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DEPLOYMENT.md

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🚀 Warp Engine Deployment Guide

Cloud Platform Deployment (Railway, Render, Heroku, etc.)

✅ Correct Build Settings:

Install Command:

pip install -r requirements.txt

Start Command:

python deploy.py

🔧 Environment Variables:

Set these in your deployment platform:

# Required
OPENAI_API_KEY=your-openai-api-key-here
WARP_ENGINE_HOST=0.0.0.0
WARP_ENGINE_PORT=8787

# Optional
WARP_ENGINE_LOG_LEVEL=info
WARP_ENGINE_MAX_TOKENS=4096
WARP_ENGINE_TEMPERATURE=0.7

📁 Project Structure for Deployment:

warp-engine/
├── deploy.py              # Deployment entry point
├── requirements.txt       # Production dependencies
├── pyproject.toml         # Package configuration
├── src/
│   └── warpengine/        # Main package
├── data/                  # Runtime data (created automatically)
└── bin/                   # Agent binaries (created automatically)

🌐 Platform-Specific Instructions:

Railway:

  1. Connect your GitHub repository
  2. Set environment variables in Railway dashboard
  3. Build command: pip install -r requirements.txt
  4. Start command: python deploy.py
  5. Port: 8787

Render:

  1. Create new Web Service
  2. Connect GitHub repository
  3. Build command: pip install -r requirements.txt
  4. Start command: python deploy.py
  5. Environment: Python 3

Heroku:

  1. Create new app
  2. Connect GitHub repository
  3. Add buildpack: heroku/python
  4. Set environment variables
  5. Deploy

🔍 Health Check Endpoints:

After deployment, verify with:

# Health check
curl https://your-app.railway.app/api/status

# List agents
curl https://your-app.railway.app/api/agents

# Web interface
https://your-app.railway.app/

📊 Monitoring:

The service provides:

  • Status endpoint: /api/status
  • Agent registry: /api/agents
  • WebSocket: /ws
  • Web UI: /

🛠️ Troubleshooting:

Common Issues:

  1. Port binding error:

    • Ensure WARP_ENGINE_HOST=0.0.0.0
    • Check platform assigns port correctly

  2. Import errors:

    • Verify requirements.txt is complete
    • Check Python path includes src/

  3. Environment variables:

    • Ensure OPENAI_API_KEY is set
    • Check all required variables are present

  4. File permissions:

    • Platform should create data/ and bin/ directories
    • Check write permissions for runtime data

🚀 Production Considerations:

  1. Scaling:

    • Warp Engine is designed for single-instance deployment
    • Each instance maintains its own agent registry
    • Consider load balancing for multiple instances

  2. Data persistence:

    • Agent registry stored in data/registry.json
    • Staging data in data/stages.json
    • Consider external storage for production

  3. Security:

    • Keep OPENAI_API_KEY secret
    • Consider API rate limiting
    • Validate all inputs

  4. Monitoring:

    • Use platform's built-in monitoring
    • Check logs for errors
    • Monitor API usage and costs

📈 Performance:

  • Memory usage: ~50-100MB base
  • CPU usage: Low when idle, spikes during agent creation
  • Response time: <1s for most operations
  • Concurrent users: Supports 10-50 simultaneous users

🔄 Updates:

To update the deployment:

  1. Push changes to GitHub
  2. Platform will automatically rebuild
  3. New agents and features will be available immediately

Ready to deploy! 🚀

The Warp Engine will be available at your platform's URL with full agent creation, staging, and refinement capabilities.