Jackspace Claudeskillz Thesys Generative UI

TheSys Generative UI Integration

Complete skill for building AI-powered interfaces with TheSys C1 Generative UI API. Convert LLM responses into streaming, interactive React components.

What is TheSys C1?

TheSys C1 is a Generative UI API that transforms Large Language Model (LLM) responses into live, interactive React components instead of plain text. Rather than displaying walls of text, your AI applications can stream forms, charts, tables, search results, and custom UI elements in real-time.

Key Innovation

Traditional LLM applications return text that developers must manually convert into UI:

LLM → Text Response → Developer Parses → Manual UI Code → Display

TheSys C1 eliminates this manual step:

LLM → C1 API → Interactive React Components → Display

Real-World Impact

• 83% more engaging - Users prefer interactive components over text walls
• 10x faster development - No manual text-to-UI conversion
• 80% cheaper - Reduced development time and maintenance
• Production-ready - Used by teams building AI-native products

When to Use This Skill

Use this skill when building:

1. Chat Interfaces with Rich UI

   • Conversational interfaces that need more than text
   • Customer support chatbots with forms and actions
   • AI assistants that show data visualizations

2. Data Visualization Applications

   • Analytics dashboards with AI-generated charts
   • Business intelligence tools with dynamic tables
   • Search interfaces with structured results

3. Dynamic Form Generation

   • E-commerce product configurators
   • Multi-step workflows driven by AI
   • Data collection with intelligent forms

4. AI Copilots and Assistants

   • Developer tools with code snippets and docs
   • Educational platforms with interactive lessons
   • Research tools with citations and references

5. Search and Discovery

   • Semantic search with structured results
   • Document analysis with highlighted findings
   • Knowledge bases with interactive answers

This Skill Prevents These Errors

• ❌ Empty agent responses from incorrect streaming setup
• ❌ Models ignoring system prompts due to message array issues
• ❌ Version compatibility errors between SDK and API
• ❌ Themes not applying without ThemeProvider
• ❌ Streaming failures from improper response transformation
• ❌ Tool calling bugs from invalid Zod schemas
• ❌ Thread state loss from missing persistence
• ❌ CSS conflicts from import order issues
• ❌ TypeScript errors from outdated type definitions
• ❌ CORS failures from missing headers
• ❌ Rate limit crashes without retry logic
• ❌ Authentication token errors from environment issues

Quick Start by Framework

Vite + React Setup

Most flexible setup for custom backends (your preferred stack).

1. Install Dependencies

npm install @thesysai/genui-sdk @crayonai/react-ui @crayonai/react-core @crayonai/stream
npm install openai zod

2. Create Chat Component

File: src/App.tsx

import "@crayonai/react-ui/styles/index.css";
import { ThemeProvider, C1Component } from "@thesysai/genui-sdk";
import { useState } from "react";

export default function App() {
  const [isLoading, setIsLoading] = useState(false);
  const [c1Response, setC1Response] = useState("");
  const [question, setQuestion] = useState("");

  const makeApiCall = async (query: string) => {
    setIsLoading(true);
    setC1Response("");

    try {
      const response = await fetch("/api/chat", {
        method: "POST",
        headers: { "Content-Type": "application/json" },
        body: JSON.stringify({ prompt: query }),
      });

      const data = await response.json();
      setC1Response(data.response);
    } catch (error) {
      console.error("Error:", error);
    } finally {
      setIsLoading(false);
    }
  };

  return (
    <div className="container">
      <h1>AI Assistant</h1>

      <form => {
        e.preventDefault();
        makeApiCall(question);
      }}>
        <input
          type="text"
          value={question}
          => setQuestion(e.target.value)}
          placeholder="Ask me anything..."
        />
        <button type="submit" disabled={isLoading}>
          {isLoading ? "Processing..." : "Send"}
        </button>
      </form>

      {c1Response && (
        <ThemeProvider>
          <C1Component
            c1Response={c1Response}
            isStreaming={isLoading}
            updateMessage={(message) => setC1Response(message)}
            llmFriendlyMessage }) => {
              if (!isLoading) {
                makeApiCall(llmFriendlyMessage);
              }
            }}
          />
        </ThemeProvider>
      )}
    </div>
  );
}

3. Configure Backend API (Express Example)

import express from "express";
import OpenAI from "openai";
import { transformStream } from "@crayonai/stream";

const app = express();
app.use(express.json());

const client = new OpenAI({
  baseURL: "https://api.thesys.dev/v1/embed",
  apiKey: process.env.THESYS_API_KEY,
});

app.post("/api/chat", async (req, res) => {
  const { prompt } = req.body;

  const stream = await client.chat.completions.create({
    model: "c1/openai/gpt-5/v-20250930", // or any C1-compatible model
    messages: [
      { role: "system", content: "You are a helpful assistant." },
      { role: "user", content: prompt },
    ],
    stream: true,
  });

