🤖 AgentForge

A hands-on, production-focused comparison of modern AI agent and multi-agent frameworks. Explore real-world implementations and evaluate trade-offs across leading tools.
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AgentForge delivers a structured, hands-on comparison of modern AI agent and multi-agent frameworks. Each framework is explored through implementation-driven examples, highlighting architecture patterns, capabilities, and real-world use cases.

🤖 Frameworks Included

Framework	Docs	Repository
Autogen
Google ADK
OpenAI Agents SDK
smolagents

📁 Structure

The repository is organized by framework. Each directory includes examples, configuration, and framework-specific documentation.

Examples range from simple agent tasks to advanced multi-agent workflows, RAG (Retrieval-Augmented Generation), and API integrations.

Main modules:

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

Some modules use PDM (pyproject.toml), while others rely on requirements.txt. Always review the local README.md before setup.

🚀 Getting Started

1. Select a framework
   Navigate to the corresponding directory.

2. Install dependencies
   Follow the setup instructions in the module’s README.md.

3. Run examples
   Execute scripts from basic agents to advanced multi-agent workflows.

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🧪 Comparison and Experiments

The study-agents-differences/ module centralizes benchmarking and cross-framework experimentation.

Key capabilities:

• Standardized agent interfaces across multiple frameworks
• Performance benchmarking (latency, token usage, tool efficiency)
• Comparative analysis of RAG pipelines, APIs, and multi-agent systems
• Interactive Streamlit UI for real-time evaluation

Run the UI:

streamlit run agent-ui.py