coeditor

name	coeditor
description	Build React applications with CopilotKit and LangGraph for AI-powered collaborative editing. Creates production-ready editor applications (text, document, or node-based) with real-time AI assistance, shared state management, and agentic workflows. Use this skill when users want to build intelligent editing interfaces with AI collaboration features.

CopilotKit + LangGraph Collaborative Editor Builder

Build production-ready React applications that combine CopilotKit's AI copilot interface with LangGraph's agentic workflows for intelligent, collaborative editing experiences.

When to Use This Skill

Use this skill when:

• User wants to build an AI-powered editor (text, document, or node-based)
• User mentions "CopilotKit", "LangGraph", "AI copilot", or "collaborative editing"
• User wants to add AI assistance to an existing React application
• User needs real-time state sharing between UI and AI agents
• User wants to build document collaboration features with AI
• User mentions "agentic workflows" or "multi-agent systems" in editing context

Overview

This skill scaffolds applications that combine:

• CopilotKit: React components for AI copilot experiences (chat, suggestions, actions)
• LangGraph: Agent orchestration framework for complex workflows
• React State Management: Shared state between editor UI and AI agents
• Editor Types: Text editors, document editors, or node-based editors

Prerequisites Check

Before starting, verify the following:

1. Node.js: Version 18.x or higher

   node --version
   

2. Package Manager: npm, yarn, or pnpm

   npm --version
   

3. Python: Version 3.9 or higher (for LangGraph backend)

   python3 --version
   

4. OpenAI API Key (or other LLM provider):

   # Will need to be configured in .env
   echo "User will need OpenAI API key or alternative LLM provider"
   

Interactive Requirements Gathering

IMPORTANT: Before scaffolding, ask the user these questions to customize the setup:

1. Editor Type Selection

"What type of editor would you like to build?"

A. Text Editor

• Rich text editing with AI assistance
• Code editor with autocomplete and suggestions
• Markdown editor with AI enhancements
• Best for: Writing tools, code editors, note-taking apps

B. Document Editor

• Structured document editing (like Google Docs)
• Multi-section documents with AI collaboration
• Template-based document creation
• Best for: Documentation tools, report builders, collaborative writing

C. Node-Based Editor

• Visual graph/flow editor with AI assistance
• Workflow builders with intelligent suggestions
• Mind mapping with AI expansion
• Best for: Workflow designers, visual programming tools, diagramming apps

2. LangGraph Agent Configuration

"What type of AI agents do you need?"

Ask the user to describe their use case, then suggest appropriate agents:

Common Agent Types:

• Writing Assistant: Helps with content creation, editing, and refinement
• Code Assistant: Provides code suggestions, explanations, and debugging
• Research Agent: Searches and incorporates external information
• Reviewer Agent: Reviews content and provides feedback
• Summarizer Agent: Creates summaries and extracts key points
• Translator Agent: Translates between languages
• Custom Agents: User-defined specialized agents

Multi-Agent Workflows:

• Ask if they need multiple agents working together
• Determine if agents should run sequentially or in parallel
• Identify handoff points between agents

3. State Management Requirements

"What data needs to be shared between the editor and AI agents?"

Common State Patterns:

• Document State: Content, structure, metadata
• Selection State: Current cursor position, selected text/nodes
• History State: Undo/redo, version tracking
• Collaboration State: Multi-user presence, changes
• Agent State: Current agent task, progress, results

4. Backend Architecture

"How would you like to deploy the LangGraph backend?"

A. Local Development Server

• FastAPI server running locally
• Best for: Development, prototyping
• Setup: Python virtual environment

B. Cloud Deployment

• Deploy to Vercel, Railway, or cloud provider
• Best for: Production, sharing with team
• Setup: Containerized deployment

C. Serverless Functions

• Deploy agents as serverless functions
• Best for: Cost-effective scaling
• Setup: Vercel Functions, AWS Lambda

5. Additional Features

Ask about optional features:

CopilotKit Features:

• Chat interface (CopilotChat)
• Inline suggestions (CopilotTextarea)
• Custom actions (CopilotAction)
• Context providers (document context)
• Keyboard shortcuts

Editor Features:

• Real-time collaboration
• Version history
• Comments and annotations
• Export formats (PDF, Markdown, etc.)
• Templates
• Search and replace

AI Features:

