home / skills / mjunaidca / mjs-agent-skills / streaming-llm-responses
This skill enables real-time streaming LLM responses in chat interfaces, managing response lifecycles, client effects, progress updates, and UI synchronization.
npx playbooks add skill mjunaidca/mjs-agent-skills --skill streaming-llm-responsesReview the files below or copy the command above to add this skill to your agents.
---
name: streaming-llm-responses
description: |
Implement real-time streaming UI patterns for AI chat applications. Use when adding response
lifecycle handlers, progress indicators, client effects, or thread state synchronization.
Covers onResponseStart/End, onEffect, ProgressUpdateEvent, and client tools.
NOT when building basic chat without real-time feedback.
---
# Streaming LLM Responses
Build responsive, real-time chat interfaces with streaming feedback.
## Quick Start
```typescript
import { useChatKit } from "@openai/chatkit-react";
const chatkit = useChatKit({
api: { url: API_URL, domainKey: DOMAIN_KEY },
onResponseStart: () => setIsResponding(true),
onResponseEnd: () => setIsResponding(false),
onEffect: ({ name, data }) => {
if (name === "update_status") updateUI(data);
},
});
```
---
## Response Lifecycle
```
User sends message
↓
onResponseStart() fires
↓
[Streaming: tokens arrive, ProgressUpdateEvents shown]
↓
onResponseEnd() fires
↓
UI unlocks, ready for next interaction
```
---
## Core Patterns
### 1. Response Lifecycle Handlers
Lock UI during AI response to prevent race conditions:
```typescript
function ChatWithLifecycle() {
const [isResponding, setIsResponding] = useState(false);
const lockInteraction = useAppStore((s) => s.lockInteraction);
const unlockInteraction = useAppStore((s) => s.unlockInteraction);
const chatkit = useChatKit({
api: { url: API_URL, domainKey: DOMAIN_KEY },
onResponseStart: () => {
setIsResponding(true);
lockInteraction(); // Disable map/canvas/form interactions
},
onResponseEnd: () => {
setIsResponding(false);
unlockInteraction();
},
onError: ({ error }) => {
console.error("ChatKit error:", error);
setIsResponding(false);
unlockInteraction();
},
});
return (
<div>
{isResponding && <LoadingOverlay />}
<ChatKit control={chatkit.control} />
</div>
);
}
```
### 2. Client Effects (Fire-and-Forget)
Server sends effects to update client UI without expecting a response:
**Backend - Streaming Effects:**
```python
from chatkit.types import ClientEffectEvent
async def respond(self, thread, item, context):
# ... agent processing ...
# Fire client effect to update UI
yield ClientEffectEvent(
name="update_status",
data={
"state": {"energy": 80, "happiness": 90},
"flash": "Status updated!"
}
)
# Another effect
yield ClientEffectEvent(
name="show_notification",
data={"message": "Task completed!"}
)
```
**Frontend - Handling Effects:**
```typescript
const chatkit = useChatKit({
api: { url: API_URL, domainKey: DOMAIN_KEY },
onEffect: ({ name, data }) => {
switch (name) {
case "update_status":
applyStatusUpdate(data.state);
if (data.flash) setFlashMessage(data.flash);
break;
case "add_marker":
addMapMarker(data);
break;
case "select_mode":
setSelectionMode(data.mode);
break;
}
},
});
```
### 3. Progress Updates
Show "Searching...", "Loading...", "Analyzing..." during long operations:
```python
from chatkit.types import ProgressUpdateEvent
@function_tool
async def search_articles(ctx: AgentContext, query: str) -> str:
"""Search for articles matching the query."""
yield ProgressUpdateEvent(message="Searching articles...")
results = await article_store.search(query)
yield ProgressUpdateEvent(message=f"Found {len(results)} articles...")
for i, article in enumerate(results):
if i % 5 == 0:
yield ProgressUpdateEvent(
message=f"Processing article {i+1}/{len(results)}..."
)
return format_results(results)
```
### 4. Thread Lifecycle Events
Track thread changes for persistence and UI updates:
```typescript
const chatkit = useChatKit({
api: { url: API_URL, domainKey: DOMAIN_KEY },
onThreadChange: ({ threadId }) => {
setThreadId(threadId);
if (threadId) localStorage.setItem("lastThreadId", threadId);
clearSelections();
},
onThreadLoadStart: ({ threadId }) => {
setIsLoadingThread(true);
},
onThreadLoadEnd: ({ threadId }) => {
setIsLoadingThread(false);
},
});
```
### 5. Client Tools (State Query)
AI needs to read client-side state to make decisions:
**Backend - Defining Client Tool:**
```python
@function_tool(name_override="get_selected_items")
async def get_selected_items(ctx: AgentContext) -> dict:
"""Get the items currently selected on the canvas.
