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Pitch Platform - AI-Powered Sales Training

An intelligent platform where sellers practice pitching to companies with real-time AI coaching and performance assessment. Built with LlamaIndex, Composio, and CopilotKit.

About this Platform

This is an AI-powered pitch training platform that helps sellers improve their sales skills through:

  • Interactive Pitch Sessions: Practice pitching to real companies with AI guidance
  • Real-time Coaching: Get instant feedback and suggestions during your pitch
  • Performance Assessment: Buyers evaluate sellers using a comprehensive scoring system
  • Company Intelligence: Access detailed information about target companies

Built on the CopilotKit canvas starter template, this platform demonstrates how AI can transform sales training and assessment.

ag-ui-canvas.mp4

LlamaIndex

LlamaIndex is a framework for building generative AI applications, in particular Document Agents, i.e. agents that process unstructured data like PDFs, PowerPoints, Word files and more. The core framework has adapters for loading and storing data, while the Workflows framework provides a way to build an agent or multi-agent system and control how data moves around. Both frameworks can make use of LlamaCloud, an enterprise offering that provides RAG and structured data extraction as a service.

LlamaIndex ->

Composio

Composio is the fastest way to enable your AI agents to take real-world actions—without dealing with individual API integrations, authentication flows, or complex tool formatting. It provides access 3000+ tools out of the box across popular apps like Slack, GitHub, Notion, and more.

More about Composio ->

CopilotKit

CopilotKit connects your app's logic, state, and user context to the AI agents that deliver the animated and interactive part of your app experience — across both embedded UIs and fully headless interfaces. It gives you the tools to build, deploy, and monitor AI-assisted features that feel intuitive, helpful, and deeply integrated.

More about CopilotKit ->

Getting Started

This repository is designed to help you hit the ground running for the hackathon. Use it as a foundation for your project, a source of inspiration, or simply as a quick way to get started. The following steps will guide you through setting up the project locally.

📚 Documentation

In case you get stuck, we highly recommend checking out the documentation.

🧑‍💻 Vibe coding

Plug-in these resources to let coding agents help you and our team!

Prerequisites

Before getting started, you'll need to the following.

Quickstart

Warning

If you run into problems getting started, make sure you have all the Prerequisites installed, or else it can fail.

  1. Install dependencies using your preferred package manager:

    This will install both your Node and Python dependencies (using uv).

    # Using pnpm (recommended)
pnpm install

# Using npm
npm install

# Using yarn
yarn install

# Using bun
bun install

    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.

  2. Setup Googlesheets Integration

    Navigate to https://app.composio.dev/developers, setup a Google Sheet integration and grab an API key.

    For the next step you'll need a Composio API key, auth config ID, and user ID.

    Show me how ->
    composio.mp4

  3. Set up your environment variables:

    There are two .env files to configure:

    Backend

    Copy agent/.env.example to agent/.env:

    # OpenAI API key
OPENAI_API_KEY=""

# Composio secrets
COMPOSIO_API_KEY=""
COMPOSIO_GOOGLESHEETS_AUTH_CONFIG_ID=""
COMPOSIO_USER_ID="default"

    Note: The OpenAI API key is required for the LlamaIndex agent to function

    Frontend (optional)

    Copy .env.local.example to .env in the root directory:

    # .env.local
COPILOT_CLOUD_PUBLIC_API_KEY="" # optional (for CopilotKit Cloud features)

  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.

  5. ✅ You're done!

    Open http://localhost:3000 to use the starter and try it out!

    Using the canvas starter->

    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"
    1. 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"
    1. Sync with Google Sheets: Use the Google Sheets button or ask the AI
    • "Create a new Google Sheet" - Creates a sheet for syncing canvas data
    • "Sync all items to Google Sheets" - Syncs current canvas state to the sheet
    • "Get the sheet URL" - Retrieves the Google Sheets link
    1. 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
    1. 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

