Octocode Plan For Agentic Application

octocode_plan.ms

# AI Agent Chat Application - Implementation Plan

## Research Summary

### Repositories Analyzed
- **langchain-ai/langgraphjs-gen-ui-examples** (347⭐) - LangGraph.js agent examples
- **assistant-ui/assistant-ui** (6,896⭐) - TypeScript/React AI chat UI library
- **FlowiseAI/Flowise** (46,168⭐) - Visual AI agent builder with LangChain
- **CopilotKit/CopilotKit** (24,685⭐) - React UI + infrastructure for AI agents

## Architecture Overview

```
┌─────────────────────────────────────────────────────┐
│                   FRONTEND (React)                   │
│  ┌───────────────────────────────────────────────┐  │
│  │  Chat UI (@assistant-ui/react components)     │  │
│  │  - Thread (message display)                   │  │
│  │  - Composer (input field)                     │  │
│  │  - Loading states & error handling            │  │
│  └───────────────────────────────────────────────┘  │
│                        │                             │
│                        │ HTTP/SSE                    │
│                        ▼                             │
└────────────────────────┼─────────────────────────────┘
                         │
┌────────────────────────┼─────────────────────────────┐
│                   BACKEND (Express)                  │
│  ┌───────────────────────────────────────────────┐  │
│  │  API Routes                                   │  │
│  │  - POST /api/chat - Main chat endpoint       │  │
│  │  - GET /api/health - Health check            │  │
│  └───────────────────────────────────────────────┘  │
│                        │                             │
│                        ▼                             │
│  ┌───────────────────────────────────────────────┐  │
│  │  LangGraph Agent System                       │  │
│  │  - StateGraph with Annotation                 │  │
│  │  - OpenAI integration                         │  │
│  │  - Tool definitions                           │  │
│  │  - State management                           │  │
│  └───────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────┘
```

## Technology Stack

### Frontend
- **React 19** - UI framework
- **TypeScript** - Type safety
- **@assistant-ui/react** - Pre-built chat components
- **Tailwind CSS** - Styling
- **Vite** - Build tool
- **lucide-react** - Icons

### Backend
- **Node.js** - Runtime
- **Express** - Web framework
- **TypeScript** - Type safety
- **@langchain/langgraph** - Agent orchestration
- **@langchain/openai** - LLM integration
- **cors** - Cross-origin support
- **dotenv** - Environment configuration

## Key Implementation Patterns

### 1. LangGraph StateGraph Pattern
From `langchain-ai/langgraphjs-gen-ui-examples`:

```typescript
import { StateGraph, Annotation, START } from "@langchain/langgraph";
import { ChatOpenAI } from "@langchain/openai";

// Define state structure
const AgentAnnotation = Annotation.Root({
  messages: Annotation<Message[]>(),
  // Add more state fields as needed
});

// Create agent node
async function agentNode(state: typeof AgentAnnotation.State) {
  const model = new ChatOpenAI({ model: "gpt-4o-mini" });
  const response = await model.invoke(state.messages);
  return { messages: [response] };
}

// Build graph
const graph = new StateGraph(AgentAnnotation)
  .addNode("agent", agentNode)
  .addEdge(START, "agent");

export const agent = graph.compile();
```

### 2. Express Backend Pattern
From `FlowiseAI/Flowise`:

```typescript
import express from "express";
import cors from "cors";

const app = express();

// Middleware
app.use(cors());
app.use(express.json());

// Chat endpoint
app.post("/api/chat", async (req, res) => {
  const { messages } = req.body;
  
  // Stream response
  res.setHeader("Content-Type", "text/event-stream");
  res.setHeader("Cache-Control", "no-cache");
  res.setHeader("Connection", "keep-alive");
  
  // Process with agent and stream
  // ...
});

app.listen(3001, () => {
  console.log("Server running on port 3001");
});
```

### 3. React Chat UI Pattern
From `assistant-ui/assistant-ui`:

```typescript
import { 
  ThreadPrimitive, 
  ComposerPrimitive, 
  MessagePrimitive 
} from "@assistant-ui/react";

export function ChatInterface() {
  return (
    <ThreadPrimitive.Root className="h-screen flex flex-col">
      <ThreadPrimitive.Viewport className="flex-1 overflow-y-auto">
        <ThreadPrimitive.Messages
          components={{
            UserMessage: UserMessage,
            AssistantMessage: AssistantMessage,
          }}
        />
      </ThreadPrimitive.Viewport>
      
      <ComposerPrimitive.Root className="border-t p-4">
        <ComposerPrimitive.Input 
          placeholder="Type your message..."
          className="w-full p-2 border rounded"
        />
        <ComposerPrimitive.Send>
          Send
        </ComposerPrimitive.Send>
      </ComposerPrimitive.Root>
    </ThreadPrimitive.Root>
  );
}
```

### 4. Loading & Error States
Pattern for UI feedback:

```typescript
// In component
const [isLoading, setIsLoading] = useState(false);
const [error, setError] = useState<string | null>(null);

// Show loading spinner while waiting
{isLoading && <LoadingSpinner />}

// Show error message if failed
{error && <ErrorMessage message={error} />}
```