This is a CLIENT TOOL - executed in browser, result comes back.
"""
yield ProgressUpdateEvent(message="Reading selection...")
pass # Actual execution happens on client
```
**Frontend - Handling Client Tools:**
```typescript
const chatkit = useChatKit({
api: { url: API_URL, domainKey: DOMAIN_KEY },
onClientTool: ({ name, params }) => {
switch (name) {
case "get_selected_items":
return { itemIds: selectedItemIds };
case "get_current_viewport":
return {
center: mapRef.current.getCenter(),
zoom: mapRef.current.getZoom(),
};
case "get_form_data":
return { values: formRef.current.getValues() };
default:
throw new Error(`Unknown client tool: ${name}`);
}
},
});
```
---
## Client Effects vs Client Tools
| Type | Direction | Response Required | Use Case |
|------|-----------|-------------------|----------|
| **Client Effect** | Server → Client | No (fire-and-forget) | Update UI, show notifications |
| **Client Tool** | Server → Client → Server | Yes (return value) | Get client state for AI decision |
---
## Common Patterns by Use Case
### Interactive Map/Canvas
```typescript
onResponseStart: () => lockCanvas(),
onResponseEnd: () => unlockCanvas(),
onEffect: ({ name, data }) => {
if (name === "add_marker") addMarker(data);
if (name === "pan_to") panTo(data.location);
},
onClientTool: ({ name }) => {
if (name === "get_selection") return getSelectedItems();
},
```
### Form-Based UI
```typescript
onResponseStart: () => setFormDisabled(true),
onResponseEnd: () => setFormDisabled(false),
onClientTool: ({ name }) => {
if (name === "get_form_values") return form.getValues();
},
```
### Game/Simulation
```typescript
onResponseStart: () => pauseSimulation(),
onResponseEnd: () => resumeSimulation(),
onEffect: ({ name, data }) => {
if (name === "update_entity") updateEntity(data);
if (name === "show_notification") showToast(data.message);
},
```
---
## Thread Title Generation
Dynamically update thread title based on conversation:
```python
class TitleAgent:
async def generate_title(self, first_message: str) -> str:
result = await Runner.run(
Agent(
name="TitleGenerator",
instructions="Generate a 3-5 word title.",
model="gpt-4o-mini", # Fast model
),
input=f"First message: {first_message}",
)
return result.final_output
# In ChatKitServer
async def respond(self, thread, item, context):
if not thread.title and item:
title = await self.title_agent.generate_title(item.content)
thread.title = title
await self.store.save_thread(thread, context)
```
---
## Anti-Patterns
1. **Not locking UI during response** - Leads to race conditions
2. **Blocking in effects** - Effects should be fire-and-forget
3. **Heavy computation in onEffect** - Use requestAnimationFrame for DOM updates
4. **Missing error handling** - Always handle onError to unlock UI
5. **Not persisting thread state** - Use onThreadChange to save context
---
## Verification
Run: `python3 scripts/verify.py`
Expected: `✓ streaming-llm-responses skill ready`
## If Verification Fails
1. Check: references/ folder has streaming-patterns.md
2. **Stop and report** if still failing
## References
- [references/streaming-patterns.md](references/streaming-patterns.md) - Complete streaming configuration
This skill implements real-time streaming UI patterns for AI chat applications. It provides lifecycle handlers, progress events, client effects, and client tools to build responsive, interactive experiences. Use it to add streaming token updates, lock UI during responses, and synchronize thread state between client and server.
The skill wires lifecycle callbacks like onResponseStart and onResponseEnd to manage interaction state and avoid race conditions. It streams tokens and ProgressUpdateEvent messages to show intermediate status, and supports fire-and-forget ClientEffectEvent messages for UI updates. Client tools let the server request client-side state and receive results back, enabling AI decisions that depend on current viewport, selection, or form data.
What is the difference between a Client Effect and a Client Tool?
Client Effects are fire-and-forget messages from server to client used to update UI without a response. Client Tools are requests from server to client that require a return value so the AI can use client state in its reasoning.
How do I avoid leaving the UI locked if an error occurs?
Always implement onError to set responding state to false and call unlockInteraction or equivalent cleanup to ensure the UI is restored.