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Repository: NirDiamant/GenAI_Agents
Branch: main
Commit: cbeab135b355
Files: 98
Total size: 13.1 MB
Directory structure:
gitextract_p_kcwhzy/
├── .github/
│ └── FUNDING.yml
├── CONTRIBUTING.md
├── LICENSE
├── README.md
├── all_agents_tutorials/
│ ├── Academic_Task_Learning_Agent_LangGraph.ipynb
│ ├── ClauseAI.ipynb
│ ├── ContentIntelligence.ipynb
│ ├── EU_Green_Compliance_FAQ_Bot.ipynb
│ ├── ShopGenie.ipynb
│ ├── Weather_Disaster_Management_AI_AGENT.ipynb
│ ├── agent_hackathon_genAI_career_assistant.ipynb
│ ├── ainsight_langgraph.ipynb
│ ├── blog_writer_swarm.ipynb
│ ├── business_meme_generator.ipynb
│ ├── car_buyer_agent_langgraph.ipynb
│ ├── chiron_learning_agent_langgraph.ipynb
│ ├── customer_support_agent_langgraph.ipynb
│ ├── database_discovery_fleet.ipynb
│ ├── e2e_testing_agent.ipynb
│ ├── essay_grading_system_langgraph.ipynb
│ ├── generate_podcast_agent_langgraph.ipynb
│ ├── gif_animation_generator_langgraph.ipynb
│ ├── graph_inspector_system_langgraph.ipynb
│ ├── grocery_management_agents_system.ipynb
│ ├── journalism_focused_ai_assistant_langgraph.ipynb
│ ├── langgraph-tutorial.ipynb
│ ├── mcp-tutorial.ipynb
│ ├── memory-agent-tutorial.ipynb
│ ├── memory_enhanced_conversational_agent.ipynb
│ ├── multi_agent_collaboration_system.ipynb
│ ├── murder_mystery_agent_langgraph.ipynb
│ ├── music_compositor_agent_langgraph.ipynb
│ ├── news_tldr_langgraph.ipynb
│ ├── project_manager_assistant_agent.ipynb
│ ├── research_team_autogen.ipynb
│ ├── sales_call_analyzer_agent.ipynb
│ ├── scientific_paper_agent_langgraph.ipynb
│ ├── scripts/
│ │ └── mcp_server.py
│ ├── search_the_internet_and_summarize.ipynb
│ ├── self_healing_code.ipynb
│ ├── self_improving_agent.ipynb
│ ├── simple_conversational_agent-pydanticai.ipynb
│ ├── simple_conversational_agent.ipynb
│ ├── simple_data_analysis_agent_notebook-pydanticai.ipynb
│ ├── simple_data_analysis_agent_notebook.ipynb
│ ├── simple_question_answering_agent.ipynb
│ ├── simple_travel_planner_langgraph.ipynb
│ ├── systematic_review_of_scientific_articles.ipynb
│ ├── task_oriented_agent.ipynb
│ ├── taskifier.ipynb
│ └── tts_poem_generator_agent_langgraph.ipynb
├── data/
│ ├── 1855.txt
│ ├── ATLAS_data/
│ │ ├── calendar_events.json
│ │ ├── profile.json
│ │ └── task.json
│ ├── ArticleAnalysis.md
│ ├── CBAM_Questions and Answers.txt
│ ├── CELEX_02003L0087-20230605_EN_TXT.md
│ ├── CELEX_02018R2066-20210101_EN_TXT.txt
│ ├── CELEX_02018R2067-20210101_EN_TXT.txt
│ ├── CELEX_32011D0753_EN_TXT.txt
│ ├── CELEX_32013R0525_EN_TXT.txt
│ ├── CELEX_32014D0955_EN_TXT.txt
│ ├── CELEX_32014R0666_EN_TXT.txt
│ ├── CELEX_32014R0749_EN_TXT.txt
│ ├── CELEX_32019D1004_EN_TXT.txt
│ ├── CELEX_32021R1119_EN_TXT.txt
│ ├── CELEX_32023D0136_EN_TXT.txt
│ ├── CELEX_32023L0959_EN_TXT.txt
│ ├── CELEX_32023L1791_EN_TXT.txt
│ ├── CELEX_32023R0839_EN_TXT.txt
│ ├── CELEX_32023R0956_EN_TXT.txt
│ ├── CELEX_32023R0957_EN_TXT.txt
│ ├── CELEX_52020PC0563_EN_TXT.txt
│ ├── COM(2019) 640 final- green deal.txt
│ ├── EU_ETS.txt
│ ├── GD0 - Annex I to EU-ETS Directive.2024.md.txt
│ ├── L_2021243EN.01000101.txt
│ ├── OJ_L_202401991_EN_TXT.txt
│ ├── PE-36-2023-INIT_en.txt
│ ├── Questions_and_Answers__EU_Biodiversity_Strategy_for_2030_-_Bringing_nature_back_into_our_lives.txt
│ ├── Questions_and_Answers__Green_Deal_Industrial_Plan_for_the_Net-Zero_Age.txt
│ ├── Questions_and_Answers__The_Net-Zero_Industry_Act_and_the_European_Hydrogen_Bank_.txt
│ ├── Questions_and_Answers_on_BEFIT_and_transfer_pricing.txt
│ ├── Taskifier data/
│ │ ├── job-application-history.txt
│ │ ├── school-assignment-history.txt
│ │ └── startup-project-history.txt
│ ├── clauses.json
│ ├── e2e_testing_agent_app.py
│ ├── e2e_testing_agent_register.html
│ ├── f2f_action-plan_2020_strategy-info_en.txt
│ ├── grocery_management_agents_system/
│ │ ├── extracted/
│ │ │ └── grocery_receipt.md
│ │ ├── input/
│ │ │ └── extract_items.js
│ │ └── output/
│ │ ├── grocery_tracker.json
│ │ └── recipe_recommendation.json
│ └── project_manager_assistant/
│ ├── project_description.txt
│ └── team.csv
└── requirements.txt
================================================
FILE CONTENTS
================================================
================================================
FILE: .github/FUNDING.yml
================================================
github: NirDiamant
================================================
FILE: CONTRIBUTING.md
================================================
# Contributing to GenAI Agents repo
Welcome to the world's most comprehensive repository of Generative AI Agent tutorials and implementations! 🌟 We're thrilled you're interested in contributing to this dynamic knowledge base. Your expertise and creativity can help us push the boundaries of GenAI agent technology.
## Join Our Community
We have a vibrant Discord community where contributors can discuss ideas, ask questions, and collaborate on GenAI agent techniques. Join us at:
[GenAI Agents Discord Server](https://discord.gg/cA6Aa4uyDX)
Don't hesitate to introduce yourself and share your thoughts!
## Ways to Contribute
We welcome contributions of all kinds! Here are some ways you can help:
1. **Add New GenAI Agents:** Create new notebooks showcasing novel agent implementations.
2. **Improve Existing Notebooks:** Enhance, update, or expand our current tutorials.
3. **Fix Bugs:** Help us squash bugs in existing code or explanations.
4. **Enhance Documentation:** Improve clarity, add examples, or fix typos in our docs.
5. **Share Creative Ideas:** Have an innovative idea for a new agent? We're all ears!
6. **Engage in Discussions:** Participate in our Discord community to help shape the future of GenAI agents.
Remember, no contribution is too small. Every improvement helps make this repository an even better resource for the community.
## Reporting Issues
Found a problem or have a suggestion? Please create an issue on GitHub, providing as much detail as possible. You can also discuss issues in our Discord community.
## Contributing Code or Content
1. **Fork and Branch:** Fork the repository and create your branch from `main`.
2. **Make Your Changes:** Implement your contribution, following our best practices.
3. **Test:** Ensure your changes work as expected.
4. **Follow the Style:** Adhere to the coding and documentation conventions used throughout the project.
5. **Commit:** Make your git commits informative and concise.
6. **Stay Updated:** The main branch is frequently updated. Before opening a pull request, make sure your code is up-to-date with the current main branch and has no conflicts.
7. **Push and Pull Request:** Push to your fork and submit a pull request.
8. **Discuss:** Use the Discord community to discuss your contribution if you need feedback or have questions.
## Adding a New GenAI Agent
When adding a new GenAI agent to the repository, please follow these additional steps:
1. Create your notebook in the `all_agents_tutorials` folder.
2. Update the README.md file:
- Add your new agent to the list of implementations in the README.
- Place it in the appropriate category based on complexity and type.
- Use the following format for the link:
```
### [Number]. [Your Agent Name 🏷️](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/your_file_name.ipynb)
```
- Replace `[Number]` with the appropriate number, `[Your Agent Name]` with your agent's name, and `your_file_name.ipynb` with the actual name of your notebook file.
- Choose an appropriate emoji that represents your agent.
- After inserting your new agent, make sure to update the numbers of all subsequent agents to maintain the correct order.
