthropic' }}\nfrom agno.models.anthropic import Claude\n\nmodel = Claude(id=\"claude-sonnet-4-20250514\")\nmem = Memori(conn=SessionLocal).llm.register(claude=model)\n```\n\n```python {{ title: 'Google Gemini' }}\nfrom agno.models.google import Gemini\n\nmodel = Gemini(id=\"gemini-2.0-flash-exp\")\nmem = Memori(conn=SessionLocal).llm.register(gemini=model)\n```\n\n```python {{ title: 'xAI' }}\nfrom agno.models.xai import xAI\n\nmodel = xAI(id=\"grok-3\")\nmem = Memori(conn=SessionLocal).llm.register(xai=model)\n```\n\n\n\n## Supported Modes\n\n| Mode | Method |\n| ------------ | ------------------------ |\n| **Sync** | `agent.run()` |\n| **Async** | `await agent.arun()` |\n| **Streamed** | `agent.run(stream=True)` |\n" }, { "path": "docs/memori-byodb/llm/anthropic.mdx", "content": "---\ntitle: Anthropic\ndescription: Using Memori with Anthropic Claude models and Memori BYODB.\n---\n\n# Anthropic\n\nMemori supports all Anthropic Claude models. Register your client once and every conversation is automatically captured and stored. Note: `max_tokens` is required for all Anthropic API calls.\n\n\n Want a zero-setup option? The Memori Cloud at\n [app.memorilabs.ai](https://app.memorilabs.ai).\n\n\n## Quick Start\n\n\n\n```python {{ title: 'Sync' }}\nfrom anthropic import Anthropic\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = Anthropic()\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"claude_assistant\")\n\nresponse = client.messages.create(\n model=\"claude-3-5-sonnet-20241022\",\n max_tokens=1024,\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n)\nprint(response.content[0].text)\n```\n\n```python {{ title: 'Async' }}\nimport asyncio\nfrom anthropic import AsyncAnthropic\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = AsyncAnthropic()\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"claude_assistant\")\n\nasync def main():\n response = await client.messages.create(\n model=\"claude-3-5-sonnet-20241022\",\n max_tokens=1024,\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n )\n print(response.content[0].text)\n\nasyncio.run(main())\n```\n\n```python {{ title: 'Streaming' }}\nfrom anthropic import Anthropic\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = Anthropic()\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"claude_assistant\")\n\nwith client.messages.stream(\n model=\"claude-3-5-sonnet-20241022\",\n max_tokens=1024,\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n) as stream:\n for text in stream.text_stream:\n print(text, end=\"\")\n```\n\n\n\n## Supported Modes\n\n| Mode | Method |\n| ------------ | -------------------------------- |\n| **Sync** | `client.messages.create()` |\n| **Async** | `await client.messages.create()` |\n| **Streamed** | `client.messages.stream()` |\n\n## System Prompts\n\nAnthropic supports a top-level `system` parameter separate from the `messages` array. Memori captures both.\n\n```python\nresponse = client.messages.create(\n model=\"claude-3-5-sonnet-20241022\",\n max_tokens=1024,\n system=\"You are a helpful coding assistant.\",\n messages=[{\"role\": \"user\", \"content\": \"Explain Python decorators.\"}]\n)\nprint(response.content[0].text)\n```\n" }, { "path": "docs/memori-byodb/llm/aws-bedrock.mdx", "content": "---\ntitle: AWS Bedrock\ndescription: Using Memori with AWS Bedrock models via the LangChain ChatBedrock adapter and Memori BYODB.\n---\n\n# AWS Bedrock\n\nMemori supports AWS Bedrock through `langchain-aws`. Register using the `chatbedrock` keyword to access Claude, Llama, Mistral, and other Bedrock models.\n\n\n Want a zero-setup option? The Memori Cloud at\n [app.memorilabs.ai](https://app.memorilabs.ai).\n\n\n## Quick Start\n\n\n\n```python {{ title: 'Sync' }}\nfrom langchain_aws import ChatBedrock\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = ChatBedrock(\n model_id=\"anthropic.claude-3-5-sonnet-20241022-v2:0\",\n region_name=\"us-east-1\"\n)\n\nmem = Memori(conn=SessionLocal).llm.register(chatbedrock=client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"bedrock_agent\")\n\nresponse = client.invoke(\"Hello!\")\nprint(response.content)\n```\n\n```python {{ title: 'Async' }}\nimport asyncio\nfrom langchain_aws import ChatBedrock\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = ChatBedrock(\n model_id=\"anthropic.claude-3-5-sonnet-20241022-v2:0\",\n region_name=\"us-east-1\"\n)\n\nmem = Memori(conn=SessionLocal).llm.register(chatbedrock=client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"bedrock_agent\")\n\nasync def main():\n response = await client.ainvoke(\"Hello!\")\n print(response.content)\n\nasyncio.run(main())\n```\n\n```python {{ title: 'Streaming' }}\nfrom langchain_aws import ChatBedrock\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = ChatBedrock(\n model_id=\"anthropic.claude-3-5-sonnet-20241022-v2:0\",\n region_name=\"us-east-1\"\n)\n\nmem = Memori(conn=SessionLocal).llm.register(chatbedrock=client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"bedrock_agent\")\n\nfor chunk in client.stream(\"Hello!\"):\n print(chunk.content, end=\"\")\n```\n\n\n\n## Supported Modes\n\n| Mode | Method |\n| ------------ | ------------------------ |\n| **Sync** | `client.invoke()` |\n| **Async** | `await client.ainvoke()` |\n| **Streamed** | `client.stream()` |\n\n## Available Models\n\n| Model | Model ID |\n| --------------------- | ------------------------------------------- |\n| **Claude 3.5 Sonnet** | `anthropic.claude-3-5-sonnet-20241022-v2:0` |\n| **Claude 3 Haiku** | `anthropic.claude-3-haiku-20240307-v1:0` |\n| **Llama 3.1 70B** | `meta.llama3-1-70b-instruct-v1:0` |\n| **Mistral Large** | `mistral.mistral-large-2407-v1:0` |\n" }, { "path": "docs/memori-byodb/llm/deepseek.mdx", "content": "---\ntitle: DeepSeek\ndescription: Using Memori with DeepSeek models via the OpenAI-compatible API and Memori BYODB.\n---\n\n# DeepSeek\n\nDeepSeek provides an OpenAI-compatible API. Use the `openai` Python package with `base_url=\"https://api.deepseek.com\"` — no special adapter needed.\n\n\n Want a zero-setup option? The Memori Cloud at\n [app.memorilabs.ai](https://app.memorilabs.ai).\n\n\n## Quick Start\n\n\n\n```python {{ title: 'Sync' }}\nimport os\nfrom memori import Memori\nfrom openai import OpenAI\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = OpenAI(\n base_url=\"https://api.deepseek.com\",\n api_key=os.getenv(\"DEEPSEEK_API_KEY\"),\n)\n\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"deepseek_assistant\")\n\nresponse = client.chat.completions.create(\n model=\"deepseek-chat\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n)\nprint(response.choices[0].message.content)\n```\n\n```python {{ title: 'Async' }}\nimport os, asyncio\nfrom memori import Memori\nfrom openai import AsyncOpenAI\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = AsyncOpenAI(\n base_url=\"https://api.deepseek.com\",\n api_key=os.getenv(\"DEEPSEEK_API_KEY\"),\n)\n\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"deepseek_assistant\")\n\nasync def main():\n response = await client.chat.completions.create(\n model=\"deepseek-chat\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n )\n print(response.choices[0].message.content)\n\nasyncio.run(main())\n```\n\n```python {{ title: 'Streaming' }}\nimport os\nfrom memori import Memori\nfrom openai import OpenAI\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = OpenAI(\n base_url=\"https://api.deepseek.com\",\n api_key=os.getenv(\"DEEPSEEK_API_KEY\"),\n)\n\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"deepseek_assistant\")\n\nstream = client.chat.completions.create(\n model=\"deepseek-chat\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}],\n stream=True\n)\nfor chunk in stream:\n if chunk.choices[0].delta.content:\n print(chunk.choices[0].delta.content, end=\"\")\n```\n\n\n\n## Supported Modes\n\n| Mode | Method |\n| ------------ | ---------------------------------------- |\n| **Sync** | `client.chat.completions.create()` |\n| **Async** | `await client.chat.completions.create()` |\n| **Streamed** | `stream=True` parameter |\n" }, { "path": "docs/memori-byodb/llm/gemini.mdx", "content": "---\ntitle: Google Gemini\ndescription: Using Memori with Google Gemini models and Memori BYODB.\n---\n\n# Google Gemini\n\nMemori integrates with Google Gemini via the `google-genai` SDK. Register the `GenerativeModel` instance and all `generate_content()` calls are automatically captured.\n\n\n Want a zero-setup option? The Memori Cloud at\n [app.memorilabs.ai](https://app.memorilabs.ai).\n\n\n## Quick Start\n\n\n\n```python {{ title: 'Sync' }}\nimport os\nfrom memori import Memori\nimport google.generativeai as genai\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\ngenai.configure(api_key=os.getenv(\"GOOGLE_API_KEY\"))\nclient = genai.GenerativeModel(\"gemini-2.0-flash-exp\")\n\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"gemini_assistant\")\n\nresponse = client.generate_content(\"Hello!\")\nprint(response.text)\n```\n\n```python {{ title: 'Async' }}\nimport os, asyncio\nfrom memori import Memori\nimport google.generativeai as genai\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\ngenai.configure(api_key=os.getenv(\"GOOGLE_API_KEY\"))\nclient = genai.GenerativeModel(\"gemini-2.0-flash-exp\")\n\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"gemini_assistant\")\n\nasync def main():\n response = await client.generate_content_async(\"Hello!\")\n print(response.text)\n\nasyncio.run(main())\n```\n\n```python {{ title: 'Streaming' }}\nimport os\nfrom memori import Memori\nimport google.generativeai as genai\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\ngenai.configure(api_key=os.getenv(\"GOOGLE_API_KEY\"))\nclient = genai.GenerativeModel(\"gemini-2.0-flash-exp\")\n\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"gemini_assistant\")\n\nresponse = client.generate_content(\"Hello!\", stream=True)\nfor chunk in response:\n print(chunk.text, end=\"\")\n```\n\n\n\n## Supported Modes\n\n| Mode | Method |\n| ------------ | --------------------------------------- |\n| **Sync** | `client.generate_content()` |\n| **Async** | `await client.generate_content_async()` |\n| **Streamed** | `stream=True` parameter |\n\n## Multi-Turn Conversations\n\nUse `start_chat()` for multi-turn interactions. Memori tracks the full conversation automatically.\n\n```python\nchat = client.start_chat()\nresponse = chat.send_message(\"My name is Alice.\")\nprint(response.text)\n\nresponse = chat.send_message(\"What's my name?\")\nprint(response.text)\n```\n" }, { "path": "docs/memori-byodb/llm/langchain.mdx", "content": "---\ntitle: LangChain\ndescription: Using Memori with LangChain chat models and Memori BYODB.\n---\n\n# LangChain\n\nMemori supports any LangChain chat model. Each class has its own registration keyword: `chatopenai` for ChatOpenAI/ChatAnthropic, `chatbedrock` for ChatBedrock, `chatgooglegenai` for ChatGoogleGenerativeAI.\n\n\n Want a zero-setup option? The Memori Cloud at\n [app.memorilabs.ai](https://app.memorilabs.ai).\n\n\n## Quick Start\n\n\n\n```python {{ title: 'Sync' }}\nfrom langchain_openai import ChatOpenAI\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = ChatOpenAI(model=\"gpt-4o-mini\")\nmem = Memori(conn=SessionLocal).llm.register(chatopenai=client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"langchain_agent\")\n\nresponse = client.invoke(\"Hello!\")\nprint(response.content)\n```\n\n```python {{ title: 'Async' }}\nimport asyncio\nfrom langchain_openai import ChatOpenAI\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = ChatOpenAI(model=\"gpt-4o-mini\")\nmem = Memori(conn=SessionLocal).llm.register(chatopenai=client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"langchain_agent\")\n\nasync def main():\n response = await client.ainvoke(\"Hello!\")\n print(response.content)\n\nasyncio.run(main())\n```\n\n```python {{ title: 'Streaming' }}\nfrom langchain_openai import ChatOpenAI\nfrom memori import Memori\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = ChatOpenAI(model=\"gpt-4o-mini\")\nmem = Memori(conn=SessionLocal).llm.register(chatopenai=client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"langchain_agent\")\n\nfor chunk in client.stream(\"Hello!\"):\n print(chunk.content, end=\"\")\n```\n\n\n\n## Different Providers\n\n| Package | Chat Model | Registration Keyword |\n| ------------------------ | ------------------------ | ------------------------ |\n| `langchain-openai` | `ChatOpenAI` | `chatopenai=client` |\n| `langchain-anthropic` | `ChatAnthropic` | `chatopenai=client` |\n| `langchain-google-genai` | `ChatGoogleGenerativeAI` | `chatgooglegenai=client` |\n| `langchain-aws` | `ChatBedrock` | `chatbedrock=client` |\n\n\n\n```python {{ title: 'Anthropic' }}\nfrom langchain_anthropic import ChatAnthropic\n\nclient = ChatAnthropic(model=\"claude-3-5-sonnet-20241022\")\nmem = Memori(conn=SessionLocal).llm.register(chatopenai=client)\n```\n\n```python {{ title: 'Google Gemini' }}\nfrom langchain_google_genai import ChatGoogleGenerativeAI\n\nclient = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash-exp\")\nmem = Memori(conn=SessionLocal).llm.register(chatgooglegenai=client)\n```\n\n```python {{ title: 'AWS Bedrock' }}\nfrom langchain_aws import ChatBedrock\n\nclient = ChatBedrock(model_id=\"anthropic.claude-3-5-sonnet-20241022-v2:0\", region_name=\"us-east-1\")\nmem = Memori(conn=SessionLocal).llm.register(chatbedrock=client)\n```\n\n\n\n## Supported Modes\n\n| Mode | Method |\n| ------------ | ------------------------ |\n| **Sync** | `client.invoke()` |\n| **Async** | `await client.ainvoke()` |\n| **Streamed** | `client.stream()` |\n" }, { "path": "docs/memori-byodb/llm/nebius.mdx", "content": "---\ntitle: Nebius AI Studio\ndescription: Using Memori with Nebius AI Studio models via the OpenAI-compatible API and Memori BYODB.\n---\n\n# Nebius AI Studio\n\nNebius AI Studio provides an OpenAI-compatible API. Use the `openai` Python package with `base_url=\"https://api.studio.nebius.com/v1/\"` — no special adapter needed.\n\n\n Want a zero-setup option? The Memori Cloud at\n [app.memorilabs.ai](https://app.memorilabs.ai).\n\n\n## Quick Start\n\n\n\n```python {{ title: 'Sync' }}\nimport os\nfrom memori import Memori\nfrom openai import OpenAI\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = OpenAI(\n base_url=\"https://api.studio.nebius.com/v1/\",\n api_key=os.getenv(\"NEBIUS_API_KEY\"),\n)\n\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"nebius_assistant\")\n\nresponse = client.chat.completions.create(\n model=\"meta-llama/Llama-3.3-70B-Instruct\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n)\nprint(response.choices[0].message.content)\n```\n\n```python {{ title: 'Async' }}\nimport os, asyncio\nfrom memori import Memori\nfrom openai import AsyncOpenAI\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = AsyncOpenAI(\n base_url=\"https://api.studio.nebius.com/v1/\",\n api_key=os.getenv(\"NEBIUS_API_KEY\"),\n)\n\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"nebius_assistant\")\n\nasync def main():\n response = await client.chat.completions.create(\n model=\"meta-llama/Llama-3.3-70B-Instruct\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n )\n print(response.choices[0].message.content)\n\nasyncio.run(main())\n```\n\n```python {{ title: 'Streaming' }}\nimport os\nfrom memori import Memori\nfrom openai import OpenAI\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = OpenAI(\n base_url=\"https://api.studio.nebius.com/v1/\",\n api_key=os.getenv(\"NEBIUS_API_KEY\"),\n)\n\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"nebius_assistant\")\n\nstream = client.chat.completions.create(\n model=\"meta-llama/Llama-3.3-70B-Instruct\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}],\n stream=True\n)\nfor chunk in stream:\n if chunk.choices[0].delta.content:\n print(chunk.choices[0].delta.content, end=\"\")\n```\n\n\n\n## Supported Modes\n\n| Mode | Method |\n| ------------ | ---------------------------------------- |\n| **Sync** | `client.chat.completions.create()` |\n| **Async** | `await client.chat.completions.create()` |\n| **Streamed** | `stream=True` parameter |\n" }, { "path": "docs/memori-byodb/llm/openai.mdx", "content": "---\ntitle: OpenAI\ndescription: Using Memori with OpenAI models including GPT-4o, GPT-4.1, and the Responses API with Memori BYODB.\n---\n\n# OpenAI\n\nMemori supports all OpenAI models and API styles. Register your client once and every call is automatically captured and stored.\n\n\n Want a zero-setup option? The Memori Cloud at\n [app.memorilabs.ai](https://app.memorilabs.ai).\n\n\n## Quick Start\n\n\n\n```python {{ title: 'Sync' }}\nfrom memori import Memori\nfrom openai import OpenAI\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = OpenAI()\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"my_agent\")\n\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n)\nprint(response.choices[0].message.content)\n```\n\n```python {{ title: 'Async' }}\nimport asyncio\nfrom memori import Memori\nfrom openai import AsyncOpenAI\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = AsyncOpenAI()\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"my_agent\")\n\nasync def main():\n response = await client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n )\n print(response.choices[0].message.content)\n\nasyncio.run(main())\n```\n\n```python {{ title: 'Streaming' }}\nfrom memori import Memori\nfrom openai import OpenAI\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = OpenAI()\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"my_agent\")\n\nstream = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}],\n stream=True\n)\nfor chunk in stream:\n if chunk.choices[0].delta.content:\n print(chunk.choices[0].delta.content, end=\"\")\n```\n\n```python {{ title: 'Responses API' }}\nfrom memori import Memori\nfrom openai import OpenAI\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = OpenAI()\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"my_agent\")\n\nresponse = client.responses.create(\n model=\"gpt-4o-mini\",\n input=\"Hello!\",\n instructions=\"You are a helpful assistant.\"\n)\nprint(response.output_text)\n```\n\n\n\n## Supported Modes\n\n| Mode | Method |\n| ----------------- | ---------------------------------------- |\n| **Sync** | `client.chat.completions.create()` |\n| **Async** | `await client.chat.completions.create()` |\n| **Streamed** | `stream=True` parameter |\n| **Responses API** | `client.responses.create()` |\n\n## Multi-Turn Conversations\n\nMemori captures each call and links them within the same session. Pass your full conversation history as usual.\n\n```python\nfrom memori import Memori\nfrom openai import OpenAI\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = OpenAI()\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"my_agent\")\n\nmessages = [{\"role\": \"user\", \"content\": \"My name is Alice.\"}]\nresponse = client.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\nmessages.append({\"role\": \"assistant\", \"content\": response.choices[0].message.content})\n\nmessages.append({\"role\": \"user\", \"content\": \"What's my name?\"})\nresponse = client.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\nprint(response.choices[0].message.content)\n```\n" }, { "path": "docs/memori-byodb/llm/overview.mdx", "content": "---\ntitle: LLM Providers Overview\ndescription: Memori is LLM-agnostic. Register any supported client with a local database connection and Memori handles memory capture, augmentation, and recall automatically.\n---\n\n# LLM Providers Overview\n\nMemori works with all major LLM providers and frameworks. Register any supported client and Memori handles memory capture, augmentation, and recall automatically.\n\n\n Want a zero-setup option? The Memori Cloud at\n [app.memorilabs.ai](https://app.memorilabs.ai).\n\n\n## Supported Providers\n\n| Provider | Integration | Install |\n| ----------------------------------------------------- | ------------------ | ------------------------------------- |\n| **[OpenAI](/docs/memori-byodb/llm/openai)** | Direct SDK wrapper | `pip install memori openai` |\n| **[Anthropic](/docs/memori-byodb/llm/anthropic)** | Direct SDK wrapper | `pip install memori anthropic` |\n| **[Google Gemini](/docs/memori-byodb/llm/gemini)** | Direct SDK wrapper | `pip install memori google-genai` |\n| **[xAI Grok](/docs/memori-byodb/llm/xai-grok)** | OpenAI-compatible | `pip install memori openai` |\n| **[AWS Bedrock](/docs/memori-byodb/llm/aws-bedrock)** | LangChain adapter | `pip install memori langchain-aws` |\n| **[LangChain](/docs/memori-byodb/llm/langchain)** | Framework support | `pip install memori langchain-openai` |\n| **[Agno](/docs/memori-byodb/llm/agno)** | Framework support | `pip install memori agno` |\n| **[Pydantic AI](/docs/memori-byodb/llm/pydantic-ai)** | Framework support | `pip install memori pydantic-ai` |\n| **[Nebius AI Studio](/docs/memori-byodb/llm/nebius)** | OpenAI-compatible | `pip install memori openai` |\n| **[DeepSeek](/docs/memori-byodb/llm/deepseek)** | OpenAI-compatible | `pip install memori openai` |\n\nAll providers support sync, async, streamed, and unstreamed modes.\n\nAny provider with an OpenAI-compatible API works by setting a custom `base_url` — including Nebius AI Studio, DeepSeek, NVIDIA NIM, Azure OpenAI, and more. See [OpenAI-Compatible Providers](#openai-compatible-providers) below.\n\n## Pydantic AI\n\nRegister the `Agent` instance directly — Memori wraps `run_sync` and `run` automatically.\n\n```python\nfrom memori import Memori\nfrom pydantic_ai import Agent\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nagent = Agent(\"openai:gpt-4o-mini\")\nmem = Memori(conn=SessionLocal).llm.register(agent)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"pydantic_agent\")\n\nresult = agent.run_sync(\"Hello!\")\nprint(result.output)\n```\n\n## OpenAI-Compatible Providers\n\nAny provider with an OpenAI-compatible API works by setting a custom `base_url`. Examples: xAI, Nebius AI Studio, DeepSeek, Azure OpenAI.\n\n```python\nimport os\nfrom memori import Memori\nfrom openai import OpenAI\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = OpenAI(\n base_url=\"https://api.studio.nebius.com/v1/\",\n api_key=os.getenv(\"NEBIUS_API_KEY\"),\n)\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"my_agent\")\n\nresponse = client.chat.completions.create(\n model=\"meta-llama/Llama-3.3-70B-Instruct\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n)\nprint(response.choices[0].message.content)\n```\n\n## OpenAI Responses API\n\n```python\nfrom memori import Memori\nfrom openai import OpenAI\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = OpenAI()\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"my_agent\")\n\nresponse = client.responses.create(\n model=\"gpt-4o-mini\",\n input=\"Hello!\",\n instructions=\"You are a helpful assistant.\"\n)\nprint(response.output_text)\n```\n" }, { "path": "docs/memori-byodb/llm/pydantic-ai.mdx", "content": "---\ntitle: Pydantic AI\ndescription: Using Memori with Pydantic AI agents and Memori BYODB.\n---\n\n# Pydantic AI\n\nMemori integrates at the agent level — register the `Agent` instance and Memori wraps `run_sync` and `run` automatically. Works with any Pydantic AI-supported model.\n\n\n Want a zero-setup option? The Memori Cloud at\n [app.memorilabs.ai](https://app.memorilabs.ai).\n\n\n## Quick Start\n\n```python\nfrom memori import Memori\nfrom pydantic_ai import Agent\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nagent = Agent(\"openai:gpt-4o-mini\")\n\nmem = Memori(conn=SessionLocal).llm.register(agent)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"pydantic_agent\")\n\nresult = agent.run_sync(\"Hello!\")\nprint(result.output)\n```\n\n## Async Usage\n\n```python\nimport asyncio\nfrom memori import Memori\nfrom pydantic_ai import Agent\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nagent = Agent(\"openai:gpt-4o-mini\")\n\nmem = Memori(conn=SessionLocal).llm.register(agent)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"pydantic_agent\")\n\nasync def main():\n result = await agent.run(\"Hello!\")\n print(result.output)\n\nasyncio.run(main())\n```\n\n## Different Providers\n\nPydantic AI supports multiple providers via model strings. Registration is identical regardless of provider.\n\n\n\n```python {{ title: 'OpenAI' }}\nagent = Agent(\"openai:gpt-4o-mini\")\nmem = Memori(conn=SessionLocal).llm.register(agent)\n```\n\n```python {{ title: 'Anthropic' }}\nagent = Agent(\"anthropic:claude-3-5-sonnet-20241022\")\nmem = Memori(conn=SessionLocal).llm.register(agent)\n```\n\n```python {{ title: 'Google Gemini' }}\nagent = Agent(\"google-gla:gemini-2.0-flash-exp\")\nmem = Memori(conn=SessionLocal).llm.register(agent)\n```\n\n\n\n## Supported Modes\n\n| Mode | Method |\n| ------------ | --------------------------- |\n| **Sync** | `agent.run_sync()` |\n| **Async** | `await agent.run()` |\n| **Streamed** | Via agent streaming support |\n" }, { "path": "docs/memori-byodb/llm/xai-grok.mdx", "content": "---\ntitle: xAI Grok\ndescription: Using Memori with xAI Grok models via the OpenAI-compatible API and Memori BYODB.\n---\n\n# xAI Grok\n\nxAI provides an OpenAI-compatible API. Use the `openai` Python package with `base_url=\"https://api.x.ai/v1\"` — no special adapter needed.\n\n\n Want a zero-setup option? The Memori Cloud at\n [app.memorilabs.ai](https://app.memorilabs.ai).\n\n\n## Quick Start\n\n\n\n```python {{ title: 'Sync' }}\nimport os\nfrom memori import Memori\nfrom openai import OpenAI\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = OpenAI(\n base_url=\"https://api.x.ai/v1\",\n api_key=os.getenv(\"XAI_API_KEY\")\n)\n\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"grok_assistant\")\n\nresponse = client.chat.completions.create(\n model=\"grok-2-latest\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n)\nprint(response.choices[0].message.content)\n```\n\n```python {{ title: 'Async' }}\nimport os, asyncio\nfrom memori import Memori\nfrom openai import AsyncOpenAI\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = AsyncOpenAI(\n base_url=\"https://api.x.ai/v1\",\n api_key=os.getenv(\"XAI_API_KEY\")\n)\n\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"grok_assistant\")\n\nasync def main():\n response = await client.chat.completions.create(\n model=\"grok-2-latest\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n )\n print(response.choices[0].message.content)\n\nasyncio.run(main())\n```\n\n```python {{ title: 'Streaming' }}\nimport os\nfrom memori import Memori\nfrom openai import OpenAI\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\n\nclient = OpenAI(\n base_url=\"https://api.x.ai/v1\",\n api_key=os.getenv(\"XAI_API_KEY\")\n)\n\nmem = Memori(conn=SessionLocal).llm.register(client)\nmem.config.storage.build()\nmem.attribution(entity_id=\"user_123\", process_id=\"grok_assistant\")\n\nstream = client.chat.completions.create(\n model=\"grok-2-latest\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}],\n stream=True\n)\nfor chunk in stream:\n if chunk.choices[0].delta.content:\n print(chunk.choices[0].delta.content, end=\"\")\n```\n\n\n\n## Supported Modes\n\n| Mode | Method |\n| ------------ | ---------------------------------------- |\n| **Sync** | `client.chat.completions.create()` |\n| **Async** | `await client.chat.completions.create()` |\n| **Streamed** | `stream=True` parameter |\n" }, { "path": "docs/memori-byodb/support/faq.mdx", "content": "---\ntitle: FAQ\ndescription: Frequently asked questions about Memori open source.\n---\n\n# FAQ\n\n\n Want a zero-setup option? Try Memori Cloud at\n [app.memorilabs.ai](https://app.memorilabs.ai).\n\n\n### What is Memori?\n\nMemori is a memory layer for LLM applications, agents, and copilots. It continuously captures interactions, extracts structured knowledge, and intelligently ranks, decays, and retrieves the relevant memories. So your AI remembers the right things at the right time across every session.\n\n### Memori Cloud vs Memori BYODB?\n\n**Memori Cloud** — managed storage, dashboard UI, just add an API key. **Memori BYODB** — bring your own database, full control. Both use the same Python SDK.\n\n### Which databases are supported?\n\nSQLite, PostgreSQL, MySQL, MariaDB, Oracle, MongoDB, CockroachDB, and OceanBase. Managed providers like Neon, Supabase, and AWS RDS/Aurora are supported through compatible PostgreSQL/MySQL engines. See the [Database guides](/docs/memori-byodb/databases/overview).\n\n### Do I need SQLAlchemy to use BYODB?\n\nNo. SQLAlchemy is one option, but not a requirement. Memori also supports direct DB-API 2.0 connection factories, Django ORM integration, and MongoDB database callables.\n\n### Which LLM providers work?\n\nOpenAI, Anthropic, Google Gemini, xAI Grok, Deepseek, Nebius AI Studio, AWS Bedrock, LangChain, Agno, and Pydantic AI. All support sync, async, and streaming. See the [LLM guides](/docs/memori-byodb/llm/overview).\n\n### Do I need a Memori API key?\n\nNo. The open-source version works without one. An API key only unlocks higher Advanced Augmentation quotas (100 free without key, 5,000/month with a free key).\n\n### Do I need to manage infrastructure?\n\nYes — you run your own database. For the simplest setup, use SQLite (single file, no server). For production, PostgreSQL is recommended.\n\n### Does it support async?\n\nYes. All providers support async mode out of the box. Just use your provider's async client.\n\n### How does augmentation work?\n\nIt runs in the background after each conversation and extracts facts, preferences, and relationships while minimizing impact on your LLM response path. See [Advanced Augmentation](/docs/memori-byodb/concepts/advanced-augmentation).\n\n### Can I use multiple LLM providers?\n\nYes. Register multiple clients on the same Memori instance. Memories are shared across providers since they're linked to entities, not providers.\n\n### Can I migrate between Memori Cloud and BYODB?\n\nYes. Both use the same SDK — just swap `conn=SessionLocal` for `api_key=\"...\"`. Existing local memories don't auto-transfer though.\n\n### What is `augmentation.wait()`?\n\nIt blocks until background augmentation finishes. Only needed in short-lived scripts that might exit before processing completes.\n\n### Still have questions?\n\n- [Troubleshooting](/docs/memori-byodb/support/troubleshooting) guide\n- [GitHub Issues](https://github.com/MemoriLabs/Memori/issues)\n- [Discord](https://discord.gg/abD4eGym6v)\n" }, { "path": "docs/memori-byodb/support/troubleshooting.mdx", "content": "---\ntitle: Troubleshooting\ndescription: Common issues and solutions when using Memori open source.\n---\n\n# Troubleshooting\n\nQuick fixes for the most common Memori issues.\n\n## Installation\n\n**`pip install memori` fails** — Requires Python 3.10+.\nRun `python --version` to check, then `pip install --upgrade pip && pip install memori`.\n\n**Missing binary deps** — Install prerequisites first: `pip install numpy>=1.24.0 sentence-transformers>=3.0.0 memori`.\n\n## Database Connection\n\n**`No connection factory provided`** — You must pass `conn` when initializing:\n\n```python\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom memori import Memori\n\nengine = create_engine(\"sqlite:///memori.db\")\nSessionLocal = sessionmaker(bind=engine)\nmem = Memori(conn=SessionLocal)\n```\n\n**`Table does not exist`** — Run `mem.config.storage.build()` once after initialization or after upgrading.\n\n**Connection pool errors** — Enable pre-ping and recycling:\n\n```python\nengine = create_engine(\"postgresql+psycopg2://user:pass@host/db\", pool_pre_ping=True, pool_recycle=300)\n```\n\n**Connection string formats:**\n\n| Database | Format |\n| ---------- | ---------------------------------------------- |\n| SQLite | `sqlite:///memori.db` |\n| PostgreSQL | `postgresql+psycopg2://user:pass@host:5432/db` |\n| MySQL | `mysql+pymysql://user:pass@host:3306/db` |\n\n## No Memories Being Created\n\n1. **Set attribution** before LLM calls — without it, no memories are stored:\n\n```python\nmem.attribution(entity_id=\"user_123\", process_id=\"my_app\")\n```\n\n2. **Wait for augmentation** in short-lived scripts:\n\n```python\nmem.augmentation.wait()\n```\n\n3. **Register your LLM client** — conversations aren't captured without registration:\n\n```python\nclient = OpenAI()\nmem = Memori(conn=SessionLocal).llm.register(client)\n```\n\n## Recall Returns Empty\n\n- Verify `entity_id` matches what was used when memories were created\n- Wait for augmentation: `mem.augmentation.wait()`\n- Increase limit: `mem.recall(\"query\", limit=10)`\n- Lower threshold: `mem.config.recall_relevance_threshold = 0.05`\n\n## Quota Exceeded\n\n```\nQuotaExceededError: your IP address is over quota\n```\n\nSign up for a free API key at [app.memorilabs.ai](https://app.memorilabs.ai) — gives 5,000/month (vs 100 without a key). Set it via `export MEMORI_API_KEY=\"your-key\"`.\n\n## Performance\n\n**Slow first run** — Memori downloads the embedding model on first use. Pre-download with `python -m memori setup`.\n\n**High memory usage** — Reduce embeddings limit: `mem.config.recall_embeddings_limit = 500`. Use PostgreSQL for production.\n\n**Network timeouts** — Increase timeout: `mem.config.request_secs_timeout = 10` and retries: `mem.config.request_num_backoff = 10`.\n\n## Debug Logging\n\n```python\nimport logging\nlogging.basicConfig(level=logging.DEBUG, format=\"%(asctime)s | %(name)s | %(levelname)s | %(message)s\")\n\nfrom memori import Memori\nmem = Memori(conn=SessionLocal, debug_truncate=False)\n```\n\n## Getting Help\n\n- [GitHub Issues](https://github.com/MemoriLabs/Memori/issues)\n- [Discord](https://discord.gg/abD4eGym6v)\n- [Examples](https://github.com/MemoriLabs/Memori/tree/main/examples)\n\nInclude your Python version, Memori version (`pip show memori`), database type, and full error trace.\n" }, { "path": "docs/memori-cloud/benchmark/experiments.mdx", "content": "---\ntitle: LoCoMo Benchmark Experiments\ndescription: These experiments evaluate the quality and accuracy of the memory assets produced by Memori's Advanced Augmentation pipeline.\n---\n\n# LoCoMo Benchmark Experiments\n\nThe primary objective of these experiments is to evaluate the quality and accuracy of the memory assets produced by Memori's Advanced Augmentation pipeline.\n\n### Dataset: The LoCoMo Benchmark\n\nThe primary dataset utilized for benchmarking is the Long Conversation Memory (LoCoMo) dataset. LoCoMo is a rigorous framework engineered to evaluate an AI agent's ability to track, retain, and synthesize information across extensive, multi-session chat histories. Unlike standard QA datasets, LoCoMo challenges models with complex state tracking, temporal reasoning, and the retrieval of subtle user preferences buried deep within noisy, unstructured conversational logs\n\nTo ensure a fair comparison with other published results on this benchmark, we excluded the adversarial category from the evaluation.\n\n### Evaluating Memory Extraction via Advanced Augmentation\n\nTo measure the quality of Memori's Advanced Augmentation, all sections of each LoCoMo conversation were processed through the pipeline. Each session produced a set of semantic triples along with a conversation-level summary. The extracted triples were embedded using the Gemma-300 embedding model, enabling efficient semantic retrieval for the benchmark’s question-answering tasks. All generated memories were indexed and stored locally using FAISS to support fast similarity search. The ultimate accuracy of the LLM’s answers serves as a direct reflection of how well the Advanced Augmentation pipeline structured, preserved, and surfaced the relevant facts.