
x-chat-provider
Focus on implementing custom Chat Provider, helping to adapt any streaming interface to Ant Design X standard format
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/ant-design/x --skill x-chat-provider- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads x-chat-provider automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
🎯 Skill Positioning
This skill focuses on solving one problem: How to quickly adapt your streaming interface to Ant Design X's Chat Provider.
Not involved: useXChat usage tutorial (that's another skill).
Table of Contents
- 📦 Technology Stack Overview
- 🚀 Quick Start
- 📋 Four Steps to Implement Custom Provider
- 🔑 Core Types and Exports
- ⚙️ XRequest Advanced Configuration
- 🔧 Common Scenario Adaptation
- ⚠️ Important Reminders
- ⚡ Quick Checklist
- 🚨 Development Rules
- 🔗 Reference Resources
📦 Technology Stack Overview
| Layer | Package Name | Core Purpose |
|---|---|---|
| UI Layer | @ant-design/x | React UI component library |
| Logic Layer | @ant-design/x-sdk | Development toolkit |
| Render Layer | @ant-design/x-markdown | Markdown renderer |
// ✅ Correct import examples
import { Bubble } from '@ant-design/x';
import { AbstractChatProvider, OpenAIChatProvider } from '@ant-design/x-sdk';
import XRequest from '@ant-design/x-sdk';
🚀 Quick Start
🎯 Provider Selection Decision Tree
graph TD
A[Start] --> B{Use standard OpenAI/DeepSeek API?}
B -->|Yes| C[Use built-in Provider]
B -->|No| D{Raw data format as message?}
D -->|Yes| E[Use DefaultChatProvider]
D -->|No| F[Custom Provider]
C --> G[OpenAIChatProvider / DeepSeekChatProvider]
E --> H[Pass-through, no conversion needed]
F --> I[Four-step custom Provider]
🏭 Built-in Provider Overview
| Provider Type | Applicable Scenario | Import |
|---|---|---|
| OpenAIChatProvider | Standard OpenAI API format | import { OpenAIChatProvider } from '@ant-design/x-sdk' |
| DeepSeekChatProvider | Standard DeepSeek API format | import { DeepSeekChatProvider } from '@ant-design/x-sdk' |
| DefaultChatProvider | Pass-through raw response, no format conversion | import { DefaultChatProvider } from '@ant-design/x-sdk' |
⚠️ Export names are
OpenAIChatProvider/DeepSeekChatProvider/DefaultChatProvider, watch spelling
DefaultChatProvider Use Case
DefaultChatProvider passes through raw response data without any conversion. Suitable for:
- The interface response format is already what you want to display
- You want full control over
Bubble.List'scontentRenderto render messages
import { DefaultChatProvider, XRequest } from '@ant-design/x-sdk';
interface ChatInput {
query: string;
stream?: boolean;
}
interface ChatOutput {
choices: Array<{ message: { content: string; role: string } }>;
}
// DefaultChatProvider generic: <ChatMessage, Input, Output>
// ChatMessage is your Output type (passed through directly)
const provider = new DefaultChatProvider<ChatOutput | ChatInput, ChatInput, ChatOutput>({
request: XRequest('https://your-api.com/chat', {
manual: true,
params: { stream: false },
}),
});
// Render using contentRender in Bubble.List's role config
// role={{ assistant: { contentRender(content) { return content?.choices?.[0]?.message?.content } } }}
⚠️ When using
DefaultChatProvider,ChatMessageis typically yourOutputtype or a union type; rendering requirescontentRender
📋 Four Steps to Implement Custom Provider
Step 1: Analyze Interface Format ⏱️ 2 minutes
| Information Type | Example Value |
|---|---|
| Interface URL | https://your-api.com/chat |
| Request Method | JSON, POST |
| Response Format | Server-Sent Events |
| Auth Method | Bearer Token |
Step 2: Create Provider Class ⏱️ 5 minutes
// MyChatProvider.ts
import { AbstractChatProvider } from '@ant-design/x-sdk';
import type { TransformMessage } from '@ant-design/x-sdk';
import type { XRequestOptions } from '@ant-design/x-sdk';
interface MyInput {
query: string;
model?: string;
stream?: boolean;
}
interface MyOutput {
content: string;
finish_reason?: string;
}
interface MyMessage {
content: string;
role: 'user' | 'assistant';
}
export class MyChatProvider extends AbstractChatProvider<MyMessage, MyInput, MyOutput> {
// Parameter conversion: merge onRequest params + XRequest default params
// options comes from XRequest(url, options), can access options.params etc.