3. Update the table in the README.md:
- Add a new row to the table with your agent's information
- Follow the existing table format with columns for:
- (number)
- Category
- Agent Name
- Framework
- Key Features
- Use the same category emojis and formatting as existing entries
- Place your agent in the appropriate category
- Increment the numbers of all subsequent agents in the table
For example:
```
| # | Category | Agent Name | Framework | Key Features |
|----|-------------------|-------------------------------|-------------------|------------------------------------------------------------------------------|
| 1 | 🌱 **Beginner** | [Simple Conversational Agent](all_agents_tutorials/simple_conversational_agent.ipynb) | LangChain/PydanticAI | Context-aware conversations, history management |
| 2 | 🌱 **Beginner** | [Your New Agent](all_agents_tutorials/your_new_agent.ipynb) | LangGraph | Feature 1, Feature 2 |
| 3 | 🌱 **Beginner** | [Next Agent](all_agents_tutorials/next_agent.ipynb) | LangChain | Feature 1, Feature 2 |
```
Remember to increment the numbers of all agents that come after your newly inserted implementation in both the list and the table.
## Notebook Structure
For new notebooks or significant additions to existing ones, please follow this structure:
1. **Title and Overview:** Clear title and brief overview of the agent.
2. **Detailed Explanation:** Cover motivation, key components, agent architecture, and benefits.
3. **Visual Representation:** Include a diagram to visualize the agent's architecture. We recommend using Mermaid syntax for creating these diagrams. Here's how to do it:
• Create a graph using Mermaid's graph TD (top-down) syntax
• You can use Claude or other AI assistants to help you design the graph if needed
• Paste your Mermaid code into [Mermaid Live Editor](https://mermaid.live/)
• In the "Actions" tab of Mermaid Live Editor, download the SVG file of your diagram
• Store the SVG file in the [images folder](https://github.com/NirDiamant/GenAI_Agents/tree/main/images) of the repository
• Use an appropriate, descriptive name for the file
• In your notebook, display the image using Markdown syntax:
```markdown
```
This process ensures consistency in our visual representations and makes it easy for others to understand and potentially modify the diagrams in the future.
4. **Required Packages:** Include all necessary package installations at the beginning of the implementation section using pip install commands. For example:
```python
!pip install package1
!pip install package2
```
5. **Implementation:** Step-by-step Python implementation with clear comments and explanations.
6. **Usage Example:** Demonstrate the agent with a practical example.
7. **Comparison:** Compare with simpler agents, both qualitatively and quantitatively if possible.
8. **Additional Considerations:** Discuss limitations, potential improvements, or specific use cases.
9. **References:** Include relevant citations or resources if you have.
## Notebook Best Practices
To ensure consistency and readability across all notebooks:
1. **Code Cell Descriptions:** Each code cell should be preceded by a markdown cell with a clear, concise title describing the cell's content or purpose.
2. **Clear Unnecessary Outputs:** Before committing your notebook, clear all unnecessary cell outputs. This helps reduce file size and avoids confusion from outdated results.
3. **Consistent Formatting:** Maintain consistent formatting throughout the notebook, including regular use of markdown headers, code comments, and proper indentation.
## Code Quality and Readability
To ensure the highest quality and readability of our code:
1. **Write Clean Code:** Follow best practices for clean, readable code, Document every function and Class.
2. **Use Comments:** Add clear and concise comments to explain complex logic.
3. **Format Your Code:** Use consistent formatting throughout your contribution.
4. **Language Model Review:** After completing your code, consider passing it through a language model for additional formatting and readability improvements. This extra step can help make your code even more accessible and maintainable.
## Documentation
Clear documentation is crucial. Whether you're improving existing docs or adding new ones, follow the same process: fork, change, test, and submit a pull request.
## Final Notes
We're grateful for all our contributors and excited to see how you'll help expand the world's most comprehensive GenAI agent resource. Don't hesitate to ask questions in our Discord community if you're unsure about anything.
Let's harness our collective knowledge and creativity to push the boundaries of GenAI agent technology together!
Happy contributing! 🚀
================================================
FILE: LICENSE
================================================
Custom License Agreement
This License Agreement ("Agreement") is a legal agreement between Nir Diamant ("Licensor") and any individual or entity ("Licensee" or "Contributor") who accesses, uses, or contributes to this repository. By accessing, using, or contributing to the Repository, you agree to be bound by the terms of this Agreement.
1. Grant of License for Non-Commercial Use
1.1 Non-Commercial Use License: The Licensor grants the Licensee a worldwide, royalty-free, non-exclusive, non-transferable license to use, reproduce, modify, and distribute the content of the Repository ("Licensed Material") for non-commercial purposes only, subject to the terms and conditions of this Agreement.
1.2 Attribution Requirement: When using or distributing the Licensed Material, the Licensee must provide appropriate credit to the Licensor by:
- Citing the Licensor's name as specified.
- Including a link to the Repository.
- Indicating if changes were made to the Licensed Material.
1.3 No Commercial Use: Licensees are expressly prohibited from using the Licensed Material, in whole or in part, for any commercial purpose without prior written permission from the Licensor.
2. Reservation of Commercial Rights
2.1 Exclusive Commercial Rights: All commercial rights to the Licensed Material are exclusively reserved by the Licensor. The Licensor retains the sole right to use, reproduce, modify, distribute, and sublicense the Licensed Material for commercial purposes.
2.2 Requesting Commercial Permission: Parties interested in using the Licensed Material for commercial purposes must obtain explicit written consent from the Licensor. Requests should be directed to the contact information provided at the end of this Agreement.
3. Contributions
3.1 Contributor License Grant: By submitting any content ("Contribution") to the Repository, the Contributor grants the Licensor an exclusive, perpetual, irrevocable, worldwide, royalty-free license to use, reproduce, modify, distribute, sublicense, and create derivative works from the Contribution for any purpose, including commercial purposes.
3.2 Non-Commercial Use by Contributor: Contributors retain the right to use their own Contributions for non-commercial purposes under the same terms as this Agreement.
3.3 Warranty of Originality: Contributors represent and warrant that their Contributions are original works and do not infringe upon the intellectual property rights of any third party.
3.4 No Commercial Rights for Contributors: Contributors acknowledge that they have no rights to use the Licensed Material or their Contributions for commercial purposes.
4. Restrictions
4.1 Prohibition of Commercial Exploitation: Licensees and Contributors may not:
- Use the Licensed Material or any Contributions for commercial purposes.
- Distribute the Licensed Material or any Contributions as part of any commercial product or service.
- Sublicense the Licensed Material or any Contributions for commercial use.
4.2 No Endorsement: Licensees and Contributors may not imply endorsement or affiliation with the Licensor without explicit written permission.
5. Term and Termination
5.1 Term: This Agreement is effective upon acceptance and continues unless terminated as provided herein.
5.2 Termination for Breach: The Licensor may terminate this Agreement immediately if the Licensee or Contributor breaches any of its terms.
5.3 Effect of Termination: Upon termination, all rights granted under this Agreement cease, and the Licensee or Contributor must destroy all copies of the Licensed Material in their possession.
5.4 Survival: Sections 2, 3, 4, 6, and 7 survive termination of this Agreement.
6. Disclaimer of Warranties and Limitation of Liability
6.1 As-Is Basis: The Licensed Material and any Contributions are provided "AS IS," without warranties or conditions of any kind, either express or implied.
6.2 Disclaimer: The Licensor expressly disclaims all warranties, including but not limited to warranties of title, non-infringement, merchantability, and fitness for a particular purpose.
6.3 Limitation of Liability: In no event shall the Licensor be liable for any direct, indirect, incidental, special, exemplary, or consequential damages arising in any way out of the use of the Licensed Material or Contributions.
7. General Provisions
7.1 Entire Agreement: This Agreement constitutes the entire agreement between the parties concerning the subject matter hereof and supersedes all prior agreements and understandings.
7.2 Modification: The Licensor reserves the right to modify this Agreement at any time. Continued use of the Repository constitutes acceptance of the modified terms.
7.3 Severability: If any provision of this Agreement is found to be unenforceable, the remainder shall remain in full force and effect.
7.4 Waiver: Failure to enforce any provision of this Agreement shall not constitute a waiver of such provision.
7.5 Governing Law: This Agreement shall be governed by and construed in accordance with the laws of [Your Jurisdiction], without regard to its conflict of law principles.
7.6 Dispute Resolution: Any disputes arising under or in connection with this Agreement shall be subject to the exclusive jurisdiction of the courts located in [Your Jurisdiction].
8. Acceptance
By accessing, using, or contributing to the Repository, you acknowledge that you have read, understood, and agree to be bound by the terms and conditions of this Agreement.
