🤖 GAIA Agent - Advanced AI Problem Solver

Version 1.0 | GAIA Benchmark - Level 1 Questions

Welcome to my final hands-on project for the Hugging Face Agent Course!

In this project, I designed my first AI agent, capable of solving complex, multi-step problems using advanced reasoning, tool orchestration, and natural language understanding.

The agent is specifically built to be evaluated on a subset of Level 1 questions from the GAIA benchmark, a framework designed to test and measure AI agents' reasoning and tool-using capabilities.

The entire system is wrapped in a Gradio-based web interface, allowing users to run full evaluations and submit answers to a scoring service.

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🎯 Key Features

🧠 Advanced Reasoning Engine

• 🔥 Chain-of-Thought Analysis: Breaks down complex problems systematically
• ⚡ Dynamic Tool Selection: Intelligently chooses appropriate tools for each task
• 🔄 Iterative Refinement: Validates and improves answers until confidence threshold is met
• 📊 Multi-Domain Problem Solving: Handles web search, data analysis, image processing, and more

🛠️ Comprehensive Tool Suite

• 🌐 Web & Research: Web search, Wikipedia lookup, webpage scraping
• 📊 Data Analysis: CSV/Excel processing with Pandas integration
• 🖼️ Multimedia Processing: Image analysis with OpenCV, YouTube transcript extraction
• 🧮 Mathematical Operations: Equation solving, string manipulation, chess analysis
• 📁 File Operations: Intelligent file download and content analysis

🔒 Enterprise-Grade Architecture

• Modular Design: Clean separation between UI, agent logic, and tools
• Error Handling: Failure recovery with retry mechanisms
• API Integration: Seamless connection to GAIA evaluation service
• Standardized Evaluation: Built-in benchmark testing and scoring

🌐 Modern Web Interface

• Gradio-Based UI: Intuitive, responsive web interface
• Real-Time Feedback: Live progress updates during evaluation
• Detailed Logging: Complete audit trail of reasoning and results
• HuggingFace Integration: Direct authentication and deployment support

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🚀 Quick Start

# 1. Clone the repository
git clone <repository-url>
cd gaia-agent

# 2. Install dependencies
pip install -r requirements.txt

# 3. Configure environment variables
cp .env.example .env
# Edit .env with your API keys

# 4. Launch the application
python app.py

Try it out:

• Open the web interface at http://localhost:7860
• Click "Run Evaluation" to test against GAIA benchmark
• Watch the agent solve complex problems in real-time!

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🏗️ System Architecture

📋 Processing Pipeline

User → Gradio UI → GaiaEvaluationRunner → GaiaAgent → Tools → LLM → Scoring Service → Results

🔧 Core Components

Component	File	Responsibility
🎨 User Interface	app.py	Gradio web app, evaluation orchestration
🧠 Agent Core	agent.py	Chain-of-thought reasoning, tool coordination
🛠️ Tool Library	tools.py	Specialized function implementations
🤖 LLM Client	llm_client.py	Groq API communication and LLM model selection layer
📝 System Prompt	gaia_system_prompt.py	Agent persona and instructions

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💡 How It Works

🎯 The GAIA Agent Problem-Solving Process

1. 📥 Question Analysis

   • Receives complex, multi-step questions from GAIA benchmark
   • Performs chain-of-thought reasoning to understand requirements
   • Identifies key information needed and potential solution paths

2. 🔧 Tool Selection & Orchestration

   # Dynamic tool selection based on question context
   if question_needs_web_search():
       result = tools.web_search(query)
   elif question_needs_data_analysis():
       result = tools.analyze_data(file_path)
   elif question_needs_image_processing():
       result = tools.analyze_image(image_path)

3. ⚡ Execution & Synthesis

   • Executes selected tools with generated parameters
   • Combines tool outputs with original question context
   • Synthesizes coherent, accurate final answers

4. 🔍 Validation & Refinement

   while confidence < threshold and attempts < max_attempts:
       answer = validate_and_refine(current_answer)
       confidence = assess_confidence(answer)
       attempts += 1

🧰 Available Tools Showcase

🌐 Research & Information Gathering

web_search(query)           # Tavily-powered web search
wiki_search(topic)          # Wikipedia knowledge lookup  
scrape_webpage(url)         # Content extraction from URLs

📊 Data Analysis & Processing

analyze_data(file_path)     # CSV/Excel analysis with Pandas
download_gaia_file(url)     # Intelligent file downloading
analyze_file_content(path)  # Multi-format content analysis

🎥 Multimedia & Advanced Processing

get_youtube_transcript(url) # Video transcript extraction
analyze_image(image_path)   # OpenCV-powered image analysis
analyze_chess_position(fen) # Chess position evaluation

🧮 Mathematical & Logical Operations

solve_equation(expression)  # Mathematical problem solving
string_operation(text, op)  # Text manipulation utilities

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📦 Installation & Setup

🔧 System Requirements

• Python 3.8 or higher
• 4GB RAM minimum (8GB recommended)
• Internet connection for API services
• Modern web browser for UI access

📥 Detailed Installation

Method 1: Standard Installation

# Clone repository
git clone https://github.com/your-username/gaia-agent.git
cd gaia-agent

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Linux/Mac
# or
venv\Scripts\activate     # Windows

# Install dependencies
pip install -r requirements.txt

Method 2: Docker Installation

# Build Docker image
docker build -t gaia-agent .

# Run container
docker run -p 7860:7860 \
  -e GROQ_API_KEY=$GROQ_API_KEY \
  -e TAVILY_API_KEY=$TAVILY_API_KEY \
  -e HF_TOKEN=$HF_TOKEN \
  gaia-agent

🔑 Environment Configuration

Create a .env file with your API credentials:

# === REQUIRED API KEYS ===
GROQ_API_KEY=gsk_your_groq_api_key_here          # Groq LLM API
TAVILY_API_KEY=tvly-your_tavily_key_here         # Web search API
HF_TOKEN=hf_your_huggingface_token_here          # HuggingFace 

Run GAIA Evaluation

1. Authentication: Ensure your HuggingFace token is configured
2. Start Evaluation: Click "Run Evaluation" button in the web interface
3. Monitor Progress: Watch real-time updates as the agent processes questions
4. View Results: Get detailed scoring and performance metrics

Example Problem-Solving Session

Question: "What is the population of the capital city of the country where the 2024 Olympics were held?"

Agent Reasoning Process:

🧠 Chain-of-Thought Analysis:
1. Need to identify where 2024 Olympics were held
2. Find the capital city of that country  
3. Look up the current population of that capital

🔧 Tool Execution:
Step 1: web_search("2024 Olympics location host city")
→ Result: Paris, France hosted 2024 Summer Olympics

Step 2: web_search("capital city of France") 
→ Result: Paris is the capital of France

Step 3: web_search("Paris France current population 2024")
→ Result: Approximately 2.16 million (city proper)

✅ Final Answer: The population of Paris, the capital city of France where the 2024 Olympics were held, is approximately 2.16 million people.

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⚙️ Advanced Configuration

🔧 Customizing Agent Behavior

Modify System Prompt (gaia_system_prompt.py):

# Adjust reasoning approach
SYSTEM_PROMPT = """
You are GAIA Agent, an advanced AI problem solver...
[Customize persona, instructions, and response format]
"""

Configure Tool Parameters (tools.py):

# Adjust search parameters
def web_search(query, max_results=5, include_raw_content=True):
    # Customize search behavior
    
# Add new custom tools
def your_custom_tool(parameters):
    """Your specialized functionality"""
    return result

Tune Agent Settings (agent.py):

class GaiaAgent:
    def __init__(self):
        self.max_attempts = 3           # Retry limit
        self.confidence_threshold = 0.8  # Answer quality bar
        self.timeout = 300              # Tool execution limit

📊 Performance Monitoring

Built-in Metrics Tracking:

# Automatic performance logging
metrics = {
    'response_time': 45.3,
    'tool_usage': {'web_search': 15, 'data_analysis': 8},
    'success_rate': 0.87,
    'confidence_scores': [0.9, 0.8, 0.95, ...]
}

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🏢 Deployment Options

🚀 HuggingFace Spaces (Recommended)

# spaces_config.yml
title: GAIA Agent - AI Problem Solver
emoji: 🤖
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 4.0.0
app_file: app.py
pinned: false

Deploy Steps:

1. Push your code to HuggingFace repository
2. Configure secrets in Space settings
3. Your agent will be available at https://huggingface.co/spaces/username/gaia-agent

☁️ Cloud Deployment

Google Cloud Run:

FROM python:3.9-slim
COPY . /app
WORKDIR /app
RUN pip install -r requirements.txt
EXPOSE 7860
CMD ["python", "app.py", "--server-name", "0.0.0.0"]

AWS EC2 with Docker:

# Launch EC2 instance
aws ec2 run-instances --image-id ami-0abcdef1234567890 --instance-type t3.medium

# Deploy container
docker run -d -p 80:7860 --name gaia-agent \
  -e GROQ_API_KEY=$GROQ_API_KEY \
  your-dockerhub-username/gaia-agent

🔧 Local Development Server

# Development mode with auto-reload
python app.py --reload --debug

# Production mode  
gunicorn -w 4 -b 0.0.0.0:7860 app:app

🧪 Development & Testing

🔬 Project Structure

Gaia_Agent_HF_Course/
├── ⚙️ .env.example              # Environment variables template
├── 📱  app.py                    # Gradio UI & evaluation runner
├── 🧠 agent.py                  # Core agent logic & reasoning
├── 🛠️ tools.py                  # Tool implementations
├── 🤖 llm_client.py             # Groq API communication and LLM model selection
├── 📝 gaia_system_prompt.py     # Agent robust system prompt
├── 📋 requirements.txt          # Python dependencies
├── ⚙️ DIAGRAM_ARCHITECTURE.md   # Diagram architecture of the project
└── 📖 README.md                 # This documentation

🔧 Contributing

1. Fork the repository on GitHub
2. Clone your fork locally
3. Create a feature branch: git checkout -b feature/amazing-improvement
4. Implement your changes with tests
5. Commit with clear messages: git commit -m "feat: add support for new tool type"
6. Push to your fork: git push origin feature/amazing-improvement
7. Create a Pull Request with detailed description

Development Guidelines:

• Follow PEP 8 style guidelines
• Add type hints to new functions
• Include docstrings for public methods
• Write tests for new functionality
• Update documentation as needed

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🎖️ Acknowledgments & References

🏆 Built With

• LangChain - Framework for connecting LLMs with tools and data sources
• Groq - Ultra-fast LLM inference with Llama and Mixtral models
• Gradio - User-friendly web interface framework
• Tavily - Advanced web search API for AI agents
• HuggingFace - ML platform and GAIA benchmark hosting
• OpenCV - Computer vision and image processing
• Pandas - Data manipulation and analysis

📚 Research & Benchmarks

• GAIA Benchmark: [Mialon et al., 2023] - "GAIA: a benchmark for General AI Assistants"
• Tool-using AI: Research on autonomous agent architectures and tool orchestration
• Chain-of-Thought: Advanced reasoning techniques for large language models

🙏 Special Thanks

• HuggingFace Team for the comprehensive AI course and evaluation framework
• GAIA Benchmark Creators for establishing rigorous AI agent evaluation standards
• Open Source Community for the excellent tools and libraries that made this project possible

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🎉 Conclusion

This GAIA Agent repository marks the creation of my first AI Agent, developed as the final project for the Hugging Face Agents Course. While its accuracy may not yet reflect state-of-the-art performance, this project served as a valuable introduction to a technology I am deeply passionate about and plan to continue developing.

The agent demonstrates capabilities such as:

✨ Advanced Reasoning with chain-of-thought problem decomposition
🛠️ Versatile Tool Integration across multiple domains and data types
🎯 Rigorous Evaluation against standardized benchmarks
🚀 Production-Ready Architecture with modern deployment options

Ready to solve problems that require human-level reasoning and tool use!

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⭐ If this project helps your research or work, please consider giving it a star!

Developed with ❤️ using Python, AI, and a generous dose of coffee ☕