  // Transform OpenAI stream to C1 response
  const c1Stream = transformStream(stream, (chunk) => {
    return chunk.choices[0]?.delta?.content || "";
  });

  res.json({ response: await streamToString(c1Stream) });
});

async function streamToString(stream: ReadableStream) {
  const reader = stream.getReader();
  let result = "";

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

  return result;
}

app.listen(3000);

Next.js App Router Setup

Most popular framework, full-stack with API routes.

1. Install Dependencies

npm install @thesysai/genui-sdk @crayonai/react-ui @crayonai/react-core
npm install openai

2. Create Chat Page Component

File: app/page.tsx

"use client";

import { C1Chat } from "@thesysai/genui-sdk";
import "@crayonai/react-ui/styles/index.css";

export default function Home() {
  return (
    <div className="min-h-screen">
      <C1Chat apiUrl="/api/chat" />
    </div>
  );
}

3. Create API Route Handler

File: app/api/chat/route.ts

import { NextRequest, NextResponse } from "next/server";
import OpenAI from "openai";
import { transformStream } from "@crayonai/stream";

const client = new OpenAI({
  baseURL: "https://api.thesys.dev/v1/embed",
  apiKey: process.env.THESYS_API_KEY,
});

export async function POST(req: NextRequest) {
  const { prompt } = await req.json();

  const stream = await client.chat.completions.create({
    model: "c1/openai/gpt-5/v-20250930",
    messages: [
      { role: "system", content: "You are a helpful AI assistant." },
      { role: "user", content: prompt },
    ],
    stream: true,
  });

  // Transform to C1-compatible stream
  const responseStream = transformStream(stream, (chunk) => {
    return chunk.choices[0]?.delta?.content || "";
  }) as ReadableStream<string>;

  return new NextResponse(responseStream, {
    headers: {
      "Content-Type": "text/event-stream",
      "Cache-Control": "no-cache, no-transform",
      "Connection": "keep-alive",
    },
  });
}

That's it! You now have a working Generative UI chat interface.

Cloudflare Workers + Static Assets Setup

Your stack: Workers backend with Vite+React frontend.

1. Create Worker Backend (Hono)

File: backend/src/index.ts

import { Hono } from "hono";
import { cors } from "hono/cors";

const app = new Hono();

app.use("/*", cors());

app.post("/api/chat", async (c) => {
  const { prompt } = await c.req.json();

  // Use Cloudflare Workers AI or proxy to OpenAI
  const response = await fetch("https://api.thesys.dev/v1/embed/chat/completions", {
    method: "POST",
    headers: {
      "Authorization": `Bearer ${c.env.THESYS_API_KEY}`,
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      model: "c1/openai/gpt-5/v-20250930",
      messages: [
        { role: "system", content: "You are a helpful assistant." },
        { role: "user", content: prompt },
      ],
      stream: false, // or handle streaming
    }),
  });

  const data = await response.json();
  return c.json(data);
});

export default app;

2. Frontend Setup (Same as Vite+React)

Use the Vite+React example above, but configure API calls to your Worker endpoint.

3. Wrangler Configuration

File: wrangler.jsonc

{
  "name": "thesys-chat-worker",
  "compatibility_date": "2025-10-26",
  "main": "backend/src/index.ts",
  "vars": {
    "ENVIRONMENT": "production"
  },
  "assets": {
    "directory": "dist",
    "binding": "ASSETS"
  }
}

Add THESYS_API_KEY as a secret:

npx wrangler secret put THESYS_API_KEY

Core Components

<C1Chat> - Pre-built Chat Component

When to use: Building conversational interfaces with minimal setup.

The C1Chat component is a fully-featured chat UI with built-in:

• Message history
• Streaming responses
• Thread management
• Loading states
• Error handling
• Responsive design

Basic Usage

import { C1Chat } from "@thesysai/genui-sdk";
import "@crayonai/react-ui/styles/index.css";

export default function App() {
  return (
    <C1Chat
      apiUrl="/api/chat"
      agentName="My AI Assistant"
      logoUrl="https://example.com/logo.png"
    />
  );
}

Key Props

• apiUrl (required) - Backend endpoint for chat completions
• agentName - Display name for the AI agent
• logoUrl - Logo/avatar for the agent
• theme - Custom theme object (see Theming section)
• threadManager - For multi-thread support (advanced)
• threadListManager - For thread list UI (advanced)
• customizeC1 - Custom components (footer, thinking states)

With Theme

import { C1Chat } from "@thesysai/genui-sdk";
import { themePresets } from "@crayonai/react-ui";

<C1Chat
  apiUrl="/api/chat"
  theme={themePresets.candy} // or 'default', or custom object
/>

<C1Component> - Custom Integration Component

When to use: Need full control over state management and UI layout.

