🤖 OpenAuditLabs Agent

AI-Powered Smart Contract Security Analysis Engine

The OpenAuditLabs Agent is an advanced multi-agent system that automatically detects security vulnerabilities in smart contracts using artificial intelligence. Built with CrewAI framework, it orchestrates specialized AI agents to perform comprehensive security analysis with 95%+ accuracy.

🚀 Quick Start

Prerequisites

• Python 3.11+
• Docker & Docker Compose
• PostgreSQL 15+
• Redis 7+

Installation

# Clone the repository
git clone https://github.com/OpenAuditLabs/agent.git
cd agent

# Install dependencies
pip install -r requirements.txt

# Setup environment variables
cp .env.example .env

# Start services
docker-compose up -d

# Run database migrations
alembic upgrade head

# Start the agent
python -m uvicorn main:app --reload

Docker Setup

# Build and run with Docker
docker-compose up --build

# API will be available at http://localhost:8000

🧠 Architecture

The Agent system employs a hierarchical multi-agent architecture powered by CrewAI:

┌─────────────────────────────────────────────────────────────┐
│                    Coordinator Agent                        │
│                 (Workflow Orchestration)                    │
└─────────────────────────┬───────────────────────────────────┘
                          │
        ┌─────────────────┼─────────────────┐
        │                 │                 │
┌───────▼──────┐ ┌────────▼────────┐ ┌─────▼─────┐
│Static Analysis│ │Dynamic Analysis │ │ML Classifier│
│    Agent      │ │     Agent       │ │   Agent     │
│   (Slither)   │ │   (Mythril)     │ │(Transformers)│
└───────────────┘ └─────────────────┘ └───────────┘
        │                 │                 │
        └─────────────────┼─────────────────┘
                          │
                ┌─────────▼─────────┐
                │Report Generation  │
                │      Agent        │
                └───────────────────┘

📁 Project Structure

agent/
├── src/
│   ├── agents/              # CrewAI agent implementations
│   │   ├── coordinator.py   # Main orchestration agent
│   │   ├── static_agent.py  # Slither integration
│   │   ├── dynamic_agent.py # Mythril integration
│   │   └── ml_agent.py      # ML classification
│   ├── api/                 # FastAPI endpoints
│   │   ├── routes/
│   │   └── models/
│   ├── core/                # Core analysis engine
│   │   ├── pipeline.py      # Analysis pipeline
│   │   └── orchestrator.py  # Agent orchestration
│   ├── models/              # ML models & schemas
│   │   ├── transformers/    # Transformer models
│   │   └── gnn/            # Graph Neural Networks
│   ├── tools/               # External tool integrations
│   │   ├── slither.py      # Static analysis
│   │   └── mythril.py      # Symbolic execution
│   └── utils/               # Utility functions
├── tests/                   # Test suites
├── data/                    # Sample contracts & datasets
├── docker/                  # Docker configurations
├── docs/                    # Documentation
└── notebooks/               # Research notebooks

🔧 Usage

REST API

Start the FastAPI server:

uvicorn main:app --host 0.0.0.0 --port 8000

Analyze a Smart Contract

curl -X POST "http://localhost:8000/analyze/contract" \
  -H "Content-Type: application/json" \
  -d '{
    "contract_code": "contract Simple { function transfer() public {} }",
    "language": "solidity",
    "analysis_type": "comprehensive"
  }'

Python SDK

from openauditlabs_agent import AnalysisClient

# Initialize client
client = AnalysisClient(api_url="http://localhost:8000")

# Analyze contract
result = client.analyze_contract(
    contract_code=contract_source,
    language="solidity"
)

# Get results
vulnerabilities = result.get_vulnerabilities()
for vuln in vulnerabilities:
    print(f"Severity: {vuln.severity}, Type: {vuln.type}")

🎯 Key Features

🔍 Multi-Modal Analysis

• Static Analysis: Slither integration with 90+ detectors
• Dynamic Analysis: Mythril symbolic execution with PoC generation
• ML Classification: Transformer and GNN models for pattern recognition
• Ensemble Methods: Combined analysis for enhanced accuracy

🌐 Multi-Language Support

• Solidity (.sol) - Complete support
• Vyper (.vy) - Full analysis pipeline
• Rust (Substrate/Ink!) - Advanced detection
• Move (Aptos/Sui) - Experimental support

🚀 Performance

• Processing Speed: 1000+ LoC analyzed in <5 minutes
• Accuracy: 95%+ vulnerability detection rate
• Scalability: 500+ concurrent analyses
• Uptime: 99.9% availability with auto-scaling

📊 Vulnerability Detection

• 50+ Vulnerability Types: Complete SWC coverage
• CVSS Scoring: Automated severity assessment
• Proof of Concept: Executable exploit generation
• Remediation: Actionable fix suggestions

