Langgraph CLI
INVOKE THIS SKILL when using the langgraph CLI to scaffold, develop, build, or deploy LangGraph applications. Covers langgraph new, dev, build, up, deploy, and langgraph.json configuration.
- Skill ID
- langchain-ai/langchain-skills/langgraph-cli
- Publisher
- langchain-ai
- Repository
- langchain-skills
- Installs
- 0
- Files
- 1
- Synced
- Sep 23, 2026
Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.
langchain-ai/langchain-skills/langgraph-cliInstalls these files- SKILL.md
What this skill tells the agent
<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.jsonconfiguration - 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 directlyAvailable 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 versionlanggraph 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 healthylanggraph 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 logsPrereq: 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 namelanggraph deploy delete
langgraph deploy delete <deployment-id> # interactive confirmation
langgraph deploy delete <deployment-id> --force # skip confirmationlanggraph 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 rangelanggraph 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 + .dockerignorelanggraph.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. |
More from langchain-ai
Use this skill in a project
Open any RiverX project, search the Skills panel for langchain-ai/langchain-skills/langgraph-cli, and install it in one click.
