langgraph-cli

Key commands:

• langgraph new — Scaffold a project from a template
• langgraph dev — Run locally with hot reload (no Docker)
• langgraph build — Build a Docker image
• langgraph up — Launch locally via Docker Compose
• langgraph deploy — Ship to LangGraph Platform
• langgraph dockerfile — Generate a Dockerfile

All commands (except new) read from a langgraph.json config file in the project root.

When to use

Use this skill when the user wants to:

• Scaffold a new LangGraph project
• Run a local development or production-like server
• Build or deploy a LangGraph application
• Understand or edit langgraph.json configuration
• Manage LangSmith Deployments (list, delete, view logs)

Installation

# Python
pip install 'langgraph-cli[inmem]'   # includes langgraph dev support
pip install langgraph-cli             # without dev server (build/up/deploy only)

# if using UV as package manager
uv add "langgraph-cli[inmem]"       # includes langgraph dev support
uv add langgraph-cli                # without dev server (build/up/deploy only)

# JavaScript
npx @langchain/langgraph-cli         # use on demand
npm install -g @langchain/langgraph-cli  # install globally (available as langgraphjs)

Commands

langgraph new [PATH]

Scaffold a new project from a template.

langgraph new                          # interactive template selection
langgraph new ./my-agent               # create in specific directory
langgraph new --template agent-python  # skip prompt, use template directly

Available templates: deep-agent-python, deep-agent-js, agent-python, new-langgraph-project-python, new-langgraph-project-js

langgraph dev

Run a local development server with hot reloading. No Docker required.

langgraph dev                              # default: localhost:2024
langgraph dev --port 8000                  # custom port
langgraph dev --config ./langgraph.json    # explicit config path
langgraph dev --no-reload                  # disable hot reload
langgraph dev --no-browser                 # don't auto-open LangGraph Studio
langgraph dev --host 0.0.0.0              # bind to all interfaces (trusted networks only)
langgraph dev --tunnel                     # expose via Cloudflare tunnel for remote access
langgraph dev --debug-port 5678            # enable remote debugger (requires debugpy)
langgraph dev --n-jobs-per-worker 20       # max concurrent jobs per worker (default: 10)

langgraph build

Build a Docker image for the LangGraph API server.

langgraph build -t my-image                # required: tag the image
langgraph build -t my-image --no-pull      # use locally-built base images
langgraph build -t my-image -c langgraph.json  # explicit config
langgraph build -t my-image --base-image langchain/langgraph-server:0.2.18  # pin base version

langgraph up

Launch the LangGraph API server via Docker Compose (includes Postgres).

langgraph up                               # default port 8123
langgraph up --port 8000                   # custom port
langgraph up --watch                       # restart on file changes
langgraph up --recreate                    # force fresh build (useful for pre-deploy validation)
langgraph up --postgres-uri postgresql://...  # external Postgres
langgraph up --no-pull                     # use local images (after langgraph build)
langgraph up --image my-image              # skip build, use pre-built image
langgraph up -d docker-compose.yml         # add extra Docker services
langgraph up --debugger-port 8124          # serve debugger UI
langgraph up --wait                        # block until services are healthy

langgraph deploy

Build and deploy to LangGraph Platform (LangSmith Deployments). Requires Docker. On Apple Silicon (M1/M2/M3), Docker Buildx is also required for cross-compiling to linux/amd64.

langgraph deploy                           # deploy, name defaults to directory name
langgraph deploy --name my-agent           # explicit deployment name
langgraph deploy --deployment-type prod    # production deployment (default: dev)
langgraph deploy --tag v1.2.0              # custom image tag (default: latest)
langgraph deploy --deployment-id <id>      # update an existing deployment by ID
langgraph deploy --config ./langgraph.json # explicit config path
langgraph deploy --no-wait                 # don't wait for deployment status
langgraph deploy --verbose                 # show detailed server logs

Prereq: LANGSMITH_API_KEY in environment or .env.

langgraph deploy also accepts build flags: --base-image, --pull/--no-pull.

langgraph deploy list

langgraph deploy list                      # list all deployments
langgraph deploy list --name-contains bot  # filter by name

langgraph deploy delete

langgraph deploy delete <deployment-id>          # interactive confirmation
langgraph deploy delete <deployment-id> --force  # skip confirmation

langgraph deploy logs

langgraph deploy logs                                  # runtime logs, last 100
langgraph deploy logs --name my-agent                  # by deployment name
langgraph deploy logs --deployment-id <id>             # by deployment ID
langgraph deploy logs --type build                     # build logs instead of runtime
langgraph deploy logs -f                               # follow/stream logs
langgraph deploy logs --level error                    # filter by level (debug|info|warning|error|critical)
langgraph deploy logs -q "timeout"                     # search filter
langgraph deploy logs --limit 500                      # more entries
langgraph deploy logs --start-time 2026-03-08T00:00:00Z  # time range

langgraph dockerfile <SAVE_PATH>

Generate a Dockerfile (and optionally Docker Compose files) without building.

langgraph dockerfile ./Dockerfile                      # generate Dockerfile
langgraph dockerfile ./Dockerfile --add-docker-compose # also generate compose + .env + .dockerignore

langgraph.json reference

The configuration file used by all CLI commands (dev, build, up, deploy). Defaults to langgraph.json in the current directory.

