qwenpaw 1.1.1b1

QwenPaw is a **personal assistant** that runs in your own environment. It talks to you over multiple channels (DingTalk, Feishu, QQ, Discord, iMessage, etc.) and runs scheduled tasks according to your configuration. **What it can do is driven by Skills — the possibilities are open-ended.** Built-in skills include cron, PDF/Office handling, news digest, file reading, and more; you can add custom skills. All data and tasks run on your machine; no third-party hosting.

QwenPaw

[Documentation] [中文] [日本語] [Русский]

Works for you, grows with you.

Your personal AI assistant — easy to install, deploy locally or in the cloud, connect across channels, extend with ease.

Core capabilities:

Under your control — Memory and personalization fully under your control. Deploy locally (data stays on your machine) or in the cloud (your chosen server). No third-party hosting, no data upload.

Skills extension — Built-in scheduling, PDF/Office processing, news digest, and more; custom skills auto-loaded, no lock-in. Skills determine what QwenPaw can do.

Multi-agent collaboration — Create multiple independent agents, each with their own role; enable collaboration skills for inter-agent communication to tackle complex tasks together.

Multi-layer security — Tool guard, file access control, skill security scanning to ensure safe operation.

Every channel — DingTalk, Feishu, WeChat, Discord, Telegram, and more. One QwenPaw, connect as needed.

What you can do with QwenPaw

• Social media: Daily hot post digests (Xiaohongshu, Zhihu, Reddit), Bilibili/YouTube video summaries.
• Productivity: Email & newsletter highlights pushed to DingTalk/Feishu/QQ; email & calendar contact organization.
• Creative & building: Describe your goal before sleep, auto-execute, wake up to a prototype; full workflow from topic selection to final video.
• Research & learning: Track tech & AI news, personal knowledge base search and reuse.
• Desktop & files: Organize and search local files, read & summarize documents, request files in chat.
• Explore more: Combine Skills with scheduled tasks into your own agentic app.

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News

[2026-04-12] CoPaw is Officially Rebranding to QwenPaw: This rebranding marks an important step forward into our next phase of open-source development.

The new name better reflects the open ecosystem we are building and the broader direction we are continuing to pursue:

Qwen — represents a deeper integration with the Qwen open-source ecosystem and a sharper focus on the model layer, including local models for agents and smart collaboration between small and large models.

Paw — carries forward our original mission to accompany users and become a personal assistant users can truly trust.

This rebranding does not change our vision and goals in the open-source community:

• To build a more practical, secure, and personalized AI assistant;
• To stay committed to open-source collaboration and move forward together with the community.

Thank you all for your support along the way. From today on, QwenPaw will stay and grow with you!

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Table of Contents

Recommended reading:

• 🚀 I'm new and want to try quickly: Quick Start → three commands to run → Configure model → chat in Console
• 💬 I want to use in DingTalk/Feishu/WeChat: Complete Quick Start → Configure model → Channel setup
• 🐍 I don't want to install Python: Desktop app or Script install or ModelScope Studio
• 💻 I want to use local models (no API key needed): Local Models → download model → start using
• 🛠️ I want to contribute code or develop new features: Install from source → Contributing

• News
• Quick Start
• API Key
• Local Models
• Documentation
• Security Features
• FAQ
• Staying ahead
• Roadmap
• Install from source
• Contributing
• Why QwenPaw?
• Built by
• License

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Quick Start

Option 1: pip install

If you prefer managing Python yourself:

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, Feishu, WeChat, etc., see the Channel setup documentation.

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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

To install with Ollama support:

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

To install with multiple extras (e.g., Ollama + local):

curl -fsSL https://qwenpaw.agentscope.io/install.sh | bash -s -- --extras ollama,local

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 0.0.2

# 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

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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 \
  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. 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 \
  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 \
  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.

Note: If you only mount /app/working without a separate volume for /app/working.secret, the entrypoint will automatically redirect secrets into /app/working/.secret so they persist on the same volume.

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.

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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.

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Option 5: Using ModelScope

No local install? ModelScope Studio one-click cloud setup. Set your Studio to non-public so others cannot control your QwenPaw.

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Option 6: 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 from GitHub Releases:

• Windows: QwenPaw-Setup-<version>.exe
• macOS: QwenPaw-<version>-macOS.zip (Apple Silicon recommended)

Features

• ✅ Zero configuration: Download and double-click to run, no need to install Python or configure environment variables
• ✅ Cross-platform: Supports Windows 10+ and macOS 14+
• ✅ Visual interface: Automatically opens browser interface, no need to manually enter addresses
• ⚠️ Beta stage: Features are continuously being improved, feedback welcome

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 browser 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:

• Right-click to open (recommended) Right-click (or Control+click) the QwenPaw app → Open → in the dialog click Open again. This tells Gatekeeper you trust the app; after that you can double-click to launch as usual.

• Allow in System Settings If it is still blocked, go to System Settings → Privacy & Security, scroll to the message like "QwenPaw was blocked because it is from an unidentified developer", and click Open Anyway or Allow.

• Remove quarantine attribute (not recommended for most users) In Terminal run: xattr -cr /Applications/QwenPaw.app (or use the path to the .app after unzipping). This clears the "downloaded from the internet" quarantine flag so the warning usually does not appear, but is less safe and controllable than using Right-click → Open.

For detailed usage instructions, troubleshooting, and common issues, see the Desktop Application Guide.

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API Key

If you use a cloud LLM API (e.g., Qianwen, Gemini, OpenAI), you must configure an API key before chatting. QwenPaw will not work until a valid key is set. See the official docs for details.

How to configure:

1. Console (recommended) — After running qwenpaw app, open http://127.0.0.1:8088/ → Settings → Models. Choose a provider, enter the API Key, and enable that provider and model.
2. qwenpaw init — When you run qwenpaw init, it will guide you through configuring the LLM provider and API key. Follow the prompts to choose a provider and enter your key.
3. Environment variable — For DashScope you can set DASHSCOPE_API_KEY in your shell or in a .env file in the working directory.

Tools that need extra keys (e.g. TAVILY_API_KEY for web search) can be set in Console Settings → Environment variables, see Config for details.

Using local models only? If you use Local Models (llama.cpp / Ollama / LM Studio), you do not need any API key.

Local Models

QwenPaw can run LLMs entirely on your machine — no API keys or cloud services required. See the official docs for details.

Backend	Best for	Install
llama.cpp	Cross-platform (macOS / Linux / Windows)	No extra installation required; click "Download Llama.cpp" in the web UI.
Ollama	Cross-platform (requires Ollama service)	Install and start the Ollama app in advance.
LM Studio	Cross-platform (requires LM Studio service)	Install and start the LM Studio app in advance.

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Documentation

Topic	Description
Introduction	What QwenPaw is and how to use it
Quick start	Install and run (local or ModelScope Studio)
Console	Web UI: chat and agent configuration
Models	Configure cloud, local, and custom providers
Channels	DingTalk, Feishu, QQ, Discord, iMessage, and more
Skills	Extend and customize capabilities
MCP	Manage MCP clients
Memory	Long-term memory
Context	Context management mechanism
Magic commands	Control conversation state without waiting for the AI
Heartbeat	Scheduled check-in and digest
Multi-Agent	Create multiple agents and enable collaboration
Config & working dir	Working directory and config file
CLI	Init, cron jobs, skills, clean
FAQ	Common questions and troubleshooting

Full docs in this repo: website/public/docs/.

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Security Features

QwenPaw includes multi-layer security mechanisms to protect your data and system:

• Tool guard — Automatically intercepts dangerous shell commands (e.g., rm -rf /, fork bombs, reverse shells, etc.)
• File access guard — Restricts agent access to sensitive paths (e.g., ~/.ssh, key files, system directories, etc.)
• Skill security scanning — Automatically scans before installing skills, detecting risks like prompt injection, command injection, hardcoded keys, data exfiltration, etc.
• Local deployment — All data and memory stored locally, no third-party upload (when using cloud LLM APIs, conversation content is sent to the corresponding API provider)
• Web Authentication — Optional login protection for the Console. Disabled by default; set QWENPAW_AUTH_ENABLED=true to enable. See Web Authentication for details.

