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LangGraph CLI

The LangGraph CLI is the official command-line interface for LangGraph, providing tools to create, develop, and deploy LangGraph applications locally and to production environments.

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What is the LangGraph CLI?

The CLI streamlines your LangGraph development workflow by:
  • Creating new projects from templates
  • Developing locally with hot reloading and debugging
  • Building Docker images for deployment
  • Testing your graphs in a production-like environment
  • Deploying to cloud platforms or self-hosted infrastructure

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Installation

Install the CLI via pip: 
pip install langgraph-cli
For development mode with hot reloading and in-memory server support: 
pip install "langgraph-cli[inmem]"
The in-memory development server requires Python 3.11 or higher.

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Quick Start

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1. Create a New Project

Start a new LangGraph project from a template: 
langgraph new my-agent --template react-agent

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2. Develop Locally

Run your agent with hot reloading: 
cd my-agent
langgraph dev
This starts a development server at http://localhost:2024 with:
  • Automatic code reloading on file changes
  • Interactive API documentation at /docs
  • LangGraph Studio integration

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3. Test with Docker

Test your agent in a production-like environment: 
langgraph up
This launches your agent in Docker with all dependencies properly containerized.

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4. Build for Production

Create a Docker image for deployment: 
langgraph build -t my-agent:latest

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Core Concepts

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Configuration File

The CLI uses a langgraph.json configuration file that defines:
  • Dependencies: Python packages required by your agent
  • Graphs: Entry points to your compiled graph objects
  • Environment: Variables and configuration for runtime
  • Python Version: Target Python version for deployment
Example langgraph.json: 
{
  "dependencies": [
    "langchain-openai",
    "langchain-anthropic",
    "."
  ],
  "graphs": {
    "agent": "./src/agent.py:graph"
  },
  "env": ".env",
  "python_version": "3.11"
}

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Development vs Production

The CLI provides two modes for running your agent:
Featurelanggraph devlanggraph up
EnvironmentIn-memory serverDocker containers
Hot ReloadYesOptional with --watch
Setup TimeFastSlower (builds image)
Use CaseDevelopmentTesting, Production
RequirementsPython 3.11+Docker

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Command Overview

The CLI provides five main commands:

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langgraph new

Create a new project from a template

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langgraph dev

Run a development server with hot reloading

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langgraph up

Launch the API server in Docker

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langgraph build

Build a Docker image for deployment

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langgraph dockerfile

Generate a Dockerfile for custom deployments

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Typical Workflow

  1. Create: Use langgraph new to scaffold a project
  2. Develop: Use langgraph dev for rapid iteration with hot reload
  3. Test: Use langgraph up to test in Docker before deploying
  4. Build: Use langgraph build to create production images
  5. Deploy: Push your image to your container registry and deploy

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Project Structure

A typical LangGraph project structure: 
my-agent/
├── langgraph.json       # CLI configuration
├── .env                 # Environment variables
├── src/
│   ├── agent.py        # Your graph definition
│   └── tools.py        # Agent tools and utilities
├── requirements.txt     # Additional dependencies (optional)
└── README.md

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Environment Variables

The CLI respects environment variables defined in:
  1. .env files (referenced in langgraph.json)
  2. System environment variables
  3. Docker Compose environment configurations
Common environment variables: 
# Development with LangSmith
LANGSMITH_API_KEY=your-api-key

# Production with license
LANGGRAPH_CLOUD_LICENSE_KEY=your-license-key

# Model providers
OPENAI_API_KEY=your-openai-key
ANTHROPIC_API_KEY=your-anthropic-key

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Next Steps

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Getting Help

View help for any command: 
langgraph --help
langgraph dev --help
langgraph build --help

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Version Information

Check your CLI version: 
langgraph --version

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