Deployment Targets

Deployment Targets

Configuration differences for local development vs. production deployment.

Local Development

Using langgraph dev

Lightweight development server for rapid iteration:

# Install CLI with in-memory backend
uv pip install "langgraph-cli[inmem]"

# Fallback if uv not available
pip install "langgraph-cli[inmem]"

# Start server
langgraph dev

Features:

• No Docker required
• Local persistence (state persisted to local directory)
• Hot reloading (auto-restart on code changes)
• Default port: 2024
• Lightweight resource usage
• Studio access requires a LangSmith API key

Access Studio:

https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024

Using langgraph up

Production-like testing environment:

# Install CLI
uv pip install langgraph-cli

# Fallback if uv not available
pip install langgraph-cli

# Start server
langgraph up

Features:

• Docker required
• PostgreSQL state persistence
• Optional hot reloading with --watch
• Default port: 8123
• Production-like environment
• Requires LangSmith API key for local dev; license key for production use

LangSmith Deployment (Cloud/Hybrid)

Configuration

No special configuration needed beyond standard langgraph.json:

{
  "dependencies": ["."],
  "graphs": {
    "agent": "./my_agent/agent.py:graph"
  },
  "env": ".env",
  "python_version": "3.11"
}

Deployment

Deploy via Git integration or the LangSmith control plane (UI/API).

Features:

• Managed infrastructure
• Auto-scaling
• Built-in monitoring
• Persistent PostgreSQL storage

Environment Variables

Set in LangSmith UI under deployment settings (not in .env file).

Self-Hosted Deployment (Standalone or Control Plane)

Requirements

• Docker and Docker Compose
• PostgreSQL database
• Redis (optional, for caching)

Configuration

{
  "dependencies": ["."],
  "graphs": {
    "agent": "./my_agent/agent.py:graph"
  },
  "python_version": "3.11",
  "base_image": "langchain/langgraph-server:0.2",
  "dockerfile_lines": [
    "RUN apt-get update && apt-get install -y postgresql-client"
  ]
}

Build and Run

# Build Docker image
langgraph build

# Run with docker-compose.yml
docker-compose up

Configuration Comparison

Best Practices

Development

• Use langgraph dev for fastest iteration
• Keep .env file for local credentials
• Use LangSmith tracing to debug

Testing

• Use langgraph up before deploying
• Test with production-like data volumes
• Verify state persistence

Production

• Set environment variables via deployment UI/config
• Enable monitoring and alerting
• Use appropriate scaling configuration
• Implement authentication
• Configure CORS policies

References

• Local Development Guide
• LangSmith Deployment Docs