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CoPaw

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CoPaw Logo

Works for you, grows with you.

Your Personal AI Assistant; easy to install, deploy on your own machine or on the cloud; supports multiple chat apps with easily extensible capabilities.

Core capabilities:

Every channel — DingTalk, Feishu, QQ, Discord, iMessage, and more. One assistant, connect as you need.

Under your control — Memory and personalization under your control. Deploy locally or in the cloud; scheduled reminders to any channel.

Skills — Built-in cron; custom skills in your workspace, auto-loaded. No lock-in.

What you can do
  • Social: daily digest of hot posts (Xiaohongshu, Zhihu, Reddit), Bilibili/YouTube summaries.
  • Productivity: newsletter digests to DingTalk/Feishu/QQ, contacts from email/calendar.
  • Creative: describe your goal, run overnight, get a draft next day.
  • Research: track tech/AI news, personal knowledge base.
  • Desktop: organize files, read/summarize docs, request files in chat.
  • Explore: combine Skills and cron into your own agentic app.

Table of Contents

Recommended reading:

Quick Start

pip install (recommended)

If you prefer managing Python yourself:

pip install copaw
copaw init --defaults
copaw app

Then open http://127.0.0.1:8088/ in your browser for the Console (chat with CoPaw, configure the agent). To talk in DingTalk, Feishu, QQ, etc., add a channel in the docs.

Console

One-line install (beta, continuously improving)

No Python required — the installer handles everything:

macOS / Linux:

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

Windows (PowerShell):

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

Then open a new terminal and run:

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

macOS / Linux:

# Install a specific version
curl -fsSL ... | bash -s -- --version 0.0.2

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

# Install from your fork repository
curl -fsSL ... | bash -s -- --from-source --repo https://github.com/<you>/CoPaw.git
COPAW_REPO=https://github.com/<you>/CoPaw.git curl -fsSL ... | bash -s -- --from-source

# With local model support
bash install.sh --extras llamacpp    # llama.cpp (cross-platform)
bash install.sh --extras mlx         # MLX (Apple Silicon)
bash install.sh --extras llamacpp,mlx

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

# Uninstall
copaw uninstall          # keeps config and data
copaw 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

# Install from your fork repository
.\install.ps1 -FromSource -Repo https://github.com/<you>/CoPaw.git
$env:COPAW_REPO="https://github.com/<you>/CoPaw.git"; .\install.ps1 -FromSource

# With local model support
.\install.ps1 -Extras llamacpp      # llama.cpp (cross-platform)
.\install.ps1 -Extras mlx           # MLX
.\install.ps1 -Extras llamacpp,mlx

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

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

Using Docker

docker pull agentscope/copaw:latest
docker run -p 8088:8088 -v copaw-data:/app/working agentscope/copaw:latest

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

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.

Using ModelScope

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

Deploy on Alibaba Cloud Kubernetes (ACK)

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

API Key

If you use a cloud LLM (e.g. DashScope, ModelScope), you must set an API key before chatting. CoPaw will not work until a valid key is configured.

Where to set it:

  1. copaw init — When you run copaw init, the command has a step to configure the LLM provider and API key. Follow the prompts to choose a provider and enter your key.
  2. Console — After copaw app, open http://127.0.0.1:8088/ → Settings → Models. Select a provider, fill in the API Key field, then activate that provider and model.
  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, or see Config for details.

Using local models only? If you use Local Models (llama.cpp or MLX), you do not need any API key.

Dual model routing (LLM + VLM)

CoPaw supports a text model slot (LLM) and a vision model slot (VLM).

  • Text-only requests use the active LLM.
  • Requests containing image blocks use LLM when it supports vision.
  • If the active LLM does not support vision, CoPaw routes the turn to VLM and then tries configured VLM fallbacks.

CLI helpers:

copaw models set-llm
copaw models set-vlm
copaw models set-vlm-fallbacks
copaw models list

Optional overrides for capability heuristics:

  • COPAW_VISION_MODELS: comma-separated model ids forced to vision-capable
  • COPAW_NON_VISION_MODELS: comma-separated model ids forced to non-vision

Vision prepass attachment policy:

  • COPAW_VISION_ATTACHMENTS_MODE: first (default) or all
  • COPAW_VISION_MAX_IMAGES: max images when mode is all (default 4)

If you add custom models, you can mark them as vision-capable:

copaw models add-model <provider_id> -m <model_id> -n <display_name> --vision

Fork-specific changes (vs upstream)

This fork keeps upstream CoPaw behavior as the baseline, and extends multimodal handling in a backward-compatible way.

  • Media prepass expanded: prepass is no longer image-only; it now supports image / audio / video inputs with per-capability settings.
  • VLM fallback chain in all surfaces: fallback list is configurable from WebUI, CLI, and API instead of being limited to one workflow.
  • New vision settings endpoints: API supports separate updates for vision.image, vision.audio, and vision.video (used by the Console models page).
  • CLI additions: new commands for multimodal settings:
    • copaw models set-vision-audio
    • copaw models set-vision-video
    • copaw models list now shows all vision/media prepass settings.
  • Capability routing generalized: model capability detection and prepass routing were refactored from vision-only checks to generic input capability checks (image, audio, video).
  • Compatibility preserved: existing single-vision env overrides and behavior remain supported for users migrating from upstream defaults.

Local Models

CoPaw can run LLMs entirely on your machine — no API keys or cloud services required.

Backend Best for Install
llama.cpp Cross-platform (macOS / Linux / Windows) pip install 'copaw[llamacpp]'
MLX Apple Silicon Macs (M1/M2/M3/M4) pip install 'copaw[mlx]'

After installing, download a model and start chatting:

copaw models download Qwen/Qwen3-4B-GGUF
copaw models # select the downloaded model
copaw app # start the server

You can also download and manage local models from the Console UI.

Documentation

Topic Description
Introduction What CoPaw is and how you use it
Quick start Install and run (local or ModelScope Studio)
Console Web UI for chat and agent config
Channels DingTalk, Feishu, QQ, Discord, iMessage, and more
Heartbeat Scheduled check-in or digest
Local Models Run models locally with llama.cpp or MLX
CLI Init, cron jobs, skills, clean
Skills Extend and customize capabilities
FAQ Common questions and troubleshooting tips
Memory Context management and long-term memory
Config Working directory and config file

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

Install from source

git clone https://github.com/agentscope-ai/CoPaw.git
cd CoPaw
pip install -e .

For forks, replace the clone URL with your own repository.

  • Dev (tests, formatting): pip install -e ".[dev]"
  • Console (build frontend): cd console && npm ci && npm run build, then copaw app from project root.

Why CoPaw?

CoPaw represents both a Co Personal Agent Workstation and a "co-paw"—a partner always by your side. More than just a cold tool, CoPaw is a warm "little paw" always ready to lend a hand (or a paw!). It is the ultimate teammate for your digital life.

Built by

AgentScope team · AgentScope · AgentScope Runtime · ReMe

License

CoPaw is released under the Apache License 2.0.

About

Your Personal AI Assistant; easy to install, deploy on your own machine or on the cloud; supports multiple chat apps with easily extensible capabilities.

copaw.agentscope.io/

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