wechat-agent-sdk 0.2.1

WeChat AI Agent SDK with ACP (Agent Client Protocol) support

wechat-agent-sdk

English | 中文

WeChat AI Agent bridge framework — connect any AI backend to WeChat with a simple Agent interface, or embed as a transport layer in your platform.

Supports ACP (Agent Client Protocol) to bridge Claude Code, Codex, Kimi, and other ACP-compatible agents directly to WeChat.

This is not an official WeChat project. Built on the iLink Bot API, for learning and research purposes only.

Architecture

The SDK has a two-layer design:

┌─────────────────────────────────────────────────┐
│           Bot Layer (full-stack mode)            │
│  WeChatBot / Builder / Manager / Middleware      │
│  Agent.chat() dispatching                        │
│  For: standalone developers                      │
└──────────────────────┬──────────────────────────┘
                       │ uses internally
┌──────────────────────▼──────────────────────────┐
│         Transport Layer (headless mode)          │
│  WeChatTransport                                 │
│  connect / messages / parse / send / login       │
│  For: platform integration                       │
└──────────────────────┬──────────────────────────┘
                       │ uses internally
┌──────────────────────▼──────────────────────────┐
│              Infrastructure                      │
│  ILinkBotClient (7 API endpoints)                │
│  MediaPipeline (AES + CDN upload/download)       │
│  AccountStorage (JSON / Redis / SQLite)          │
└─────────────────────────────────────────────────┘

Standalone developers use the Bot layer — implement Agent.chat(), call bot.run(), done.

Platform integrators use the Transport layer — consume transport.messages(), handle routing/agents yourself, call transport.send_text() to reply.

Installation

pip install wechat-agent-sdk

Optional dependencies:

pip install 'wechat-agent-sdk[qr]'       # Terminal QR code display
pip install 'wechat-agent-sdk[acp]'       # ACP protocol (Claude Code / Codex / Kimi)
pip install 'wechat-agent-sdk[openai]'    # OpenAI integration
pip install 'wechat-agent-sdk[redis]'     # Redis storage backend
pip install 'wechat-agent-sdk[sqlite]'    # SQLite storage backend
pip install 'wechat-agent-sdk[all]'       # Everything

Requires Python >= 3.11

Quick Start (Bot Layer)

For standalone developers who want a bot running in 5 minutes:

import asyncio
from wechat_agent_sdk import Agent, ChatRequest, ChatResponse, WeChatBot

class EchoAgent(Agent):
    async def chat(self, request: ChatRequest) -> ChatResponse:
        return ChatResponse(text=f"You said: {request.text}")

async def main():
    bot = WeChatBot(agent=EchoAgent())
    await bot.run()

asyncio.run(main())

Using the Builder pattern:

bot = (
    WeChatBot.builder()
    .agent(EchoAgent())
    .account_id("my_bot")
    .storage(RedisStorage("redis://localhost"))
    .middleware(rate_limit_middleware)
    .on_error(my_error_handler)
    .build()
)
await bot.run()

Transport Layer (Platform Integration)

For platforms that have their own agent execution layer, pipeline, and session management. The Transport layer provides only: connect, receive, parse, send.

import asyncio
from wechat_agent_sdk import WeChatTransport, ParsedMessage

async def main():
    transport = WeChatTransport(
        account_id="bot_1",
        storage=my_redis_storage,
    )

    # Web login (for platforms with web UI)
    session = await transport.request_login()
    # ... show session.qr_url in your web UI ...
    result = await transport.check_login(session)

    # Or terminal login
    # await transport.login_terminal()

    # Receive messages
    await transport.connect()
    async for raw_msg in transport.messages():
        parsed = transport.parse(raw_msg)
        if not parsed:
            continue

        # Your platform handles everything from here:
        # permission checks, session management, agent execution, SSE streaming...
        reply = await your_pipeline.handle(parsed)

        # Send reply via transport
        await transport.send_text(parsed.conversation_id, reply, parsed.context_token)

    await transport.disconnect()

asyncio.run(main())

