This repository provides a hands-on comparison of modern AI agent and multi-agent frameworks, serving as a foundational resource for learning, exploring, and testing. It targets engineers and researchers seeking to understand and evaluate the unique features, capabilities, and use cases of various state-of-the-art open-source AI agent frameworks through practical examples.

How It Works

The project organizes code by framework, offering practical examples ranging from simple agent tasks to advanced multi-agent workflows, RAG, and API integrations. A dedicated study-agents-differences/ directory facilitates direct comparison, featuring unified agent interfaces for several key frameworks and performance benchmarks measuring response time, token usage, and tool utilization.

Quick Start & Requirements

To get started, users select a framework folder, navigate to it, and install its specific dependencies as detailed in each module's README.md. Examples can then be run directly. Some modules utilize PDM (pyproject.toml) for dependency management, while others use requirements.txt. .env.example files are provided for necessary API keys and settings. An interactive Streamlit UI is available for real-time comparison via streamlit run agent-ui.py.

Highlighted Details

• Comprehensive comparison of frameworks including Autogen, CrewAI, LangGraph, LlamaIndex, OpenAI Agents SDK, and others.
• Unified agent interfaces for Agno, LangGraph, LlamaIndex, OpenAI, and Pydantic-AI within the comparison module.
• Performance benchmarks for response time, token usage, and tool utilization.
• Interactive Streamlit UI for visualizing framework comparisons.

Maintenance & Community

Contributions are welcomed through issues and pull requests for new examples, frameworks, or improvements.

Licensing & Compatibility

Licensing information is not explicitly detailed in the provided README. Compatibility for commercial use or closed-source linking would require further investigation into the licenses of individual frameworks included.

Limitations & Caveats

The repository uses a mixed approach to dependency management (PDM and requirements.txt), necessitating careful attention to each module's specific setup instructions. Users will likely need to acquire and configure API keys for various AI services, as indicated by the presence of .env.example files.