Contact Information
For any questions or requests regarding this Agreement, please contact:
Name: Nir Diamant
Email: nirdiamant21@gmail.com
================================================
FILE: README.md
================================================
[](http://makeapullrequest.com)
[](https://www.linkedin.com/in/nir-diamant-759323134/)
[](https://www.reddit.com/r/EducationalAI/)
[](https://twitter.com/NirDiamantAI)
[](https://discord.gg/cA6Aa4uyDX)
> 🌟 **Support This Project:** Your sponsorship fuels innovation in GenAI agent development. **[Become a sponsor](https://github.com/sponsors/NirDiamant)** to help maintain and expand this valuable resource!
# GenAI Agents: Comprehensive Repository for Development and Implementation 🚀
Welcome to one of the most extensive and dynamic collections of Generative AI (GenAI) agent tutorials and implementations available today. This repository serves as a comprehensive resource for learning, building, and sharing GenAI agents, ranging from simple conversational bots to complex, multi-agent systems.
## 🏆 Sponsors
<div align="center">
<a href="https://coderabbit.link/nir"><img src="images/coderabbit_Light_Type_Mark_Orange.png" height="80" alt="CodeRabbit" /></a>
</div>
## 📫 Stay Updated!
<div align="center">
<table>
<tr>
<td align="center">🚀<br><b>Cutting-edge<br>Updates</b></td>
<td align="center">💡<br><b>Expert<br>Insights</b></td>
<td align="center">🎯<br><b>Top 0.1%<br>Content</b></td>
</tr>
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*Join over 50,000 AI enthusiasts getting unique cutting-edge insights and free tutorials!* ***Plus, subscribers get exclusive early access and special 33% discounts to my book and the upcoming RAG Techniques course!***
</div>
[](https://diamantai.substack.com/?r=336pe4&utm_campaign=pub-share-checklist)
## Introduction
Generative AI agents are at the forefront of artificial intelligence, revolutionizing the way we interact with and leverage AI technologies. This repository is designed to guide you through the development journey, from basic agent implementations to advanced, cutting-edge systems.
<div align="center">
<table>
<tr>
<td>
<h3>📚 Learn to Build Your First AI Agent</h3>
<p><strong><a href="https://diamantai.substack.com/p/your-first-ai-agent-simpler-than">Your First AI Agent: Simpler Than You Think</a></strong></p>
<p>This detailed blog post complements the repository by providing a complete A-Z walkthrough with in-depth explanations of core concepts, step-by-step implementation, and the theory behind AI agents. It's designed to be incredibly simple to follow while covering everything you need to know to build your first working agent from scratch.</p>
<p><em>💡 Plus: Subscribe to the newsletter for exclusive early access to tutorials and special discounts on upcoming courses and books!</em></p>
</td>
</tr>
</table>
</div>
Our goal is to provide a valuable resource for everyone - from beginners taking their first steps in AI to seasoned practitioners pushing the boundaries of what's possible. By offering a range of examples from foundational to complex, we aim to facilitate learning, experimentation, and innovation in the rapidly evolving field of GenAI agents.
Furthermore, this repository serves as a platform for showcasing innovative agent creations. Whether you've developed a novel agent architecture or found an innovative application for existing techniques, we encourage you to share your work with the community.
## Related Projects
🚀 Level up with my **[Agents Towards Production](https://github.com/NirDiamant/agents-towards-production)** repository. It delivers horizontal, code-first tutorials that cover every tool and step in the lifecycle of building production-grade GenAI agents, guiding you from spark to scale with proven patterns and reusable blueprints for real-world launches, making it the smartest place to start if you're serious about shipping agents to production.
📚 Dive into my **[comprehensive guide on RAG techniques](https://github.com/NirDiamant/RAG_Techniques)** to learn about integrating external knowledge into AI systems, enhancing their capabilities with up-to-date and relevant information retrieval.
🖋️ Explore my **[Prompt Engineering Techniques guide](https://github.com/NirDiamant/Prompt_Engineering)** for an extensive collection of prompting strategies, from fundamental concepts to advanced methods, improving your ability to communicate effectively with AI language models.
## A Community-Driven Knowledge Hub
**This repository grows stronger with your contributions!** Join our vibrant communities - the central hubs for shaping and advancing this project together 🤝
**[Educational AI Subreddit](https://www.reddit.com/r/EducationalAI/)**
**[GenAI Agents Discord Community](https://discord.gg/cA6Aa4uyDX)**
Whether you're a novice eager to learn or an expert ready to share your knowledge, your insights can shape the future of GenAI agents. Join us to propose ideas, get feedback, and collaborate on innovative implementations. For contribution guidelines, please refer to our **[CONTRIBUTING.md](https://github.com/NirDiamant/GenAI_Agents/blob/main/CONTRIBUTING.md)** file. Let's advance GenAI agent technology together!
🔗 For discussions on GenAI, agents, or to explore knowledge-sharing opportunities, feel free to **[connect on LinkedIn](https://www.linkedin.com/in/nir-diamant-759323134/)**.
## Key Features
- 🎓 Learn to build GenAI agents from beginner to advanced levels
- 🧠 Explore a wide range of agent architectures and applications
- 📚 Step-by-step tutorials and comprehensive documentation
- 🛠️ Practical, ready-to-use agent implementations
- 🌟 Regular updates with the latest advancements in GenAI
- 🤝 Share your own agent creations with the community
## GenAI Agent Implementations
Below is a comprehensive overview of our GenAI agent implementations, organized by category and functionality. Each implementation is designed to showcase different aspects of AI agent development, from basic conversational agents to complex multi-agent systems.
| # | Category | Agent Name | Framework | Key Features |
|----|-------------------|-------------------------------|-------------------|------------------------------------------------------------------------------|
| 1 | 🌱 **Beginner** | [Simple Conversational Agent](all_agents_tutorials/simple_conversational_agent.ipynb) | LangChain/PydanticAI | Context-aware conversations, history management |
| 2 | 🌱 **Beginner** | [Simple Question Answering](all_agents_tutorials/simple_question_answering_agent.ipynb) | LangChain | Query understanding, concise answers |
| 3 | 🌱 **Beginner** | [Simple Data Analysis](all_agents_tutorials/simple_data_analysis_agent_notebook.ipynb) | LangChain/PydanticAI | Dataset interpretation, natural language queries |
| 4 | 🔧 **Framework** | [Introduction to LangGraph](all_agents_tutorials/langgraph-tutorial.ipynb) | LangGraph | Modular AI workflows, state management |
| 5 | 🔧 **Framework** | [Model Context Protocol (MCP)](all_agents_tutorials/mcp-tutorial.ipynb) | MCP | AI-external resource integration |
| 6 | 🎓 **Educational**| [ATLAS: Academic Task System](all_agents_tutorials/Academic_Task_Learning_Agent_LangGraph.ipynb) | LangGraph | Multi-agent academic planning, note-taking |
| 7 | 🎓 **Educational**| [Scientific Paper Agent](all_agents_tutorials/scientific_paper_agent_langgraph.ipynb) | LangGraph | Literature review automation |
| 8 | 🎓 **Educational**| [Chiron - Feynman Learning](all_agents_tutorials/chiron_learning_agent_langgraph.ipynb) | LangGraph | Adaptive learning, checkpoint system |
| 9 | 💼 **Business** | [Customer Support Agent](all_agents_tutorials/customer_support_agent_langgraph.ipynb) | LangGraph | Query categorization, sentiment analysis |
| 10 | 💼 **Business** | [Essay Grading Agent](all_agents_tutorials/essay_grading_system_langgraph.ipynb) | LangGraph | Automated grading, multiple criteria |
| 11 | 💼 **Business** | [Travel Planning Agent](all_agents_tutorials/simple_travel_planner_langgraph.ipynb) | LangGraph | Personalized itineraries |
| 12 | 💼 **Business** | [GenAI Career Assistant](all_agents_tutorials/agent_hackathon_genAI_career_assistant.ipynb) | LangGraph | Career guidance, learning paths |
| 13 | 💼 **Business** | [Project Manager Assistant](all_agents_tutorials/project_manager_assistant_agent.ipynb) | LangGraph | Task generation, risk assessment |
| 14 | 💼 **Business** | [Contract Analysis Assistant](all_agents_tutorials/ClauseAI.ipynb) | LangGraph | Clause analysis, compliance checking |
| 15 | 💼 **Business** | [E2E Testing Agent](all_agents_tutorials/e2e_testing_agent.ipynb) | LangGraph | Test automation, browser control |
| 16 | 🎨 **Creative** | [GIF Animation Generator](all_agents_tutorials/gif_animation_generator_langgraph.ipynb) | LangGraph | Text-to-animation pipeline |
| 17 | 🎨 **Creative** | [TTS Poem Generator](all_agents_tutorials/tts_poem_generator_agent_langgraph.ipynb) | LangGraph | Text classification, speech synthesis |