• Streaming responses
• Multi-turn conversations
• Agent memory/context
• Custom prompts/instructions
• Fine-tuned models

Project Structure

Based on user selections, create this structure:

<app-name>/
├── frontend/                   # React application
│   ├── src/
│   │   ├── components/
│   │   │   ├── Editor/        # Editor components
│   │   │   │   ├── TextEditor.tsx
│   │   │   │   ├── DocumentEditor.tsx
│   │   │   │   └── NodeEditor.tsx
│   │   │   ├── Copilot/       # CopilotKit components
│   │   │   │   ├── CopilotProvider.tsx
│   │   │   │   ├── ChatPanel.tsx
│   │   │   │   └── Actions.tsx
│   │   │   └── shared/        # Shared UI components
│   │   ├── hooks/             # Custom React hooks
│   │   │   ├── useEditor.ts
│   │   │   ├── useAgents.ts
│   │   │   └── useSharedState.ts
│   │   ├── state/             # State management
│   │   │   ├── editorStore.ts
│   │   │   ├── agentStore.ts
│   │   │   └── types.ts
│   │   ├── lib/               # Utilities
│   │   │   ├── copilot-config.ts
│   │   │   └── api-client.ts
│   │   ├── App.tsx
│   │   └── main.tsx
│   ├── package.json
│   ├── vite.config.ts
│   └── tsconfig.json
│
├── backend/                    # LangGraph backend
│   ├── agents/                # Agent definitions
│   │   ├── __init__.py
│   │   ├── writing_assistant.py
│   │   ├── code_assistant.py
│   │   └── custom_agents.py
│   ├── graphs/                # LangGraph workflows
│   │   ├── __init__.py
│   │   ├── editor_graph.py
│   │   └── multi_agent_graph.py
│   ├── api/                   # FastAPI endpoints
│   │   ├── __init__.py
│   │   ├── main.py
│   │   └── routes.py
│   ├── state/                 # Shared state management
│   │   ├── __init__.py
│   │   └── state_manager.py
│   ├── requirements.txt
│   └── pyproject.toml
│
├── shared/                     # Shared types/schemas
│   ├── types.ts
│   └── schemas.py
│
├── .env.example
├── .gitignore
├── docker-compose.yml          # Optional: for containerized dev
├── README.md
└── package.json               # Root workspace config

Step-by-Step Implementation

Step 1: Project Initialization

1.1 Create React Frontend (Vite + TypeScript)

npm create vite@latest <app-name> -- --template react-ts
cd <app-name>
mv <app-name> frontend

1.2 Install CopilotKit Dependencies

cd frontend
npm install @copilotkit/react-core @copilotkit/react-ui @copilotkit/react-textarea

1.3 Install Editor Dependencies

Choose based on editor type:

For Text/Code Editor:

npm install @monaco-editor/react      # VS Code editor
# OR
npm install @tiptap/react @tiptap/starter-kit  # Rich text

For Document Editor:

npm install slate slate-react         # Document framework
# OR
npm install @lexical/react lexical     # Facebook's editor framework

For Node-Based Editor:

npm install reactflow                  # Flow/graph editor
# OR
npm install @xyflow/react              # Advanced node editor

1.4 Install State Management

npm install zustand                    # Lightweight state management
# OR
npm install @tanstack/react-query      # Server state management
# OR
npm install jotai                      # Atomic state management

1.5 Initialize Python Backend

cd ..
mkdir backend
cd backend
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

1.6 Install LangGraph Dependencies

pip install langgraph langchain langchain-openai fastapi uvicorn python-dotenv pydantic websockets

Create requirements.txt:

pip freeze > requirements.txt

Step 2: Configure CopilotKit

2.1 Create CopilotKit Provider (frontend/src/components/Copilot/CopilotProvider.tsx)

import { CopilotKit } from "@copilotkit/react-core";
import { CopilotSidebar } from "@copilotkit/react-ui";
import "@copilotkit/react-ui/styles.css";

interface CopilotProviderProps {
  children: React.ReactNode;
}

export function CopilotProvider({ children }: CopilotProviderProps) {
  return (
    <CopilotKit
      runtimeUrl="/api/copilotkit"
      // Alternatively, connect directly to LangGraph backend:
      // runtimeUrl="http://localhost:8000/copilot"
    >
      <CopilotSidebar>
        {children}
      </CopilotSidebar>
    </CopilotKit>
  );
}

