Langgraph Cli

<overview> The langgraph CLI manages the full lifecycle of LangGraph applications — from scaffolding a new project to deploying it to LangGraph Platform (LangSmith Deployments).

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. </overview>

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

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

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.