CrewAI Agent Deployment Orchestrator

Cross-platform deployment orchestration for CrewAI agents. Analyze agent codebases and generate optimized Docker configurations with one command.

Quick Start

# Install
pip install crewai-deploy-orchestrator

# Analyze an agent and generate deployment configs
crewai-deploy /path/to/agent --output ./deployment

# Or use directly with Python
python -m orchestrator /path/to/agent --output ./deployment

Installation

From PyPI

pip install crewai-deploy-orchestrator

From Source

git clone https://github.com/yourorg/crewai-deploy-orchestrator.git
cd crewai-deploy-orchestrator
pip install -r requirements.txt

Development Installation

pip install -e ".[dev]"

Features

• Agent Analysis: Parse CrewAI agent definitions to extract dependencies
• Dependency Detection: Identify Python packages and system requirements
• Docker Generation: Create optimized Dockerfile and docker-compose.yml
• Health Monitoring: Built-in health checks and monitoring setup
• Multi-Platform: Ready for cloud deployment (AWS, GCP, Azure, etc.)

Usage Examples

Basic Usage

# Analyze an agent and generate deployment configs
crewai-deploy /path/to/agent --output ./deployment

# With custom agent name
crewai-deploy /path/to/agent --output ./deployment --agent-name my-agent

Example with Included Examples

# Test with included examples
crewai-deploy examples/simple_agent --output simple_deployment

# Deploy
cd simple_deployment
docker-compose up -d

Output Structure

The tool generates the following files:

deployment/
├── Dockerfile           # Optimized container definition
├── docker-compose.yml   # Multi-service orchestration
├── requirements.txt     # Python dependencies
├── health_check.py      # Health monitoring endpoint
├── prometheus.yml       # Monitoring configuration
└── logs/                # Log directory (mounted volume)

How It Works

1. Agent Analysis

The tool scans your CrewAI agent codebase for:

• Python imports and dependencies
• Agent class definitions (Agent, CrewAI patterns)
• Requirements files (requirements.txt, setup.py, pyproject.toml)
• System package requirements (apt-get install patterns)

2. Dependency Resolution

• Extracts all Python package dependencies
• Identifies system-level requirements
• Creates optimized installation order

3. Docker Configuration

• Generates optimized Dockerfile with caching
• Creates docker-compose.yml with health checks
• Includes monitoring setup (Prometheus)
• Sets up volume mounts for persistence

4. Health Monitoring

• Built-in FastAPI health endpoint
• Resource usage monitoring (CPU, memory)
• Agent status reporting
• Ready for integration with monitoring stacks

Testing

Run the test suite:

python test_orchestrator.py

The test suite includes:

• Simple agent analysis
• Web research agent analysis
• CLI interface testing
• Docker config generation

Example Agents

The repository includes two example agents:

1. Simple Agent (examples/simple_agent/)

• Basic data analysis agent
• Report generation workflow
• Minimal dependencies

2. Web Research Agent (examples/web_research_agent/)

• Multi-agent research system
• Web scraping capabilities
• Async processing
• Comprehensive reporting

Deployment Options

Local Development

docker-compose up -d

Production Considerations

• Set environment variables for API keys
• Configure resource limits in docker-compose.yml
• Add SSL/TLS termination
• Set up proper logging and monitoring
• Implement backup strategies

Cloud Deployment

• AWS: Deploy to ECS/EKS with Load Balancer
• GCP: Deploy to Cloud Run or GKE
• Azure: Deploy to Container Apps or AKS
• Kubernetes: Use generated manifests with kompose convert

Configuration

Environment Variables

# Agent configuration
OPENAI_API_KEY=your_key_here
LOG_LEVEL=INFO
PYTHONPATH=/app

# Docker configuration
MEMORY_LIMIT=2g
CPU_LIMIT=1.0

Customizing Deployment

Edit generated files:

• Dockerfile: Add custom build steps
• docker-compose.yml: Adjust resource limits, add services
• prometheus.yml: Configure monitoring targets
• health_check.py: Add custom health checks

Limitations (MVP)

• Currently supports Python/CrewAI agents only
• Basic dependency resolution (no conflict handling)
• Assumes FastAPI for health endpoints
• Limited system package detection
• No automatic cloud deployment integration

Roadmap

• Advanced dependency conflict resolution
• Cloud provider templates (AWS, GCP, Azure)
• Kubernetes manifests generation
• Database and cache integration
• Secret management
• CI/CD pipeline templates
• Multi-language agent support
• Performance optimization recommendations

Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Submit a pull request

License

MIT License - See LICENSE file for details.

Community

• GitHub Issues: Report bugs and feature requests
• Discussions: Share ideas and ask questions
• CrewAI Discord: Join the CrewAI community

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Built for the CrewAI ecosystem • Deploy agents anywhere with confidence