Langgraph project init

Skill dor-rondel/agent-skills-mcp/resources/langgraph/langgraph-project-init

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Use this skill when initializing a brand new Python LangGraph project from scratch. Covers installing uv, bootstrapping directories, generating the Makefile, configuring LangGraph Studio, and setting up the GitHub Actions CI workflow.

SKILL.md

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LangGraph Python Project Initialization & CI Setup

This skill provides step-by-step instructions for scaffolding a new LangGraph project using uv and setting up both local task runners and automated remote CI pipelines.

🏃‍♂️ Step-by-Step Initialization Workflow

1. Initialize the Directory & Environment

Create the project folder structure, including the hidden GitHub workflows path, and initialize a new uv environment: mkdir -p langgraph-agent/src langgraph-agent/tests langgraph-agent/scripts langgraph-agent/.github/workflows cd langgraph-agent uv init

2. Add LangGraph & Core Dependencies

Install the required production packages and local quality tools directly into your lockfile: uv add langgraph langchain-openai dotenv uv add --dev ruff pylint mypy pytest codespell

3. Generate the Standard Makefile

Write the unified Makefile into the root directory to handle linting, formatting, tests, and graph generation:

.PHONY: install format lint test graph spell check clean

    PYTHON ?= uv run python
    SRC_DIR := src
    TEST_DIR := tests

    install:
    	uv sync

    format:
    	uv run ruff format .

    lint:
    	uv run ruff check .
    	uv run pylint $(SRC_DIR)
    	uv run mypy $(SRC_DIR)

    test:
    	uv run pytest $(TEST_DIR)

    graph:
    	PYTHONPATH=. uv run python scripts/generate_graph.py

    spell:
    	uv run codespell .

    check:
    	uv run ruff format --check .
    	uv run ruff check .
    	uv run pylint $(SRC_DIR)
    	uv run mypy $(SRC_DIR)
    	uv run pytest $(TEST_DIR)
    	uv run codespell .

    clean:
    	find . -type d -name "__pycache__" -exec rm -rf {} +
    	find . -type f -name "*.py[cod]" -delete
    	find . -type f -name ".coverage" -delete
    	rm -rf .pytest_cache
    	rm -rf .ruff_cache
    	rm -rf .mypy_cache
    	rm -rf .venv

4. Configure LangGraph Studio

Create a standard langgraph.json file so the workspace instantly mounts to the LangGraph visual development desktop application or server:

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

5. Establish GitHub Actions CI Workflow

Inject the continuous integration yaml block into your workspace to automatically run tests and code validation on every pull request to main or master in .github/workflows/ci.yml

name: CI

    on:
      pull_request:
        branches: [ main, master ]

    jobs:
      check:
        runs-on: ubuntu-latest
        steps:
          - uses: actions/checkout@v4

          - name: Install uv
            uses: astral-sh/setup-uv@v5
            with:
              enable-cache: true
              version: "latest"

          - name: Set up Python
            run: uv python install 3.12

          - name: Install dependencies
            run: make install

          - name: Run checks
            run: make check

🛠️ Verification Run

Always execute an environment-wide check directly following initialization to ensure everything passes locally before pushing the workspace to GitHub:

make install
make check