langgraph-project-setup

Initialize and configure LangGraph projects with proper structure, langgraph.json configuration, environment variables, and dependency management. Use when users want to (1) create a new LangGraph project, (2) set up langgraph.json for deployment, (3) configure environment variables for LLM providers, (4) initialize project structure for agents, (5) set up local development with LangGraph Studio, (6) configure dependencies (pyproject.toml, requirements.txt, package.json), or (7) troubleshoot project configuration issues.

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Install skill "langgraph-project-setup" with this command: npx skills add lubu-labs/langchain-agent-skills/lubu-labs-langchain-agent-skills-langgraph-project-setup

LangGraph Project Setup

Initialize and configure LangGraph projects for local development and deployment.

Quick Start

Python Project

# Initialize new project
uv run scripts/init_langgraph_project.py my-agent

# Fallback if uv not available
python3 scripts/init_langgraph_project.py my-agent

# Or with options
uv run scripts/init_langgraph_project.py my-agent \
  --pattern multiagent \
  --python-version 3.12

# Fallback if uv not available
python3 scripts/init_langgraph_project.py my-agent \
  --pattern multiagent \
  --python-version 3.12

JavaScript Project

# Initialize new project
node scripts/init_langgraph_project.js my-agent

# TypeScript project
node scripts/init_langgraph_project.js my-agent --typescript

# Multi-agent pattern
node scripts/init_langgraph_project.js my-agent \
  --pattern multiagent \
  --typescript

Setup Workflow

Step 1: Choose Project Pattern

Simple Pattern: Single agent with straightforward workflow

  • Best for: Getting started, prototypes, single-purpose agents
  • Structure: Minimal files, agent.py/agent.ts at package root

Multi-Agent Pattern: Modular architecture with separated concerns

  • Best for: Complex workflows, multiple agents, production applications
  • Structure: utils/ directory with state.py, nodes.py, tools.py

Step 2: Initialize Project

Run the init script with your chosen pattern:

# Python - simple
uv run scripts/init_langgraph_project.py my-agent

# Fallback if uv not available
python3 scripts/init_langgraph_project.py my-agent

# Python - multi-agent
uv run scripts/init_langgraph_project.py my-agent --pattern multiagent

# Fallback if uv not available
python3 scripts/init_langgraph_project.py my-agent --pattern multiagent

# JavaScript/TypeScript - simple
node scripts/init_langgraph_project.js my-agent --typescript

# JavaScript/TypeScript - multi-agent
node scripts/init_langgraph_project.js my-agent --pattern multiagent --typescript

The script creates:

  • Project directory structure
  • langgraph.json configuration
  • .env template
  • Dependency files (pyproject.toml or package.json)
  • .gitignore
  • Boilerplate code with TODO comments

Step 3: Install Dependencies

Python:

cd my-agent
uv venv --python 3.12
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv pip install -e '.[dev]'

# Fallback if uv not available
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -e '.[dev]'

JavaScript:

cd my-agent
npm install  # or: yarn install / pnpm install

Step 4: Configure Environment Variables

Option A: Interactive Setup (Recommended)

uv run scripts/setup_providers.py

Follow the prompts to configure:

  • OpenAI
  • Anthropic (Claude)
  • Google (Gemini)
  • AWS Bedrock
  • LangSmith (tracing)
  • Tavily (search)

Option B: Manual Configuration

Edit .env file directly:

# Required: Choose at least one LLM provider
OPENAI_API_KEY=sk-...
# or
ANTHROPIC_API_KEY=sk-ant-...

# Optional: Enable tracing
LANGSMITH_API_KEY=lsv2_...
LANGSMITH_TRACING=true
LANGSMITH_PROJECT=my-project

See references/provider-configuration.md for provider-specific setup.

Step 5: Implement Agent Logic

Replace TODO comments in generated files:

Python Simple:

  • Edit my_agent/agent.py
  • Configure LLM in call_model function

Python Multi-Agent:

  • Define state schema in my_agent/utils/state.py
  • Implement node logic in my_agent/utils/nodes.py
  • Add tools in my_agent/utils/tools.py
  • Build graph in my_agent/agent.py

JavaScript/TypeScript:

  • Similar structure in src/ directory
  • Import appropriate LangChain packages

Step 6: Configure langgraph.json

The init script creates a basic configuration. Customize as needed:

{
  "dependencies": ["."],
  "graphs": {
    "agent": "./my_agent/agent.py:graph"
  },
  "env": ".env",
  "python_version": "3.11"
}

Key configuration options:

  • dependencies: Package dependencies location
  • graphs: Mapping of graph IDs to code paths
  • env: Path to environment file
  • python_version or node_version: Runtime version

For complete schema reference, see references/langgraph-json-schema.md.

