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).

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 (Python package manager)
Any of the following package managers:
- npm (default)
- pnpm
- yarn
- bun
OpenAI API Key (for the LangGraph agent)

Getting Started

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

npm install

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

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

Start the development server:

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 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:

Catalog — a set of component definitions (schema) paired with React renderers. Registered once in layout.tsx via <CopilotKitProvider a2ui={{ catalog: demonstrationCatalog }}>.
Surface — a rendered UI instance. The agent creates a surface, sets its components, and binds data to it.
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

Define the component schema in definitions.ts:

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

Render it in renderers.tsx:
```
MyWidget: ({ props }) => (
  <div>{props.title}: {props.value}</div>
),
```
Renderers are type-checked against the definitions — TypeScript will error if props don't match.
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

Create a JSON schema file in agent/src/a2ui/schemas/ describing the component tree.
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.

Documentation

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

README.md

Generative UI Starter Project