| 18 | 🎨 **Creative** | [Music Compositor](all_agents_tutorials/music_compositor_agent_langgraph.ipynb) | LangGraph | AI music composition |
| 19 | 🎨 **Creative** | [Content Intelligence](all_agents_tutorials/ContentIntelligence.ipynb) | LangGraph | Multi-platform content generation |
| 20 | 🎨 **Creative** | [Business Meme Generator](all_agents_tutorials/business_meme_generator.ipynb) | LangGraph | Brand-aligned meme creation |
| 21 | 🎨 **Creative** | [Murder Mystery Game](all_agents_tutorials/murder_mystery_agent_langgraph.ipynb) | LangGraph | Procedural story generation |
| 22 | 📊 **Analysis** | [Memory-Enhanced Conversational](all_agents_tutorials/memory_enhanced_conversational_agent.ipynb)| LangChain | Short/long-term memory integration |
| 23 | 📊 **Analysis** | [Multi-Agent Collaboration](all_agents_tutorials/multi_agent_collaboration_system.ipynb) | LangChain | Historical research, data analysis |
| 24 | 📊 **Analysis** | [Self-Improving Agent](all_agents_tutorials/self_improving_agent.ipynb) | LangChain | Learning from interactions |
| 25 | 📊 **Analysis** | [Task-Oriented Agent](all_agents_tutorials/task_oriented_agent.ipynb) | LangChain | Text summarization, translation |
| 26 | 📊 **Analysis** | [Internet Search Agent](all_agents_tutorials/search_the_internet_and_summarize.ipynb) | LangChain | Web research, summarization |
| 27 | 📊 **Analysis** | [Research Team - Autogen](all_agents_tutorials/research_team_autogen.ipynb) | AutoGen | Multi-agent research collaboration |
| 28 | 📊 **Analysis** | [Sales Call Analyzer](all_agents_tutorials/sales_call_analyzer_agent.ipynb) | LangGraph | Audio transcription, NLP analysis |
| 29 | 📊 **Analysis** | [Weather Emergency System](all_agents_tutorials/Weather_Disaster_Management_AI_AGENT.ipynb) | LangGraph | Real-time data processing |
| 30 | 📊 **Analysis** | [Self-Healing Codebase](all_agents_tutorials/self_healing_code.ipynb) | LangGraph | Error detection, automated fixes |
| 31 | 📊 **Analysis** | [DataScribe](all_agents_tutorials/database_discovery_fleet.ipynb) | LangGraph | Database exploration, query planning |
| 32 | 📊 **Analysis** | [Memory-Enhanced Email](all_agents_tutorials/memory-agent-tutorial.ipynb) | LangGraph | Email triage, response generation |
| 33 | 📰 **News** | [News TL;DR](all_agents_tutorials/news_tldr_langgraph.ipynb) | LangGraph | News summarization, API integration |
| 34 | 📰 **News** | [AInsight](all_agents_tutorials/ainsight_langgraph.ipynb) | LangGraph | AI/ML news aggregation |
| 35 | 📰 **News** | [Journalism Assistant](all_agents_tutorials/journalism_focused_ai_assistant_langgraph.ipynb) | LangGraph | Fact-checking, bias detection |
| 36 | 📰 **News** | [Blog Writer](all_agents_tutorials/blog_writer_swarm.ipynb) | OpenAI Swarm | Collaborative content creation |
| 37 | 📰 **News** | [Podcast Generator](all_agents_tutorials/generate_podcast_agent_langgraph.ipynb) | LangGraph | Content search, audio generation |
| 38 | 🛍️ **Shopping** | [ShopGenie](all_agents_tutorials/ShopGenie.ipynb) | LangGraph | Product comparison, recommendations |
| 39 | 🛍️ **Shopping** | [Car Buyer Agent](all_agents_tutorials/car_buyer_agent_langgraph.ipynb) | LangGraph | Web scraping, decision support |
| 40 | 🎯 **Task Management** | [Taskifier](all_agents_tutorials/taskifier.ipynb) | LangGraph | Work style analysis, task breakdown |
| 41 | 🎯 **Task Management** | [Grocery Management](all_agents_tutorials/grocery_management_agents_system.ipynb) | CrewAI | Inventory tracking, recipe suggestions |
| 42 | 🔍 **QA** | [LangGraph Inspector](all_agents_tutorials/graph_inspector_system_langgraph.ipynb) | LangGraph | System testing, vulnerability detection |
| 43 | 🔍 **QA** | [EU Green Deal Bot](all_agents_tutorials/EU_Green_Compliance_FAQ_Bot.ipynb) | LangGraph | Regulatory compliance, FAQ system |
| 44 | 🔍 **QA** | [Systematic Review](all_agents_tutorials/systematic_review_of_scientific_articles.ipynb) | LangGraph | Academic paper processing, draft generation |
| 45 | 🌟 **Advanced** | [Controllable RAG Agent](https://github.com/NirDiamant/Controllable-RAG-Agent) | Custom | Complex question answering, deterministic graph |
Explore our extensive list of GenAI agent implementations, sorted by categories:
### 🌱 Beginner-Friendly Agents
1. **Simple Conversational Agent**
- **[LangChain](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/simple_conversational_agent.ipynb)**
- **[PydanticAI](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/simple_conversational_agent-pydanticai.ipynb)**
#### Overview 🔎
A context-aware conversational AI maintains information across interactions, enabling more natural dialogues.
#### Implementation 🛠️
Integrates a language model, prompt template, and history manager to generate contextual responses and track conversation sessions.
2. **[Simple Question Answering Agent](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/simple_question_answering_agent.ipynb)**
#### Overview 🔎
Answering (QA) agent using LangChain and OpenAI's language model understands user queries and provides relevant, concise answers.
#### Implementation 🛠️
Combines OpenAI's GPT model, a prompt template, and an LLMChain to process user questions and generate AI-driven responses in a streamlined manner.
3. **Simple Data Analysis Agent**
- **[LangChain](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/simple_data_analysis_agent_notebook.ipynb)**
- **[PydanticAI](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/simple_data_analysis_agent_notebook-pydanticai.ipynb)**
#### Overview 🔎
An AI-powered data analysis agent interprets and answers questions about datasets using natural language, combining language models with data manipulation tools for intuitive data exploration.
#### Implementation 🛠️
Integrates a language model, data manipulation framework, and agent framework to process natural language queries and perform data analysis on a synthetic dataset, enabling accessible insights for non-technical users.
### 🔧 Framework Tutorial
4. **[Introduction to LangGraph: Building Modular AI Workflows](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/langgraph-tutorial.ipynb)**
#### Overview 🔎
This tutorial introduces LangGraph, a powerful framework for creating modular, graph-based AI workflows. Learn how to leverage LangGraph to build more complex and flexible AI agents that can handle multi-step processes efficiently.
#### Implementation 🛠️
Step-by-step guide on using LangGraph to create a StateGraph workflow. The tutorial covers key concepts such as state management, node creation, and graph compilation. It demonstrates these principles by constructing a simple text analysis pipeline, serving as a foundation for more advanced agent architectures.
#### Additional Resources 📚
- **[Blog Post](https://open.substack.com/pub/diamantai/p/your-first-ai-agent-simpler-than?r=336pe4&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false)**
5. **[Model Context Protocol (MCP): Seamless Integration of AI and External Resources](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/mcp-tutorial.ipynb)**
#### Overview 🔎
This tutorial introduces the Model Context Protocol (MCP), an open standard for connecting AI models with external data sources and tools. Learn how MCP serves as a universal bridge between GenAI agents and the wider digital ecosystem, enabling more capable and context-aware AI applications.
#### Implementation 🛠️
Provides a hands-on guide to implementing MCP servers and clients, demonstrating how to connect language models with external tools and data sources. The tutorial covers server setup, tool definition, and integration with AI clients, with practical examples of building useful agent capabilities through the protocol.
#### Additional Resources 📚
- **[Blog Post](https://open.substack.com/pub/diamantai/p/model-context-protocol-mcp-explained?r=336pe4&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false)**
- **[Official MCP Documentation](https://modelcontextprotocol.io/introduction)**
- **[MCP GitHub Repository](https://github.com/modelcontextprotocol)**
### 🎓 Educational and Research Agents
6. **[ATLAS: Academic Task and Learning Agent System](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/Academic_Task_Learning_Agent_LangGraph.ipynb)**
#### Overview 🔎
ATLAS demonstrates how to build an intelligent multi-agent system that transforms academic support through AI-powered assistance. The system leverages LangGraph's workflow framework to coordinate multiple specialized agents that provide personalized academic planning, note-taking, and advisory support.
#### Implementation 🛠️
Implements a state-managed multi-agent architecture using four specialized agents (Coordinator, Planner, Notewriter, and Advisor) working in concert through LangGraph's workflow framework. The system features sophisticated workflows for profile analysis and academic support, with continuous adaptation based on student performance and feedback.
#### Additional Resources 📚
- **[YouTube Explanation](https://www.youtube.com/watch?v=yxowMLL2dDI)**
- **[Blog Post](https://open.substack.com/pub/diamantai/p/atlas-when-artificial-intelligence?r=336pe4&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false)**
7. **[Scientific Paper Agent - Literature Review](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/scientific_paper_agent_langgraph.ipynb)**
#### Overview 🔎
An intelligent research assistant that helps users navigate, understand, and analyze scientific literature through an orchestrated workflow. The system combines academic APIs with sophisticated paper processing techniques to automate literature review tasks, enabling researchers to efficiently extract insights from academic papers while maintaining research rigor and quality control.
#### Implementation 🛠️
Leverages LangGraph to create a five-node workflow system including decision making, planning, tool execution, and quality validation nodes. The system integrates the CORE API for paper access, PDFplumber for document processing, and advanced language models for analysis. Key features include a retry mechanism for robust paper downloads, structured data handling through Pydantic models, and quality-focused improvement cycles with human-in-the-loop validation options.
#### Additional Resources 📚
- **[YouTube Explanation](https://youtu.be/Bc4YtpHY6Ws)**
- **[Blog Post](https://open.substack.com/pub/diamantai/p/nexus-ai-the-revolutionary-research?r=336pe4&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false)**
8. **[Chiron - A Feynman-Enhanced Learning Agent](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/chiron_learning_agent_langgraph.ipynb)**
#### Overview 🔎
An adaptive learning agent that guides users through educational content using a structured checkpoint system and Feynman-style teaching. The system processes learning materials (either user-provided or web-retrieved), verifies understanding through interactive checkpoints, and provides simplified explanations when needed, creating a personalized learning experience that mimics one-on-one tutoring.
#### Implementation 🛠️
Uses LangGraph to orchestrate a learning workflow that includes checkpoint definition, context building, understanding verification, and Feynman teaching nodes. The system integrates web search for dynamic content retrieval, employs semantic chunking for context processing, and manages embeddings for relevant information retrieval. Key features include a 70% understanding threshold for progression, interactive human-in-the-loop validation, and structured output through Pydantic models for consistent data handling.
#### Additional Resources 📚
- **[YouTube Explanation](https://www.youtube.com/watch?v=qsdiTGkB8mk)**
### 💼 Business and Professional Agents
9. **[Customer Support Agent (LangGraph)](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/customer_support_agent_langgraph.ipynb)**
#### Overview 🔎
An intelligent customer support agent using LangGraph categorizes queries, analyzes sentiment, and provides appropriate responses or escalates issues.
#### Implementation 🛠️
Utilizes LangGraph to create a workflow combining state management, query categorization, sentiment analysis, and response generation.
10. **[Essay Grading Agent (LangGraph)](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/essay_grading_system_langgraph.ipynb)**
#### Overview 🔎
An automated essay grading system using LangGraph and an LLM model evaluates essays based on relevance, grammar, structure, and depth of analysis.
#### Implementation 🛠️
Utilizes a state graph to define the grading workflow, incorporating separate grading functions for each criterion.
11. **[Travel Planning Agent (LangGraph)](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/simple_travel_planner_langgraph.ipynb)**
#### Overview 🔎
A Travel Planner using LangGraph demonstrates how to build a stateful, multi-step conversational AI application that collects user input and generates personalized travel itineraries.
#### Implementation 🛠️
Utilizes StateGraph to define the application flow, incorporates custom PlannerState for process management.
12. **[GenAI Career Assistant Agent](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/agent_hackathon_genAI_career_assistant.ipynb)**
#### Overview 🔎
The GenAI Career Assistant demonstrates how to create a multi-agent system that provides personalized guidance for careers in Generative AI. Using LangGraph and Gemini LLM, the system delivers customized learning paths, resume assistance, interview preparation, and job search support.
#### Implementation 🛠️
Leverages a multi-agent architecture using LangGraph to coordinate specialized agents (Learning, Resume, Interview, Job Search) through TypedDict-based state management. The system employs sophisticated query categorization and routing while integrating with external tools like DuckDuckGo for job searches and dynamic content generation.
#### Additional Resources 📚
- **[YouTube Explanation](https://www.youtube.com/watch?v=IcKh0ltXO_8)**
13. **[Project Manager Assistant Agent](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/project_manager_assistant_agent.ipynb)**
#### Overview 🔎
An AI agent designed to assist in project management tasks by automating the process of creating actionable tasks from project descriptions, identifying dependencies, scheduling work, and assigning tasks to team members based on expertise. The system includes risk assessment and self-reflection capabilities to optimize project plans through multiple iterations, aiming to minimize overall project risk.
#### Implementation 🛠️
Leverages LangGraph to orchestrate a workflow of specialized nodes including task generation, dependency mapping, scheduling, allocation, and risk assessment. Each node uses GPT-4o-mini for structured outputs following Pydantic models. The system implements a feedback loop for self-improvement, where risk scores trigger reflection cycles that generate insights to optimize the project plan. Visualization tools display Gantt charts of the generated schedules across iterations.
#### Additional Resources 📚
- **[YouTube Explanation](https://www.youtube.com/watch?v=R7YWjzg3LpI)**
14. **[Contract Analysis Assistant (ClauseAI)](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/ClauseAI.ipynb)**
#### Overview 🔎
ClauseAI demonstrates how to build an AI-powered contract analysis system using a multi-agent approach. The system employs specialized AI agents for different aspects of contract review, from clause analysis to compliance checking, and leverages LangGraph for workflow orchestration and Pinecone for efficient clause retrieval and comparison.
#### Implementation 🛠️
Implements a sophisticated state-based workflow using LangGraph to coordinate multiple AI agents through contract analysis stages. The system features Pydantic models for data validation, vector storage with Pinecone for clause comparison, and LLM-based analysis for generating comprehensive contract reports. The implementation includes parallel processing capabilities and customizable report generation based on user requirements.
#### Additional Resources 📚
- **[YouTube Explanation](https://www.youtube.com/watch?v=rP8uv_tXuSI)**
15. **[E2E Testing Agent](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/e2e_testing_agent.ipynb)**
#### Overview 🔎
The E2E Testing Agent demonstrates how to build an AI-powered system that converts natural language test instructions into executable end-to-end web tests. Using LangGraph for workflow orchestration and Playwright for browser automation, the system enables users to specify test cases in plain English while handling the complexity of test generation and execution.
#### Implementation 🛠️
Implements a structured workflow using LangGraph to coordinate test generation, validation, and execution. The system features TypedDict state management, integration with Playwright for browser automation, and LLM-based code generation for converting natural language instructions into executable test scripts. The implementation includes DOM state analysis, error handling, and comprehensive test reporting.
#### Additional Resources 📚
- **[YouTube Explanation](https://www.youtube.com/watch?v=jPXtpzcCtyA)**
### 🎨 Creative and Content Generation Agents
16. **[GIF Animation Generator Agent (LangGraph)](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/gif_animation_generator_langgraph.ipynb)**
#### Overview 🔎
A GIF animation generator that integrates LangGraph for workflow management, GPT-4 for text generation, and DALL-E for image creation, producing custom animations from user prompts.
#### Implementation 🛠️
Utilizes LangGraph to orchestrate a workflow that generates character descriptions, plots, and image prompts using GPT-4, creates images with DALL-E 3, and assembles them into GIFs using PIL. Employs asynchronous programming for efficient parallel processing.
17. **[TTS Poem Generator Agent (LangGraph)](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/tts_poem_generator_agent_langgraph.ipynb)**
#### Overview 🔎
An advanced text-to-speech (TTS) agent using LangGraph and OpenAI's APIs classifies input text, processes it based on content type, and generates corresponding speech output.
#### Implementation 🛠️
Utilizes LangGraph to orchestrate a workflow that classifies input text using GPT models, applies content-specific processing, and converts the processed text to speech using OpenAI's TTS API. The system adapts its output based on the identified content type (general, poem, news, or joke).
18. **[Music Compositor Agent (LangGraph)](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/music_compositor_agent_langgraph.ipynb)**
#### Overview 🔎
An AI Music Compositor using LangGraph and OpenAI's language models generates custom musical compositions based on user input. The system processes the input through specialized components, each contributing to the final musical piece, which is then converted to a playable MIDI file.
#### Implementation 🛠️
LangGraph orchestrates a workflow that transforms user input into a musical composition, using ChatOpenAI (GPT-4) to generate melody, harmony, and rhythm, which are then style-adapted. The final AI-generated composition is converted to a MIDI file using music21 and can be played back using pygame.
19. **[Content Intelligence: Multi-Platform Content Generation Agent](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/ContentIntelligence.ipynb)**
#### Overview 🔎
Content Intelligence demonstrates how to build an advanced content generation system that transforms input text into platform-optimized content across multiple social media channels. The system employs LangGraph for workflow orchestration to analyze content, conduct research, and generate tailored content while maintaining brand consistency across different platforms.
