ChatKit Integration Skill (Tier 1: Foundation)

ChatKit Integration Skill (Tier 1: Foundation)

Overview

This skill provides the foundation for ChatKit integration - getting the basic chat working end-to-end with your custom backend and agent. It covers:

• Backend: ChatKitServer setup, database persistence, agent wiring
• Frontend: useChatKit configuration, authentication, context injection
• Infrastructure: Script loading, httpOnly cookie proxy (Next.js)

For advanced capabilities, see related skills:

• chatkit-streaming (Tier 2): Response lifecycle, progress updates, client effects
• chatkit-actions (Tier 3): Interactive widgets, entity tagging, composer tools

Persona

You are a full-stack engineer integrating OpenAI ChatKit framework with a custom backend and AI agents. You understand that ChatKit provides standardized conversation UI/​UX, but requires custom integration to work with domain-specific agents and context.

Questions to Ask Before Implementing

1. Backend Integration:

   • What agent framework are you using? (OpenAI Agents SDK, LangChain, custom)
   • What tools does your agent need? (RAG search, custom functions)
   • What context does your agent need? (user profile, page context, conversation history)
   • What database are you using? (PostgreSQL, MongoDB, Redis)

2. Frontend Integration:

   • What frontend framework? (React, Next.js, Docusaurus)
   • How is authentication handled? (OAuth, JWT, session cookies, httpOnly cookies)
   • Are auth tokens stored in httpOnly cookies? (Requires server-side proxy)
   • What context can you extract client-side? (page URL, title, DOM content)
   • Do you need custom UI features? (text selection, personalization menu)

3. Context Requirements:

   • What user information is available? (name, email, role, preferences)
   • What page context is needed? (URL, title, headings, content)
   • How should context be transmitted? (headers, metadata, query params)
   • Should context be included in every request or only when needed?

4. Persistence Requirements:

   • Do conversations need to persist across sessions?
   • What's the expected conversation volume? (affects database choice)
   • Do you need multi-tenancy? (organization isolation)
   • What's the retention policy? (how long to keep conversations)

Principles

Backend Principles

1. Extend ChatKit Server, Don't Replace

   • Inherit from ChatKitServer[RequestContext]
   • Override only respond() method for agent execution
   • Let base class handle read-only operations (threads.list, items.list)
   • Rationale: ChatKit handles protocol, you handle agent logic

2. Context Injection in Prompt

   • Include conversation history as string in system prompt (CarFixer pattern)
   • Include user context (name, profile) in system prompt
   • Include page context (current page) in system prompt
   • Rationale: Agent SDK receives single prompt, history must be in prompt

3. User Isolation via RequestContext

   • All operations scoped by user_id in RequestContext
   • Store operations filter by user_id automatically
   • Never expose data across users
   • Rationale: Multi-tenant safety, data privacy

4. Graceful Degradation

   • System starts even if database unavailable (ChatKit disabled)
   • RAG search can fail without blocking ChatKit
   • Log warnings but don't crash
   • Rationale: Partial functionality better than no functionality

5. Connection Pool Warmup

   • Pre-warm database connections on startup
   • Avoids 7+ second first-request delay
   • Test connections before use (pool_pre_ping=True)
   • Rationale: Production-ready performance

Frontend Principles

1. Custom Fetch Interceptor

   • Provide custom fetch function to useChatKit config
   • Intercept all ChatKit requests
   • Add authentication headers (X-User-ID)
   • Add metadata (userInfo, pageContext) to request body
   • Rationale: ChatKit doesn't handle auth natively, you must inject it

2. Script Loading Detection

   • Check for ChatKit custom element before rendering
   • Listen for script load events
   • Only render ChatKit component when script ready
   • Handle script load failures gracefully
   • Rationale: External script required, component fails without it

3. Page Context Extraction

   • Extract client-side (DOM, window.location)
   • Include: URL, title, path, headings, meta description
   • Send with every message (in metadata)
   • Rationale: Agent needs to know what user is viewing

4. Build-Time Configuration

   • Read env vars in docusaurus.config.ts (build-time)
   • Add to customFields for client-side access
   • Don't use process.env in browser code
   • Rationale: Static sites bake config at build time

5. Authentication Gate

   • Require login before allowing chat access
   • Show login prompt if not authenticated
   • Redirect to OAuth flow
   • Rationale: User ID required for conversation persistence

