langgraph/libs/cli

LangGraph CLI

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Quick Install

uv add langgraph-cli

🤔 What is this?

The LangGraph CLI is the official command-line interface for LangGraph. It provides tools to create, develop, build, and run LangGraph applications locally or in Docker.

📖 Documentation

For full documentation, see the LangGraph CLI reference. For conceptual guides and tutorials, see the LangGraph Docs.

For development mode with hot reloading:

uv add "langgraph-cli[inmem]"

Commands

langgraph new 🌱

Create a new LangGraph project from a template.

langgraph new [PATH] --template TEMPLATE_NAME

langgraph dev 🏃‍♀️

Run LangGraph API server in development mode with hot reloading.

langgraph dev [OPTIONS]
  --host TEXT                 Host to bind to (default: 127.0.0.1)
  --port INTEGER             Port to bind to (default: 2024)
  --no-reload               Disable auto-reload
  --debug-port INTEGER      Enable remote debugging
  --no-browser             Skip opening browser window
  -c, --config FILE        Config file path (default: langgraph.json)

langgraph up 🚀

Launch LangGraph API server in Docker.

langgraph up [OPTIONS]
  -p, --port INTEGER        Port to expose (default: 8123)
  --wait                   Wait for services to start
  --watch                  Restart on file changes
  --verbose               Show detailed logs
  -c, --config FILE       Config file path
  -d, --docker-compose    Additional services file

langgraph build

Build a Docker image for your LangGraph application.

langgraph build -t IMAGE_TAG [OPTIONS]
  --platform TEXT          Target platforms (e.g., linux/amd64,linux/arm64)
  --pull / --no-pull      Use latest/local base image
  -c, --config FILE       Config file path

langgraph dockerfile

Generate a Dockerfile for custom deployments.

langgraph dockerfile SAVE_PATH [OPTIONS]
  -c, --config FILE       Config file path

Configuration

The CLI uses a langgraph.json configuration file with these key settings:

{
  "dependencies": ["langchain_openai", "./your_package"],
  "graphs": {
    "my_graph": "./your_package/file.py:graph"
  },
  "env": "./.env",
  "python_version": "3.11",
  "pip_config_file": "./pip.conf",
  "dockerfile_lines": []
}

Git dependencies should use credential-free URLs. The CLI conservatively scans direct langgraph.json dependencies, common Python package files, uv project and lock files, and common Node.js package and lock files for HTTP Git URLs with userinfo. This check is not exhaustive: generated Docker builds can copy other files, including nested requirement or constraint files, into image layers without scanning them. For private dependencies, provide short-lived credentials through your build environment's secret-backed Git credential helper. Do not store credentials in copied files such as langgraph.json or pip_config_file.

See the full documentation for detailed configuration options.

Development

To develop the CLI itself:

1. Clone the repository
2. Navigate to the CLI directory: cd libs/cli
3. Install development dependencies: uv sync
4. Make your changes to the CLI code
5. Test your changes:

# Run CLI commands directly
uv run langgraph --help

# Or use the examples
cd examples
uv sync
uv run langgraph dev  # or other commands

📕 Releases & Versioning

See our Releases and Versioning policies.

💁 Contributing

As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.

For detailed information on how to contribute, see the Contributing Guide.