Transport API

Method	Description
connect() / disconnect()	Manage connection lifecycle
messages()	Async iterator — long-poll for messages (auto dedup + cursor)
parse(raw)	Parse raw iLink message → ParsedMessage
send_text(chat_id, text)	Send text (auto markdown strip + split)
send_text_raw(chat_id, text)	Send raw text (no processing)
send_media(chat_id, data, type)	Encrypt + upload + send media
download_media(media)	Download + decrypt media attachment
send_typing(chat_id, start)	Typing indicator
request_login()	Get QR URL for web login
check_login(session)	Poll login status
login_terminal()	Interactive terminal QR login
logout()	Clear token, force re-login
activate_token(token)	Inject token (after platform re-login)
needs_login	Property: True when no valid token

Integration Patterns

Pattern A: Claude Code via ACP

npm install -g @zed-industries/claude-code-acp
pip install 'wechat-agent-sdk[acp,qr]'

from wechat_agent_sdk import WeChatBot
from wechat_agent_sdk.acp.adapter import AcpAgent

agent = AcpAgent(command="claude-agent-acp", permission_mode="bypassPermissions")
bot = WeChatBot(agent=agent)
await bot.run()

Supported ACP agents:

Agent	Install	Command
Claude Code	npm i -g @zed-industries/claude-code-acp	claude-agent-acp
Codex	npm i -g @openai/codex-acp	codex-acp
Kimi CLI	npm i -g kimi-cli	kimi (args: ["acp"])

Pattern B: OpenAI / Compatible APIs

class OpenAIAgent(Agent):
    async def on_start(self):
        from openai import AsyncOpenAI
        self._client = AsyncOpenAI()

    async def chat(self, request: ChatRequest) -> ChatResponse:
        resp = await self._client.chat.completions.create(
            model="gpt-4o",
            messages=[{"role": "user", "content": request.text}],
        )
        return ChatResponse(text=resp.choices[0].message.content)

Pattern C: Multi-Account

from wechat_agent_sdk import WeChatBotManager

manager = WeChatBotManager(storage=my_storage, auto_restart=True)
manager.add_bot("bot_1", agent=AgentA())
manager.add_bot("bot_2", agent=AgentB())

await manager.start_all()
# manager.get_status() → {"bot_1": RUNNING, "bot_2": RUNNING}
await manager.stop_all()

Middleware

Bot layer supports an onion middleware chain (inspired by aiogram / Bot Framework):

async def logging_mw(ctx, next_fn):
    print(f"Received: {ctx.request.text}")
    await next_fn()  # call next middleware / handler
    print(f"Replied: {ctx.response.text if ctx.response else 'None'}")

async def rate_limit(ctx, next_fn):
    if is_rate_limited(ctx.request.conversation_id):
        ctx.response = ChatResponse(text="Please slow down")
        return  # short-circuit, don't call next
    await next_fn()

bot = WeChatBot.builder().agent(my_agent).middleware(logging_mw).middleware(rate_limit).build()

Error handling via middleware:

async def my_error_handler(ctx, error):
    await alert_ops_team(error)
    return ChatResponse(text="Something went wrong, please retry")

bot = WeChatBot.builder().agent(my_agent).on_error(my_error_handler).build()

Concurrent Message Handling

By default, the Bot layer processes up to 10 messages concurrently. When agent.chat() takes a long time (e.g. AcpAgent calling Claude Code), other users' messages are handled in parallel instead of queuing.

# Default: 10 concurrent handlers
bot = WeChatBot(agent=my_agent)

# Custom concurrency limit
bot = WeChatBot(agent=my_agent, max_concurrent=20)

# Via builder
bot = WeChatBot.builder().agent(my_agent).max_concurrent(5).build()

Bounded by asyncio.Semaphore — when the limit is reached, new messages wait until a slot opens. Transport layer does not manage concurrency; platform integrators control their own.