6. httpOnly Cookie Proxy (Next.js)

   • httpOnly cookies cannot be accessed from JavaScript (security feature)
   • Create server-side API route to read cookies and add Authorization header
   • Proxy forwards requests to backend with JWT token
   • Rationale: Secure token storage requires server-side access

7. Next.js Script Loading Strategy

   • Use beforeInteractive for web component scripts in layout.tsx
   • Place Script in <head> element
   • Script must load before React hydration for custom elements
   • Rationale: Web components must be defined before React renders them

Implementation Patterns

Pattern 1: ChatKit Server with Custom Agent

When: Integrating ChatKit with OpenAI Agents SDK

Implementation:

from chatkit.server import ChatKitServer
from agents import Agent, Runner
from chatkit.agents import stream_agent_response

class CustomChatKitServer(ChatKitServer[RequestContext]):
    """Extend ChatKit server with custom agent."""
    
    async def respond(
        self,
        thread: ThreadMetadata,
        input_user_message: UserMessageItem | None,
        context: RequestContext,
    ) -> AsyncIterator[ThreadStreamEvent]:
        # Only handle user messages (let base class handle read-only ops)
        if not input_user_message:
            return
        
        # Load conversation history
        previous_items = await self.store.load_thread_items(
            thread.id, after=None, limit=10, order="desc", context=context
        )
        
        # Build history string for prompt
        history_str = "\n".join([
            f"{item.role}: {item.content}" 
            for item in reversed(previous_items.data)
        ])
        
        # Extract context from metadata
        user_info = context.metadata.get('userInfo', {})
        page_context = context.metadata.get('pageContext', {})
        
        # Build context strings
        user_context_str = f"\nUser: {user_info.get('name')}\n"
        page_context_str = f"\nPage: {page_context.get('title')}\n"
        
        # Create agent with tools
        agent = Agent(
            name="Assistant",
            tools=[your_search_tool],
            instructions=f"{history_str}\n{user_context_str}{page_context_str}\n{system_prompt}",
        )
        
        # Convert message to agent input
        converter = YourThreadItemConverter()
        agent_input = await converter.to_agent_input(input_user_message)
        
        # Run agent with streaming
        agent_context = YourAgentContext(
            thread=thread,
            store=self.store,
            request_context=context,
        )
        result = Runner.run_streamed(agent, agent_input, context=agent_context)
        
        # Stream results
        async for event in stream_agent_response(agent_context, result):
            yield event

Evidence: rag-agent/​chatkit_server.py:100-270

Pattern 2: Custom Fetch Interceptor

When: Adding authentication and context to ChatKit requests

Implementation:

const { control, sendUserMessage } = useChatKit({
  api: {
    url: `${backendUrl}/​chatkit`,
    domainKey: domainKey,
    
    // Custom fetch to inject auth and context
    fetch: async (url: string, options: RequestInit) => {
      // Check authentication
      if (!isLoggedIn) {
        throw new Error('User must be logged in');
      }
      
      const userId = session.user.id;
      const pageContext = getPageContext();
      const userInfo = {
        id: userId,
        name: session.user.name,
        email: session.user.email,
        // ... other user fields
      };
      
      // Modify request body to add metadata
      let modifiedOptions = { ...options };
      if (modifiedOptions.body && typeof modifiedOptions.body === 'string') {
        const parsed = JSON.parse(modifiedOptions.body);
        if (parsed.type === 'threads.create' && parsed.params?.input) {
          parsed.params.input.metadata = {
            userId,
            userInfo,
            pageContext,
            ...parsed.params.input.metadata,
          };
          modifiedOptions.body = JSON.stringify(parsed);
        } else if (parsed.type === 'threads.run' && parsed.params?.input) {
          if (!parsed.params.input.metadata) {
            parsed.params.input.metadata = {};
          }
          parsed.params.input.metadata.userInfo = userInfo;
          parsed.params.input.metadata.pageContext = pageContext;
          modifiedOptions.body = JSON.stringify(parsed);
        }
      }
      
      // Add authentication header
      return fetch(url, {
        ...modifiedOptions,
        headers: {
          ...modifiedOptions.headers,
          'X-User-ID': userId,
          'Content-Type': 'application/​json',
        },
      });
    },
  },
});

