# Generative UI Starter Project

> A chat-driven kanban board where you and the agent work the same task list. Built with CopilotKit AG-UI and LangGraph( on top of Next.js. Also doubles as a starter for declarative gen UI via A2UI( (flight-search example included).

- Source: https://doutorai.pt/en/apps/generative-ui-starter-project
- Category: Generative Interfaces
- Difficulty: Advanced
- Models: OpenAI
- Frameworks: LangChain
- Runs locally?: Needs cloud APIs
- Required keys: OPENAI_API_KEY
- License: Apache-2.0
- Site: Doutor AI (https://doutorai.pt/en) - AI tools for small businesses

## Full tutorial

# Generative UI Starter Project

A chat-driven kanban board where you and the agent work the same task list. Built with [CopilotKit](https://github.com/CopilotKit/CopilotKit), [AG-UI](https://github.com/ag-ui-protocol/ag-ui), and [LangGraph](https://www.langchain.com/langgraph) on top of Next.js. Also doubles as a starter for declarative gen UI via [A2UI](https://a2ui.org/specification/) (flight-search example included).

**Gen UI concept - shared agent state.** The board (To Do / Done columns) lives in the agent and syncs bidirectionally with React via `useAgent()`. The agent moves cards through tool calls; you click, edit, and reorder them in the UI. Both sides observe and react to the same state - no separate frontend store, no manual sync.

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

## Prerequisites

- Node.js 18+
- Python 3.8+
- [uv](https://docs.astral.sh/uv/) (Python package manager)
- Any of the following package managers:
  - npm (default)
  - [pnpm](https://pnpm.io/installation)
  - [yarn](https://classic.yarnpkg.com/lang/en/docs/install/)
  - [bun](https://bun.sh/)
- OpenAI API Key (for the LangGraph agent)

## Getting Started

1. Install dependencies (npm, or pnpm/yarn/bun):

```bash
npm install
```

This will also install the Python agent dependencies via `uv sync`.

2. Set up your environment variables:

```bash
cp .env.example .env
```

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

```bash
OPENAI_API_KEY=your-openai-api-key-here
```

3. Start the development server:

```bash
npm 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 Python dependencies for the agent

## 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 Python agent
│   ├── main.py                  # Agent entry point
│   └── src/
│       ├── todos.py             # Todo tools and state schema
│       └── query.py             # Example data query tool
├── scripts/                     # Agent setup and run scripts
│   ├── setup-agent.sh / .bat
│   └── run-agent.sh / .bat
├── public/                      # Static assets
├── next.config.ts
├── tsconfig.json
└── package.json
```

## A2UI - Agent-to-User Interface

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.

### 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 `a2ui.render(operations=[...])` from a tool, which the middleware streams to the frontend.

### Two patterns

| Pattern            | Description                                                                   | Agent tool       | Frontend                                    |
| ------------------ | ----------------------------------------------------------------------------- | ---------------- | ------------------------------------------- |
| **Fixed schema**   | Pre-defined component layout. Only the data changes per invocation.           | `search_flights` | Schema in `a2ui/schemas/flight_schema.json` |
| **Dynamic schema** | A secondary LLM generates both components and data based on the conversation. | `generate_a2ui`  | Components decided at runtime               |

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

### Key files

| Purpose                              | Path                                               |
| ------------------------------------ | -------------------------------------------------- |
| Catalog definitions (Zod schemas)    | `src/app/declarative-generative-ui/definitions.ts` |
| Catalog renderers (React components) | `src/app/declarative-generative-ui/renderers.tsx`  |
| Catalog registration                 | `src/app/layout.tsx`                               |
| Fixed-schema agent tool              | `agent/src/a2ui_fixed_schema.py`                   |
| Dynamic-schema agent tool            | `agent/src/a2ui_dynamic_schema.py`                 |
| Flight schema JSON                   | `agent/src/a2ui/schemas/flight_schema.json`        |
| Showcase config                      | `showcase.json`                                    |

### Adding a custom component

1. **Define** the component schema in `definitions.ts`:

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

2. **Render** it in `renderers.tsx`:

   ```typescript
   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 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.

### 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](https://a2ui.org/specification/)
- [CopilotKit A2UI Documentation](https://docs.copilotkit.ai)

## Documentation

- [LangGraph Documentation](https://langchain-ai.github.io/langgraph/) - Learn more about LangGraph and its features
- [CopilotKit Documentation](https://docs.copilotkit.ai) - Explore CopilotKit's capabilities

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Content by Doutor AI (https://doutorai.pt/en). When quoting, please cite the source: https://doutorai.pt/en/apps/generative-ui-starter-project