See Security documentation for details.

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FAQ

For common questions, troubleshooting tips, and known issues, please visit the FAQ page.

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Staying ahead

Star QwenPaw on GitHub and be instantly notified of new releases.

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Roadmap

Area	Item	Status
Horizontal Expansion	More channels, models, skills, MCPs — community contributions welcome	Seeking Contributors
Existing Feature Extension	Display optimization, download hints, Windows path compatibility, etc. — community contributions welcome	Seeking Contributors
Console Web UI	Expose more info/config in the Console	In Progress
Multi-agent	Agentic Ralph Loop	In Progress
Multimodal	Voice/video calls and real-time interaction	In Progress
Small + Large Model Collaboration	Multi-model routing; different models for different tasks	In Progress
Memory System	Experience distillation & skill extraction	In Progress
	Memory mechanism switching	In Progress
	Multimodal memory fusion	Planned
	Context-aware proactive delivery	Planned
Sandbox	Deeper integration with AgentScope Runtime sandboxes	In Progress
Cloud-native	Deeper integration with AgentScope Runtime; leverage cloud compute, storage, tools, and skills	In Progress
Skills Hub	Enrich the AgentScope Skills repository and improve discoverability of high-quality skills	Planned

Status: In Progress — actively being worked on; Planned — queued or under design, also welcome contributions; Seeking Contributors — we strongly encourage community contributions.

Install from source

git clone https://github.com/agentscope-ai/QwenPaw.git
cd QwenPaw

# Build console frontend first (required for web UI)
cd console && npm ci && npm run build
cd ..

# Copy console build output to package directory
mkdir -p src/qwenpaw/console
cp -R console/dist/. src/qwenpaw/console/

# Install Python package
pip install -e .

• Dev (tests, formatting): pip install -e ".[dev,full]"
• Then: Run qwenpaw init --defaults, then qwenpaw app.

Note for updates: When updating to a new major version after git pull, please also rebuild the frontend, reinstall the package (pip install -e .), restart qwenpaw app, and clear your browser cache with Ctrl+Shift+R (or Cmd+Shift+R on macOS).

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Contributing

QwenPaw evolves through open collaboration, and we welcome all forms of contribution! Check the Roadmap above (especially items marked Seeking Contributors) to find areas that interest you, and read CONTRIBUTING to get started. We particularly welcome:

• Horizontal expansion — new channels, model providers, skills, MCPs.
• Existing feature extension & refinement — display and interaction improvements, download hints, Windows path compatibility, etc.

Join GitHub Discussions to discuss ideas or pick up tasks.

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Why QwenPaw?

QwenPaw stands for Qwen Personal Agent Workstation, and also embodies the wisdom of Qwen and the warmth of a Paw. We hope it is not a cold tool, but an intelligent and warm "little paw" always ready to help—a most intuitive partner in your digital life.

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Built by

AgentScope team · AgentScope · AgentScope Runtime · ReMe

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Contact us

Discord	X (Twitter)	DingTalk	RedNote

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Telemetry

QwenPaw collects anonymous usage data during qwenpaw init to help us understand our user base and prioritize improvements. Data is sent once per version — when you upgrade QwenPaw, telemetry is re-collected so we can track version adoption.

What we collect:

• QwenPaw version (e.g., 0.0.7)
• Install method (pip, Docker, or desktop app)
• OS and version (e.g., macOS 14.0, Ubuntu 22.04)
• Python version (e.g., 3.13)
• CPU architecture (e.g., x86_64, arm64)
• GPU availability (yes/no)

What we do NOT collect: No personal data, no files, no credentials, no IP addresses, no identifiable information.

When running qwenpaw init interactively, you will be asked whether to opt in. If you choose --defaults, telemetry is accepted automatically. The prompt appears once per version and never affects QwenPaw's functionality.

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License

QwenPaw is released under the Apache License 2.0.

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Contributors

All thanks to our contributors: