QwenPaw README

The project's README from github.com/agentscope-ai/QwenPaw. As one markdown file: README.md.

QwenPaw

GitHub Repo PyPI Documentation Python Version Last Commit License Code Style GitHub Stars GitHub Forks DeepWiki Discord X DingTalk AgentScope Platform

agentscope-ai%2FQwenPaw | Trendshift

[Documentation] [中文] [日本語] [Русский]

QwenPaw Logo

Works for you, grows with you.

Your personal AI assistant — deploy locally or in the cloud, extend with Skills & Plugins, connect across every channel.

Never forgets Three-layer memory — live working context, full verbatim history, and a self-evolving personal knowledge base powered by ReMe. Conversations and resources continuously become readable, editable, searchable, and linked Markdown memory.
Local or cloud, runs free QwenPaw-Flash models (2B / 4B / 9B) trained for agent tasks. Built-in QwenPaw Local runtime — no API key, no cloud dependency. Also works with Ollama, LM Studio, or 14+ cloud providers.
Security built in Kernel-level Sandbox, Tool Guard, File Guard, Skill Scanner, and Access Policy. Dangerous commands are blocked before they run.
Multi-agent & parallel Spawn independent agents with their own memory and skills. Sub-agents at runtime. Agent Communication Protocol (ACP) for cross-system orchestration.
File workspace Unified file navigation, preview, editing, diffs, upload, and download across project and Agent files.
Extensible Skills for scheduling, documents, browser, news, and more. Plugin architecture with a marketplace. MCP integration for external tools. Combine them into purpose-built workflows.
Reachable anywhere DingTalk, Lark, WeChat, Discord, Telegram, iMessage, QQ — one instance, all channels. Console, TUI, and desktop app for direct access.
Yours, not ours Deploy locally — data stays on your machine. No third-party hosting, no data upload.
What you can do with QwenPaw
  • Automation & scheduling: Set up recurring tasks — news digests, report generation, multi-channel broadcasting — all on your schedule.
  • Code & development: Read, edit, review, and test code in your projects with the unified file workspace.
  • Document processing: Read, write, and convert PDF, Word, Excel, and PowerPoint files.
  • Information gathering: Search the web, follow subscriptions, summarize videos, and find what you need in your personal knowledge base.
  • Multi-channel ops: Push alerts, summaries, or AI-generated content to DingTalk, Lark, Discord, Telegram, and more — simultaneously or per channel.
  • Custom workflows: Combine built-in capabilities, plugins, and scheduled tasks into workflows tailored to your needs.

News

All release notes →


Table of Contents


Quick Start

Option 1: Pip Install

If you prefer managing Python yourself (requires Python >= 3.11, < 3.14):

pip install qwenpaw
qwenpaw init --defaults
qwenpaw app

Then open the Console in your browser at http://127.0.0.1:8088/ to configure your model. To chat in DingTalk, Lark, WeChat, etc., see the Channel setup documentation.

Console


Option 2: Script Install

No Python setup required, one command installs everything. The script will automatically download uv (Python package manager), create a virtual environment, and install QwenPaw with all dependencies (including Node.js and frontend assets). Note: May not work in restricted network environments or corporate firewalls.

macOS / Linux:

curl -fsSL https://qwenpaw.agentscope.io/install.sh | bash

Windows (CMD):

curl -fsSL https://qwenpaw.agentscope.io/install.bat -o install.bat && install.bat

Windows (PowerShell):

irm https://qwenpaw.agentscope.io/install.ps1 | iex

Note: The installer will automatically check the status of uv. If it is not installed, it will attempt to download and configure it automatically. If the automatic installation fails, please follow the on-screen prompts or execute python -m pip install -U uv, then rerun the installer.

⚠️ Special Notice for Windows Enterprise LTSC Users

If you are using Windows LTSC or an enterprise environment governed by strict security policies, PowerShell may run in Constrained Language Mode, potentially causing the following issue:

  1. If using CMD (.bat): Script executes successfully but fails to write to Path

    The script completes file installation. Due to Constrained Language Mode, it cannot automatically update environment variables. Manually configure as follows:

    • Locate the installation directory:
      • Check if uv is available: Enter uv --version in CMD. If a version number appears, only configure the QwenPaw path. If you receive the prompt 'uv' is not recognized as an internal or external command, operable program or batch file, configure both paths.
      • uv path (choose one based on installation location; use if uv fails): Typically %USERPROFILE%\.local\bin, %USERPROFILE%\AppData\Local\uv, or the Scripts folder within your Python installation directory
      • QwenPaw path: Typically located at %USERPROFILE%\.qwenpaw\bin.
    • Manually add to the system's Path environment variable:
      • Press Win + R, type sysdm.cpl and press Enter to open System Properties.
      • Click “Advanced” -> “Environment Variables”.
      • Under “System variables”, locate and select Path, then click “Edit”.
      • Click “New”, enter both directory paths sequentially, then click OK to save.
  2. If using PowerShell (.ps1): Script execution interrupted

Due to Constrained Language Mode, the script may fail to automatically download uv.

  • Manually install uv: Refer to the GitHub Release to download uv.exe and place it in %USERPROFILE%\.local\bin or %USERPROFILE%\AppData\Local\uv; or ensure Python is installed and run python -m pip install -U uv.
  • Configure uv environment variables: Add the uv directory and %USERPROFILE%\.qwenpaw\bin to your system's Path variable.
  • Re-run the installation: Open a new terminal and execute the installation script again to complete the QwenPaw installation.
  • Configure the QwenPaw environment variable: Add %USERPROFILE%\.qwenpaw\bin to your system's Path variable.

Once installed, open a new terminal and run:

qwenpaw init --defaults   # or: qwenpaw init (interactive)
qwenpaw app
Install options

macOS / Linux:

# Install a specific version
curl -fsSL ... | bash -s -- --version 1.1.0

# Install from source (dev/testing)
curl -fsSL ... | bash -s -- --from-source

# Upgrade — just re-run the installer
curl -fsSL ... | bash

# Uninstall
qwenpaw uninstall          # keeps config and data
qwenpaw uninstall --purge  # removes everything

Windows (PowerShell):

# Install a specific version
irm ... | iex; .\install.ps1 -Version 1.1.12

# Install from source (dev/testing)
.\install.ps1 -FromSource

# Upgrade — just re-run the installer
irm ... | iex

# Uninstall
qwenpaw uninstall          # keeps config and data
qwenpaw uninstall --purge  # removes everything

Option 3: Docker

Images are on Docker Hub (agentscope/qwenpaw). Image tags: latest (stable); pre (PyPI pre-release).

docker pull agentscope/qwenpaw:latest
docker run -p 127.0.0.1:8088:8088 \
  -v qwenpaw-data:/app/working \
  -v qwenpaw-secrets:/app/working.secret \
  -v qwenpaw-backups:/app/working.backups \
  agentscope/qwenpaw:latest

Also available on Alibaba Cloud Container Registry (ACR) for users in China: agentscope-registry.ap-southeast-1.cr.aliyuncs.com/agentscope/qwenpaw (same tags).

Then open http://127.0.0.1:8088/ for the Console. Config, memory, and skills are stored in the qwenpaw-data volume; model provider settings and API keys are in the qwenpaw-secrets volume; backup archives are stored in the qwenpaw-backups volume. To pass API keys (e.g. DASHSCOPE_API_KEY), add -e VAR=value or --env-file .env to docker run.

Connecting to Ollama or other services on the host machine

Inside a Docker container, localhost refers to the container itself, not your host machine. If you run Ollama (or other model services) on the host and want QwenPaw in Docker to reach them, use one of these approaches:

Option A — Explicit host binding (all platforms):

docker run -p 127.0.0.1:8088:8088 \
  --add-host=host.docker.internal:host-gateway \
  -v qwenpaw-data:/app/working \
  -v qwenpaw-secrets:/app/working.secret \
  -v qwenpaw-backups:/app/working.backups \
  agentscope/qwenpaw:latest

Then in QwenPaw Settings → Models, change the Base URL to http://host.docker.internal:<port> — for example, http://host.docker.internal:11434 for Ollama, or http://host.docker.internal:1234/v1 for LM Studio.

Option B — Host networking (Linux only):

docker run --network=host \
  -v qwenpaw-data:/app/working \
  -v qwenpaw-secrets:/app/working.secret \
  -v qwenpaw-backups:/app/working.backups \
  agentscope/qwenpaw:latest

No port mapping (-p) is needed; the container shares the host network directly. Note that all container ports are exposed on the host, which may cause conflicts if the port is already in use.

The image is built from scratch. To build the image yourself, please refer to the Build Docker image section in scripts/README.md, and then push to your registry.


Option 4: Deploy on Alibaba Cloud ECS

To run QwenPaw on Alibaba Cloud (ECS), use the one-click deployment: open the QwenPaw on Alibaba Cloud (ECS) deployment link and follow the prompts. For step-by-step instructions, see Alibaba Cloud Developer: Deploy your AI assistant in 3 minutes.


Option 5: AgentScope Platform

AgentScope Platform provides one-click cloud QwenPaw deployment, plugin sharing, and a Skill marketplace. Free, 7/24 online.


Option 6: Using ModelScope

ModelScope Studio also supports cloud QwenPaw deployment. Note: set your Studio to non-public so others cannot control your QwenPaw.


Option 7: Desktop Application (Beta)

Beta Notice: The desktop application is currently in Beta testing phase with the following known limitations:

  • Incomplete compatibility testing: Not fully tested across all system versions and hardware configurations
  • Potential performance issues: Startup time, memory usage, and other performance aspects may need further optimization
  • Features under development: Some features may be unstable or missing

If you're not comfortable with command-line tools, you can download and use QwenPaw's desktop application without manually configuring Python environments or running commands.

Download

Download the desktop app (Tauri build) from the official download page:

Features

First Launch

Important: The first launch may take 10-60 seconds (depending on your system configuration). The application needs to initialize the Python environment and load dependencies. Please wait patiently for the window to open automatically.

macOS: Bypass System Security Restrictions

When you download the QwenPaw macOS app from Releases, macOS may show: "Apple cannot verify that 'QwenPaw' contains no malicious software". This happens because the app is not notarized. You can still open it as follows:

QwenPaw (agentscope-ai/QwenPaw)

qwen
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35.8K ·GitHub
What is this?QwenPaw is a personal AI assistant that you deploy yourself (locally or in the cloud), which remembers context across sessions, runs tasks/automations, and reaches you through chat apps like Discord, Telegram, and WeChat. It is extensible with Skills, Plugins, and MCP tools.
Is this just a chat UI or something bigger?It's a full agent runtime (built on the AgentScope framework) with an 'Agent OS' architecture, not a single app — the same agent core is exposed through a Console, a Terminal UI, a desktop app, and multiple chat channels.
How do I get it running?Install via pip (Python >= 3.11, auto-bootstraps uv), pull the Docker image agentscope/qwenpaw from Docker Hub, one-click deploy on Alibaba Cloud ECS or the AgentScope Platform/ModelScope, or download the Beta desktop app (Tauri build) for Windows/macOS.
Do I need an API key to use it?No — it ships a built-in local runtime with QwenPaw-Flash models (2B/4B/9B) requiring no API key or cloud dependency, but you can also hook it up to Ollama, LM Studio, or 14+ cloud model providers.
Is the desktop app ready to use right now?It's explicitly labeled Beta: on macOS it isn't notarized so Gatekeeper blocks it by default (you must right-click → Open), first launch can take 10-60 seconds to initialize, and some features are still unstable.
Which chat apps can it connect to?DingTalk, Lark, WeChat, Discord, Telegram, iMessage, and QQ, in addition to its own Console, Terminal UI, and desktop app.
Where does my data go when I self-host it?Everything stays on your machine when self-hosted; there is no third-party hosting or data upload, and config/memory/skills are kept in local Docker volumes.
What does the three-layer memory system actually store?It keeps live working context, the full verbatim conversation history, and a self-evolving personal knowledge base (ReMe) that turns conversations and files into searchable, linked Markdown memory.
How does it stop the agent from running dangerous commands?It has a kernel-level Sandbox plus Tool Guard, File Guard, Skill Scanner, and an Access Policy engine that block risky commands before execution.
Can it run more than one agent at a time?Yes, it can spawn independent sub-agents each with their own memory and skills, coordinated via the Agent Communication Protocol (ACP) for cross-system orchestration.
What changed in the 2.0 rewrite?Version 2.0 is a ground-up rewrite on AgentScope 2.0 adding the Agent OS architecture, Loop Engineering agent-loop templates, 'Scroll Context' (nothing gets summarized away), and a bundled Terminal UI.
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