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CoPaw

A self-hostable personal agent workstation on AgentScope with multi-channel chat, local LLM execution, and modular skills and memory for everyday automation.
1.1kPythonApache License 2.0
#ai-agent#personal-ai-assistant#multi-channel-chat#local-llm#agent-workstation
#skills
#long-term-memory
#scheduled-automation
#self-hosted
#alternative-to-autogpt
#crewai-like

What is it?

CoPaw is a self-hostable personal agent workstation that decouples models, skills, memory, channels, and scheduling. One assistant can live across multiple chat apps, while the model layer supports cloud APIs and local inference via Ollama, llama.cpp, or MLX to keep sensitive data on-device. Built on AgentScope, it turns schedulable, composable skills into a durable personal workflow engine.

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.

Architecture Deep Dive

Modular personal agent workstation
CoPaw is organized into interchangeable modules: model routing, skills, memory, channels, and scheduling. The model layer abstracts over cloud APIs and local backends so your workflow is not locked to a single provider. A unified channel layer normalizes heterogeneous chat events into shared types and a single pipeline, keeping skills focused on actions rather than platform quirks. Built-in scheduling enables both reactive chat handling and proactive routines, turning ad-hoc prompts into durable automation.

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 SceneTarget AudienceSolutionOutcome
Unified multi-channel assistantRemote teamsRun one assistant across chat apps with consistent to-dos and remindersLess context switching and more consistent responses
Privacy-first local doc helperEngineers and legal rolesUse local models for private summarization and Q&A with stored preferencesKeep data on-device while improving daily efficiency
Scheduled reports automationOps and PMsSchedule skills to compile updates and deliver daily/weekly reports to a channelSave repetitive work and increase delivery cadence

Limitations & Gotchas

Limitations & Gotchas
  • Multi-channel connections often require per-platform bot/app credentials and permissions; some platforms add review and rate limits.
  • Local inference depends on hardware and model size; heavy tasks may need a cloud model or stronger machines.
  • Always-on scheduling relies on process stability; server deployments benefit from monitoring and logs.

Frequently Asked Questions

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 focuses on autonomous task execution and 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, llama.cpp, or 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.
View on GitHub

Project Metrics

Stars1.1 k
LanguagePython
LicenseApache License 2.0
Deploy DifficultyEasy

Table of Contents

  1. 01What is it?
  2. 02Pain Points vs Innovation
  3. 03Architecture Deep Dive
  4. 04Deployment Guide
  5. 05Use Cases
  6. 06Limitations & Gotchas
  7. 07Frequently Asked Questions

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