LangGraph CLI

To help you ship LangGraph apps to production faster, check out LangSmith. LangSmith is a unified developer platform for building, testing, and monitoring LLM applications.

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 Agent 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 Agent 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

langgraph deploy

Build and deploy an Agent Server image to LangSmith. Use --agent-server-id to update an existing Agent Server and --agent-server-type to select its type. The legacy --deployment-id and --deployment-type spellings remain supported as hidden compatibility aliases, with no removal scheduled. Older Python CLI versions and the JavaScript CLI may still require the legacy spellings; check command help for your installed version.

langgraph deploy metrics

Fetch Agent Server monitoring time series as JSON through the LangSmith control plane. Use langgraph deploy list to find the Agent Server ID. Authentication uses the same API key and workspace configuration as the other deploy commands.

langgraph deploy metrics AGENT_SERVER_ID \
  --start-time 2026-10-28T00:00:00Z \
  --end-time 2026-10-28T01:00:00Z \
  --metrics cpu_usage,memory_usage,worker_count

--metrics accepts a comma-separated list of these names (also listed in langgraph deploy metrics --help):

• Resource usage: memory_usage, cpu_usage, disk_usage
• Counts: restart_count, replica_count, worker_count, lg_run_count
• HTTP traffic: responses_per_second, error_responses_per_second, p95_latency
• Queue: run_queue_wait_time

These are exact metric names, not free-text search terms. Availability depends on the Agent Server and platform; a supported metric may have no data. Use lg_run_count for Agent Server runs, not the UI schema's placeholder run_count.

Omit --metrics to fetch all available metrics. Time bounds default to the last 15 minutes, and the backend picks the resolution from the time range. This command never prompts; for an organization-scoped key, set LANGSMITH_TENANT_ID to the workspace ID.

Default output is compact JSON grouped by section title, chart title, and series name. Each series contains its real project_metric as metric, inferred unit (percent, count, or null), latest, min, max, avg, and points: [[iso_timestamp, value], ...]. Values are rounded to three decimals; statistics use unrounded finite values and ignore missing samples. latest is the latest non-missing value. Missing samples remain null gaps, and series with no values have null statistics. Empty sections are marked {"_empty":true}.

The top level includes deployment_id, start_time, end_time, and step_seconds. Bounds always describe the requested window, not the observed point bounds. The CLI resolves omitted bounds once: end defaults to now (UTC), and start defaults to 15 minutes before the resolved end. With only --end-time, start is 15 minutes before that value; with only --start-time, end is now. Both values are sent explicitly to the backend and output, even when there are no points. step_seconds is the most common gap between consecutive timestamps within series, with ties broken by the smallest gap. Whole seconds are integers; fractional seconds are preserved. It is null only when every series has fewer than two timestamps. UI metadata and repeated series IDs are omitted. The host-backend response itself is unchanged.

langgraph deploy metrics AGENT_SERVER_ID --summary
langgraph deploy metrics AGENT_SERVER_ID --raw

--summary returns the same shape without points. --raw returns the original UI chart schema without reshaping or rounding. These flags are mutually exclusive. Neither mode returns traces.

Size comparison using the realistic synthetic unit-test fixture (compact JSON serialization for all modes; not a live Agent Server benchmark):

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.