2.2 Configure Context Providers

CopilotKit needs access to editor state to provide relevant suggestions:

import { useCopilotReadable, useCopilotAction } from "@copilotkit/react-core";

export function useEditorCopilot(editorState: EditorState) {
  // Make editor state readable by AI
  useCopilotReadable({
    description: "The current document content and structure",
    value: editorState,
  });

  // Define actions AI can perform
  useCopilotAction({
    name: "insertText",
    description: "Insert text at current cursor position",
    parameters: [
      {
        name: "text",
        type: "string",
        description: "The text to insert",
        required: true,
      },
    ],
    handler: async ({ text }) => {
      // Insert text into editor
      editorState.insertText(text);
    },
  });

  useCopilotAction({
    name: "replaceSelection",
    description: "Replace currently selected text",
    parameters: [
      {
        name: "newText",
        type: "string",
        description: "The replacement text",
        required: true,
      },
    ],
    handler: async ({ newText }) => {
      editorState.replaceSelection(newText);
    },
  });
}

Step 3: Set Up LangGraph Backend

3.1 Define State Schema (backend/state/state_manager.py)

from typing import TypedDict, Annotated, Sequence
from langchain_core.messages import BaseMessage
import operator

class EditorState(TypedDict):
    """Shared state between frontend and agents."""
    # Document content
    content: str
    # Current selection
    selection: dict
    # Chat messages
    messages: Annotated[Sequence[BaseMessage], operator.add]
    # Agent context
    current_agent: str
    # Task tracking
    task_status: str
    # Additional metadata
    metadata: dict

3.2 Create Base Agent (backend/agents/base_agent.py)

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough

class BaseAgent:
    def __init__(self, name: str, system_prompt: str):
        self.name = name
        self.llm = ChatOpenAI(model="gpt-4", temperature=0.7)
        self.prompt = ChatPromptTemplate.from_messages([
            ("system", system_prompt),
            ("human", "{input}"),
        ])
        self.chain = self.prompt | self.llm

    async def process(self, state: EditorState) -> EditorState:
        """Process state and return updated state."""
        raise NotImplementedError

3.3 Implement Specific Agents

Based on user requirements, create specialized agents:

Writing Assistant (backend/agents/writing_assistant.py)

from .base_agent import BaseAgent
from ..state.state_manager import EditorState

class WritingAssistant(BaseAgent):
    def __init__(self):
        super().__init__(
            name="writing_assistant",
            system_prompt="""You are an expert writing assistant.
            Help users improve their writing by:
            - Suggesting better phrasing
            - Fixing grammar and style issues
            - Expanding on ideas
            - Maintaining consistent tone

            Current document context: {content}
            Current selection: {selection}
            """
        )

    async def process(self, state: EditorState) -> EditorState:
        # Get current content and selection
        content = state.get("content", "")
        selection = state.get("selection", {})

        # Process with LLM
        result = await self.chain.ainvoke({
            "input": state["messages"][-1].content,
            "content": content,
            "selection": selection,
        })

        # Update state
        state["messages"].append(result)
        state["task_status"] = "completed"

        return state

Code Assistant (backend/agents/code_assistant.py)

from .base_agent import BaseAgent
from ..state.state_manager import EditorState

class CodeAssistant(BaseAgent):
    def __init__(self):
        super().__init__(
            name="code_assistant",
            system_prompt="""You are an expert programming assistant.
            Help users with:
            - Code completion and suggestions
            - Bug finding and fixing
            - Code explanation
            - Refactoring suggestions

            Current code: {content}
            Selected code: {selection}
            """
        )

    async def process(self, state: EditorState) -> EditorState:
        content = state.get("content", "")
        selection = state.get("selection", {})

        result = await self.chain.ainvoke({
            "input": state["messages"][-1].content,
            "content": content,
            "selection": selection,
        })

        state["messages"].append(result)
        return state

3.4 Create LangGraph Workflow (backend/graphs/editor_graph.py)

from langgraph.graph import StateGraph, END
from ..state.state_manager import EditorState
from ..agents.writing_assistant import WritingAssistant
from ..agents.code_assistant import CodeAssistant

def create_editor_graph():
    """Create LangGraph workflow for editor agents."""

    # Initialize agents
    writing_agent = WritingAssistant()
    code_agent = CodeAssistant()

    # Create graph
    workflow = StateGraph(EditorState)

    # Add nodes
    workflow.add_node("writing_assistant", writing_agent.process)
    workflow.add_node("code_assistant", code_agent.process)

    # Add conditional routing
    def route_agent(state: EditorState) -> str:
        """Route to appropriate agent based on state."""
        current_agent = state.get("current_agent", "writing_assistant")
        return current_agent

    # Set entry point
    workflow.set_conditional_entry_point(
        route_agent,
        {
            "writing_assistant": "writing_assistant",
            "code_assistant": "code_assistant",
        }
    )

    # Add edges
    workflow.add_edge("writing_assistant", END)
    workflow.add_edge("code_assistant", END)

    return workflow.compile()

3.5 Create FastAPI Server (backend/api/main.py)

from fastapi import FastAPI, WebSocket
from fastapi.middleware.cors import CORSMiddleware
from ..graphs.editor_graph import create_editor_graph
from ..state.state_manager import EditorState
import json

app = FastAPI()

# CORS configuration
app.add_middleware(
    CORSMiddleware,
    allow_origins=["http://localhost:5173"],  # Vite dev server
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Initialize graph
graph = create_editor_graph()

@app.post("/copilot")
async def copilot_endpoint(request: dict):
    """CopilotKit-compatible endpoint."""
    # Extract state from request
    state: EditorState = {
        "content": request.get("context", {}).get("content", ""),
        "selection": request.get("context", {}).get("selection", {}),
        "messages": request.get("messages", []),
        "current_agent": request.get("agent", "writing_assistant"),
        "task_status": "pending",
        "metadata": {},
    }

    # Process through graph
    result = await graph.ainvoke(state)

    # Return response
    return {
        "message": result["messages"][-1].content,
        "state": result,
    }

@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
    """WebSocket for real-time updates."""
    await websocket.accept()

    try:
        while True:
            # Receive state update from frontend
            data = await websocket.receive_text()
            state = json.loads(data)

            # Process through graph
            result = await graph.ainvoke(state)

            # Send back to frontend
            await websocket.send_json(result)
    except Exception as e:
        print(f"WebSocket error: {e}")
    finally:
        await websocket.close()

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)

Step 4: Implement Editor Component

Based on editor type, create the appropriate component:

4.1 Text Editor (Monaco/VS Code)

// frontend/src/components/Editor/TextEditor.tsx
import Editor from '@monaco-editor/react';
import { useEditorStore } from '../../state/editorStore';
import { useEditorCopilot } from '../../hooks/useEditorCopilot';

export function TextEditor() {
  const { content, updateContent, selection } = useEditorStore();

  // Connect to CopilotKit
  useEditorCopilot({ content, selection });

  return (
    <Editor
      height="90vh"
      defaultLanguage="typescript"
      value={content}
      onChange={(value) => updateContent(value || '')}
      onCursorSelectionChange={(selection) => {
        useEditorStore.setState({ selection });
      }}
      theme="vs-dark"
      options={{
        minimap: { enabled: true },
        fontSize: 14,
        wordWrap: 'on',
      }}
    />
  );
}

4.2 Document Editor (Slate)

// frontend/src/components/Editor/DocumentEditor.tsx
import { createEditor } from 'slate';
import { Slate, Editable, withReact } from 'slate-react';
import { useState, useMemo } from 'react';
import { useEditorCopilot } from '../../hooks/useEditorCopilot';

export function DocumentEditor() {
  const editor = useMemo(() => withReact(createEditor()), []);
  const [value, setValue] = useState(initialValue);

  // Connect to CopilotKit
  useEditorCopilot({ content: JSON.stringify(value) });

  return (
    <Slate editor={editor} value={value} onChange={setValue}>
      <Editable
        placeholder="Start writing..."
        renderLeaf={renderLeaf}
        renderElement={renderElement}
      />
    </Slate>
  );
}

4.3 Node-Based Editor (ReactFlow)

// frontend/src/components/Editor/NodeEditor.tsx
import ReactFlow, {
  MiniMap,
  Controls,
  Background,
  useNodesState,
  useEdgesState,
} from 'reactflow';
import 'reactflow/dist/style.css';
import { useEditorCopilot } from '../../hooks/useEditorCopilot';

export function NodeEditor() {
  const [nodes, setNodes, onNodesChange] = useNodesState(initialNodes);
  const [edges, setEdges, onEdgesChange] = useEdgesState(initialEdges);