Evidence: robolearn-interface/​src/​components/​ChatKitWidget/​index.tsx:197-240

Pattern 3: Script Loading Detection

When: ChatKit requires external script, component must wait

Implementation:

const [scriptStatus, setScriptStatus] = useState<'pending' | 'ready' | 'error'>(
  isBrowser && window.customElements?.get('openai-chatkit') ? 'ready' : 'pending'
);

useEffect(() => {
  if (scriptStatus !== 'pending') return;
  
  // Check if already loaded
  if (window.customElements?.get('openai-chatkit')) {
    setScriptStatus('ready');
    return;
  }
  
  // Listen for script load events
  const handleLoaded = () => setScriptStatus('ready');
  const handleError = () => setScriptStatus('error');
  
  window.addEventListener('chatkit-script-loaded', handleLoaded);
  window.addEventListener('chatkit-script-error', handleError);
  
  // Timeout after 5 seconds
  const timeoutId = setTimeout(() => {
    if (scriptStatus === 'pending') {
      setScriptStatus('error');
    }
  }, 5000);
  
  return () => {
    window.removeEventListener('chatkit-script-loaded', handleLoaded);
    window.removeEventListener('chatkit-script-error', handleError);
    clearTimeout(timeoutId);
  };
}, [scriptStatus]);

// Only render ChatKit when script ready
{isOpen && scriptStatus === 'ready' && (
  <ChatKit control={control} />
)}

Evidence: robolearn-interface/​src/​components/​ChatKitWidget/​index.tsx:67-113

Pattern 4: Page Context Extraction

When: Agent needs to know what page user is viewing

Implementation:

const getPageContext = useCallback(() => {
  if (typeof window === 'undefined') return null;
  
  // Extract meta tags
  const metaDescription = document.querySelector('meta[name="description"]')
    ?.getAttribute('content') || '';
  
  // Find main content
  const mainContent = document.querySelector('article') || 
                     document.querySelector('main') || 
                     document.body;
  
  // Extract headings
  const headings = Array.from(mainContent.querySelectorAll('h1, h2, h3'))
    .slice(0, 5)
    .map(h => h.textContent?.trim())
    .filter(Boolean)
    .join(', ');
  
  return {
    url: window.location.href,
    title: document.title,
    path: window.location.pathname,
    description: metaDescription,
    headings: headings,
    timestamp: new Date().toISOString(),
  };
}, []);

Evidence: robolearn-interface/​src/​components/​ChatKitWidget/​index.tsx:121-151

Pattern 5: Text Selection "Ask" Feature

When: Users want to ask questions about selected content

Implementation:

// Detect text selection
useEffect(() => {
  const handleSelection = () => {
    const selection = window.getSelection();
    if (!selection || selection.rangeCount === 0) {
      setSelectedText('');
      setSelectionPosition(null);
      return;
    }
    
    const selectedText = selection.toString().trim();
    if (selectedText.length > 0) {
      setSelectedText(selectedText);
      
      // Get selection position
      const range = selection.getRangeAt(0);
      const rect = range.getBoundingClientRect();
      setSelectionPosition({
        x: rect.left + rect.width / 2,
        y: rect.top - 10,
      });
    }
  };
  
  document.addEventListener('selectionchange', handleSelection);
  document.addEventListener('mouseup', handleSelection);
  
  return () => {
    document.removeEventListener('selectionchange', handleSelection);
    document.removeEventListener('mouseup', handleSelection);
  };
}, []);

// Send selected text
const handleAskSelectedText = useCallback(async () => {
  const pageContext = getPageContext();
  const messageText = `Can you explain this from "${pageContext.title}":\n\n"${selectedText}"`;
  
  if (!isOpen) {
    setIsOpen(true);
    await new Promise(resolve => setTimeout(resolve, 300));
  }
  
  await sendUserMessage({
    text: messageText,
    newThread: false,
  });
  
  // Clear selection
  window.getSelection()?.removeAllRanges();
  setSelectedText('');
  setSelectionPosition(null);
}, [selectedText, isOpen, sendUserMessage, getPageContext]);

Evidence: robolearn-interface/​src/​components/​ChatKitWidget/​index.tsx:153-187, 273-331

Pattern 6: httpOnly Cookie Proxy (Next.js App Router)

When: Authentication tokens stored in httpOnly cookies (cannot be read by JavaScript)

Implementation:

