LangGraph Starter Kit

The fastest way to build production-ready multi-agent apps with LangGraph.
7 patterns. 5 providers. One command.

Quick Start • Patterns • Providers • Examples • API • Contributing

Why This Exists

Building multi-agent systems with LangGraph means writing the same boilerplate over and over — setting up supervisors, wiring handoff tools, configuring providers, adding persistence. This starter kit gives you all of that out of the box so you can focus on your agent logic, not infrastructure.

npx create-langgraph-app

What you get:

• Pick your LLM provider (OpenAI, Anthropic, Google, Groq, or local Ollama)
• Choose which agent patterns you need
• Get a ready-to-run project with tests, types, and a Fastify server

Or clone the full kit with all 7 patterns included.

Architecture

graph TD
    A[LangGraph Starter Kit] --> B["CLI Demo · npm run dev"]
    A --> C["HTTP Server · npm run dev:http"]
    A --> D[LangGraph Studio]

    B --> P
    C --> P
    D --> P

    subgraph P["7 Agent Patterns"]
        P1[Supervisor]
        P2[Swarm]
        P3[HITL]
        P4[Structured Output]
        P5[Research Agent]
        P6[RAG]
        P7[Customer Support]
    end

    subgraph F["Agent Factory"]
        F1["makeAgent()"]
        F2["makeSupervisor()"]
        F3["makeSwarm()"]
    end

    subgraph T["Tools"]
        T1[Local]
        T2[Web Search]
        T3[RAG]
        T4[Support]
        T5[MCP]
    end

    P --> L
    F --> L
    T --> L

    subgraph L["5 LLM Providers"]
        L1[OpenAI]
        L2[Anthropic]
        L3[Google]
        L4[Groq]
        L5["Ollama · local"]
    end

Features

Quick Start

Option A: Scaffold a new project (recommended)

npx create-langgraph-app

Interactive CLI — pick your provider, choose your patterns, get a project:

  ╔═══════════════════════════════════════╗
  ║     create-langgraph-app              ║
  ╚═══════════════════════════════════════╝

  Project name (my-langgraph-app): my-agents
  
  LLM provider?
    1. OpenAI (gpt-4o-mini)
    2. Anthropic (Claude Sonnet)
    3. Google (Gemini 2.0 Flash)
    4. Groq (Llama 3.3 70B)
    5. Ollama (local, no API key)

  Which patterns?
    1. Supervisor
    2. Swarm
    3. Human-in-the-Loop
    4. Structured Output
    5. RAG

  Done! cd my-agents && npm run dev

Option B: Clone the full starter kit

git clone https://github.com/ac12644/langgraph-starter-kit.git
cd langgraph-starter-kit
npm install
cp .env.example .env    # Add your API key
npm run dev             # Run all 7 patterns
npm run dev:http        # Start HTTP server on :3000

LLM Providers

Switch providers with one env var. Each has a sensible default model:

# .env — just two lines to switch
LLM_PROVIDER=anthropic
ANTHROPIC_API_KEY=sk-ant-...

Agent Patterns

1. Supervisor

A central coordinator routes tasks to specialized workers. Best for: structured workflows with clear task delegation.

curl -X POST http://localhost:3000/supervisor/invoke \
  -H "Content-Type: application/json" \
  -d '{"messages": [{"role": "user", "content": "sum 10 and 15, then write a summary"}]}'

2. Swarm

Agents hand off to each other peer-to-peer using transfer tools. Best for: open-ended conversations where the right agent depends on context.

curl -X POST http://localhost:3000/swarm/invoke \
  -H "Content-Type: application/json" \
  -d '{"messages": [{"role": "user", "content": "talk to bob then add 5 and 7"}]}'

3. Human-in-the-Loop

Pauses the graph for human approval before dangerous actions. Best for: high-stakes operations — deletions, payments, emails.

# Trigger an action that needs approval
curl -X POST http://localhost:3000/interrupt/invoke \
  -H "Content-Type: application/json" \
  -d '{"messages": [{"role": "user", "content": "delete record rec_2"}], "thread_id": "hitl-1"}'

# Approve it
curl -X POST http://localhost:3000/interrupt/resume \
  -H "Content-Type: application/json" \
  -d '{"thread_id": "hitl-1", "decision": "yes"}'

4. Structured Output

Returns typed JSON validated by Zod. Best for: extracting structured data — summaries, classifications, entities.

curl -X POST http://localhost:3000/analyst/invoke \
  -H "Content-Type: application/json" \
  -d '{"messages": [{"role": "user", "content": "Analyze: Revenue grew 25% but churn increased 8%"}]}'

5. Research Agent

Web search + URL scraping coordinated by a supervisor. Best for: gathering and synthesizing information from the web.

