LangGraph CLI - Docs by LangChain

The LangGraph command line interface includes commands to build and run a LangGraph Platform API server locally in Docker. For development and testing, you can use the CLI to deploy a local API server.

Installation

Ensure that Docker is installed (e.g. docker --version).
Install the CLI package:
pip install langgraph-cli
Run the command langgraph --help or npx @langchain/langgraph-cli --help to confirm that the CLI is working correctly.

Configuration File

The LangGraph CLI requires a JSON configuration file that follows this schema. It contains the following properties:

The LangGraph CLI defaults to using the configuration file langgraph.json in the current directory.

Key	Description
`dependencies`	Required. Array of dependencies for LangGraph Platform API server. Dependencies can be one of the following: A single period (`"."`), which will look for local Python packages. The directory path where `pyproject.toml`, `setup.py` or `requirements.txt` is located. For example, if `requirements.txt` is located in the root of the project directory, specify `"./"`. If it’s located in a subdirectory called `local_package`, specify `"./local_package"`. Do not specify the string `"requirements.txt"` itself. A Python package name.
`graphs`	Required. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: `./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.state.CompiledStateGraph` `./your_package/your_file.py:make_graph`, where `make_graph` is a function that takes a config dictionary (`langchain_core.runnables.RunnableConfig`) and returns an instance of `langgraph.graph.state.StateGraph` or `langgraph.graph.state.CompiledStateGraph`. See how to rebuild a graph at runtime for more details.
`auth`	(Added in v0.0.11) Auth configuration containing the path to your authentication handler. Example: `./your_package/auth.py:auth`, where `auth` is an instance of `langgraph_sdk.Auth`. See authentication guide for details.
`base_image`	Optional. Base image to use for the LangGraph API server. Defaults to `langchain/langgraph-api` or `langchain/langgraphjs-api`. Use this to pin your builds to a particular version of the langgraph API, such as `"langchain/langgraph-server:0.2"`. See https://hub.docker.com/r/langchain/langgraph-server/tags for more details. (added in `langgraph-cli==0.2.8`)
`image_distro`	Optional. Linux distribution for the base image. Must be either `"debian"` or `"wolfi"`. If omitted, defaults to `"debian"`. Available in `langgraph-cli>=0.2.11`.
`env`	Path to `.env` file or a mapping from environment variable to its value.
`store`	Configuration for adding semantic search and/or time-to-live (TTL) to the BaseStore. Contains the following fields: `index` (optional): Configuration for semantic search indexing with fields `embed`, `dims`, and optional `fields`. `ttl` (optional): Configuration for item expiration. An object with optional fields: `refresh_on_read` (boolean, defaults to `true`), `default_ttl` (float, lifespan in minutes, defaults to no expiration), and `sweep_interval_minutes` (integer, how often to check for expired items, defaults to no sweeping).
`ui`	Optional. Named definitions of UI components emitted by the agent, each pointing to a JS/TS file. (added in `langgraph-cli==0.1.84`)
`python_version`	`3.11`, `3.12`, or `3.13`. Defaults to `3.11`.
`node_version`	Specify `node_version: 20` to use LangGraph.js.
`pip_config_file`	Path to `pip` config file.
`pip_installer`	(Added in v0.3) Optional. Python package installer selector. It can be set to `"auto"`, `"pip"`, or `"uv"`. From version 0.3 onward the default strategy is to run `uv pip`, which typically delivers faster builds while remaining a drop-in replacement. In the uncommon situation where `uv` cannot handle your dependency graph or the structure of your `pyproject.toml`, specify `"pip"` here to revert to the earlier behaviour.
`keep_pkg_tools`	(Added in v0.3.4) Optional. Control whether to retain Python packaging tools (`pip`, `setuptools`, `wheel`) in the final image. Accepted values: `true` : Keep all three tools (skip uninstall). `false` / omitted : Uninstall all three tools (default behaviour). `list[str]` : Names of tools to retain. Each value must be one of “pip”, “setuptools”, “wheel”. . By default, all three tools are uninstalled.
`dockerfile_lines`	Array of additional lines to add to Dockerfile following the import from parent image.
`checkpointer`	Configuration for the checkpointer. Contains a `ttl` field which is an object with the following keys: `strategy`: How to handle expired checkpoints (e.g., `"delete"`). `sweep_interval_minutes`: How often to check for expired checkpoints (integer). `default_ttl`: Default time-to-live for checkpoints in minutes (integer). Defines how long checkpoints are kept before the specified strategy is applied.
`http`	HTTP server configuration with the following fields: `app`: Path to custom Starlette/FastAPI app (e.g., `"./src/agent/webapp.py:app"`). See custom routes guide. `cors`: CORS configuration with fields for `allow_origins`, `allow_methods`, `allow_headers`, etc. `configurable-headers`: Define which request headers to exclude or include as a run’s configurable values. `disable_assistants`: Disable `/assistants` routes `disable_mcp`: Disable `/mcp` routes `disable_meta`: Disable `/ok`, `/info`, `/metrics`, and `/docs` routes `disable_runs`: Disable `/runs` routes `disable_store`: Disable `/store` routes `disable_threads`: Disable `/threads` routes `disable_ui`: Disable `/ui` routes `disable_webhooks`: Disable webhooks calls on run completion in all routes `mount_prefix`: Prefix for mounted routes (e.g., “/my-deployment/api”)
`api_version`	(Added in v0.3.7) Which semantic version of the LangGraph API server to use (e.g., `"0.3"`). Defaults to latest. Check the server changelog for details on each release.

Examples

Basic Configuration

{
  "dependencies": ["."],
  "graphs": {
    "chat": "chat.graph:graph"
  }
}

Using Wolfi Base Images

You can specify the Linux distribution for your base image using the image_distro field. Valid options are debian or wolfi. Wolfi is the recommended option as it provides smaller and more secure images. This is available in langgraph-cli>=0.2.11.

{
  "dependencies": ["."],
  "graphs": {
    "chat": "chat.graph:graph"
  },
  "image_distro": "wolfi"
}

Adding semantic search to the store

All deployments come with a DB-backed BaseStore. Adding an “index” configuration to your langgraph.json will enable semantic search within the BaseStore of your deployment.The index.fields configuration determines which parts of your documents to embed:

If omitted or set to ["$"], the entire document will be embedded
To embed specific fields, use JSON path notation: ["metadata.title", "content.text"]
Documents missing specified fields will still be stored but won’t have embeddings for those fields
You can still override which fields to embed on a specific item at put time using the index parameter

{
  "dependencies": ["."],
  "graphs": {
    "memory_agent": "./agent/graph.py:graph"
  },
  "store": {
    "index": {
      "embed": "openai:text-embedding-3-small",
      "dims": 1536,
      "fields": ["$"]
    }
  }
}

Common model dimensions

openai:text-embedding-3-large: 3072
openai:text-embedding-3-small: 1536
openai:text-embedding-ada-002: 1536
cohere:embed-english-v3.0: 1024
cohere:embed-english-light-v3.0: 384
cohere:embed-multilingual-v3.0: 1024
cohere:embed-multilingual-light-v3.0: 384

Semantic search with a custom embedding function

If you want to use semantic search with a custom embedding function, you can pass a path to a custom embedding function:

{
  "dependencies": ["."],
  "graphs": {
    "memory_agent": "./agent/graph.py:graph"
  },
  "store": {
    "index": {
      "embed": "./embeddings.py:embed_texts",
      "dims": 768,
      "fields": ["text", "summary"]
    }
  }
}

The embed field in store configuration can reference a custom function that takes a list of strings and returns a list of embeddings. Example implementation:

# embeddings.py
def embed_texts(texts: list[str]) -> list[list[float]]:
    """Custom embedding function for semantic search."""
    # Implementation using your preferred embedding model
    return [[0.1, 0.2, ...] for _ in texts]  # dims-dimensional vectors

Adding custom authentication

{
  "dependencies": ["."],
  "graphs": {
    "chat": "chat.graph:graph"
  },
  "auth": {
    "path": "./auth.py:auth",
    "openapi": {
      "securitySchemes": {
        "apiKeyAuth": {
          "type": "apiKey",
          "in": "header",
          "name": "X-API-Key"
        }
      },
      "security": [{ "apiKeyAuth": [] }]
    },
    "disable_studio_auth": false
  }
}

See the authentication conceptual guide for details, and the setting up custom authentication guide for a practical walk through of the process.

Configuring Store Item Time-to-Live

You can configure default data expiration for items/memories in the BaseStore using the store.ttl key. This determines how long items are retained after they are last accessed (with reads potentially refreshing the timer based on refresh_on_read). Note that these defaults can be overwritten on a per-call basis by modifying the corresponding arguments in get, search, etc.The ttl configuration is an object containing optional fields:

refresh_on_read: If true (the default), accessing an item via get or search resets its expiration timer. Set to false to only refresh TTL on writes (put).
default_ttl: The default lifespan of an item in minutes. If not set, items do not expire by default.
sweep_interval_minutes: How frequently (in minutes) the system should run a background process to delete expired items. If not set, sweeping does not occur automatically.

Here is an example enabling a 7-day TTL (10080 minutes), refreshing on reads, and sweeping every hour:

{
  "dependencies": ["."],
  "graphs": {
    "memory_agent": "./agent/graph.py:graph"
  },
  "store": {
    "ttl": {
      "refresh_on_read": true,
      "sweep_interval_minutes": 60,
      "default_ttl": 10080
    }
  }
}

Configuring Checkpoint Time-to-Live

You can configure the time-to-live (TTL) for checkpoints using the checkpointer key. This determines how long checkpoint data is retained before being automatically handled according to the specified strategy (e.g., deletion). The ttl configuration is an object containing:

strategy: The action to take on expired checkpoints (currently "delete" is the only accepted option).
sweep_interval_minutes: How frequently (in minutes) the system checks for expired checkpoints.
default_ttl: The default lifespan of a checkpoint in minutes.

Here’s an example setting a default TTL of 30 days (43200 minutes):

{
  "dependencies": ["."],
  "graphs": {
    "chat": "chat.graph:graph"
  },
  "checkpointer": {
    "ttl": {
      "strategy": "delete",
      "sweep_interval_minutes": 10,
      "default_ttl": 43200
    }
  }
}

In this example, checkpoints older than 30 days will be deleted, and the check runs every 10 minutes.

Pinning API Version

(Added in v0.3.7)You can pin the API version of the LangGraph server by using the api_version key. This is useful if you want to ensure that your server uses a specific version of the API. By default, builds in Cloud deployments use the latest stable version of the server. This can be pinned by setting the api_version key to a specific version.

{
  "dependencies": ["."],
  "graphs": {
    "chat": "chat.graph:graph"
  },
  "api_version": "0.2"
}

Commands

Usage

The base command for the LangGraph CLI is langgraph.

langgraph [OPTIONS] COMMAND [ARGS]

`dev`

Run LangGraph API server in development mode with hot reloading and debugging capabilities. This lightweight server requires no Docker installation and is suitable for development and testing. State is persisted to a local directory.

Currently, the CLI only supports Python >= 3.11.

InstallationThis command requires the “inmem” extra to be installed:

pip install -U "langgraph-cli[inmem]"

Usage

langgraph dev [OPTIONS]

Options

Option	Default	Description
`-c, --config FILE`	`langgraph.json`	Path to configuration file declaring dependencies, graphs and environment variables
`--host TEXT`	`127.0.0.1`	Host to bind the server to
`--port INTEGER`	`2024`	Port to bind the server to
`--no-reload`		Disable auto-reload
`--n-jobs-per-worker INTEGER`		Number of jobs per worker. Default is 10
`--debug-port INTEGER`		Port for debugger to listen on
`--wait-for-client`	`False`	Wait for a debugger client to connect to the debug port before starting the server
`--no-browser`		Skip automatically opening the browser when the server starts
`--studio-url TEXT`		URL of the LangGraph Studio instance to connect to. Defaults to https://smith.langchain.com
`--allow-blocking`	`False`	Do not raise errors for synchronous I/O blocking operations in your code (added in `0.2.6`)
`--tunnel`	`False`	Expose the local server via a public tunnel (Cloudflare) for remote frontend access. This avoids issues with browsers like Safari or networks blocking localhost connections
`--help`		Display command documentation