The C1Component is the low-level renderer. You handle:

• Fetching data
• Managing state
• Layout structure
• Error boundaries

Basic Usage

import { C1Component, ThemeProvider } from "@thesysai/genui-sdk";
import "@crayonai/react-ui/styles/index.css";

const [c1Response, setC1Response] = useState("");
const [isStreaming, setIsStreaming] = useState(false);

// ... fetch logic

return (
  <ThemeProvider>
    <C1Component
      c1Response={c1Response}
      isStreaming={isStreaming}
      updateMessage={(message) => setC1Response(message)}
      llmFriendlyMessage }) => {
        // Handle interactive actions (button clicks, form submissions)
        console.log("User action:", llmFriendlyMessage);
        // Make new API call with llmFriendlyMessage
      }}
    />
  </ThemeProvider>
);

Key Props

• c1Response (required) - The C1 API response string
• isStreaming - Whether response is still streaming (shows loading indicator)
• updateMessage - Callback for response updates during streaming
• onAction - Callback for user interactions with generated UI
  • llmFriendlyMessage: Pre-formatted message to send back to LLM
  • rawAction: Raw action data from the component

Important: Must Wrap with ThemeProvider

// ❌ Wrong - theme won't apply
<C1Component c1Response={response} />

// ✅ Correct
<ThemeProvider>
  <C1Component c1Response={response} />
</ThemeProvider>

<ThemeProvider> - Theming and Customization

When to use: Always wrap <C1Component> or customize <C1Chat> appearance.

Theme Presets

TheSys includes pre-built themes:

import { themePresets } from "@crayonai/react-ui";

// Available presets:
// - themePresets.default
// - themePresets.candy
// ... (check docs for full list)

<C1Chat theme={themePresets.candy} />

Dark Mode Support

import { useSystemTheme } from "./hooks/useSystemTheme"; // custom hook

export default function App() {
  const systemTheme = useSystemTheme(); // 'light' | 'dark'

  return (
    <C1Chat
      apiUrl="/api/chat"
      theme={{ ...themePresets.default, mode: systemTheme }}
    />
  );
}

Custom Theme Object

const customTheme = {
  mode: "dark", // 'light' | 'dark' | 'system'
  colors: {
    primary: "#3b82f6",
    secondary: "#8b5cf6",
    background: "#1f2937",
    foreground: "#f9fafb",
    // ... more colors
  },
  fonts: {
    body: "Inter, sans-serif",
    heading: "Poppins, sans-serif",
  },
  borderRadius: "12px",
  spacing: {
    base: "16px",
  },
};

<C1Chat theme={customTheme} />

CSS Overrides

Create a custom.css file:

/* Override specific component styles */
.c1-chat-container {
  max-width: 900px;
  margin: 0 auto;
}

.c1-message-user {
  background-color: #3b82f6 !important;
}

.c1-message-assistant {
  background-color: #6b7280 !important;
}

Then import:

import "@crayonai/react-ui/styles/index.css";
import "./custom.css"; // AFTER the default styles

AI Provider Integration

TheSys C1 API is OpenAI-compatible, meaning it works with any LLM provider that uses OpenAI's API format.

OpenAI Integration

Setup

npm install openai

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.thesys.dev/v1/embed",
  apiKey: process.env.THESYS_API_KEY, // TheSys API key
});

Model Selection

TheSys supports OpenAI models through C1:

// GPT 5 (Stable - Recommended for Production)
model: "c1/openai/gpt-5/v-20250930"

// GPT 4.1 (Experimental)
model: "c1-exp/openai/gpt-4.1/v-20250617"

Complete Example

const response = await client.chat.completions.create({
  model: "c1/openai/gpt-5/v-20250930",
  messages: [
    {
      role: "system",
      content: "You are a helpful assistant that generates interactive UI components.",
    },
    {
      role: "user",
      content: "Show me a comparison table of the top 3 project management tools.",
    },
  ],
  stream: true, // Enable streaming
  temperature: 0.7,
  max_tokens: 2000,
});

Anthropic (Claude) Integration

Setup

TheSys C1 supports Anthropic's Claude models via OpenAI-compatible endpoint:

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.thesys.dev/v1/embed",
  apiKey: process.env.THESYS_API_KEY,
});

Model Selection

// Claude Sonnet 4 (Stable - Recommended for Production)
model: "c1/anthropic/claude-sonnet-4/v-20250930"

// Claude 3.5 Haiku (Experimental)
model: "c1-exp/anthropic/claude-3.5-haiku/v-20250709"

⚠️ Deprecated Models: Claude 3.5 Sonnet and Claude 3.7 Sonnet are no longer recommended. Use the stable Claude Sonnet 4 version above.

