title	emoji	colorFrom	colorTo	sdk	sdk_version	app_file	pinned	hf_oauth	hf_oauth_expiration_minutes
Final Assigment Submission GAIA Agent	🕵🏻‍♂️	indigo	indigo	gradio	5.25.2	app.py	true	true	480

🚀 HF Agent GAIA 🕵🏻‍♂️

This repository contains an AI agent developed for the Hugging Face Agents Course. The primary goal of this project is to create a robust agent capable of tackling tasks from the GAIA Benchmark.

The agent is built using Python and leverages the power of LangGraph for creating a stateful, multi-actor agent. It interacts with various tools to gather information, perform actions, and ultimately solve complex problems.

📸 Demo

🛠️ Setup with uv

This project uses uv for Python package management. uv is a fast Python package installer and resolver, written in Rust.

To set up the environment:

1. Install uv: Follow the instructions at https://github.com/astral-sh/uv.
2. Create a virtual environment and install dependencies:

   uv venv
   uv pip install -r requirements.txt

   Or, if you prefer to use the pyproject.toml:

   uv sync

🔑 API Keys

To use this agent, you will need API keys for the following services:

• Groq: For fast LLM inference. You can get a key from GroqCloud.
• Tavily AI: For the comprehensive web search tool. You can get a key from Tavily AI.

Once you have your keys, create a .env file in the root of the project and add your keys like this:

GROQ_API_KEY="gsk_YOUR_GROQ_API_KEY"
TAVILY_API_KEY="tvly-YOUR_TAVILY_API_KEY"

Replace gsk_YOUR_GROQ_API_KEY and tvly-YOUR_TAVILY_API_KEY with your actual API keys. The agent will load these keys automatically.

🤖 Agent Architecture

The core of the agent is built with LangGraph. It follows a ReAct (Reason + Act) prompting strategy.

1. LLM: The agent uses a Large Language Model (LLM) hosted on Groq (e.g., qwen/qwen3-32b or llama3-8b-8192) for its reasoning capabilities.
2. Prompting: A base prompt (base_prompt.txt) guides the LLM's behavior, instructing it on how to use the available tools and respond to user queries.
3. Tools: The agent has access to a suite of tools to interact with the external world. These tools allow it to:
   • Perform mathematical calculations (e.g., calculator, multiply, ...).
   • Search the web and specific platforms (e.g., web_search via Tavily, wiki_search, arxiv_search).
   • Read and write files (e.g., read_file, write_file, list_files).
   • Download files from URLs (download_file).
   • Fetch and parse web page content (get_url, get_url_text).
   • Process images (captioning with image_captioner, OCR with ocr).
4. Graph: The LangGraph framework orchestrates the flow of information between the LLM and the tools. The agent.py file defines the graph structure, including:
   • An AgentState to hold the current state of the conversation and any input files.
   • An assistant node that calls the LLM.
   • A ToolNode that executes the chosen tool.
   • Conditional edges that determine the next step based on the LLM's output (e.g., call a tool or respond to the user).

The create_react_agent function from langgraph.prebuilt is used to quickly set up a ReAct agent with the specified LLM and tools.

📁 Repository Structure

• .github/workflows/sync-to-hf.yml: GitHub Actions workflow to automatically sync the repository to a Hugging Face Space.
• agent.py: Defines the LangGraph agent, its state, and the interaction logic.
• app.py: Gradio application to interact with the agent.
• base_prompt.txt: The system prompt for the LLM.
• pyproject.toml: Project metadata and dependencies for uv.
• requirements.txt: List of Python dependencies (can be generated from pyproject.toml).
• tools.py: Defines all the tools available to the agent.