CoPaw

CoPaw is a self-developed personal AI assistant project, not developed based on OpenClaw. The two only have similar functions, and you can choose according to your own needs.

Notes for Safe Use

To ensure data security, it is strongly recommended to install and run CoPaw in a virtual machine or on an independent computer without any important files and data; if you configure the API key of cloud large models during use, you need to keep the key confidential to avoid leakage; you should also pay attention to the permission setting of the working directory when running locally to prevent unauthorized access.

Core Project Information

1. Development Team and Underlying Technology: Developed by the AgentScope team, the project is built on AgentScope Runtime and ReMe at the bottom. It is also compatible with local large model running backends such as llama.cpp, MLX and Ollama, and can connect to cloud large model services such as DashScope and ModelScope.
2. Core Features: Compared with similar original tools, CoPaw has a significantly lower threshold for installation and use. It supports one-click script installation, desktop end running without environment configuration, and also provides multiple deployment methods such as Docker containerization deployment and one-click deployment on Alibaba Cloud ECS. No complex technical operations are required, and ordinary users can get started quickly.
3. Core Functions: As a full-featured personal AI assistant, CoPaw can connect to multiple social/office channels such as DingTalk, Feishu, QQ and Discord to achieve one-stop intelligent interaction; it supports practical functions such as hot content summary, document reading and summarization, file organization, schedule reminder, technical information tracking, and can also customize skills with automatic loading. It also has the capabilities of flexible local/cloud deployment, personalized memory management, token usage statistics, and risky tool call protection.
4. Target Users and Application Scenarios: It is for all groups with AI assistant needs such as ordinary individual users, office workers and research enthusiasts. It can be applied to scenarios such as daily social information sorting, office efficiency improvement, creative content generation, research data tracking, local file management, and can also build personalized intelligent agent applications through the combination of skills and scheduled tasks.