langgraph/docs/docs/cloud/reference/cli.md

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 as an alternative to the Studio desktop app.

Installation

1. Ensure that Docker is installed (e.g. docker --version).

2. Install the langgraph-cli package:

   === "pip" bash pip install langgraph-cli

   === "Homebrew (MacOS only)" bash brew install langgraph-cli

3. Run the command langgraph --help to confirm that the CLI is installed.

{#langgraph.json}

Configuration File

The LangGraph CLI requires a JSON configuration file with the following keys:

Key	Description
dependencies	Required. Array of dependencies for LangGraph Cloud API server. Dependencies can be one of the following: (1) ".", which will look for local Python packages, (2) pyproject.toml, setup.py or requirements.txt in the app directory "./local_package", or (3) a 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 to the BaseStore. Contains the following fields: index: Configuration for semantic search indexing with fields: embed: Embedding provider (e.g., "openai:text-embedding-3-small") or path to custom embedding function dims: Dimension size of the embedding model. Used to initialize the vector table. fields (optional): List of fields to index. Defaults to ["$"], meaningto index entire documents. Can be specific fields like ["text", "summary", "some.value"]
python_version	3.11 or 3.12. Defaults to 3.11.
pip_config_file	Path to pip config file.
dockerfile_lines	Array of additional lines to add to Dockerfile following the import from parent image.

Note

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

Examples

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 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": ["$"]
    }
  }
}

!!! note "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.

Commands

The base command for the LangGraph CLI is langgraph.

Usage

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.

!!! note "Python only"

Currently, the CLI only supports Python >= 3.11.
JS support is coming soon.

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
--no-browser		Disable automatic browser opening
--debug-port INTEGER		Port for debugger to listen on
--help		Display command documentation

build

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.

up

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.

dockerfile

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"}'

???+ note "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.