// app/​api/​chatkit/​route.ts
import { NextRequest, NextResponse } from "next/​server";
import { cookies } from "next/​headers";

const API_BASE = process.env.NEXT_PUBLIC_API_URL || "http://localhost:8000";

export async function POST(request: NextRequest) {
  const cookieStore = await cookies();

  // Read httpOnly cookie (only accessible server-side)
  const idToken = cookieStore.get("taskflow_id_token")?.value;

  if (!idToken) {
    return NextResponse.json({ error: "Not authenticated" }, { status: 401 });
  }

  // Build target URL - note: ChatKit endpoint is at /​chatkit, NOT /​api/​chatkit
  const url = new URL("/​chatkit", API_BASE);

  try {
    const body = await request.text();

    // Forward request with Authorization header
    const response = await fetch(url.toString(), {
      method: "POST",
      headers: {
        Authorization: `Bearer ${idToken}`,
        "Content-Type": "application/​json",
        // Forward custom headers
        "X-User-ID": request.headers.get("X-User-ID") || "",
        "X-Page-URL": request.headers.get("X-Page-URL") || "",
      },
      body: body || undefined,
    });

    // Handle SSE streaming responses
    if (response.headers.get("content-type")?.includes("text/​event-stream")) {
      return new Response(response.body, {
        status: response.status,
        headers: {
          "Content-Type": "text/​event-stream",
          "Cache-Control": "no-cache",
          "Connection": "keep-alive",
        },
      });
    }

    // Return JSON for non-streaming responses
    const data = await response.json().catch(() => null);
    return NextResponse.json(data, { status: response.status });
  } catch (error) {
    console.error("[ChatKit Proxy] Error:", error);
    return NextResponse.json({ error: "ChatKit proxy request failed" }, { status: 500 });
  }
}

Evidence: web-dashboard/​src/​app/​api/​chatkit/​route.ts

Pattern 7: Next.js Script Loading for Web Components

When: ChatKit script must load before React hydration

Implementation:

// app/​layout.tsx
import Script from "next/​script";

export default function RootLayout({ children }: { children: React.ReactNode }) {
  return (
    <html lang="en">
      <head>
        {/​* MUST be in <head> with beforeInteractive for web components */​}
        <Script
          src="https://cdn.platform.openai.com/​deployments/​chatkit/​chatkit.js"
          strategy="beforeInteractive"
        />
      </​head>
      <body>{children}</​body>
    </​html>
  );
}

In ChatKit Widget - wait for custom element:

// components/​chat/​ChatKitWidget.tsx
const [scriptStatus, setScriptStatus] = useState<'pending' | 'ready' | 'error'>(
  isBrowser && window.customElements?.get('openai-chatkit') ? 'ready' : 'pending'
);

useEffect(() => {
  if (!isBrowser) return;

  // Already registered
  if (window.customElements?.get('openai-chatkit')) {
    setScriptStatus('ready');
    return;
  }

  // Wait for custom element to be defined
  customElements.whenDefined('openai-chatkit').then(() => {
    setScriptStatus('ready');
  });
}, []);

// Only render when ready
{isOpen && scriptStatus === 'ready' && <ChatKit control={control} />}

Evidence: web-dashboard/​src/​app/​layout.tsx, web-dashboard/​src/​components/​chat/​ChatKitWidget.tsx:42-93

Pattern 8: Custom Fetch with Proxy Authentication (Next.js)

When: Using useChatKit with httpOnly cookie proxy

Implementation:

const chatkitProxyUrl = "/​api/​chatkit"; // Proxy handles auth

const { control, sendUserMessage } = useChatKit({
  api: {
    url: chatkitProxyUrl,
    domainKey: domainKey,

    // Custom fetch - auth handled by proxy, inject context
    fetch: async (input: RequestInfo | URL, options?: RequestInit) => {
      const url = typeof input === 'string' ? input : input.toString();

      // Client-side auth check (proxy will verify token)
      if (!isAuthenticated) {
        throw new Error('User must be logged in');
      }

      const userId = user.sub;
      const pageContext = getPageContext();