`build`

Build LangGraph Platform API server Docker image.Usage

langgraph build [OPTIONS]

Options

Option	Default	Description
`--platform TEXT`		Target platform(s) to build the Docker image for. Example: `langgraph build --platform linux/amd64,linux/arm64`
`-t, --tag TEXT`		Required. Tag for the Docker image. Example: `langgraph build -t my-image`
`--pull / --no-pull`	`--pull`	Build with latest remote Docker image. Use `--no-pull` for running the LangGraph Platform API server with locally built images.
`-c, --config FILE`	`langgraph.json`	Path to configuration file declaring dependencies, graphs and environment variables.
`--help`		Display command documentation.

`up`

Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Platform. Requires a license key for production use.Usage

langgraph up [OPTIONS]

Options

Option	Default	Description
`--wait`		Wait for services to start before returning. Implies —detach
`--base-image TEXT`	`langchain/langgraph-api`	Base image to use for the LangGraph API server. Pin to specific versions using version tags.
`--image TEXT`		Docker image to use for the langgraph-api service. If specified, skips building and uses this image directly.
`--postgres-uri TEXT`	Local database	Postgres URI to use for the database.
`--watch`		Restart on file changes
`--debugger-base-url TEXT`	`http://127.0.0.1:[PORT]`	URL used by the debugger to access LangGraph API.
`--debugger-port INTEGER`		Pull the debugger image locally and serve the UI on specified port
`--verbose`		Show more output from the server logs.
`-c, --config FILE`	`langgraph.json`	Path to configuration file declaring dependencies, graphs and environment variables.
`-d, --docker-compose FILE`		Path to docker-compose.yml file with additional services to launch.
`-p, --port INTEGER`	`8123`	Port to expose. Example: `langgraph up --port 8000`
`--pull / --no-pull`	`pull`	Pull latest images. Use `--no-pull` for running the server with locally-built images. Example: `langgraph up --no-pull`
`--recreate / --no-recreate`	`no-recreate`	Recreate containers even if their configuration and image haven’t changed
`--help`		Display command documentation.

`dockerfile`

Generate a Dockerfile for building a LangGraph Platform API server Docker image.Usage

langgraph dockerfile [OPTIONS] SAVE_PATH

Options

Option	Default	Description
`-c, --config FILE`	`langgraph.json`	Path to the configuration file declaring dependencies, graphs and environment variables.
`--help`		Show this message and exit.

Example:

langgraph dockerfile -c langgraph.json Dockerfile

This generates a Dockerfile that looks similar to:

FROM langchain/langgraph-api:3.11

ADD ./pipconf.txt /pipconfig.txt

RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt langchain_community langchain_anthropic langchain_openai wikipedia scikit-learn

ADD ./graphs /deps/__outer_graphs/src
RUN set -ex && \
    for line in '[project]' \
                'name = "graphs"' \
                'version = "0.1"' \
                '[tool.setuptools.package-data]' \
                '"*" = ["**/*"]'; do \
        echo "$line" >> /deps/__outer_graphs/pyproject.toml; \
    done

RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*

ENV LANGSERVE_GRAPHS='{"agent": "/deps/__outer_graphs/src/agent.py:graph", "storm": "/deps/__outer_graphs/src/storm.py:graph"}'

The langgraph dockerfile command translates all the configuration in your langgraph.json file into Dockerfile commands. When using this command, you will have to re-run it whenever you update your langgraph.json file. Otherwise, your changes will not be reflected when you build or run the dockerfile.

​Installation

​Configuration File

​Examples

​Basic Configuration

​Using Wolfi Base Images

​Adding semantic search to the store

​Semantic search with a custom embedding function

​Adding custom authentication

​Configuring Store Item Time-to-Live

​Configuring Checkpoint Time-to-Live

​Pinning API Version

​Commands

​dev

​build

​up

​dockerfile

Installation

Configuration File

Examples

Basic Configuration

Using Wolfi Base Images

Adding semantic search to the store

Semantic search with a custom embedding function

Adding custom authentication

Configuring Store Item Time-to-Live

Configuring Checkpoint Time-to-Live

Pinning API Version

Commands

`dev`

`build`

`up`

`dockerfile`