transformParams(
requestParams: Partial<MyInput>,
options: XRequestOptions<MyInput, MyOutput, MyMessage>,
): MyInput {
return {
...(options?.params || {}),
query: requestParams.query || '',
model: 'gpt-3.5-turbo',
stream: true,
};
}
// Local message: convert onRequest params to the user-side display message (can return array)
transformLocalMessage(requestParams: Partial<MyInput>): MyMessage {
return {
content: requestParams.query || '',
role: 'user',
};
}
// Response conversion:
// info.originMessage: previous content of this message (for stream accumulation)
// info.chunk: current streaming chunk
// info.chunks: all received chunks (used in onSuccess)
// info.status: current status
// ⚠️ Return only MyMessage type; do NOT add a status field
transformMessage(info: TransformMessage<MyMessage, MyOutput>): MyMessage {
const { originMessage, chunk } = info;
if (!chunk?.content || chunk.content === '[DONE]') {
return { ...(originMessage || { content: '', role: 'assistant' }) };
}
return {
content: `${originMessage?.content || ''}${chunk.content}`,
role: 'assistant',
};
}
}
Step 3: Verify ⏱️ 1 minute
| Check Item | Description |
|---|---|
| Only 3 methods | transformParams, transformLocalMessage, transformMessage |
| transformParams signature | Must include second parameter options: XRequestOptions<...> |
| No status in return | transformMessage return value has no status field |
| No request method | Confirm no request method implemented |
| Type check passes | tsc --noEmit no errors |
Step 4: Use Provider ⏱️ 1 minute
import { MyChatProvider } from './MyChatProvider';
import XRequest from '@ant-design/x-sdk';
// ⚠️ Must pass manual: true, otherwise AbstractChatProvider constructor will throw
const provider = new MyChatProvider({
request: XRequest('https://your-api.com/chat', {
manual: true,
headers: {
Authorization: 'Bearer your-token',
'Content-Type': 'application/json',
},
params: {
model: 'gpt-3.5-turbo',
stream: true,
},
}),
});
export { provider };
🔑 Core Types and Exports
Key types exported from @ant-design/x-sdk:
import type {
// OpenAI standard message format
XModelMessage, // { role: string; content: string | { text: string; type: string } }
XModelParams, // Full OpenAI request params type (model, messages, stream, temperature, etc.)
XModelResponse, // Full OpenAI response type (choices, usage, etc.)
// SSE stream field types
SSEFields, // 'data' | 'event' | 'id' | 'retry'
SSEOutput, // Partial<Record<SSEFields, any>>
// Provider related
TransformMessage, // { originMessage, chunk, chunks, status, responseHeaders }
// XRequest related
XRequestOptions, // Full request config
XRequestCallbacks, // { onUpdate, onSuccess, onError }
// Message related
MessageInfo, // { id, message, status, extraInfo }
} from '@ant-design/x-sdk';
XModelMessage Structure (OpenAI message format)
// XModelMessage is the standard OpenAI message format
// Used for OpenAIChatProvider / DeepSeekChatProvider ChatMessage generic
const userMessage: XModelMessage = { role: 'user', content: 'Hello' };
const systemMessage: XModelMessage = { role: 'system', content: 'You are an assistant' };
const developerMessage: XModelMessage = { role: 'developer', content: 'System prompt' };
SSEOutput and SSEFields
// SSEOutput is the type for raw SSE stream data
// { data?: string; event?: string; id?: string; retry?: number }
// DeepSeekChatProvider uses Partial<Record<SSEFields, XModelResponse>>
import { DeepSeekChatProvider, XRequest } from '@ant-design/x-sdk';
import type { SSEFields, XModelParams, XModelResponse } from '@ant-design/x-sdk';
const provider = new DeepSeekChatProvider({
request: XRequest<XModelParams, Partial<Record<SSEFields, XModelResponse>>>(
'https://api.deepseek.com/v1/chat/completions',
{
manual: true,
params: { model: 'deepseek-chat', stream: true },
},
),
});
⚙️ XRequest Advanced Configuration
callbacks
callbacks allows monitoring request events at the Provider level. The third parameter in callbacks is the MessageInfo processed by transformMessage:
const provider = new OpenAIChatProvider({
request: XRequest<XModelParams, XModelResponse, XModelMessage>(BASE_URL, {
manual: true,
callbacks: {
// onUpdate: triggered on each streaming chunk arrival
// chunk: current chunk; responseHeaders: response headers; message: current MessageInfo
onUpdate: (chunk, responseHeaders, message) => {
console.log('Stream update:', message?.message?.content);
},
// onSuccess: triggered when all chunks are received
// chunks: all chunks array; message: final MessageInfo
onSuccess: (chunks, responseHeaders, message) => {
console.log('Request complete:', message?.message?.content);
// Good place for analytics, logging, etc.
},
// onError: triggered on request failure (including AbortError)
// error: error object; errorInfo: extra error info; message: MessageInfo at failure
onError: (error, errorInfo, responseHeaders, message) => {
console.error('Request failed:', error.message);
},
},
params: { model: 'gpt-4o', stream: true },
}),
});
⚠️
callbacksanduseXChat'srequestFallbackdo not conflict — both execute.callbacksis better for logging/reporting;requestFallbackcontrols UI display.
retryInterval Retry
const request = XRequest('https://your-api.com/chat', {
manual: true,
// Retry interval after failure (ms)
retryInterval: 3000,
// Max retry count (unlimited if not set)
retryTimes: 3,
// onError can also return a number to dynamically set retry interval
callbacks: {
onError: (error) => {
if (error.name === 'AbortError') return; // Don't retry on user cancel
return 5000; // Return number = retry after 5s (higher priority than retryInterval)
},
},
});
transformStream Custom Stream
Use when the server returns a non-standard SSE stream format:
const request = XRequest('https://your-api.com/chat', {
manual: true,
// Fixed TransformStream
transformStream: new TransformStream({
transform(chunk, controller) {
controller.enqueue(JSON.parse(chunk));
},
}),
// Or decide dynamically based on URL and response headers
transformStream: (baseURL, responseHeaders) => {
if (responseHeaders.get('x-stream-type') === 'ndjson') {
return new TransformStream({/* ... */});
}
return undefined; // Use default SSE parsing
},
});
🔧 Common Scenario Adaptation
📖 Complete Examples: EXAMPLES.md
| Scenario Type | Difficulty | Description |
|---|---|---|
| Standard OpenAI | 🟢 | Use built-in OpenAIChatProvider directly |
| Standard DeepSeek | 🟢 | Use built-in DeepSeekChatProvider directly |
| Pass-through raw data | 🟢 | Use DefaultChatProvider |
| Private SSE API | 🟡 | Four-step custom Provider |
| Multi-field response | 🟡 | Custom Provider + complex ChatMessage |
| Non-SSE stream | 🔴 | Custom Provider + transformStream |
⚠️ Important Reminders
🚨 Mandatory Rule: Never write a request method!
// ❌ Serious error
class MyProvider extends AbstractChatProvider {
async request(params: any) {
/* Forbidden! */
}
}
// ✅ Only correct approach: implement only the three conversion methods
class MyProvider extends AbstractChatProvider {
transformParams(params, options) {
/* ... */
}
transformLocalMessage(params) {
/* ... */
}
transformMessage(info) {
/* ... */
}
}
⚠️ transformMessage must not return status
// ❌ Wrong
transformMessage(info) {
return { content: '...', status: 'error' }; // ❌ status is managed by the framework
}
// ✅ Correct
transformMessage(info) {
return { content: '...' }; // ✅
}
⚠️ Provider instantiation notes
// ✅ In React components, use useState to ensure only created once
const [provider] = React.useState(
new MyChatProvider({
request: XRequest(URL, { manual: true }),
}),
);
// ❌ Don't create directly in render function (creates new instance on every render)
// const provider = new MyChatProvider(...); // inside component body causes issues
⚡ Quick Checklist
Before creating Provider:
- Have interface docs and response format
- Confirmed whether custom is needed (or if built-in Provider suffices)
- Defined Input, Output, ChatMessage types
After completion:
- Only implemented the three required methods
- transformParams includes second parameter options
- transformMessage return value has no status field
- XRequest configured with manual: true
- Absolutely no request method implemented
- Provider wrapped with
useStatein React component - Type check passes (
tsc --noEmit)
🚨 Development Rules
- If the user does not explicitly need test cases, do not add test files
- After completion, must check types: Run
tsc --noEmitto ensure no type errors - Keep code clean: Remove all unused variables and imports
🔗 Reference Resources
📚 Core Reference Documentation
- EXAMPLES.md - Practical example code
🌐 SDK Official Documentation
- useXChat Official Documentation
- XRequest Official Documentation
- Chat Provider Official Documentation
💻 Example Code
FAQ
What does the x-chat-provider skill do?
Focus on implementing custom Chat Provider, helping to adapt any streaming interface to Ant Design X standard format
How do I install x-chat-provider?
Run npx skills add https://github.com/ant-design/x --skill x-chat-provider in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/x-chat-provider/ (personal) or .claude/skills/x-chat-provider/ (project).
Which AI agents can use x-chat-provider?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is x-chat-provider safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains x-chat-provider?
It is published by ant-design in ant-design/x. It has 78 recorded installs.
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