#### Implementation 🛠️
Implements a sophisticated workflow using LangGraph to coordinate multiple specialized nodes (Summary, Research, Platform-Specific) through the content generation process. The system features TypedDict and Pydantic models for state management, integration with Tavily Search for research enhancement, and platform-specific content generation using GPT-4. The implementation includes parallel processing for multiple platforms and customizable content templates.
#### Additional Resources 📚
- **[YouTube Explanation](https://www.youtube.com/watch?v=DPMtPbKmWnU)**
20. **[Business Meme Generator Using LangGraph and Memegen.link](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/business_meme_generator.ipynb)**
#### Overview 🔎
The Business Meme Generator demonstrates how to create an AI-powered system that generates contextually relevant memes based on company website analysis. Using LangGraph for workflow orchestration, the system combines Groq's Llama model for text analysis and the Memegen.link API to automatically produce brand-aligned memes for digital marketing.
#### Implementation 🛠️
Implements a state-managed workflow using LangGraph to coordinate website content analysis, meme concept generation, and image creation. The system features Pydantic models for data validation, asynchronous processing with aiohttp, and integration with external APIs (Groq, Memegen.link) to create a complete meme generation pipeline with customizable templates.
#### Additional Resources 📚
- **[YouTube Explanation](https://youtu.be/lsdDaGmkSCw?si=oF3CGfhbRqz1_Vm8)**
21. **[Murder Mystery Game with LLM Agents](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/murder_mystery_agent_langgraph.ipynb)**
#### Overview 🔎
A text-based detective game that utilizes autonomous LLM agents as interactive characters in a procedurally generated murder mystery. Drawing inspiration from the UNBOUNDED paper, the system creates unique scenarios each time, with players taking on the role of Sherlock Holmes to solve the case through character interviews and deductive reasoning.
#### Implementation 🛠️
Leverages two LangGraph workflows - a main game loop for story/character generation and game progression, and a conversation sub-graph for character interactions. The system uses a combination of LLM-powered narrative generation, character AI, and structured game mechanics to create an immersive investigative experience with replayable storylines.
#### Additional Resources 📚
- **[YouTube Explanation](https://www.youtube.com/watch?v=_3cJYlk2EmA)**
### 📊 Analysis and Information Processing Agents
22. **[Memory-Enhanced Conversational Agent](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/memory_enhanced_conversational_agent.ipynb)**
#### Overview 🔎
A memory-enhanced conversational AI agent incorporates short-term and long-term memory systems to maintain context within conversations and across multiple sessions, improving interaction quality and personalization.
#### Implementation 🛠️
Integrates a language model with separate short-term and long-term memory stores, utilizes a prompt template incorporating both memory types, and employs a memory manager for storage and retrieval. The system includes an interaction loop that updates and utilizes memories for each response.
23. **[Multi-Agent Collaboration System](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/multi_agent_collaboration_system.ipynb)**
#### Overview 🔎
A multi-agent collaboration system combining historical research with data analysis, leveraging large language models to simulate specialized agents working together to answer complex historical questions.
#### Implementation 🛠️
Utilizes a base Agent class to create specialized HistoryResearchAgent and DataAnalysisAgent, orchestrated by a HistoryDataCollaborationSystem. The system follows a five-step process: historical context provision, data needs identification, historical data provision, data analysis, and final synthesis.
24. **[Self-Improving Agent](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/self_improving_agent.ipynb)**
#### Overview 🔎
A Self-Improving Agent using LangChain engages in conversations, learns from interactions, and continuously improves its performance over time through reflection and adaptation.
#### Implementation 🛠️
Integrates a language model with chat history management, response generation, and a reflection mechanism. The system employs a learning system that incorporates insights from reflection to enhance future performance, creating a continuous improvement loop.
25. **[Task-Oriented Agent](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/task_oriented_agent.ipynb)**
#### Overview 🔎
A language model application using LangChain that summarizes text and translates the summary to Spanish, combining custom functions, structured tools, and an agent for efficient text processing.
#### Implementation 🛠️
Utilizes custom functions for summarization and translation, wrapped as structured tools. Employs a prompt template to guide the agent, which orchestrates the use of tools. An agent executor manages the process, taking input text and producing both an English summary and its Spanish translation.
26. **[Internet Search and Summarize Agent](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/search_the_internet_and_summarize.ipynb)**
#### Overview 🔎
An intelligent web research assistant that combines web search capabilities with AI-powered summarization, automating the process of gathering information from the internet and distilling it into concise, relevant summaries.
#### Implementation 🛠️
Integrates a web search module using DuckDuckGo's API, a result parser, and a text summarization engine leveraging OpenAI's language models. The system performs site-specific or general searches, extracts relevant content, generates concise summaries, and compiles attributed results for efficient information retrieval and synthesis.
27. **[Multi agent research team - Autogen](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/research_team_autogen.ipynb)**
#### Overview 🔎
This technique explores a multi-agent system for collaborative research using the AutoGen library. It employs agents to solve tasks collaboratively, focusing on efficient execution and quality assurance. The system enhances research by distributing tasks among specialized agents.
#### Implementation 🛠️
Agents are configured with specific roles using the GPT-4 model, including admin, developer, planner, executor, and quality assurance. Interaction management ensures orderly communication with defined transitions. Task execution involves collaborative planning, coding, execution, and quality checking, demonstrating a scalable framework for various domains.
#### Additional Resources 📚
- **[comprehensive solution with UI](https://github.com/yanivvak/dream-team)**
- **[Blogpost](https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/build-your-dream-team-with-autogen/ba-p/4157961)**
28. **[Sales Call Analyzer](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/sales_call_analyzer_agent.ipynb)**
#### Overview 🔎
An intelligent system that automates the analysis of sales call recordings by combining audio transcription with advanced natural language processing. The analyzer transcribes audio using OpenAI's Whisper, processes the text using NLP techniques, and generates comprehensive reports including sentiment analysis, key phrases, pain points, and actionable recommendations to improve sales performance.
#### Implementation 🛠️
Utilizes multiple components in a structured workflow: OpenAI Whisper for audio transcription, CrewAI for task automation and agent management, and LangChain for orchestrating the analysis pipeline. The system processes audio through a series of steps from transcription to detailed analysis, leveraging custom agents and tasks to generate structured JSON reports containing insights about customer sentiment, sales opportunities, and recommended improvements.
#### Additional Resources 📚
- **[YouTube Explanation](https://www.youtube.com/watch?v=SKAt_PvznDw)**
29. **[Weather Emergency & Response System](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/Weather_Disaster_Management_AI_AGENT.ipynb)**
#### Overview 🔎
A comprehensive system demonstrating two agent graph implementations for weather emergency response: a real-time graph processing live weather data, and a hybrid graph combining real and simulated data for testing high-severity scenarios. The system handles complete workflow from data gathering through emergency plan generation, with automated notifications and human verification steps.
#### Implementation 🛠️
Utilizes LangGraph for orchestrating complex workflows with state management, integrating OpenWeatherMap API for real-time data, and Gemini for analysis and response generation. The system incorporates email notifications, social media monitoring simulation, and severity-based routing with configurable human verification for low/medium severity events.
#### Additional Resources 📚
- **[YouTube Explanation](https://www.youtube.com/watch?v=AgiOAJl_apw)**
30. **[Self-Healing Codebase System](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/self_healing_code.ipynb)**
#### Overview 🔎
An intelligent system that automatically detects, diagnoses, and fixes runtime code errors using LangGraph workflow orchestration and ChromaDB vector storage. The system maintains a memory of encountered bugs and their fixes through vector embeddings, enabling pattern recognition for similar errors across the codebase.
#### Implementation 🛠️
Utilizes a state-based graph workflow that processes function definitions and runtime arguments through specialized nodes for error detection, code analysis, and fix generation. Incorporates ChromaDB for vector-based storage of bug patterns and fixes, with automated search and retrieval capabilities for similar error patterns, while maintaining code execution safety through structured validation steps.
#### Additional Resources 📚
- **[YouTube Explanation](https://www.youtube.com/watch?v=ga7ShvIXOvE)**
31. **[DataScribe: AI-Powered Schema Explorer](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/database_discovery_fleet.ipynb)**
#### Overview 🔎
An intelligent agent system that enables intuitive exploration and querying of relational databases through natural language interactions. The system utilizes a fleet of specialized agents, coordinated by a stateful Supervisor, to handle schema discovery, query planning, and data analysis tasks while maintaining contextual understanding through vector-based relationship graphs.
#### Implementation 🛠️
Leverages LangGraph for orchestrating a multi-agent workflow including discovery, inference, and planning agents, with NetworkX for relationship graph visualization and management. The system incorporates dynamic state management through TypedDict classes, maintains database context between sessions using a db_graph attribute, and includes safety measures to prevent unauthorized database modifications.
32. **[Memory-Enhanced Email Agent (LangGraph & LangMem)](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/memory-agent-tutorial.ipynb)**
#### Overview 🔎
An intelligent email assistant that combines three types of memory (semantic, episodic, and procedural) to create a system that improves over time. The agent can triage incoming emails, draft contextually appropriate responses using stored knowledge, and enhance its performance based on user feedback.
#### Implementation 🛠️
Leverages LangGraph for workflow orchestration and LangMem for sophisticated memory management across multiple memory types. The system implements a triage workflow with memory-enhanced decision making, specialized tools for email composition and calendar management, and a self-improvement mechanism that updates its own prompts based on feedback and past performance.
#### Additional Resources 📚
- **[Blog Post](https://open.substack.com/pub/diamantai/p/building-an-ai-agent-with-memory?r=336pe4&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false)
### 📰 News and Information Agents
33. **[News TL;DR using LangGraph](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/news_tldr_langgraph.ipynb)**
#### Overview 🔎
A news summarization system that generates concise TL;DR summaries of current events based on user queries. The system leverages large language models for decision making and summarization while integrating with news APIs to access up-to-date content, allowing users to quickly catch up on topics of interest through generated bullet-point summaries.
#### Implementation 🛠️
Utilizes LangGraph to orchestrate a workflow combining multiple components: GPT-4o-mini for generating search terms and article summaries, NewsAPI for retrieving article metadata, BeautifulSoup for web scraping article content, and Asyncio for concurrent processing. The system follows a structured pipeline from query processing through article selection and summarization, managing the flow between components to produce relevant TL;DRs of current news articles.
#### Additional Resources 📚
- **[YouTube Explanation](https://www.youtube.com/watch?v=0fRxW6miybI)**
- **[Blog Post](https://open.substack.com/pub/diamantai/p/stop-reading-start-understanding?r=336pe4&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false)**
34. **[AInsight: AI/ML Weekly News Reporter](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/ainsight_langgraph.ipynb)**
#### Overview 🔎
AInsight demonstrates how to build an intelligent news aggregation and summarization system using a multi-agent architecture. The system employs three specialized agents (NewsSearcher, Summarizer, Publisher) to automatically collect, process and summarize AI/ML news for general audiences through LangGraph-based workflow orchestration.
#### Implementation 🛠️
Implements a state-managed multi-agent system using LangGraph to coordinate the news collection (Tavily API), technical content summarization (GPT-4), and report generation processes. The system features modular architecture with TypedDict-based state management, external API integration, and markdown report generation with customizable templates.
#### Additional Resources 📚
- **[YouTube Explanation](https://www.youtube.com/watch?v=kH5S1is2D_0)**
35. **[Journalism-Focused AI Assistant](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/journalism_focused_ai_assistant_langgraph.ipynb)**
#### Overview 🔎
A specialized AI assistant that helps journalists tackle modern journalistic challenges like misinformation, bias, and information overload. The system integrates fact-checking, tone analysis, summarization, and grammar review tools to enhance the accuracy and efficiency of journalistic work while maintaining ethical reporting standards.
#### Implementation 🛠️
Leverages LangGraph to orchestrate a workflow of specialized components including language models for analysis and generation, web search integration via DuckDuckGo's API, document parsing tools like PyMuPDFLoader and WebBaseLoader, text splitting with RecursiveCharacterTextSplitter, and structured JSON outputs. Each component works together through a unified workflow to analyze content, verify facts, detect bias, extract quotes, and generate comprehensive reports.
36. **[Blog Writer (Open AI Swarm)](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/blog_writer_swarm.ipynb)**
#### Overview 🔎
A multi-agent system for collaborative blog post creation using OpenAI's Swarm package. It leverages specialized agents to perform research, planning, writing, and editing tasks efficiently.
#### Implementation 🛠️
Utilizes OpenAI's Swarm Package to manage agent interactions. Includes an admin, researcher, planner, writer, and editor, each with specific roles. The system follows a structured workflow: topic setting, outlining, research, drafting, and editing. This approach enhances content creation through task distribution, specialization, and collaborative problem-solving.
#### Additional Resources 📚
- **[Swarm Repo](https://github.com/openai/swarm)**
37. **[Podcast Internet Search and Generate Agent 🎙️](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/generate_podcast_agent_langgraph.ipynb)**
#### Overview 🔎
A two step agent that first searches the internet for a given topic and then generates a podcast on the topic found. The search step uses a search agent and search function to find the most relevant information. The second step uses a podcast generation agent and generation function to create a podcast on the topic found.
#### Implementation 🛠️
Utilizes LangGraph to orchestrate a two-step workflow. The first step involves a search agent and function to gather information from the internet. The second step uses a podcast generation agent and function to create a podcast based on the gathered information.
### 🛍️ Shopping and Product Analysis Agents
38. **[ShopGenie - Redefining Online Shopping Customer Experience](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/ShopGenie.ipynb)**
#### Overview 🔎
An AI-powered shopping assistant that helps customers make informed purchasing decisions even without domain expertise. The system analyzes product information from multiple sources, compares specifications and reviews, identifies the best option based on user needs, and delivers recommendations through email with supporting video reviews, creating a comprehensive shopping experience.
#### Implementation 🛠️
Uses LangGraph to orchestrate a workflow combining Tavily for web search, Llama-3.1-70B for structured data analysis and product comparison, and YouTube API for review video retrieval. The system processes search results through multiple nodes including schema mapping, product comparison, review identification, and email generation. Key features include structured Pydantic models for consistent data handling, retry mechanisms for robust API interactions, and email delivery through SMTP for sharing recommendations.
#### Additional Resources 📚
- **[YouTube Explanation](https://www.youtube.com/watch?v=Js0sK0u53dQ)**
39. **[Car Buyer AI Agent](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/car_buyer_agent_langgraph.ipynb)**
#### Overview 🔎
The Smart Product Buyer AI Agent demonstrates how to build an intelligent system that assists users in making informed purchasing decisions. Using LangGraph and LLM-based intelligence, the system processes user requirements, scrapes product listings from websites like AutoTrader, and provides detailed analysis and recommendations for car purchases.
#### Implementation 🛠️
Implements a state-based workflow using LangGraph to coordinate user interaction, web scraping, and decision support. The system features TypedDict state management, async web scraping with Playwright, and integrates with external APIs for comprehensive product analysis. The implementation includes a Gradio interface for real-time chat interaction and modular scraper architecture for easy extension to additional product categories.
#### Additional Resources 📚
- **[YouTube Explanation](https://www.youtube.com/watch?v=I61I1fp0qys)**
### 🎯 Task Management and Productivity Agents
40. **[Taskifier - Intelligent Task Allocation & Management](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/taskifier.ipynb)**
#### Overview 🔎
An intelligent task management system that analyzes user work styles and creates personalized task breakdown strategies, born from the observation that procrastination often stems from task ambiguity among students and early-career professionals. The system evaluates historical work patterns, gathers relevant task information through web search, and generates customized step-by-step approaches to optimize productivity and reduce workflow paralysis.
#### Implementation 🛠️
Leverages LangGraph for orchestrating a multi-step workflow including work style analysis, information gathering via Tavily API, and customized plan generation. The system maintains state through the process, integrating historical work pattern data with fresh task research to output detailed, personalized task execution plans aligned with the user's natural working style.
#### Additional Resources 📚
- **[YouTube Explanation](https://www.youtube.com/watch?v=1W_p_RVi9KE&t=25s)**
41. **[Grocery Management Agents System](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/grocery_management_agents_system.ipynb)**
#### Overview 🔎
A multi-agent system built with CrewAI that automates grocery management tasks including receipt interpretation, expiration date tracking, inventory management, and recipe recommendations. The system uses specialized agents to extract data from receipts, estimate product shelf life, track consumption, and suggest recipes to minimize food waste.
#### Implementation 🛠️
Implements four specialized agents using CrewAI - a Receipt Interpreter that extracts item details from receipts, an Expiration Date Estimator that determines shelf life using online sources, a Grocery Tracker that maintains inventory based on consumption, and a Recipe Recommender that suggests meals using available ingredients. Each agent has specific tools and tasks orchestrated through a crew workflow.
#### Additional Resources 📚
- **[YouTube Explanation](https://www.youtube.com/watch?v=FlMu5pKSaHI)**
### 🔍 Quality Assurance and Testing Agents
42. **[LangGraph-Based Systems Inspector](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/graph_inspector_system_langgraph.ipynb)**
#### Overview 🔎
A comprehensive testing and validation tool for LangGraph-based applications that automatically analyzes system architecture, generates test cases, and identifies potential vulnerabilities through multi-agent inspection. The inspector employs specialized AI testers to evaluate different aspects of the system, from basic functionality to security concerns and edge cases.
#### Implementation 🛠️
Integrates LangGraph for workflow orchestration, multiple LLM-powered testing agents, and a structured evaluation pipeline that includes static analysis, test case generation, and results verification. The system uses Pydantic for data validation, NetworkX for graph representation, and implements a modular architecture that allows for parallel test execution and comprehensive result analysis.
