agentscope-ai/QwenPaw — A Self-Hosted Personal Agent That Bridges Every Chat App

A Personal Agent You Actually Own

agentscope-ai/QwenPaw (formerly CoPaw, rebranded April 2026) lets you run one personal assistant that's reachable across WhatsApp, Telegram, WeChat, QQ, Discord, and Slack — on your own machine or cloud, no third-party data plane in the middle. v1.1.5 (April 29) added memory-search optimization, context-compaction fallback, and QQ voice ASR. 16,200 stars, ~230/day right now.

The data-sovereignty story is the real story. Keeping personal messages off OpenAI/Anthropic clouds while still getting one always-available agent across every chat app you use isn't just convenience — it's a structural shift. QwenPaw makes the shift OSS-affordable.

Background

agentscope-ai is the team behind Alibaba's AgentScope, a multi-agent orchestration framework. QwenPaw is the application-layer "personal assistant" sitting on top. The CoPaw → QwenPaw rebrand in April signals Alibaba's intent to push Qwen as the global default agent model.

Macro context: Chinese OSS shipping accelerated through April — DeepSeek V4, Qwen3, GLM-5.1 all in one month. QwenPaw is that surge's application-layer evidence, with multilingual and Chinese-messenger (WeChat, QQ) coverage that US OSS (OpenInterpreter, Continue) doesn't reach.

Core Features

Three groups. Multi-chat bridge — adapter-pattern wrap of WhatsApp Web, Telegram Bot API, WeChat unofficial, QQ Bot, Discord, Slack into one normalized message queue. Same agent answers everywhere.

Memory + context compaction (v1.1.5). Running 24/7 means you accumulate conversation history. QwenPaw separates session-level and long-term memory; when context fills, it summarizes older turns into the long-term store and retrieves on demand.

QQ voice ASR. Voice messages dominate Chinese QQ usage; v1.1.5 ships a Whisper-Qwen2-ASR pipeline, opening a fully OSS route. Niche to non-Chinese users, decisive for the Chinese base.

git clone https://github.com/agentscope-ai/QwenPaw.git
cd QwenPaw && pip install -e .
qwenpaw init && qwenpaw run

Stack + Architecture

Python + AgentScope + Qwen + any OpenAI-compatible API + Home Assistant plugin. BYOK at the model layer, so Qwen, OpenAI, Anthropic-compatible endpoints all plug in. Architecturally it's a bidirectional streaming agent, not a one-way command router — incoming messages preserve which chat platform they came from, the agent decides memory/tool/model, then dispatches a reply plus follow-up actions (calendar entry, smart-home command). A watchdog with auto-restart covers 24/7 reliability.

Compete Set

Project	Self-host	Multi-chat bridge	Voice	Home Assistant
agentscope-ai/QwenPaw	Yes	6 platforms	QQ voice + ASR	Yes (plugin)
nexu-io/nexu	Yes	Desktop-focused	No	No
OpenInterpreter	Yes	CLI-focused	No	No
open-claw/claw	Yes	Desktop-focused	No	Yes

QwenPaw's wedge is the breadth of chat-platform coverage. The Chinese-messenger support is essentially unique among global OSS.

Why Now

Three currents collided. One: OpenAI's mandatory macOS app update (deadline May 8) plus the Google + Forcepoint indirect-prompt-injection report turned cloud-data-exfil into a live security worry — self-hosting reads as the safer default. Two: April's Chinese OSS surge (DeepSeek V4, Qwen3, GLM-5.1) made "Qwen + self-host" technically viable for non-Chinese users; v1.1.5's improved English/Korean ASR is helping. Three: the cloud-agent news cycle (Genspark + MS, OpenAI macOS) is generating a counter-cohort that wants self-hosted, and QwenPaw is its standard-bearer on r/LocalLLaMA and r/selfhosted.

Getting Started + Pitfalls

Two pitfalls. One: Telegram/Discord/Slack official APIs need token issuance; WeChat/QQ unofficial APIs can break with platform changes. For stable ops, lean on the four official APIs and treat the unofficial ones as bonus channels. Two: memory search wants a vector DB (Qdrant recommended); the included docker-compose brings one up alongside.

Limits and Outlook

iMessage bridge is missing — a real gap for macOS users; the May roadmap adds it. Video calling and screen sharing are out of scope (June targeted). Long-term, QwenPaw is a referendum on whether self-hosted assistants can match cloud-only ones on quality + reliability + integration. Its trajectory is the dial for the OSS-assistant category.

Tomorrow Morning

Developers: qwenpaw init and bridge one chat app you actually use within five minutes. Privacy-conscious users: simulate moving WhatsApp/Telegram daily chat to the agent and you'll get a clear feel for where data sovereignty trades off against convenience. Investors/founders: track adoption against vertical SaaS assistants — that delta is the 12-24 month story.

References

• Repo: https://github.com/agentscope-ai/QwenPaw
• AgentScope: https://github.com/modelscope/agentscope
• Qwen: https://github.com/QwenLM/Qwen
• Home Assistant: https://www.home-assistant.io/
• OpenInterpreter: https://github.com/OpenInterpreter/open-interpreter