  // Connect to CopilotKit
  useEditorCopilot({
    content: JSON.stringify({ nodes, edges }),
    selection: selectedNodes,
  });

  return (
    <div style={{ width: '100vw', height: '100vh' }}>
      <ReactFlow
        nodes={nodes}
        edges={edges}
        onNodesChange={onNodesChange}
        onEdgesChange={onEdgesChange}
        fitView
      >
        <Controls />
        <MiniMap />
        <Background variant="dots" gap={12} size={1} />
      </ReactFlow>
    </div>
  );
}

Step 5: State Management

5.1 Create Editor Store (Zustand)

// frontend/src/state/editorStore.ts
import { create } from 'zustand';

interface EditorState {
  content: string;
  selection: {
    start: number;
    end: number;
    text: string;
  };
  history: string[];
  currentAgent: string;

  // Actions
  updateContent: (content: string) => void;
  updateSelection: (selection: EditorState['selection']) => void;
  setAgent: (agent: string) => void;
  undo: () => void;
  redo: () => void;
}

export const useEditorStore = create<EditorState>((set, get) => ({
  content: '',
  selection: { start: 0, end: 0, text: '' },
  history: [],
  currentAgent: 'writing_assistant',

  updateContent: (content) =>
    set((state) => ({
      content,
      history: [...state.history, state.content],
    })),

  updateSelection: (selection) =>
    set({ selection }),

  setAgent: (agent) =>
    set({ currentAgent: agent }),

  undo: () => {
    const { history } = get();
    if (history.length > 0) {
      set({
        content: history[history.length - 1],
        history: history.slice(0, -1),
      });
    }
  },

  redo: () => {
    // Implement redo logic
  },
}));

5.2 Create Agent Store

// frontend/src/state/agentStore.ts
import { create } from 'zustand';

interface Agent {
  id: string;
  name: string;
  description: string;
  status: 'idle' | 'working' | 'completed' | 'error';
}

interface AgentState {
  agents: Agent[];
  activeAgent: string | null;
  taskQueue: Task[];

  // Actions
  setActiveAgent: (agentId: string) => void;
  updateAgentStatus: (agentId: string, status: Agent['status']) => void;
  addTask: (task: Task) => void;
}

export const useAgentStore = create<AgentState>((set) => ({
  agents: [
    {
      id: 'writing_assistant',
      name: 'Writing Assistant',
      description: 'Helps with writing and editing',
      status: 'idle',
    },
    {
      id: 'code_assistant',
      name: 'Code Assistant',
      description: 'Helps with coding and debugging',
      status: 'idle',
    },
  ],
  activeAgent: null,
  taskQueue: [],

  setActiveAgent: (agentId) =>
    set({ activeAgent: agentId }),

  updateAgentStatus: (agentId, status) =>
    set((state) => ({
      agents: state.agents.map((agent) =>
        agent.id === agentId ? { ...agent, status } : agent
      ),
    })),

  addTask: (task) =>
    set((state) => ({
      taskQueue: [...state.taskQueue, task],
    })),
}));

Step 6: Connect Frontend to Backend

6.1 Create API Client (frontend/src/lib/api-client.ts)

interface CopilotRequest {
  context: {
    content: string;
    selection: any;
  };
  messages: any[];
  agent: string;
}

export class AgentAPIClient {
  private baseUrl: string;

  constructor(baseUrl: string = 'http://localhost:8000') {
    this.baseUrl = baseUrl;
  }

  async sendMessage(request: CopilotRequest) {
    const response = await fetch(`${this.baseUrl}/copilot`, {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
      },
      body: JSON.stringify(request),
    });

    if (!response.ok) {
      throw new Error(`API error: ${response.statusText}`);
    }

    return response.json();
  }

  // WebSocket connection for real-time updates
  connectWebSocket(onMessage: (data: any) => void) {
    const ws = new WebSocket(`ws://localhost:8000/ws`);

    ws.onmessage = (event) => {
      const data = JSON.parse(event.data);
      onMessage(data);
    };

    return ws;
  }
}

6.2 Create Custom Hook (frontend/src/hooks/useAgents.ts)

import { useEffect, useState } from 'react';
import { AgentAPIClient } from '../lib/api-client';
import { useEditorStore } from '../state/editorStore';
import { useAgentStore } from '../state/agentStore';