#### Additional Resources 📚
- **[YouTube Explanation](https://www.youtube.com/watch?v=fQd6lXc-Y9A)**
- **[Blog Post](https://open.substack.com/pub/diamantai/p/langgraph-systems-inspector-an-ai?r=336pe4&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false)**
43. **[EU Green Deal FAQ Bot](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/EU_Green_Compliance_FAQ_Bot.ipynb)**
#### Overview 🔎
The EU Green Deal FAQ Bot demonstrates how to build a RAG-based AI agent that helps businesses understand EU green deal policies. The system processes complex regulatory documents into manageable chunks and provides instant, accurate answers to common questions about environmental compliance, emissions reporting, and waste management requirements.
#### Implementation 🛠️
Implements a sophisticated RAG pipeline using FAISS vectorstore for document storage, semantic chunking for preprocessing, and multiple specialized agents (Retriever, Summarizer, Evaluator) for query processing. The system features query rephrasing for improved accuracy, cross-reference with gold Q&A datasets for answer validation, and comprehensive evaluation metrics to ensure response quality and relevance.
#### Additional Resources 📚
- **[YouTube Explanation](https://www.youtube.com/watch?v=Av0kBQjwU-Y)**
44. **[Systematic Review Automation System + Paper Draft Creation](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/systematic_review_of_scientific_articles.ipynb)**
#### Overview 🔎
A comprehensive system for automating academic systematic reviews using a directed graph architecture and LangChain components. The system generates complete, publication-ready systematic review papers, automatically processing everything from literature search through final draft generation with multiple revision cycles.
#### Implementation 🛠️
Utilizes a state-based graph workflow that handles paper search and selection (up to 3 papers), PDF processing, and generates a complete academic paper with all standard sections (abstract, introduction, methods, results, conclusions, references). The system incorporates multiple revision cycles with automated critique and improvement phases, all orchestrated through LangGraph state management.
#### Additional Resources 📚
- **[YouTube Explanation](https://www.youtube.com/watch?v=qi35mGGkCtg)**
### 🌟 Special Advanced Technique 🌟
45. **[Sophisticated Controllable Agent for Complex RAG Tasks 🤖](https://github.com/NirDiamant/Controllable-RAG-Agent)**
#### Overview 🔎
An advanced RAG solution designed to tackle complex questions that simple semantic similarity-based retrieval cannot solve. This approach uses a sophisticated deterministic graph as the "brain" 🧠 of a highly controllable autonomous agent, capable of answering non-trivial questions from your own data.
#### Implementation 🛠️
• Implement a multi-step process involving question anonymization, high-level planning, task breakdown, adaptive information retrieval and question answering, continuous re-planning, and rigorous answer verification to ensure grounded and accurate responses.
## Getting Started
To begin exploring and building GenAI agents:
1. Clone this repository:
```
git clone https://github.com/NirDiamant/GenAI_Agents.git
```
2. Navigate to the technique you're interested in:
```
cd all_agents_tutorials/technique-name
```
3. Follow the detailed implementation guide in each technique's notebook.
## Contributing
We welcome contributions from the community! If you have a new technique or improvement to suggest:
1. Fork the repository
2. Create your feature branch: `git checkout -b feature/AmazingFeature`
3. Commit your changes: `git commit -m 'Add some AmazingFeature'`
4. Push to the branch: `git push origin feature/AmazingFeature`
5. Open a pull request
## Contributors
[](https://github.com/NirDiamant/GenAI_Agents/graphs/contributors)
## License
This project is licensed under a custom non-commercial license - see the [LICENSE](LICENSE) file for details.
---
⭐️ If you find this repository helpful, please consider giving it a star!
Keywords: GenAI, Generative AI, Agents, NLP, AI, Machine Learning, Natural Language Processing, LLM, Conversational AI, Task-Oriented AI
================================================
FILE: all_agents_tutorials/Academic_Task_Learning_Agent_LangGraph.ipynb
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"# **ATLAS** : Academic Task and Learning Agent System\n",
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"## **Overview**\n",
"ATLAS demonstrates how to build an intelligent multi-agent system that transforms the way students manage their academic life. Using LangGraph's workflow framework, we'll create a network of specialized AI agents that work together to provide personalized academic support, from automated scheduling to intelligent lectures summarization.\n",
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"- Coordinator Agent: Orchestrates the interaction between specialized agents and manages the overall system state\n",
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"- Notewriter Agent: Processes academic content and generates study materials\n",
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"## **Implementation Method**\n",
" ATLAS begins with a comprehensive initial assessment to understand each student's unique profile. The system conducts a thorough evaluation of learning preferences, cognitive styles, and current academic commitments while identifying specific challenges that require support. This information forms the foundation of a detailed student profile that drives personalized assistance throughout their academic journey.\n",
" At its core, ATLAS operates through a sophisticated multi-agent system architecture. The implementation leverages LangGraph's workflow framework to coordinate four specialized AI agents working in concert. The Coordinator Agent serves as the central orchestrator, managing workflow and ensuring seamless communication between components. The Planner Agent focuses on schedule optimization and time management, while the Notewriter Agent processes academic content and generates tailored study materials. The Advisor Agent rounds out the team by providing personalized guidance and support strategies.\n",
" The workflow orchestration implements a state management system that tracks student progress and coordinates agent activities. Using LangGraph's framework, the system maintains consistent communication channels between agents and defines clear transition rules for different academic scenarios. This structured approach ensures that each agent's specialized capabilities are deployed effectively to support student needs.\n",
" Learning process optimization forms a key part of the implementation. The system generates personalized study schedules that adapt to student preferences and energy patterns while creating customized learning materials that match individual learning styles. Real-time monitoring enables continuous adjustment of strategies based on student performance and engagement. The implementation incorporates proven learning techniques such as spaced repetition and active recall, automatically adjusting their application based on observed effectiveness.\n",
" Resource management and integration extend the system's capabilities through connections with external academic tools and platforms. ATLAS synchronizes with academic calendars, integrates with digital learning environments, and coordinates access to additional educational resources. This comprehensive integration ensures students have seamless access to all necessary tools and materials within their personalized academic support system.\n",
" The implementation maintains flexibility through continuous adaptation and improvement mechanisms. By monitoring performance metrics and gathering regular feedback, the system refines its recommendations and adjusts support strategies. This creates a dynamic learning environment that evolves with each student's changing needs and academic growth.\n",
" Emergency and support protocols are woven throughout the implementation to provide immediate assistance when needed. The system includes mechanisms for detecting academic stress, managing approaching deadlines, and providing intervention strategies during challenging periods. These protocols ensure students receive timely support while maintaining progress toward their academic goals.\n",
" Through this comprehensive implementation approach, ATLAS creates an intelligent, adaptive academic support system that grows increasingly effective at meeting each student's unique needs over time. The system's architecture enables seamless coordination between different support functions while maintaining focus on individual student success.\n",
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"## **Conclusion**\n",
"ATLAS : Academic Task and Learning Agent System demonstrates the potential of combining language models with structured workflows to create an effective educational support system. By breaking down the academic support process into discrete steps and leveraging AI capabilities, we can provide personalized assistance that adapts to each student's needs. This approach opens up new possibilities for AI-assisted learning and academic success.\n",
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" 640 final- green deal.txt │ ├── EU_ETS.txt │ ├── GD0 - Annex I to EU-ETS Directive.2024.md.txt │ ├── L_2021243EN.01000101.txt │ ├── OJ_L_202401991_EN_TXT.txt │ ├── PE-36-2023-INIT_en.txt │ ├── Questions_and_Answers__EU_Biodiversity_Strategy_for_2030_-_Bringing_nature_back_into_our_lives.txt │ ├── Questions_and_Answers__Green_Deal_Industrial_Plan_for_the_Net-Zero_Age.txt │ ├── Questions_and_Answers__The_Net-Zero_Industry_Act_and_the_European_Hydrogen_Bank_.txt │ ├── Questions_and_Answers_on_BEFIT_and_transfer_pricing.txt │ ├── Taskifier data/ │ │ ├── job-application-history.txt │ │ ├── school-assignment-history.txt │ │ └── startup-project-history.txt │ ├── clauses.json │ ├── e2e_testing_agent_app.py │ ├── e2e_testing_agent_register.html │ ├── f2f_action-plan_2020_strategy-info_en.txt │ ├── grocery_management_agents_system/ │ │ ├── extracted/ │ │ │ └── grocery_receipt.md │ │ ├── input/ │ │ │ └── extract_items.js │ │ └── output/ │ │ ├── grocery_tracker.json │ │ └── recipe_recommendation.json │ └── project_manager_assistant/ │ ├── project_description.txt │ └── team.csv └── requirements.txt
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]
About this extraction
This page contains the full source code of the NirDiamant/GenAI_Agents GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 98 files (13.1 MB), approximately 3.4M tokens, and a symbol index with 3 extracted functions, classes, methods, constants, and types. 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.