What makes CoPaw different from AutoGPT and CrewAI?

CoPaw is designed as a long-running personal workstation with multi-channel chat and strong on-device control, while [AutoGPT](https://github.com/Significant-Gravitas/AutoGPT) focuses on autonomous task execution and [CrewAI](https://github.com/crewAIInc/crewAI) emphasizes multi-agent orchestration. If you want an assistant that lives inside real communication channels and keeps operating daily, CoPaw is the more direct fit.

Can I run it without cloud API keys?

Yes. CoPaw supports local inference via [Ollama](https://ollama.com), [llama.cpp](https://github.com/ggerganov/llama.cpp), or [MLX](https://github.com/ml-explore/mlx) so private content can stay on-device, and you can still add cloud models for heavier reasoning when needed.

Is it friendly to customization?

Yes. Its core is modular: prompts, hooks, tools, memory, and channels can be replaced or extended. A pragmatic path is to start by adding skills, then evolve memory and model routing as your workflow matures.

CoPaw Deep Dive: Self-hosted Assistant Alternative

Pain Points vs Innovation

✕Traditional Pain Points	✓Innovative Solutions
Assistants are often trapped in a single channel, making cross-app reuse inconsistent.	Uses the modular paradigm of AgentScope to decouple channels, skills, memory, and scheduling into swappable parts.
Cloud-only models raise privacy and cost concerns, while local stacks are fragmented.	A unified model layer enables hybrid cloud APIs and local inference so sensitive tasks can stay on-device.

Deployment Guide

1. Install via pip and initialize the workspace (Python 3.10+).

bash

1pip install copaw && copaw init

2. Start the local console and open the web UI.

bash

1copaw app  # http://127.0.0.1:8088/

3. Deploy with Docker and persist data to a volume.

bash

1docker pull agentscope/copaw:latest && docker run -p 8088:8088 -v copaw-data:/app/working agentscope/copaw:latest

Use Cases

Core Scene	Target Audience	Solution	Outcome
Unified multi-channel assistant	Remote teams	Run one assistant across chat apps with consistent to-dos and reminders	Less context switching and more consistent responses
Privacy-first local doc helper	Engineers and legal roles	Use local models for private summarization and Q&A with stored preferences	Keep data on-device while improving daily efficiency
Scheduled reports automation	Ops and PMs	Schedule skills to compile updates and deliver daily/weekly reports to a channel	Save repetitive work and increase delivery cadence

CoPaw

What is it?

Pain Points vs Innovation

Architecture Deep Dive

Deployment Guide

1. Install via pip and initialize the workspace (Python 3.10+).

2. Start the local console and open the web UI.

3. Deploy with Docker and persist data to a volume.

Use Cases

Limitations & Gotchas

Frequently Asked Questions