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

News

• [2026-07-10] v2.0.0 — QwenPaw 2.0 Official Release 🎉 | An AgentScope 2.0 based ground-up rewrite delivering the Agent OS architecture, Loop Engineering, Scroll Context, ReMe v0.4.0 Long-term Memory, and a bundled Terminal UI.

  Highlight	What's new
  Agent OS — Workspace	Three pillars per agent: Resources (transparent on disk), Governance (allow/deny/ask/sandbox), Sandbox (macOS / Linux / Windows).
  Agent OS — Drivers	Protocol-neutral MCP / A2A / ACP connector layer with encrypted credentials and per-call policy gate.
  Loop Engineering	Advanced agent loop templates (Coding Mode, Mission Mode, more to come) with composable approval gates.
  Scroll Context	Every turn persisted; evicted turns indexed with on-demand recall — nothing summarized away.
  ReMe v0.4.0 Long-term Memory	Turn-based auto tracking, usage-aware search, and backend-specific embeddings.
  Terminal UI (TUI)	Full-screen terminal chat — same agent, memory, and sessions as Console and channels.

  Built on Agent OS, we will be launching out-of-box QwenPaw applications — such as QwenPaw Creator and QwenPaw Insight — stay tuned. v2.0.0 Release Notes →

• [2026-06-17] v1.1.12 — Models Page Overhaul & Simple Mode | Redesigned Models page with provider aggregation; new Simple Mode for streamlined navigation. v1.1.12 Release Notes →

• [2026-06-11] AgentScope Platform is live — Free QwenPaw deployment, plugin sharing, and Skill marketplace. Try it now →

• [2026-06-10] v1.1.11 — Free Model OAuth, Plugin Market, MCP Tool Whitelisting. v1.1.11 Release Notes →

All release notes →

Table of Contents

• News
• Quick Start
• API Key
• Local Models
• Security Features
• Documentation
• FAQ
• Roadmap
• Contributing
• Install From Source
• Why QwenPaw?
• Built By
• License

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.

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 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

# 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:

• Windows: QwenPaw-Tauri-<version>-Windows-setup.exe
• macOS: QwenPaw-Tauri-<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 the app window, 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 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 Desktop.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.

What's Next?

After installation, configure your model in Console → Settings → Models, then explore:

• Add Skills to extend capabilities (PDF, Office, browser, news, etc.)
• Set up Channels for DingTalk, Lark, Discord, and more
• Configure Cron for scheduled tasks and automation
• Try Coding Mode for IDE-style code collaboration
• See the full Quick Start guide for all options and verification steps

Terminal UI (TUI)

Prefer to stay in the terminal? Run qwenpaw to open a full-screen chat TUI that drives the same agent as the Console and the IM Channels — same memory, skills, MCP tools, and sessions — without leaving your keyboard.

qwenpaw                     # open a chat with the active agent
qwenpaw tui --resume <id>   # resume a previous session
qwenpaw .                   # start in the current repo (Coding Mode)

It supports streaming replies, slash commands (/help, /resume, /theme, plus the agent's own /model, /clear, …), pasting files/long text as attachments, and inline tool-permission prompts. See the Terminal UI guide for details.

API Key

If you use a cloud LLM API (e.g., DashScope / Qwen, OpenAI, Anthropic, Google Gemini, DeepSeek, Kimi, OpenRouter, and more), 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 (QwenPaw Local / 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.

QwenPaw also provides the QwenPaw-Flash series — purpose-trained 2B / 4B / 9B models for agent scenarios, with Q4 and Q8 quantizations. Available on ModelScope and Hugging Face.

Security Features

QwenPaw includes four core security layers:

• Sandbox — Kernel-level execution isolation using Seatbelt (macOS), Bubblewrap / Landlock (Linux), and AppContainer (Windows). Shell commands run inside a restricted filesystem view.
• Tool Guard — YAML rule engine with ShellEvasionGuardian inspects every tool call before execution, detecting command injection, path traversal, reverse shells, and obfuscated attacks. Configurable approval levels: STRICT / SMART / AUTO / OFF.
• File Guard — Independent of Tool Guard; blocks agent access to sensitive files and directories (default-protects ~/.qwenpaw.secret/, ~/.ssh, etc.).
• Skill Scanner — Pre-activation scanning with block / warn / off modes and whitelist support. Detects prompt injection, hardcoded secrets, data exfiltration, and more.

See Security for details.

Documentation

Full documentation: qwenpaw.agentscope.io/docs

FAQ

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

Roadmap

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

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.

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).

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.

Built By

AgentScope team · AgentScope · AgentScope Runtime · ReMe

Contact Us

Discord	X (Twitter)	DingTalk	RedNote

Staying Ahead

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

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., 1.1.12)
• 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.

License

QwenPaw is released under the Apache License 2.0.

Contributors

All thanks to our contributors: