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gitextract__hqle3k9/
├── LICENSE
├── README.md
├── contributing.md
└── package.json
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# 🦜🔗 Awesome LangChain [](https://awesome.re) 
> Curated list of tools and projects using LangChain.
LangChain is an amazing framework to get LLM projects done in a matter of no time, and the ecosystem is growing fast.
Here is an attempt to keep track of the initiatives around LangChain.
**[Subscribe to the newsletter](https://awesomelangchain.substack.com/)** to stay informed about the Awesome LangChain. We send a couple of emails per month about the articles, videos, projects, and tools that grabbed our attention
Contributions welcome. Add links through pull requests or create an issue to start a discussion. Please read the [contribution guidelines](contributing.md) before contributing.
## Table of Contents
- [🦜🔗 Awesome LangChain ](#-awesome-langchain--)
- [Table of Contents](#table-of-contents)
- [LangChain Framework](#langchain-framework)
- [Ports to other languages](#ports-to-other-languages)
- [Tools](#tools)
- [Low-code](#low-code)
- [Services](#services)
- [Agents](#agents)
- [Templates](#templates)
- [Platforms](#platforms)
- [Open Source Projects](#open-source-projects)
- [Knowledge Management](#knowledge-management)
- [Other / Chatbots](#other--chatbots)
- [Learn](#learn)
- [Notebooks](#notebooks)
- [Videos Playlists](#videos-playlists)
- [Other LLM Frameworks](#other-llm-frameworks)
- [Complement to this list](#complement-to-this-list)
- [Unmaintained](#unmaintained)
## LangChain Framework
- [LangChain](https://github.com/hwchase17/langchain): the original 🐍 
- [LangChain.js](https://github.com/hwchase17/langchainjs): the js brother ✨ 
- [Concepts](https://docs.langchain.com/docs/): Langchain concepts doc
- [Twitter account](https://twitter.com/LangChainAI): follow to get fresh updates
- [Youtube Channel](https://www.youtube.com/channel/UCC-lyoTfSrcJzA1ab3APAgw)
- [Langchain Blog](https://blog.langchain.dev/): The Official Langchain blog
- [LangServe](https://github.com/langchain-ai/langserve): LangServe helps developers deploy LangChain runnables and chains as a REST API. 
## Ports to other languages
List of non-official ports of LangChain to other languages.
- [Langchain Go](https://github.com/tmc/langchaingo): Golang Langchain 
- [LangchainRb](https://github.com/andreibondarev/langchainrb): Ruby Langchain 
- [LangChain4j](https://github.com/langchain4j/langchain4j): LangChain for Java 
- [LangChainDart](https://github.com/davidmigloz/langchain_dart): Build powerful LLM-based Dart/Flutter applications. 
- [Langchain-hs](https://github.com/tusharad/langchain-hs): Haskell implementation of Haskell. 
- [Langchain](https://github.com/brainlid/langchain): Elixir implementation of a LangChain 
- [Langchain-rust](https://github.com/Abraxas-365/langchain-rust): LangChain for Rust 
## Tools
### Low-code
- [Flowise](https://github.com/FlowiseAI/Flowise): Drag & drop UI to build your customized LLM flow using LangchainJS 
- [Langflow](https://github.com/logspace-ai/langflow): LangFlow is a UI for LangChain 
- [Flock](https://github.com/Onelevenvy/flock): Flock is a workflow-based low-code platform for rapidly building chatbots, RAG, and coordinating multi-agent teams
### Services
- [GPTCache](https://github.com/zilliztech/GPTCache): A Library for Creating Semantic Cache for LLM Queries 
- [Gorilla](https://github.com/ShishirPatil/gorilla): An API store for LLMs 
- [LlamaHub](https://github.com/emptycrown/llama-hub): a library of data loaders for LLMs made by the community 
- [Auto-evaluator](https://github.com/PineappleExpress808/auto-evaluator): a lightweight evaluation tool for question-answering using Langchain 
- [Langchain visualizer](https://github.com/amosjyng/langchain-visualizer): visualization and debugging tool for LangChain workflows 
- [LLM Strategy](https://github.com/BlackHC/llm-strategy): implementing the Strategy Pattern using LLMs 
- [datasetGPT](https://github.com/radi-cho/datasetGPT): A command-line interface to generate textual and conversational datasets with LLMs. 
- [Auto Evaluator](https://github.com/langchain-ai/auto-evaluator): Langchain auto evaluator 
- [Jina](https://github.com/jina-ai/langchain-serve): Langchain Apps on Production with Jina 
- [Dify](https://github.com/langgenius/dify): One API for plugins and datasets, one interface for prompt engineering and visual operation, all for creating powerful AI applications. 
- [Chainlit](https://github.com/Chainlit/chainlit): Build Python LLM apps in minutes ⚡️ 
- [Langchain Decorators](https://github.com/ju-bezdek/langchain-decorators): a layer on the top of LangChain that provides syntactic sugar 🍭 for writing custom langchain prompts and chains 
- [AilingBot](https://github.com/ericzhang-cn/ailingbot): Quickly integrate applications built on Langchain into IM such as Slack, WeChat Work, Feishu, DingTalk.
- [Llama2 Embedding Server](https://github.com/Dicklesworthstone/llama_embeddings_fastapi_service): Llama2 Embeddings FastAPI Service using LangChain 
- [ChatAbstractions](https://github.com/andrewnguonly/ChatAbstractions): LangChain chat model abstractions for dynamic failover, load balancing, chaos engineering, and more! 
- [MindSQL](https://github.com/Mindinventory/MindSQL) - A python package for Txt-to-SQL with self hosting functionalities and RESTful APIs compatible with proprietary as well as open source LLM.
- [Llama-github](https://github.com/JetXu-LLM/llama-github): Llama-github is a python library which built with Langchain framework that helps you retrieve the most relevant code snippets, issues, and repository information from GitHub 
- [CopilotKit](https://github.com/CopilotKit/CopilotKit): A framework for building custom AI Copilots 🤖 in-app AI chatbots, in-app AI Agents, & AI-powered Textareas 
- [LangFair](https://github.com/cvs-health/langfair): LangFair is a Python library for conducting use-case-specific LLM bias and fairness assessments 
- [LangWatch](https://github.com/langwatch/langwatch): An Open Source tool for observing, evaluating and optimising your llm apps and prompts, which supports LangChain out of the box! 
- [Agentic Radar](https://github.com/splx-ai/agentic-radar) - Open-source CLI security scanner for agentic workflows. Scans your workflow’s source code, detects vulnerabilities, and generates an interactive visualization along with a detailed security report. 
- [UQLM](https://github.com/cvs-health/uqlm): UQLM: Uncertainty Quantification for Language Models, is a Python library for LLM hallucination detection using state-of-the-art uncertainty quantification techniques 
- [far-search-tool](https://github.com/blueskylineassets/far-search-tool): LangChain tool for semantic search over Federal Acquisition Regulations (FAR). Enables AI agents to query U.S. government contracting rules and compliance requirements. 
- [Tenuo](https://github.com/tenuo-ai/tenuo): Capability-based authorization for AI agents. Task-scoped tokens with offline verification, proof-of-possession binding, and native LangChain/LangGraph integration. 
- [Veritensor](https://github.com/arsbr/Veritensor) - Native security wrappers for LangChain DocumentLoaders to block prompt injections, stealth attacks, and PII leaks during RAG data ingestion. 
- [Mengram](https://github.com/alibaizhanov/mengram): Long-term memory for LangChain agents — semantic, episodic & procedural memory with Graph RAG. Includes MengramRetriever for RAG pipelines. 
- [Ziran](https://github.com/taoq-ai/ziran): Open-source security testing framework for AI agents. Discovers dangerous tool chain compositions via graph analysis, detects execution-level side effects, and runs multi-phase trust exploitation campaigns. 
### Agents
- [Private GPT](https://github.com/imartinez/privateGPT): Interact privately with your documents using the power of GPT, 100% privately, no data leaks 
- [CollosalAI Chat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): implement LLM with RLHF, powered by the Colossal-AI project 
- [CrewAI](https://github.com/joaomdmoura/crewai): Cutting-edge framework for orchestrating role-playing, autonomous AI agents. 
- [AgentGPT](https://github.com/reworkd/AgentGPT): AI Agents with Langchain & OpenAI (Vercel / Nextjs) 
- [Local GPT](https://github.com/PromtEngineer/localGPT): Inspired on Private GPT with the GPT4ALL model replaced with the Vicuna-7B model and using the InstructorEmbeddings instead of LlamaEmbeddings 
- [GPT Researcher](https://github.com/assafelovic/gpt-researcher): GPT Researcher is an autonomous agent designed for comprehensive online research on a variety of tasks. 
- [ThinkGPT](https://github.com/alaeddine-13/thinkgpt): Agent techniques to augment your LLM and push it beyond its limits 
- [Camel-AutoGPT](https://github.com/SamurAIGPT/Camel-AutoGPT): role-playing approach for LLMs and auto-agents like BabyAGI & AutoGPT 
- [RasaGPT](https://github.com/paulpierre/RasaGPT): RasaGPT is the first headless LLM chatbot platform built on top of Rasa and Langchain. 