\n\n### Answer Generation\n\nEach question in the LoCoMo benchmark was answered using GPT-4.1-mini, conditioned on the retrieved triples and their corresponding summaries (the utilized prompt is presented in the appendix). Triples were retrieved using a hybrid search approach that combines cosine similarity over embeddings with BM25 keyword matching.\n\n### Performance Metrics: LLM-as-a-Judge\n\nWe employ an LLM-as-a-Judge methodology (the utilized prompt is presented in the Appendix), using GPT-4.1-mini as the evaluator. The judge model analyzes the user query, the ground-truth answer, and the generated response to provide a nuanced assessment. \n\n### Token-Driven Cost Analysis \n\nBeyond response quality, practical deployment considerations are paramount for enterprise AI applications. We evaluate Memori against traditional architectures (e.g., standard RAG) by systematically measuring system efficiency as a function of context consumption.. \n\nThe absolute number of tokens added to the LLM prompt is the primary driver of operational costs in conversational AI. We measure the exact number of tokens extracted during retrieval and injected into the prompt context. This metric highlights a critical architectural distinction: while traditional architectures consume massive token budgets by indiscriminately injecting large, raw text chunks or full histories into the prompt, Memori retrieves highly concise, structured memory facts. By minimizing the context footprint, Memori directly curtails API expenditure and optimizes operational economics.\n" }, { "path": "docs/memori-cloud/benchmark/overview.mdx", "content": "---\ntitle: LoCoMo Benchmark Overview\ndescription: Memori - A Persistent Memory Layer for Efficient, Context-Aware LLM Agents\n---\n\n# LoCoMo Benchmark Overview\n\nAs large language models (LLMs) evolve into autonomous agents, persistent memory at the API layer is essential for enabling context-aware behavior across LLMs and multi-session interactions. Existing approaches force vendor lock-in and rely on injecting large volumes of raw conversation into prompts, leading to high token costs and degraded performance.\n\nWe introduce Memori, a LLM-agnostic persistent memory layer that treats memory as a data structuring problem. Its Advanced Augmentation pipeline converts unstructured dialogue into compact semantic triples and conversation summaries, enabling precise retrieval and coherent reasoning.\n\nEvaluated on the LoCoMo benchmark, Memori achieves 81.95% accuracy, outperforming existing memory systems while using only 1,294 tokens per query (\\~5% of full context). This results in substantial cost reductions, including 67% fewer tokens than competing approaches and over 20× savings compared to full-context methods.\n\nThese results show that effective memory in LLM agents depends on structured representations instead of larger context windows, enabling scalable and cost-efficient deployment.\n\n\n Download the Paper → [Memori LoCoMo Benchmark PDF](https://s3.us-east-1.amazonaws.com/files.memorilabs.ai/memori-locomo-benchmark.pdf)\n\n\n## Introduction\n\nIn recent years, large language models (LLMs) have rapidly evolved into fully sophisticated AI agents. These foundation-model-powered systems have demonstrated strong performance across diverse domains, including research, software engineering, and scientific discovery, pushing the trajectory toward more general and adaptive intelligence. As the field matures, it is evident that modern agents extend beyond a pure LLM backbone and incorporate additional capabilities such as reasoning, planning, perception, memory, and tool use. Together, these components transform LLMs from static conditional generators into adaptive systems capable of interacting with external environments and improving over time.\n\nLarge language models (LLMs) have quickly become sophisticated AI agents. These foundation-model-powered systems perform well in research, software engineering, and scientific discovery, driving the move toward general intelligence. Modern agents now go beyond using only LLMs by adding reasoning, planning, perception, memory, and tool use. These components let LLMs act as adaptive systems that interact with their environments and improve over time.\n\nAmong these capabilities, memory stands out as a foundational pillar. Unlike reasoning or tool use, which are increasingly internalized within model parameters, memory remains largely dependent on external system design. This dependency arises because LLM parameters cannot be updated in real time during deployment. Memory mechanisms, therefore, play a key role in enabling agents to persist information across interactions, adapt to user context, and evolve based on experience.\n\nThis reliance on external memory is especially apparent from an application perspective: persistent memory is essential. Domains such as personalized assistants, recommendation systems, social simulations, and complex investigative workflows all require agents to retain and reason over historical information. Without memory, these systems behave as stateless responders, repeatedly reprocessing context and failing to build continuity over time. From a broader research perspective, agents’ ability to continually evolve through interaction is central to the pursuit of general intelligence. This capacity is fundamentally grounded in memory\n\nEnabling long-term, cross-session, cross-model memory introduces significant challenges. Naively storing and injecting past interactions into the prompt leads to rapidly growing context windows. This increases both cost and instability. As context size grows, models become more prone to overlooking critical information. They may produce inconsistent outputs and suffer from what is commonly referred to as *context rot*, in which relevant information is present but not effectively used.\n\nThese limitations highlight a key insight: memory in LLM systems is not simply a storage problem, but a structuring problem. The challenge is to transform noisy, unstructured conversational data into representations that are efficient to retrieve. These representations must also be effective for downstream reasoning.\n\nMemori implements this as a persistent memory layer that incrementally distills conversational data into structured representations. This process is handled by Advanced Augmentation, a memory creation pipeline that extracts, compresses, and organizes high-signal information from raw interactions for efficient retrieval and downstream use. Through empirical evaluation on the LoCoMo benchmark, we demonstrate that high-quality memory structuring enables strong reasoning performance while dramatically reducing the number of tokens required in the prompt, thereby improving the cost-efficiency and scalability of LLM agents.\n\n\n## System Architecture\n\nAs depicted in Figure 1, Memori operates as a decoupled memory layer positioned between the application logic and the underlying LLM. The system integrates via a lightweight **Memori SDK**, seamlessly wrapping existing LLM clients to intercept requests and manage memory natively.\n\n![\"Memori Cloud System Overview\"](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/memori-cloud-architecture-detail.webp)\n\nThe core differentiators of the Memori architecture lie in how it structures unstructured data and how it intelligently retrieves that data for reasoning.\n\n### Advanced Augmentation: Structuring the Unstructured\n\nRaw conversation logs are noisy, filled with colloquialisms, pleasantries, self-corrections, and tangential discussions. When these raw, unstructured transcripts are directly chunked and embedded, as is standard in traditional RAG architectures, the resulting vector space becomes heavily cluttered. Direct retrieval from this noisy data is highly inefficient, leading to false positives, contradictory context, and massively inflated token consumption during the generation phase.\n\nTo solve this, **Advanced Augmentation** functions as an automated cognitive filter. It is a background memory creation pipeline designed to distill raw dialogue into searchable memory assets, shifting the system's memory from mere text storage to an organized knowledge base.\n\n* **Semantic Extraction & Triple Generation:** Rather than saving sentences, the pipeline deconstructs dialogue messages into atomic units of knowledge. It actively scans conversations for concrete facts, user preferences, constraints, and evolving attributes, structuring them into semantic triples (subject–predicate–object). Each triple is then linked to the exact conversation in which it was mentioned. This design delivers two key advantages. First, it produces a low-noise, high-signal index that improves vector search retrieval accuracy. Second, it functions as a compression layer. \n* **Conversation Summarization:** While semantic triples excel at capturing granular, static facts, they inherently strip away the surrounding context. An isolated triple might state what a user prefers, but it lacks the narrative of *why* a decision was made or how a user’s goal evolved throughout a specific interaction. To bridge this gap, the pipeline simultaneously generates Conversation Summaries. These are concise, high-level overviews of specific conversational threads that capture the user’s overarching intent, the dialogue's chronological progression, and the task's implicit context. Because triples are tied to their source, each individual triple can be directly linked to the proper summary of the conversation in which it appears, allowing the system to easily retrieve the background story behind any isolated fact.\n\nAdvanced Augmentation creates an interconnected, dual-layered memory asset: Triples provide the precise, token-efficient facts needed for exact recall, while Conversation Summaries provide the cohesive narrative flow required for the LLM to understand temporal changes and execute complex reasoning. By linking atomic triples directly to the summaries of the conversations they originated from, the system ensures that granular facts are never divorced from their broader context.\n" }, { "path": "docs/memori-cloud/benchmark/results.mdx", "content": "---\ntitle: LoCoMo Benchmark Results\ndescription: See how Memori’s Advanced Augmentation performed on the LoCoMo benchmark. Using our LLM-as-a-Judge framework, we evaluated four reasoning categories (Multi-Hop, Temporal, Open-Domain, and Single-Hop) and compared Memori against several memory baselines and a Full-Context ceiling.\n---\n# LoCoMo Benchmark Results\n\nThis section summarizes how Memori’s Advanced Augmentation performed on the LoCoMo benchmark. Using our LLM-as-a-Judge framework, we evaluated four reasoning categories (Multi-Hop, Temporal, Open-Domain, and Single-Hop) and compared Memori against several memory baselines and a Full-Context ceiling. We also examined the vital tradeoff between output accuracy and token cost efficiency. The results are presented in Table 1\\.\n\n| Table 1: LLM-as-a-Judge Evaluation Results on the LoCoMo Benchmark | | | | | |\n| ----- | :---: | :---: | :---: | :---: | :---: |\n| Method | Single-hop (%) | Multi-hop (%) | Open-domain (%) | Temporal (%) | Overall (%) |\n| Memori | 87.87 | 72.70 | 63.54 | 80.37 | 81.95 |\n| Zep | 79.43 | 69.16 | 73.96 | 83.33 | 79.09 |\n| LangMem | 74.47 | 61.06 | 67.71 | 86.92 | 78.05 |\n| Mem0 | 62.41 | 57.32 | 44.79 | 66.47 | 62.47 |\n| Full-Context (Ceiling) | 88.53 | 77.70 | 71.88 | 92.70 | 87.52 |\n\nThis table compares the factual accuracy and reasoning capabilities of Memori’s Advanced Augmentation assets against state-of-the-art baselines and a full-context ceiling. Memori performance values were computed using the average of three rounds.\n\nGraphical representation of Memori's average accuracy along with the standard deviation is presented in Figure 2\\.\n\n![\"Memori's average accuracy along with the standard deviation\"](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/memori-locomo-benchmark.webp)\n\n## **Overall Performance**\n\nAs expected, the Full-Context setup achieved the highest score (87.52%). However, passing the entire conversation history into the prompt is fundamentally impractical in production due to prohibitive token costs, uncontrolled context expansion, and context degradation over time.\n\nAmong retrieval-based systems, Memori achieved a leading overall score of 81.95%, successfully outperforming Zep (79.09%), LangMem (78.05%), and Mem0 (62.47%). This validates the assumption that by structuring unstructured chat logs into semantic triples and summaries, Memori effectively isolates high-signal knowledge. This structured memory design significantly narrows the gap to the Full-Context ceiling while keeping context windows highly manageable and operational token costs low.\n\n## **Performance by Category**\n\nAnalyzing the results across reasoning categories highlights the specific strengths of Memori's extracted memory assets:\n\n* **Single-Hop Reasoning (87.87%):** Memori excels in direct fact retrieval, outperforming both LangMem (74.47%) and Zep (79.43%). By minimizing conversational noise and structuring data cleanly, the LLM is fed exact, undeniable facts, minimizing token consumption while maximizing direct recall. \n* **Temporal Reasoning (80.37%):** Memori outperforms Mem0 (66.47%) but trails LangMem (86.92%) and Zep (83.33%) in temporal tracking. Isolated semantic triples capture static facts but often miss the temporal context needed to identify changes in user states or preferences across sessions. Memori’s summaries help rebuild this timeline, but the results show it needs better temporal reasoning. \n* **Multi-Hop Reasoning (72.70%):** Memori performs strongly when asked to connect disparate pieces of information, outperforming Zep (69.16%) and trailing LangMem (61.06%) by a narrow margin. The combination of precise triples and cohesive summaries provides the necessary backdrop that helps the LLM connect isolated facts without needing the entire conversational transcript injected into the prompt. \n* **Open-Domain Reasoning (63.54%):** This category remains challenging across all retrieval-based systems. Open-ended questions often lack clear retrieval anchors, making them difficult to match with granular triples. Furthermore, these queries require broad synthesis across massive contexts rather than simple fact extraction. While Memori lags slightly behind LangMem (67.71%) here, it is important to note that improving open-domain scores typically requires retrieving significantly larger chunks of text, which actively works against the system's core operational goal: strictly minimizing the number of tokens added to the context to control API costs.\n\n## **Token Usage and Cost Efficiency**\n\nTraditional memory architectures and standard RAG setups often rely on retrieving raw, uncompressed text chunks. This indiscriminately injects conversational noise and redundant dialogue into the prompt, consuming context limits and inflating API bills.\n\nMemori’s Advanced Augmentation pipeline completely bypasses this inefficiency by acting as an intelligent cognitive filter. Rather than retrieving raw text, it compresses chat logs into structured representations, including dense semantic triples and concise conversation-level summaries. As a result, only high-signal, structured information is passed to the LLM, minimizing context overhead while preserving relevant context. \n\n| Table 2: Token Usage and Cost Efficiency | | | |\n| ----- | :---: | :---: | :---: |\n| Method | Added Tokens to Context (mean) | Context Cost ($) | Context Footprint (%) |\n| **Memori** | **1,294** | **0.001035** | **4.97** |\n| Full-context | 26,031 | 0.020825 | 100.00 |\n| Mem0 | 1,764 | 0.001411 | 6.78 |\n| Zep | 3,911 | 0.003129 | 15.02 |\n\n This table analyzes the operational efficiency of each method by measuring the absolute number of tokens added to the context and the resulting cost per query. Costs are computed based on current gpt-4.1-mini pricing: $0.8 per 1M tokens.\n\nMemori requires an average of only 1,294 tokens to ground each LLM response. This token footprint represents just 4.97% of the full conversational context, while achieving the 81.95% overall accuracy detailed in the previous section.\n\nWhen compared to competing memory frameworks, the operational advantages become even more pronounced:\n\n* **Compared to Zep (3,911 tokens):** Memori reduces the prompt size by roughly 67% per query, directly cutting API inference costs by the same margin, while simultaneously delivering a higher accuracy score (81.95% vs. 79.09%). \n* **Compared to the Full-Context approach (26,031 tokens):** Passing the entire history is financially unsustainable for persistent agents, costing over 20 times more per turn than Memori. Furthermore, repeatedly injecting 26K+ tokens drastically increases the risk of \"lost in the middle\" hallucinations.\n\n## **Conclusion**\n\nFor LLM agents to scale in production, persistent memory must address two fundamental challenges: context degradation and rapidly increasing token costs. Memori approaches this not as a storage issue, but as a data structuring problem.\n\nThrough its Advanced Augmentation memory creation pipeline, Memori transforms noisy conversational logs into compact, high-signal representations, combining precise semantic triples with coherent conversation-level summaries. This dual representation enables accurate fact retrieval alongside strong temporal and contextual reasoning, without inflating the prompt with unnecessary tokens.\n\nOur evaluation on the LoCoMo benchmark demonstrates the effectiveness of this approach:\n\n* **High-Quality Reasoning:** Memori achieves state-of-the-art performance among retrieval-based systems, with particularly strong gains in temporal and single-hop reasoning \\- highlighting the impact of structured memory on reasoning fidelity. \n* **Minimal Context Footprint:** Responses are grounded using a small fraction of the original conversation, showing that well-structured memory can replace large, unfiltered context without sacrificing accuracy. \n* **Cost-Efficient Scaling:** By significantly reducing the number of tokens injected into the prompt, Memori directly lowers inference costs and enables sustainable deployment of long-running agents.\n\nThese results highlight a fundamental shift in memory design for LLM systems: performance is determined not by how much context is used, but by the quality of its structure. \n\nMemori eliminates the traditional tradeoff between reasoning quality and operational cost. By delivering accurate, cross-session recall with a compact context footprint, it provides a practical and scalable foundation for deploying persistent AI agents in real-world environments.\n" }, { "path": "docs/memori-cloud/concepts/advanced-augmentation.mdx", "content": "---\ntitle: Advanced Augmentation\ndescription: How Memori's Advanced Augmentation engine extracts structured facts, preferences, and knowledge from your AI conversations — all managed in Memori Cloud.\n---\n\n# Advanced Augmentation\n\nAdvanced Augmentation is the AI engine inside Memori Cloud that turns raw conversations into structured, searchable memories. It runs asynchronously in the background to minimize impact on your response path.\n\n## What It Does\n\nWhen your application has a conversation through a Memori-wrapped LLM client, the augmentation engine:\n\n1. Reads the full conversation (user messages and AI responses)\n2. Identifies facts, preferences, skills, and attributes\n3. Extracts semantic triples (subject-predicate-object relationships)\n4. Generates vector embeddings for semantic search\n5. Stores everything in your managed memory space\n\nNo extra code required — just initialize Memori and set attribution.\n\n## How It Works\n\nThe augmentation flow is fully asynchronous and designed to avoid blocking your main request path.\n\n1. Your app makes an LLM call through the wrapped client\n2. Memori returns the response immediately\n3. In the background, the conversation is queued for processing\n4. The augmentation engine extracts structured memories\n5. Memories are stored in Memori Cloud for future recall\n\n\n\n```python {{ title: 'Python' }}\nfrom memori import Memori\nfrom openai import OpenAI\n\nclient = OpenAI()\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"my_agent\")\n\n# This returns immediately — no augmentation delay\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[\n {\"role\": \"user\", \"content\": \"I love hiking in the mountains.\"}\n ]\n)\nprint(response.choices[0].message.content)\n```\n\n```typescript {{ title: 'TypeScript' }}\nimport OpenAI from 'openai';\nimport { Memori } from '@memorilabs/memori';\n\nconst client = new OpenAI();\nconst mem = new Memori().llm.register(client);\nmem.attribution('user_123', 'my_agent');\n\n// This returns immediately — no augmentation delay\nconst response = await client.chat.completions.create({\n model: 'gpt-4o-mini',\n messages: [\n { role: 'user', content: 'I love hiking in the mountains.' },\n ],\n});\nconsole.log(response.choices[0].message.content);\n```\n\n\n\n## Extraction Types\n\n| Type | What it captures | Scope |\n| ---------------------- | ------------------------------------------------------- | ------------------------------------ |\n| **Facts** | Objective information with vector embeddings | Per entity — shared across processes |\n| **Preferences** | User choices, opinions, and tastes | Per entity |\n| **Skills & Knowledge** | Abilities and expertise levels | Per entity |\n| **Attributes** | Process-level information about what your agent handles | Per process |\n\n## Semantic Triples\n\nAdvanced Augmentation uses named-entity recognition to extract semantic triples (subject, predicate, object). These form the building blocks of the [Knowledge Graph](/docs/memori-cloud/concepts/knowledge-graph).\n\n**Example** — from _\"My favorite database is PostgreSQL and I use it with FastAPI\"_:\n\n| Subject | Predicate | Object |\n| ------- | ----------------- | -------------------- |\n| user | favorite_database | PostgreSQL |\n| user | uses | FastAPI |\n| user | uses_with | PostgreSQL + FastAPI |\n\nMemori automatically deduplicates triples — if the same fact is mentioned multiple times, it increments the mention count and updates the timestamp.\n\n## Context Recall\n\nWhen a query is sent to an LLM through a wrapped client, Memori automatically:\n\n1. Intercepts the outbound LLM call\n2. Uses semantic search to find entity facts matching the query\n3. Ranks facts by vector similarity\n4. Injects the most relevant facts into the system prompt\n5. Forwards the enriched request to the LLM provider\n\n\n For Memori to provide all Advanced Augmentation capabilities, attribution must\n be set before making LLM calls. Without attribution, Memori cannot create or\n recall memories.\n\n" }, { "path": "docs/memori-cloud/concepts/architecture.mdx", "content": "---\ntitle: Architecture\ndescription: Understand how Memori's Cloud platform is designed — from your app to Memori Cloud, with managed storage, augmentation, and recall.\n---\n\n# Architecture\n\nMemori Cloud is a managed memory platform for AI applications. Connect your LLM client, set attribution, and Memori handles the rest — storage, augmentation, knowledge graph construction, and recall.\n\n## System Overview\n\n![\"Memori Cloud System Overview\"](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/memori-cloud-architecture-detail.webp)\n\n## Core Components\n\n### Your Application\n\nYour code and existing LLM client. It sends requests through the Memori SDK and receives model responses as usual.\n\n### Memori SDK\n\nThe integration layer between your app and Memori Cloud. It provides LLM wrappers, attribution, and the Recall API.\n\n### Memori Cloud\n\nThe managed backend that processes captured conversations and powers storage, augmentation, and recall services.\n\n### Managed Storage\n\nStores conversations, sessions, and facts for each attribution scope.\n\n### Advanced Augmentation\n\nProcesses raw conversation data into structured memory through fact extraction, embeddings, and knowledge graph construction.\n\n### Recall Engine\n\nSurfaces the right memories at the right time — semantic search over stored memory, intelligent ranking and decay, and seamless injection of relevant context into every LLM call so your AI stays contextually aware.\n\n## Configuration\n\nSetting up Memori requires only your API key and attribution:\n\n\n\n```python {{ title: 'Python' }}\nfrom memori import Memori\nfrom openai import OpenAI\n\n# Set MEMORI_API_KEY as an environment variable\n# export MEMORI_API_KEY=\"your-memori-api-key\"\n\nclient = OpenAI()\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"my_agent\")\n```\n\n```typescript {{ title: 'TypeScript' }}\nimport OpenAI from 'openai';\nimport { Memori } from '@memorilabs/memori';\n\n// Set MEMORI_API_KEY as an environment variable\n// export MEMORI_API_KEY=\"your-memori-api-key\"\n\nconst client = new OpenAI();\nconst mem = new Memori().llm.register(client);\nmem.attribution('user_123', 'my_agent');\n```\n\n\n\n## Data Flow\n\n1. **Conversation Capture** — Every LLM call through the wrapped client is captured and sent to Memori Cloud. Your app gets the response immediately.\n\n2. **Attribution Tracking** — Attribution links every conversation to a specific entity and process so memories are properly scoped and indexed.\n\n3. **Augmentation** — After a conversation completes, Memori Cloud processes it asynchronously — extracts facts, generates embeddings, and builds knowledge graph triples.\n\n4. **Recall** — On the next LLM call, Memori embeds the query, performs vector search across the entity's stored facts, and injects the most relevant memories into the context.\n" }, { "path": "docs/memori-cloud/concepts/async-patterns.mdx", "content": "---\ntitle: Async Patterns\ndescription: Best practices for using Memori with async/await in Python and TypeScript.\n---\n\n# Async Patterns\n\nMemori works with async/await out of the box in both Python and TypeScript. In Python, use `AsyncOpenAI` or `AsyncAnthropic` instead of their sync counterparts — everything else stays the same. TypeScript is natively async.\n\n## When to Use Async\n\n| Scenario | Python | TypeScript | Why |\n| ------------------------ | ------ | ---------- | --------------------------- |\n| Web servers | Yes | Default | Concurrent request handling |\n| Chatbots with many users | Yes | Default | Non-blocking I/O |\n| CLI scripts | No | Default | Sync is simpler in Python |\n| Jupyter notebooks | No | — | Event loop already running |\n\n\n TypeScript is natively async — all Memori SDK calls return Promises. No special async client or `asyncio` setup is needed.\n\n\n## Basic Async Setup\n\n\n\n```python {{ title: 'Python' }}\nimport asyncio\nfrom memori import Memori\nfrom openai import AsyncOpenAI\n\nclient = AsyncOpenAI()\n\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"async_agent\")\n\nasync def main():\n response = await client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"I prefer async Python.\"}]\n )\n print(response.choices[0].message.content)\n\nasyncio.run(main())\n```\n\n```typescript {{ title: 'TypeScript' }}\nimport OpenAI from 'openai';\nimport { Memori } from '@memorilabs/memori';\n\nconst client = new OpenAI();\nconst mem = new Memori().llm.register(client);\nmem.attribution('user_123', 'async_agent');\n\nconst response = await client.chat.completions.create({\n model: 'gpt-4o-mini',\n messages: [{ role: 'user', content: 'I prefer TypeScript.' }],\n});\nconsole.log(response.choices[0].message.content);\n```\n\n\n\n## Web Server Example\n\n\n\n```python\nimport os\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\nfrom memori import Memori\nfrom openai import AsyncOpenAI\n\napp = FastAPI()\n\nclass ChatRequest(BaseModel):\n message: str\n\n@app.post(\"/chat/{user_id}\")\nasync def chat(user_id: str, req: ChatRequest):\n client = AsyncOpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\n mem = Memori().llm.register(client)\n mem.attribution(entity_id=user_id, process_id=\"fastapi_async\")\n\n response = await client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": req.message}]\n )\n return {\"response\": response.choices[0].message.content}\n```\n\n```typescript\nimport express from 'express';\nimport OpenAI from 'openai';\nimport { Memori } from '@memorilabs/memori';\n\nconst app = express();\napp.use(express.json());\n\napp.post('/chat/:userId', async (req, res) => {\n const client = new OpenAI();\n const mem = new Memori().llm.register(client);\n mem.attribution(req.params.userId, 'express_async');\n\n const response = await client.chat.completions.create({\n model: 'gpt-4o-mini',\n messages: [{ role: 'user', content: req.body.message }],\n });\n res.json({ response: response.choices[0].message.content });\n});\n\napp.listen(3000);\n```\n\n\n" }, { "path": "docs/memori-cloud/concepts/how-memory-works.mdx", "content": "---\ntitle: How Memori Works\ndescription: Understand the core concepts behind Memori — entities, processes, sessions, memory types, attribution, and how recall brings it all together.\n---\n\n# How Memori Works\n\nMemori gives your AI application long-term memory. Instead of forgetting everything after each conversation, your AI can remember facts, preferences, and context across sessions and across different applications.\n\n## Attribution\n\nEvery memory in Memori is tagged with three dimensions: **who** (entity), **what** (process), and **which conversation** (session).\n\n- **Entity (`entity_id`)** — The person, place, or thing generating memories. Typically a user ID (e.g., `\"user_alice\"`, `\"company_acme\"`).\n- **Process (`process_id`)** — The agent, program, or workflow creating memories (e.g., `\"support_bot\"`, `\"code_review_agent\"`).\n- **Session (`session_id`)** — Groups related LLM interactions into a conversation thread. Auto-generated as a UUID by default.\n\nThe combination of `entity_id` + `process_id` + `session_id` creates a unique memory scope — different users have isolated memories, the same user can have different context in different applications, and each conversation is tracked separately.\n\n\n\n```python {{ title: 'Python' }}\nfrom memori import Memori\nfrom openai import OpenAI\n\nclient = OpenAI()\nmem = Memori().llm.register(client)\n\n# Set attribution before any LLM calls\nmem.attribution(\n entity_id=\"user_alice\",\n process_id=\"support_bot\"\n)\n# session_id is auto-generated\n\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[\n {\"role\": \"user\", \"content\": \"I prefer dark mode.\"}\n ]\n)\n```\n\n```typescript {{ title: 'TypeScript' }}\nimport OpenAI from 'openai';\nimport { Memori } from '@memorilabs/memori';\n\nconst client = new OpenAI();\nconst mem = new Memori().llm.register(client);\n\n// Set attribution before any LLM calls\nmem.attribution('user_alice', 'support_bot');\n// session ID is auto-generated\n\nconst response = await client.chat.completions.create({\n model: 'gpt-4o-mini',\n messages: [\n { role: 'user', content: 'I prefer dark mode.' },\n ],\n});\n```\n\n\n\n## Memory Types\n\nWhen you have a conversation through a Memori-wrapped LLM client, Advanced Augmentation extracts structured memories in the background:\n\n| Type | What it captures | Example |\n| ---------------------- | ----------------------------------------- | ----------------------------------------------- |\n| **Facts** | Objective information with embeddings | \"User uses PostgreSQL for production databases\" |\n| **Preferences** | Choices, opinions, and tastes | \"Prefers concise answers\" |\n| **Skills & Knowledge** | Abilities and expertise levels | \"Experienced with React (5 years)\" |\n| **Attributes** | Process-level information about the agent | \"Handles billing and subscription queries\" |\n\n## How Recall Works\n\nRecall brings stored memories back into your AI conversations. There are two modes.\n\n### Automatic Recall (Default)\n\nOn every LLM call, Memori automatically:\n\n1. Intercepts the outbound request\n2. Uses semantic search to find relevant facts for the current entity\n3. Injects the most relevant memories into the system prompt\n4. Forwards the enriched request to the LLM\n\nNo extra code required — it happens transparently.\n\n### Manual Recall\n\nUse mem.recall() to retrieve memories explicitly — useful for building custom prompts, displaying memories in a UI, or debugging.\n\n\n\n```python {{ title: 'Python' }}\nfrom memori import Memori\n\nmem = Memori()\nmem.attribution(entity_id=\"user_alice\", process_id=\"support_bot\")\n\nfacts = mem.recall(\"coding preferences\", limit=5)\n\nfor fact in facts:\n print(f\"Fact: {fact.content}\")\n print(f\"Score: {fact.similarity:.4f}\")\n```\n\n```typescript {{ title: 'TypeScript' }}\nimport { Memori } from '@memorilabs/memori';\n\nconst mem = new Memori();\nmem.attribution('user_alice', 'support_bot');\n\nconst facts = await mem.recall('coding preferences');\n\nfor (const fact of facts) {\n console.log(`Fact: ${fact.content}`);\n console.log(`Score: ${fact.score.toFixed(4)}`);\n}\n```\n\n\n\nEach returned fact includes `id`, `content`, `similarity` (0–1 relevance score), `rank_score`, and `date_created`.\n\n### Recall Configuration\n\nMemori uses semantic search (vector similarity) to find relevant facts. You can tune recall behavior with:\n\n| Option | Default | Description |\n| --------------------------------------- | ------- | -------------------------------------------------- |\n| `mem.config.recall_relevance_threshold` | `0.1` | Minimum similarity score for a fact to be included |\n| `mem.config.recall_embeddings_limit` | `1000` | Maximum number of embeddings to compare against |\n\n\n\n```python {{ title: 'Python' }}\n# Example: tune recall for broader or narrower results\nmem.config.recall_relevance_threshold = 0.05 # Lower = more results\nmem.config.recall_embeddings_limit = 500 # Reduce for lower memory usage\n```\n\n```typescript {{ title: 'TypeScript' }}\n// Example: tune recall for broader or narrower results\nmem.config.recallRelevanceThreshold = 0.05; // Lower = more results\n```\n\n\n\n## Memory Lifecycle\n\n![\"Memori Lifecycle\"](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/memori-lifecycle.webp)\n\n1. **Conversation** — Your user talks to your AI through the wrapped LLM client\n2. **Capture** — Memori intercepts and stores the raw conversation\n3. **Augmentation** — Advanced Augmentation processes the conversation asynchronously, extracting structured memories\n4. **Extraction** — Facts, preferences, skills, and attributes are identified\n5. **Storage** — Extracted memories are stored in Memori Cloud with vector embeddings\n6. **Recall** — On the next LLM call, relevant memories are retrieved and injected into context\n" }, { "path": "docs/memori-cloud/concepts/knowledge-graph.mdx", "content": "---\ntitle: Knowledge Graph\ndescription: How Memori automatically builds a knowledge graph from your AI conversations using semantic triples, and how to query it through the Recall API.\n---\n\n# Knowledge Graph\n\nMemori automatically builds a knowledge graph from your AI conversations. Each time Advanced Augmentation processes a conversation, it extracts structured relationships — semantic triples — and connects them into a graph. This powers richer recall and gives your AI deeper understanding of each user.