curl -X POST http://localhost:3000/researcher/invoke \
  -H "Content-Type: application/json" \
  -d '{"messages": [{"role": "user", "content": "Research multi-agent AI systems"}]}'

6. RAG (Retrieval-Augmented Generation)

In-memory vector store with semantic search. Best for: answering questions about your own documents/knowledge base.

curl -X POST http://localhost:3000/rag/invoke \
  -H "Content-Type: application/json" \
  -d '{"messages": [{"role": "user", "content": "What is the supervisor pattern?"}]}'

7. Customer Support Bot

Multi-agent support system with a router that delegates to billing, tech support, and returns specialists. Includes escalation to human operators. Best for: customer-facing products with different support domains.

curl -X POST http://localhost:3000/support/invoke \
  -H "Content-Type: application/json" \
  -d '{"messages": [{"role": "user", "content": "I am customer C-1002. I was charged $29.99 but my plan is free. Can you help?"}]}'

Streaming

Every app supports SSE for real-time token streaming:

curl -N http://localhost:3000/supervisor/stream \
  -H "Content-Type: application/json" \
  -d '{"messages": [{"role": "user", "content": "what is 2+2?"}]}'

MCP Integration

Extend your agents with external tools via Model Context Protocol:

cp mcp-servers.example.json mcp-servers.json
# Edit mcp-servers.json with your MCP server configs
# Set MCP_SERVERS_PATH=./mcp-servers.json in .env

Supports both stdio (local) and http (remote) transports. Tools are auto-injected into swarm and supervisor apps.

Observability

LangGraph Studio

langgraph dev    # Visual graph debugging

LangSmith Tracing

LANGCHAIN_TRACING_V2=true
LANGSMITH_API_KEY=ls_...
LANGSMITH_PROJECT=langgraph-starter-kit

API Reference

Apps: swarm supervisor interrupt analyst researcher rag support

Examples

Real-world agent apps with full documentation:

Each example has its own README with architecture diagrams, tool reference, usage examples, and customization guide.

Project Structure

src/
├── config/
│   ├── env.ts              # Environment + provider validation
│   ├── llm.ts              # Multi-provider LLM factory
│   ├── embeddings.ts       # Multi-provider embeddings factory
│   └── checkpointer.ts     # Memory (dev) / Postgres (prod)
├── tools/
│   ├── local.ts            # Built-in tools (add, multiply, echo)
│   ├── web.ts              # Web search + URL scraping
│   ├── rag.ts              # Vector store + retrieval
│   ├── support.ts          # Customer support tools
│   └── mcp.ts              # MCP external tool loader
├── agents/
│   ├── factory.ts          # makeAgent() — agent builder
│   ├── supervisor.ts       # makeSupervisor() wrapper
│   ├── swarm.ts            # makeSwarm() wrapper
│   └── handoff.ts          # createHandoffTool() — transfers
├── apps/
│   ├── supervisor.ts       # Supervisor pattern
│   ├── swarm.ts            # Swarm pattern
│   ├── interrupt.ts        # Human-in-the-loop
│   ├── analyst.ts          # Structured output
│   ├── researcher.ts       # Research agent
│   ├── rag.ts              # RAG agent
│   └── support.ts          # Customer support bot
├── server/index.ts         # Fastify HTTP server
└── index.ts                # CLI demo
examples/
├── customer-support/       # Full customer support bot docs
├── research-agent/         # Research agent docs
└── rag-agent/              # RAG agent docs

Deploy

# Docker Compose (includes Postgres)
docker compose up

# Standalone Docker
docker build -t langgraph-starter .
docker run -p 3000:3000 --env-file .env langgraph-starter

Adding Your Own Agent

Create a file, wire it up, done:

// src/apps/my-agent.ts
import { llm } from "../config/llm";
import { makeAgent } from "../agents/factory";
import { makeSupervisor } from "../agents/supervisor";

export function createMyApp() {
  const agent = makeAgent({
    name: "my_agent",
    llm,
    tools: [/* your tools */],
    system: "You are a helpful assistant.",
  });

  return makeSupervisor({
    agents: [agent],
    llm,
    outputMode: "last_message",
    supervisorName: "my_supervisor",
  });
}

Register in src/server/index.ts and you're live.

Contributing

Contributions are welcome! Whether it's a new agent pattern, bug fix, documentation improvement, or just a typo — every bit helps.

See CONTRIBUTING.md for guidelines.

First time contributing? Look for issues labeled good first issue.

Community

• Questions? Open a Discussion
• Bug? File an Issue
• Want to contribute? See CONTRIBUTING.md
• Like it? Give it a star — it helps others find the project

License

Apache License 2.0 — same license as LangChain. Use it freely in personal and commercial projects.