Step 7: Start Development Server

Option A: langgraph dev (Recommended for development)

langgraph dev
  • No Docker required
  • In-memory state persistence
  • Hot reloading enabled
  • Default port: 2024

Option B: langgraph up (Production-like testing)

langgraph up
  • Docker required
  • PostgreSQL state persistence
  • Production environment simulation
  • Default port: 8123

Step 8: Connect to LangGraph Studio

Access Studio in your browser:

https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024

Safari users: Use --tunnel flag:

langgraph dev --tunnel

Validation

Validate Configuration

uv run scripts/validate_langgraph_config.py

Checks:

  • Required fields (dependencies, graphs)
  • File paths and references
  • Optional field formats
  • Common configuration errors

Test Agent Locally

# Start server
langgraph dev

# In another terminal, test with curl
curl -X POST http://localhost:2024/invoke \
  -H "Content-Type: application/json" \
  -d '{"input": {"messages": [{"role": "user", "content": "Hello"}]}}'

Common Configurations

Python with OpenAI

# pyproject.toml
[project.optional-dependencies]
openai = ["langchain-openai>=1.1.0"]

# agent.py
from langchain_openai import ChatOpenAI

model = ChatOpenAI(model="gpt-4o-mini")

Python with Anthropic

# pyproject.toml
[project.optional-dependencies]
anthropic = ["langchain-anthropic>=1.1.0"]

# agent.py
from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(model="claude-haiku-4-5-20251001")

JavaScript with OpenAI

// package.json
{
  "dependencies": {
    "@langchain/openai": "^1.1.0"
  }
}

// agent.ts
import { ChatOpenAI } from "@langchain/openai";

const model = new ChatOpenAI({ model: "gpt-4o-mini" });

Project Structure Reference

  • Python structures: references/python-project-structure.md
  • JavaScript structures: references/javascript-project-structure.md
  • langgraph.json schema: references/langgraph-json-schema.md
  • Provider setup: references/provider-configuration.md
  • Deployment options: references/deployment-targets.md

Troubleshooting

"Module not found" errors

Ensure dependencies are installed:

# Python
uv pip install -e '.[dev]'

# Fallback if uv not available
pip install -e '.[dev]'

# JavaScript
npm install

"Graph not found" in langgraph.json

Check graph path format:

  • Python: ./package_name/agent.py:graph
  • JavaScript: ./src/agent.ts:graph

Validate: uv run scripts/validate_langgraph_config.py (fallback: python3 scripts/validate_langgraph_config.py)

Environment variables not loading

  • Check .env file exists in project root
  • Verify "env": ".env" in langgraph.json
  • Ensure no quotes around values in .env
  • Restart development server after changes

Studio connection issues

  • Verify server is running: langgraph dev
  • Check correct port (default: 2024)
  • Safari users: use --tunnel flag
  • Check firewall/security software

Hot reload not working

  • Ensure using langgraph dev (not langgraph up)
  • Check file is in correct directory
  • Try manual restart if needed

Next Steps

After setup:

  1. Implement agent logic (replace TODOs)
  2. Add tools and nodes as needed
  3. Test with Studio
  4. Write tests (see langgraph-testing-evaluation skill)
  5. Deploy to LangSmith (see langsmith-deployment skill)

Scripts Reference

init_langgraph_project.py

Initialize Python project:

uv run scripts/init_langgraph_project.py <name> [--pattern simple|multiagent] [--python-version 3.11|3.12|3.13]

# Fallback if uv not available
python3 scripts/init_langgraph_project.py <name> [--pattern simple|multiagent] [--python-version 3.11|3.12|3.13]

init_langgraph_project.js

Initialize JavaScript project:

node scripts/init_langgraph_project.js <name> [--pattern simple|multiagent] [--typescript]

validate_langgraph_config.py

Validate langgraph.json:

uv run scripts/validate_langgraph_config.py [path/to/langgraph.json]

# Fallback if uv not available
python3 scripts/validate_langgraph_config.py [path/to/langgraph.json]

setup_providers.py

Interactive provider setup:

uv run scripts/setup_providers.py [--output .env]

# Fallback if uv not available
python3 scripts/setup_providers.py [--output .env]

Additional Resources

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