CopilotKit <> LlamaIndex AG-UI Canvas Starter

This is a starter template for building AI-powered canvas applications using LlamaIndex and CopilotKit. It provides a modern Next.js application with an integrated LlamaIndex agent that manages a visual canvas of interactive cards with real-time AI synchronization.

ag-ui-canvas.mp4

🚀 Key Features

• Visual Canvas Interface: Drag-free canvas displaying cards in a responsive grid layout
• Four Card Types:
  • Project: Includes text fields, dropdown, date picker, and checklist
  • Entity: Features text fields, dropdown, and multi-select tags
  • Note: Simple rich text content area
  • Chart: Visual metrics with percentage-based bar charts
• Real-time AI Sync: Bidirectional synchronization between the AI agent and UI canvas
• Multi-step Planning: AI can create and execute plans with visual progress tracking
• Human-in-the-Loop (HITL): Intelligent interrupts for clarification when needed
• JSON View: Toggle between visual canvas and raw JSON state
• Responsive Design: Optimized for both desktop (sidebar chat) and mobile (popup chat)

Prerequisites

• Node.js 18+
• Python 3.8+
• OpenAI API Key (for the LlamaIndex agent)
• uv
• Any of the following package managers:
  • pnpm (recommended)
  • npm
  • yarn
  • bun

Note: This repository ignores lock files (package-lock.json, yarn.lock, pnpm-lock.yaml, bun.lockb) to avoid conflicts between different package managers. Each developer should generate their own lock file using their preferred package manager. After that, make sure to delete it from the .gitignore.

Getting Started

1. Install dependencies using your preferred package manager:

# Using pnpm (recommended)
pnpm install

# Using npm
npm install

# Using yarn
yarn install

# Using bun
bun install

2. Install Python dependencies for the LlamaIndex agent (requires uv). If you don't have uv installed, install it first using one of the following:
   • macOS (Homebrew): brew install uv
   • macOS/Linux (official installer): curl -LsSf https://astral.sh/uv/install.sh | sh
   • Or with pipx: pipx install uv

# Using pnpm
pnpm install:agent

# Using npm
npm run install:agent

# Using yarn
yarn install:agent

# Using bun
bun run install:agent

Note: This will automatically setup a .venv (virtual environment) inside the agent directory.

To activate the virtual environment manually, you can run:

source agent/.venv/bin/activate

3. Set up your OpenAI API key:

export OPENAI_API_KEY="your-openai-api-key-here"

4. Start the development server:

# Using pnpm
pnpm dev

# Using npm
npm run dev

# Using yarn
yarn dev

# Using bun
bun run dev

This will start both the UI and agent servers concurrently.

Getting Started with the Canvas

Once the application is running, you can:

1. Create Cards: Use the "New Item" button or ask the AI to create cards

   • "Create a new project"
   • "Add an entity and a note"
   • "Create a chart with sample metrics"

2. Edit Cards: Click on any field to edit directly, or ask the AI

   • "Set the project field1 to 'Q1 Planning'"
   • "Add a checklist item 'Review budget'"
   • "Update the chart metrics"

3. Execute Plans: Give the AI multi-step instructions

   • "Create 3 projects with different priorities and add 2 checklist items to each"
   • The AI will create a plan and execute it step by step with visual progress

4. View JSON: Toggle between the visual canvas and JSON view using the button at the bottom

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 LlamaIndex agent server
• install:agent - Installs Python dependencies for the agent
• build - Builds the Next.js application for production
• start - Starts the production server
• lint - Runs ESLint for code linting

Architecture Overview

graph TB
    subgraph "Frontend (Next.js)"
        UI[Canvas UI<br/>page.tsx]
        Actions[Frontend Actions<br/>useCopilotAction]
        State[State Management<br/>useCoAgent]
        Chat[CopilotChat]
    end
    
    subgraph "Backend (Python)"
        Agent[LlamaIndex Agent<br/>agent.py]
        Tools[Backend Tools<br/>- set_plan<br/>- update_plan_progress<br/>- complete_plan]
        AgentState[Workflow Context<br/>State Management]
        Model[LLM<br/>GPT-4o]
    end
    
    subgraph "Communication"
        Runtime[CopilotKit Runtime<br/>:9000]
    end
    
    UI <--> State
    State <--> Runtime
    Chat <--> Runtime
    Actions --> Runtime
    Runtime <--> Agent
    Agent --> Tools
    Agent --> AgentState
    Agent --> Model
    
    style UI fill:#e1f5fe
    style Agent fill:#fff3e0
    style Runtime fill:#f3e5f5
    
    click UI "https://github.com/CopilotKit/canvas-with-llamaindex/blob/main/src/app/page.tsx"
    click Agent "https://github.com/CopilotKit/canvas-with-llamaindex/blob/main/agent/agent/agent.py"

