🚀 Meridian CrewAI Agent Deployment Orchestrator

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| Repository | Current Stars | Target | |------------|---------------|--------| | meridian-mcp-deploy | 0 | First 10 stars | | meridian-crewai-deploy-orchestrator | 0 | First 10 stars | | meridian-context-compression | 0 | First 10 stars |

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1. ⭐ meridian-context-compression - 22x token reduction for CrewAI workflows
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3. ⭐ meridian-crewai-deploy-orchestrator - Automated CrewAI agent deployment

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⭐ Star This Repository

If this tool helps you deploy CrewAI agents faster, please star it!

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Deploy CrewAI agents anywhere with one command. Automatically analyze agent codebases and generate optimized Docker configurations for cloud deployment. The easiest way to containerize and scale your CrewAI agents.

⭐ Why this matters

🏠 Meridian Tooling Family

This tool is part of the Meridian Tooling Suite - a collection of AI deployment and optimization tools:

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🔗 Related Tools

| Tool | Purpose | Key Feature | |------|---------|-------------| | meridian-context-compression | Reduce LLM token usage | 22x compression for CrewAI workflows | | meridian-crewai-deploy-orchestrator | Deploy CrewAI agents | One-command Docker & cloud deployment | | meridian-mcp-deploy | Deploy MCP servers | Automatic configuration & CI/CD |

📦 Install All Meridian Tools

# Install the complete Meridian Tooling Suite
pip install meridian-context-compression meridian-crewai-deploy-orchestrator meridian-mcp-deploy

🤝 Cross-Tool Integration

These tools are designed to work together:

1. Use context-compression to optimize your agent prompts
2. Use crewai-deploy-orchestrator to containerize and deploy
3. Use mcp-deploy to set up MCP servers for your deployed agents

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• Stop manually writing Dockerfiles and docker-compose.yml
• Deploy CrewAI agents to AWS, GCP, Azure in minutes
• Built-in health checks and monitoring
• Perfect for production deployments

🚀 Quick Start

# Install
pip install meridian-crewai-deploy-orchestrator

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

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

📦 Installation

From PyPI (recommended)

pip install meridian-crewai-deploy-orchestrator

From Source (for development)

git clone https://github.com/meridianmindx/meridian-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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🔗 See Also

Part of the Meridian Tooling Suite – Complementary tools for AI agent development:

🚀 Meridian MCP Deploy Framework

Deploy MCP servers with one command. Save hours of manual Docker configuration for Model Context Protocol (MCP) servers.

Perfect companion for CrewAI deployments: Deploy your MCP servers alongside CrewAI agents using the same unified approach.

🔄 Meridian Context Compression

Reduce LLM token usage by 22x while preserving semantic meaning. Cut your OpenAI and Anthropic API costs dramatically with intelligent context compression.

Works great with CrewAI: Compress prompts before sending to expensive LLMs, especially useful when CrewAI agents retrieve large documents.

📦 Installation Bundle

# Install the complete Meridian tooling suite
pip install meridian-crewai-deploy-orchestrator meridian-context-compression
# Or individually
pip install meridian-mcp-deploy

🎯 Why Use All Three?

1. meridian-crewai-deploy-orchestrator: Deploy your CrewAI agents
2. meridian-mcp-deploy: Containerize and deploy your MCP servers
3. meridian-context-compression: Optimize token usage for both

Together, they provide a complete AI agent deployment and optimization stack.

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❤️ Support this project

If this tool helps you deploy CrewAI agents faster, please consider starring this repository on GitHub. Stars help other developers discover useful tools!

⭐ Star this repo • 💬 Start a discussion • 🐛 Report issues

Built for the CrewAI ecosystem • Deploy agents anywhere with confidence

💰 Affiliate Disclosure

We participate in affiliate programs for cloud providers. If you sign up using our links, we may earn a commission at no extra cost to you. This helps support the project.

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