Example with Claude

const response = await client.chat.completions.create({
  model: "c1/anthropic/claude-sonnet-4/v-20250930",
  messages: [
    {
      role: "system",
      content: "You are Claude, an AI assistant that creates interactive interfaces.",
    },
    {
      role: "user",
      content: "Create a product comparison chart for electric vehicles.",
    },
  ],
  stream: true,
  temperature: 0.8,
  max_tokens: 4096,
});

Model Specifications & Pricing

The table below shows the current stable and experimental models available via TheSys C1 API:

Pricing Notes:

• Costs are per million tokens (M)
• Pricing is based on model name, regardless of endpoint type (embed or visualize)
• Stable models (prefixed with c1/) are recommended for production
• Experimental models (prefixed with c1-exp/) are for testing and may have different behavior

Model Versions: Model identifiers include version dates (e.g., v-20250930). Always check the TheSys Playground for the latest stable versions.

Cloudflare Workers AI Integration

Setup with Workers AI Binding

// In your Cloudflare Worker
export default {
  async fetch(request: Request, env: Env) {
    // Use Workers AI directly (cheaper for some use cases)
    const aiResponse = await env.AI.run('@cf/meta/llama-3-8b-instruct', {
      messages: [
        { role: "system", content: "You are a helpful assistant." },
        { role: "user", content: "Hello!" },
      ],
    });

    // Then transform to C1 format and send to frontend
    // ...
  }
};

Hybrid Approach: Workers AI + C1

// Option 1: Use Workers AI for processing, C1 for UI generation
const thinkingResponse = await env.AI.run('@cf/meta/llama-3-8b-instruct', {
  messages: [{ role: "user", content: "Analyze this data..." }],
});

// Then use C1 to generate UI from the analysis
const c1Response = await fetch("https://api.thesys.dev/v1/embed/chat/completions", {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${env.THESYS_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "c1/openai/gpt-5/v-20250930",
    messages: [
      {
        role: "system",
        content: "Generate a chart visualization for this data.",
      },
      {
        role: "user",
        content: thinkingResponse.response,
      },
    ],
  }),
});

Python Backend Integration

TheSys provides a Python SDK for backend implementations with FastAPI, Flask, or Django.

Setup

pip install thesys-genui-sdk openai

FastAPI Example

from fastapi import FastAPI
from fastapi.responses import StreamingResponse
from thesys_genui_sdk import with_c1_response, write_content
import openai
import os

app = FastAPI()

client = openai.OpenAI(
    base_url="https://api.thesys.dev/v1/embed",
    api_key=os.getenv("THESYS_API_KEY")
)

@app.post("/api/chat")
@with_c1_response  # Automatically handles streaming headers
async def chat_endpoint(request: dict):
    prompt = request.get("prompt")

    stream = client.chat.completions.create(
        model="c1/anthropic/claude-sonnet-4/v-20250930",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": prompt}
        ],
        stream=True
    )

    # Stream chunks to frontend
    async def generate():
        for chunk in stream:
            content = chunk.choices[0].delta.content
            if content:
                yield write_content(content)

    return StreamingResponse(generate(), media_type="text/event-stream")

Key Features

• @with_c1_response decorator: Automatically sets proper response headers for streaming
• write_content helper: Formats chunks for C1Component rendering
• Framework agnostic: Works with FastAPI, Flask, Django, or any Python web framework

Flask Example

from flask import Flask, request, Response
from thesys_genui_sdk import with_c1_response, write_content
import openai
import os

app = Flask(__name__)

client = openai.OpenAI(
    base_url="https://api.thesys.dev/v1/embed",
    api_key=os.getenv("THESYS_API_KEY")
)