## Project Structure

```
ai-agent-chat/
├── backend/
│   ├── src/
│   │   ├── index.ts              # Express server setup
│   │   ├── agent/
│   │   │   ├── index.ts          # LangGraph agent
│   │   │   └── types.ts          # State definitions
│   │   ├── routes/
│   │   │   └── chat.ts           # Chat API routes
│   │   └── utils/
│   │       └── logger.ts         # Logging utilities
│   ├── package.json
│   ├── tsconfig.json
│   └── .env.example
│
├── frontend/
│   ├── src/
│   │   ├── main.tsx              # Entry point
│   │   ├── App.tsx               # Main app component
│   │   ├── components/
│   │   │   ├── ChatInterface.tsx # Main chat UI
│   │   │   ├── MessageList.tsx   # Message display
│   │   │   ├── ChatInput.tsx     # Input component
│   │   │   ├── LoadingSpinner.tsx
│   │   │   └── ErrorMessage.tsx
│   │   ├── lib/
│   │   │   └── api.ts            # API client
│   │   └── styles/
│   │       └── index.css         # Global styles
│   ├── package.json
│   ├── tsconfig.json
│   ├── vite.config.ts
│   └── index.html
│
└── README.md                      # Setup instructions
```

## Key Dependencies

### Backend (`backend/package.json`)
```json
{
  "dependencies": {
    "@langchain/langgraph": "^0.2.64",
    "@langchain/openai": "^0.5.5",
    "@langchain/core": "^0.3.45",
    "express": "^4.18.2",
    "cors": "^2.8.5",
    "dotenv": "^16.4.5"
  },
  "devDependencies": {
    "@types/express": "^4.17.21",
    "@types/cors": "^2.8.17",
    "@types/node": "^22.0.0",
    "typescript": "^5.7.2",
    "tsx": "^4.7.0"
  }
}
```

### Frontend (`frontend/package.json`)
```json
{
  "dependencies": {
    "react": "^19.0.0",
    "react-dom": "^19.0.0",
    "@assistant-ui/react": "^0.8.0",
    "@assistant-ui/react-markdown": "^0.8.0",
    "lucide-react": "^0.476.0",
    "clsx": "^2.1.1",
    "tailwind-merge": "^3.0.2"
  },
  "devDependencies": {
    "@types/react": "^19.0.8",
    "@types/react-dom": "^19.0.3",
    "@vitejs/plugin-react": "^4.3.4",
    "typescript": "^5.7.2",
    "vite": "^6.1.0",
    "tailwindcss": "^4.0.6",
    "autoprefixer": "^10.4.20",
    "postcss": "^8.4.47"
  }
}
```

## Implementation Steps

### Phase 1: Backend Setup
1. ✅ Initialize Node.js project with TypeScript
2. ✅ Install dependencies (@langchain/langgraph, express, etc.)
3. ✅ Create Express server with CORS
4. ✅ Implement LangGraph agent with StateGraph
5. ✅ Create chat API endpoint with streaming support
6. ✅ Add error handling and logging

### Phase 2: Frontend Setup
1. ✅ Initialize Vite + React + TypeScript project
2. ✅ Install @assistant-ui/react and dependencies
3. ✅ Set up Tailwind CSS
4. ✅ Create chat interface with Thread components
5. ✅ Implement message input with Composer
6. ✅ Add loading spinner component
7. ✅ Add error message display component

### Phase 3: Integration
1. ✅ Connect frontend to backend API
2. ✅ Implement API client with fetch
3. ✅ Handle streaming responses
4. ✅ Add real-time message updates
5. ✅ Test end-to-end flow

### Phase 4: Polish
1. ✅ Add proper TypeScript types
2. ✅ Implement error boundaries
3. ✅ Add loading states for all async operations
4. ✅ Create README with setup instructions
5. ✅ Add environment variable examples

## Environment Variables

### Backend (`.env`)
```env
OPENAI_API_KEY=your_openai_api_key_here
PORT=3001
NODE_ENV=development
```

### Frontend (`.env`)
```env
VITE_API_URL=http://localhost:3001
```

## API Endpoints

### POST /api/chat
Request:
```json
{
  "messages": [
    { "role": "user", "content": "Hello!" }
  ]
}
```

Response (streaming):
```
data: {"type":"message","content":"Hello!"}
data: {"type":"message","content":" How"}
data: {"type":"message","content":" can"}
data: {"type":"done"}
```

### GET /api/health
Response:
```json
{
  "status": "ok",
  "timestamp": "2025-10-30T12:00:00Z"
}
```

## Best Practices Identified

1. **State Management**: Use LangGraph Annotation for type-safe state
2. **Streaming**: Implement Server-Sent Events for real-time updates
3. **Error Handling**: Comprehensive try-catch with user-friendly messages
4. **TypeScript**: Strong typing throughout for reliability
5. **Component Structure**: Modular, reusable components
6. **Loading States**: Always show feedback during async operations
7. **CORS**: Properly configured for development and production
8. **Environment Config**: Separate configs for different environments

## References

### GitHub Repositories Explored
- [langchain-ai/langgraphjs-gen-ui-examples](https://github.com/langchain-ai/langgraphjs-gen-ui-examples) - LangGraph.js examples
- [assistant-ui/assistant-ui](https://github.com/assistant-ui/assistant-ui) - Chat UI components
- [FlowiseAI/Flowise](https://github.com/FlowiseAI/Flowise) - Production LangChain app
- [CopilotKit/CopilotKit](https://github.com/CopilotKit/CopilotKit) - AI copilot infrastructure

### Documentation
- [LangGraph Documentation](https://langchain-ai.github.io/langgraphjs/)
- [Assistant UI Documentation](https://docs.assistant-ui.com/)
- [OpenAI API Reference](https://platform.openai.com/docs)

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