chore: import upstream snapshot with attribution
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wehub-resource-sync
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# CopilotKit <> LangGraph Starter
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This is a starter template for building AI agents using [LangGraph](https://www.langchain.com/langgraph) and [CopilotKit](https://copilotkit.ai). It provides a modern Next.js application with an integrated LangGraph agent to be built on top of.
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https://github.com/user-attachments/assets/47761912-d46a-4fb3-b9bd-cb41ddd02e34
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## Prerequisites
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- Node.js 18+
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- Python 3.12+
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- [uv](https://docs.astral.sh/uv/) (Python package manager)
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- Any of the following package managers:
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- npm (default)
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- [pnpm](https://pnpm.io/installation)
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- [yarn](https://classic.yarnpkg.com/lang/en/docs/install/)
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- [bun](https://bun.sh/)
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- OpenAI API Key (for the LangGraph agent)
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## Getting Started
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1. Install dependencies using your preferred package manager:
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```bash
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# Using npm (default)
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npm install
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# Using pnpm
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pnpm install
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# Using yarn
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yarn install
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# Using bun
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bun install
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```
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This will also install the Python agent dependencies via `uv sync`.
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2. Set up your environment variables:
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```bash
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cp .env.example .env
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```
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Then edit the `.env` file and add your OpenAI API key:
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```bash
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OPENAI_API_KEY=your-openai-api-key-here
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```
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3. Start the development server:
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```bash
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# Using npm (default)
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npm run dev
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# Using pnpm
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pnpm dev
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# Using yarn
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yarn dev
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# Using bun
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bun run dev
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```
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This will start both the UI and agent servers concurrently.
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## Available Scripts
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The following scripts can also be run using your preferred package manager:
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- `dev` - Starts both UI and agent servers in development mode
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- `dev:debug` - Starts development servers with debug logging enabled
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- `dev:ui` - Starts only the Next.js UI server
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- `dev:agent` - Starts only the LangGraph agent server
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- `build` - Builds the Next.js application for production
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- `start` - Starts the production server
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- `install:agent` - Installs Python dependencies for the agent
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## Project Structure
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```
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├── src/ # Next.js frontend source
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│ ├── app/
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│ │ ├── page.tsx # Main page
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│ │ └── api/copilotkit/ # CopilotKit API route
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│ ├── components/
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│ │ ├── example-canvas/ # Todo list UI
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│ │ ├── example-layout/ # Layout: chat + canvas side-by-side
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│ │ └── generative-ui/ # Example generative UI components
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│ └── hooks/
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├── agent/ # LangGraph Python agent
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│ ├── main.py # Agent entry point
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│ └── src/
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│ ├── todos.py # Todo tools and state schema
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│ └── query.py # Example data query tool
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├── scripts/ # Agent setup and run scripts
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│ ├── setup-agent.sh / .bat
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│ └── run-agent.sh / .bat
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├── public/ # Static assets
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├── next.config.ts
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├── tsconfig.json
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└── package.json
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```
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## A2UI — Agent-to-User Interface
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This starter includes [A2UI](https://a2ui.org/specification/) 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.
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### How it works
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A2UI uses three concepts:
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1. **Catalog** — a set of component definitions (schema) paired with React renderers. Registered once in `layout.tsx` via `<CopilotKitProvider a2ui={{ catalog: demonstrationCatalog }}>`.
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2. **Surface** — a rendered UI instance. The agent creates a surface, sets its components, and binds data to it.
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3. **Operations** — the agent returns `a2ui.render(operations=[...])` from a tool, which the middleware streams to the frontend.
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### Two patterns
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| Pattern | Description | Agent tool | Frontend |
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| ------------------ | ----------------------------------------------------------------------------- | ---------------- | ------------------------------------------- |
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| **Fixed schema** | Pre-defined component layout. Only the data changes per invocation. | `search_flights` | Schema in `a2ui/schemas/flight_schema.json` |
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| **Dynamic schema** | A secondary LLM generates both components and data based on the conversation. | `generate_a2ui` | Components decided at runtime |
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Both patterns use the same catalog on the frontend — the difference is where the component tree comes from.
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### Key files
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| Purpose | Path |
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| ------------------------------------ | -------------------------------------------------- |
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| Catalog definitions (Zod schemas) | `src/app/declarative-generative-ui/definitions.ts` |
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| Catalog renderers (React components) | `src/app/declarative-generative-ui/renderers.tsx` |
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| Catalog registration | `src/app/layout.tsx` |
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| Fixed-schema agent tool | `agent/src/a2ui_fixed_schema.py` |
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| Dynamic-schema agent tool | `agent/src/a2ui_dynamic_schema.py` |
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| Flight schema JSON | `agent/src/a2ui/schemas/flight_schema.json` |
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| Showcase config | `showcase.json` |
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### Adding a custom component
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1. **Define** the component schema in `definitions.ts`:
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```typescript
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MyWidget: {
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description: "A brief description for the agent.",
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props: z.object({ title: z.string(), value: z.number() }),
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},
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```
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2. **Render** it in `renderers.tsx`:
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```typescript
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MyWidget: ({ props }) => (
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<div>{props.title}: {props.value}</div>
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),
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```
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Renderers are type-checked against the definitions — TypeScript will error if props don't match.
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3. **Use it** from the agent. The component is automatically available to both fixed-schema templates and the dynamic-schema LLM.
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### Adding a new fixed-schema tool
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1. Create a JSON schema file in `agent/src/a2ui/schemas/` describing the component tree.
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2. Create a Python tool that loads the schema with `a2ui.load_schema()` and returns `a2ui.render(operations=[...])` with your data. See `a2ui_fixed_schema.py` for the pattern.
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### Showcase mode
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`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`.
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### Further reading
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- [A2UI Specification](https://a2ui.org/specification/)
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- [CopilotKit A2UI Documentation](https://docs.copilotkit.ai)
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## Documentation
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- [LangGraph Documentation](https://langchain-ai.github.io/langgraph/) - Learn more about LangGraph and its features
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- [CopilotKit Documentation](https://docs.copilotkit.ai) - Explore CopilotKit's capabilities
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## Contributing
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Feel free to submit issues and enhancement requests! This starter is designed to be easily extensible.
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## License
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This project is licensed under the MIT License - see the LICENSE file for details.
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## Troubleshooting
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### Agent Connection Issues
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If you see "I'm having trouble connecting to my tools", make sure:
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1. The LangGraph agent is running on port 8123
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2. Your OpenAI API key is set correctly
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3. Both servers started successfully
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### Python Dependencies
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If you encounter Python import errors:
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```bash
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npm run install:agent
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```
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