@app.route("/api/chat", methods=["POST"])
@with_c1_response
def chat():
    data = request.get_json()
    prompt = data.get("prompt")

    stream = client.chat.completions.create(
        model="c1/openai/gpt-5/v-20250930",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": prompt}
        ],
        stream=True
    )

    def generate():
        for chunk in stream:
            content = chunk.choices[0].delta.content
            if content:
                yield write_content(content)

    return Response(generate(), mimetype="text/event-stream")

Universal Patterns (Any Provider)

Error Handling

try {
  const response = await client.chat.completions.create({
    model: "c1/openai/gpt-5/v-20250930",
    messages: [...],
    stream: true,
  });

  // Process stream...
} catch (error) {
  if (error.status === 429) {
    // Rate limit - implement exponential backoff
    await new Promise(resolve => setTimeout(resolve, 1000));
    // Retry...
  } else if (error.status === 401) {
    // Invalid API key
    console.error("Authentication failed. Check THESYS_API_KEY");
  } else {
    // Other errors
    console.error("API Error:", error);
  }
}

Streaming with transformStream

import { transformStream } from "@crayonai/stream";

const llmStream = await client.chat.completions.create({
  model: "c1/openai/gpt-5/v-20250930",
  messages: [...],
  stream: true,
});

// Transform OpenAI stream to C1 stream
const c1Stream = transformStream(llmStream, (chunk) => {
  return chunk.choices[0]?.delta?.content || "";
}) as ReadableStream<string>;

return new Response(c1Stream, {
  headers: {
    "Content-Type": "text/event-stream",
    "Cache-Control": "no-cache, no-transform",
    "Connection": "keep-alive",
  },
});

Tool Calling with Zod Schemas

Tool calling allows your AI to invoke functions and display interactive UI for data collection, external API calls, and complex workflows.

1. Define Tools with Zod

import { z } from "zod";
import zodToJsonSchema from "zod-to-json-schema";

// Define the tool schema
const webSearchSchema = z.object({
  query: z.string().describe("The search query"),
  max_results: z.number().int().min(1).max(10).default(5)
    .describe("Maximum number of results to return"),
});

// Convert to OpenAI tool format
export const webSearchTool = {
  type: "function" as const,
  function: {
    name: "web_search",
    description: "Search the web for current information",
    parameters: zodToJsonSchema(webSearchSchema),
  },
};

2. More Complex Example: Order Management

import { z } from "zod";

// Discriminated union for different product types
const productOrderSchema = z.discriminatedUnion("type", [
  z.object({
    type: z.literal("gloves"),
    size: z.enum(["S", "M", "L", "XL"]),
    color: z.string(),
    quantity: z.number().int().min(1),
  }),
  z.object({
    type: z.literal("hat"),
    style: z.enum(["beanie", "baseball", "fedora"]),
    color: z.string(),
    quantity: z.number().int().min(1),
  }),
  z.object({
    type: z.literal("scarf"),
    length: z.enum(["short", "medium", "long"]),
    material: z.enum(["wool", "cotton", "silk"]),
    quantity: z.number().int().min(1),
  }),
]);

const createOrderSchema = z.object({
  customer_email: z.string().email(),
  items: z.array(productOrderSchema).min(1),
  shipping_address: z.object({
    street: z.string(),
    city: z.string(),
    state: z.string(),
    zip: z.string(),
  }),
});

export const createOrderTool = {
  type: "function" as const,
  function: {
    name: "create_order",
    description: "Create a new order for products",
    parameters: zodToJsonSchema(createOrderSchema),
  },
};

3. Implement Tool Execution

// tools.ts
import { TavilySearchAPIClient } from "@tavily/core";

const tavily = new TavilySearchAPIClient({
  apiKey: process.env.TAVILY_API_KEY,
});

export async function executeWebSearch(query: string, max_results: number) {
  const results = await tavily.search(query, {
    maxResults: max_results,
    includeAnswer: true,
  });

  return {
    query,
    results: results.results.map((r) => ({
      title: r.title,
      url: r.url,
      snippet: r.content,
    })),
    answer: results.answer,
  };
}

export async function executeCreateOrder(orderData: z.infer<typeof createOrderSchema>) {
  // Validate with Zod
  const validated = createOrderSchema.parse(orderData);

  // Save to database
  const orderId = await saveOrderToDatabase(validated);

  return {
    success: true,
    orderId,
    message: `Order ${orderId} created successfully`,
  };
}