Storage Backends

Backend	Install	Usage
JSON file (default)	built-in	JsonFileStorage()
Redis	pip install 'wechat-agent-sdk[redis]'	from wechat_agent_sdk.account.redis_storage import RedisStorage
SQLite	pip install 'wechat-agent-sdk[sqlite]'	from wechat_agent_sdk.account.sqlite_storage import SqliteStorage

from wechat_agent_sdk.account.redis_storage import RedisStorage

storage = RedisStorage(url="redis://localhost:6379", prefix="wechat-sdk")
bot = WeChatBot(agent=my_agent, storage=storage)

API Reference

ParsedMessage (Transport layer)

Field	Type	Description
conversation_id	str	User wxid (DM) or group ID
text	str	Text content (voice auto-transcribed)
media	list[MediaInfo]	All media attachments (supports multi-image)
message_id	str	Unique message ID
context_token	str	Must echo back when replying
group_id	str | None	Group ID (group chats)
sender_id	str | None	Sender wxid (group chats)
sender_name	str | None	Sender nickname (group chats)
is_at_bot	bool	Whether bot was @mentioned
raw	dict | None	Raw iLink message

ChatRequest (Bot layer)

Same fields as ParsedMessage except media is MediaInfo | None (first attachment only) and no context_token (handled internally).

ChatResponse

Field	Type	Description
text	str | None	Reply text (markdown auto-stripped)
media	MediaResponseInfo | None	Reply media (image/video/file)

MediaInfo

Field	Type	Description
type	str	"image" / "audio" / "video" / "file"
cdn_param	str	CDN encrypted query param (for download)
aes_key	str	Base64 AES key
file_name	str | None	Original filename

Download media: data = await transport.download_media(media_info)

Supported Message Types

Inbound (WeChat → Agent)

Type	Behavior
Text	text contains the message
Image	text is "[图片]", media contains CDN reference for download
Voice	Transcribed text if available, else "[语音]"
Video	text is "[视频]", media contains CDN reference
File	text is "[文件: xxx.pdf]", media contains CDN reference
Quote	Quoted text prepended: "[引用: original] new message"

Outbound (Agent → WeChat)

Type	Usage
Text	ChatResponse(text="...")
Long text	Auto-split at paragraph boundaries (max 2000 chars)
Markdown	Auto-stripped to plain text
Image/Video/File	ChatResponse(media=MediaResponseInfo(type="image", url="path_or_url"))

Project Structure

src/wechat_agent_sdk/
├── __init__.py              # Public API exports
├── agent.py                 # Agent abstract base class
├── types.py                 # ChatRequest, ChatResponse, MediaInfo
├── transport.py             # WeChatTransport + ParsedMessage (transport layer)
├── middleware.py             # MiddlewareChain + Context (bot layer)
├── api/
│   ├── client.py            # ILinkBotClient (7 API endpoints)
│   ├── auth.py              # Login flow + LoginSession/LoginResult
│   └── types.py             # iLink API data models
├── messaging/
│   ├── process.py           # Inbound message parsing
│   ├── send.py              # Text splitting + markdown conversion
│   └── monitor.py           # Legacy monitor (kept for backward compat)
├── media/
│   ├── crypto.py            # AES-128-ECB + dual-format key decoding
│   └── cdn.py               # CDN upload / download
├── acp/
│   └── adapter.py           # AcpAgent: ACP subprocess bridge
├── account/
│   ├── manager.py           # WeChatBot + WeChatBotBuilder
│   ├── bot_manager.py       # WeChatBotManager (multi-account)
│   ├── storage.py           # AccountStorage ABC + JsonFileStorage
│   ├── redis_storage.py     # RedisStorage (optional)
│   └── sqlite_storage.py    # SqliteStorage (optional)
└── utils/
    └── markdown.py          # strip_markdown()

Acknowledgements

• wong2/weixin-agent-sdk — architecture reference
• m1heng/claude-plugin-weixin — Claude integration reference
• Agent Client Protocol — ACP specification

License

MIT