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🤖 AI Agent Frameworks

A hands-on comparison of modern AI agent and multi-agent frameworks. Get started with practical examples and explore the unique features of each framework.
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This repository provides a comprehensive, hands-on comparison of modern AI agent and multi-agent frameworks. Each framework is explored through practical examples, highlighting its unique features, capabilities, and use cases.

🤖 Frameworks Included

Framework Docs Repository
AG2 Docs GitHub
Agno Docs GitHub
Autogen Autogen Docs GitHub
CrewAI Docs GitHub
Google ADK Google ADK Docs GitHub
LangGraph LangGraph Docs GitHub
LlamaIndex LlamaIndex Docs GitHub
OpenAI Agents SDK OpenAI Agents SDK Docs GitHub
Pydantic-AI Docs GitHub
smolagents smolagents Docs GitHub

📁 Structure

The repository is organized by framework, with each top-level folder containing examples, configuration, and a README.md for that framework. Examples range from simple agent tasks to advanced multi-agent workflows, RAG (Retrieval-Augmented Generation), API integration, and more.

Main modules:

  • ag2/
  • agno/
  • autogen/
  • crewai/
  • google-adk/
  • langgraph/
  • llama-index/
  • openai-agents-sdk/
  • pydantic-ai/
  • smolagents/
  • study-agents-differences/

Some modules are standalone, while others are PDM projects or use requirements.txt for dependency management. Always check the README.md in each module for specific setup and usage instructions.

🚀 Getting Started

  1. Choose a framework: Navigate to the relevant folder for the agent framework you want to explore.
  2. Install dependencies: See each module’s README.md for installation instructions.
  3. Run examples

🧪 Comparison and Experiments

The study-agents-differences/ folder contains comprehensive scripts and utilities for comparing frameworks on common tasks, including RAG, API integration, and multi-agent workflows. It provides:

  • Unified agent interfaces for Agno, LangGraph, LlamaIndex, OpenAI, and Pydantic-AI
  • Performance benchmarks measuring response time, token usage, and tool utilization
  • Detailed results and analysis comparing different agent designs and tool integrations
  • Interactive Streamlit UI for real-time comparison (streamlit run agent-ui.py)

🤝 Contributing

All contributions are welcome! If you have suggestions for new examples, frameworks to add, or improvements to existing content, please open an issue or submit a pull request.


Notes

  • Some modules use PDM (pyproject.toml), others use requirements.txt. Check each module’s README.md for installation and usage.
  • Install dependencies before running examples.
  • Example .env.example files are provided where needed for API keys and settings.

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Foundational code for testing and exploring various AI Multi-Agent Frameworks

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  • Jupyter Notebook 57.7%
  • Python 42.3%