\n\n## How It Works\n\n1. **Conversation captured** — Your user talks to your AI through the Memori-wrapped LLM client\n2. **Augmentation processes** — Memori Cloud analyzes the conversation in the background\n3. **NER extraction** — Named-entity recognition identifies key entities and relationships\n4. **Triple creation** — Relationships are expressed as subject-predicate-object triples\n5. **Graph storage** — Triples are stored and deduplicated in the knowledge graph\n6. **Recall ready** — The graph is available for semantic search on subsequent LLM calls\n\n![Memori Cloud Graph](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/entities-knowledge-graph.webp)\n\n## Semantic Triples\n\nEvery fact in the knowledge graph is a semantic triple — a three-part statement: **[Subject]** **[Predicate]** **[Object]**.\n\n- \"Alice\" \"prefers\" \"dark mode\"\n- \"PostgreSQL\" \"is\" \"a relational database\"\n- \"The project\" \"uses\" \"FastAPI\"\n\n### Example Extraction\n\nFrom _\"My favorite database is PostgreSQL and I use it with FastAPI for our REST APIs. I've been using Python for about 8 years\"_:\n\n| Subject | Predicate | Object |\n| ------- | ----------------- | -------------------- |\n| user | favorite_database | PostgreSQL |\n| user | uses | FastAPI |\n| user | uses_for | REST APIs |\n| user | uses_with | PostgreSQL + FastAPI |\n| user | experience_years | Python (8 years) |\n\nOver time, as more conversations happen, the graph grows richer. Memori connects new facts to existing ones, building a comprehensive picture of each entity.\n\n## Visualizing the Graph\n\nThe Memori Playground at [app.memorilabs.ai](https://app.memorilabs.ai) includes a **Memory Graph Viewer** that shows:\n\n| Element | What it shows |\n| ------------------ | ---------------------------------------------- |\n| **Nodes** | Subjects and objects from semantic triples |\n| **Edges** | Predicates (relationships) between nodes |\n| **Mention counts** | How often a fact was discussed across sessions |\n| **Timestamps** | When facts were first and last seen |\n\n## Scope\n\nThe knowledge graph follows the same scoping rules as other memory types:\n\n| Aspect | Scope |\n| -------------- | --------------------------------------------------------------- |\n| **Triples** | Per entity — shared across all processes |\n| **Visibility** | All processes for an entity can see and use the graph |\n| **Growth** | Conversations from any process contribute to the entity's graph |\n\nIf Alice tells your support bot about PostgreSQL, your code assistant also knows she uses PostgreSQL.\n\n## Querying the Graph\n\nThe knowledge graph is automatically used during recall. When you call `mem.recall()` or make an LLM call through a wrapped client, Memori searches across both extracted facts and the knowledge graph to find the most relevant context.\n" }, { "path": "docs/memori-cloud/concepts/multi-user-support.mdx", "content": "---\ntitle: Multi-User Support\ndescription: How Memori isolates memories across users, applications, and sessions so each user gets a personalized experience.\n---\n\n# Multi-User Support\n\nMemori provides built-in multi-user and multi-process isolation through its attribution system. Each combination of entity, process, and session creates an isolated memory space — user A never sees user B's memories, and your support bot has different context than your sales bot.\n\n## Isolation Model\n\n![\"Memori - Multi User Support\"](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/memori-multi-user-support.webp)\n\n## What's Shared vs Isolated\n\n| Data | Scope |\n| ------------------- | ------------------------------------------ |\n| **Facts** | Per entity — shared across all processes |\n| **Preferences** | Per entity |\n| **Skills** | Per entity |\n| **Attributes** | Per process |\n| **Conversations** | Per entity + process + session |\n| **Sessions** | Per entity + process (auto-generated UUID) |\n| **Knowledge Graph** | Per entity |\n\n## Examples\n\n\n\n```python\nfrom memori import Memori\nfrom openai import OpenAI\n\nclient = OpenAI()\nmem = Memori().llm.register(client)\n\n# User A's conversations\nmem.attribution(entity_id=\"user_alice\", process_id=\"support_bot\")\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"I prefer dark mode\"}]\n)\n\n# User B's conversations — completely isolated\nmem.attribution(entity_id=\"user_bob\", process_id=\"support_bot\")\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"What are my preferences?\"}]\n)\n# Bob will NOT see Alice's preferences\n```\n```typescript\nimport OpenAI from 'openai';\nimport { Memori } from '@memorilabs/memori';\n\nconst client = new OpenAI();\nconst mem = new Memori().llm.register(client);\n\n// User A's conversations\nmem.attribution('user_alice', 'support_bot');\nawait client.chat.completions.create({\n model: 'gpt-4o-mini',\n messages: [{ role: 'user', content: 'I prefer dark mode' }],\n});\n\n// User B's conversations — completely isolated\nmem.attribution('user_bob', 'support_bot');\nconst response = await client.chat.completions.create({\n model: 'gpt-4o-mini',\n messages: [{ role: 'user', content: 'What are my preferences?' }],\n});\n// Bob will NOT see Alice's preferences\n```\n\n\n\n```python\nfrom memori import Memori\nfrom openai import OpenAI\n\nclient = OpenAI()\nmem = Memori().llm.register(client)\n\n# Same user, different processes\nmem.attribution(entity_id=\"user_alice\", process_id=\"support_bot\")\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[\n {\"role\": \"user\", \"content\": \"I use PostgreSQL for my databases\"}\n ]\n)\n\n# Switch to a different process for the same user\nmem.attribution(entity_id=\"user_alice\", process_id=\"sales_bot\")\n# The sales bot can recall Alice's facts (like \"uses PostgreSQL\")\n# because facts are shared across processes for the same entity.\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[\n {\"role\": \"user\", \"content\": \"What databases do I use?\"}\n ]\n)\n```\n\n```typescript\nimport OpenAI from 'openai';\nimport { Memori } from '@memorilabs/memori';\n\nconst client = new OpenAI();\nconst mem = new Memori().llm.register(client);\n\n// Same user, different processes\nmem.attribution('user_alice', 'support_bot');\nawait client.chat.completions.create({\n model: 'gpt-4o-mini',\n messages: [\n { role: 'user', content: 'I use PostgreSQL for my databases' },\n ],\n});\n\n// Switch to a different process for the same user\nmem.attribution('user_alice', 'sales_bot');\n// The sales bot can recall Alice's facts (like \"uses PostgreSQL\")\n// because facts are shared across processes for the same entity.\nconst response = await client.chat.completions.create({\n model: 'gpt-4o-mini',\n messages: [\n { role: 'user', content: 'What databases do I use?' },\n ],\n});\n```\n\n\n\n```python\nfrom memori import Memori\nfrom openai import OpenAI\n\nclient = OpenAI()\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_alice\", process_id=\"support_bot\")\n\n# Get the current session ID\ncurrent_session = mem.config.session_id\n\n# Start a new conversation group\nmem.new_session()\n\n# Or restore a previous session\nmem.set_session(current_session)\n```\n\n```typescript\nimport OpenAI from 'openai';\nimport { Memori } from '@memorilabs/memori';\n\nconst client = new OpenAI();\nconst mem = new Memori().llm.register(client);\nmem.attribution('user_alice', 'support_bot');\n\n// Get the current session ID\nconst currentSession = mem.session.id;\n\n// Start a new conversation group\nmem.resetSession();\n\n// Or restore a previous session\nmem.setSession(currentSession);\n```\n\n\n## Common Patterns\n\n### Web Application\n\nSet the entity ID from the authenticated user. Works with Flask, FastAPI, Django, or any web framework.\n\n\n```python\nfrom memori import Memori\nfrom openai import OpenAI\n\ndef handle_chat(user_id: str, message: str):\n client = OpenAI()\n mem = Memori().llm.register(client)\n mem.attribution(entity_id=user_id, process_id=\"web_assistant\")\n\n response = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": message}]\n )\n return response.choices[0].message.content\n```\n\n```typescript\nimport express from 'express';\nimport OpenAI from 'openai';\nimport { Memori } from '@memorilabs/memori';\n\nconst app = express();\napp.use(express.json());\n\napp.post('/chat/:userId', async (req, res) => {\n const client = new OpenAI();\n const mem = new Memori().llm.register(client);\n mem.attribution(req.params.userId, 'web_assistant');\n\n const response = await client.chat.completions.create({\n model: 'gpt-4o-mini',\n messages: [{ role: 'user', content: req.body.message }],\n });\n res.json({ response: response.choices[0].message.content });\n});\n```\n\n\n### Multi-Agent System\n\nGive each agent a unique process ID. Facts are shared across agents for the same entity, but each agent maintains its own conversation history.\n\n\n```python\nfrom memori import Memori\nfrom openai import OpenAI\n\ndef create_agent(user_id: str, agent_name: str):\n client = OpenAI()\n mem = Memori().llm.register(client)\n mem.attribution(entity_id=user_id, process_id=agent_name)\n return client\n\n# Three agents, one user, shared facts\nsupport = create_agent(\"user_alice\", \"support_agent\")\nsales = create_agent(\"user_alice\", \"sales_agent\")\nonboard = create_agent(\"user_alice\", \"onboarding_agent\")\n```\n\n```typescript\nimport OpenAI from 'openai';\nimport { Memori } from '@memorilabs/memori';\n\nfunction createAgent(userId: string, agentName: string) {\n const client = new OpenAI();\n const mem = new Memori().llm.register(client);\n mem.attribution(userId, agentName);\n return client;\n}\n\n// Three agents, one user, shared facts\nconst support = createAgent('user_alice', 'support_agent');\nconst sales = createAgent('user_alice', 'sales_agent');\nconst onboard = createAgent('user_alice', 'onboarding_agent');\n```\n\n" }, { "path": "docs/memori-cloud/dashboard/analytics.mdx", "content": "---\ntitle: Analytics\ndescription: Understand memory volume, retrieval performance, usage quotas, and top subjects in Memori Cloud analytics.\n---\n\n# Analytics\n\nUse **Analytics** in [app.memorilabs.ai](https://app.memorilabs.ai) to monitor memory creation, recall activity, traffic trends, and quota usage.\n\nUse the date selector (for example **Last 7 days**) to scope metrics, and check **Last refreshed** for data freshness.\n\n**What Each Data Point Means:**\n\n| Data point | Meaning |\n| --------------------------------- | ------------------------------------------------------------------------------------------ |\n| **Unique memories** | Total number of unique memory records in the selected time range. |\n| **Entities** | Number of unique extracted entities attached to memories in the selected range. |\n| **Recalls** | Number of memory retrieval events served in the selected range. |\n| **Cache hit rate** | Percentage of recall requests served from cache instead of a full retrieval path. |\n| **Total conversations** | Number of conversation threads processed by Memori. |\n| **Total sessions** | Number of distinct sessions in the selected range. |\n| **Trend chart** | Time-series trend for the active activity metric across the selected period. |\n| **Monthly quota resets on ...** | Date when monthly limits reset for your organization. |\n| **Memories created** (usage bar) | Current month created-memory usage vs monthly plan limit. |\n| **Memories recalled** (usage bar) | Current month recall usage vs monthly plan limit. |\n| **Top subjects** | Ranked subjects/entities by memory volume, with entity type tags and relative volume bars. |\n\n![Analytics page with KPI cards, activity chart, monthly usage, and top subjects](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/analytics-overview.webp)\n\n## Read the Page in Order\n\n1. Start with the top KPI cards to understand overall volume and retrieval load.\n2. Check **Activity and performance** for traffic trends and directional changes (up/down percentages).\n3. Review **Monthly usage** to confirm distance from quota limits.\n4. Use **Top subjects** to see which entities dominate memory creation.\n\n## Interpreting Directional Changes\n\n- Green upward changes indicate growth versus the comparison period.\n- Red downward changes indicate decreases versus the comparison period.\n- Combine change percentages with absolute counts to avoid overreacting to small-volume shifts.\n" }, { "path": "docs/memori-cloud/dashboard/api-keys.mdx", "content": "---\ntitle: API Keys\ndescription: Create and manage Memori API keys from the dashboard.\n---\n\n# API Keys\n\nUse the **API keys** page in [app.memorilabs.ai](https://app.memorilabs.ai)\nto create and manage keys for your organization.\n\n## Overview\n\nThe table includes:\n\n- **Name**\n- **Key** (masked)\n- **Created at**\n- **Last used**\n\nIf no keys are connected yet, the table shows **No connected APIs.**\n\n## Create a New API Key\n\n1. Open **API keys** in the dashboard sidebar.\n2. Click **Create API key**.\n3. In **New API key**, enter a value in **Name**.\n4. Click **Create key**.\n\n![API keys page with empty table state and New API key modal](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/new-api-key-modal.webp)\n\n## Copy the Key (Shown Once)\n\nAfter creating a key, the **Your API key** modal appears with the full key\nvalue and a **Copy** button.\n\n\n The full key value is shown only once. Copy it and store it securely before\n clicking **Continue**.\n\n\n1. Click **Copy**.\n2. Click **Continue** to return to the API keys table.\n\n![API keys page with table row and Your API key modal for one-time copy](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/your-api-key-modal.webp)\n\n## Security Best Practices\n\n\n Never commit API keys to version control. Treat API keys like passwords.\n\n\n- Store keys in environment variables or a secure secrets manager\n- Rotate keys immediately if you suspect exposure\n- Use separate keys for development, staging, and production\n" }, { "path": "docs/memori-cloud/dashboard/memories.mdx", "content": "---\ntitle: Memories\ndescription: Explore memories and entities in table and graph views, then drill into details, associations, and retrieval behavior.\n---\n\n# Memories\n\nUse **Memories** in [app.memorilabs.ai](https://app.memorilabs.ai) to inspect all memories created by your configured applications and agents.\n\nThe page supports two ways to explore data:\n\n- **Table** view for row-based inspection\n- **Knowledge graph** view for relationship-first exploration\n\n## Start in Table View\n\nThe default table flow uses two tabs:\n\n- **Memories** for individual memory rows\n- **Subjects** for extracted entities/subjects and their counts\n\nUse **Semantic search** to find relevant rows quickly, then open the view menu to switch between **Table** and **Knowledge graph**.\n\n### Memories tab\n\n- **Summary**: The memory text captured from conversations\n- **Entity ID**: The associated entity for that memory\n- **Recalls**: Number of times this memory was retrieved\n- **# of mentions**: How often this memory was mentioned in context\n- **Last retrieved**: Most recent retrieval time\n- **Created at**: When the memory was first created\n\n![Memories table view with semantic search and table or knowledge graph switcher](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/memories-overview-table.webp)\n\n### Subjects tab\n\n- **Subject**: Extracted subject/entity value\n- **Type**: Entity class (for example `GPE`, `PRODUCT`, `ORGANIZATION`, `MONEY`)\n- **Memory count**: Number of memories linked to that subject\n- **Created at**: Subject creation date\n\nThe **All types** filter helps narrow the table by subject type.\n\n![Subjects table view with all types filter and subject memory counts](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/memories-detail-drawer.webp)\n\n## Open Memory Details\n\n1. In **Memories**, click any row.\n2. Review the **Memory details** panel.\n3. Use the embedded **Subjects** graph and list to inspect associations.\n\nThe **Details** section includes:\n\n- **Memory ID**\n- **Entity ID**\n- **Importance score**\n- **Created at**\n- **Last retrieved**\n- **Recalls**\n- **# of mentions**\n\n![Memory details drawer showing details fields and associated subjects graph](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/memories-subjects-table.webp)\n\n## Switch to Knowledge Graph\n\n1. Open the view switcher and select **Knowledge graph**.\n2. Use the **Entities** tab to explore nodes and relationships.\n3. Filter with **Search memories** and time range (for example **All time**).\n\n![Entities knowledge graph view with search and time filter](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/entities-knowledge-graph.webp)\n\n## Recommended Exploration Workflow\n\n1. Start in **Memories** table to validate recency and retrieval metrics.\n2. Open **Memory details** to verify entity associations and subject links.\n3. Switch to **Knowledge graph** to follow cross-entity relationships.\n4. Open an entity and trace back through **Associated memories**.\n" }, { "path": "docs/memori-cloud/dashboard/overview.mdx", "content": "---\ntitle: Dashboard Overview\ndescription: An overview of the Memori Cloud dashboard at app.memorilabs.ai for managing API keys, testing memories, and monitoring usage.\n---\n\n# Dashboard Overview\n\nThe Memori Cloud dashboard at [app.memorilabs.ai](https://app.memorilabs.ai) is where you manage API keys, test memory in real-time, and monitor usage.\n\n## Dashboard Sections\n\n### API Keys\n\nCreate and manage your Memori API keys. The free plan unlocks 5,000 memories created and 15,000 recalled per\nmonth. [Manage API Keys](/docs/memori-cloud/dashboard/api-keys).\n\n### Playground\n\nChat with an AI and watch memories get extracted in real-time. The fastest way\nto understand how Memori works.\n[Try the Playground](/docs/memori-cloud/dashboard/playground).\n\n## Memory Usage\n\n| Plan | Memories Created / month | Memories Recalled / month | Requirements |\n| ----------------------- | ------------------------ | ------------------------- | ---------------------------------------------------------- |\n| **Free (with API key)** | 5,000 | 15,000 | [Sign up and create an API key](https://app.memorilabs.ai) |\n| **Starter** | 25,000 | 100,000 | [Sign up and subscribe](/pricing) |\n| **Pro** | 150,000 | 500,000 | [Sign up and subscribe](/pricing) |\n| **Enterprise** | Custom | Custom | [Contact us](/pricing) |\n\n## Getting Started\n\n1. **Sign up** — Go to [app.memorilabs.ai](https://app.memorilabs.ai) and create an account\n2. **Create an API key** — Navigate to API Keys and generate your first key\n3. **Try the Playground** — Chat with an AI and see memories being extracted live\n4. **Integrate** — Copy your API key into your application code\n\n```python\nimport os\nfrom memori import Memori\nfrom openai import OpenAI\n\nclient = OpenAI()\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"my-agent\")\n```\n" }, { "path": "docs/memori-cloud/dashboard/playground.mdx", "content": "---\ntitle: Playground\ndescription: Use Playground to validate memory extraction end to end with chat, extracted memories, and the memory graph.\n---\n\n# Playground\n\nUse **Playground** in [app.memorilabs.ai](https://app.memorilabs.ai) to test how conversation turns become extracted memories and relationship graphs.\n\n## Playground Layout\n\nThe screen is split into three working areas:\n\n- Left navigation: **Memories**, **API keys**, **Playground**, **Documentation**, **Settings**\n- Center panel: **Playground** conversation and composer\n- Right column: **Memory usage**, **Extracted memories** with **Clean**, and **Memory graph** with expand\n\nThis layout keeps input, extracted output, and relationship visualization on one page so you can validate memory behavior quickly.\n\n![Playground overview with conversation, memory usage, extracted memories, and memory graph](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/playground-memory-graph-expanded.webp)\n\n## Core Workflow\n\n1. Chat in **Playground** with user details or preferences.\n2. Watch new records appear in **Extracted memories**.\n3. Verify those same facts and links in **Memory graph**.\n4. Use the graph expand control for deeper inspection when needed.\n\n## Memory Graph\n\n**Memory graph** is a relationship view over the same memory set shown in **Extracted memories**.\n\n- Nodes represent subjects and entities (for example: `User`, `Emma`, `Queens`, `NYC Juniors`).\n- Edges show relation text (for example: `has name`, `lives in`, `plays as`).\n- Use the expand icon to open a larger graph canvas for graph-first review.\n\n![Memory graph card in Playground](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/playground-overview.webp)\n\n## Extracted Memories and Persistence\n\nThe **Extracted memories** list shows normalized memory statements such as:\n\n- `Northeastern University located in Boston, Massachusetts`\n- `User plays as libero`\n- `User played club volleyball growing up for NYC Juniors`\n\nThese memories are persisted as records in the database and are generated asynchronously through advanced augmentation after conversation turns are processed.\n\n![Extracted memories panel with Clean action](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/playground-extracted-memories.webp)\n" }, { "path": "docs/memori-cloud/getting-started/installation.mdx", "content": "---\ntitle: Installation\ndescription: Install Memori and set up your API key for the Memori Cloud.\n---\n\n# Installation\n\nGet Memori installed and connected to Memori Cloud in a few steps.\n\n## Python\n\n### Install Memori\n\n\n\n```bash {{ title: 'pip' }}\npip install memori\n```\n\n```bash {{ title: 'poetry' }}\npoetry add memori\n```\n\n```bash {{ title: 'uv' }}\nuv add memori\n```\n\n\n\n### Install Your LLM Provider\n\nInstall the SDK for your preferred LLM provider:\n\n\n\n```bash {{ title: 'OpenAI' }}\npip install openai\n```\n\n```bash {{ title: 'Anthropic' }}\npip install anthropic\n```\n\n```bash {{ title: 'Google Gemini' }}\npip install google-genai\n```\n\n```bash {{ title: 'xAI Grok' }}\npip install openai\n```\n\n```bash {{ title: 'Nebius AI Studio' }}\npip install openai\n```\n\n```bash {{ title: 'AWS Bedrock' }}\npip install langchain-aws\n```\n\n```bash {{ title: 'LangChain' }}\npip install langchain-openai\n```\n\n```bash {{ title: 'Pydantic AI' }}\npip install pydantic-ai\n```\n\n\n\n## TypeScript\n\n\n The TypeScript SDK is for Memori Cloud only. For self-hosted setups, use the [Python SDK](/docs/memori-byodb/getting-started/installation).\n\n\n### Install Memori\n\n\n\n```bash {{ title: 'npm' }}\nnpm install @memorilabs/memori\n```\n\n```bash {{ title: 'yarn' }}\nyarn add @memorilabs/memori\n```\n\n```bash {{ title: 'pnpm' }}\npnpm add @memorilabs/memori\n```\n\n\n\n### Install Your LLM Provider\n\n\n\n```bash {{ title: 'OpenAI' }}\nnpm install openai\n```\n\n```bash {{ title: 'Anthropic' }}\nnpm install @anthropic-ai/sdk\n```\n\n```bash {{ title: 'Google Gemini' }}\nnpm install @google/genai\n```\n\n\n\n## Set Your API Key\n\nSign up at [app.memorilabs.ai](https://app.memorilabs.ai) to get your Memori API key. The SDK reads it automatically from the environment:\n\n\n \n Your Memori API key from [app.memorilabs.ai](https://app.memorilabs.ai)\n connects your application to Memori Cloud.\n \n\n\nSet this as an environment variable. The Memori SDK reads it automatically on initialization.\n\n\n\n```bash {{ title: 'Environment Variable' }}\nexport MEMORI_API_KEY=\"your-memori-api-key\"\n```\n\n```bash {{ title: '.env File' }}\n# Create a .env file in your project root\nMEMORI_API_KEY=your-memori-api-key\nOPENAI_API_KEY=your-openai-api-key\n```\n\n\n\n## Set Your LLM Provider Key\n\nYou'll also need an API key for your LLM provider:\n\n```bash\n# OpenAI\nexport OPENAI_API_KEY=\"your-openai-key\"\n\n# Anthropic\nexport ANTHROPIC_API_KEY=\"your-anthropic-key\"\n\n# Google Gemini\nexport GOOGLE_API_KEY=\"your-google-key\"\n```\n\n## Verify Installation\n\n\n\n```bash {{ title: 'Python' }}\npip show memori\n```\n\n```bash {{ title: 'TypeScript' }}\nnpx tsx -e \"import { Memori } from '@memorilabs/memori'; console.log('Memori installed successfully')\"\n```\n\n\n" }, { "path": "docs/memori-cloud/getting-started/python-quickstart.mdx", "content": "---\ntitle: Python SDK Quickstart\ndescription: Get started with Memori Cloud in under 3 minutes.\n---\n\n# Python SDK Quickstart\n\nGet started with Memori in under 3 minutes using Python. No database setup required — just your Memori API key and your favorite LLM provider.\n\n\n In this example, we'll use Memori with OpenAI. Check out our [Integration\n guides](/docs/memori-cloud/llm/overview) for other LLM providers and\n frameworks.\n\n\n\n## Prerequisites\n\n- Python 3.10 or higher\n- An OpenAI API key\n- A Memori API key from [app.memorilabs.ai](https://app.memorilabs.ai)\n\n## Step 1: Install Libraries\n\nInstall Memori and the OpenAI SDK:\n\n\n\n```bash {{ title: 'pip' }}\npip install memori openai\n```\n\n```bash {{ title: 'poetry' }}\npoetry add memori openai\n```\n\n```bash {{ title: 'uv' }}\nuv add memori openai\n```\n\n\n\n## Step 2: Set Environment Variables\n\nSet your API keys as environment variables:\n\n```bash\nexport MEMORI_API_KEY=\"your-memori-api-key\"\nexport OPENAI_API_KEY=\"your-openai-api-key\"\n```\n\n## Step 3: Run Your First Memori Application\n\nCreate a new Python file `quickstart.py` and add the following code:\n\n### Setup & Configuration\n\nImport libraries and initialize Memori with your API key and OpenAI client.\n\n- Memori reads your `MEMORI_API_KEY` from the environment automatically\n- `llm.register()` wraps your LLM client for automatic memory capture\n- `attribution()` links memories to a specific user and process\n\n```python\nimport os\nfrom memori import Memori\nfrom openai import OpenAI\n\nclient = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"test-ai-agent\")\n```\n\n### First Conversation\n\nTell the LLM a fact about yourself. Memori automatically captures the\nconversation and processes it through Advanced Augmentation.\n\n```python\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[\n {\"role\": \"user\", \"content\": \"My favorite color is blue.\"}\n ]\n)\nprint(response.choices[0].message.content + \"\\n\")\n```\n\n### Memory Recall\n\nCreate a completely new client and Memori instance — no prior context\ncarried over. Memori automatically injects relevant facts via semantic\nsearch, so the second response should correctly recall your favorite\ncolor. This verifies recall from stored memories, not prior in-memory message history.\n\n```python\nclient = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\n\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"test-ai-agent\")\n\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[\n {\"role\": \"user\", \"content\": \"What's my favorite color?\"}\n ]\n)\nprint(response.choices[0].message.content + \"\\n\")\n```\n\n## Step 4: Run the Application\n\nExecute your Python file:\n\n```bash\npython quickstart.py\n```\n\nYou should see the AI respond to both questions, with the second response correctly recalling that your favorite color is blue!\n\n## Step 5: Check the Dashboard\n\nVisit [app.memorilabs.ai](https://app.memorilabs.ai) to see your memory usage and try the Graph Explorer to interact with your memories visually.\n" }, { "path": "docs/memori-cloud/getting-started/typescript-quickstart.mdx", "content": "---\ntitle: Typescript SDK Quickstart\ndescription: Get started with Memori Cloud and the TypeScript SDK in under 3 minutes.\n---\n\n# Typescript SDK Quickstart\n\nGet started with Memori in under 3 minutes using TypeScript. No database setup required — just your Memori API key and your favorite LLM provider.\n\n\n In this example, we'll use Memori with OpenAI. Check out our [Integration\n guides](/docs/memori-cloud/llm/overview) for other LLM providers.\n\n\n## Prerequisites\n\n- Node.js 18 or higher\n- An OpenAI API key\n- A Memori API key from [app.memorilabs.ai](https://app.memorilabs.ai)\n\n## Step 1: Install Libraries\n\nInstall Memori and the OpenAI SDK:\n\n\n\n```bash {{ title: 'npm' }}\nnpm install @memorilabs/memori openai\n```\n\n```bash {{ title: 'yarn' }}\nyarn add @memorilabs/memori openai\n```\n\n```bash {{ title: 'pnpm' }}\npnpm add @memorilabs/memori openai\n```\n\n\n\n## Step 2: Set Environment Variables\n\nSet your API keys as environment variables:\n\n```bash\nexport MEMORI_API_KEY=\"your-memori-api-key\"\nexport OPENAI_API_KEY=\"your-openai-api-key\"\n```\n\n## Step 3: Run Your First Memori Application\n\nCreate a new file `quickstart.ts` and add the following code:\n\n### Setup & Configuration\n\nImport libraries and initialize Memori with your API key and OpenAI client.\n\n- Memori reads your `MEMORI_API_KEY` from the environment automatically\n- `llm.register()` wraps your LLM client for automatic memory capture\n- `attribution()` links memories to a specific user and process\n\n```typescript\nimport OpenAI from 'openai';\nimport { Memori } from '@memorilabs/memori';\n\nconst client = new OpenAI();\nconst mem = new Memori().llm.register(client);\nmem.attribution('user_123', 'test-ai-agent');\n```\n\n### First Conversation\n\nTell the LLM a fact about yourself. Memori automatically captures the\nconversation and processes it through Advanced Augmentation.\n\n```typescript\nconst response = await client.chat.completions.create({\n model: 'gpt-4o-mini',\n messages: [\n { role: 'user', content: 'My favorite color is blue.' }\n ],\n});\nconsole.log(response.choices[0].message.content + '\\n');\n```\n\n### Memory Recall\n\nCreate a completely new client and Memori instance — no prior context\ncarried over. Memori automatically injects relevant facts via semantic\nsearch, so the second response should correctly recall your favorite\ncolor. This verifies recall from stored memories, not prior in-memory message history.\n\n```typescript\nconst client2 = new OpenAI();\n\nconst mem2 = new Memori().llm.register(client2);\nmem2.attribution('user_123', 'test-ai-agent');\n\nconst response2 = await client2.chat.completions.create({\n model: 'gpt-4o-mini',\n messages: [\n { role: 'user', content: \"What's my favorite color?\" }\n ],\n});\nconsole.log(response2.choices[0].message.content + '\\n');\n```\n\n## Step 4: Run the Application\n\nExecute your TypeScript file:\n\n```bash\nnpx tsx quickstart.ts\n```\n\nYou should see the AI respond to both questions, with the second response correctly recalling that your favorite color is blue!\n\n## Step 5: Check the Dashboard\n\nVisit [app.memorilabs.ai](https://app.memorilabs.ai) to see your memory usage and try the Graph Explorer to interact with your memories visually.\n" }, { "path": "docs/memori-cloud/getting-started/use-cases.mdx", "content": "---\ntitle: Use Cases\ndescription: Common use cases and applications for Memori.\n---\n\n# Use Cases\n\nMemori is designed for any application where AI agents need to remember context across conversations. Here are the most common use cases.\n\n## Customer Support Chatbots\n\nBuild support bots that remember customer history, preferences, and past issues. No more \"Can you repeat your account number?\" — Memori recalls context automatically.\n\n**Benefits:**\n\n- Remember customer preferences and history\n- Recall previous support tickets and resolutions\n- Personalize responses based on past interactions\n- Track issues across multiple sessions\n\n\n\n```python {{ title: 'Python' }}\nfrom memori import Memori\nfrom openai import OpenAI\n\nclient = OpenAI()\nmem = Memori().llm.register(client)\n\n# Each customer gets their own memory space\nmem.attribution(\n entity_id=\"customer_456\",\n process_id=\"support_bot\"\n)\n\n# Memori automatically recalls relevant context\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\n \"role\": \"user\",\n \"content\": \"I'm having that issue again\"\n }]\n)\n# Memori injects: \"Customer previously reported\n# login timeout issues on 2024-01-15\"\n```\n\n```typescript {{ title: 'TypeScript' }}\nimport OpenAI from 'openai';\nimport { Memori } from '@memorilabs/memori';\n\nconst client = new OpenAI();\nconst mem = new Memori().llm.register(client);\n\n// Each customer gets their own memory space\nmem.attribution('customer_456', 'support_bot');\n\n// Memori automatically recalls relevant context\nconst response = await client.chat.completions.create({\n model: 'gpt-4o-mini',\n messages: [{\n role: 'user',\n content: \"I'm having that issue again\",\n }],\n});\n// Memori injects: \"Customer previously reported\n// login timeout issues on 2024-01-15\"\n```\n\n\n\n## Personalized AI Assistants\n\nCreate AI assistants that learn and adapt to each user over time. Memori builds a profile of preferences, skills, and context that makes every interaction more relevant.\n\n**Benefits:**\n\n- Learn coding preferences and tech stack\n- Remember project context across sessions\n- Adapt communication style to user preferences\n- Build long-term user profiles automatically\n\n\n\n```python {{ title: 'Python' }}\nfrom memori import Memori\nfrom anthropic import Anthropic\n\nclient = Anthropic()\nmem = Memori().llm.register(client)\n\nmem.attribution(\n entity_id=\"developer_789\",\n process_id=\"code_assistant\"\n)\n\n# Over time, Memori learns:\n# - \"Uses Python 3.12 with FastAPI\"\n# - \"Prefers type hints and dataclasses\"\n# - \"Works on e-commerce platform\"\nresponse = client.messages.create(\n model=\"claude-sonnet-4-5-20250929\",\n max_tokens=1024,\n messages=[{\n \"role\": \"user\",\n \"content\": \"How should I structure this endpoint?\"\n }]\n)\n```\n\n```typescript {{ title: 'TypeScript' }}\nimport Anthropic from '@anthropic-ai/sdk';\nimport { Memori } from '@memorilabs/memori';\n\nconst client = new Anthropic();\nconst mem = new Memori().llm.register(client);\n\nmem.attribution('developer_789', 'code_assistant');\n\n// Over time, Memori learns:\n// - \"Uses TypeScript with Express\"\n// - \"Prefers strict types and interfaces\"\n// - \"Works on e-commerce platform\"\nconst response = await client.messages.create({\n model: 'claude-sonnet-4-5-20250929',\n max_tokens: 1024,\n messages: [{\n role: 'user',\n content: 'How should I structure this endpoint?',\n }],\n});\n```\n\n\n\n## Multi-Agent Workflows\n\nCoordinate multiple AI agents that share context through Memori. Each agent contributes to a shared memory space while maintaining its own process identity and conversation history.\n\n**Benefits:**\n\n- Share context between specialized agents\n- Track which agent contributed what information\n- Maintain conversation continuity across handoffs\n- Build collective knowledge graphs\n\n\n\n```python {{ title: 'Python' }}\nfrom memori import Memori\nfrom openai import OpenAI\n\nclient = OpenAI()\nmem = Memori().llm.register(client)\n\n# Research agent gathers information\nmem.attribution(\n entity_id=\"project_alpha\",\n process_id=\"research_agent\"\n)\nclient.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\n \"role\": \"user\",\n \"content\": \"Research competitor pricing\"\n }]\n)\n\n# Analysis agent recalls research findings\nmem.attribution(\n entity_id=\"project_alpha\",\n process_id=\"analysis_agent\"\n)\n# Memori shares context across agents\n# for the same entity\n```\n\n```typescript {{ title: 'TypeScript' }}\nimport OpenAI from 'openai';\nimport { Memori } from '@memorilabs/memori';\n\nconst client = new OpenAI();\nconst mem = new Memori().llm.register(client);\n\n// Research agent gathers information\nmem.attribution('project_alpha', 'research_agent');\nawait client.chat.completions.create({\n model: 'gpt-4o-mini',\n messages: [{\n role: 'user',\n content: 'Research competitor pricing',\n }],\n});\n\n// Analysis agent recalls research findings\nmem.attribution('project_alpha', 'analysis_agent');\n// Memori shares context across agents\n// for the same entity\n```\n\n\n\n\n For runnable versions of these examples and more, see the [examples\n folder](https://github.com/MemoriLabs/Memori/tree/main/examples) on GitHub.\n\n" }, { "path": "docs/memori-cloud/index.mdx", "content": "---\ntitle: Introduction\ndescription: Memori gives your AI agents structured, persistent memory — no database setup required.\n---\n\n# What is Memori?\n\n__Memori__ is a memory layer for LLM applications, agents, and copilots. It continuously captures interactions, extracts structured knowledge, and intelligently ranks, decays, and retrieves the relevant memories. So your AI remembers the right things at the right time across every session.\n\n## Why Memori Cloud?\n\nWith the Memori Cloud platform: [app.memorilabs.ai](https://app.memorilabs.ai), you skip all database configuration. Sign up, get a Memori API key, and start building AI agents with memory in minutes.\n\n### LLM Provider Support\n\nOpenAI, Anthropic, Gemini, and Grok (xAI) via direct SDK wrappers. Bedrock is\nsupported via LangChain `ChatBedrock`. OpenAI-compatible providers (Nebius,\nDeepseek, NVIDIA NIM, Azure OpenAI, and more) work through OpenAI's `base_url`\nparameter. Supports sync, async, streamed, and unstreamed modes, plus LangChain\n, Agno, and Pydantic AI.\n\n### Zero Configuration\n\nNo database setup needed. Connect your LLM client with an API key and start\nbuilding memories immediately.\n\n### Framework Integration\n\nNative support for LangChain, Agno, and Pydantic AI with seamless integration\ninto your existing workflows.\n\n### Advanced Augmentation\n\nBackground AI processing extracts facts, preferences, and relationships from\nyour conversations automatically.\n\n### Intelligent Recall\n\nIntelligent Recall surfaces the right memories at the right time. Memories are\nranked by relevance and importance, with intelligent decay so older or less\nrelevant facts recede — so your AI stays contextually aware without clutter.\nUse manual recall when you need to display memories in your UI, build custom\nprompts, or debug. See [How Memori\nWorks](/docs/memori-cloud/concepts/how-memory-works#how-recall-works) for\nautomatic vs manual recall and tuning.\n\n\n\n```python {{ title: 'Python' }}\nfrom memori import Memori\nfrom openai import OpenAI\n\n# Requires MEMORI_API_KEY and OPENAI_API_KEY in your environment\nclient = OpenAI()\nmem = Memori().llm.register(client)\n\n# Track conversations by user and process\nmem.attribution(entity_id=\"user_123\", process_id=\"support_agent\")\n\n# All conversations automatically persisted and recalled\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"My favorite color is blue.\"}]\n)\n```\n\n```typescript {{ title: 'TypeScript' }}\nimport OpenAI from 'openai';\nimport { Memori } from '@memorilabs/memori';\n\n// Requires MEMORI_API_KEY and OPENAI_API_KEY in your environment\nconst client = new OpenAI();\nconst mem = new Memori().llm.register(client);\n\n// Track conversations by user and process\nmem.attribution('user_123', 'support_agent');\n\n// All conversations automatically persisted and recalled\nconst response = await client.chat.completions.create({\n model: 'gpt-4o-mini',\n messages: [{ role: 'user', content: 'My favorite color is blue.' }],\n});\n```\n\n\n\n## Core Concepts\n\n| Concept | Description | Example |\n| ---------------- | ----------------------------------------------------- | ---------------------------------------- |\n| **Entity** | Person, place, or thing (like a user) | `entity_id=\"user_123\"` |\n| **Process** | Your agent, LLM interaction, or program | `process_id=\"support_agent\"` |\n| **Session** | Groups LLM interactions together | Auto-generated UUID, manually manageable |\n| **Augmentation** | Background AI enhancement of memories | Auto-runs after wrapped LLM calls |\n| **Recall** | Retrieve relevant memories from previous interactions | Auto-injects recalled memories |\n\n## Architecture Overview\n\nThe diagram has three lanes: your app, the Memori SDK, and Memori Cloud. Your app calls the LLM normally, Memori captures context on the response path, and memory processing continues in the background.\n\nSynchronous capture: conversation messages are sent to Memori Cloud while your normal LLM flow continues.\n\nRecall injection: relevant memories are fetched from managed storage and injected into later prompts.\n\nAsync augmentation: Memori Cloud extracts facts, preferences, rules, events, and relationships from conversations without blocking your app.\n\n![\"Memori Cloud architecture\"](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/docs/memori-cloud-architecture-detail.webp)\n" }, { "path": "docs/memori-cloud/llm/agno.mdx", "content": "---\ntitle: Agno\ndescription: Using Memori with Agno agents on Memori Cloud.\n---\n\n# Agno\n\nMemori Cloud integrates with Agno at the model layer. Register your Agno model with `llm.register(...)` and Memori captures `run()`, `arun()`, and streamed responses automatically.\n\n\n TypeScript support for Agno is coming soon. The TypeScript SDK currently supports [OpenAI](/docs/memori-cloud/llm/openai), [Anthropic](/docs/memori-cloud/llm/anthropic), and [Gemini](/docs/memori-cloud/llm/gemini).\n\n\n## Quick Start\n\n\n\n```python {{ title: 'Sync' }}\nfrom agno.agent import Agent\nfrom agno.models.openai import OpenAIChat\nfrom memori import Memori\n\nmodel = OpenAIChat(id=\"gpt-4o-mini\")\n\nmem = Memori().llm.register(openai_chat=model)\nmem.attribution(entity_id=\"user_123\", process_id=\"agno_agent\")\n\nagent = Agent(\n model=model,\n instructions=[\"Be helpful and concise\"],\n markdown=True,\n)\n\nresponse = agent.run(\"Hello!\", session_id=\"support-session\")\nprint(response.content)\n```\n\n```python {{ title: 'Async' }}\nimport asyncio\nfrom agno.agent import Agent\nfrom agno.models.openai import OpenAIChat\nfrom memori import Memori\n\nmodel = OpenAIChat(id=\"gpt-4o-mini\")\n\nmem = Memori().llm.register(openai_chat=model)\nmem.attribution(entity_id=\"user_123\", process_id=\"agno_agent\")\n\nagent = Agent(\n model=model,\n instructions=[\"Be helpful and concise\"],\n markdown=True,\n)\n\nasync def main():\n response = await agent.arun(\"Hello!\", session_id=\"support-session\")\n print(response.content)\n\nasyncio.run(main())\n```\n\n```python {{ title: 'Streaming' }}\nfrom agno.agent import Agent\nfrom agno.models.openai import OpenAIChat\nfrom memori import Memori\n\nmodel = OpenAIChat(id=\"gpt-4o-mini\")\n\nmem = Memori().llm.register(openai_chat=model)\nmem.attribution(entity_id=\"user_123\", process_id=\"agno_agent\")\n\nagent = Agent(\n model=model,\n instructions=[\"Be helpful and concise\"],\n markdown=True,\n)\n\nstream = agent.run(\"Hello!\", session_id=\"support-session\", stream=True)\nfor chunk in stream:\n if hasattr(chunk, \"content\") and chunk.content:\n print(chunk.content, end=\"\")\n```\n\n\n\n## Different Providers\n\nAgno supports multiple model families. Use the matching registration keyword in Memori.\n\n| Package | Model Class | Registration Keyword |\n| ----------------------- | ------------ | -------------------- |\n| `agno.models.openai` | `OpenAIChat` | `openai_chat=model` |\n| `agno.models.anthropic` | `Claude` | `claude=model` |\n| `agno.models.google` | `Gemini` | `gemini=model` |\n| `agno.models.xai` | `xAI` | `xai=model` |\n\n\n\n```python {{ title: 'OpenAI' }}\nfrom agno.models.openai import OpenAIChat\n\nmodel = OpenAIChat(id=\"gpt-4o-mini\")\nmem = Memori().llm.register(openai_chat=model)\n```\n\n```python {{ title: 'Anthropic' }}\nfrom agno.models.anthropic import Claude\n\nmodel = Claude(id=\"claude-sonnet-4-20250514\")\nmem = Memori().llm.register(claude=model)\n```\n\n```python {{ title: 'Google Gemini' }}\nfrom agno.models.google import Gemini\n\nmodel = Gemini(id=\"gemini-2.0-flash-exp\")\nmem = Memori().llm.register(gemini=model)\n```\n\n```python {{ title: 'xAI' }}\nfrom agno.models.xai import xAI\n\nmodel = xAI(id=\"grok-3\")\nmem = Memori().llm.register(xai=model)\n```\n\n\n\n## Supported Modes\n\n| Mode | Method |\n| ------------ | ------------------------ |\n| **Sync** | `agent.run()` |\n| **Async** | `await agent.arun()` |\n| **Streamed** | `agent.run(stream=True)` |\n" }, { "path": "docs/memori-cloud/llm/anthropic.mdx", "content": "---\ntitle: Anthropic\ndescription: Using Memori with Anthropic Claude models on Memori Cloud.\n---\n\n# Anthropic\n\nMemori Cloud supports all Anthropic Claude models. The `max_tokens` parameter is required for all Anthropic API calls.\n\n## Quick Start\n\n\n\n```python {{ title: 'Python' }}\nfrom anthropic import Anthropic\nfrom memori import Memori\n\nclient = Anthropic()\n\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"claude_assistant\")\n\nresponse = client.messages.create(\n model=\"claude-sonnet-4-5-20250929\",\n max_tokens=1024,\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n)\nprint(response.content[0].text)\n```\n\n```typescript {{ title: 'TypeScript' }}\nimport Anthropic from '@anthropic-ai/sdk';\nimport { Memori } from '@memorilabs/memori';\n\nconst client = new Anthropic();\n\nconst mem = new Memori().llm.register(client);\nmem.attribution('user_123', 'claude_assistant');\n\nconst response = await client.messages.create({\n model: 'claude-sonnet-4-5-20250929',\n max_tokens: 1024,\n messages: [{ role: 'user', content: 'Hello!' }],\n});\nconsole.log(response.content[0].text);\n```\n\n\n\n## Supported Modes\n\n| Mode | Python | TypeScript |\n| ------------ | -------------------------------- | -------------------------------------- |\n| **Sync** | `client.messages.create()` | — |\n| **Async** | `await client.messages.create()` | `await client.messages.create()` |\n| **Streamed** | `client.messages.stream()` | `stream: true` parameter |\n\n## Additional Modes\n\n### Async (Python)\n\n```python\nimport asyncio\nfrom anthropic import AsyncAnthropic\nfrom memori import Memori\n\nclient = AsyncAnthropic()\n\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"claude_assistant\")\n\nasync def main():\n response = await client.messages.create(\n model=\"claude-sonnet-4-5-20250929\",\n max_tokens=1024,\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n )\n print(response.content[0].text)\n\nasyncio.run(main())\n```\n\n### Streaming\n\n\n\n```python {{ title: 'Python' }}\nfrom anthropic import Anthropic\nfrom memori import Memori\n\nclient = Anthropic()\n\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"claude_assistant\")\n\nwith client.messages.stream(\n model=\"claude-sonnet-4-5-20250929\",\n max_tokens=1024,\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n) as stream:\n for text in stream.text_stream:\n print(text, end=\"\")\n```\n\n```typescript {{ title: 'TypeScript' }}\nimport Anthropic from '@anthropic-ai/sdk';\nimport { Memori } from '@memorilabs/memori';\n\nconst client = new Anthropic();\n\nconst mem = new Memori().llm.register(client);\nmem.attribution('user_123', 'claude_assistant');\n\nconst stream = await client.messages.create({\n model: 'claude-sonnet-4-5-20250929',\n max_tokens: 1024,\n stream: true,\n messages: [{ role: 'user', content: 'Hello!' }],\n});\nfor await (const event of stream) {\n if (event.type === 'content_block_delta' && event.delta.type === 'text_delta') {\n process.stdout.write(event.delta.text);\n }\n}\n```\n\n\n\n## System Prompts\n\nAnthropic supports a top-level `system` parameter separate from the `messages` array. Memori captures both.\n\n```python\nfrom anthropic import Anthropic\nfrom memori import Memori\n\nclient = Anthropic()\n\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"claude_assistant\")\n\nresponse = client.messages.create(\n model=\"claude-sonnet-4-5-20250929\",\n max_tokens=1024,\n system=\"You are a helpful coding assistant.\",\n messages=[\n {\"role\": \"user\", \"content\": \"Explain Python decorators.\"}\n ]\n)\nprint(response.content[0].text)\n```\n\n### TypeScript\n\n```typescript\nimport Anthropic from '@anthropic-ai/sdk';\nimport { Memori } from '@memorilabs/memori';\n\nconst client = new Anthropic();\n\nconst mem = new Memori().llm.register(client);\nmem.attribution('user_123', 'claude_assistant');\n\nconst response = await client.messages.create({\n model: 'claude-sonnet-4-5-20250929',\n max_tokens: 1024,\n system: 'You are a helpful coding assistant.',\n messages: [\n { role: 'user', content: 'Explain TypeScript generics.' },\n ],\n});\nconsole.log(response.content[0].text);\n```\n" }, { "path": "docs/memori-cloud/llm/aws-bedrock.mdx", "content": "---\ntitle: AWS Bedrock\ndescription: Using Memori with AWS Bedrock models via the LangChain ChatBedrock adapter on Memori Cloud.\n---\n\n# AWS Bedrock\n\nMemori supports AWS Bedrock through `langchain-aws`. Register using the `chatbedrock` keyword to access Claude, Llama, Mistral, and other Bedrock models.\n\n\n TypeScript support for AWS Bedrock is coming soon. The TypeScript SDK currently supports [OpenAI](/docs/memori-cloud/llm/openai), [Anthropic](/docs/memori-cloud/llm/anthropic), and [Gemini](/docs/memori-cloud/llm/gemini).\n\n\n## Quick Start\n\n\n\n```python {{ title: 'Sync' }}\nfrom langchain_aws import ChatBedrock\nfrom memori import Memori\n\nclient = ChatBedrock(\n model_id=\"anthropic.claude-sonnet-4-5-20250929\",\n region_name=\"us-east-1\"\n)\n\nmem = Memori().llm.register(chatbedrock=client)\nmem.attribution(entity_id=\"user_123\", process_id=\"bedrock_agent\")\n\nresponse = client.invoke(\"Hello!\")\nprint(response.content)\n```\n\n```python {{ title: 'Async' }}\nimport asyncio\nfrom langchain_aws import ChatBedrock\nfrom memori import Memori\n\nclient = ChatBedrock(\n model_id=\"anthropic.claude-sonnet-4-5-20250929\",\n region_name=\"us-east-1\"\n)\n\nmem = Memori().llm.register(chatbedrock=client)\nmem.attribution(entity_id=\"user_123\", process_id=\"bedrock_agent\")\n\nasync def main():\n response = await client.ainvoke(\"Hello!\")\n print(response.content)\n\nasyncio.run(main())\n```\n\n```python {{ title: 'Streaming' }}\nfrom langchain_aws import ChatBedrock\nfrom memori import Memori\n\nclient = ChatBedrock(\n model_id=\"anthropic.claude-sonnet-4-5-20250929\",\n region_name=\"us-east-1\"\n)\n\nmem = Memori().llm.register(chatbedrock=client)\nmem.attribution(entity_id=\"user_123\", process_id=\"bedrock_agent\")\n\nfor chunk in client.stream(\"Hello!\"):\n print(chunk.content, end=\"\")\n```\n\n\n\n## Supported Modes\n\n| Mode | Method |\n| ------------ | ------------------------ |\n| **Sync** | `client.invoke()` |\n| **Async** | `await client.ainvoke()` |\n| **Streamed** | `client.stream()` |\n\n## Available Models\n\n| Model | Model ID |\n| --------------------- | ------------------------------------------- |\n| **Claude 3.5 Sonnet** | `anthropic.claude-3-5-sonnet-20241022-v2:0` |\n| **Claude 3 Haiku** | `anthropic.claude-3-haiku-20240307-v1:0` |\n| **Llama 3.1 70B** | `meta.llama3-1-70b-instruct-v1:0` |\n| **Mistral Large** | `mistral.mistral-large-2407-v1:0` |\n" }, { "path": "docs/memori-cloud/llm/deepseek.mdx", "content": "---\ntitle: DeepSeek\ndescription: Using Memori with DeepSeek models via the OpenAI-compatible API on Memori Cloud.\n---\n\n# DeepSeek\n\nDeepSeek provides an OpenAI-compatible API. Use the `openai` Python package with `base_url=\"https://api.deepseek.com\"` — no special adapter needed.\n\n\n TypeScript support for DeepSeek is coming soon. The TypeScript SDK currently supports [OpenAI](/docs/memori-cloud/llm/openai), [Anthropic](/docs/memori-cloud/llm/anthropic), and [Gemini](/docs/memori-cloud/llm/gemini).\n\n\n## Quick Start\n\n\n\n```python {{ title: 'Sync' }}\nimport os\nfrom memori import Memori\nfrom openai import OpenAI\n\nclient = OpenAI(\n base_url=\"https://api.deepseek.com\",\n api_key=os.getenv(\"DEEPSEEK_API_KEY\"),\n)\n\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"deepseek_assistant\")\n\nresponse = client.chat.completions.create(\n model=\"deepseek-chat\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n)\nprint(response.choices[0].message.content)\n```\n\n```python {{ title: 'Async' }}\nimport os, asyncio\nfrom memori import Memori\nfrom openai import AsyncOpenAI\n\nclient = AsyncOpenAI(\n base_url=\"https://api.deepseek.com\",\n api_key=os.getenv(\"DEEPSEEK_API_KEY\"),\n)\n\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"deepseek_assistant\")\n\nasync def main():\n response = await client.chat.completions.create(\n model=\"deepseek-chat\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n )\n print(response.choices[0].message.content)\n\nasyncio.run(main())\n```\n\n```python {{ title: 'Streaming' }}\nimport os\nfrom memori import Memori\nfrom openai import OpenAI\n\nclient = OpenAI(\n base_url=\"https://api.deepseek.com\",\n api_key=os.getenv(\"DEEPSEEK_API_KEY\"),\n)\n\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"deepseek_assistant\")\n\nstream = client.chat.completions.create(\n model=\"deepseek-chat\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}],\n stream=True\n)\nfor chunk in stream:\n if chunk.choices[0].delta.content:\n print(chunk.choices[0].delta.content, end=\"\")\n```\n\n\n\n## Supported Modes\n\n| Mode | Method |\n| ------------ | ---------------------------------------- |\n| **Sync** | `client.chat.completions.create()` |\n| **Async** | `await client.chat.completions.create()` |\n| **Streamed** | `stream=True` parameter |\n" }, { "path": "docs/memori-cloud/llm/gemini.mdx", "content": "---\ntitle: Google Gemini\ndescription: Using Memori with Google Gemini models on Memori Cloud.\n---\n\n# Google Gemini\n\nMemori integrates with Google Gemini via the `google-genai` SDK. Register the `GenerativeModel` instance and all `generate_content()` calls are automatically captured.\n\n## Quick Start\n\n\n\n```python {{ title: 'Python' }}\nimport os\nfrom memori import Memori\nimport google.generativeai as genai\n\ngenai.configure(api_key=os.getenv(\"GOOGLE_API_KEY\"))\nclient = genai.GenerativeModel(\"gemini-2.0-flash-exp\") # Use any Gemini model\n\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"gemini_assistant\")\n\nresponse = client.generate_content(\"Hello!\")\nprint(response.text)\n```\n\n```typescript {{ title: 'TypeScript' }}\nimport { GoogleGenAI } from '@google/genai';\nimport { Memori } from '@memorilabs/memori';\n\nconst client = new GoogleGenAI({ apiKey: process.env.GOOGLE_API_KEY });\n\nconst mem = new Memori().llm.register(client);\nmem.attribution('user_123', 'gemini_assistant');\n\nconst response = await client.models.generateContent({\n model: 'gemini-2.0-flash',\n contents: 'Hello!',\n});\nconsole.log(response.text);\n```\n\n\n\n## Supported Modes\n\n| Mode | Python | TypeScript |\n| ------------ | --------------------------------------- | ----------------------------------------------- |\n| **Sync** | `client.generate_content()` | — |\n| **Async** | `await client.generate_content_async()` | `await client.models.generateContent()` |\n| **Streamed** | `stream=True` parameter | `client.models.generateContentStream()` |\n\n## Additional Modes\n\n### Async (Python)\n\n```python\nimport os, asyncio\nfrom memori import Memori\nimport google.generativeai as genai\n\ngenai.configure(api_key=os.getenv(\"GOOGLE_API_KEY\"))\nclient = genai.GenerativeModel(\"gemini-2.0-flash-exp\") # Use any Gemini model\n\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"gemini_assistant\")\n\nasync def main():\n response = await client.generate_content_async(\"Hello!\")\n print(response.text)\n\nasyncio.run(main())\n```\n\n### Streaming\n\n\n\n```python {{ title: 'Python' }}\nimport os\nfrom memori import Memori\nimport google.generativeai as genai\n\ngenai.configure(api_key=os.getenv(\"GOOGLE_API_KEY\"))\nclient = genai.GenerativeModel(\"gemini-2.0-flash-exp\") # Use any Gemini model\n\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"gemini_assistant\")\n\nresponse = client.generate_content(\"Hello!\", stream=True)\nfor chunk in response:\n print(chunk.text, end=\"\")\n```\n\n```typescript {{ title: 'TypeScript' }}\nimport { GoogleGenAI } from '@google/genai';\nimport { Memori } from '@memorilabs/memori';\n\nconst client = new GoogleGenAI({ apiKey: process.env.GOOGLE_API_KEY });\n\nconst mem = new Memori().llm.register(client);\nmem.attribution('user_123', 'gemini_assistant');\n\nconst stream = await client.models.generateContentStream({\n model: 'gemini-2.0-flash',\n contents: 'Hello!',\n});\nfor await (const chunk of stream) {\n process.stdout.write(chunk.text ?? '');\n}\n```\n\n\n\n## Multi-Turn Conversations\n\nUse `start_chat()` for multi-turn interactions. Memori tracks the full conversation automatically.\n\n```python\nchat = client.start_chat()\nresponse = chat.send_message(\"My name is Alice.\")\nprint(response.text)\n\nresponse = chat.send_message(\"What's my name?\")\nprint(response.text)\n```\n\n### TypeScript\n\nMulti-turn conversations in TypeScript use the standard messages array pattern:\n\n```typescript\nimport { GoogleGenAI } from '@google/genai';\nimport { Memori } from '@memorilabs/memori';\n\nconst client = new GoogleGenAI({ apiKey: process.env.GOOGLE_API_KEY });\n\nconst mem = new Memori().llm.register(client);\nmem.attribution('user_123', 'gemini_assistant');\n\nconst response = await client.models.generateContent({\n model: 'gemini-2.0-flash',\n contents: [\n { role: 'user', parts: [{ text: 'My name is Alice.' }] },\n ],\n});\nconsole.log(response.text);\n\nconst response2 = await client.models.generateContent({\n model: 'gemini-2.0-flash',\n contents: [\n { role: 'user', parts: [{ text: 'My name is Alice.' }] },\n { role: 'model', parts: [{ text: response.text ?? '' }] },\n { role: 'user', parts: [{ text: \"What's my name?\" }] },\n ],\n});\nconsole.log(response2.text);\n```\n" }, { "path": "docs/memori-cloud/llm/langchain.mdx", "content": "---\ntitle: LangChain\ndescription: Using Memori with LangChain chat models on Memori Cloud.\n---\n\n# LangChain\n\nMemori Cloud supports any LangChain chat model. Each class has its own registration keyword: `ChatOpenAI` for OpenAI, `ChatAnthropic` for Anthropic, `ChatBedrock` for BedRock, `ChatGoogleGenerativeAI` for Google Gen AI models.\n\n\n TypeScript support for LangChain is coming soon. The TypeScript SDK currently supports [OpenAI](/docs/memori-cloud/llm/openai), [Anthropic](/docs/memori-cloud/llm/anthropic), and [Gemini](/docs/memori-cloud/llm/gemini).\n\n\n## Quick Start\n\n\n\n```python {{ title: 'Sync' }}\nfrom langchain_openai import ChatOpenAI\nfrom memori import Memori\n\nclient = ChatOpenAI(model=\"gpt-4o-mini\")\n\nmem = Memori().llm.register(chatopenai=client)\nmem.attribution(entity_id=\"user_123\", process_id=\"langchain_agent\")\n\nresponse = client.invoke(\"Hello!\")\nprint(response.content)\n```\n\n```python {{ title: 'Async' }}\nimport asyncio\nfrom langchain_openai import ChatOpenAI\nfrom memori import Memori\n\nclient = ChatOpenAI(model=\"gpt-4o-mini\")\n\nmem = Memori().llm.register(chatopenai=client)\nmem.attribution(entity_id=\"user_123\", process_id=\"langchain_agent\")\n\nasync def main():\n response = await client.ainvoke(\"Hello!\")\n print(response.content)\n\nasyncio.run(main())\n```\n\n```python {{ title: 'Streaming' }}\nfrom langchain_openai import ChatOpenAI\nfrom memori import Memori\n\nclient = ChatOpenAI(model=\"gpt-4o-mini\")\n\nmem = Memori().llm.register(chatopenai=client)\nmem.attribution(entity_id=\"user_123\", process_id=\"langchain_agent\")\n\nfor chunk in client.stream(\"Hello!\"):\n print(chunk.content, end=\"\")\n```\n\n\n\n## Different Providers\n\n| Package | Chat Model | Registration Keyword |\n| ------------------------ | ------------------------ | ------------------------ |\n| `langchain-openai` | `ChatOpenAI` | `chatopenai=client` |\n| `langchain-anthropic` | `ChatAnthropic` | `chatanthropic=client` |\n| `langchain-google-genai` | `ChatGoogleGenerativeAI` | `chatgooglegenai=client` |\n| `langchain-aws` | `ChatBedrock` | `chatbedrock=client` |\n\n\n\n```python {{ title: 'Anthropic' }}\nfrom langchain_anthropic import ChatAnthropic\nfrom memori import Memori\n\nclient = ChatAnthropic(model=\"claude-sonnet-4-5-20250929\")\nmem = Memori().llm.register(chatopenai=client)\n```\n\n```python {{ title: 'Google Gemini' }}\nfrom langchain_google_genai import ChatGoogleGenerativeAI\nfrom memori import Memori\n\nclient = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash-exp\")\nmem = Memori().llm.register(chatgooglegenai=client)\n```\n\n```python {{ title: 'AWS Bedrock' }}\nfrom langchain_aws import ChatBedrock\nfrom memori import Memori\n\nclient = ChatBedrock(model_id=\"anthropic.claude-sonnet-4-5-20250929\", region_name=\"us-east-1\")\nmem = Memori().llm.register(chatbedrock=client)\n```\n\n\n\n## Supported Modes\n\n| Mode | Method |\n| ------------ | ------------------------ |\n| **Sync** | `client.invoke()` |\n| **Async** | `await client.ainvoke()` |\n| **Streamed** | `client.stream()` |\n" }, { "path": "docs/memori-cloud/llm/nebius.mdx", "content": "---\ntitle: Nebius AI Studio\ndescription: Using Memori with Nebius AI Studio models via the OpenAI-compatible API on Memori Cloud.\n---\n\n# Nebius AI Studio\n\nNebius AI Studio provides an OpenAI-compatible API. Use the `openai` Python package with `base_url=\"https://api.studio.nebius.com/v1/\"` — no special adapter needed.\n\n\n TypeScript support for Nebius AI Studio is coming soon. The TypeScript SDK currently supports [OpenAI](/docs/memori-cloud/llm/openai), [Anthropic](/docs/memori-cloud/llm/anthropic), and [Gemini](/docs/memori-cloud/llm/gemini).\n\n\n## Quick Start\n\n\n\n```python {{ title: 'Sync' }}\nimport os\nfrom memori import Memori\nfrom openai import OpenAI\n\nclient = OpenAI(\n base_url=\"https://api.studio.nebius.com/v1/\",\n api_key=os.getenv(\"NEBIUS_API_KEY\"),\n)\n\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"nebius_assistant\")\n\nresponse = client.chat.completions.create(\n model=\"meta-llama/Llama-3.3-70B-Instruct\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n)\nprint(response.choices[0].message.content)\n```\n\n```python {{ title: 'Async' }}\nimport os, asyncio\nfrom memori import Memori\nfrom openai import AsyncOpenAI\n\nclient = AsyncOpenAI(\n base_url=\"https://api.studio.nebius.com/v1/\",\n api_key=os.getenv(\"NEBIUS_API_KEY\"),\n)\n\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"nebius_assistant\")\n\nasync def main():\n response = await client.chat.completions.create(\n model=\"meta-llama/Llama-3.3-70B-Instruct\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n )\n print(response.choices[0].message.content)\n\nasyncio.run(main())\n```\n\n```python {{ title: 'Streaming' }}\nimport os\nfrom memori import Memori\nfrom openai import OpenAI\n\nclient = OpenAI(\n base_url=\"https://api.studio.nebius.com/v1/\",\n api_key=os.getenv(\"NEBIUS_API_KEY\"),\n)\n\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"nebius_assistant\")\n\nstream = client.chat.completions.create(\n model=\"meta-llama/Llama-3.3-70B-Instruct\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}],\n stream=True\n)\nfor chunk in stream:\n if chunk.choices[0].delta.content:\n print(chunk.choices[0].delta.content, end=\"\")\n```\n\n\n\n## Supported Modes\n\n| Mode | Method |\n| ------------ | ---------------------------------------- |\n| **Sync** | `client.chat.completions.create()` |\n| **Async** | `await client.chat.completions.create()` |\n| **Streamed** | `stream=True` parameter |\n" }, { "path": "docs/memori-cloud/llm/openai.mdx", "content": "---\ntitle: OpenAI\ndescription: Using Memori with OpenAI models including GPT-4o, GPT-4.1, and the Responses API on Memori Cloud.\n---\n\n# OpenAI\n\nMemori supports all OpenAI Chat Completions and Responses APIs. Both sync and async clients are fully supported.\n\n## Quick Start\n\n\n\n```python {{ title: 'Python' }}\nfrom memori import Memori\nfrom openai import OpenAI\n\nclient = OpenAI()\n\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"my_agent\")\n\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n)\nprint(response.choices[0].message.content)\n```\n\n```typescript {{ title: 'TypeScript' }}\nimport OpenAI from 'openai';\nimport { Memori } from '@memorilabs/memori';\n\nconst client = new OpenAI();\n\nconst mem = new Memori().llm.register(client);\nmem.attribution('user_123', 'my_agent');\n\nconst response = await client.chat.completions.create({\n model: 'gpt-4o-mini',\n messages: [{ role: 'user', content: 'Hello!' }],\n});\nconsole.log(response.choices[0].message.content);\n```\n\n\n\n## Supported Modes\n\n| Mode | Python | TypeScript |\n| ----------------- | ---------------------------------------- | ---------------------------------------- |\n| **Sync** | `client.chat.completions.create()` | — |\n| **Async** | `await client.chat.completions.create()` | `await client.chat.completions.create()` |\n| **Streamed** | `stream=True` parameter | `stream: true` parameter |\n| **Responses API** | `client.responses.create()` | — |\n\n## Additional Modes\n\n### Async (Python)\n\n```python\nimport asyncio\nfrom memori import Memori\nfrom openai import AsyncOpenAI\n\nclient = AsyncOpenAI()\n\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"my_agent\")\n\nasync def main():\n response = await client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n )\n print(response.choices[0].message.content)\n\nasyncio.run(main())\n```\n\n### Streaming\n\n\n\n```python {{ title: 'Python' }}\nfrom memori import Memori\nfrom openai import OpenAI\n\nclient = OpenAI()\n\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"my_agent\")\n\nstream = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}],\n stream=True\n)\nfor chunk in stream:\n if chunk.choices[0].delta.content:\n print(chunk.choices[0].delta.content, end=\"\")\n```\n\n```typescript {{ title: 'TypeScript' }}\nimport OpenAI from 'openai';\nimport { Memori } from '@memorilabs/memori';\n\nconst client = new OpenAI();\n\nconst mem = new Memori().llm.register(client);\nmem.attribution('user_123', 'my_agent');\n\nconst stream = await client.chat.completions.create({\n model: 'gpt-4o-mini',\n messages: [{ role: 'user', content: 'Hello!' }],\n stream: true,\n});\nfor await (const chunk of stream) {\n if (chunk.choices[0]?.delta?.content) {\n process.stdout.write(chunk.choices[0].delta.content);\n }\n}\n```\n\n\n\n### Responses API (Python)\n\n```python\nfrom memori import Memori\nfrom openai import OpenAI\n\nclient = OpenAI()\n\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"my_agent\")\n\nresponse = client.responses.create(\n model=\"gpt-4o-mini\",\n input=\"Hello!\",\n instructions=\"You are a helpful assistant.\"\n)\nprint(response.output_text)\n```\n\n## Multi-Turn Conversations\n\nMemori automatically captures each interaction and links them within the same session.\n\n\n```python\nfrom memori import Memori\nfrom openai import OpenAI\n\nclient = OpenAI()\n\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"my_agent\")\n\nmessages = [\n {\"role\": \"user\", \"content\": \"My name is Alice.\"}\n]\n\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=messages\n)\nmessages.append({\n \"role\": \"assistant\",\n \"content\": response.choices[0].message.content\n})\n\nmessages.append({\n \"role\": \"user\",\n \"content\": \"What's my name?\"\n})\nresponse = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=messages\n)\nprint(response.choices[0].message.content)\n```\n\n```typescript\nimport OpenAI from 'openai';\nimport { Memori } from '@memorilabs/memori';\n\nconst client = new OpenAI();\n\nconst mem = new Memori().llm.register(client);\nmem.attribution('user_123', 'my_agent');\n\nconst messages: OpenAI.ChatCompletionMessageParam[] = [\n { role: 'user', content: 'My name is Alice.' },\n];\n\nconst response = await client.chat.completions.create({\n model: 'gpt-4o-mini',\n messages,\n});\nmessages.push({\n role: 'assistant',\n content: response.choices[0].message.content!,\n});\n\nmessages.push({\n role: 'user',\n content: \"What's my name?\",\n});\nconst response2 = await client.chat.completions.create({\n model: 'gpt-4o-mini',\n messages,\n});\nconsole.log(response2.choices[0].message.content);\n```\n" }, { "path": "docs/memori-cloud/llm/overview.mdx", "content": "---\ntitle: Integration Overview\ndescription: Memori is LLM-agnostic. Register any supported client and Memori handles memory capture, augmentation, and recall automatically.\n---\n\n# Integration Overview\n\nMemori Cloud works with all major LLM providers and frameworks. Register any supported client and Memori handles memory capture, augmentation, and recall automatically — with your Memori API key and provider credentials, no database setup required.\n\n## Supported Providers\n\n| Provider | Integration | Python Install | TypeScript Install |\n| ----------------------------------------------------- | ------------------ | ------------------------------------- | --------------------------------------------------- |\n| **[OpenAI](/docs/memori-cloud/llm/openai)** | Direct SDK wrapper | `pip install memori openai` | `npm install @memorilabs/memori openai` |\n| **[Anthropic](/docs/memori-cloud/llm/anthropic)** | Direct SDK wrapper | `pip install memori anthropic` | `npm install @memorilabs/memori @anthropic-ai/sdk` |\n| **[Google Gemini](/docs/memori-cloud/llm/gemini)** | Direct SDK wrapper | `pip install memori google-genai` | `npm install @memorilabs/memori @google/genai` |\n| **[xAI Grok](/docs/memori-cloud/llm/xai-grok)** | OpenAI-compatible | `pip install memori openai` | Coming soon |\n| **[Nebius AI Studio](/docs/memori-cloud/llm/nebius)** | OpenAI-compatible | `pip install memori openai` | Coming soon |\n| **[DeepSeek](/docs/memori-cloud/llm/deepseek)** | OpenAI-compatible | `pip install memori openai` | Coming soon |\n| **[AWS Bedrock](/docs/memori-cloud/llm/aws-bedrock)** | LangChain adapter | `pip install memori langchain-aws` | Coming soon |\n| **[LangChain](/docs/memori-cloud/llm/langchain)** | Framework support | `pip install memori langchain-openai` | Coming soon |\n| **[Agno](/docs/memori-cloud/llm/agno)** | Framework support | `pip install memori agno` | Coming soon |\n| **[Pydantic AI](/docs/memori-cloud/llm/pydantic-ai)** | Framework support | `pip install memori pydantic-ai` | Coming soon |\n\nAll providers support sync, async, streamed, and unstreamed modes.\n\n## Pydantic AI\n\nRegister the `Agent` instance directly — Memori wraps `run_sync` and `run` automatically.\n\n```python\nfrom memori import Memori\nfrom pydantic_ai import Agent\n\nagent = Agent(\"openai:gpt-4o-mini\")\n\nmem = Memori().llm.register(agent)\nmem.attribution(entity_id=\"user_123\", process_id=\"pydantic_agent\")\n\nresult = agent.run_sync(\"Hello!\")\nprint(result.output)\n```\n\n## OpenAI-Compatible Providers\n\nAny provider with an OpenAI-compatible API works by setting a custom `base_url`. Dedicated guides: [xAI Grok](/docs/memori-cloud/llm/xai-grok), [Nebius AI Studio](/docs/memori-cloud/llm/nebius), [DeepSeek](/docs/memori-cloud/llm/deepseek). Same pattern works for Azure OpenAI, NVIDIA NIM, and others.\n\n```python\nimport os\nfrom memori import Memori\nfrom openai import OpenAI\n\nclient = OpenAI(\n base_url=\"https://api.studio.nebius.com/v1/\",\n api_key=os.getenv(\"NEBIUS_API_KEY\"),\n)\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"my_agent\")\n\nresponse = client.chat.completions.create(\n model=\"meta-llama/Llama-3.3-70B-Instruct\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n)\nprint(response.choices[0].message.content)\n```\n\n## OpenAI Responses API\n\n```python\nfrom memori import Memori\nfrom openai import OpenAI\n\nclient = OpenAI()\n\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"my_agent\")\n\nresponse = client.responses.create(\n model=\"gpt-4o-mini\",\n input=\"Hello!\",\n instructions=\"You are a helpful assistant.\"\n)\nprint(response.output_text)\n```\n" }, { "path": "docs/memori-cloud/llm/pydantic-ai.mdx", "content": "---\ntitle: Pydantic AI\ndescription: Using Memori with Pydantic AI agents on Memori Cloud.\n---\n\n# Pydantic AI\n\nMemori integrates at the agent level — register the `Agent` instance and Memori wraps `run_sync` and `run` automatically. Works with any Pydantic AI-supported model.\n\n\n TypeScript support for Pydantic AI is coming soon. The TypeScript SDK currently supports [OpenAI](/docs/memori-cloud/llm/openai), [Anthropic](/docs/memori-cloud/llm/anthropic), and [Gemini](/docs/memori-cloud/llm/gemini).\n\n\n## Quick Start\n\n```python\nfrom memori import Memori\nfrom pydantic_ai import Agent\n\nagent = Agent(\"openai:gpt-4o-mini\")\n\nmem = Memori().llm.register(agent)\nmem.attribution(entity_id=\"user_123\", process_id=\"pydantic_agent\")\n\nresult = agent.run_sync(\"Hello!\")\nprint(result.output)\n```\n\n## Async Usage\n\n```python\nimport asyncio\nfrom memori import Memori\nfrom pydantic_ai import Agent\n\nagent = Agent(\"openai:gpt-4o-mini\")\n\nmem = Memori().llm.register(agent)\nmem.attribution(entity_id=\"user_123\", process_id=\"pydantic_agent\")\n\nasync def main():\n result = await agent.run(\"Hello!\")\n print(result.output)\n\nasyncio.run(main())\n```\n\n## Different Providers\n\nPydantic AI supports multiple providers via model strings. Registration is identical regardless of provider.\n\n\n\n```python {{ title: 'OpenAI' }}\nagent = Agent(\"openai:gpt-4o-mini\")\nmem = Memori().llm.register(agent)\n```\n\n```python {{ title: 'Anthropic' }}\nagent = Agent(\"anthropic:claude-sonnet-4-5-20250929\")\nmem = Memori().llm.register(agent)\n```\n\n```python {{ title: 'Google Gemini' }}\nagent = Agent(\"google-gla:gemini-2.0-flash-exp\")\nmem = Memori().llm.register(agent)\n```\n\n\n\n## Supported Modes\n\n| Mode | Method |\n| ------------ | --------------------------- |\n| **Sync** | `agent.run_sync()` |\n| **Async** | `await agent.run()` |\n| **Streamed** | Via agent streaming support |\n" }, { "path": "docs/memori-cloud/llm/xai-grok.mdx", "content": "---\ntitle: xAI Grok\ndescription: Using Memori with xAI Grok models via the OpenAI-compatible API on Memori Cloud.\n---\n\n# xAI Grok\n\nxAI provides an OpenAI-compatible API. Use the `openai` Python package with `base_url=\"https://api.x.ai/v1\"` — no special adapter needed.\n\n\n TypeScript support for xAI Grok is coming soon. The TypeScript SDK currently supports [OpenAI](/docs/memori-cloud/llm/openai), [Anthropic](/docs/memori-cloud/llm/anthropic), and [Gemini](/docs/memori-cloud/llm/gemini).\n\n\n## Quick Start\n\n\n\n```python {{ title: 'Sync' }}\nimport os\nfrom memori import Memori\nfrom openai import OpenAI\n\nclient = OpenAI(\n base_url=\"https://api.x.ai/v1\",\n api_key=os.getenv(\"XAI_API_KEY\")\n)\n\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"grok_assistant\")\n\nresponse = client.chat.completions.create(\n model=\"grok-2-latest\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n)\nprint(response.choices[0].message.content)\n```\n\n```python {{ title: 'Async' }}\nimport os, asyncio\nfrom memori import Memori\nfrom openai import AsyncOpenAI\n\nclient = AsyncOpenAI(\n base_url=\"https://api.x.ai/v1\",\n api_key=os.getenv(\"XAI_API_KEY\")\n)\n\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"grok_assistant\")\n\nasync def main():\n response = await client.chat.completions.create(\n model=\"grok-2-latest\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n )\n print(response.choices[0].message.content)\n\nasyncio.run(main())\n```\n\n```python {{ title: 'Streaming' }}\nimport os\nfrom memori import Memori\nfrom openai import OpenAI\n\nclient = OpenAI(\n base_url=\"https://api.x.ai/v1\",\n api_key=os.getenv(\"XAI_API_KEY\")\n)\n\nmem = Memori().llm.register(client)\nmem.attribution(entity_id=\"user_123\", process_id=\"grok_assistant\")\n\nstream = client.chat.completions.create(\n model=\"grok-2-latest\",\n messages=[{\"role\": \"user\", \"content\": \"Hello!\"}],\n stream=True\n)\nfor chunk in stream:\n if chunk.choices[0].delta.content:\n print(chunk.choices[0].delta.content, end=\"\")\n```\n\n\n\n## Supported Modes\n\n| Mode | Method |\n| ------------ | ---------------------------------------- |\n| **Sync** | `client.chat.completions.create()` |\n| **Async** | `await client.chat.completions.create()` |\n| **Streamed** | `stream=True` parameter |\n" }, { "path": "docs/memori-cloud/mcp/agent-skills.mdx", "content": "---\ntitle: Agent Skills\ndescription: Add a SKILL.md file to teach your IDE agent when and how to use Memori MCP tools for reliable recall and memory storage.\n---\n\n# Agent Skills\n\nIn addition to the MCP server config, we strongly recommend adding an **Agent Skills file** (`SKILL.md`) that teaches your IDE agent _when_ and _how_ to use the Memori MCP tools.\n\n## Why Add a Skills File\n\n- **Reliability** — the agent consistently calls `recall` at the start of a turn and `advanced_augmentation` after responding\n- **Lower prompt friction** — you don't need to re-explain the memory workflow every session\n- **Focused context** — most clients only load the full skill instructions when the skill is triggered\n\n## The SKILL.md File\n\nCreate a `SKILL.md` file with the content below. This teaches the agent the full decision flow for when to recall, when to store memories, and what to skip.\n\n````markdown\n---\nname: memori-mcp-usage\ndescription: Decides when to call Memori MCP tools for recall and advanced augmentation. Use when the user mentions remembering, preferences, \"last time\", personalization, prior context, recurring patterns, or when consistency across turns matters.\n---\n\n# Memori MCP usage\n\n### Tool contract\n- `entity_id` and `process_id` are provided via MCP headers (do not pass as tool args unless the tool schema requires it).\n- `session_id` is generated by the server.\n\n## Decision flow per turn (follow in this order)\n\n1. Is this a trivial, administrative, or closing message?\n Examples: \"thanks\", \"ok\", \"got it\", \"goodbye\", \"ignore everything before\"\n → Skip both recall **and** augment. Answer directly.\n\n2. Does the user explicitly say **not** to remember / store / save / log / keep anything from this turn?\n → Recall if the question needs prior context (step 3), but **never** augment this turn.\n\n3. Should we recall prior memory? Check conditions below.\n → Yes → Call `recall` (usually once per turn)\n → Use results to ground / personalize the answer\n → No useful memories? → Proceed normally\n\n4. After drafting the final assistant response → Should we augment? Check conditions below.\n → Yes → Call `advanced_augmentation` with the full turn `[user message, assistant response]`\n → No → Skip\n\nSkip memory calls entirely for very short / purely reactive turns unless personalization is clearly relevant.\n\n## When to recall (`recall`)\n\nCall `recall` when **any** of these are true:\n\n- User references prior context: \"as I said\", \"last time\", \"you already know\", \"same as before\", \"remember when\", \"like we did\"\n- Preferences or style matter: tone, verbosity, length, formatting, units, structure, templates, language, emoji usage, etc.\n- Personal / project constraints apply: timezone, location, environment, setup, repo conventions, tooling stack, recurring workflows\n- Consistency across answers is important: decision patterns, naming conventions, approval thresholds, recurring questions\n- User asks to be reminded of something they previously told you\n\nQuery strategies (ranked best to worst):\n\n1. **Best**: user's exact latest message (preserves nuance, wording, typos, personal style)\n2. **Good**: very short rephrased intent when message is long/noisy\n3. **Avoid**: overly generic queries (\"preferences\", \"remember\", \"info about me\") — poor precision\n **Avoid**: dumping full conversation history as query\n\nPrefer **one** recall per turn in almost all cases.\n\n## When to write (`advanced_augmentation`)\n\nCall `advanced_augmentation` **after** drafting your response **only** when the turn reveals a **durable, stable fact or preference** the user would want applied in future sessions. Ask yourself: *\"Would this information still be useful to recall weeks from now, in a completely different conversation?\"* If the answer is no, skip augment.\n\nAugment-worthy signals:\n\n- Explicit preferences (\"always do X\", \"from now on\", \"default to...\", \"prefer...\")\n- Identity / profile facts user wants persisted (timezone, role, typical environment, location hints)\n- Long-lived project or workflow context (naming conventions, tooling choices, architecture decisions, coding principles)\n- Relationships or team structure (\"Alice owns the auth service\", \"report to Bob\")\n\n### Do NOT call advanced_augmentation\n\n- Secrets, API keys, tokens, passwords (even partially redacted)\n- Large logs, stack traces, error dumps\n- Clearly ephemeral info: one-time codes, temp links, live prices\n- Debugging marked temporary (\"just this once\", \"for this error only\")\n- Role-play, hypotheticals, fiction\n- Purely reactive chit-chat with no lasting preference\n- Session activity / progress updates: test results, lint output, command exit codes\n- Task management actions: to-do completions, progress checkpoints, commit messages\n- Routine code changes: refactoring steps, file renames, import cleanups\n- Tool output narration: summarizing what a tool returned\n- Conversation-scoped decisions: one-off choices that don't represent a lasting rule\n\n**Rule of thumb**: if the information describes *what happened during this session* rather than *a fact or preference that transcends sessions*, do not augment.\n\n### Examples\n\n**Should augment:**\n- \"From now on keep answers short, include TL;DR, max 250 words\"\n- \"My location is Paris, France; I prefer Central time and 12-hour format\"\n- \"This project uses ruff + mypy + pytest; strongly prefer KISS/DRY/YAGNI\"\n\n**Should NOT augment:**\n- \"All 57 tests passed after the refactor\"\n- \"Renamed `_utils.py` to `_helpers.py`\"\n- \"The linter found 3 errors, now fixed\"\n\n**Should NOT use Memori tools at all:**\n- \"Thanks man!\"\n- \"Temp login code is 493021\"\n- \"What's the weather like in KC right now?\"\n````\n\n## Installing Skills by Client\n\n### Cursor\n\nCursor supports skill folders containing a `SKILL.md`:\n\n- **Project (recommended for teams):** `.cursor/skills/memori-mcp/SKILL.md`\n- **Personal (all projects):** `~/.cursor/skills/memori-mcp/SKILL.md`\n\n```bash\nmkdir -p .cursor/skills/memori-mcp\n# Add SKILL.md content to .cursor/skills/memori-mcp/SKILL.md\n```\n\nRestart Cursor so it re-discovers skills.\n\n### Claude Code\n\nClaude Code skills live in either:\n\n- **Personal:** `~/.claude/skills/memori-mcp/SKILL.md`\n- **Project:** `.claude/skills/memori-mcp/SKILL.md`\n\n```bash\nmkdir -p .claude/skills/memori-mcp\n# Add SKILL.md content to .claude/skills/memori-mcp/SKILL.md\n```\n\nRestart Claude Code or reload the session.\n\n### OpenAI Codex\n\nCodex scans repo-level skills under `.agents/skills`:\n\n```bash\nmkdir -p .agents/skills/memori-mcp\n# Add SKILL.md content to .agents/skills/memori-mcp/SKILL.md\n```\n\nRestart Codex if it doesn't pick up changes automatically.\n\n### Warp\n\nWarp supports custom agent rules that serve the same purpose as a skills file. Add the SKILL.md content to your Warp rules configuration:\n\n- **Project:** `.warp/rules/memori-mcp.md`\n- **Global:** `~/.warp/rules/memori-mcp.md`\n\n```bash\nmkdir -p .warp/rules\n# Add SKILL.md content to .warp/rules/memori-mcp.md\n```\n\nRestart Warp so the rules take effect.\n\n### Antigravity\n\nAntigravity skills can be stored at:\n\n- **Workspace:** `/.agent/skills/memori-mcp/SKILL.md`\n- **Global:** `~/.gemini/antigravity/skills/memori-mcp/SKILL.md`\n\n```bash\nmkdir -p .agent/skills/memori-mcp\n# Add SKILL.md content to .agent/skills/memori-mcp/SKILL.md\n```\n\n### LangChain\n\nLangChain agents use Memori MCP to persist user preferences and project context across invocations. The integration logic lives in your application layer using the `langchain-mcp-adapters` package.\n\nTo implement this, instantiate `MultiServerMCPClient` per request with the end-user's ID set as `X-Memori-Entity-Id`, as shown on the Client Setup page.\n\n### Slack\n\nA Slack bot backed by an LLM uses Memori MCP to persist user preferences across all channels. As Slack is a cloud based service, the integration logic lives in your bot's backend.\n\nTo implement this, set the headers dynamically in your server's outgoing MCP tool calls using the Slack User ID from the incoming event payload as shown on the Client Setup page.\n\n### Notion\n\nCustom Notion integrations use Memori MCP to maintain a consistent memory of a user's writing style and project context across different pages and databases.\n\nTo implement this, your middleware must map the Notion User ID to the Memori headers as shown on the Client Setup page.\nThis ensures that regardless of which workspace or page the user is on, the AI assistant retrieves the same persistent preferences.\n\n\n## How It All Works Together\n\nOnce both the MCP config and skills file are installed:\n\n- The **MCP config** ensures the tools exist (`recall`, `advanced_augmentation`)\n- The **skills file** ensures the agent uses them _consistently and correctly_ — recall before responding, store after responding\n\n\n After installing both, try telling your agent a preference like \"I always use tabs over spaces.\" In a later session, ask it to write code — it should recall and apply the preference automatically.\n\n" }, { "path": "docs/memori-cloud/mcp/client-setup.mdx", "content": "---\ntitle: Client Setup\ndescription: Configure the Memori MCP server in Cursor, Claude Code, Codex, Warp, Antigravity, and other MCP clients with custom connectors.\n---\n\n# Client Setup\n\nConnect your IDE agent to the Memori MCP server. Each client uses a local MCP configuration with your API key and attribution headers.\n\n## Prerequisites\n\n- A Memori API key from [app.memorilabs.ai](https://app.memorilabs.ai)\n- An `entity_id` (identifies the end user) and optional `process_id` (identifies the agent or workflow)\n\nSet these as environment variables or replace the placeholders directly in the config:\n\n```bash\nexport MEMORI_API_KEY=\"your-memori-api-key\"\nexport MEMORI_ENTITY_ID=\"user_123\"\nexport MEMORI_PROCESS_ID=\"my_agent\" # optional\n```\n\n## Cursor\n\nCreate `~/.cursor/mcp.json` (global) or `.cursor/mcp.json` (project-level):\n\n```json\n{\n \"mcpServers\": {\n \"memori\": {\n \"url\": \"https://api.memorilabs.ai/mcp/\",\n \"headers\": {\n \"X-Memori-API-Key\": \"${MEMORI_API_KEY}\",\n \"X-Memori-Entity-Id\": \"${MEMORI_ENTITY_ID}\",\n \"X-Memori-Process-Id\": \"${MEMORI_PROCESS_ID}\"\n }\n }\n }\n}\n```\n\nRestart Cursor after saving the file.\n\n## Claude Code\n\nYou can configure via the CLI or a `.mcp.json` file.\n\n### CLI\n\n```bash\nclaude mcp add --transport http memori https://api.memorilabs.ai/mcp/ \\\n --header \"X-Memori-API-Key: ${MEMORI_API_KEY}\" \\\n --header \"X-Memori-Entity-Id: ${MEMORI_ENTITY_ID}\" \\\n --header \"X-Memori-Process-Id: ${MEMORI_PROCESS_ID}\"\n```\n\n### Project Config\n\nCreate `.mcp.json` in your project root:\n\n```json\n{\n \"mcpServers\": {\n \"memori\": {\n \"type\": \"http\",\n \"url\": \"https://api.memorilabs.ai/mcp/\",\n \"headers\": {\n \"X-Memori-API-Key\": \"${MEMORI_API_KEY}\",\n \"X-Memori-Entity-Id\": \"${MEMORI_ENTITY_ID}\",\n \"X-Memori-Process-Id\": \"${MEMORI_PROCESS_ID}\"\n }\n }\n }\n}\n```\n\nRun `/mcp` inside Claude Code to verify the server status.\n\n## OpenAI Codex\n\nCodex reads MCP server configuration from `~/.codex/config.toml`. Add this:\n\n```toml\n[mcp_servers.memori]\nenabled = true\nurl = \"https://api.memorilabs.ai/mcp/\"\n\n[mcp_servers.memori.http_headers]\nX-Memori-API-Key = \"${MEMORI_API_KEY}\"\nX-Memori-Entity-Id = \"${MEMORI_ENTITY_ID}\"\nX-Memori-Process-Id = \"${MEMORI_PROCESS_ID}\"\n```\n\n`X-Memori-Process-Id` is optional.\n\nYou can also add the server from the Codex UI: **Settings > Settings > MCP Servers > + Add Server**.\n\n## Warp\n\nWarp cloud agents support MCP JSON config with remote URLs. Add the Memori server to your Warp MCP configuration:\n\n```json\n{\n \"memori\": {\n \"serverUrl\": \"https://api.memorilabs.ai/mcp/\",\n \"headers\": {\n \"X-Memori-API-Key\": \"your-memori-api-key\",\n \"X-Memori-Entity-Id\": \"user_123\",\n \"X-Memori-Process-Id\": \"my_agent\"\n }\n }\n}\n```\n\n`X-Memori-Process-Id` is optional.\n\n## Antigravity\n\nOpen **Manage MCP Servers** and edit `mcp_config.json`:\n\n```json\n{\n \"mcpServers\": {\n \"memori\": {\n \"serverUrl\": \"https://api.memorilabs.ai/mcp/\",\n \"headers\": {\n \"X-Memori-API-Key\": \"your-memori-api-key\",\n \"X-Memori-Entity-Id\": \"user_123\",\n \"X-Memori-Process-Id\": \"my_agent\"\n }\n }\n }\n}\n```\n\nSave and restart Antigravity so the tool list refreshes.\n\n## LangChain\n\nSet headers dynamically based on the current user session:\n\n```python\nfrom langchain_mcp_adapters.client import MultiServerMCPClient\n\nclient = MultiServerMCPClient({\n \"memori\": {\n \"transport\": \"streamable_http\",\n \"url\": \"https://api.memorilabs.ai/mcp/\",\n \"headers\": {\n \"X-Memori-API-Key\": \"your-memori-api-key\",\n \"X-Memori-Entity-Id\": \"user_123\",\n \"X-Memori-Process-Id\": \"langchain_agent\"\n }\n }\n})\n\n# Load the tools to be used by your LangChain agent\ntools = await client.get_tools()\n```\n\n## Slack\n\nSet headers dynamically per request using the Slack user ID from the event payload:\n\n```ts\nconst memoriHeaders = {\n \"X-Memori-API-Key\": process.env.MEMORI_API_KEY,\n \"X-Memori-Entity-Id\": slackEvent.user, // e.g. \"U04ABCDEF\"\n \"X-Memori-Process-Id\": \"supportbot\",\n};\n```\n\nPass these headers in every MCP tool call. `session_id` is derived automatically from `entity_id` and the current UTC hour.\nUse `process_id` to isolate memories by integration or workspace so preferences from a personal workspace don't bleed into a team workspace.\n\n## Notion\n\nSet the entity and process IDs from the Notion API user object:\n\n```ts\nconst memoriHeaders = {\n \"X-Memori-API-Key\": process.env.MEMORI_API_KEY,\n \"X-Memori-Entity-Id\": notionUser.id, // e.g. \"a1b2c3d4-e5f6-7890-abcd-ef1234567890\"\n \"X-Memori-Process-Id\": \"notion_writing_assistant\",\n};\n```\n\nPass these headers in every MCP tool call. `session_id` is derived automatically from `entity_id` and the current UTC hour.\nUse `process_id` to isolate memories by integration or workspace so preferences from a personal workspace don't bleed into a team workspace.\n\n## Verifying the Connection\n\nAfter configuring any client:\n\n1. Confirm the MCP server shows as **connected** in your client's UI\n2. Check that `recall` and `advanced_augmentation` appear in the tools list\n3. Send a test message — `recall` should return a response (even if empty for new entities)\n4. Verify `advanced_augmentation` returns `memory being created`\n\nIf you receive 401 errors, double-check your `X-Memori-API-Key` value. See [Troubleshooting](/docs/memori-cloud/support/troubleshooting) for more help." }, { "path": "docs/memori-cloud/mcp/overview.mdx", "content": "---\ntitle: Memori MCP Overview\ndescription: Use the Model Context Protocol (MCP) to connect AI agents directly to Memori for real-time recall and memory storage.\n---\n\n# Memori MCP (Model Context Protocol)\n\nMCP (Model Context Protocol) is a standard way for AI agents to connect to external tools and APIs. With Memori's MCP server, your agent can retrieve relevant memories before answering and store durable facts after responding — keeping context across sessions without any SDK integration.\n\n## Why Use MCP\n\nWithout MCP, your agent only sees the current conversation. With MCP, it can:\n\n- **Store** stable user facts and preferences after answering using the `advanced_augmentation` tool\n- **Recall** relevant memories before answering using the `recall` tool\n- **Maintain context** across sessions using `entity_id`, `process_id`, and `session_id`\n\nMCP is ideal for IDE agents (Cursor, Claude Code, Codex, Warp, Antigravity) and other MCP-compatible clients where you want persistent memory without writing integration code.\n\n## Server Details\n\n| Property | Value |\n| ------------- | ---------------------------------------- |\n| **Server** | Memori MCP |\n| **Endpoint** | `https://api.memorilabs.ai/mcp/` |\n| **Transport** | Stateless HTTP |\n| **Auth** | API key via request headers |\n\n### Headers\n\n\n \n Your Memori API key from [app.memorilabs.ai](https://app.memorilabs.ai).\n \n \n Stable end-user or entity identifier (e.g. `user_123`).\n \n \n Optional process, app, or workflow identifier (e.g. `my_agent`) for memory isolation.\n \n\n\n\n `session_id` is derived automatically as `-`. You do not need to provide it.\n\n\n## Tools\n\n### `recall`\n\nFetches relevant memories at the start of a user turn.\n\n\n \n The latest user message — typically passed verbatim.\n \n\n\n`entity_id` is read from MCP headers. `process_id` is read when provided. Returns a structured memory payload with relevant context.\n\n### `advanced_augmentation`\n\nStores durable memory after the agent has drafted a response.\n\n\n \n The user's message for this turn.\n \n \n The assistant's response for this turn.\n \n\n\n`entity_id` is read from MCP headers. `process_id` is read when provided. Returns a confirmation: `memory being created`.\n\n## Example Agent Flow\n\nGiven the user message: _\"I prefer Python and use uv for dependency management.\"_\n\n1. Agent calls `recall` with the user message as `query`\n2. Agent composes a response using any returned facts\n3. Agent sends the response to the user\n4. Agent calls `advanced_augmentation` with the `user_message` and `assistant_response`\n\nOn a later turn like _\"Write a hello world script\"_, the agent recalls the Python + uv preference and personalizes its response.\n\n## Validation Checklist\n\nAfter configuring your client, verify the setup:\n\n- MCP server shows as connected and healthy in your client UI\n- Tools list includes `recall` and `advanced_augmentation`\n- Calls return non-401 responses\n- `recall` returns memories for known entities\n- `advanced_augmentation` returns `memory being created`\n" }, { "path": "docs/memori-cloud/openclaw/overview.mdx", "content": "---\ntitle: OpenClaw Overview\ndescription: Give your OpenClaw agents structured, persistent memory with the Memori plugin — auto-recall and auto-capture across every session.\n---\n\n# OpenClaw Overview\n\nBy default, OpenClaw agents forget everything between sessions. The Memori plugin fixes that. It watches conversations, extracts what matters, and brings it back when relevant — automatically.\n\n## The Problem with OpenClaw's Built-In Memory\n\nOpenClaw ships with a basic memory layer, but it has fundamental limitations that break down at scale:\n\n| Limitation | What happens |\n| --- | --- |\n| **Flat markdown files** | Memory lives in plain text files with no structure or relationships. The agent reads and writes large chunks of text with no way to index, query, or deduplicate facts. |\n| **Context compaction** | As sessions grow longer, important details begin to disappear. Memory gets lost when context is compressed to stay within token limits. |\n| **No relationship reasoning** | OpenClaw retrieves semantically similar text, but cannot understand relationships between facts or connect them to each other. |\n| **Cross-project noise** | When working across multiple projects, memories are not isolated. Searches return irrelevant results from other contexts, polluting the agent's responses. |\n| **No user isolation** | Users do not have their own isolated memories. Memories bleed across users and data is mixed, creating privacy and relevance issues. |\n\n## What Changes When You Add Memori?\n\nThe Memori plugin replaces OpenClaw's flat-file memory workflow with managed, structured memory that is scoped by `entity_id`, `process_id`, and `session_id` and enriched automatically through OpenClaw's existing hooks.\n\n### 1. Structured memory storage\n\nInstead of raw markdown blobs, Memori stores conversations, facts, preferences, and knowledge-graph triples as structured records tied to an entity, process, and session. Facts are extracted as subject-predicate-object relationships, deduplicated over time, and connected into a graph so related memories stay queryable instead of being buried in text files.\n\n### 2. Advanced Augmentation\n\nAfter each conversation, Memori processes the user and assistant exchange asynchronously in the background, identifies facts, preferences, skills, and attributes, generates embeddings for semantic search, and updates the knowledge graph without blocking the agent's response path.\n\n### 3. Intelligent Recall\n\nBefore the agent responds, Memori searches the current entity's stored facts and knowledge graph, ranks memories by semantic relevance and importance, and injects the most useful context into the prompt so durable knowledge survives context-window compression.\n\n### 4. Production-ready observability\n\nMemori Cloud gives you dashboard visibility into memory creation, recalls, cache hit rate, sessions, quota usage, top subjects, per-memory retrieval metrics, and knowledge-graph relationships, so you can inspect what was stored and how recall is behaving in production.\n\n> The plugin still remains drop-in: OpenClaw handles the agent loop, while Memori adds recall, augmentation, sanitization, and observability around it.\n\n\n## How It Works\n\nThe plugin hooks into two points in OpenClaw's event lifecycle:\n\n### 1. `before_prompt_build` — Intelligent Recall\n\nWhen a user sends a message, the plugin:\n\n1. Intercepts the event and extracts the user's prompt\n2. Sanitizes the prompt (strips metadata, timestamps, system messages)\n3. Queries the Memori API for relevant memories\n4. Prepends matching memories to the agent's system context\n\nThe agent then responds with full awareness of prior context.\n\n### 2. `agent_end` — Advanced Augmentation\n\nAfter the agent finishes responding, the plugin:\n\n1. Extracts the last user and assistant messages\n2. Sanitizes both (removes thinking blocks, metadata fences, timestamps)\n3. Sends the cleaned exchange to the Memori API\n4. Memori extracts durable facts, updates stale ones, and merges duplicates\n\nThis runs asynchronously and never blocks the agent's response.\n" }, { "path": "docs/memori-cloud/openclaw/quickstart.mdx", "content": "---\ntitle: Quickstart\ndescription: Install and configure the Memori plugin for OpenClaw in under 5 minutes.\n---\n\n# OpenClaw Quickstart\n\nGet persistent memory running in your OpenClaw gateway in three steps.\n\n## Prerequisites\n\n- [OpenClaw](https://openclaw.ai) `v2026.3.2` or later\n- A Memori API key from [app.memorilabs.ai](https://app.memorilabs.ai)\n- An Entity ID to attribute memories to (e.g., a user ID or agent name)\n\n## 1. Install and Enable\n\n```bash\n# Install the plugin from npm\nopenclaw plugins install @memorilabs/openclaw-memori\n\n# Enable it in your workspace\nopenclaw plugins enable openclaw-memori\n\n# Restart the gateway to load the plugin\nopenclaw gateway restart\n```\n\n## 2. Configure\n\nYou need two values: your Memori API key and an Entity ID.\n\n### Option A: Via CLI (Recommended)\n\n```bash\nopenclaw config set plugins.entries.openclaw-memori.config.apiKey \"YOUR_MEMORI_API_KEY\"\nopenclaw config set plugins.entries.openclaw-memori.config.entityId \"your-app-user-id\"\n```\n\n### Option B: Via `openclaw.json`\n\nAdd the following to `~/.openclaw/openclaw.json`:\n\n```json\n{\n \"plugins\": {\n \"entries\": {\n \"openclaw-memori\": {\n \"enabled\": true,\n \"config\": {\n \"apiKey\": \"your-memori-api-key\",\n \"entityId\": \"your-app-user-id\"\n }\n }\n }\n }\n}\n```\n\n### Configuration Options\n\n\n \n Your Memori API key from [app.memorilabs.ai](https://app.memorilabs.ai).\n \n \n The unique identifier for the entity (user, agent, or tenant) to attribute memories to.\n \n\n\n## 3. Verify\n\nAfter configuring, restart the gateway and check the logs:\n\n```bash\nopenclaw gateway restart\nopenclaw gateway logs --filter \"[Memori]\"\n```\n\nYou should see:\n\n```\n[Memori] === INITIALIZING PLUGIN ===\n[Memori] Tracking Entity ID: your-app-user-id\n```\n\n### Test the Memory Loop\n\n1. Send a message with a preference:\n > \"I always use TypeScript and prefer functional patterns.\"\n\n2. Check the logs for a successful augmentation:\n ```\n [Memori] === AUGMENTATION HOOK START ===\n [Memori] Capturing conversation turn...\n [Memori] Augmentation successful!\n ```\n\n3. In a new session, ask a question that should trigger recall:\n > \"Write a hello world script.\"\n\n4. The agent should recall and apply your TypeScript preference automatically:\n ```\n [Memori] === RECALL HOOK START ===\n [Memori] Executing SDK Recall...\n [Memori] Successfully injected memory context.\n ```\n\n## What Happens Under the Hood\n\nOnce installed, the plugin works automatically on every conversation turn:\n\n| Phase | Hook | What the plugin does |\n| --- | --- | --- |\n| **Before response** | `before_prompt_build` | Queries Memori for relevant memories and injects them into the agent's context |\n| **After response** | `agent_end` | Extracts the user/assistant exchange, sanitizes it, and sends it to Memori for fact extraction |\n\nNo changes to your agent's code or prompts are required. The plugin handles everything through OpenClaw's event lifecycle.\n\n\n The plugin is fully stateless and thread-safe. You can run it across multiple agents in the same gateway without any shared state or concurrency issues.\n\n" }, { "path": "docs/memori-cloud/support/faq.mdx", "content": "---\ntitle: FAQ\ndescription: Frequently asked questions about Memori Cloud.\n---\n\n# FAQ\n\n## What is Memori?\n\n__Memori__ is a memory layer for LLM applications, agents, and copilots. It continuously captures interactions, extracts structured knowledge, and intelligently ranks, decays, and retrieves the relevant memories. So your AI remembers the right things at the right time across every session.\n\n## Which languages are supported?\n\nMemori Cloud supports both **Python** and **TypeScript** SDKs. The Python SDK (`memori`) works with both Memori Cloud and BYODB. The TypeScript SDK (`@memorilabs/memori`) is currently Cloud-only. Both SDKs support OpenAI, Anthropic, and Gemini, with additional provider support in Python.\n\n## Memori Cloud vs Memori BYODB?\n\n**Memori Cloud** — managed storage, dashboard UI, just add an API key. Available for Python and TypeScript. **Memori BYODB** — bring your own database & maintain full control. Python only.\n\n## Which LLM providers are supported?\n\n**Python:** OpenAI, Anthropic, Google Gemini, xAI Grok, Deepseek, Nebius AI Studio, AWS Bedrock, LangChain, Agno, and Pydantic AI. All support sync, async, and streaming.\n\n**TypeScript:** OpenAI, Anthropic, and Google Gemini. More providers coming soon.\n\nSee the [Integration Overview](/docs/memori-cloud/llm/overview).\n\n## Do I need to set up a database?\n\nNo. The Memori Cloud platform manages all storage. Just initialize with `mem = Memori()`.\n\n## Is there a free tier?\n\nYes. 5000 memory read & writes per month. Sign up at [app.memorilabs.ai](https://app.memorilabs.ai).\n\n## What counts as a \"memory\"?\n\nEach fact, preference, or relationship extracted by Advanced Augmentation counts as one memory. A single conversation can generate multiple memories.\n\n## Does it support async?\n\nYes. In Python, use your provider's async client (e.g. `AsyncOpenAI`). TypeScript is natively async — all SDK calls return Promises.\n\n## How does augmentation work?\n\nIt runs in the background after each conversation and extracts facts, preferences, and relationships while minimizing impact on your LLM response path. See [Advanced Augmentation](/docs/memori-cloud/concepts/advanced-augmentation).\n\n## Can I use multiple LLM providers?\n\nYes. Register multiple clients on the same Memori instance. Memories are shared across providers since they're scoped to entities, not to providers.\n\n## Can I migrate between Memori Cloud and Memori BYODB?\n\nYes. In Python, both use the same SDK — just remove `conn=SessionLocal` for Memori Cloud or add it for Memori BYODB. Existing memories don't auto-transfer. The TypeScript SDK is Cloud-only.\n\n## Still have questions?\n\n- [Troubleshooting guide](/docs/memori-cloud/support/troubleshooting)\n- [GitHub Issues](https://github.com/MemoriLabs/Memori/issues)\n- [Discord](https://discord.gg/abD4eGym6v)\n" }, { "path": "docs/memori-cloud/support/troubleshooting.mdx", "content": "---\ntitle: Troubleshooting\ndescription: Common issues and solutions when using Memori Cloud.\n---\n\n# Troubleshooting\n\nQuick fixes for the most common Memori issues.\n\n## Installation\n\n### Python\n\n**If `pip install memori` fails** — Requires Python 3.10+.\nRun `python --version` to check, then `pip install --upgrade pip && pip install memori`.\n\n### TypeScript\n\n**If `npm install @memorilabs/memori` fails** — Requires Node.js 18+.\nRun `node --version` to check, then update Node.js and retry.\n\n**Module resolution errors** — Ensure your `tsconfig.json` uses `\"moduleResolution\": \"node\"` or `\"bundler\"`. The SDK ships ESM with TypeScript declarations.\n\n## API Key Issues\n\n**Invalid or missing API key** — Set the `MEMORI_API_KEY` environment variable:\n\n```bash\nexport MEMORI_API_KEY=\"your-memori-api-key\"\n```\n\n**Quota exceeded** — Upgrade your account at [app.memorilabs.ai/settings/billing](https://app.memorilabs.ai/settings/billing).\n\n## No Memories Being Created\n\n1. **Register your LLM client** — conversations aren't captured without registration:\n\n\n\n```python {{ title: 'Python' }}\nclient = OpenAI()\nmem = Memori().llm.register(client)\n```\n\n```typescript {{ title: 'TypeScript' }}\nconst client = new OpenAI();\nconst mem = new Memori().llm.register(client);\n```\n\n\n\n2. **Set attribution** before LLM calls — without it, no memories are stored:\n\n\n\n```python {{ title: 'Python' }}\nmem.attribution(entity_id=\"user_123\", process_id=\"my_app\")\n```\n\n```typescript {{ title: 'TypeScript' }}\nmem.attribution('user_123', 'my_app');\n```\n\n\n\n## Recall Returns Empty\n\n- Verify `entity_id` matches what was used when memories were created\n- Increase limit: `mem.recall(\"query\", limit=10)`\n- Lower threshold: `mem.config.recall_relevance_threshold = 0.05`\n\n## Performance\n\n**Network timeouts** — Increase timeout: `mem.config.request_secs_timeout = 10` and retries: `mem.config.request_num_backoff = 10`.\n\n## Debug Logging\n\nEnable debug logging to inspect request flow, attribution, and augmentation behavior when troubleshooting missing memories or API errors. Use it temporarily in development, since logs can include sensitive request metadata.\n\n\n\n```python {{ title: 'Python' }}\nimport logging\nlogging.basicConfig(level=logging.DEBUG, format=\"%(asctime)s | %(name)s | %(levelname)s | %(message)s\")\n\nfrom memori import Memori\nmem = Memori()\n```\n\n```typescript {{ title: 'TypeScript' }}\nimport { Memori } from '@memorilabs/memori';\nconst mem = new Memori({ debug: true });\n```\n\n\n\n## Getting Help\n\n- [GitHub Issues](https://github.com/MemoriLabs/Memori/issues)\n- [Discord](https://discord.gg/abD4eGym6v)\n- [Examples](https://github.com/MemoriLabs/Memori/tree/main/examples)\n\nInclude your language, runtime version (Python/Node.js), Memori version (`pip show memori` or check `package.json`), and full error trace.\n" }, { "path": "examples/agno/README.md", "content": "# Memori + Agno Example\n\nExample showing how to use Memori with Agno agents to add persistent memory across conversations.\n\n## Quick Start\n\n1. **Install dependencies**:\n ```bash\n uv sync\n ```\n\n2. **Set your OpenAI API key**:\n Create a `.env` file:\n ```bash\n OPENAI_API_KEY=your_api_key_here\n ```\n\n3. **Run the example**:\n ```bash\n uv run python main.py\n ```\n\n## What This Example Demonstrates\n\n- **Agno integration**: Use Memori with Agno's agent framework\n- **Persistent memory**: Conversations are stored in SQLite and recalled automatically\n- **Context awareness**: The agent remembers details from earlier in the conversation\n- **Customer support use case**: Shows a realistic scenario where memory is valuable\n" }, { "path": "examples/agno/main.py", "content": "\"\"\"\nMemori + Agno + SQLite Example\n\nDemonstrates how Memori adds persistent memory to Agno agents.\n\"\"\"\n\nimport os\n\nfrom agno.agent import Agent\nfrom agno.models.openai import OpenAIChat\nfrom dotenv import load_dotenv\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nfrom memori import Memori\n\nload_dotenv()\n\ndb_path = os.getenv(\"DATABASE_PATH\", \"memori_agno.db\")\nengine = create_engine(f\"sqlite:///{db_path}\")\nSession = sessionmaker(bind=engine)\n\nmodel = OpenAIChat(id=\"gpt-4o-mini\")\n\nmem = Memori(conn=Session).llm.register(openai_chat=model)\nmem.attribution(entity_id=\"customer-456\", process_id=\"support-agent\")\nmem.config.storage.build()\n\nagent = Agent(\n model=model,\n instructions=[\n \"You are a helpful customer support agent.\",\n \"Remember customer preferences and history from previous conversations.\",\n ],\n markdown=True,\n)\n\nif __name__ == \"__main__\":\n print(\"Customer: Hi, I'd like to order a large pepperoni pizza with extra cheese\")\n response1 = agent.run(\n \"Hi, I'd like to order a large pepperoni pizza with extra cheese\"\n )\n print(f\"Agent: {response1.content}\\n\")\n\n print(\"Customer: Actually, can you remind me what I just ordered?\")\n response2 = agent.run(\"Actually, can you remind me what I just ordered?\")\n print(f\"Agent: {response2.content}\\n\")\n\n print(\"Customer: Perfect! And what size was that again?\")\n response3 = agent.run(\"Perfect! And what size was that again?\")\n print(f\"Agent: {response3.content}\")\n\n # Advanced Augmentation runs asynchronously to efficiently\n # create memories. For this example, a short lived command\n # line program, we need to wait for it to finish.\n mem.augmentation.wait()\n" }, { "path": "examples/agno/pyproject.toml", "content": "[project]\nname = \"memori-agno-example\"\nversion = \"0.1.0\"\nrequires-python = \">=3.9\"\ndependencies = [\n \"memori\",\n \"agno\",\n \"sqlalchemy\",\n \"python-dotenv\",\n]\n" }, { "path": "examples/cockroachdb/README.md", "content": "# Memori + CockroachDB Example\n\n**Memori + CockroachDB** brings durable, distributed memory to AI - instantly, globally, and at any scale. Memori transforms conversations into structured, queryable intelligence, while CockroachDB keeps that memory available, resilient, and consistently accurate across regions. Deploy and scale effortlessly from prototype to production with zero downtime on enterprise-grade infrastructure. Give your AI a foundation to remember, reason, and evolve - with the simplicity of cloud and the reliability and power of distributed SQL.\n\n## Getting Started\n\nInstall Memori:\n\n```bash\npip install memori\n```\n\nSign up for [CockroachDB Cloud](https://www.cockroachlabs.com/product/cloud/).\n\nYou may need to record the database connection string for your implementation. Once you've signed up, your database is provisioned and ready for use with Memori.\n\n## Quick Start\n\n1. **Install dependencies**:\n ```bash\n uv sync\n ```\n\n2. **Set environment variables**:\n ```bash\n export OPENAI_API_KEY=your_api_key_here\n export COCKROACHDB_CONNECTION_STRING=postgresql://user:password@host:26257/defaultdb?sslmode=verify-full\n ```\n\n3. **Run the example**:\n ```bash\n uv run python main.py\n ```\n\n## What This Example Demonstrates\n\n- **Serverless CockroachDB**: Connect to CockroachDB's cloud serverless Postgres with zero database management\n- **Automatic persistence**: All conversation messages are automatically stored in your CockroachDB database\n- **Context preservation**: Memori injects relevant conversation history into each LLM call\n- **Interactive chat**: Type messages and see how Memori maintains context across the conversation\n" }, { "path": "examples/cockroachdb/main.py", "content": "\"\"\"\nQuickstart: Memori + OpenAI + CockroachDB\n\nDemonstrates how Memori adds memory across conversations.\n\"\"\"\n\nimport os\n\nimport psycopg2\nfrom openai import OpenAI\n\nfrom memori import Memori\n\nclient = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\n\n\ndef get_conn():\n return psycopg2.connect(os.getenv(\"COCKROACHDB_CONNECTION_STRING\"))\n\n\nmem = Memori(conn=get_conn).llm.register(client)\nmem.attribution(entity_id=\"user-123\", process_id=\"my-app\")\nmem.config.storage.build()\n\nif __name__ == \"__main__\":\n print(\"You: My favorite color is blue and I live in Paris\")\n response1 = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[\n {\"role\": \"user\", \"content\": \"My favorite color is blue and I live in Paris\"}\n ],\n )\n print(f\"AI: {response1.choices[0].message.content}\\n\")\n\n print(\"You: What's my favorite color?\")\n response2 = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"What's my favorite color?\"}],\n )\n print(f\"AI: {response2.choices[0].message.content}\\n\")\n\n print(\"You: What city do I live in?\")\n response3 = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"What city do I live in?\"}],\n )\n print(f\"AI: {response3.choices[0].message.content}\")\n\n # Advanced Augmentation runs asynchronously to efficiently\n # create memories. For this example, a short lived command\n # line program, we need to wait for it to finish.\n mem.augmentation.wait()\n" }, { "path": "examples/cockroachdb/pyproject.toml", "content": "[project]\nname = \"memori-cockroachdb-example\"\nversion = \"0.1.0\"\ndescription = \"Memori SDK example with CockroachDB\"\nreadme = \"README.md\"\nrequires-python = \">=3.10\"\ndependencies = [\n \"memori>=3.0.0\",\n \"openai>=2.6.1\",\n \"psycopg2-binary>=2.9.11\",\n \"python-dotenv>=1.2.1\",\n]\n" }, { "path": "examples/digitalocean/README.md", "content": "# Memori + DigitalOcean Gradient Example\n\nExample showing how to use Memori with DigitalOcean Gradient AI Agents to add persistent memory across conversations.\n\n## Quick Start\n\n1. **Install dependencies**:\n ```bash\n uv sync\n ```\n\n2. **Set environment variables**:\n Create a `.env` file:\n ```bash\n AGENT_ENDPOINT=your_gradient_agent_endpoint\n AGENT_ACCESS_KEY=your_gradient_access_key\n DATABASE_CONNECTION_STRING=postgresql+psycopg2://user:password@localhost:5432/dbname\n ```\n\n3. **Run the example**:\n ```bash\n uv run python main.py\n ```\n\n## What This Example Demonstrates\n\n- **DigitalOcean Gradient integration**: Use Memori with DigitalOcean's Gradient AI platform\n- **Persistent memory**: Conversations are stored in PostgreSQL and recalled automatically\n- **OpenAI-compatible API**: Gradient agents use OpenAI's API format for easy integration\n- **Context awareness**: The agent remembers details from earlier in the conversation\n" }, { "path": "examples/digitalocean/main.py", "content": "\"\"\"\nMemori + DigitalOcean Gradient AI Example\n\nDemonstrates how Memori adds persistent memory to DigitalOcean Gradient AI Agents.\n\"\"\"\n\nimport os\n\nfrom dotenv import load_dotenv\nfrom openai import OpenAI\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nfrom memori import Memori\n\nload_dotenv()\n\nagent_endpoint = os.getenv(\"AGENT_ENDPOINT\")\nagent_access_key = os.getenv(\"AGENT_ACCESS_KEY\")\n\nif not agent_endpoint or not agent_access_key:\n raise ValueError(\"AGENT_ENDPOINT and AGENT_ACCESS_KEY must be set in .env\")\n\nbase_url = (\n agent_endpoint\n if agent_endpoint.endswith(\"/api/v1/\")\n else f\"{agent_endpoint}/api/v1/\"\n)\nclient = OpenAI(base_url=base_url, api_key=agent_access_key)\n\ndatabase_connection_string = os.getenv(\"DATABASE_CONNECTION_STRING\")\nif not database_connection_string:\n raise ValueError(\"DATABASE_CONNECTION_STRING must be set in .env\")\n\nengine = create_engine(database_connection_string)\nSession = sessionmaker(bind=engine)\n\nmem = Memori(conn=Session).llm.register(client)\nmem.attribution(entity_id=\"user-123\", process_id=\"gradient-agent\")\nmem.config.storage.build()\n\nif __name__ == \"__main__\":\n print(\"You: My favorite color is blue and I live in Paris\")\n response1 = client.chat.completions.create(\n model=\"n/a\",\n messages=[\n {\"role\": \"user\", \"content\": \"My favorite color is blue and I live in Paris\"}\n ],\n )\n print(f\"AI: {response1.choices[0].message.content}\\n\")\n\n print(\"You: What's my favorite color?\")\n response2 = client.chat.completions.create(\n model=\"n/a\",\n messages=[{\"role\": \"user\", \"content\": \"What's my favorite color?\"}],\n )\n print(f\"AI: {response2.choices[0].message.content}\\n\")\n\n print(\"You: What city do I live in?\")\n response3 = client.chat.completions.create(\n model=\"n/a\",\n messages=[{\"role\": \"user\", \"content\": \"What city do I live in?\"}],\n )\n print(f\"AI: {response3.choices[0].message.content}\")\n\n # Advanced Augmentation runs asynchronously to efficiently\n # create memories. For this example, a short lived command\n # line program, we need to wait for it to finish.\n mem.augmentation.wait()\n" }, { "path": "examples/digitalocean/pyproject.toml", "content": "[project]\nname = \"memori-digitalocean-example\"\nversion = \"0.1.0\"\ndescription = \"Memori SDK example with DigitalOcean Gradient AI\"\nreadme = \"README.md\"\nrequires-python = \">=3.10\"\ndependencies = [\n \"memori\",\n \"openai>=2.6.1\",\n \"SQLAlchemy>=2.0.0\",\n \"psycopg2-binary>=2.9.0\",\n \"python-dotenv>=1.2.1\",\n]\n" }, { "path": "examples/mongodb/README.md", "content": "# Memori + MongoDB Example\n\nExample showing how to use Memori with MongoDB.\n\n## Quick Start\n\n1. **Install dependencies**:\n ```bash\n uv sync\n ```\n\n2. **Set environment variables**:\n ```bash\n export OPENAI_API_KEY=your_api_key_here\n export MONGODB_CONNECTION_STRING=mongodb+srv://user:password@cluster.mongodb.net/dbname\n ```\n\n3. **Run the example**:\n ```bash\n uv run python main.py\n ```\n\n## What This Example Demonstrates\n\n- **NoSQL flexibility**: Store conversation data in MongoDB's document model\n- **Automatic persistence**: All conversation messages are automatically stored in MongoDB collections\n- **Context preservation**: Memori injects relevant conversation history into each LLM call\n- **Interactive chat**: Type messages and see how Memori maintains context across the conversation\n- **Cloud-ready**: Works seamlessly with MongoDB Atlas free tier\n" }, { "path": "examples/mongodb/main.py", "content": "\"\"\"\nQuickstart: Memori + OpenAI + MongoDB\n\nDemonstrates how Memori adds memory across conversations.\n\"\"\"\n\nimport os\n\nfrom openai import OpenAI\nfrom pymongo import MongoClient\n\nfrom memori import Memori\n\nclient = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\n\nmongo_client = MongoClient(os.getenv(\"MONGODB_CONNECTION_STRING\"))\ndb = mongo_client[\"memori\"]\n\nmem = Memori(conn=lambda: db).llm.register(client)\nmem.attribution(entity_id=\"user-123\", process_id=\"my-app\")\nmem.config.storage.build()\n\nif __name__ == \"__main__\":\n print(\"You: My favorite color is blue and I live in Paris\")\n response1 = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[\n {\"role\": \"user\", \"content\": \"My favorite color is blue and I live in Paris\"}\n ],\n )\n print(f\"AI: {response1.choices[0].message.content}\\n\")\n\n print(\"You: What's my favorite color?\")\n response2 = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"What's my favorite color?\"}],\n )\n print(f\"AI: {response2.choices[0].message.content}\\n\")\n\n print(\"You: What city do I live in?\")\n response3 = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"What city do I live in?\"}],\n )\n print(f\"AI: {response3.choices[0].message.content}\")\n\n # Advanced Augmentation runs asynchronously to efficiently\n # create memories. For this example, a short lived command\n # line program, we need to wait for it to finish.\n mem.augmentation.wait()\n" }, { "path": "examples/mongodb/pyproject.toml", "content": "[project]\nname = \"memori-mongodb-example\"\nversion = \"0.1.0\"\ndescription = \"Memori SDK example with MongoDB\"\nreadme = \"README.md\"\nrequires-python = \">=3.10\"\ndependencies = [\n \"memori>=3.0.0\",\n \"openai>=2.6.1\",\n \"pymongo>=4.7.0\",\n \"python-dotenv>=1.2.1\",\n]\n" }, { "path": "examples/nebius/README.md", "content": "# Memori + Nebius AI Studio Example\n\nExample showing how to use Memori with Nebius AI Studio to add persistent memory across conversations.\n\n[Nebius AI Studio](https://nebius.com/ai-studio) provides an OpenAI-compatible API for state-of-the-art open-source models including Llama, Qwen, DeepSeek, and more. Since Nebius uses the OpenAI SDK, it works seamlessly with Memori's automatic instrumentation.\n\n## Quick Start\n\n1. **Install dependencies**:\n ```bash\n uv sync\n ```\n\n2. **Get a Nebius API key**:\n - Sign up at [https://studio.nebius.ai/](https://studio.nebius.ai/)\n - Get your API key from the dashboard\n\n3. **Set your API key**:\n Create a `.env` file:\n ```bash\n NEBIUS_API_KEY=your_api_key_here\n ```\n\n4. **Run the example**:\n ```bash\n uv run python main.py\n ```\n\n## What This Example Demonstrates\n\n- **Nebius AI Studio integration**: Use Memori with Nebius's OpenAI-compatible API\n- **Open-source models**: Access to models like Llama 3.1 70B with persistent memory\n- **Automatic registration**: Simply use `.llm.register(client)` - Memori auto-detects Nebius\n- **Persistent memory**: Conversations are stored in SQLite and recalled automatically\n- **Context awareness**: The LLM remembers details from earlier in the conversation\n\n## Available Models\n\nNebius AI Studio supports 60+ open-source models. Check the [model catalog](https://studio.nebius.ai/) for the full list including:\n- Meta Llama 3.1 (8B, 70B, 405B)\n- Qwen 2.5\n- DeepSeek\n- Mistral\n- And many more\n\n## Alternative Base URLs\n\nNebius offers multiple endpoints:\n- Studio API: `https://api.studio.nebius.com/v1/`\n- Token Factory: `https://api.tokenfactory.nebius.com/`\n\nSimply change the `base_url` parameter to use a different endpoint.\n" }, { "path": "examples/nebius/main.py", "content": "\"\"\"\nMemori + Nebius AI Studio + SQLite Example\n\nDemonstrates how Memori adds persistent memory to Nebius AI Studio LLMs.\nNebius AI Studio provides an OpenAI-compatible API with state-of-the-art open-source models.\n\"\"\"\n\nimport os\n\nfrom dotenv import load_dotenv\nfrom openai import OpenAI\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nfrom memori import Memori\n\nload_dotenv()\n\ndb_path = os.getenv(\"DATABASE_PATH\", \"memori_nebius.db\")\nengine = create_engine(f\"sqlite:///{db_path}\")\nSession = sessionmaker(bind=engine)\n\nclient = OpenAI(\n base_url=\"https://api.studio.nebius.com/v1/\",\n api_key=os.getenv(\"NEBIUS_API_KEY\"),\n)\n\nmem = Memori(conn=Session).llm.register(client)\nmem.attribution(entity_id=\"user-789\", process_id=\"nebius-chat-app\")\nmem.config.storage.build()\n\nif __name__ == \"__main__\":\n print(\"User: My favorite color is blue and I live in Paris\")\n response1 = client.chat.completions.create(\n model=\"meta-llama/Llama-3.3-70B-Instruct\",\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"My favorite color is blue and I live in Paris.\",\n }\n ],\n )\n print(f\"Assistant: {response1.choices[0].message.content}\\n\")\n\n print(\"User: What's my favorite color?\")\n response2 = client.chat.completions.create(\n model=\"meta-llama/Llama-3.3-70B-Instruct\",\n messages=[{\"role\": \"user\", \"content\": \"What's my favorite color?\"}],\n )\n print(f\"Assistant: {response2.choices[0].message.content}\\n\")\n\n print(\"User: Where do I live?\")\n response3 = client.chat.completions.create(\n model=\"meta-llama/Llama-3.3-70B-Instruct\",\n messages=[{\"role\": \"user\", \"content\": \"Where do I live?\"}],\n )\n print(f\"Assistant: {response3.choices[0].message.content}\")\n\n # Advanced Augmentation runs asynchronously to efficiently\n # create memories. For this example, a short lived command\n # line program, we need to wait for it to finish.\n mem.augmentation.wait()\n" }, { "path": "examples/nebius/pyproject.toml", "content": "[project]\nname = \"memori-nebius-example\"\nversion = \"0.1.0\"\nrequires-python = \">=3.9\"\ndependencies = [\n \"memori\",\n \"openai\",\n \"sqlalchemy\",\n \"python-dotenv\",\n]\n" }, { "path": "examples/neon/README.md", "content": "# Memori + Neon Example\n\nSign up for [Neon serverless Postgres](https://neon.tech).\n\nYou may need to record the database connection string for your implementation. Once you've signed up, your database is provisioned and ready for use with Memori.\n\n## Quick Start\n\n1. **Install dependencies**:\n ```bash\n uv sync\n ```\n\n2. **Set environment variables**:\n ```bash\n export OPENAI_API_KEY=your_api_key_here\n export NEON_CONNECTION_STRING=postgresql://user:pass@ep-xyz-123.us-east-2.aws.neon.tech/dbname?sslmode=require\n ```\n\n3. **Run the example**:\n ```bash\n uv run python main.py\n ```\n\n## What This Example Demonstrates\n\n- **Serverless PostgreSQL**: Connect to Neon's serverless Postgres with zero database management\n- **Automatic persistence**: All conversation messages are automatically stored in your Neon database\n- **Context preservation**: Memori injects relevant conversation history into each LLM call\n- **Interactive chat**: Type messages and see how Memori maintains context across the conversation\n" }, { "path": "examples/neon/main.py", "content": "\"\"\"\nQuickstart: Memori + OpenAI + Neon\n\nDemonstrates how Memori adds memory across conversations.\n\"\"\"\n\nimport os\n\nfrom openai import OpenAI\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nfrom memori import Memori\n\nclient = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\n\nneon_connection_string = os.getenv(\"NEON_CONNECTION_STRING\")\nif not neon_connection_string:\n raise ValueError(\"NEON_CONNECTION_STRING must be set in the environment\")\n\nengine = create_engine(neon_connection_string)\nSession = sessionmaker(bind=engine)\n\nmem = Memori(conn=Session).llm.register(client)\nmem.attribution(entity_id=\"user-123\", process_id=\"my-app\")\nmem.config.storage.build()\n\nif __name__ == \"__main__\":\n print(\"You: My favorite color is blue and I live in Paris\")\n response1 = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[\n {\"role\": \"user\", \"content\": \"My favorite color is blue and I live in Paris\"}\n ],\n )\n print(f\"AI: {response1.choices[0].message.content}\\n\")\n\n print(\"You: What's my favorite color?\")\n response2 = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"What's my favorite color?\"}],\n )\n print(f\"AI: {response2.choices[0].message.content}\\n\")\n\n print(\"You: What city do I live in?\")\n response3 = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"What city do I live in?\"}],\n )\n print(f\"AI: {response3.choices[0].message.content}\")\n\n # Advanced Augmentation runs asynchronously to efficiently\n # create memories. For this example, a short lived command\n # line program, we need to wait for it to finish.\n mem.augmentation.wait()\n" }, { "path": "examples/neon/pyproject.toml", "content": "[project]\nname = \"memori-neon-example\"\nversion = \"0.1.0\"\ndescription = \"Memori SDK example with Neon serverless Postgres\"\nreadme = \"README.md\"\nrequires-python = \">=3.10\"\ndependencies = [\n \"memori\",\n \"openai>=2.6.1\",\n \"SQLAlchemy>=2.0.0\",\n \"psycopg[binary]>=3.2.0\",\n \"psycopg2-binary>=2.9.0\",\n \"python-dotenv>=1.2.1\",\n]\n\n[tool.uv.sources]\nmemori = { path = \"../../..\", editable = true }\n" }, { "path": "examples/oceanbase/README.md", "content": "# Memori + OceanBase Example\n\nExample showing how to use Memori with OceanBase (or SeekDB).\n\n## Quick Start\n\n1. **Install dependencies**:\n ```bash\n uv sync\n ```\n\n2. **Set environment variables**:\n ```bash\n export OPENAI_API_KEY=your_api_key_here\n export DATABASE_CONNECTION_STRING=mysql+oceanbase://root:@localhost:2881/memori_test?charset=utf8mb4\n ```\n\n3. **Run the example**:\n ```bash\n uv run python main.py\n ```\n\n## What This Example Demonstrates\n\n- **OceanBase integration**: Connect to OceanBase or SeekDB using the pyobvector dialect\n- **Automatic persistence**: Conversation messages are stored in OceanBase tables\n- **Context preservation**: Memori injects relevant history into each LLM call\n" }, { "path": "examples/oceanbase/main.py", "content": "\"\"\"\nQuickstart: Memori + OpenAI + OceanBase\n\nDemonstrates how Memori adds memory across conversations.\n\"\"\"\n\nimport os\n\nfrom openai import OpenAI\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.dialects import registry\nfrom sqlalchemy.orm import sessionmaker\n\nfrom memori import Memori\n\nclient = OpenAI(\n api_key=os.getenv(\"OPENAI_API_KEY\"),\n base_url=os.getenv(\"OPENAI_BASE_URL\"),\n)\n\nregistry.register(\"mysql.oceanbase\", \"pyobvector.schema.dialect\", \"OceanBaseDialect\")\n\ndatabase_connection_string = os.getenv(\"DATABASE_CONNECTION_STRING\")\nif not database_connection_string:\n raise ValueError(\"DATABASE_CONNECTION_STRING must be set in the environment\")\n\nengine = create_engine(database_connection_string, pool_pre_ping=True)\nSession = sessionmaker(bind=engine)\n\nmem = Memori(conn=Session).llm.register(client)\nmem.attribution(entity_id=\"user-123\", process_id=\"my-app\")\nmem.config.storage.build()\n\nif __name__ == \"__main__\":\n model = os.getenv(\"OPENAI_MODEL\", \"qwen-plus\")\n print(\"You: My favorite color is blue and I live in Paris\")\n response1 = client.chat.completions.create(\n model=model,\n messages=[\n {\"role\": \"user\", \"content\": \"My favorite color is blue and I live in Paris\"}\n ],\n )\n print(f\"AI: {response1.choices[0].message.content}\\n\")\n\n print(\"You: What's my favorite color?\")\n response2 = client.chat.completions.create(\n model=model,\n messages=[{\"role\": \"user\", \"content\": \"What's my favorite color?\"}],\n )\n print(f\"AI: {response2.choices[0].message.content}\\n\")\n\n print(\"You: What city do I live in?\")\n response3 = client.chat.completions.create(\n model=model,\n messages=[{\"role\": \"user\", \"content\": \"What city do I live in?\"}],\n )\n print(f\"AI: {response3.choices[0].message.content}\")\n\n # Wait for background augmentation in short-lived scripts.\n mem.augmentation.wait()\n" }, { "path": "examples/postgres/README.md", "content": "# Memori + PostgreSQL Example\n\nExample showing how to use Memori with PostgreSQL.\n\n## Quick Start\n\n1. **Install dependencies**:\n ```bash\n uv sync\n ```\n\n2. **Set environment variables**:\n ```bash\n export OPENAI_API_KEY=your_api_key_here\n export DATABASE_CONNECTION_STRING=postgresql+psycopg://user:password@localhost:5432/dbname\n ```\n\n3. **Run the example**:\n ```bash\n uv run python main.py\n ```\n\n## What This Example Demonstrates\n\n- **PostgreSQL integration**: Connect to any PostgreSQL database (local, AWS RDS, or other managed database services)\n- **Automatic persistence**: All conversation messages are automatically stored in your database\n- **Context preservation**: Memori injects relevant conversation history into each LLM call\n- **Interactive chat**: Type messages and see how Memori maintains context across the conversation\n" }, { "path": "examples/postgres/main.py", "content": "\"\"\"\nQuickstart: Memori + OpenAI + PostgreSQL\n\nDemonstrates how Memori adds memory across conversations.\n\"\"\"\n\nimport os\n\nfrom openai import OpenAI\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nfrom memori import Memori\n\nclient = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\n\ndatabase_connection_string = os.getenv(\"DATABASE_CONNECTION_STRING\")\nif not database_connection_string:\n raise ValueError(\"DATABASE_CONNECTION_STRING must be set in the environment\")\n\nengine = create_engine(database_connection_string)\nSession = sessionmaker(bind=engine)\n\nmem = Memori(conn=Session).llm.register(client)\nmem.attribution(entity_id=\"user-123\", process_id=\"my-app\")\nmem.config.storage.build()\n\nif __name__ == \"__main__\":\n print(\"You: My favorite color is blue and I live in Paris\")\n response1 = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[\n {\"role\": \"user\", \"content\": \"My favorite color is blue and I live in Paris\"}\n ],\n )\n print(f\"AI: {response1.choices[0].message.content}\\n\")\n\n print(\"You: What's my favorite color?\")\n response2 = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"What's my favorite color?\"}],\n )\n print(f\"AI: {response2.choices[0].message.content}\\n\")\n\n print(\"You: What city do I live in?\")\n response3 = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"What city do I live in?\"}],\n )\n print(f\"AI: {response3.choices[0].message.content}\")\n\n # Advanced Augmentation runs asynchronously to efficiently\n # create memories. For this example, a short lived command\n # line program, we need to wait for it to finish.\n mem.augmentation.wait()\n" }, { "path": "examples/postgres/pyproject.toml", "content": "[project]\nname = \"memori-postgres-example\"\nversion = \"0.1.0\"\ndescription = \"Memori SDK example with PostgreSQL\"\nreadme = \"README.md\"\nrequires-python = \">=3.10\"\ndependencies = [\n \"memori>=3.0.0\",\n \"openai>=2.6.1\",\n \"SQLAlchemy>=2.0.0\",\n \"psycopg[binary]>=3.2.0\",\n \"python-dotenv>=1.2.1\",\n]\n" }, { "path": "examples/sqlite/README.md", "content": "# Memori + SQLite Example\n\nExample showing how to use Memori with SQLite.\n\n## Quick Start\n\n1. **Install dependencies**:\n ```bash\n uv sync\n ```\n\n2. **Set environment variables**:\n ```bash\n export OPENAI_API_KEY=your_api_key_here\n ```\n\n3. **Run the example**:\n ```bash\n uv run python main.py\n ```\n\n## What This Example Demonstrates\n\n- **Automatic persistence**: All conversation messages are automatically stored in the SQLite database\n- **Context preservation**: Memori injects relevant conversation history into each LLM call\n- **Interactive chat**: Type messages and see how Memori maintains context across the conversation\n- **Portable**: The database file can be copied, backed up, or shared easily\n" }, { "path": "examples/sqlite/main.py", "content": "\"\"\"\nQuickstart: Memori + OpenAI + SQLite\n\nDemonstrates how Memori adds memory across conversations.\n\"\"\"\n\nimport os\n\nfrom openai import OpenAI\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nfrom memori import Memori\n\n# Setup OpenAI\nclient = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\", \"\"))\n\n# Setup SQLite\nengine = create_engine(\"sqlite:///memori.db\")\nSession = sessionmaker(bind=engine)\n\n# Setup Memori - that's it!\nmem = Memori(conn=Session).llm.register(client)\nmem.attribution(entity_id=\"user-123\", process_id=\"my-app\")\nmem.config.storage.build()\n\nif __name__ == \"__main__\":\n # First conversation - establish facts\n print(\"You: My favorite color is blue and I live in Paris\")\n response1 = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[\n {\"role\": \"user\", \"content\": \"My favorite color is blue and I live in Paris\"}\n ],\n )\n print(f\"AI: {response1.choices[0].message.content}\\n\")\n\n # Second conversation - Memori recalls context automatically\n print(\"You: What's my favorite color?\")\n response2 = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"What's my favorite color?\"}],\n )\n print(f\"AI: {response2.choices[0].message.content}\\n\")\n\n # Third conversation - context is maintained\n print(\"You: What city do I live in?\")\n response3 = client.chat.completions.create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"What city do I live in?\"}],\n )\n print(f\"AI: {response3.choices[0].message.content}\")\n\n # Advanced Augmentation runs asynchronously to efficiently\n # create memories. For this example, a short lived command\n # line program, we need to wait for it to finish.\n mem.augmentation.wait()\n" }, { "path": "examples/sqlite/pyproject.toml", "content": "[project]\nname = \"memori-sqlite-example\"\nversion = \"0.1.0\"\ndescription = \"Memori SDK example with SQLite\"\nreadme = \"README.md\"\nrequires-python = \">=3.10\"\ndependencies = [\n \"memori>=3.0.0\",\n \"openai>=2.6.1\",\n \"SQLAlchemy>=2.0.0\",\n \"python-dotenv>=1.2.1\",\n]\n" }, { "path": "integrations/openclaw/.prettierrc.json", "content": "{\n \"semi\": true,\n \"trailingComma\": \"es5\",\n \"singleQuote\": true,\n \"printWidth\": 100,\n \"tabWidth\": 2,\n \"arrowParens\": \"always\"\n}\n" }, { "path": "integrations/openclaw/README.md", "content": "[![Memori Labs](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/banner.png)](https://memorilabs.ai/)\n\n

\n The memory fabric for enterprise AI\n

\n\n

\n Give OpenClaw persistent, structured memory with Memori. Capture what matters, recall it when relevant, and move from lightweight experimentation to production-ready memory infrastructure.\n

\n\n

\n \n \"NPM\n \n \n \"NPM\n \n \n \"License\"\n \n \n \"Discord\"\n \n

\n\n---\n\n## Why Memori for OpenClaw?\n\nOpenClaw ships with a simple file-first memory system designed for lightweight experimentation. As deployments scale into production environments, teams often run into memory problems that need more structured, deterministic infrastructure.\n\nMemori provides a drop-in memory layer purpose-built for agentic systems running OpenClaw in production. It works through OpenClaw's plugin lifecycle, so you get persistent, structured memory without changing your agent logic.\n\n## Common Challenges with Default OpenClaw Memory\n\n### 1. Fact conflicts in long-running agents\n\nOpenClaw stores memory as plain markdown files. When facts change or contradict over time, there is no deterministic conflict resolution or lifecycle management.\n\nMemori introduces structured memory with update logic, decay policies, and deterministic fact handling.\n\n### 2. Context loss from token limits\n\nAs sessions grow, context must be compacted to fit within model token limits. Important details can be dropped during compression.\n\nMemori stores memory outside the prompt and retrieves the right facts at query time, eliminating compaction loss.\n\n### 3. No relationship reasoning\n\nOpenClaw retrieves semantically similar text but does not model relationships between entities.\n\nMemori builds structured memory graphs that let agents reason across linked facts, not just retrieve similar chunks.\n\n### 4. Cross-project noise\n\nWhen multiple projects share memory storage, irrelevant context can bleed across workflows.\n\nMemori supports scoped memory namespaces to isolate projects and workflows.\n\n### 5. No user-level isolation\n\nDefault memory systems do not provide deterministic isolation across users.\n\nMemori enforces user-scoped memory boundaries for secure multi-user deployments.\n\n## What Changes When You Add Memori?\n\nThe Memori plugin replaces OpenClaw's flat-file memory workflow with managed, structured memory that is scoped by `entity_id`, `process_id`, and `session_id` and enriched automatically through OpenClaw's existing hooks.\n\n| Capability | What changes |\n| --- | --- |\n| **Structured memory storage** | Instead of raw markdown blobs, Memori stores conversations, facts, preferences, and knowledge-graph triples as structured records tied to an entity, process, and session. Facts are extracted as subject-predicate-object relationships, deduplicated over time, and connected into a graph so related memories stay queryable instead of being buried in text files. |\n| **Advanced Augmentation** | After each conversation, Memori processes the user and assistant exchange asynchronously in the background, identifies facts, preferences, skills, and attributes, generates embeddings for semantic search, and updates the knowledge graph without blocking the agent's response path. |\n| **Intelligent Recall** | Before the agent responds, Memori searches the current entity's stored facts and knowledge graph, ranks memories by semantic relevance and importance, and injects the most useful context into the prompt so durable knowledge survives context-window compression. |\n| **Production-ready observability** | Memori Cloud gives you dashboard visibility into memory creation, recalls, cache hit rate, sessions, quota usage, top subjects, per-memory retrieval metrics, and knowledge-graph relationships, so you can inspect what was stored and how recall is behaving in production. |\n\nThe plugin still remains drop-in: OpenClaw handles the agent loop, while Memori adds recall, augmentation, sanitization, and observability around it.\n\n\n## Quickstart\n\nGet persistent memory running in your OpenClaw gateway in three steps.\n\n### Prerequisites\n\n- [OpenClaw](https://openclaw.ai) `v2026.3.2` or later\n- A Memori API key from [app.memorilabs.ai](https://app.memorilabs.ai)\n- An Entity ID to attribute memories to, such as a user ID, tenant ID, or agent name\n\n### 1. Install and Enable\n\nRun the following commands in your terminal to install and enable the plugin:\n\n```bash\n# 1. Install the plugin from npm\nopenclaw plugins install @memorilabs/openclaw-memori\n\n# 2. Enable it in your workspace\nopenclaw plugins enable openclaw-memori\n\n# 3. Restart the OpenClaw gateway\nopenclaw gateway restart\n```\n\n### 2. Configure\n\nThe plugin needs your Memori API key and an Entity ID to function. You can configure this via the OpenClaw CLI or your `openclaw.json` file.\n\n### Option A: Via OpenClaw CLI (Recommended)\n\n```bash\nopenclaw config set plugins.entries.openclaw-memori.config.apiKey \"YOUR_MEMORI_API_KEY\"\nopenclaw config set plugins.entries.openclaw-memori.config.entityId \"your-app-user-id\"\n```\n\n### Option B: Via `openclaw.json`\n\nAdd the following to your `~/.openclaw/openclaw.json` file:\n\n```json\n{\n \"plugins\": {\n \"entries\": {\n \"openclaw-memori\": {\n \"enabled\": true,\n \"config\": {\n \"apiKey\": \"your-memori-api-key\",\n \"entityId\": \"your-app-user-id\"\n }\n }\n }\n }\n}\n```\n\n### Configuration Options\n\n| Option | Type | Required | Description |\n| ---------- | -------- | -------- | --------------------------------------------------------------------------------------------------- |\n| `apiKey` | `string` | **Yes** | Your Memori API key. |\n| `entityId` | `string` | **Yes** | The unique identifier for the entity (e.g., user, agent, or tenant) to attribute these memories to. |\n\n### 3. Verify\n\nRestart the gateway and inspect the logs:\n\n```bash\nopenclaw gateway restart\nopenclaw gateway logs --filter \"[Memori]\"\n```\n\nYou should see:\n\n```text\n[Memori] === INITIALIZING PLUGIN ===\n[Memori] Tracking Entity ID: your-app-user-id\n```\n\nTo test the full memory loop:\n\n1. Send a message with a durable preference:\n `I always use TypeScript and prefer functional patterns.`\n2. Confirm augmentation ran:\n `Augmentation successful!`\n3. Start a new session and ask:\n `Write a hello world script.`\n4. Confirm recall ran:\n `Successfully injected memory context.`\n\n## How It Works\n\nThis plugin integrates with OpenClaw's event lifecycle to provide persistent memory without interfering with the agent's core logic:\n\n1. **`before_prompt_build` (Intelligent Recall):** When a user sends a message, the plugin intercepts the event, queries the Memori API, and safely prepends relevant memories to the agent's system context.\n2. **`agent_end` (Advanced Augmentation):** Once the agent finishes generating its response, the plugin captures the final `user` and `assistant` messages, sanitizes them, and sends them to the Memori integration endpoint for long-term storage and entity mapping.\n\n## Contributing\n\nWe welcome contributions from the community! Please see our [Contributing Guidelines](https://github.com/MemoriLabs/Memori/blob/main/CONTRIBUTING.md) for details on code style, standards, and submitting pull requests.\n\nTo build from source:\n\n```bash\n# Clone the repository\ngit clone https://github.com/memorilabs/openclaw-memori.git\ncd openclaw-memori\n\n# Install dependencies and build\nnpm install\nnpm run build\n\n# Run formatting, linting, and type checking\nnpm run check\n```\n\n---\n\n## Support\n\n- **Documentation**: [https://memorilabs.ai/docs](https://memorilabs.ai/docs)\n- **Discord**: [https://discord.gg/abD4eGym6v](https://discord.gg/abD4eGym6v)\n- **Issues**: [GitHub Issues](https://github.com/memorilabs/openclaw-memori/issues)\n\n---\n\n## License\n\nApache 2.0 - see [LICENSE](https://github.com/MemoriLabs/Memori/blob/main/LICENSE)\n" }, { "path": "integrations/openclaw/eslint.config.js", "content": "import eslint from '@eslint/js';\nimport tseslint from 'typescript-eslint';\nimport prettier from 'eslint-config-prettier';\nimport tsdoc from 'eslint-plugin-tsdoc';\n\nexport default tseslint.config(\n eslint.configs.recommended,\n ...tseslint.configs.strictTypeChecked,\n prettier,\n {\n languageOptions: {\n parserOptions: {\n projectService: true,\n tsconfigRootDir: import.meta.dirname,\n },\n },\n plugins: {\n tsdoc: tsdoc,\n },\n rules: {\n '@typescript-eslint/no-unused-vars': [\n 'error',\n { argsIgnorePattern: '^_', varsIgnorePattern: '^_' },\n ],\n '@typescript-eslint/no-explicit-any': 'warn',\n '@typescript-eslint/no-extraneous-class': 'off',\n '@typescript-eslint/restrict-template-expressions': 'off',\n 'tsdoc/syntax': 'warn',\n },\n },\n {\n files: ['tests/**/*.ts'],\n rules: {\n '@typescript-eslint/no-explicit-any': 'off',\n '@typescript-eslint/no-unsafe-assignment': 'off',\n '@typescript-eslint/no-unsafe-member-access': 'off',\n '@typescript-eslint/no-unsafe-argument': 'off',\n '@typescript-eslint/no-unsafe-return': 'off',\n '@typescript-eslint/no-unsafe-call': 'off',\n '@typescript-eslint/require-await': 'off',\n '@typescript-eslint/no-non-null-assertion': 'off',\n '@typescript-eslint/unbound-method': 'off',\n },\n },\n {\n ignores: [\n 'dist/**',\n 'node_modules/**',\n '*.config.js',\n '*.config.ts',\n 'tests/**',\n '**/*.test.ts',\n '**/*.spec.ts',\n 'coverage',\n ],\n }\n);\n" }, { "path": "integrations/openclaw/openclaw.plugin.json", "content": "{\n \"id\": \"openclaw-memori\",\n \"name\": \"Memori System\",\n \"version\": \"0.0.2\",\n \"description\": \"Hosted memory backend\",\n \"kind\": \"memory\",\n \"main\": \"dist/index.js\",\n \"uiHints\": {\n \"apiKey\": {\n \"label\": \"Memori API Key\",\n \"sensitive\": true,\n \"placeholder\": \"Enter your Memori API key...\",\n \"help\": \"API key from memorilabs.ai (or use MEMORI_API_KEY env var)\"\n },\n \"entityId\": {\n \"label\": \"Entity ID\",\n \"placeholder\": \"e.g., your-app-user-id\",\n \"help\": \"Required. The unique identifier to attribute these memories to.\"\n }\n },\n \"configSchema\": {\n \"type\": \"object\",\n \"properties\": {\n \"apiKey\": {\n \"type\": \"string\",\n \"title\": \"API Key\",\n \"description\": \"Memori API Key\"\n },\n \"entityId\": {\n \"type\": \"string\",\n \"title\": \"Entity ID\",\n \"description\": \"Required. Hardcode a specific Entity ID for memories.\"\n }\n },\n \"required\": []\n }\n}\n" }, { "path": "integrations/openclaw/package-lock.json", "content": "{\n \"name\": \"@memorilabs/openclaw-memori\",\n \"version\": \"0.0.4\",\n \"lockfileVersion\": 3,\n \"requires\": true,\n \"packages\": {\n \"\": {\n \"name\": \"@memorilabs/openclaw-memori\",\n \"version\": \"0.0.4\",\n \"license\": \"Apache-2.0\",\n \"dependencies\": {\n \"@memorilabs/memori\": \"^0.0.5\"\n },\n \"devDependencies\": {\n \"@eslint/js\": \"^9.0.0\",\n \"@types/node\": \"^20.0.0\",\n \"@vitest/coverage-v8\": \"^4.0.18\",\n \"eslint\": \"^9.0.0\",\n \"eslint-config-prettier\": \"^10.1.8\",\n \"eslint-plugin-tsdoc\": \"^0.5.1\",\n \"prettier\": \"^3.0.0\",\n \"typescript\": \"^5.0.0\",\n \"typescript-eslint\": \"^8.0.0\",\n \"vitest\": \"^4.0.18\"\n },\n \"engines\": {\n \"node\": \">=22.0.0\"\n },\n \"peerDependencies\": {\n \"openclaw\": \"^2026.3.2\"\n }\n },\n \"node_modules/@agentclientprotocol/sdk\": {\n 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\"MIT\",\n \"engines\": {\n \"node\": \">=10\"\n },\n \"funding\": {\n \"url\": \"https://github.com/sponsors/sindresorhus\"\n }\n },\n \"node_modules/yoctocolors\": {\n \"version\": \"2.1.2\",\n \"resolved\": \"https://registry.npmjs.org/yoctocolors/-/yoctocolors-2.1.2.tgz\",\n \"integrity\": \"sha512-CzhO+pFNo8ajLM2d2IW/R93ipy99LWjtwblvC1RsoSUMZgyLbYFr221TnSNT7GjGdYui6P459mw9JH/g/zW2ug==\",\n \"license\": \"MIT\",\n \"peer\": true,\n \"engines\": {\n \"node\": \">=18\"\n },\n \"funding\": {\n \"url\": \"https://github.com/sponsors/sindresorhus\"\n }\n },\n \"node_modules/zod\": {\n \"version\": \"4.3.6\",\n \"resolved\": \"https://registry.npmjs.org/zod/-/zod-4.3.6.tgz\",\n \"integrity\": \"sha512-rftlrkhHZOcjDwkGlnUtZZkvaPHCsDATp4pGpuOOMDaTdDDXF91wuVDJoWoPsKX/3YPQ5fHuF3STjcYyKr+Qhg==\",\n \"license\": \"MIT\",\n \"peer\": true,\n \"funding\": {\n \"url\": \"https://github.com/sponsors/colinhacks\"\n }\n },\n \"node_modules/zod-to-json-schema\": {\n \"version\": \"3.25.1\",\n \"resolved\": \"https://registry.npmjs.org/zod-to-json-schema/-/zod-to-json-schema-3.25.1.tgz\",\n \"integrity\": \"sha512-pM/SU9d3YAggzi6MtR4h7ruuQlqKtad8e9S0fmxcMi+ueAK5Korys/aWcV9LIIHTVbj01NdzxcnXSN+O74ZIVA==\",\n \"license\": \"ISC\",\n \"peer\": true,\n \"peerDependencies\": {\n \"zod\": \"^3.25 || ^4\"\n }\n }\n }\n}\n" }, { "path": "integrations/openclaw/package.json", "content": "{\n \"name\": \"@memorilabs/openclaw-memori\",\n \"version\": \"0.0.4\",\n \"description\": \"Official MemoriLabs.ai long-term memory plugin for OpenClaw\",\n \"type\": \"module\",\n \"main\": \"./dist/index.js\",\n \"types\": \"./dist/index.d.ts\",\n \"files\": [\n \"dist\",\n \"openclaw.plugin.json\"\n ],\n \"scripts\": {\n \"build\": \"tsc\",\n \"build:dev\": \"tsc --watch\",\n \"typecheck\": \"tsc --noEmit\",\n \"lint\": \"eslint .\",\n \"lint:fix\": \"eslint . --fix\",\n \"format\": \"prettier --write .\",\n \"format:check\": \"prettier --check .\",\n \"check\": \"npm run format:check && npm run lint && npm run typecheck\",\n \"test\": \"vitest run\",\n \"test:coverage\": \"vitest run --coverage\"\n },\n \"peerDependencies\": {\n \"openclaw\": \"^2026.3.2\"\n },\n \"devDependencies\": {\n \"@eslint/js\": \"^9.0.0\",\n \"@types/node\": \"^20.0.0\",\n \"@vitest/coverage-v8\": \"^4.0.18\",\n \"eslint\": \"^9.0.0\",\n \"eslint-config-prettier\": \"^10.1.8\",\n \"eslint-plugin-tsdoc\": \"^0.5.1\",\n \"prettier\": \"^3.0.0\",\n \"typescript\": \"^5.0.0\",\n \"typescript-eslint\": \"^8.0.0\",\n \"vitest\": \"^4.0.18\"\n },\n \"keywords\": [\n \"openclaw\",\n \"plugin\",\n \"memory\",\n \"memori\",\n \"long-term-memory\"\n ],\n \"author\": \"Memori Labs\",\n \"license\": \"Apache-2.0\",\n \"repository\": {\n \"type\": \"git\",\n \"url\": \"https://github.com/MemoriLabs/Memori.git\",\n \"directory\": \"integrations/openclaw\"\n },\n \"bugs\": {\n \"url\": \"https://github.com/MemoriLabs/Memori/issues\"\n },\n \"homepage\": \"https://github.com/MemoriLabs/Memori\",\n \"openclaw\": {\n \"extensions\": [\n \"./dist/index.js\"\n ]\n },\n \"engines\": {\n \"node\": \">=22.0.0\"\n },\n \"dependencies\": {\n \"@memorilabs/memori\": \"^0.0.5\"\n },\n \"overrides\": {\n \"@hono/node-server\": \"^1.19.10\"\n }\n}\n" }, { "path": "integrations/openclaw/src/constants.ts", "content": "export const PLUGIN_CONFIG = {\n ID: 'openclaw-memori',\n NAME: 'Memori System',\n LOG_PREFIX: '[Memori]',\n} as const;\n\nexport const RECALL_CONFIG = {\n MIN_PROMPT_LENGTH: 2,\n} as const;\n\nexport const AUGMENTATION_CONFIG = {\n MAX_CONTEXT_MESSAGES: 5,\n} as const;\n" }, { "path": "integrations/openclaw/src/handlers/augmentation.ts", "content": "import { IntegrationRequest, IntegrationMetadata } from '@memorilabs/memori/integrations';\nimport { OpenClawEvent, OpenClawContext, MemoriPluginConfig } from '../types.js';\nimport { extractContext, MemoriLogger, initializeMemoriClient } from '../utils/index.js';\nimport { cleanText, isSystemMessage } from '../sanitizer.js';\nimport { AUGMENTATION_CONFIG } from '../constants.js';\nimport { SDK_VERSION } from '../version.js';\n\nfunction extractLLMMetadata(event: OpenClawEvent): IntegrationMetadata {\n const messages = event.messages || [];\n const lastAssistant = messages.findLast((m) => m.role === 'assistant');\n\n return {\n provider: (lastAssistant?.provider as string) || null,\n model: (lastAssistant?.model as string) || null,\n sdkVersion: null,\n integrationSdkVersion: SDK_VERSION,\n platform: 'openclaw',\n };\n}\n\nexport async function handleAugmentation(\n event: OpenClawEvent,\n ctx: OpenClawContext,\n config: MemoriPluginConfig,\n logger: MemoriLogger\n): Promise {\n logger.section('AUGMENTATION HOOK START');\n\n if (!event.success || !event.messages || event.messages.length < 2) {\n logger.info('No messages or unsuccessful event. Skipping augmentation.');\n logger.endSection('AUGMENTATION HOOK END');\n return;\n }\n\n try {\n const recentMessages = event.messages.slice(-AUGMENTATION_CONFIG.MAX_CONTEXT_MESSAGES);\n\n let lastUserMsg: { role: string; content: string } | undefined;\n let lastAiMsg: { role: string; content: string } | undefined;\n\n for (let i = recentMessages.length - 1; i >= 0; i--) {\n const msg = recentMessages[i];\n const role = msg.role;\n\n if (role !== 'user' && role !== 'assistant') continue;\n\n const cleanedContent = cleanText(msg.content);\n if (!cleanedContent) continue;\n\n let finalContent = cleanedContent;\n if (role === 'assistant') {\n finalContent = finalContent.replace(/^\\[\\[.*?\\]\\]\\s*/, '');\n }\n\n if (role === 'assistant' && !lastAiMsg) {\n lastAiMsg = { role, content: finalContent };\n }\n\n if (role === 'user' && !lastUserMsg) {\n lastUserMsg = { role, content: finalContent };\n }\n\n if (lastUserMsg && lastAiMsg) break;\n }\n\n if (!lastUserMsg || !lastAiMsg) {\n logger.info('Missing user or assistant message. Skipping.');\n logger.endSection('AUGMENTATION HOOK END');\n return;\n }\n\n if (isSystemMessage(lastUserMsg.content)) {\n logger.info('User message is a system message. Skipping augmentation.');\n logger.endSection('AUGMENTATION HOOK END');\n return;\n }\n\n if (lastAiMsg.content === 'NO_REPLY' || lastAiMsg.content === 'SILENT_REPLY') {\n logger.info('Assistant used tool-based messaging. Using synthetic response.');\n lastAiMsg = {\n role: 'assistant',\n content: \"Okay, I'll remember that for you.\",\n };\n }\n\n const context = extractContext(event, ctx, config.entityId);\n const memoriClient = initializeMemoriClient(config.apiKey, context);\n\n const payload: IntegrationRequest = {\n userMessage: lastUserMsg.content,\n agentResponse: lastAiMsg.content,\n metadata: extractLLMMetadata(event),\n };\n\n await memoriClient.augmentation(payload);\n logger.info('Augmentation successful!');\n } catch (err) {\n logger.error(`Augmentation failed: ${err instanceof Error ? err.message : String(err)}`);\n } finally {\n logger.endSection('AUGMENTATION HOOK END');\n }\n}\n" }, { "path": "integrations/openclaw/src/handlers/recall.ts", "content": "import { cleanText, isSystemMessage } from '../sanitizer.js';\nimport { OpenClawEvent, OpenClawContext, MemoriPluginConfig } from '../types.js';\nimport { RECALL_CONFIG } from '../constants.js';\nimport { extractContext, MemoriLogger, initializeMemoriClient } from '../utils/index.js';\n\nexport async function handleRecall(\n event: OpenClawEvent,\n ctx: OpenClawContext,\n config: MemoriPluginConfig,\n logger: MemoriLogger\n): Promise<{ prependContext: string } | undefined> {\n logger.section('RECALL HOOK START');\n\n try {\n const context = extractContext(event, ctx, config.entityId);\n const promptText = cleanText(event.prompt);\n\n if (\n !promptText ||\n promptText.length < RECALL_CONFIG.MIN_PROMPT_LENGTH ||\n isSystemMessage(promptText)\n ) {\n logger.info('Prompt too short or is a system message. Aborting recall.');\n return undefined;\n }\n\n const memoriClient = initializeMemoriClient(config.apiKey, context);\n\n logger.info('Executing SDK Recall...');\n\n const recallText = await memoriClient.recall(promptText);\n const hookReturn = recallText ? { prependContext: recallText } : undefined;\n\n if (hookReturn) {\n logger.info('Successfully injected memory context.');\n } else {\n logger.info('No relevant memories found.');\n }\n\n return hookReturn;\n } catch (err) {\n logger.error(`Recall failed: ${err instanceof Error ? err.message : String(err)}`);\n return undefined;\n } finally {\n logger.endSection('RECALL HOOK END');\n }\n}\n" }, { "path": "integrations/openclaw/src/index.ts", "content": "import type { OpenClawPluginApi } from 'openclaw/plugin-sdk';\nimport { handleRecall } from './handlers/recall.js';\nimport { handleAugmentation } from './handlers/augmentation.js';\nimport { OpenClawEvent, OpenClawContext, MemoriPluginConfig } from './types.js';\nimport { PLUGIN_CONFIG } from './constants.js';\nimport { MemoriLogger } from './utils/index.js';\n\nconst memoriPlugin = {\n id: PLUGIN_CONFIG.ID,\n name: PLUGIN_CONFIG.NAME,\n description: 'Hosted memory backend',\n\n register(api: OpenClawPluginApi) {\n const rawConfig = api.pluginConfig;\n\n const config: MemoriPluginConfig = {\n apiKey: rawConfig?.apiKey as string,\n entityId: rawConfig?.entityId as string,\n };\n\n if (!config.apiKey || !config.entityId) {\n api.logger.warn(\n `${PLUGIN_CONFIG.LOG_PREFIX} Missing apiKey or entityId in config. Plugin disabled.`\n );\n return;\n }\n\n const logger = new MemoriLogger(api);\n\n logger.info(`\\n=== ${PLUGIN_CONFIG.LOG_PREFIX} INITIALIZING PLUGIN ===`);\n logger.info(`${PLUGIN_CONFIG.LOG_PREFIX} Tracking Entity ID: ${config.entityId}`);\n\n api.on('before_prompt_build', (event: unknown, ctx: unknown) =>\n handleRecall(event as OpenClawEvent, ctx as OpenClawContext, config, logger)\n );\n\n api.on('agent_end', (event: unknown, ctx: unknown) =>\n handleAugmentation(event as OpenClawEvent, ctx as OpenClawContext, config, logger)\n );\n },\n};\n\nexport default memoriPlugin;\n" }, { "path": "integrations/openclaw/src/sanitizer.ts", "content": "import { OpenClawMessageBlock } from './types.js';\n\nconst SYSTEM_MESSAGE_PATTERNS = [\n 'a new session was started',\n '/new or /reset',\n 'session startup sequence',\n 'use persona',\n] as const;\n\nconst TIMESTAMP_PREFIX_REGEX =\n /^\\[[A-Z][a-z]{2}\\s+\\d{4}-\\d{2}-\\d{2}\\s+\\d{2}:\\d{2}\\s+[A-Z]{2,4}\\]\\s*/;\n\nfunction isMessageBlockArray(value: unknown): value is OpenClawMessageBlock[] {\n return Array.isArray(value) && value.every((item) => item !== null && typeof item === 'object');\n}\n\nexport function isSystemMessage(text: string): boolean {\n if (!text) return true;\n const lowerText = text.toLowerCase();\n return SYSTEM_MESSAGE_PATTERNS.some((pattern) => lowerText.includes(pattern));\n}\n\n/**\n * Extracts the actual user message from OpenClaw's formatted content.\n *\n * OpenClaw wraps metadata in markdown code fences (triple tick).\n * The actual message is always after the LAST closing fence.\n * The message might aslo contain a timestamp prefix like: [Day YYYY-MM-DD HH:MM TZ], that will need to be removed\n */\nfunction extractRawUserMessage(content: string): string {\n let message = content;\n\n if (message.includes('```')) {\n const lastFenceIndex = message.lastIndexOf('```');\n if (lastFenceIndex !== -1) {\n message = message.substring(lastFenceIndex + 3).trim();\n }\n }\n\n const timestampMatch = message.match(TIMESTAMP_PREFIX_REGEX);\n if (timestampMatch) {\n message = message.substring(timestampMatch[0].length).trim();\n }\n\n return message;\n}\n\nfunction extractMessageText(content: unknown): string {\n if (!content) return '';\n\n if (typeof content === 'string') {\n return content;\n }\n\n if (isMessageBlockArray(content)) {\n return content\n .filter((block) => (block.type === 'text' || typeof block.text === 'string') && block.text)\n .map((block) => block.text)\n .join('\\n\\n');\n }\n\n return '';\n}\n\nexport function cleanText(rawContent: unknown): string {\n let text = extractMessageText(rawContent);\n\n if (!text) return '';\n\n text = extractRawUserMessage(text);\n\n text = text.replace(/[\\s\\S]*?<\\/memori_context>\\s*/g, '');\n\n return text.trim();\n}\n" }, { "path": "integrations/openclaw/src/types.ts", "content": "export interface MemoriPluginConfig {\n apiKey: string;\n entityId: string;\n}\n\nexport interface OpenClawMessageBlock {\n type?: string;\n text?: string;\n thinking?: string;\n [key: string]: unknown;\n}\n\nexport interface OpenClawMessage {\n role: 'user' | 'assistant' | 'system';\n content: string | OpenClawMessageBlock[];\n timestamp?: number;\n [key: string]: unknown;\n}\n\nexport interface OpenClawEvent {\n prompt?: string;\n messages?: OpenClawMessage[];\n completion?: string;\n success?: boolean;\n error?: string;\n durationMs?: number;\n userId?: string;\n sessionId?: string;\n messageProvider?: string;\n}\n\nexport interface OpenClawContext {\n agentId?: string;\n sessionKey?: string;\n sessionId?: string;\n workspaceDir?: string;\n messageProvider?: string;\n}\n" }, { "path": "integrations/openclaw/src/utils/context.ts", "content": "import { OpenClawEvent, OpenClawContext } from '../types.js';\n\n/**\n * Extracted context information from OpenClaw events\n */\nexport interface ExtractedContext {\n entityId: string;\n sessionId: string;\n provider: string;\n}\n\n/**\n * Extracts and normalizes context information from OpenClaw event and context objects.\n * Throws an error if required context fields cannot be resolved.\n *\n * @param event - OpenClaw event object\n * @param ctx - OpenClaw context object\n * @param configuredEntityId - Hardcoded entity ID from plugin config\n * @returns Normalized context with entityId, sessionId, and provider\n * @throws Error If entityId, sessionId, or provider cannot be determined\n */\nexport function extractContext(\n event: OpenClawEvent,\n ctx: OpenClawContext,\n configuredEntityId: string\n): ExtractedContext {\n const sessionId = ctx.sessionKey || event.sessionId;\n const provider = ctx.messageProvider || event.messageProvider;\n\n if (!sessionId) {\n throw new Error('Failed to extract context: Missing sessionId in OpenClaw context and event.');\n }\n\n if (!provider) {\n throw new Error(\n 'Failed to extract context: Missing message provider in OpenClaw context and event.'\n );\n }\n\n return {\n entityId: configuredEntityId,\n sessionId,\n provider,\n };\n}\n" }, { "path": "integrations/openclaw/src/utils/index.ts", "content": "export { extractContext, type ExtractedContext } from './context.js';\nexport { MemoriLogger } from './logger.js';\nexport { initializeMemoriClient } from './memori-client.js';\n" }, { "path": "integrations/openclaw/src/utils/logger.ts", "content": "import type { OpenClawPluginApi } from 'openclaw/plugin-sdk';\nimport { PLUGIN_CONFIG } from '../constants.js';\n\nexport class MemoriLogger {\n constructor(private api: OpenClawPluginApi) {}\n\n private prefix(msg: string): string {\n return `${PLUGIN_CONFIG.LOG_PREFIX} ${msg}`;\n }\n\n info(message: string): void {\n this.api.logger.info(this.prefix(message));\n }\n\n warn(message: string): void {\n this.api.logger.warn(this.prefix(message));\n }\n\n error(message: string): void {\n this.api.logger.error(this.prefix(message));\n }\n\n section(title: string): void {\n this.api.logger.info(`\\n=== ${this.prefix(title)} ===`);\n }\n\n endSection(title: string): void {\n this.api.logger.info(`=== ${this.prefix(title)} ===\\n`);\n }\n}\n" }, { "path": "integrations/openclaw/src/utils/memori-client.ts", "content": "import { Memori } from '@memorilabs/memori';\nimport { OpenClawIntegration } from '@memorilabs/memori/integrations';\nimport { ExtractedContext } from './context.js';\n\n/**\n * Initializes and configures a Memori OpenClaw integration instance\n *\n * @param apiKey - Memori API key\n * @param context - Extracted context information\n * @returns Configured OpenClawIntegration instance\n */\nexport function initializeMemoriClient(\n apiKey: string,\n context: ExtractedContext\n): OpenClawIntegration {\n const memori = new Memori();\n memori.config.apiKey = apiKey;\n\n const openclaw = memori.integrate(OpenClawIntegration);\n openclaw.setAttribution(context.entityId, context.provider);\n openclaw.setSession(context.sessionId);\n\n return openclaw;\n}\n" }, { "path": "integrations/openclaw/src/version.ts", "content": "// This file is auto-generated during CI builds.\nexport const SDK_VERSION = '0.0.0';\n" }, { "path": "integrations/openclaw/tests/handlers/augmentation.test.ts", "content": "import { describe, it, expect, vi, beforeEach } from 'vitest';\nimport { handleAugmentation } from '../../src/handlers/augmentation.js';\nimport type {\n OpenClawEvent,\n OpenClawContext,\n MemoriPluginConfig,\n OpenClawMessage,\n} from '../../src/types.js';\nimport type { MemoriLogger } from '../../src/utils/logger.js';\nimport { SDK_VERSION } from '../../src/version.js';\n\nvi.mock('../../src/sanitizer.js', () => ({\n cleanText: vi.fn((content) => {\n if (typeof content === 'string') return content;\n return 'cleaned text';\n }),\n isSystemMessage: vi.fn(() => false),\n}));\n\nvi.mock('../../src/utils/index.js', () => ({\n extractContext: vi.fn(() => ({\n entityId: 'test-entity',\n sessionId: 'test-session',\n provider: 'test-provider',\n })),\n initializeMemoriClient: vi.fn(() => ({\n augmentation: vi.fn(async () => {}),\n })),\n}));\n\ndescribe('handlers/augmentation', () => {\n let mockLogger: MemoriLogger;\n let config: MemoriPluginConfig;\n let event: OpenClawEvent;\n let ctx: OpenClawContext;\n\n beforeEach(() => {\n vi.clearAllMocks();\n\n mockLogger = {\n section: vi.fn(),\n endSection: vi.fn(),\n info: vi.fn(),\n error: vi.fn(),\n } as unknown as MemoriLogger;\n\n config = {\n apiKey: 'test-api-key',\n entityId: 'test-entity-id',\n };\n\n event = {\n success: true,\n messages: [\n { role: 'user', content: 'Hello, how are you?' },\n { role: 'assistant', content: \"I'm doing well, thank you!\" },\n ],\n };\n\n ctx = {\n sessionKey: 'session-123',\n messageProvider: 'test-provider',\n };\n });\n\n describe('successful augmentation', () => {\n it('should send user and assistant messages to memori', async () => {\n const { initializeMemoriClient } = await import('../../src/utils/index.js');\n\n await handleAugmentation(event, ctx, config, mockLogger);\n\n const client = vi.mocked(initializeMemoriClient).mock.results[0].value;\n expect(client.augmentation).toHaveBeenCalledWith(\n expect.objectContaining({\n userMessage: 'Hello, how are you?',\n agentResponse: \"I'm doing well, thank you!\",\n })\n );\n });\n\n it('should include metadata in request', async () => {\n const { initializeMemoriClient } = await import('../../src/utils/index.js');\n\n await handleAugmentation(event, ctx, config, mockLogger);\n\n const client = vi.mocked(initializeMemoriClient).mock.results[0].value;\n expect(client.augmentation).toHaveBeenCalledWith(\n expect.objectContaining({\n metadata: expect.objectContaining({\n platform: 'openclaw',\n integrationSdkVersion: SDK_VERSION,\n }),\n })\n );\n });\n });\n\n describe('event validation', () => {\n it('should skip when event is unsuccessful', async () => {\n event.success = false;\n\n await handleAugmentation(event, ctx, config, mockLogger);\n\n expect(mockLogger.info).toHaveBeenCalledWith(\n 'No messages or unsuccessful event. Skipping augmentation.'\n );\n\n const { initializeMemoriClient } = await import('../../src/utils/index.js');\n expect(initializeMemoriClient).not.toHaveBeenCalled();\n });\n\n it('should skip when messages array is empty', async () => {\n event.messages = [];\n\n await handleAugmentation(event, ctx, config, mockLogger);\n\n expect(mockLogger.info).toHaveBeenCalledWith(\n 'No messages or unsuccessful event. Skipping augmentation.'\n );\n });\n\n it('should skip when messages has only one message', async () => {\n event.messages = [{ role: 'user', content: 'Hello' }];\n\n await handleAugmentation(event, ctx, config, mockLogger);\n\n expect(mockLogger.info).toHaveBeenCalledWith(\n 'No messages or unsuccessful event. Skipping augmentation.'\n );\n });\n\n it('should skip when messages is undefined', async () => {\n event.messages = undefined;\n\n await handleAugmentation(event, ctx, config, mockLogger);\n\n expect(mockLogger.info).toHaveBeenCalledWith(\n 'No messages or unsuccessful event. Skipping augmentation.'\n );\n });\n });\n\n describe('message extraction', () => {\n it('should extract last user and assistant messages from recent history', async () => {\n event.messages = [\n { role: 'user', content: 'First message' },\n { role: 'assistant', content: 'First response' },\n { role: 'user', content: 'Second message' },\n { role: 'assistant', content: 'Second response' },\n { role: 'user', content: 'Third message' },\n { role: 'assistant', content: 'Third response' },\n ];\n\n const { initializeMemoriClient } = await import('../../src/utils/index.js');\n\n await handleAugmentation(event, ctx, config, mockLogger);\n\n const client = vi.mocked(initializeMemoriClient).mock.results[0].value;\n expect(client.augmentation).toHaveBeenCalledWith(\n expect.objectContaining({\n userMessage: 'Third message',\n agentResponse: 'Third response',\n })\n );\n });\n\n it('should only consider last 5 messages', async () => {\n event.messages = [\n { role: 'user', content: 'Message 1' },\n { role: 'assistant', content: 'Response 1' },\n { role: 'user', content: 'Message 2' },\n { role: 'assistant', content: 'Response 2' },\n { role: 'user', content: 'Message 3' },\n { role: 'assistant', content: 'Response 3' },\n { role: 'user', content: 'Message 4' },\n { role: 'assistant', content: 'Response 4' },\n { role: 'user', content: 'Latest message' },\n { role: 'assistant', content: 'Latest response' },\n ];\n\n const { initializeMemoriClient } = await import('../../src/utils/index.js');\n\n await handleAugmentation(event, ctx, config, mockLogger);\n\n const client = vi.mocked(initializeMemoriClient).mock.results[0].value;\n expect(client.augmentation).toHaveBeenCalledWith(\n expect.objectContaining({\n userMessage: 'Latest message',\n agentResponse: 'Latest response',\n })\n );\n });\n\n it('should skip system role messages', async () => {\n event.messages = [\n { role: 'system', content: 'System prompt' },\n { role: 'user', content: 'User message' },\n { role: 'assistant', content: 'Assistant response' },\n ];\n\n const { initializeMemoriClient } = await import('../../src/utils/index.js');\n\n await handleAugmentation(event, ctx, config, mockLogger);\n\n const client = vi.mocked(initializeMemoriClient).mock.results[0].value;\n expect(client.augmentation).toHaveBeenCalledWith(\n expect.objectContaining({\n userMessage: 'User message',\n agentResponse: 'Assistant response',\n })\n );\n });\n\n it('should skip when user or assistant message is missing', async () => {\n event.messages = [\n { role: 'system', content: 'Sys' },\n { role: 'user', content: 'Only user message' },\n ];\n\n await handleAugmentation(event, ctx, config, mockLogger);\n\n expect(mockLogger.info).toHaveBeenCalledWith('Missing user or assistant message. Skipping.');\n\n const { initializeMemoriClient } = await import('../../src/utils/index.js');\n expect(initializeMemoriClient).not.toHaveBeenCalled();\n });\n\n it('should skip messages with empty cleaned content', async () => {\n const { cleanText } = await import('../../src/sanitizer.js');\n vi.mocked(cleanText).mockReturnValueOnce('');\n\n event.messages = [\n { role: 'user', content: 'Some content' },\n { role: 'assistant', content: 'Some response' },\n ];\n\n await handleAugmentation(event, ctx, config, mockLogger);\n\n expect(mockLogger.info).toHaveBeenCalledWith('Missing user or assistant message. Skipping.');\n });\n });\n\n describe('system message filtering', () => {\n it('should skip when user message is a system message', async () => {\n const { isSystemMessage } = await import('../../src/sanitizer.js');\n vi.mocked(isSystemMessage).mockReturnValueOnce(true);\n\n await handleAugmentation(event, ctx, config, mockLogger);\n\n expect(mockLogger.info).toHaveBeenCalledWith(\n 'User message is a system message. Skipping augmentation.'\n );\n\n const { initializeMemoriClient } = await import('../../src/utils/index.js');\n expect(initializeMemoriClient).not.toHaveBeenCalled();\n });\n });\n\n describe('synthetic responses', () => {\n it('should replace NO_REPLY with synthetic response', async () => {\n event.messages = [\n { role: 'user', content: 'Remember my name is John' },\n { role: 'assistant', content: 'NO_REPLY' },\n ];\n\n const { initializeMemoriClient } = await import('../../src/utils/index.js');\n\n await handleAugmentation(event, ctx, config, mockLogger);\n\n const client = vi.mocked(initializeMemoriClient).mock.results[0].value;\n expect(client.augmentation).toHaveBeenCalledWith(\n expect.objectContaining({\n agentResponse: \"Okay, I'll remember that for you.\",\n })\n );\n\n expect(mockLogger.info).toHaveBeenCalledWith(\n 'Assistant used tool-based messaging. Using synthetic response.'\n );\n });\n\n it('should replace SILENT_REPLY with synthetic response', async () => {\n event.messages = [\n { role: 'user', content: 'Save this for later' },\n { role: 'assistant', content: 'SILENT_REPLY' },\n ];\n\n const { initializeMemoriClient } = await import('../../src/utils/index.js');\n\n await handleAugmentation(event, ctx, config, mockLogger);\n\n const client = vi.mocked(initializeMemoriClient).mock.results[0].value;\n expect(client.augmentation).toHaveBeenCalledWith(\n expect.objectContaining({\n agentResponse: \"Okay, I'll remember that for you.\",\n })\n );\n });\n });\n\n describe('thinking block removal', () => {\n it('should strip thinking blocks from assistant messages', async () => {\n const { cleanText } = await import('../../src/sanitizer.js');\n vi.mocked(cleanText).mockImplementationOnce((content) => {\n if (typeof content === 'string' && content.includes('[[')) {\n return '[[This is my thought process.]]\\n\\nActual response here.';\n }\n return content as string;\n });\n\n event.messages = [\n { role: 'user', content: 'Question?' },\n {\n role: 'assistant',\n content: '[[This is my thought process.]]\\n\\nActual response here.',\n },\n ];\n\n const { initializeMemoriClient } = await import('../../src/utils/index.js');\n\n await handleAugmentation(event, ctx, config, mockLogger);\n\n const client = vi.mocked(initializeMemoriClient).mock.results[0].value;\n expect(client.augmentation).toHaveBeenCalledWith(\n expect.objectContaining({\n agentResponse: 'Actual response here.',\n })\n );\n });\n });\n\n describe('metadata extraction', () => {\n it('should extract provider and model from last assistant message', async () => {\n event.messages = [\n { role: 'user', content: 'Question' },\n {\n role: 'assistant',\n content: 'Answer',\n provider: 'anthropic' as any,\n model: 'claude-3-sonnet' as any,\n },\n ];\n\n const { initializeMemoriClient } = await import('../../src/utils/index.js');\n\n await handleAugmentation(event, ctx, config, mockLogger);\n\n const client = vi.mocked(initializeMemoriClient).mock.results[0].value;\n expect(client.augmentation).toHaveBeenCalledWith(\n expect.objectContaining({\n metadata: expect.objectContaining({\n provider: 'anthropic',\n model: 'claude-3-sonnet',\n }),\n })\n );\n });\n\n it('should use null for missing provider and model', async () => {\n const { initializeMemoriClient } = await import('../../src/utils/index.js');\n\n await handleAugmentation(event, ctx, config, mockLogger);\n\n const client = vi.mocked(initializeMemoriClient).mock.results[0].value;\n expect(client.augmentation).toHaveBeenCalledWith(\n expect.objectContaining({\n metadata: expect.objectContaining({\n provider: null,\n model: null,\n }),\n })\n );\n });\n });\n\n describe('error handling', () => {\n it('should handle errors gracefully', async () => {\n const { extractContext } = await import('../../src/utils/index.js');\n vi.mocked(extractContext).mockImplementationOnce(() => {\n throw new Error('Context extraction failed');\n });\n\n await handleAugmentation(event, ctx, config, mockLogger);\n\n expect(mockLogger.error).toHaveBeenCalledWith(\n 'Augmentation failed: Context extraction failed'\n );\n });\n\n it('should log non-Error objects as strings', async () => {\n const { extractContext } = await import('../../src/utils/index.js');\n vi.mocked(extractContext).mockImplementationOnce(() => {\n throw 'String error';\n });\n\n await handleAugmentation(event, ctx, config, mockLogger);\n\n expect(mockLogger.error).toHaveBeenCalledWith('Augmentation failed: String error');\n });\n\n it('should handle API errors from memori client', async () => {\n const { initializeMemoriClient } = await import('../../src/utils/index.js');\n vi.mocked(initializeMemoriClient).mockReturnValueOnce({\n augmentation: vi.fn(async () => {\n throw new Error('API connection failed');\n }),\n } as any);\n\n await handleAugmentation(event, ctx, config, mockLogger);\n\n expect(mockLogger.error).toHaveBeenCalledWith('Augmentation failed: API connection failed');\n });\n });\n});\n" }, { "path": "integrations/openclaw/tests/handlers/recall.test.ts", "content": "import { describe, it, expect, vi, beforeEach } from 'vitest';\nimport { handleRecall } from '../../src/handlers/recall.js';\nimport type { OpenClawEvent, OpenClawContext, MemoriPluginConfig } from '../../src/types.js';\nimport type { MemoriLogger } from '../../src/utils/logger.js';\n\nvi.mock('../../src/sanitizer.js', () => ({\n cleanText: vi.fn((text) => text),\n isSystemMessage: vi.fn(() => false),\n}));\n\nvi.mock('../../src/utils/index.js', () => ({\n extractContext: vi.fn(() => ({\n entityId: 'test-entity',\n sessionId: 'test-session',\n provider: 'test-provider',\n })),\n initializeMemoriClient: vi.fn(() => ({\n recall: vi.fn(async () => 'Relevant memories'),\n })),\n}));\n\ndescribe('handlers/recall', () => {\n let mockLogger: MemoriLogger;\n let config: MemoriPluginConfig;\n let event: OpenClawEvent;\n let ctx: OpenClawContext;\n\n beforeEach(() => {\n vi.clearAllMocks();\n\n mockLogger = {\n section: vi.fn(),\n endSection: vi.fn(),\n info: vi.fn(),\n error: vi.fn(),\n } as unknown as MemoriLogger;\n\n config = {\n apiKey: 'test-api-key',\n entityId: 'test-entity-id',\n };\n\n event = {\n prompt: 'What is the weather today?',\n };\n\n ctx = {\n sessionKey: 'session-123',\n messageProvider: 'test-provider',\n };\n });\n\n describe('successful recall', () => {\n it('should return prependContext when memories are found', async () => {\n const result = await handleRecall(event, ctx, config, mockLogger);\n\n expect(result).toEqual({\n prependContext: 'Relevant memories',\n });\n });\n\n it('should call extractContext with correct parameters', async () => {\n const { extractContext } = await import('../../src/utils/index.js');\n await handleRecall(event, ctx, config, mockLogger);\n\n expect(extractContext).toHaveBeenCalledWith(event, ctx, 'test-entity-id');\n });\n\n it('should call initializeMemoriClient with correct parameters', async () => {\n const { initializeMemoriClient } = await import('../../src/utils/index.js');\n await handleRecall(event, ctx, config, mockLogger);\n\n expect(initializeMemoriClient).toHaveBeenCalledWith('test-api-key', {\n entityId: 'test-entity',\n sessionId: 'test-session',\n provider: 'test-provider',\n });\n });\n });\n\n describe('no memories found', () => {\n it('should return undefined when memori returns empty string', async () => {\n const { initializeMemoriClient } = await import('../../src/utils/index.js');\n vi.mocked(initializeMemoriClient).mockReturnValueOnce({\n recall: vi.fn(async () => ''),\n } as any);\n\n const result = await handleRecall(event, ctx, config, mockLogger);\n\n expect(result).toBeUndefined();\n expect(mockLogger.info).toHaveBeenCalledWith('No relevant memories found.');\n });\n\n it('should return undefined when memori returns null', async () => {\n const { initializeMemoriClient } = await import('../../src/utils/index.js');\n vi.mocked(initializeMemoriClient).mockReturnValueOnce({\n recall: vi.fn(async () => null),\n } as any);\n\n const result = await handleRecall(event, ctx, config, mockLogger);\n\n expect(result).toBeUndefined();\n });\n });\n\n describe('prompt validation', () => {\n it('should abort when prompt is empty after cleaning', async () => {\n const { cleanText } = await import('../../src/sanitizer.js');\n vi.mocked(cleanText).mockReturnValueOnce('');\n\n const result = await handleRecall(event, ctx, config, mockLogger);\n\n expect(result).toBeUndefined();\n expect(mockLogger.info).toHaveBeenCalledWith(\n 'Prompt too short or is a system message. Aborting recall.'\n );\n });\n\n it('should abort when prompt is too short', async () => {\n const { cleanText } = await import('../../src/sanitizer.js');\n vi.mocked(cleanText).mockReturnValueOnce('a');\n\n const result = await handleRecall(event, ctx, config, mockLogger);\n\n expect(result).toBeUndefined();\n expect(mockLogger.info).toHaveBeenCalledWith(\n 'Prompt too short or is a system message. Aborting recall.'\n );\n });\n\n it('should abort when prompt is a system message', async () => {\n const { isSystemMessage } = await import('../../src/sanitizer.js');\n vi.mocked(isSystemMessage).mockReturnValueOnce(true);\n\n const result = await handleRecall(event, ctx, config, mockLogger);\n\n expect(result).toBeUndefined();\n expect(mockLogger.info).toHaveBeenCalledWith(\n 'Prompt too short or is a system message. Aborting recall.'\n );\n });\n\n it('should proceed when prompt meets minimum length', async () => {\n const { cleanText } = await import('../../src/sanitizer.js');\n vi.mocked(cleanText).mockReturnValueOnce('Hello');\n\n const result = await handleRecall(event, ctx, config, mockLogger);\n\n expect(result).toBeDefined();\n });\n });\n\n describe('error handling', () => {\n it('should handle errors gracefully and return undefined', async () => {\n const { extractContext } = await import('../../src/utils/index.js');\n vi.mocked(extractContext).mockImplementationOnce(() => {\n throw new Error('Context extraction failed');\n });\n\n const result = await handleRecall(event, ctx, config, mockLogger);\n\n expect(result).toBeUndefined();\n expect(mockLogger.error).toHaveBeenCalledWith('Recall failed: Context extraction failed');\n });\n\n it('should log non-Error objects as strings', async () => {\n const { extractContext } = await import('../../src/utils/index.js');\n vi.mocked(extractContext).mockImplementationOnce(() => {\n throw 'String error';\n });\n\n await handleRecall(event, ctx, config, mockLogger);\n\n expect(mockLogger.error).toHaveBeenCalledWith('Recall failed: String error');\n });\n\n it('should handle API errors from memori client', async () => {\n const { initializeMemoriClient } = await import('../../src/utils/index.js');\n vi.mocked(initializeMemoriClient).mockReturnValueOnce({\n recall: vi.fn(async () => {\n throw new Error('API connection failed');\n }),\n } as any);\n\n const result = await handleRecall(event, ctx, config, mockLogger);\n\n expect(result).toBeUndefined();\n expect(mockLogger.error).toHaveBeenCalledWith('Recall failed: API connection failed');\n });\n });\n\n describe('edge cases', () => {\n it('should handle missing prompt gracefully', async () => {\n const eventWithoutPrompt: OpenClawEvent = {};\n\n const result = await handleRecall(eventWithoutPrompt, ctx, config, mockLogger);\n\n expect(result).toBeUndefined();\n });\n\n it('should handle null prompt', async () => {\n const eventWithNullPrompt: OpenClawEvent = { prompt: null as any };\n\n const result = await handleRecall(eventWithNullPrompt, ctx, config, mockLogger);\n\n expect(result).toBeUndefined();\n });\n\n it('should call cleanText with the prompt', async () => {\n const { cleanText } = await import('../../src/sanitizer.js');\n\n await handleRecall(event, ctx, config, mockLogger);\n\n expect(cleanText).toHaveBeenCalledWith('What is the weather today?');\n });\n });\n});\n" }, { "path": "integrations/openclaw/tests/index.test.ts", "content": "import { describe, it, expect, vi, beforeEach } from 'vitest';\nimport memoriPlugin from '../src/index.js';\nimport type { OpenClawPluginApi } from 'openclaw/plugin-sdk';\n\nvi.mock('../src/handlers/recall.js', () => ({\n handleRecall: vi.fn(),\n}));\n\nvi.mock('../src/handlers/augmentation.js', () => ({\n handleAugmentation: vi.fn(),\n}));\n\ndescribe('plugin index', () => {\n let mockApi: OpenClawPluginApi;\n\n beforeEach(() => {\n vi.clearAllMocks();\n\n mockApi = {\n pluginConfig: {\n apiKey: 'test-api-key',\n entityId: 'test-entity-id',\n },\n logger: {\n info: vi.fn(),\n warn: vi.fn(),\n error: vi.fn(),\n },\n on: vi.fn(),\n } as unknown as OpenClawPluginApi;\n });\n\n describe('plugin metadata', () => {\n it('should have correct id', () => {\n expect(memoriPlugin.id).toBe('openclaw-memori');\n });\n\n it('should have correct name', () => {\n expect(memoriPlugin.name).toBe('Memori System');\n });\n\n it('should have description', () => {\n expect(memoriPlugin.description).toBe('Hosted memory backend');\n });\n });\n\n describe('register', () => {\n it('should register hooks when config is valid', () => {\n memoriPlugin.register(mockApi);\n\n expect(mockApi.on).toHaveBeenCalledWith('before_prompt_build', expect.any(Function));\n expect(mockApi.on).toHaveBeenCalledWith('agent_end', expect.any(Function));\n expect(mockApi.on).toHaveBeenCalledTimes(2);\n });\n\n it('should not register when apiKey is missing', () => {\n mockApi.pluginConfig = {\n entityId: 'test-entity-id',\n };\n\n memoriPlugin.register(mockApi);\n\n expect(mockApi.logger.warn).toHaveBeenCalledWith(\n expect.stringContaining('Missing apiKey or entityId')\n );\n expect(mockApi.on).not.toHaveBeenCalled();\n });\n\n it('should not register when entityId is missing', () => {\n mockApi.pluginConfig = {\n apiKey: 'test-api-key',\n };\n\n memoriPlugin.register(mockApi);\n\n expect(mockApi.logger.warn).toHaveBeenCalledWith(\n expect.stringContaining('Missing apiKey or entityId')\n );\n expect(mockApi.on).not.toHaveBeenCalled();\n });\n\n it('should not register when both apiKey and entityId are missing', () => {\n mockApi.pluginConfig = {};\n\n memoriPlugin.register(mockApi);\n\n expect(mockApi.logger.warn).toHaveBeenCalledWith(\n expect.stringContaining('Missing apiKey or entityId')\n );\n expect(mockApi.on).not.toHaveBeenCalled();\n });\n\n it('should not register when pluginConfig is undefined', () => {\n mockApi.pluginConfig = undefined;\n\n memoriPlugin.register(mockApi);\n\n expect(mockApi.logger.warn).toHaveBeenCalledWith(\n expect.stringContaining('Missing apiKey or entityId')\n );\n expect(mockApi.on).not.toHaveBeenCalled();\n });\n\n it('should not register when apiKey is empty string', () => {\n mockApi.pluginConfig = {\n apiKey: '',\n entityId: 'test-entity-id',\n };\n\n memoriPlugin.register(mockApi);\n\n expect(mockApi.logger.warn).toHaveBeenCalledWith(\n expect.stringContaining('Missing apiKey or entityId')\n );\n expect(mockApi.on).not.toHaveBeenCalled();\n });\n\n it('should not register when entityId is empty string', () => {\n mockApi.pluginConfig = {\n apiKey: 'test-api-key',\n entityId: '',\n };\n\n memoriPlugin.register(mockApi);\n\n expect(mockApi.logger.warn).toHaveBeenCalledWith(\n expect.stringContaining('Missing apiKey or entityId')\n );\n expect(mockApi.on).not.toHaveBeenCalled();\n });\n });\n\n describe('hook handlers', () => {\n it('should call handleRecall for before_prompt_build event', async () => {\n const { handleRecall } = await import('../src/handlers/recall.js');\n\n memoriPlugin.register(mockApi);\n\n const beforePromptBuildHandler = vi\n .mocked(mockApi.on)\n .mock.calls.find((call) => call[0] === 'before_prompt_build')?.[1];\n\n expect(beforePromptBuildHandler).toBeDefined();\n\n const mockEvent = { prompt: 'test' } as any;\n const mockCtx = { sessionKey: 'session-123' } as any;\n\n await beforePromptBuildHandler?.(mockEvent, mockCtx);\n\n expect(handleRecall).toHaveBeenCalledWith(\n mockEvent,\n mockCtx,\n { apiKey: 'test-api-key', entityId: 'test-entity-id' },\n expect.any(Object)\n );\n });\n\n it('should call handleAugmentation for agent_end event', async () => {\n const { handleAugmentation } = await import('../src/handlers/augmentation.js');\n\n memoriPlugin.register(mockApi);\n\n const agentEndHandler = vi\n .mocked(mockApi.on)\n .mock.calls.find((call) => call[0] === 'agent_end')?.[1];\n\n expect(agentEndHandler).toBeDefined();\n\n const mockEvent = { success: true, messages: [] } as any;\n const mockCtx = { sessionKey: 'session-123' } as any;\n\n await agentEndHandler?.(mockEvent, mockCtx);\n\n expect(handleAugmentation).toHaveBeenCalledWith(\n mockEvent,\n mockCtx,\n { apiKey: 'test-api-key', entityId: 'test-entity-id' },\n expect.any(Object)\n );\n });\n });\n\n describe('configuration handling', () => {\n it('should extract apiKey from pluginConfig', () => {\n mockApi.pluginConfig = {\n apiKey: 'custom-key-123',\n entityId: 'entity-456',\n };\n\n memoriPlugin.register(mockApi);\n\n expect(mockApi.on).toHaveBeenCalled();\n });\n\n it('should handle additional config properties gracefully', () => {\n mockApi.pluginConfig = {\n apiKey: 'test-api-key',\n entityId: 'test-entity-id',\n extraProperty: 'should be ignored',\n anotherExtra: 12345,\n };\n\n memoriPlugin.register(mockApi);\n\n expect(mockApi.on).toHaveBeenCalledTimes(2);\n });\n });\n\n describe('logger creation', () => {\n it('should create MemoriLogger with api', () => {\n memoriPlugin.register(mockApi);\n\n expect(mockApi.logger.info).toHaveBeenCalledWith(expect.stringContaining('[Memori]'));\n });\n });\n});\n" }, { "path": "integrations/openclaw/tests/sanitizer.test.ts", "content": "import { describe, it, expect } from 'vitest';\nimport { isSystemMessage, cleanText } from '../src/sanitizer.js';\nimport type { OpenClawMessageBlock } from '../src/types.js';\n\ndescribe('sanitizer', () => {\n describe('isSystemMessage', () => {\n it('should return true for empty text', () => {\n expect(isSystemMessage('')).toBe(true);\n });\n\n it('should return true for system startup messages', () => {\n expect(isSystemMessage('a new session was started')).toBe(true);\n expect(isSystemMessage('A NEW SESSION WAS STARTED')).toBe(true);\n });\n\n it('should return true for reset command messages', () => {\n expect(isSystemMessage('/new or /reset')).toBe(true);\n expect(isSystemMessage('Use /new or /reset to start fresh')).toBe(true);\n });\n\n it('should return true for session startup sequence', () => {\n expect(isSystemMessage('session startup sequence initiated')).toBe(true);\n });\n\n it('should return true for persona messages', () => {\n expect(isSystemMessage('use persona: helpful assistant')).toBe(true);\n });\n\n it('should return false for regular user messages', () => {\n expect(isSystemMessage('Hello, how are you?')).toBe(false);\n expect(isSystemMessage('What is the weather today?')).toBe(false);\n expect(isSystemMessage('Can you help me with this code?')).toBe(false);\n });\n });\n\n describe('cleanText', () => {\n describe('string content', () => {\n it('should return the string as-is when no special formatting', () => {\n const input = 'Hello, world!';\n expect(cleanText(input)).toBe('Hello, world!');\n });\n\n it('should extract message after last code fence', () => {\n const input = '```metadata\\nsome: data\\n```\\nActual message here';\n expect(cleanText(input)).toBe('Actual message here');\n });\n\n it('should remove timestamp prefix from message', () => {\n const input = '```metadata```\\n[Mon 2024-03-09 14:30 UTC] Hello there';\n expect(cleanText(input)).toBe('Hello there');\n });\n\n it('should handle timestamp with timezone offset', () => {\n const input = '[Tue 2024-03-10 09:15 EST] Good morning';\n expect(cleanText(input)).toBe('Good morning');\n });\n\n it('should remove memori_context tags', () => {\n const input = 'Previous facts\\nNew message';\n expect(cleanText(input)).toBe('New message');\n });\n\n it('should handle multiple memori_context blocks', () => {\n const input =\n 'Block 1\\nText\\nBlock 2';\n expect(cleanText(input)).toBe('Text');\n });\n\n it('should handle complex combination of formatting', () => {\n const input = `\\`\\`\\`metadata\nsessionId: abc-123\n\\`\\`\\`\n[Wed 2024-03-11 16:45 UTC] Previous context\nWhat is the capital of France?`;\n expect(cleanText(input)).toBe('What is the capital of France?');\n });\n\n it('should return empty string when only metadata present', () => {\n const input = '```metadata\\ndata\\n```';\n expect(cleanText(input)).toBe('');\n });\n\n it('should handle content without code fences', () => {\n const input = '[Thu 2024-03-12 10:00 PST] Hello';\n expect(cleanText(input)).toBe('Hello');\n });\n });\n\n describe('message block array content', () => {\n it('should extract text from text blocks', () => {\n const blocks: OpenClawMessageBlock[] = [\n { type: 'text', text: 'First part' },\n { type: 'text', text: 'Second part' },\n ];\n expect(cleanText(blocks)).toBe('First part\\n\\nSecond part');\n });\n\n it('should filter out non-text blocks', () => {\n const blocks: OpenClawMessageBlock[] = [\n { type: 'text', text: 'User message' },\n { type: 'thinking', thinking: 'Internal thought' },\n { type: 'tool_use', name: 'search' },\n ];\n expect(cleanText(blocks)).toBe('User message');\n });\n\n it('should handle blocks without explicit type but with text', () => {\n const blocks: OpenClawMessageBlock[] = [{ text: 'Message 1' }, { text: 'Message 2' }];\n expect(cleanText(blocks)).toBe('Message 1\\n\\nMessage 2');\n });\n\n it('should filter out empty text blocks', () => {\n const blocks: OpenClawMessageBlock[] = [\n { type: 'text', text: '' },\n { type: 'text', text: 'Valid message' },\n { type: 'text', text: '' },\n ];\n expect(cleanText(blocks)).toBe('Valid message');\n });\n\n it('should handle empty array', () => {\n expect(cleanText([])).toBe('');\n });\n\n it('should apply post-processing to extracted text', () => {\n const blocks: OpenClawMessageBlock[] = [\n {\n type: 'text',\n text: '```metadata```\\n[Fri 2024-03-13 11:20 UTC] Clean this message',\n },\n ];\n expect(cleanText(blocks)).toBe('Clean this message');\n });\n });\n\n describe('edge cases', () => {\n it('should return empty string for null input', () => {\n expect(cleanText(null)).toBe('');\n });\n\n it('should return empty string for undefined input', () => {\n expect(cleanText(undefined)).toBe('');\n });\n\n it('should handle objects that are not arrays or strings', () => {\n expect(cleanText({ random: 'object' })).toBe('');\n });\n\n it('should handle number input', () => {\n expect(cleanText(42)).toBe('');\n });\n\n it('should trim whitespace from final result', () => {\n expect(cleanText(' \\n Hello \\n ')).toBe('Hello');\n });\n\n it('should preserve internal whitespace', () => {\n const input = 'Hello world\\n\\nNew paragraph';\n expect(cleanText(input)).toBe('Hello world\\n\\nNew paragraph');\n });\n });\n });\n});\n" }, { "path": "integrations/openclaw/tests/utils/context.test.ts", "content": "import { describe, it, expect } from 'vitest';\nimport { extractContext } from '../../src/utils/context.js';\nimport type { OpenClawEvent, OpenClawContext } from '../../src/types.js';\n\ndescribe('utils/context', () => {\n describe('extractContext', () => {\n const configuredEntityId = 'test-entity-123';\n\n it('should extract context successfully with all required fields', () => {\n const event: OpenClawEvent = {\n sessionId: 'event-session-456',\n messageProvider: 'event-provider',\n };\n const ctx: OpenClawContext = {\n sessionKey: 'ctx-session-789',\n messageProvider: 'ctx-provider',\n };\n\n const result = extractContext(event, ctx, configuredEntityId);\n\n expect(result).toEqual({\n entityId: 'test-entity-123',\n sessionId: 'ctx-session-789',\n provider: 'ctx-provider',\n });\n });\n\n it('should use event sessionId when ctx.sessionKey is missing', () => {\n const event: OpenClawEvent = {\n sessionId: 'event-session-456',\n messageProvider: 'event-provider',\n };\n const ctx: OpenClawContext = {\n messageProvider: 'ctx-provider',\n };\n\n const result = extractContext(event, ctx, configuredEntityId);\n\n expect(result.sessionId).toBe('event-session-456');\n });\n\n it('should use event messageProvider when ctx.messageProvider is missing', () => {\n const event: OpenClawEvent = {\n sessionId: 'event-session-456',\n messageProvider: 'event-provider',\n };\n const ctx: OpenClawContext = {\n sessionKey: 'ctx-session-789',\n };\n\n const result = extractContext(event, ctx, configuredEntityId);\n\n expect(result.provider).toBe('event-provider');\n });\n\n it('should throw error when sessionId cannot be determined', () => {\n const event: OpenClawEvent = {\n messageProvider: 'event-provider',\n };\n const ctx: OpenClawContext = {\n messageProvider: 'ctx-provider',\n };\n\n expect(() => extractContext(event, ctx, configuredEntityId)).toThrow(\n 'Failed to extract context: Missing sessionId in OpenClaw context and event.'\n );\n });\n\n it('should throw error when provider cannot be determined', () => {\n const event: OpenClawEvent = {\n sessionId: 'event-session-456',\n };\n const ctx: OpenClawContext = {\n sessionKey: 'ctx-session-789',\n };\n\n expect(() => extractContext(event, ctx, configuredEntityId)).toThrow(\n 'Failed to extract context: Missing message provider in OpenClaw context and event.'\n );\n });\n\n it('should throw error when both sessionId and provider are missing', () => {\n const event: OpenClawEvent = {};\n const ctx: OpenClawContext = {};\n\n expect(() => extractContext(event, ctx, configuredEntityId)).toThrow();\n });\n\n it('should handle empty string sessionId as missing', () => {\n const event: OpenClawEvent = {\n messageProvider: 'provider',\n };\n const ctx: OpenClawContext = {\n sessionKey: '',\n messageProvider: 'ctx-provider',\n };\n\n expect(() => extractContext(event, ctx, configuredEntityId)).toThrow(\n 'Failed to extract context: Missing sessionId'\n );\n });\n\n it('should handle empty string provider as missing', () => {\n const event: OpenClawEvent = {\n sessionId: 'session-123',\n };\n const ctx: OpenClawContext = {\n sessionKey: 'ctx-session-789',\n messageProvider: '',\n };\n\n expect(() => extractContext(event, ctx, configuredEntityId)).toThrow(\n 'Failed to extract context: Missing message provider'\n );\n });\n\n it('should always use configuredEntityId from plugin config', () => {\n const event: OpenClawEvent = {\n sessionId: 'session-123',\n messageProvider: 'provider',\n userId: 'different-user-id',\n };\n const ctx: OpenClawContext = {\n sessionKey: 'session-789',\n messageProvider: 'ctx-provider',\n agentId: 'agent-id',\n };\n\n const result = extractContext(event, ctx, 'my-configured-entity');\n\n expect(result.entityId).toBe('my-configured-entity');\n });\n });\n});\n" }, { "path": "integrations/openclaw/tests/utils/logger.test.ts", "content": "import { describe, it, expect, vi, beforeEach } from 'vitest';\nimport { MemoriLogger } from '../../src/utils/logger.js';\nimport type { OpenClawPluginApi } from 'openclaw/plugin-sdk';\n\ndescribe('utils/logger', () => {\n let mockApi: OpenClawPluginApi;\n let logger: MemoriLogger;\n\n beforeEach(() => {\n mockApi = {\n logger: {\n info: vi.fn(),\n warn: vi.fn(),\n error: vi.fn(),\n },\n } as unknown as OpenClawPluginApi;\n\n logger = new MemoriLogger(mockApi);\n });\n\n describe('info', () => {\n it('should call api.logger.info with prefixed message', () => {\n logger.info('Test message');\n expect(mockApi.logger.info).toHaveBeenCalledWith('[Memori] Test message');\n });\n\n it('should handle empty strings', () => {\n logger.info('');\n expect(mockApi.logger.info).toHaveBeenCalledWith('[Memori] ');\n });\n\n it('should preserve message content exactly', () => {\n logger.info('Message with spaces\\nand newlines');\n expect(mockApi.logger.info).toHaveBeenCalledWith(\n '[Memori] Message with spaces\\nand newlines'\n );\n });\n });\n\n describe('warn', () => {\n it('should call api.logger.warn with prefixed message', () => {\n logger.warn('Warning message');\n expect(mockApi.logger.warn).toHaveBeenCalledWith('[Memori] Warning message');\n });\n });\n\n describe('error', () => {\n it('should call api.logger.error with prefixed message', () => {\n logger.error('Error message');\n expect(mockApi.logger.error).toHaveBeenCalledWith('[Memori] Error message');\n });\n\n it('should handle error messages with special characters', () => {\n logger.error('Error: Failed to connect (code: 500)');\n expect(mockApi.logger.error).toHaveBeenCalledWith(\n '[Memori] Error: Failed to connect (code: 500)'\n );\n });\n });\n\n describe('section', () => {\n it('should log section start with formatting', () => {\n logger.section('HOOK START');\n expect(mockApi.logger.info).toHaveBeenCalledWith('\\n=== [Memori] HOOK START ===');\n });\n\n it('should handle multi-word section titles', () => {\n logger.section('AUGMENTATION HOOK START');\n expect(mockApi.logger.info).toHaveBeenCalledWith(\n '\\n=== [Memori] AUGMENTATION HOOK START ==='\n );\n });\n });\n\n describe('endSection', () => {\n it('should log section end with formatting', () => {\n logger.endSection('HOOK END');\n expect(mockApi.logger.info).toHaveBeenCalledWith('=== [Memori] HOOK END ===\\n');\n });\n });\n\n describe('prefix consistency', () => {\n it('should use consistent prefix across all log levels', () => {\n logger.info('info');\n logger.warn('warn');\n logger.error('error');\n\n expect(mockApi.logger.info).toHaveBeenCalledWith('[Memori] info');\n expect(mockApi.logger.warn).toHaveBeenCalledWith('[Memori] warn');\n expect(mockApi.logger.error).toHaveBeenCalledWith('[Memori] error');\n });\n });\n\n describe('call count verification', () => {\n it('should call underlying logger exactly once per method call', () => {\n logger.info('test');\n expect(mockApi.logger.info).toHaveBeenCalledTimes(1);\n\n logger.warn('test');\n expect(mockApi.logger.warn).toHaveBeenCalledTimes(1);\n\n logger.error('test');\n expect(mockApi.logger.error).toHaveBeenCalledTimes(1);\n });\n\n it('should accumulate calls correctly', () => {\n logger.info('first');\n logger.info('second');\n logger.info('third');\n\n expect(mockApi.logger.info).toHaveBeenCalledTimes(3);\n });\n });\n});\n" }, { "path": "integrations/openclaw/tests/utils/memori-client.test.ts", "content": "import { describe, it, expect, vi, beforeEach } from 'vitest';\nimport { initializeMemoriClient } from '../../src/utils/memori-client.js';\nimport type { ExtractedContext } from '../../src/utils/context.js';\n\nvi.mock('@memorilabs/memori', () => {\n const mockOpenClawIntegration = {\n setAttribution: vi.fn(),\n setSession: vi.fn(),\n };\n\n const mockMemori = {\n config: {},\n integrate: vi.fn(() => mockOpenClawIntegration),\n };\n\n return {\n Memori: vi.fn(function () {\n return mockMemori;\n }),\n };\n});\n\nvi.mock('@memorilabs/memori/integrations', () => ({\n OpenClawIntegration: class MockOpenClawIntegration {},\n}));\n\ndescribe('utils/memori-client', () => {\n beforeEach(() => {\n vi.clearAllMocks();\n });\n\n describe('initializeMemoriClient', () => {\n it('should create Memori instance with API key', async () => {\n const { Memori } = await import('@memorilabs/memori');\n const apiKey = 'test-api-key-123';\n const context: ExtractedContext = {\n entityId: 'entity-456',\n sessionId: 'session-789',\n provider: 'test-provider',\n };\n\n initializeMemoriClient(apiKey, context);\n\n expect(Memori).toHaveBeenCalled();\n const memoriInstance = vi.mocked(Memori).mock.results[0].value;\n expect(memoriInstance.config.apiKey).toBe('test-api-key-123');\n });\n\n it('should integrate with OpenClawIntegration', async () => {\n const { Memori } = await import('@memorilabs/memori');\n const { OpenClawIntegration } = await import('@memorilabs/memori/integrations');\n\n const apiKey = 'test-api-key';\n const context: ExtractedContext = {\n entityId: 'entity-123',\n sessionId: 'session-456',\n provider: 'provider-789',\n };\n\n initializeMemoriClient(apiKey, context);\n\n const memoriInstance = vi.mocked(Memori).mock.results[0].value;\n expect(memoriInstance.integrate).toHaveBeenCalledWith(OpenClawIntegration);\n });\n\n it('should set attribution with entityId and provider', async () => {\n const { Memori } = await import('@memorilabs/memori');\n\n const apiKey = 'test-api-key';\n const context: ExtractedContext = {\n entityId: 'user-abc',\n sessionId: 'session-xyz',\n provider: 'openai',\n };\n\n initializeMemoriClient(apiKey, context);\n\n const memoriInstance = vi.mocked(Memori).mock.results[0].value;\n const integration = memoriInstance.integrate.mock.results[0].value;\n expect(integration.setAttribution).toHaveBeenCalledWith('user-abc', 'openai');\n });\n\n it('should set session with sessionId', async () => {\n const { Memori } = await import('@memorilabs/memori');\n\n const apiKey = 'test-api-key';\n const context: ExtractedContext = {\n entityId: 'entity-123',\n sessionId: 'my-session-id',\n provider: 'anthropic',\n };\n\n initializeMemoriClient(apiKey, context);\n\n const memoriInstance = vi.mocked(Memori).mock.results[0].value;\n const integration = memoriInstance.integrate.mock.results[0].value;\n expect(integration.setSession).toHaveBeenCalledWith('my-session-id');\n });\n\n it('should return the OpenClawIntegration instance', async () => {\n const { Memori } = await import('@memorilabs/memori');\n\n const apiKey = 'test-api-key';\n const context: ExtractedContext = {\n entityId: 'entity-123',\n sessionId: 'session-456',\n provider: 'provider-789',\n };\n\n const result = initializeMemoriClient(apiKey, context);\n\n const memoriInstance = vi.mocked(Memori).mock.results[0].value;\n const expectedIntegration = memoriInstance.integrate.mock.results[0].value;\n expect(result).toBe(expectedIntegration);\n });\n\n it('should configure client with correct sequence', async () => {\n const { Memori } = await import('@memorilabs/memori');\n\n const apiKey = 'secret-key';\n const context: ExtractedContext = {\n entityId: 'user-999',\n sessionId: 'sess-888',\n provider: 'google',\n };\n\n const result = initializeMemoriClient(apiKey, context);\n\n expect(Memori).toHaveBeenCalled();\n const memoriInstance = vi.mocked(Memori).mock.results[0].value;\n expect(memoriInstance.config.apiKey).toBe('secret-key');\n expect(memoriInstance.integrate).toHaveBeenCalled();\n expect(result.setAttribution).toHaveBeenCalledWith('user-999', 'google');\n expect(result.setSession).toHaveBeenCalledWith('sess-888');\n });\n });\n});\n" }, { "path": "integrations/openclaw/tsconfig.json", "content": "{\n \"compilerOptions\": {\n \"target\": \"ES2023\",\n \"lib\": [\"ES2023\", \"DOM\"],\n \"module\": \"NodeNext\",\n \"moduleResolution\": \"NodeNext\",\n \"declaration\": true,\n \"outDir\": \"dist\",\n \"rootDir\": \"src\",\n \"strict\": true,\n \"esModuleInterop\": true,\n \"skipLibCheck\": true\n },\n \"include\": [\"src\"],\n \"exclude\": [\"test\", \"**/*.test.ts\", \"**/*.spec.ts\"]\n}\n" }, { "path": "integrations/openclaw/vitest.config.ts", "content": "import { defineConfig } from 'vitest/config';\nimport path from 'node:path';\n\nexport default defineConfig({\n test: {\n globals: true,\n environment: 'node',\n coverage: {\n provider: 'v8',\n reporter: ['text', 'json', 'html'],\n include: ['src/**'],\n exclude: [\n 'node_modules/',\n 'dist/',\n 'tests/',\n 'examples/',\n '**/*.config.ts',\n '**/*.d.ts',\n '**/types/**',\n '**/index.ts',\n ],\n },\n include: ['tests/**/*.test.ts'],\n exclude: ['node_modules/', 'dist/'],\n },\n resolve: {\n alias: {\n '@': path.resolve(__dirname, './src'),\n },\n },\n});\n" }, { "path": "memori/__init__.py", "content": "r\"\"\"\n __ __ _\n| \\/ | ___ _ __ ___ ___ _ __(_)\n| |\\/| |/ _ \\ '_ ` _ \\ / _ \\| '__| |\n| | | | __/ | | | | | (_) | | | |\n|_| |_|\\___|_| |_| |_|\\___/|_| |_|\n perfectam memoriam\n memorilabs.ai\n\"\"\"\n\nimport os\nfrom collections.abc import Callable\nfrom typing import Any\nfrom uuid import uuid4\n\nfrom memori._config import Config\nfrom memori._exceptions import (\n MissingMemoriApiKeyError,\n MissingPsycopgError,\n QuotaExceededError,\n UnsupportedLLMProviderError,\n warn_if_legacy_memorisdk_installed,\n)\nfrom memori.embeddings import embed_texts\nfrom memori.llm._providers import Agno as LlmProviderAgno\nfrom memori.llm._providers import Anthropic as LlmProviderAnthropic\nfrom memori.llm._providers import Google as LlmProviderGoogle\nfrom memori.llm._providers import LangChain as LlmProviderLangChain\nfrom memori.llm._providers import OpenAi as LlmProviderOpenAi\nfrom memori.llm._providers import PydanticAi as LlmProviderPydanticAi\nfrom memori.llm._providers import XAi as LlmProviderXAi\nfrom memori.memory.augmentation import Manager as AugmentationManager\nfrom memori.memory.recall import Recall\nfrom memori.storage import Manager as StorageManager\n\n__all__ = [\"Memori\", \"QuotaExceededError\", \"UnsupportedLLMProviderError\"]\n\nwarn_if_legacy_memorisdk_installed()\n\n\nclass LlmRegistry:\n def __init__(self, memori):\n self.memori = memori\n\n def register(\n self,\n client=None,\n openai_chat=None,\n claude=None,\n gemini=None,\n xai=None,\n chatbedrock=None,\n chatgooglegenai=None,\n chatopenai=None,\n chatvertexai=None,\n ):\n from memori.llm._registry import register_llm\n\n return register_llm(\n self.memori,\n client=client,\n openai_chat=openai_chat,\n claude=claude,\n gemini=gemini,\n xai=xai,\n chatbedrock=chatbedrock,\n chatgooglegenai=chatgooglegenai,\n chatopenai=chatopenai,\n chatvertexai=chatvertexai,\n )\n\n\nclass Memori:\n def __init__(\n self,\n conn: Callable[[], Any] | Any | None = None,\n debug_truncate: bool = True,\n ):\n from memori._logging import set_truncate_enabled\n\n self.config = Config()\n self.config.api_key = os.environ.get(\"MEMORI_API_KEY\", None)\n self.config.session_id = uuid4()\n self.config.debug_truncate = debug_truncate\n set_truncate_enabled(debug_truncate)\n\n if conn is None:\n conn = self._get_default_connection()\n else:\n self.config.cloud = False\n\n self.config.storage = StorageManager(self.config).start(conn)\n self.config.augmentation = AugmentationManager(self.config).start(conn)\n\n self.augmentation = self.config.augmentation\n self.llm = LlmRegistry(self)\n self.agno = LlmProviderAgno(self)\n self.anthropic = LlmProviderAnthropic(self)\n self.google = LlmProviderGoogle(self)\n self.langchain = LlmProviderLangChain(self)\n self.openai = LlmProviderOpenAi(self)\n self.pydantic_ai = LlmProviderPydanticAi(self)\n self.xai = LlmProviderXAi(self)\n\n def _get_default_connection(self) -> Callable[[], Any] | None:\n connection_string = os.environ.get(\"MEMORI_COCKROACHDB_CONNECTION_STRING\", None)\n if connection_string:\n try:\n import psycopg\n except ImportError as e:\n raise MissingPsycopgError(\"CockroachDB\") from e\n\n self.config.cloud = False\n return lambda: psycopg.connect(connection_string)\n\n self.config.cloud = True\n api_key = os.environ.get(\"MEMORI_API_KEY\", None)\n if api_key is None or api_key == \"\":\n raise MissingMemoriApiKeyError()\n return None\n\n def attribution(self, entity_id=None, process_id=None):\n if entity_id is not None:\n entity_id = str(entity_id)\n\n if len(entity_id) > 100:\n raise RuntimeError(\"entity_id cannot be greater than 100 characters\")\n\n if process_id is not None:\n process_id = str(process_id)\n\n if len(process_id) > 100:\n raise RuntimeError(\"process_id cannot be greater than 100 characters\")\n\n self.config.entity_id = entity_id\n self.config.process_id = process_id\n\n return self\n\n def new_session(self):\n self.config.session_id = uuid4()\n self.config.reset_cache()\n return self\n\n def set_session(self, id):\n self.config.session_id = id\n return self\n\n def recall(self, query: str, limit: int | None = None):\n return Recall(self.config).search_facts(query, limit)\n\n def close(self) -> None:\n \"\"\"Close the underlying storage connection/session, if any.\n\n This is especially important for long-running processes (e.g. web servers)\n where you want to explicitly release database connections.\n \"\"\"\n storage = getattr(self.config, \"storage\", None)\n adapter = getattr(storage, \"adapter\", None) if storage is not None else None\n if adapter is None:\n return\n try:\n adapter.close()\n except Exception: # nosec B110\n pass\n\n def __enter__(self) -> \"Memori\":\n return self\n\n def __exit__(self, exc_type, exc, tb) -> None:\n self.close()\n\n def embed_texts(self, texts: str | list[str], *, async_: bool = False) -> Any:\n embeddings_cfg = self.config.embeddings\n return embed_texts(\n texts,\n model=embeddings_cfg.model,\n async_=async_,\n )\n" }, { "path": "memori/__main__.py", "content": "r\"\"\"\n __ __ _\n| \\/ | ___ _ __ ___ ___ _ __(_)\n| |\\/| |/ _ \\ '_ ` _ \\ / _ \\| '__| |\n| | | | __/ | | | | | (_) | | | |\n|_| |_|\\___|_| |_| |_|\\___/|_| |_|\n perfectam memoriam\n memorilabs.ai\n\"\"\"\n\nimport sys\nfrom typing import Any\n\nfrom memori._cli import Cli\nfrom memori._config import Config\nfrom memori._setup import Manager as SetupManager\nfrom memori.api._quota import Manager as ApiQuotaManager\nfrom memori.api._sign_up import Manager as ApiSignUpManager\nfrom memori.storage.cockroachdb._cluster_manager import (\n ClusterManager as CockroachDBClusterManager,\n)\n\n\ndef main():\n cli = Cli(Config())\n cli.banner()\n\n options: dict[str, dict[str, Any]] = {\n \"cockroachdb\": {\n \"description\": \"Manager a CockroachDB cluster\",\n \"params\": [\"cluster\", \"\"],\n \"obj\": CockroachDBClusterManager,\n },\n \"quota\": {\n \"description\": \"Check your quota\",\n \"params\": [],\n \"obj\": ApiQuotaManager,\n },\n \"setup\": {\n \"description\": \"Execute suggested setup steps\",\n \"params\": [],\n \"obj\": SetupManager,\n },\n \"sign-up\": {\n \"description\": \"Sign up for an API key\",\n \"params\": [\"\"],\n \"obj\": ApiSignUpManager,\n },\n }\n\n if len(sys.argv) <= 1 or sys.argv[1] not in options:\n cli.print(\"{:<15}{:<45}{:<6}\".format(\"Option\", \"Description\", \"Params\"))\n cli.print(\"{:<15}{:<45}{:<6}\".format(\"------\", \"-----------\", \"------\"))\n\n for key, value in options.items():\n params = value[\"params\"]\n cli.print(\n \"{:<15}{:<45}{:>6}\".format(\n key, value[\"description\"], \"Y\" if len(params) > 0 else \"N\"\n )\n )\n\n cli.print(\"\\nusage: python -m memori