Minimal config (Python)

{
    "dependencies": ["."],
    "graphs": {
        "agent": "./my_agent/agent.py:graph"
    },
    "env": "./.env"
}

Minimal config (JavaScript)

{
    "dependencies": ["."],
    "graphs": {
        "agent": "./src/agent.js:graph"
    },
    "env": "./.env"
}

Full config with all keys

{
    "dependencies": [".", "langchain_openai", "./local_package"],
    "graphs": {
        "agent": "./my_agent/agent.py:graph",
        "retriever": "./my_agent/rag.py:rag_graph"
    },
    "env": "./.env",
    "python_version": "3.12",
    "pip_config_file": "./pip.conf",
    "dockerfile_lines": [
        "RUN apt-get update && apt-get install -y ffmpeg"
    ]
}

Key reference

Key	Required	Description
dependencies	Yes	Array of dependencies. "." looks for local packages via pyproject.toml, setup.py, requirements.txt, or package.json. Can also be paths to subdirectories ("./my_pkg") or package names ("langchain_openai").
graphs	Yes	Mapping of graph ID to path. Format: ./path/to/file.py:variable (Python) or ./path/to/file.js:function (JS). The variable must be a CompiledGraph or a function returning one. Multiple graphs supported.
env	No	Path to a .env file (string) OR an inline mapping of env var names to values (object). Used by langgraph dev and langgraph up locally. langgraph deploy reads from this file and adds the variables as deployment secrets.
python_version	No	"3.11", "3.12", or "3.13". Defaults to "3.11".
node_version	No	Node.js version for JS projects.
pip_config_file	No	Path to a pip config file for custom package indexes.
dockerfile_lines	No	Array of additional Dockerfile lines appended after the base image import. Use for system packages, binaries, or custom setup.

Typical workflow

1. Scaffold — langgraph new to create a project from a template.
2. Configure — Edit langgraph.json: set dependencies, point graphs at your compiled graph(s), add .env.
3. Develop — langgraph dev for rapid local iteration with hot reload (no Docker, port 2024).
4. Validate — langgraph up --recreate to test in a production-like Docker stack (port 8123, includes Postgres).
5. Deploy — langgraph deploy to ship to LangGraph Platform (LangSmith Deployments).
6. Monitor — langgraph deploy logs -f to tail runtime logs; --type build for build logs.

langgraph dev vs langgraph up

Feature	langgraph dev	langgraph up
Docker required	No	Yes
Install	pip install 'langgraph-cli[inmem]'	pip install langgraph-cli
Primary use	Rapid development & testing	Production-like validation
State persistence	In-memory / pickled to local dir	PostgreSQL
Hot reloading	Yes (default)	Optional (--watch)
Default port	2024	8123
Resource usage	Lightweight	Heavier (Docker containers for server, Postgres, Redis)
IDE debugging	Built-in DAP support (--debug-port)	Container debugging

Gotchas

• langgraph deploy requires Docker — On Apple Silicon (M1/M2/M3), Docker Buildx is also required for cross-compiling to linux/amd64.
• langgraph deploy can only update its own deployments — Deployments created through the LangSmith UI or GitHub integration cannot be updated with langgraph deploy. Use the UI for those.
• dependencies must include all packages — The dependencies array in langgraph.json must point to where your package config lives (e.g., "." for root). The actual packages are resolved from pyproject.toml, requirements.txt, or package.json at that location.
• langgraph dev runs without Docker — It runs directly in your environment. If your code depends on system packages (e.g., ffmpeg), they must be installed locally. Use langgraph up to validate Docker builds.
• JavaScript CLI — Use npx @langchain/langgraph-cli <command> (or langgraphjs if installed globally via npm install -g @langchain/langgraph-cli).
• API key — LANGSMITH_API_KEY is required for langgraph deploy. For langgraph dev, it is optional — the server runs without it, but you won't get traces in LangSmith. Can also be set via LANGGRAPH_HOST_API_KEY or LANGCHAIN_API_KEY.