      // Inject metadata into request body
      let modifiedOptions = { ...options } as RequestInit;
      if (modifiedOptions.body && typeof modifiedOptions.body === 'string') {
        try {
          const parsed = JSON.parse(modifiedOptions.body);
          if (parsed.type === 'threads.create' && parsed.params?.input) {
            parsed.params.input.metadata = {
              userId,
              userInfo: { id: userId, name: user.name },
              pageContext,
              ...parsed.params.input.metadata,
            };
            modifiedOptions.body = JSON.stringify(parsed);
          } else if (parsed.type === 'threads.run' && parsed.params?.input) {
            parsed.params.input.metadata = {
              ...parsed.params.input.metadata,
              userInfo: { id: userId, name: user.name },
              pageContext,
            };
            modifiedOptions.body = JSON.stringify(parsed);
          }
        } catch { /​* Ignore non-JSON */ }
      }

      return fetch(url, {
        ...modifiedOptions,
        credentials: 'include', // Include cookies for proxy auth
        headers: {
          ...modifiedOptions.headers,
          'X-User-ID': userId,
          'X-Page-URL': pageContext?.url || '',
          'Content-Type': 'application/​json',
        },
      });
    },
  },
});

Evidence: web-dashboard/​src/​components/​chat/​ChatKitWidget.tsx:168-296

When to Apply

• Integrating ChatKit with custom backend
• Adding authentication to ChatKit
• Injecting context (user, page) into agent prompts
• Implementing text selection "Ask" functionality
• Building conversational AI interfaces
• Next.js App Router with httpOnly cookies (use Pattern 6-8)
• External web component scripts in Next.js (use Pattern 7)

Contraindications

• Simple chat without persistence: ChatKit may be overkill
• No user authentication: ChatKit requires user_id for isolation
• Serverless functions: Connection pooling doesn't work well
• Very low traffic: Overhead not justified

Common Pitfalls

1. History Not in Prompt: Agent doesn't remember conversation

   • Fix: Include history as string in system prompt (not as messages)

2. Context Not Transmitted: Agent doesn't receive user/​page context

   • Fix: Add to request metadata, extract in backend, include in prompt

3. Script Not Loaded: ChatKit component fails to render

   • Fix: Detect script loading, wait before rendering

4. Auth Headers Missing: Backend rejects requests

   • Fix: Use custom fetch interceptor to add headers

5. Database Not Warmed: First request takes 7+ seconds

   • Fix: Pre-warm connection pool on startup

6. httpOnly Cookies Not Accessible (Next.js): Can't read auth token from JavaScript

   • Symptom: document.cookie returns nothing, "id_token not found"
   • Why: httpOnly cookies are a security feature - cannot be read by JavaScript
   • Fix: Create server-side API route proxy that reads cookies and adds Authorization header

7. Script Loading Too Late (Next.js): "ChatKit web component is unavailable"

   • Symptom: 404 errors, custom element not defined
   • Why: afterInteractive strategy loads after React hydration
   • Fix: Use beforeInteractive in <head> within layout.tsx

8. Wrong Backend Endpoint: 404 on ChatKit requests

   • Symptom: /​api/​proxy/​chatkit returns 404
   • Why: General proxy adds /​api/ prefix, but ChatKit is at /​chatkit
   • Fix: Create dedicated proxy that routes to exact endpoint path

9. MCP Tools Missing Auth Token: Agent calls MCP tools without credentials

   • Fix: Pass token via system prompt so LLM includes in tool calls

10. E402 Linter Error: Imports after load_dotenv()

• Fix: Add # noqa: E402 to intentional imports after dotenv

1. Type Annotation vs Runtime Mismatch (Action context parameter)

• Symptom: ValidationError trying to create RequestContext from RequestContext
• Why: Type hint says context: dict[str, Any] but ChatKit SDK passes RequestContext object
• Fix: Use context directly, don't wrap it in RequestContext constructor
• Detection: Error message shows Input should be a valid dictionary [input_value=RequestContext(...)]

1. Python Auto-Reload Not Working: Changes don't take effect

• Symptom: Old code still runs despite file changes
• Why: Python bytecode cache (.pyc files) or uvicorn reload mechanism lag
• Fix: Kill process manually and restart, or delete pycache directories
• Prevention: Use --reload flag with uvicorn, but be aware it's not 100% reliable

1. Missing Pydantic Model Required Fields: ValidationError on ChatKit types

• Symptom: "Field required" errors when creating UserMessageItem, Action, etc.
• Why: Pydantic models have strict validation, all required fields must be present
• Fix: Check ChatKit type definitions, include all required fields with correct types
• Common mistakes: Missing id, thread_id, created_at, inference_options fields

Pattern 6: MCP Agent Authentication (NEW)

When: MCP tools need to call authenticated APIs

Problem: MCP protocol doesn't forward auth headers. Tools need user credentials.