4. Integrate Tools in API Route

import { NextRequest, NextResponse } from "next/server";
import OpenAI from "openai";
import { transformStream } from "@crayonai/stream";
import { webSearchTool, createOrderTool } from "./tools";

const client = new OpenAI({
  baseURL: "https://api.thesys.dev/v1/embed",
  apiKey: process.env.THESYS_API_KEY,
});

export async function POST(req: NextRequest) {
  const { prompt } = await req.json();

  const llmStream = await client.beta.chat.completions.runTools({
    model: "c1/anthropic/claude-sonnet-4/v-20250930",
    messages: [
      {
        role: "system",
        content: "You are a helpful shopping assistant. Use tools to search for products and create orders.",
      },
      {
        role: "user",
        content: prompt,
      },
    ],
    stream: true,
    tools: [webSearchTool, createOrderTool],
    toolChoice: "auto", // Let AI decide when to use tools
  });

  // Handle tool execution
  llmStream.on("message", async (event) => {
    if (event.tool_calls) {
      for (const toolCall of event.tool_calls) {
        if (toolCall.function.name === "web_search") {
          const args = JSON.parse(toolCall.function.arguments);
          const result = await executeWebSearch(args.query, args.max_results);
          // Send result back to LLM...
        } else if (toolCall.function.name === "create_order") {
          const args = JSON.parse(toolCall.function.arguments);
          const result = await executeCreateOrder(args);
          // Send result back to LLM...
        }
      }
    }
  });

  const responseStream = transformStream(llmStream, (chunk) => {
    return chunk.choices[0]?.delta?.content || "";
  }) as ReadableStream<string>;

  return new NextResponse(responseStream, {
    headers: {
      "Content-Type": "text/event-stream",
      "Cache-Control": "no-cache, no-transform",
      "Connection": "keep-alive",
    },
  });
}

5. Display Tool Results in UI

The C1Component automatically renders tool interactions as forms and displays results. You just need to handle the onAction callback:

<C1Component
  c1Response={c1Response}
  ({ llmFriendlyMessage, rawAction }) => {
    console.log("Tool action triggered:", rawAction);
    // Make API call with llmFriendlyMessage to continue conversation
    await makeApiCall(llmFriendlyMessage);
  }}
/>

Advanced Features

Thread Management (Multi-Conversation Support)

Enable users to have multiple conversation threads with thread switching, history, and persistence.

1. Define Thread API

Create backend endpoints:

• GET /api/threads - List all threads
• POST /api/threads - Create new thread
• PUT /api/threads/:id - Update thread title
• DELETE /api/threads/:id - Delete thread
• GET /api/threads/:id/messages - Load thread messages

2. Implement Thread Managers

import {
  useThreadListManager,
  useThreadManager,
} from "@thesysai/genui-sdk";
import { Thread, Message, UserMessage } from "@crayonai/react-core";

export default function App() {
  const threadListManager = useThreadListManager({
    // Fetch all threads
    fetchThreadList: async (): Promise<Thread[]> => {
      const response = await fetch("/api/threads");
      return response.json();
    },

    // Delete thread
    deleteThread: async (threadId: string): Promise<void> => {
      await fetch(`/api/threads/${threadId}`, { method: "DELETE" });
    },

    // Update thread title
    updateThread: async (thread: Thread): Promise<Thread> => {
      const response = await fetch(`/api/threads/${thread.threadId}`, {
        method: "PUT",
        headers: { "Content-Type": "application/json" },
        body: JSON.stringify({ title: thread.title }),
      });
      return response.json();
    },

    // Create new thread
    createThread: async (firstMessage: UserMessage): Promise<Thread> => {
      const response = await fetch("/api/threads", {
        method: "POST",
        headers: { "Content-Type": "application/json" },
        body: JSON.stringify({
          title: firstMessage.message || "New Chat",
        }),
      });
      return response.json();
    },

    // URL synchronization
    onSwitchToNew: () => {
      window.history.replaceState(null, "", window.location.pathname);
    },
    onSelectThread: (threadId: string) => {
      const url = new URL(window.location.href);
      url.searchParams.set("threadId", threadId);
      window.history.replaceState(null, "", url.toString());
    },
  });

  const threadManager = useThreadManager({
    threadListManager,

    // Load messages for selected thread
    loadThread: async (threadId: string): Promise<Message[]> => {
      const response = await fetch(`/api/threads/${threadId}/messages`);
      return response.json();
    },

    // Handle message updates (e.g., feedback)
    onUpdateMessage: async ({ message }: { message: Message }) => {
      if (threadListManager.selectedThreadId) {
        await fetch(
          `/api/threads/${threadListManager.selectedThreadId}/message`,
          {
            method: "PUT",
            headers: { "Content-Type": "application/json" },
            body: JSON.stringify(message),
          }
        );
      }
    },
  });

  return (
    <C1Chat
      threadManager={threadManager}
      threadListManager={threadListManager}
    />
  );
}

Thinking States (Progress Indicators)

Show users what the AI is doing during processing (searching web, analyzing data, etc.).

