CopilotKit <> LangGraph Starter

This is a starter template for building AI agents using LangGraph and CopilotKit. It provides a modern Next.js application with an integrated LangGraph agent (TypeScript) to be built on top of.

https://github.com/user-attachments/assets/47761912-d46a-4fb3-b9bd-cb41ddd02e34

Prerequisites

• Node.js 20+
• Any of the following package managers:
  • npm (default)
  • pnpm
  • yarn
  • bun
• OpenAI API Key (for the LangGraph agent)

Getting Started

1. Install dependencies using your preferred package manager:

# Using npm (default)
npm install

# Using pnpm
pnpm install

# Using yarn
yarn install

# Using bun
bun install

This also installs the agent dependencies via npm install inside agent/.

2. Set up your environment variables:

cp .env.example .env

Then edit the .env file and add your OpenAI API key:

OPENAI_API_KEY=your-openai-api-key-here

3. Start the development server:

# Using npm (default)
npm run dev

# Using pnpm
pnpm dev

# Using yarn
yarn dev

# Using bun
bun run dev

This will start both the UI and agent servers concurrently.

Available Scripts

The following scripts can also be run using your preferred package manager:

• dev - Starts both UI and agent servers in development mode
• dev:debug - Starts development servers with debug logging enabled
• dev:ui - Starts only the Next.js UI server
• dev:agent - Starts only the LangGraph agent server
• build - Builds the Next.js application for production
• start - Starts the production server
• install:agent - Installs agent (Node) dependencies

Project Structure

├── src/                         # Next.js frontend source
│   ├── app/
│   │   ├── page.tsx             # Main page
│   │   └── api/copilotkit/      # CopilotKit API route
│   ├── components/
│   │   ├── example-canvas/      # Todo list UI
│   │   ├── example-layout/      # Layout: chat + canvas side-by-side
│   │   └── generative-ui/       # Example generative UI components
│   └── hooks/
├── agent/                       # LangGraph TypeScript agent
│   ├── src/
│   │   ├── agent.ts             # Agent entry point (createAgent)
│   │   ├── todos.ts             # Todo tools and state schema
│   │   ├── query.ts             # Example data query tool
│   │   ├── a2ui.ts              # A2UI operation helpers
│   │   ├── a2ui_fixed_schema.ts # Fixed-schema A2UI tool (flights)
│   │   └── a2ui_dynamic_schema.ts # Dynamic-schema A2UI tool
│   └── langgraph.json
├── scripts/                     # Agent run scripts
│   └── run-agent.sh / .bat
├── public/                      # Static assets
├── next.config.ts
├── tsconfig.json
└── package.json

A2UI — Agent-to-User Interface

This starter includes A2UI support, allowing the agent to generate rich, interactive UI surfaces declaratively. Instead of returning plain text, the agent sends a JSON description of the UI it wants to render, and the frontend turns it into real components.

How it works

A2UI uses three concepts:

1. Catalog — a set of component definitions (schema) paired with React renderers. Registered once in layout.tsx via <CopilotKitProvider a2ui={{ catalog: demonstrationCatalog }}>.
2. Surface — a rendered UI instance. The agent creates a surface, sets its components, and binds data to it.
3. Operations — the agent returns render(operations=[...]) from a tool, which the middleware streams to the frontend.

Two patterns

Both patterns use the same catalog on the frontend — the difference is where the component tree comes from.

Key files

Adding a custom component

1. Define the component schema in definitions.ts:

   typescriptCopy

   MyWidget: {
     description: "A brief description for the agent.",
     props: z.object({ title: z.string(), value: z.number() }),
   },
   

2. Render it in renderers.tsx:

   typescriptCopy

   MyWidget: ({ props }) => (
     <div>{props.title}: {props.value}</div>
   ),
   

   Renderers are type-checked against the definitions — TypeScript will error if props don't match.

3. Use it from the agent. The component is automatically available to both fixed-schema templates and the dynamic-schema LLM.

Adding a new fixed-schema tool

1. Create a JSON schema file in agent/src/a2ui/schemas/ describing the component tree.
2. Create a TypeScript tool that loads the schema with loadSchema() and returns render([...]) with your data. See a2ui_fixed_schema.ts for the pattern.

Showcase mode

showcase.json controls which suggestion pills are visually highlighted. Set "showcase": "a2ui" to highlight the A2UI demos, or "showcase": "default" for no highlights. This is configured automatically when scaffolding via npx copilotkit create --framework a2ui.

Further reading

• A2UI Specification
• CopilotKit A2UI Documentation

Documentation

• LangGraph Documentation - Learn more about LangGraph and its features
• CopilotKit Documentation - Explore CopilotKit's capabilities

Contributing

Feel free to submit issues and enhancement requests! This starter is designed to be easily extensible.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Troubleshooting

Agent Connection Issues

If you see "I'm having trouble connecting to my tools", make sure:

1. The LangGraph agent is running on port 8123
2. Your OpenAI API key is set correctly
3. Both servers started successfully

Agent Dependencies

If you encounter agent import errors:

npm run install:agent