export function useAgents() {
  const [client] = useState(() => new AgentAPIClient());
  const { content, selection, currentAgent } = useEditorStore();
  const { updateAgentStatus } = useAgentStore();

  const sendToAgent = async (message: string) => {
    updateAgentStatus(currentAgent, 'working');

    try {
      const response = await client.sendMessage({
        context: { content, selection },
        messages: [{ role: 'user', content: message }],
        agent: currentAgent,
      });

      updateAgentStatus(currentAgent, 'completed');
      return response;
    } catch (error) {
      updateAgentStatus(currentAgent, 'error');
      throw error;
    }
  };

  return { sendToAgent };
}

Step 7: Configuration Files

7.1 Environment Variables

Create .env.example:

# Frontend
VITE_API_URL=http://localhost:8000

# Backend
OPENAI_API_KEY=your-api-key-here
ANTHROPIC_API_KEY=optional-claude-key
LANGCHAIN_API_KEY=optional-langsmith-key
LANGCHAIN_TRACING_V2=true
LANGCHAIN_PROJECT=coeditor-project

7.2 Docker Compose (Optional)

# docker-compose.yml
version: '3.8'

services:
  frontend:
    build: ./frontend
    ports:
      - "5173:5173"
    environment:
      - VITE_API_URL=http://backend:8000
    depends_on:
      - backend

  backend:
    build: ./backend
    ports:
      - "8000:8000"
    environment:
      - OPENAI_API_KEY=${OPENAI_API_KEY}
    volumes:
      - ./backend:/app

Step 8: Development Scripts

8.1 Root Package.json

{
  "name": "coeditor-app",
  "private": true,
  "workspaces": ["frontend", "backend"],
  "scripts": {
    "dev": "concurrently \"npm run dev:frontend\" \"npm run dev:backend\"",
    "dev:frontend": "cd frontend && npm run dev",
    "dev:backend": "cd backend && python -m uvicorn api.main:app --reload",
    "build": "npm run build:frontend",
    "build:frontend": "cd frontend && npm run build",
    "type-check": "cd frontend && npm run type-check"
  },
  "devDependencies": {
    "concurrently": "^8.2.0"
  }
}

Advanced Patterns

Multi-Agent Workflows

For complex tasks requiring multiple agents:

# backend/graphs/multi_agent_graph.py
from langgraph.graph import StateGraph, END

def create_multi_agent_workflow():
    workflow = StateGraph(EditorState)

    # Add multiple agents
    workflow.add_node("researcher", researcher_agent.process)
    workflow.add_node("writer", writer_agent.process)
    workflow.add_node("reviewer", reviewer_agent.process)

    # Create workflow: research → write → review
    workflow.set_entry_point("researcher")
    workflow.add_edge("researcher", "writer")
    workflow.add_edge("writer", "reviewer")

    # Conditional loop back for revisions
    workflow.add_conditional_edges(
        "reviewer",
        should_revise,
        {
            "revise": "writer",
            "approve": END,
        }
    )

    return workflow.compile()

Streaming Responses

For better UX with long-running tasks:

// Frontend streaming
async function* streamAgentResponse(message: string) {
  const response = await fetch('/api/copilot/stream', {
    method: 'POST',
    body: JSON.stringify({ message }),
  });

  const reader = response.body?.getReader();
  const decoder = new TextDecoder();

  while (true) {
    const { done, value } = await reader.read();
    if (done) break;

    const chunk = decoder.decode(value);
    yield chunk;
  }
}

Context-Aware Actions

Make agents aware of editor context:

useCopilotAction({
  name: "improveSelection",
  description: "Improve the currently selected text",
  handler: async () => {
    const { selection } = useEditorStore.getState();
    const improved = await sendToAgent(
      `Improve this text: ${selection.text}`
    );
    replaceSelection(improved);
  },
});

Testing

Frontend Tests

// frontend/src/components/Editor/__tests__/TextEditor.test.tsx
import { render, screen } from '@testing-library/react';
import { TextEditor } from '../TextEditor';

describe('TextEditor', () => {
  it('renders editor', () => {
    render(<TextEditor />);
    expect(screen.getByRole('textbox')).toBeInTheDocument();
  });

  it('updates content on change', async () => {
    // Test implementation
  });
});

Backend Tests

# backend/tests/test_agents.py
import pytest
from agents.writing_assistant import WritingAssistant

@pytest.mark.asyncio
async def test_writing_assistant():
    agent = WritingAssistant()
    state = {
        "content": "This is a test",
        "messages": [{"role": "user", "content": "Improve this"}],
    }
    result = await agent.process(state)
    assert "messages" in result

Deployment

Frontend Deployment (Vercel)

cd frontend
vercel deploy --prod

Backend Deployment (Railway/Render)

# Dockerfile for backend
FROM python:3.11-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .
CMD ["uvicorn", "api.main:app", "--host", "0.0.0.0", "--port", "8000"]

Best Practices Checklist

When building the application, ensure:

Architecture

• Clear separation between frontend and backend
• Type-safe communication (TypeScript + Pydantic)
• Shared state schema between frontend and backend
• Proper error handling and fallbacks

CopilotKit Integration

• Context providers configured for editor state
• Custom actions defined for common tasks
• Proper streaming for long responses
• Keyboard shortcuts configured

LangGraph Implementation

• Agents properly isolated with clear responsibilities
• State transitions well-defined
• Error recovery mechanisms
• Logging and observability (LangSmith)

Editor Experience

• Responsive UI with loading states
• Undo/redo functionality
• Auto-save and persistence
• Keyboard shortcuts
• Accessibility (ARIA labels)

Performance

• Debounced state updates
• Lazy loading for heavy components
• Optimized bundle size
• Caching for API responses

Security

• API key protection (server-side only)
• Input validation
• Rate limiting
• CORS properly configured
• Sanitized user inputs

Common Pitfalls to Avoid

• ❌ Exposing API keys in frontend code
• ❌ Not handling agent failures gracefully
• ❌ Blocking UI while waiting for agent responses
• ❌ Not validating state updates from agents
• ❌ Forgetting to cleanup WebSocket connections
• ❌ Not implementing proper error boundaries
• ❌ Ignoring TypeScript errors
• ❌ Not testing on different screen sizes
• ❌ Hardcoding backend URLs
• ❌ Not implementing proper loading states

Example Use Cases

1. AI-Powered Writing App

• Editor: Rich text (TipTap)
• Agents: Writing assistant, grammar checker, style improver
• Features: Real-time suggestions, tone adjustment, expansion

2. Code Collaboration Tool

• Editor: Monaco (VS Code)
• Agents: Code assistant, reviewer, documentation generator
• Features: Code completion, bug detection, auto-documentation

3. Workflow Designer

• Editor: ReactFlow nodes
• Agents: Workflow optimizer, validator, template suggester
• Features: Smart node suggestions, validation, auto-layout

Reference Documentation

For detailed implementation patterns and API references:

• references/copilotkit-patterns.md - CopilotKit integration patterns
• references/langgraph-agents.md - LangGraph agent implementations
• references/state-management.md - State synchronization patterns
• references/editor-integrations.md - Editor-specific implementations

Post-Setup Checklist

After scaffolding, guide the user to:

1. ✅ Configure environment variables (.env)
2. ✅ Set up OpenAI or LLM provider API key
3. ✅ Test frontend development server
4. ✅ Test backend API server
5. ✅ Verify CopilotKit connection
6. ✅ Test agent responses
7. ✅ Customize agent prompts
8. ✅ Add custom actions
9. ✅ Implement additional features
10. ✅ Deploy to production

Troubleshooting

CopilotKit not connecting

• Verify runtimeUrl is correct
• Check CORS configuration on backend
• Ensure backend is running

Agents not responding

• Check API keys are configured
• Verify LangGraph state schema matches
• Check backend logs for errors

Editor state not syncing

• Verify useCopilotReadable is called
• Check WebSocket connection
• Ensure state updates are properly dispatched

Version Compatibility

This skill targets:

• React: 18+
• CopilotKit: Latest (@copilotkit/react-core)
• LangGraph: Latest (langgraph)
• Python: 3.9+
• Node.js: 18+
• TypeScript: 5+

Next Steps

After scaffolding:

1. Customize agent prompts for your use case
2. Add domain-specific actions
3. Implement additional editor features
4. Set up analytics and monitoring
5. Plan deployment strategy
6. Create user documentation