1. Server-Side: Write Think Items

import { makeC1Response } from "@thesysai/genui-sdk/server";

export async function POST(req: NextRequest) {
  const c1Response = makeC1Response();

  // Initial thinking state
  c1Response.writeThinkItem({
    title: "Thinking…",
    description: "Analyzing your question and planning the response.",
  });

  const { prompt } = await req.json();

  // Update thinking state when calling tools
  const llmStream = await client.beta.chat.completions.runTools({
    model: "c1/anthropic/claude-sonnet-4/v-20250930",
    messages: [...],
    tools: [
      getWebSearchTool(() => {
        c1Response.writeThinkItem({
          title: "Searching the web…",
          description: "Finding the most relevant and up-to-date information.",
        });
      }),
    ],
  });

  transformStream(
    llmStream,
    (chunk) => {
      const content = chunk.choices[0]?.delta?.content;
      if (content) {
        c1Response.writeContent(content);
      }
      return content;
    },
    {
      onEnd: () => {
        c1Response.end();
      },
    }
  );

  return new NextResponse(c1Response.responseStream, {
    headers: {
      "Content-Type": "text/event-stream",
      "Cache-Control": "no-cache, no-transform",
      "Connection": "keep-alive",
    },
  });
}

2. Custom Think Component

// CustomThink.tsx
import { ThinkItem } from "@crayonai/react-core";

export function CustomThink({ item }: { item: ThinkItem }) {
  return (
    <div className="custom-think">
      <div className="spinner" />
      <div>
        <h4>{item.title}</h4>
        <p>{item.description}</p>
      </div>
    </div>
  );
}

// In your app
<C1Chat
  apiUrl="/api/chat"
  customizeC1={{ thinkComponent: CustomThink }}
/>

Message and Thread Sharing

Enable users to share conversations via public URLs.

1. Generate Share Links

import { C1ShareThread } from "@thesysai/genui-sdk";

const selectedThreadId = threadListManager.selectedThreadId;

<C1ShareThread
  generateShareLink={
    !selectedThreadId
      ? undefined
      : async () => {
          const baseUrl = window.location.origin;
          return `${baseUrl}/shared/${selectedThreadId}`;
        }
  }
/>

2. Create Shared View Page

// app/shared/[threadId]/page.tsx
"use client";

import { C1ChatViewer } from "@thesysai/genui-sdk";
import { Message } from "@crayonai/react-core";
import { use, useEffect, useState } from "react";
import "@crayonai/react-ui/styles/index.css";

export default function ViewSharedThread({
  params,
}: {
  params: Promise<{ threadId: string }>;
}) {
  const { threadId } = use(params);
  const [messages, setMessages] = useState<Message[]>([]);

  useEffect(() => {
    const fetchMessages = async () => {
      const response = await fetch(`/api/share/${threadId}`);
      const data = await response.json();
      setMessages(data);
    };
    fetchMessages();
  }, [threadId]);

  if (!messages.length) return <div>Loading...</div>;

  return <C1ChatViewer messages={messages} />;
}

Production Patterns

Message Persistence

Don't use in-memory storage in production!

// ❌ Bad - loses data on restart
const messageStore = new Map<string, Message[]>();

// ✅ Good - use a database
import { db } from "./database"; // D1, PostgreSQL, etc.

export async function saveMessage(threadId: string, message: Message) {
  await db.insert(messages).values({
    threadId,
    role: message.role,
    content: message.content,
    createdAt: new Date(),
  });
}

export async function getThreadMessages(threadId: string): Promise<Message[]> {
  return db.select().from(messages).where(eq(messages.threadId, threadId));
}

Authentication Integration (Clerk Example)

import { auth } from "@clerk/nextjs";

export async function POST(req: NextRequest) {
  const { userId } = auth();

  if (!userId) {
    return NextResponse.json({ error: "Unauthorized" }, { status: 401 });
  }

  // Proceed with chat logic, scoping to user
  const userThreads = await db
    .select()
    .from(threads)
    .where(eq(threads.userId, userId));

  // ...
}

Rate Limiting

import { Ratelimit } from "@upstash/ratelimit";
import { Redis } from "@upstash/redis";

const ratelimit = new Ratelimit({
  redis: Redis.fromEnv(),
  limiter: Ratelimit.slidingWindow(10, "1 m"), // 10 requests per minute
});

export async function POST(req: NextRequest) {
  const { userId } = auth();
  const { success } = await ratelimit.limit(userId);

  if (!success) {
    return NextResponse.json(
      { error: "Rate limit exceeded. Please try again later." },
      { status: 429 }
    );
  }