Solution: Pass auth credentials via agent system prompt. LLM includes them in every tool call.

Implementation:

# 1. Extract token at endpoint
@app.post("/​chatkit")
async def chatkit_endpoint(request: Request):
    auth_header = request.headers.get("Authorization")
    access_token = None
    if auth_header and auth_header.startswith("Bearer "):
        access_token = auth_header[7:]  # Remove "Bearer " prefix

    # Add to metadata
    metadata["access_token"] = access_token

# 2. System prompt template
SYSTEM_PROMPT = """You are Assistant.

## Authentication Context
- User ID: {user_id}
- Access Token: {access_token}

CRITICAL: When calling ANY MCP tool, you MUST ALWAYS include:
- user_id: "{user_id}"
- access_token: "{access_token}"

{rest_of_prompt}
"""

# 3. Format with credentials
instructions = SYSTEM_PROMPT.format(
    user_id=context.user_id,
    access_token=context.metadata.get("access_token", ""),
    ...
)

Flow:

Frontend (Authorization: Bearer <jwt>)
    → /​chatkit endpoint (extract token)
    → ChatKit server (add to metadata)
    → Agent prompt (include credentials)
    → LLM (includes in tool params)
    → MCP tool (receives token)
    → API call (Authorization header)

Evidence: packages/​api/​src/​taskflow_api/​main.py, packages/​api/​src/​taskflow_api/​services/​chat_agent.py

Pattern 7: Separate ChatKit Store Configuration

When: ChatKit needs its own database schema/​connection

Implementation:

# config.py - Ignore ChatKit env vars in main Settings
class Settings(BaseSettings):
    model_config = SettingsConfigDict(
        extra="ignore",  # Ignore TASKFLOW_CHATKIT_* vars
    )

    @property
    def chat_enabled(self) -> bool:
        return os.getenv("TASKFLOW_CHATKIT_DATABASE_URL") is not None

# chatkit_store/​config.py - Separate config for ChatKit
class StoreConfig(BaseSettings):
    model_config = SettingsConfigDict(
        env_prefix="TASKFLOW_CHATKIT_",  # Reads TASKFLOW_CHATKIT_DATABASE_URL
    )
    database_url: str
    schema_name: str = "taskflow_chat"  # Separate schema

Evidence: packages/​api/​src/​taskflow_api/​config.py, packages/​api/​src/​taskflow_api/​chatkit_store/​config.py

Tier Boundaries

This Skill Covers (Tier 1: Foundation)

• ✅ ChatKitServer setup with respond() method
• ✅ useChatKit basic configuration
• ✅ Custom fetch interceptor for authentication
• ✅ Context injection (user info, page context)
• ✅ Script loading detection
• ✅ httpOnly cookie proxy (Next.js)
• ✅ Database persistence setup
• ✅ MCP tool authentication via prompt

Use chatkit-streaming For (Tier 2: Real-time)

• ⏭️ onResponseStart / onResponseEnd handlers
• ⏭️ onEffect for fire-and-forget client updates
• ⏭️ ProgressUpdateEvent for loading states
• ⏭️ Thread lifecycle events
• ⏭️ Thread title generation

Use chatkit-actions For (Tier 3: Interactive)

• ⏭️ Widget templates (.widget files)
• ⏭️ widgets.onAction handler
• ⏭️ action() method in ChatKitServer
• ⏭️ sendCustomAction() for widget updates
• ⏭️ Entity tagging (@mentions)
• ⏭️ Composer tools (mode selection)
• ⏭️ ThreadItemReplacedEvent

References

• ChatKit Server Spec: specs/​007-chatkit-server/​spec.md
• ChatKit UI Spec: specs/​008-chatkit-ui-widget/​spec.md
• Reference Docs: references/​nextjs-httponly-cookie-proxy.md, references/​nextjs-script-loading.md
• Blueprints: blueprints/​openai-chatkit-advanced-samples-main/

Related Skills

• chatkit-streaming - Response lifecycle, progress updates, client effects
• chatkit-actions - Interactive widgets, entity tagging, composer tools