README.md

GAIA Research Agent 🤖

A high-performance, tool-augmented AI agent built using LangGraph and LangChain to solve complex, multi-step reasoning questions from the GAIA benchmark. This agent is designed to perform research, use external tools dynamically, and ensure outputs follow a strict format.

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🌟 Project Overview

The GAIA Research Agent tackles "hidden" reasoning problems that require more than just internal model knowledge. It follows a structured protocol: analyze the question, determine if external tools (Search, arXiv, Math) are needed, execute the research, and synthesize a precise response.

Core Goal: Solve complex reasoning problems and return:
FINAL ANSWER: <answer>

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🏗️ Architecture & Workflow

The system is built as a stateful LangGraph workflow with the following nodes:

1. Retriever Node: Injects the system prompt, compacts message history, and optionally retrieves similar examples from a Supabase vector store (RAG).
2. Assistant Node: The "brain" of the agent. It uses an LLM to decide reasoning steps and whether to call tools or answer directly.
3. Tool Node: Executes external tools such as:
   • Math Operations: Add, subtract, multiply, divide, modulus.
   • Search: Tavily Web Search, Wikipedia, and arXiv.

The Flow: User Question → Retriever → Assistant ↔ Tools (if needed) → Assistant → FINAL ANSWER

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🚀 Key Features

• Multi-LLM Support: Configurable for Groq (LLaMA models), Google Gemini, and HuggingFace endpoints.
• Tool-Call Repair: Automatically fixes malformed tool calls using regex and fallback prompts.
• Direct Fallback: If tools fail or recursion limits are hit, the agent defaults to internal reasoning.
• Context Management: Limits message history to avoid token overflow.
• RAG (Retrieval-Augmented Generation): Optional similarity search via Supabase to improve reasoning accuracy.

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📂 File Structure

File	Description
agent.py	Full LangGraph logic, tools, LLM setup, and fallback handling.
app.py	Gradio UI for running the evaluation suite and scoring.
System_prompt.txt	Custom system prompt defining the agent's persona.
requirements.txt	Python dependencies.