LangGraph CLI¶

The LangGraph command line interface includes commands to build and run a LangGraph Cloud 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:

PythonJS

pip install langgraph-cli

npx @langchain/langgraph-cli

# Install globally, will be available as `langgraphjs`
npm install -g @langchain/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:

Note

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

PythonJS

Key	Description
`dependencies`	Required. Array of dependencies for LangGraph Cloud 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 creates an instance of `langgraph.graph.state.StateGraph` / `langgraph.graph.state.CompiledStateGraph`.
`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.
`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).
`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.
`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. `disable_assistants`: Disable `/assistants` routes `disable_threads`: Disable `/threads` routes `disable_runs`: Disable `/runs` routes `disable_store`: Disable `/store` routes `disable_meta`: Disable `/ok`, `/info`, `/metrics`, and `/docs` routes `cors`: CORS configuration with fields for `allow_origins`, `allow_methods`, `allow_headers`, etc.

Key	Description
`graphs`	Required. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: `./src/graph.ts:variable`, where `variable` is an instance of `CompiledStateGraph` `./src/graph.ts:makeGraph`, where `makeGraph` is a function that takes a config dictionary (`LangGraphRunnableConfig`) and creates an instance of `StateGraph` / `CompiledStateGraph`.
`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).
`node_version`	Specify `node_version: 20` to use LangGraph.js.
`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.

Examples¶

PythonJS

Basic Configuration¶

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

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.py: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 (TTL)¶

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 (TTL)¶

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.py: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.

Basic Configuration¶

{
  "graphs": {
    "chat": "./src/graph.ts:graph"
  }
}

Commands¶

Usage

PythonJS

The base command for the LangGraph CLI is langgraph.

langgraph [OPTIONS] COMMAND [ARGS]

The base command for the LangGraph.js CLI is langgraphjs.

npx @langchain/langgraph-cli [OPTIONS] COMMAND [ARGS]

We recommend using npx to always use the latest version of the CLI.

`dev`¶

PythonJS

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.

Note

Currently, the CLI only supports Python >= 3.11.

Installation

This 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

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

Usage

npx @langchain/langgraph-cli 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
`--tunnel`	`False`	Expose the local server via a public tunnel (Cloudflare) for remote frontend access. This avoids issues with browsers or networks blocking localhost connections
`--help`		Display command documentation

`build`¶

PythonJS

Build LangGraph Cloud 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 Cloud 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.

Build LangGraph Cloud API server Docker image.

Usage

npx @langchain/langgraph-cli 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`
`--no-pull`		Use locally built images. Defaults to `false` to build with latest remote Docker image.
`-c, --config FILE`	`langgraph.json`	Path to configuration file declaring dependencies, graphs and environment variables.
`--help`		Display command documentation.

`up`¶

PythonJS

Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Cloud closed beta. 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
`--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.

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

Usage

npx @langchain/langgraph-cli up [OPTIONS]

Options

Option	Default	Description
`--wait`		Wait for services to start before returning. Implies --detach
`--postgres-uri TEXT`	Local database	Postgres URI to use for the database.
`--watch`		Restart on file changes
`-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`
`--no-pull`		Use locally built images. Defaults to `false` to build with latest remote Docker image.
`--recreate`		Recreate containers even if their configuration and image haven't changed
`--help`		Display command documentation.

`dockerfile`¶

PythonJS

Generate a Dockerfile for building a LangGraph Cloud 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"}'

Updating your langgraph.json file

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.

Generate a Dockerfile for building a LangGraph Cloud API server Docker image.

Usage

npx @langchain/langgraph-cli 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:

npx @langchain/langgraph-cli dockerfile -c langgraph.json Dockerfile

This generates a Dockerfile that looks similar to:

FROM langchain/langgraphjs-api:20

ADD . /deps/agent

RUN cd /deps/agent && yarn install

ENV LANGSERVE_GRAPHS='{"agent":"./src/react_agent/graph.ts:graph"}'

WORKDIR /deps/agent

RUN (test ! -f /api/langgraph_api/js/build.mts && echo "Prebuild script not found, skipping") || tsx /api/langgraph_api/js/build.mts

Updating your langgraph.json file

The npx @langchain/langgraph-cli 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.

LangGraph CLI¶

Installation¶

Configuration File¶

Examples¶

Basic Configuration¶

Adding semantic search to the store¶

Semantic search with a custom embedding function¶

Adding custom authentication¶

Configuring Store Item Time-to-Live (TTL)¶

Configuring Checkpoint Time-to-Live (TTL)¶

Basic Configuration¶

Commands¶

dev¶

build¶

up¶

dockerfile¶

Comments

`dev`¶

`build`¶

`up`¶

`dockerfile`¶