Reflexion Agent with LangGraph🕸

Implementation of a sophisticated Reflexion agent using LangGraph and LangChain, designed to generate high-quality responses through self-reflection and iterative improvement.

This project demonstrates advanced AI agent capabilities using LangGraph's state-of-the-art control flow mechanisms for self-reflection and response refinement.

---
config:
  flowchart:
    curve: linear
---
graph LR;
        __start__([<p>__start__</p>]):::first
        draft(draft)
        execute_tools(execute_tools)
        revise(revise)
        __end__([<p>__end__</p>]):::last
        __start__ --> draft;
        draft --> execute_tools;
        execute_tools --> revise;
        revise -.-> draft;
        revise -.-> execute_tools;
        revise -.-> __end__;
        classDef default fill:#f2f0ff,line-height:1.2
        classDef first fill-opacity:0
        classDef last fill:#bfb6fc

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Features

• Self-Reflection: Implements sophisticated reflection mechanisms for response improvement
• Iterative Refinement: Uses a graph-based approach to iteratively enhance responses
• Production-Ready: Built with scalability and real-world applications in mind
• Integrated Search: Leverages Tavily search for enhanced response accuracy
• Structured Output: Uses Pydantic models for reliable data handling

Architecture

The agent uses a graph-based architecture with the following components:

• Entry Point: draft node for initial response generation
• Processing Nodes: execute_tools and revise for refinement
• Maximum Iterations: 2 (configurable)
• Chain Components: First responder and revisor using GPT-4
• Tool Integration: Tavily Search for web research