🛠️ Configuration

Environment Variables

# Database
DATABASE_URL=postgresql://user:pass@localhost:5432/openauditlabs
REDIS_URL=redis://localhost:6379

# API Configuration
API_HOST=0.0.0.0
API_PORT=8000
SECRET_KEY=your-secret-key

# Agent Configuration
CREWAI_API_KEY=your-crewai-key
OPENAI_API_KEY=your-openai-key

# Tool Configuration
SLITHER_VERSION=0.10.0
MYTHRIL_VERSION=0.24.2

# ML Models
MODEL_CACHE_DIR=./models
TRANSFORMER_MODEL=microsoft/codebert-base
GNN_MODEL_PATH=./models/gnn_classifier.pt

Agent Configuration

# agents/config.py
AGENT_CONFIG = {
    "coordinator": {
        "model": "gpt-4",
        "temperature": 0.1,
        "max_tokens": 2000
    },
    "static_agent": {
        "slither_detectors": ["all"],
        "timeout": 300,
        "gas_analysis": True
    },
    "dynamic_agent": {
        "mythril_timeout": 600,
        "max_depth": 3,
        "create_poc": True
    },
    "ml_agent": {
        "confidence_threshold": 0.8,
        "ensemble_voting": "soft",
        "model_batch_size": 32
    }
}

🧪 Development

Setup Development Environment

# Install development dependencies
pip install -r requirements-dev.txt

# Setup pre-commit hooks
pre-commit install

# Run tests
pytest tests/ -v

# Code formatting
black src/
isort src/

# Type checking
mypy src/

Running Tests

# Unit tests
pytest tests/unit/

# Integration tests
pytest tests/integration/

# End-to-end tests
pytest tests/e2e/

# Load tests
pytest tests/load/ --load-test

Adding New Agents

1. Create agent class in src/agents/
2. Implement required methods:
   • analyze(): Main analysis logic
   • get_tools(): Return required tools
   • get_config(): Return agent configuration
3. Register in src/core/orchestrator.py
4. Add tests in tests/agents/

Example:

from crewai import Agent
from typing import Dict, List

class CustomAgent(Agent):
    def __init__(self, config: Dict):
        super().__init__(
            role="Custom Analyzer",
            goal="Detect specific vulnerability patterns",
            backstory="Specialized security expert",
            tools=self.get_tools(),
            **config
        )
    
    def analyze(self, contract_code: str) -> List[Dict]:
        # Implement custom analysis logic
        return []

🔐 Security

Input Validation

• All contract inputs are sanitized and validated
• File size limits enforced (max 10MB)
• Rate limiting on API endpoints
• Input encoding detection and normalization

Data Protection

• Contract source code encrypted at rest (AES-256)
• Analysis results stored with access controls
• Audit logs for all operations
• Automatic data retention policies

Authentication

• JWT-based API authentication
• Role-based access control (RBAC)
• API key management for integrations
• Session management and timeout

📈 Monitoring

Metrics

• Analysis throughput and latency
• Vulnerability detection accuracy
• Agent performance metrics
• Resource utilization

Health Checks

# System health
curl http://localhost:8000/health

# Agent status
curl http://localhost:8000/agents/status

# Database connectivity
curl http://localhost:8000/health/db

Logging

• Structured JSON logging
• Correlation IDs for request tracking
• Error aggregation and alerting
• Performance monitoring

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Workflow

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Code Standards

• Follow PEP 8 style guide
• Add type hints to all functions
• Write comprehensive tests
• Update documentation
• Ensure all checks pass

📚 Documentation

• API Documentation
• Agent Development Guide
• ML Model Training
• Deployment Guide
• Troubleshooting

🚀 Deployment

Docker Production

# Build production image
docker build -t openauditlabs/agent:latest .

# Run with docker-compose
docker-compose -f docker-compose.prod.yml up -d

Kubernetes

# Deploy to Kubernetes
kubectl apply -f k8s/

# Check deployment
kubectl get pods -n openauditlabs

IDE Plugins

• VS Code extension available
• Vim/Neovim integration
• JetBrains plugin support
• Sublime Text package

📊 Performance Benchmarks

Metric	Value
Analysis Speed	< 5 minutes per 1000 LoC
Accuracy	95%+ vulnerability detection
False Positives	< 5%
Throughput	500+ analyses/day
Uptime	99.9%
Memory Usage	< 2GB per analysis

🆘 Support

• Documentation: docs.openauditlabs.com
• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [email protected]
• Discord: OpenAuditLabs Community

📄 License

This project is licensed under the GNU Affero General Public License v3 (AGPLv3) - see the LICENSE file for details.

🙏 Acknowledgments

• CrewAI for multi-agent orchestration
• Slither for static analysis
• Mythril for symbolic execution
• OpenZeppelin for smart contract security standards
• Smart Contract Weakness Classification for vulnerability taxonomy

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🛡️ Securing the Future of Smart Contracts with AI 🛡️

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