- [SkyAGI](https://github.com/litanlitudan/skyagi): Emerging human-behavior simulation capability in LLM agents 
- [PyCodeAGI](https://github.com/chakkaradeep/pyCodeAGI): A small AGI experiment to generate a Python app given what app the user wants to build 
- [BabyAGI UI](https://github.com/miurla/babyagi-ui): Make it easier to run and develop with babyagi in a web app, like a ChatGPT 
- [SuperAgent](https://github.com/homanp/superagent): Deploy LLM Agents to production 
- [Voyager](https://github.com/MineDojo/Voyager): An Open-Ended Embodied Agent with Large Language Models 
- [ix](https://github.com/kreneskyp/ix): Autonomous GPT-4 agent platform 
- [DuetGPT](https://github.com/kristoferlund/duet-gpt): A conversational semi-autonomous developer assistant, AI pair programming without the copypasta. 
- [Multi-Modal LangChain agents in Production](https://github.com/steamship-packages/langchain-agent-production-starter): Deploy LangChain Agents and connect them to Telegram 
- [DemoGPT](https://github.com/melih-unsal/DemoGPT): DemoGPT enables you to create quick demos by just using prompt. It applies ToT approach on Langchain documentation tree. 
- [SuperAGI](https://github.com/TransformerOptimus/SuperAGI): SuperAGI - A dev-first open source autonomous AI agent framework 
- [Autonomous HR Chatbot](https://github.com/stepanogil/autonomous-hr-chatbot): An autonomous agent that can answer HR related queries autonomously using the tools it has on hand 
- [BlockAGI](https://github.com/blockpipe/blockagi): BlockAGI conducts iterative, domain-specific research, and outputs detailed narrative reports to showcase its findings 
- [waggledance.ai](https://github.com/agi-merge/waggle-dance): An opinionated, concurrent system of AI Agents. It implements Plan-Validate-Solve with data and tools for general goal-solving. 
### Templates
- [AI](https://github.com/vercel-labs/ai): Vercel template to build AI-powered applications with React, Svelte, and Vue, first class support for LangChain 
- [create-t3-turbo-ai](https://github.com/zckly/create-t3-turbo-ai): t3 based, Langchain-friendly boilerplate for building type-safe, full-stack, LLM-powered web apps with Nextjs and Prisma 
- [LangChain.js LLM Template](https://github.com/Conner1115/LangChain.js-LLM-Template): LangChain LLM template that allows you to train your own custom AI LLM model. 
- [Streamlit Template](https://github.com/hwchase17/langchain-streamlit-template): template for how to deploy a LangChain on Streamlit 
- [Codespaces Template](https://github.com/lostintangent/codespaces-langchain): a Codespaces template for getting up-and-running with LangChain in seconds! 
- [Gradio Template](https://github.com/hwchase17/langchain-gradio-template): template for how to deploy a LangChain on Gradio 
- [AI Getting Started](https://github.com/a16z-infra/ai-getting-started): A Javascript AI getting started stack for weekend projects, including image/text models, vector stores, auth, and deployment configs 
- [Embedchain](https://github.com/embedchain/embedchain): Framework to easily create LLM powered bots over any dataset. 
### Platforms
- [Openllmetry](https://github.com/traceloop/openllmetry): Open-source observability for your LLM application, based on OpenTelemetry 
## Open Source Projects
### Knowledge Management
- [Quiver](https://github.com/StanGirard/quiver): Dump your brain into your GenerativeAI Vault 
- [DocsGPT](https://github.com/arc53/docsgpt): GPT-powered chat for documentation search & assistance. 
- [Chaindesk](https://github.com/gmpetrov/databerry): The no-code platform for semantic search and documents retrieval 
- [Knowledge GPT](https://github.com/mmz-001/knowledge_gpt): Accurate answers and instant citations for your documents. 
- [Knowledge](https://github.com/KnowledgeCanvas/knowledge): Knowledge is a tool for saving, searching, accessing, and exploring all of your favorite websites, documents and files. 
- [Anything LLM](https://github.com/Mintplex-Labs/anything-llm): A full-stack application that turns any documents into an intelligent chatbot with a sleek UI and easier way to manage your workspaces. 
- [DocNavigator](https://github.com/vgulerianb/DocNavigator): AI-powered chatbot builder that is designed to improve the user experience on product documentation/support websites 
- [ChatFiles](https://github.com/guangzhengli/ChatFiles): Upload your document and then chat with it. Powered by GPT / Embedding / TS / NextJS. 
- [DataChad](https://github.com/gustavz/DataChad): A streamlit app that lets you chat with any data source. Supporting both OpenAI and local mode with GPT4All. 
- [Second Brain AI Agent](https://github.com/flepied/second-brain-agent): A streamlit app dialog with your second brain notes using OpenAI and ChromaDB locally. 
- [examor](https://github.com/codeacme17/examor): A website application that allows you to take exams based on your knowledge notes. Let you really remember what you have learned and written. 
- [Repochat](https://github.com/pnkvalavala/repochat): Chatbot assistant enabling GitHub repository interaction using LLMs with Retrieval Augmented Generation 
- [SolidGPT](https://github.com/AI-Citizen/SolidGPT): Chat everything with your code repository, ask repository level code questions, and discuss your requirements 
- [Minima](https://github.com/dmayboroda/minima): Chat with local documents, connect local environment to ChatGPT or Claude 
### Other / Chatbots
- [DB GPT](https://github.com/csunny/DB-GPT): Interact your data and environment using the local GPT, no data leaks, 100% privately, 100% security 
- [AudioGPT](https://github.com/AIGC-Audio/AudioGPT): Understanding and Generating Speech, Music, Sound, and Talking Head 
- [Paper QA](https://github.com/whitead/paper-qa): LLM Chain for answering questions from documents with citations 
- [Chat Langchain](https://github.com/hwchase17/chat-langchain): locally hosted chatbot specifically focused on question answering over the LangChain documentation 
- [Langchain Chat](https://github.com/zahidkhawaja/langchain-chat-nextjs): another Next.js frontend for LangChain Chat. 
- [Book GPT](https://github.com/fraserxu/book-gpt): drop a book, start asking question. 
- [Doc Search](https://github.com/namuan/dr-doc-search): converse with book - Built with GPT-3 
- [Fact Checker](https://github.com/jagilley/fact-checker): fact-checking LLM outputs with langchain 
- [MM ReAct](https://github.com/microsoft/MM-REACT): Multi Modal ReAct Design
- [QABot](https://github.com/hardbyte/qabot): Query local or remote files or databases with natural language queries powered by langchain and openai 
- [FlowGPT](https://github.com/nilooy/flowgpt): Generate diagram with AI 
- [langchain-text-summarizer](https://github.com/alphasecio/langchain-text-summarizer): A sample streamlit application summarizing text using LangChain 
- [Langchain Chat Websocket](https://github.com/pors/langchain-chat-websockets): About LangChain LLM chat with streaming response over websockets 
- [langchain_yt_tools](https://github.com/venuv/langchain_yt_tools): Langchain tools to search/extract/transcribe text transcripts of Youtube videos 
- [SmartPilot](https://github.com/jaredkirby/SmartPilot): A Python program leveraging OpenAI's language models to generate, analyze, and select the best answer to a given question 
- [ThoughtSource⚡](https://github.com/OpenBioLink/ThoughtSource): A framework for the science of machine thinking 
- [ChatGPT Langchain](https://huggingface.co/spaces/JavaFXpert/Chat-GPT-LangChain): ChatGPT clone using langchain on Huggingface
- [Chat Math Techniques](https://huggingface.co/spaces/JavaFXpert/gpt-math-techniques): langchain chat with math techniques on Huggingface
- [Notion QA](https://github.com/hwchase17/notion-qa): Notion Question-Answering Bot 
- [QNimGPT](https://huggingface.co/spaces/rituthombre/QNim): Play Nim against an IBM Quantum Computer simulator or OpenAI GPT-3.5
- [ChatPDF](https://github.com/akshata29/chatpdf): ChatGPT + Enterprise data with Azure OpenAI 
- [Chat with Scanned Documents](https://github.com/tony-xlh/Chat-with-Scanned-Documents): A demo chatting with documents scanned with Dynamic Web TWAIN.
- [snowChat ❄️](https://github.com/kaarthik108/snowChat): Chat with you're snowflake database 
- [TutorGPT](https://github.com/plastic-labs/tutor-gpt): Dynamic few-shot metaprompting for the task of tutoring. 
- [Cheshire Cat](https://github.com/cheshire-cat-ai/core): Custom AGI boT with ready-to-use chat integration and plugins development platform. 
- [Dialoqbase](https://github.com/n4ze3m/dialoqbase): web application that allows you to create custom chatbots with your own knowledge base 
- [CSV-AI 🧠](https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/snowflake.html): CSV-AI is the ultimate app powered by LangChain that allows you to unlock hidden insights in your CSV files.
- [MindGeniusAI](https://github.com/xianjianlf2/MindGeniusAI): Auto generate MindMap with ChatGPT 
- [Robby-Chatbot](https://github.com/yvann-hub/Robby-chatbot): AI chatbot 🤖 for chat with CSV, PDF, TXT files 📄 and YTB videos 🎥 | using Langchain🦜 | OpenAI | Streamlit ⚡.
- [AI Chatbot](https://github.com/vercel-labs/ai-chatbot): A full-featured, hackable Next.js AI chatbot built by Vercel Labs 
- [Instrukt](https://github.com/blob42/Instrukt): A fully-fledged AI environment in the terminal. Build, test and instruct agents. 
- [OpenChat](https://github.com/openchatai/OpenChat/): LLMs custom-chatbots console ⚡. 
- [GPT Migrate](https://github.com/0xpayne/gpt-migrate): Easily migrate your codebase from one framework or language to another.
- [Code Interpreter API](https://github.com/shroominic/codeinterpreter-api): About Open source implementation of the ChatGPT Code Interpreter 
- [Lobe Chat](https://github.com/lobehub/lobe-chat) An open-source, extensible (Function Calling), high-performance chatbot framework 
- [Funcchain](https://github.com/shroominic/funcchain): write prompts, pythonic 
- [PersonalityChatbot](https://github.com/btrcm00/chatbot-with-langchain): Langchain chatbot for chat with personality using Langchain🦜 | LangSmith | MongoDB. 
- [XAgent](https://github.com/OpenBMB/XAgent): An Autonomous LLM Agent for Complex Task Solving 
- [MemFree](https://github.com/memfreeme/memfree) - Open Source Hybrid AI Search Engine, Instantly Get Accurate Answers from the Internet, Bookmarks, Notes, and Docs. Support One-Click Deployment. 
- [JARVIS](https://github.com/hyhmrright/JARVIS): Self-hosted AI assistant platform with RAG, multi-LLM support (DeepSeek/OpenAI/Anthropic), plugin SDK, and multi-channel gateway (Slack/Discord/Telegram). Built with FastAPI and LangGraph. 
## Learn
### Notebooks
- [Langchain Tutorials](https://github.com/gkamradt/langchain-tutorials): overview and tutorial of the LangChain Library 
- [LangChain Chinese Getting Started Guide](https://github.com/liaokongVFX/LangChain-Chinese-Getting-Started-Guide): Chinese LangChain Tutorial for Beginners 
- [Flan5 LLM](https://colab.research.google.com/drive/1AVh9dOsG9DKzfK7gOFrJuitPIcLPqlbO?usp=sharing): PDF QA using LangChain for chain of thought and multi-task instructions, Flan5 on HuggingFace
- [LangChain Handbook](https://github.com/pinecone-io/examples/tree/master/generation/langchain/handbook): Pinecone / James Briggs' LangChain handbook
- [Query the YouTube video transcripts](https://colab.research.google.com/drive/1sKSTjt9cPstl_WMZ86JsgEqFG-aSAwkn?usp=sharing): Query the YouTube video transcripts, returning timestamps as sources to legitimize the answers
- [llm-lobbyist](https://github.com/JohnNay/llm-lobbyist): Large Language Models as Corporate Lobbyists
- [Langchain Semantic Search](https://github.com/venuv/langchain_semantic_search): Search and indexing your own Google Drive Files using GPT3, LangChain, and Python
- [GPT Political Compass](https://colab.research.google.com/drive/1xt2IsFPGYMEQdoJFNgWNAjWGxa60VXdV)
- [llm-grovers-search-party](https://github.com/JavaFXpert/llm-grovers-search-party): Leveraging Qiskit, OpenAI and LangChain to demonstrate Grover's algorithm
- [TextWorld ReAct Agent](https://colab.research.google.com/drive/19WTIWC3prw5LDMHmRMvqNV2loD9FHls6?usp=sharing)
- [LangChain <> Wolfram Alpha](https://colab.research.google.com/drive/1AAyEdTz-Z6ShKvewbt1ZHUICqak0MiwR?usp=sharing)
- [BYO Knowledge Graph](https://github.com/prof-frink-lab/slangchain/blob/main/docs/modules/knowledge_graph/examples/byo_knowledge_graph.ipynb)
- [Large Language Models Course](https://github.com/peremartra/Large-Language-Model-Notebooks-Course) 
- [Learn LangChain (JS)](https://github.com/iparesh18/Learn-LangChain) – A structured, example-driven LangChain JS learning repository covering prompts, chains, tools, embeddings, RAG, agents, Puppeteer scraping, and LangGraph-based multi-agent workflows.
### Videos Playlists
- [LangChain Series by Sam Witteveen](https://www.youtube.com/watch?v=J_0qvRt4LNk&list=PL8motc6AQftk1Bs42EW45kwYbyJ4jOdiZ)
- [LangChain Tutorials Playlist](https://www.youtube.com/playlist?list=PL611FKPtL866MnlDPHvI3KwVGqCB-QJAx)
- [LangChain James Briggs' Playlist](https://www.youtube.com/watch?v=nE2skSRWTTs&list=PLIUOU7oqGTLieV9uTIFMm6_4PXg-hlN6F)
- [Greg Kamradt Playlist](https://www.youtube.com/watch?v=_v_fgW2SkkQ&list=PLqZXAkvF1bPNQER9mLmDbntNfSpzdDIU5)
## Other LLM Frameworks
- [Transformers Agents](https://huggingface.co/docs/transformers/transformers_agents): Provides a natural language API on top of transformers
- [LlamaIndex](https://github.com/jerryjliu/llama_index): provides a central interface to connect your LLM's with external data. 
- [Botpress](https://github.com/botpress/botpress): The building blocks for building chatbots 
- [Haystack](https://github.com/deepset-ai/haystack): NLP framework to interact with your data using Transformer models and LLMs 
- [Semantic Kernel](https://github.com/microsoft/semantic-kernel): Microsoft C# SDK to integrate cutting-edge LLM technology quickly and easily into your apps 
- [Promptify](https://github.com/promptslab/Promptify): Prompt Engineering | Use GPT or other prompt based models to get structured output. 
- [PromptSource](https://github.com/bigscience-workshop/promptsource): About Toolkit for creating, sharing and using natural language prompts. 
- [Agent-LLM](https://github.com/Josh-XT/Agent-LLM): An Artificial Intelligence Automation Platform. 
- [LLM Agents](https://github.com/mpaepper/llm_agents): Build agents which are controlled by LLMs 
- [MiniChain](https://github.com/srush/MiniChain): A tiny library for coding with large language models. 
- [Griptape](https://github.com/griptape-ai/griptape): Python framework for AI workflows and pipelines with chain of thought reasoning, external tools, and memory. 
- [llm-chain](https://github.com/sobelio/llm-chain): is a powerful rust crate for building chains in LLMs allowing you to summarise text and complete complex tasks. 
- [OpenLM](https://github.com/r2d4/openlm): a drop-in OpenAI-compatible library that can call LLMs from any other hosted inference API. Also [Typescript](https://github.com/r2d4/llm.ts) 
- [Dust](https://github.com/dust-tt/dust): Design and Deploy Large Language Model Apps 
- [e2b](https://github.com/e2b-dev/e2b): Open-source platform for building & deploying virtual developers’ agents
- [SmartGPT](https://github.com/Cormanz/smartgpt): A program that provides LLMs with the ability to complete complex tasks using plugins. 
- [TermGPT](https://github.com/Sentdex/TermGPT): Giving LLMs like GPT-4 the ability to plan and execute terminal commands 
- [ReLLM](https://github.com/r2d4/rellm): Regular Expressions for Language Model Completions. 
- [OpenDAN](https://github.com/fiatrete/OpenDAN-Personal-AI-OS): open source Personal AI OS , which consolidates various AI modules in one place for your personal use. 
- [OpenLLM](https://github.com/bentoml/OpenLLM): An open platform for operating large language models (LLMs) in production. Fine-tune, serve, deploy, and monitor any LLMs with ease using OpenLLM. 
- [FlagAI](https://github.com/FlagAI-Open/FlagAI): FlagAI (Fast LArge-scale General AI models) is a fast, easy-to-use and extensible toolkit for large-scale model. 
- [AI.JSX](https://github.com/fixie-ai/ai-jsx): The AI Application Framework for Javascript 
- [AI Utils](https://github.com/lgrammel/ai-utils.js): TypeScript-first library for building AI apps, chatbots, and agents. 
- [MetaGPT](https://github.com/geekan/MetaGPT): The Multi-Agent Meta Programming Framework: Given one line Requirement, return PRD, Design, Tasks, Repo and CI 
- [Hyv](https://github.com/failfa-st/hyv): Probably the easiest way to use any AI Model in Node.js and create complex interactions with ease. 
- [Autochain](https://github.com/Forethought-Technologies/AutoChain): Build lightweight, extensible, and testable LLM Agents with AutoChain. 
- [TypeChat](https://github.com/microsoft/TypeChat): TypeChat is a library that makes it easy to build natural language interfaces using types. 
- [Marvin](https://github.com/PrefectHQ/marvin): ✨ Build AI interfaces that spark joy 
- [LMQL](https://github.com/eth-sri/lmql): A programming language for large language models. 
- [LLMFlow](https://github.com/stoyan-stoyanov/llmflows): Simple, Explicit and Transparent LLM Apps 
- [Ax](https://github.com/axilla-io/ax): A comprehensive AI framework for TypeScript 
- [TextAI](https://github.com/neuml/txtai): 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows. 
- [AgentFlow](https://github.com/simonmesmith/agentflow): About Complex LLM Workflows from Simple JSON. 
- [Outlines](https://github.com/normal-computing/outlines): Fast and reliable neural text generation. 
- [SimpleAIChat](https://github.com/minimaxir/simpleaichat): Python package for easily interfacing with chat apps, with robust features and minimal code complexity. 
- [LLFn](https://github.com/orgexyz/LLFn): A light-weight framework for creating applications using LLMs 
- [LLMStack](https://github.com/trypromptly/LLMStack): No code platform for building LLM-powered applications with custom data. 
- [Lagent](https://github.com/InternLM/lagent): A lightweight framework for building LLM-based agents 
- [Embedbase](https://github.com/different-ai/embedbase): The native Software 3.0 stack for building AI-powered applications. 
- [Rivet](https://github.com/Ironclad/rivet): An IDE for creating complex AI agents and prompt chaining, and embedding it in your application. 
- [Promptfoo](https://github.com/promptfoo/promptfoo): Test your prompts. Evaluate and compare LLM outputs, catch regressions, and improve prompt quality. 
- [RestGPT](https://github.com/Yifan-Song793/RestGPT): An LLM-based autonomous agent controlling real-world applications via RESTful APIs 
- [LangStream](https://github.com/LangStream/langstream): Framework for building and running event-driven LLM applications using no-code and Python (including LangChain-based) agents. 
- [Magentic](https://github.com/jackmpcollins/magentic): Seamlessly integrate LLMs as Python functions 
- [Autogen](https://github.com/microsoft/autogen): Enable Next-Gen Large Language Model Applications.
- [AgentVerse](https://github.com/openbmb/agentverse) Provides a flexible framework that simplifies the process of building custom multi-agent environments for LLMs 
- [Flappy](https://github.com/pleisto/flappy): Production-Ready LLM Agent SDK for Every Developer 
- [MemGPT](https://github.com/cpacker/MemGPT): Teaching LLMs memory management for unbounded context 
- [Agentlabs](https://github.com/agentlabs-inc/agentlabs): Universal AI Agent Frontend. Build your backend we handle the rest. 
- [axflow](https://github.com/axflow/axflow): The TypeScript framework for AI development 
- [bondai](https://github.com/krohling/bondai): AI-powered assistant with a lightweight, versatile API for seamless integration into your own applications 
- [Chidori](https://github.com/ThousandBirdsInc/chidori): A reactive runtime for building durable AI agents 
- [Langroid](https://github.com/langroid/langroid): an intuitive, lightweight, extensible and principled Python framework to easily build LLM-powered applications. 
- [Langstream](https://github.com/rogeriochaves/langstream): Build robust LLM applications with true composability 🔗 
- [Agency](https://github.com/neurocult/agency): 🕵️♂️ Library designed for developers eager to explore the potential of Large Language Models (LLMs) and other generative AI through a clean, effective, and Go-idiomatic approach 
- [TaskWeaver](https://github.com/microsoft/TaskWeaver): A code-first agent framework for seamlessly planning and executing data analytics tasks. 
- [MicroAgent](https://github.com/aymenfurter/microagents): Agents Capable of Self-Editing Their Prompts / Python Code 
- [Casibase](https://github.com/casibase/casibase): Open-source AI LangChain-like RAG (Retrieval-Augmented Generation) knowledge database with web UI and Enterprise SSO⚡️, supports OpenAI, Azure, LLaMA, Google Gemini, HuggingFace, Claude, Grok, etc 
- [Fructose](https://github.com/bananaml/fructose): Fructose is a python package to create a dependable, strongly-typed interface around an LLM call. 
- [R2R](https://github.com/SciPhi-AI/R2R): A framework for rapid development and deployment of production-ready RAG systems 
- [uAgents](https://github.com/fetchai/uAgents): A fast and lightweight framework for creating decentralized agents with ease. 
- [Codel](https://github.com/semanser/codel): ✨ Fully autonomous AI Agent that can perform complicated tasks and projects using terminal, browser, and editor. 
- [LLocalSearch](https://github.com/nilsherzig/LLocalSearch): LLocalSearch is a completely locally running search aggregator using LLM Agents. The user can ask a question and the system will use a chain of LLMs to find the answer. The user can see the progress of the agents and the final answer. No OpenAI or Google API keys are needed. 
- [Plandex](https://github.com/plandex-ai/plandex): An AI coding engine for complex tasks 
- [Maestro](https://github.com/Doriandarko/maestro): A framework for Claude Opus to intelligently orchestrate subagents. 
- [GPT Pilot](https://github.com/Pythagora-io/gpt-pilot): GPT Pilot is the core technology for the Pythagora VS Code extension that aims to provide the first real AI developer companion. 
- [SWE Agent](https://github.com/princeton-nlp/swe-agent): SWE-agent takes a GitHub issue and tries to automatically fix it, using GPT-4, or your LM of choice. 
- [Gateway](https://github.com/Portkey-AI/gateway): A Blazing Fast AI Gateway. Route to 100+ LLMs with 1 fast & friendly API. 
- [AgentRun](https://github.com/Jonathan-Adly/AgentRun): The easiest, and fastest way to run AI-generated Python code safely 
- [LLama Cpp Agent](https://github.com/Maximilian-Winter/llama-cpp-agent): The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models 
- [FinRobot](https://github.com/AI4Finance-Foundation/FinRobot): An Open-Source AI Agent Platform for Financial Applications using LLMs 
- [Groq Ruby](https://github.com/drnic/groq-ruby): Groq Cloud runs LLM models fast and cheap. This is a convenience client library for Ruby. 
- [AgentScope](https://github.com/modelscope/agentscope): Start building LLM-empowered multi-agent applications in an easier way. 
- [Memary](https://github.com/kingjulio8238/memary): Longterm Memory for Autonomous Agents. 
- [Llmware](https://github.com/llmware-ai/llmware): Providing enterprise-grade LLM-based development framework, tools, and fine-tuned models. 
- [Pipecat](https://github.com/pipecat-ai/pipecat): Open Source framework for voice and multimodal conversational AI. 
- [Phidata](https://github.com/phidatahq/phidata): Build AI Assistants with memory, knowledge and tools. 
- [Rigging](https://github.com/dreadnode/rigging): Lightweight LLM Interaction Framework (rust) 
- [Vision agent](https://github.com/landing-ai/vision-agent): Vision Agent is a library that helps you utilize agent frameworks to generate code to solve your vision task. 
- [llama-agents](https://github.com/run-llama/llama-agents): llama-agents is an async-first framework for building, iterating, and productionizing multi-agent systems, including multi-agent communication, distributed tool execution, human-in-the-loop, and more 
- [Claude Engineer](https://github.com/Doriandarko/claude-engineer): Claude Engineer is an interactive command-line interface (CLI) that leverages the power of Anthropic's Claude-3.5-Sonnet model to assist with software development tasks. 
- [AI Scientist](https://github.com/SakanaAI/AI-Scientist): The AI Scientist: Towards Fully Automated Open-Ended Scientific 
- [DSPy](https://github.com/stanfordnlp/dspy): The framework for programming—not prompting—foundation models 
- [Eino](https://github.com/cloudwego/eino): Eino provides a Golang AI application development framework with various component integrations and the ability to orchestrate Chains and Graphs similar to LangChain and LangGraph. 
- [TensorZero](https://github.com/tensorzero/tensorzero): An open-source framework for building production-grade LLM applications. It unifies an LLM gateway, observability, optimization, evaluations, and experimentation. 
- [Bifrost](https://github.com/maximhq/bifrost): Bifrost is the fastest LLM gateway, with just 11μs overhead at 5,000 RPS, making it 50x faster than LiteLLM. 
- [Mastra AI](https://github.com/mastra-ai/mastra): a framework for building AI-powered applications and agents with a modern TypeScript stack.
## Complement to this list
- [Open LLMs](https://github.com/eugeneyan/open-llms): A list of open LLMs available for commercial use 
- [Awesome LLM](https://github.com/Hannibal046/Awesome-LLM): Awesome-LLM: a curated list of Large Language Model resources. 
- [LLaMA Cult and More](https://github.com/shm007g/LLaMA-Cult-and-More): Keeping Track of Affordable LLMs, 🦙 Cult and More 
- [Awesome Language Agents](https://github.com/ysymyth/awesome-language-agents): List of language agents based on paper "Cognitive Architectures for Language Agents" 
================================================
FILE: contributing.md
================================================
# Contribution Guidelines
The Awesome Langchain curates content and projects using or supporting Langchain ecosystem. The contribution needs to be open source. The list is curated in a way that only the best content is listed. This means that not all content will be listed. The content that is listed should be of high quality and have a clear added value.
We do not list content that is:
- brand new vibe coded repo without demonstrated traction.
- not in English.
- not related to Langchain.
- not maintained anymore.
- not online anymore.
- not open source.
- not adding value to existing content.
-
When adding a new item, please place it at the _bottom_ of the list.
We do not aim to list all Langchain-supported platforms already listed in the Langchain documentation, but we may list articles and examples demonstrating their usage.
Submit a PR, not an issue. Any PR that does not follow the guidelines will be automatically closed.
## Adding something to awesome langchain
If you have something awesome to contribute to an awesome list, this is how you do it.
You'll need a [GitHub account](https://github.com/join)!
1. Access the awesome list's GitHub page. For example: https://github.com/kyrolabs/awesome-langchain
2. Click on the `readme.md` file: 
3. Now click on the edit icon. 
4. You can start editing the text of the file in the in-browser editor. Make sure you follow guidelines above. You can use [GitHub Flavored Markdown](https://help.github.com/articles/github-flavored-markdown/). 
5. Say why you're proposing the changes, and then click on "Propose file change". 
6. Submit the [pull request](https://help.github.com/articles/using-pull-requests/)!
## Updating your Pull Request
Sometimes, a maintainer of an awesome list will ask you to edit your Pull Request before it is included. This is normally due to spelling errors or because your PR didn't match the awesome-\* list guidelines.
[Here](https://github.com/RichardLitt/knowledge/blob/master/github/amending-a-commit-guide.md) is a write up on how to change a Pull Request, and the different ways you can do that.
================================================
FILE: package.json
================================================
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"author": "",
"license": "ISC",
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About this extraction
This page contains the full source code of the kyrolabs/awesome-langchain GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 4 files (62.7 KB), approximately 17.0k tokens. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.