metadata

title: 🚀 GAIA Multi-Agent System - BENCHMARK OPTIMIZED
emoji: 🕵🏻‍♂️
colorFrom: indigo
colorTo: indigo
sdk: gradio
sdk_version: 5.25.2
app_file: app.py
pinned: false
hf_oauth: true
hf_oauth_expiration_minutes: 480

🚀 GAIA Multi-Agent System - BENCHMARK OPTIMIZED

A GAIA benchmark-optimized AI agent system specifically designed for exact-match evaluation with aggressive response cleaning and direct answer formatting.

🎯 GAIA Benchmark Compliance

🔥 Exact-Match Optimization

Direct Answers Only: No "The answer is" prefixes or explanations
Clean Responses: Complete removal of thinking processes and reasoning
Perfect Formatting: Numbers, facts, or comma-separated lists as required
API-Ready: Responses formatted exactly for GAIA submission

🧠 Multi-Model AI Integration

10+ AI Models: DeepSeek-R1, GPT-4o, Llama-3.3-70B, Kimi-Dev-72B, and more
6 AI Providers: Together, Novita, Featherless-AI, Fireworks-AI, HuggingFace, OpenAI
Priority-Based Fallback: Intelligent model selection with graceful degradation
Aggressive Cleaning: Specialized extraction for benchmark compliance

⚡ Performance Features

Fallback Speed: <100ms responses for common questions
High Accuracy: Optimized for GAIA Level 1 questions (targeting 30%+ score)
Exact Match: Designed for GAIA's strict evaluation criteria
Response Validation: Built-in compliance checking

🏗️ GAIA-Optimized Architecture

Core Components

🎯 GAIA Benchmark-Optimized System
├── 🤖 BasicAgent (GAIA Interface)
├── 🧠 MultiModelGAIASystem (Optimized Core)
├── 🔧 Multi-Provider AI Clients (10+ Models)
│   ├── 🔥 Together (DeepSeek-R1, Llama-3.3-70B)
│   ├── ⚡ Novita (MiniMax-M1-80k, DeepSeek variants)
│   ├── 🪶 Featherless-AI (Kimi-Dev-72B, Jan-nano)
│   ├── 🚀 Fireworks-AI (Llama-3.1-8B)
│   ├── 🤗 HF-Inference (Specialized tasks)
│   └── 🤖 OpenAI (GPT-4o, GPT-3.5-turbo)
├── 🛡️ Enhanced Fallback System (Exact answers)
├── 🧽 Aggressive Response Cleaning (Benchmark compliance)
└── 🎨 Gradio Interface (GAIA evaluation ready)

GAIA Processing Pipeline

Question Analysis → Determine question type and expected format
Fallback Check → Fast, accurate answers for simple questions
AI Model Query → Multi-model reasoning with DeepSeek-R1 priority
Response Extraction → Aggressive cleaning to remove all reasoning
Format Compliance → Final validation for exact-match submission

🚀 Getting Started

Installation

# Clone the repository
git clone <your-repo-url>
cd Final_Assignment_Template

# 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

Configuration

Set HF Token (Required for AI models):
```
export HF_TOKEN="your_hf_token_here"
```

Set OpenAI Key (Optional, for GPT models):

export OPENAI_API_KEY="your_openai_key_here"

Test GAIA Compliance:
```
python test_gaia.py
```
Launch Web Interface:
```
python app.py
```

🧪 Testing & Validation

GAIA Compliance Testing

# Run comprehensive GAIA compliance tests
python test_gaia.py

# Expected output:
# ✅ Responses are GAIA compliant
# ✅ Reasoning is properly cleaned  
# ✅ API format is correct
# ✅ Ready for exact-match evaluation

Expected GAIA Results

✅ Math: "What is 15 + 27?" → "42" (not "The answer is 42")
✅ Geography: "What is the capital of Germany?" → "Berlin" (not "The capital of Germany is Berlin")
✅ Science: "How many planets are in our solar system?" → "8" (not "There are 8 planets")

📊 GAIA Benchmark Performance

Target Metrics

Level 1 Questions: Targeting 30%+ accuracy for course completion
Response Time: <5 seconds average per question
Compliance Rate: 90%+ exact-match format compliance
Fallback Coverage: 100% availability even without AI models

Question Types Optimized

Type	GAIA Format	Example Response
🧮 Mathematical	Just the number	"42"
🌍 Geographical	Just the place name	"Paris"
🔬 Scientific	Just the fact/value	"8"
📝 Factual	Concise answer	"H2O"
📊 Lists	Comma-separated	"apples, oranges, bananas"

🔧 Technical Implementation

Response Cleaning Process

# GAIA-optimized cleaning pipeline:
1. Remove <think> tags completely
2. Extract explicit answer markers
3. Remove reasoning phrases
4. Clean formatting artifacts  
5. Validate compliance
6. Return direct answer only

Key Dependencies

gradio>=5.34.2          # Web interface with OAuth
huggingface_hub         # Multi-model AI integration  
transformers            # Model support
requests                # API communication
pandas                  # Results handling
openai                  # GPT model access

Environment Variables

# Required for HuggingFace models
HF_TOKEN="hf_your_token_here"

# Required for OpenAI models
OPENAI_API_KEY="sk-your_openai_key_here"

# Auto-set in HuggingFace Spaces
SPACE_ID="your_space_id"
SPACE_HOST="your_space_host"

🌟 GAIA Optimization Features

Aggressive Response Cleaning

Thinking Process Removal: Complete elimination of tags
Reasoning Extraction: Removes "Let me think", "First", "Therefore"
Answer Isolation: Extracts only the final answer value
Format Standardization: Numbers, names, lists only

Exact-Match Compliance

No Prefixes: Removes "The answer is", "Result:", etc.
Clean Numbers: "42" not "42." or "The result is 42"
Direct Facts: "Paris" not "The capital is Paris"
Concise Lists: "red, blue, green" not "The colors are red, blue, and green"

API Submission Ready

JSON Format: Perfect structure for GAIA API
Error Handling: Graceful failures with default responses
Validation: Built-in compliance checking before submission
Logging: Detailed tracking for debugging

📈 Deployment

Local Development

python app.py
# Access at http://localhost:7860

Hugging Face Spaces

Fork this repository
Create new Space on Hugging Face
Set HF_TOKEN and OPENAI_API_KEY as repository secrets
Deploy automatically with OAuth enabled

Production Optimization

Multi-model fallback ensures high availability
Aggressive caching for common questions
API rate limit management
Comprehensive error handling

🎯 GAIA Benchmark Ready!

Your GAIA-optimized multi-agent system is specifically designed for:

🎯 Exact-Match Evaluation with clean, direct answers
🧠 Multi-Model Intelligence via DeepSeek-R1 and 9 other models
🛡️ Reliable Fallback for 100% question coverage
📏 Perfect Compliance with GAIA submission requirements
🚀 Production Ready with comprehensive testing

Target Achievement: 30%+ score on GAIA Level 1 questions for course completion

Next Steps:

Set your HF_TOKEN and OPENAI_API_KEY
Run python test_gaia.py to verify compliance
Deploy to HuggingFace Spaces
Submit to GAIA benchmark! 🚀

Note: The system provides reliable fallback responses even without API keys, ensuring baseline functionality for all question types.