AI tool comparison
QwenPaw vs QwenPaw
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Personal AI
QwenPaw
Self-hosted personal AI assistant that runs in your own environment
75%
Panel ship
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Community
Free
Entry
QwenPaw (formerly CoPaw) is an open-source personal AI assistant you run on your own machine or cloud server. It connects to multiple chat platforms — Discord, DingTalk, Feishu, QQ, iMessage — and handles scheduled tasks, custom skills, and document processing all from a single local process. Nothing leaves your infrastructure. The April 22 v1.1.3 release added a Backup & Restore system, the ability to run QwenPaw as an ACP (Agent Communication Protocol) server, proactive agent messaging, a Console Plugin System, and a Shell Evasion Guard for security. It's built on the AgentScope framework and is now deeply integrated with the Qwen open-source model ecosystem, including local model support. QwenPaw sits in a sweet spot between consumer AI apps (which own your data) and raw agent frameworks (which require heavy engineering). The skills system makes it extensible without requiring code changes for each new capability — built-in skills handle PDF/Office files, news digests, and cron jobs, with custom skills easily added.
Personal AI
QwenPaw
Self-hosted personal AI with evolving memory, runs on 6+ chat apps
75%
Panel ship
—
Community
Free
Entry
QwenPaw (formerly CoPaw, rebranded April 2026) is an open-source personal AI assistant built by the AgentScope team at Alibaba. You deploy it locally or on a cloud VM, connect it to messaging apps like Telegram, Discord, WeChat, DingTalk, or Feishu, and interact with a persistent, memory-evolving agent that learns your preferences and proactively surfaces relevant information. Version 1.1.4, released April 24, brings a refactored memory and context architecture, built-in DeepSeek V4 models, ACP Server exposure for multi-agent communication, and a console plugin system. For LLM backends it supports cloud APIs (Qianwen, DeepSeek, OpenAI) and fully offline local inference via Ollama, LM Studio, or llama.cpp — meaning you can run it with zero API costs on your own hardware. The built-in skill library covers daily news digests, video summarization, email triage, PDF/Office processing, and calendar management. The multi-agent capability — where you can spin up specialized agents that collaborate — puts it in interesting territory between a personal assistant and a lightweight team-of-agents platform. Desktop apps for Windows and macOS are in beta.
Reviewer scorecard
“The ACP server mode in v1.1.3 is underrated — it means QwenPaw can act as an agent backend for other tools. Apache 2.0 license, multi-channel support, and local Qwen model integration make this a genuinely solid self-hosted assistant stack.”
“The Ollama backend support is the key feature — this is the first personal assistant I've seen where you can genuinely go fully offline and fully free. The ACP server in v1.1.4 opens it up for multi-agent coordination that's actually useful for automating dev workflows.”
“The Qwen branding pivot is a bit of a red flag — it suggests this is now more of a Alibaba/Qwen showcase than a truly independent project. The multi-channel support sounds good but each integration adds surface area for breakage when APIs change.”
“The skill library looks impressive on paper but most of the demos are China-centric platforms (Xiaohongshu, Zhihu, DingTalk). International users will find meaningful gaps and will need to build their own skills. The documentation is also still primarily in Chinese despite multilingual README efforts.”
“Local-first AI assistants that run across all your communication channels are the next wave of personal productivity. QwenPaw's Shell Evasion Guard and offline-capable architecture show the team understands that security and privacy are table stakes for self-hosted agents.”
“The future of personal AI is self-hosted, memory-persistent, and connected to where you actually communicate. QwenPaw's architecture — LLM backend agnostic, multi-platform, multi-agent — is the right shape for that future. The Alibaba team building this in the open is a meaningful contribution.”
“Having your AI assistant available in Discord, iMessage, AND DingTalk from one local setup is genuinely useful. The proactive messaging feature means it can push you reminders and digests without you having to ask — that's where personal assistants actually earn their keep.”
“The 'describe your goal before sleep, wake up to a prototype' workflow is the creator feature I didn't know I needed. Video pipeline automation and newsletter digests pushed to Telegram cover 80% of my daily content research. This one's getting installed.”
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