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Frontend (Next.js + CopilotKit)

The main UI component is in src/app/page.tsx. It includes:

• Canvas Management: Visual grid of cards with create, read, update, and delete operations
• State Synchronization: Uses useCoAgent hook for real-time state sync with the agent
• Frontend Actions: Exposed as tools to the AI agent via useCopilotAction
• Plan Visualization: Shows multi-step plan execution with progress indicators
• HITL (Tool-based): Uses useCopilotAction with renderAndWaitForResponse for disambiguation prompts (e.g., choosing an item or card type)

Backend (LlamaIndex Agent)

The agent logic is in agent/agent/agent.py. It features:

• Workflow Context: Uses LlamaIndex's Context for state management and event streaming
• Tool Integration: Backend tools for planning, frontend tools integration via CopilotKit
• Strict Grounding: Enforces data consistency by always using shared state as truth
• Loop Control: Prevents infinite loops and redundant operations
• Planning System: Can create and execute multi-step plans with status tracking
• FastAPI Router: Uses get_ag_ui_workflow_router for seamless integration

Card Field Schema

Each card type has specific fields defined in the agent:

• Project: field1 (text), field2 (select), field3 (date), field4 (checklist)
• Entity: field1 (text), field2 (select), field3 (tags), field3_options (available tags)
• Note: field1 (textarea content)
• Chart: field1 (array of metrics with label and value 0-100)

Data Flow

sequenceDiagram
    participant User
    participant UI as Canvas UI
    participant CK as CopilotKit
    participant Agent as LlamaIndex Agent
    participant Tools
    
    User->>UI: Interact with canvas
    UI->>CK: Update state via useCoAgent
    CK->>Agent: Send state + message
    Agent->>Agent: Process with GPT-4o
    Agent->>Tools: Execute tools
    Tools-->>Agent: Return results
    Agent->>CK: Return updated state
    CK->>UI: Sync state changes
    UI->>User: Display updates
    
    Note over Agent: Maintains ground truth
    Note over UI,CK: Real-time bidirectional sync

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Customization Guide

Adding New Card Types

1. Define the data schema in src/lib/canvas/types.ts
2. Add the card type to the CardType union
3. Create rendering logic in src/components/canvas/CardRenderer.tsx
4. Update the agent's field schema in agent/agent/agent.py
5. Add corresponding frontend actions in src/app/page.tsx

Modifying Existing Cards

• Field definitions are in the agent's FIELD_SCHEMA constant
• UI components are in CardRenderer.tsx
• Frontend actions follow the pattern: set[Type]Field[Number]

Styling

• Global styles: src/app/globals.css
• Component styles use Tailwind CSS with shadcn/ui components
• Theme colors can be modified via CSS custom properties

📚 Documentation

• LlamaIndex Documentation - Learn more about LlamaIndex and its features
• CopilotKit Documentation - Explore CopilotKit's capabilities
• Next.js Documentation - Learn about Next.js features and API

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 LlamaIndex agent is running on port 9000 (check terminal output)
2. Your OpenAI API key is set correctly as an environment variable
3. Both servers started successfully (UI and agent)

Port Already in Use

If you see "[Errno 48] Address already in use":

1. The agent might still be running from a previous session
2. Kill the process using the port: lsof -ti:9000 | xargs kill -9
3. For the UI port: lsof -ti:3000 | xargs kill -9

State Synchronization Issues

If the canvas and AI seem out of sync:

1. Check the browser console for errors
2. Ensure all frontend actions are properly registered
3. Verify the agent is using the latest shared state (not cached values)

Python Dependencies

If you encounter Python import errors:

cd agent
uv sync

Dependency Conflicts

If issues persist, recreate the virtual environment:

cd agent
rm -rf .venv
uv venv
uv sync

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Important

Some features are still under active development and may not yet work as expected. If you encounter a problem using this template, please report an issue to this repository.