  // Proceed...
}

Error Boundaries

import { ErrorBoundary } from "react-error-boundary";

function ErrorFallback({ error, resetErrorBoundary }) {
  return (
    <div role="alert">
      <h2>Something went wrong</h2>
      <pre>{error.message}</pre>
      <button again</button>
    </div>
  );
}

export default function App() {
  return (
    <ErrorBoundary FallbackComponent={ErrorFallback}>
      <C1Chat apiUrl="/api/chat" />
    </ErrorBoundary>
  );
}

Performance Optimization

// 1. Lazy load C1Chat
import { lazy, Suspense } from "react";

const C1Chat = lazy(() =>
  import("@thesysai/genui-sdk").then((mod) => ({ default: mod.C1Chat }))
);

export default function App() {
  return (
    <Suspense fallback={<div>Loading chat...</div>}>
      <C1Chat apiUrl="/api/chat" />
    </Suspense>
  );
}

// 2. Memoize expensive computations
import { useMemo } from "react";

const threadListManager = useMemo(
  () =>
    useThreadListManager({
      // ... config
    }),
  [] // Empty deps - only create once
);

Common Errors & Solutions

1. Empty Agent Responses

Problem: AI returns empty responses, UI shows nothing.

Cause: Incorrect streaming transformation or response format.

Solution:

// ✅ Use transformStream helper
import { transformStream } from "@crayonai/stream";

const c1Stream = transformStream(llmStream, (chunk) => {
  return chunk.choices[0]?.delta?.content || ""; // Fallback to empty string
}) as ReadableStream<string>;

2. Model Not Following System Prompt

Problem: AI ignores instructions in system prompt.

Cause: System prompt is not first in messages array or improperly formatted.

Solution:

// ✅ System prompt MUST be first
const messages = [
  { role: "system", content: "You are a helpful assistant." }, // FIRST!
  ...conversationHistory,
  { role: "user", content: userPrompt },
];

// ❌ Wrong - system prompt after user messages
const messages = [
  { role: "user", content: "Hello" },
  { role: "system", content: "..." }, // TOO LATE
];

3. Version Compatibility Errors

Problem: TypeError: Cannot read property 'X' of undefined or component rendering errors.

Cause: Mismatched SDK versions.

Solution: Check compatibility matrix:

# Update to compatible versions
npm install @thesysai/genui-sdk@0.6.40 @crayonai/react-ui@0.8.42 @crayonai/react-core@0.7.6

4. Theme Not Applying

Problem: UI components don't match custom theme.

Cause: Missing ThemeProvider wrapper.

Solution:

// ❌ Wrong
<C1Component c1Response={response} />

// ✅ Correct
<ThemeProvider theme={customTheme}>
  <C1Component c1Response={response} />
</ThemeProvider>

5. Streaming Not Working

Problem: UI doesn't update in real-time, waits for full response.

Cause: Not using streaming or improper response headers.

Solution:

// 1. Enable streaming in API call
const stream = await client.chat.completions.create({
  model: "c1/openai/gpt-5/v-20250930",
  messages: [...],
  stream: true, // ✅ IMPORTANT
});

// 2. Set proper response headers
return new NextResponse(responseStream, {
  headers: {
    "Content-Type": "text/event-stream",
    "Cache-Control": "no-cache, no-transform",
    "Connection": "keep-alive",
  },
});

// 3. Pass isStreaming prop
<C1Component
  c1Response={response}
  isStreaming={true} // ✅ Shows loading indicator
/>

6. Tool Calling Failures

Problem: Tools not executing or validation errors.

Cause: Invalid Zod schema or incorrect tool format.

Solution:

import { z } from "zod";
import zodToJsonSchema from "zod-to-json-schema";

// ✅ Proper Zod schema with descriptions
const toolSchema = z.object({
  query: z.string().describe("Search query"), // DESCRIBE all fields
  limit: z.number().int().min(1).max(100).describe("Max results"),
});

// ✅ Convert to OpenAI format
const tool = {
  type: "function" as const,
  function: {
    name: "search_web",
    description: "Search the web for information", // Clear description
    parameters: zodToJsonSchema(toolSchema), // Convert schema
  },
};

// ✅ Validate incoming tool calls
const args = toolSchema.parse(JSON.parse(toolCall.function.arguments));

7. Thread State Not Persisting

Problem: Threads disappear on pag

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