High-level -> 
graph TB
    subgraph "Frontend (Next.js)"
        UI[Canvas UI<br/>page.tsx]
        Actions[Frontend Actions<br/>useCopilotAction]
        State[State Management<br/>useCoAgent]
        Chat[CopilotChat]
        SheetsBtn[Google Sheets<br/>Menu]
    end
    
    subgraph "Backend (Python)"
        Agent[LlamaIndex Agent<br/>agent.py]
        Tools[Backend Tools<br/>- set_plan<br/>- update_plan_progress<br/>- complete_plan]
        SheetsTools[Sheets Tools<br/>- sheets_sync_all<br/>- sheets_create_new<br/>- sheets_get_url]
        AgentState[Workflow Context<br/>State Management]
        Model[LLM<br/>GPT-4o]
    end
    
    subgraph "External Services"
        Composio[Composio API<br/>Tool Integration]
        GSheets[Google Sheets<br/>API]
    end
    
    subgraph "Communication"
        Runtime[CopilotKit Runtime<br/>:9000]
    end
    
    UI <--> State
    SheetsBtn --> UI
    State <--> Runtime
    Chat <--> Runtime
    Actions --> Runtime
    Runtime <--> Agent
    Agent --> Tools
    Agent --> SheetsTools
    SheetsTools --> Composio
    Composio --> GSheets
    Agent --> AgentState
    Agent --> Model
    
    style UI text-decoration:none,fill:#e1f5fe
    style Agent text-decoration:none,fill:#fff3e0
    style Runtime text-decoration:none,fill:#f3e5f5,color:#111111
    style Composio text-decoration:none,fill:#e8f5e9,color:#111111
    style GSheets text-decoration:none,fill:#fff9c4,color:#111111
    
    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"
Loading
Data flow -> 
sequenceDiagram
    participant User
    participant UI as Canvas UI
    participant CK as CopilotKit
    participant Agent as LlamaIndex Agent
    participant Tools
    participant Composio
    participant GSheets as Google Sheets
    
    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
    
    alt Google Sheets Sync
        Agent->>Composio: Execute sheets_sync_all
        Composio->>GSheets: Update spreadsheet
        GSheets-->>Composio: Confirm update
        Composio-->>Agent: Return status
    end
    
    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
    Note over Composio,GSheets: External tool integration
Loading

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
  • Google Sheets Integration: Dropdown menu for creating sheets and syncing data
  • 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
  • Composio Integration: Leverages Composio for external service connections (Google Sheets, etc.)
  • 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
Schema ->

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)

Concepts

Backend tools ->

With LlamaIndex's get_ag_ui_workflow_router, you can define backend tools.

def hello_world(name: str) -> str:
  return f"Hello, {str}" # tool result

agentic_chat_router = get_ag_ui_workflow_router(
    llm=OpenAI(model="gpt-4.1"),
    backend_tools=[hello_world]
)

You can then render this with CopilotKit's useCopilotAction

// page.tsx
import { useCopilotAction } from "@copilotkit/core"

const Main = () => {
  // ...

  useCopilotAction({
    name: "hello_world"
    render: () => {
      return <div>Called hello_world tool...</div>
    }
  })

  // ...
}
Frontend tools ->

With LlamaIndex's get_ag_ui_workflow_router, you can also define frontend tools. They get called by the agent but handled on the frontend.

def hello_world(name: str) -> str:
  return "called hello_world"

agentic_chat_router = get_ag_ui_workflow_router(
    llm=OpenAI(model="gpt-4.1"),
    frontend_tools=[hello_world]
)

You can then handle this tool with CopilotKit's useCopilotAction

// page.tsx
import { useCopilotAction } from "@copilotkit/core"

const Main = () => {
  // ...

  useCopilotAction({
    name: "say_hello",
    description: "Say hello to the name, make sure to get it first",
    available: "remote",
    parameters: [
      { name: "name", type: "string", required: true, description: "The name to say hi to" },
    ],
    handler: ({ description }: { description: string }) => {
      return "Hello, Tyler" // tool_result
    },
  });

  // ...
}

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
  • See CopilotKit's customization docs for the chat window

Troubleshooting

Setup errors ->

If you encounter errors while setting up the project, make sure you have all the Prerequisites installed. Missing prerequisites like Node.js, Python, or uv can cause installation or build failures.

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 in agent/.env
  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)
Google Sheets integration issues ->

If Google Sheets sync is not working:

  1. Verify your Composio API key is set correctly in agent/.env
  2. Check if you need to authenticate with Google Sheets (the agent will provide an auth URL)
  3. Ensure the COMPOSIO_USER_ID is set (defaults to "default")
  4. For first-time setup, you may need to configure Google Sheets auth in your Composio dashboard
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

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.

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.