Want to learn how to USE AI technology to make money and/or your life easier? Join our FREE AI community here: https://www.skool.com/ai-with-apex/about

Alibaba Open-Sources CoPaw: A “Personal Agent Workstation” With Memory, Skills, and Multi-Channel Chat

Personal AI agents don’t fail because the model can’t chat—they fail because real life requires memory, scheduling, integrations, and a place to run continuously. CoPaw is Alibaba’s attempt to package those missing pieces into a developer-friendly “workstation” layer.

TL;DR

• Alibaba’s AgentScope team open-sourced CoPaw, framing it as a personal agent workstation/runtime rather than a standalone chatbot.
• CoPaw is described as built on AgentScope, AgentScope Runtime, and ReMe for long-term memory management.
• It supports multi-channel chat so one agent can be reached from platforms like DingTalk, Lark/Feishu, Discord, QQ, iMessage, and a local console.
• A skills system lets developers add Python-based capabilities via a custom directory, without editing the core engine.
• Built-in scheduling/cron enables proactive digests and reminders pushed into your chosen chat channel.

Alibaba open-sources CoPaw — a “personal agent workstation” (not just a chatbot)

What happened (2–3 sentences)
Alibaba’s AgentScope team released CoPaw as an open-source project positioned as a standardized workstation/runtime for building and running persistent personal AI agents. The project emphasizes long-term memory, multi-channel chat connectivity, scheduling, and an extensible skills system.

Why it matters (2–3 sentences)
Most agent prototypes break down when they have to live beyond a single chat session or a single interface. CoPaw’s framing is pragmatic: treat an “agent” like a durable software product with an operations layer—memory, connectors, jobs, and extensions—so it can actually run in the background and show up where users already are.

Key details (3–6 bullets; only include specifics that are supported)

• Architecture: CoPaw is described as built on AgentScope (agent communication/logic), AgentScope Runtime (execution environment), and ReMe for memory management supporting local or cloud options.
• Skills extensibility: Developers can add capabilities as Python-based functions placed in a custom skill directory that CoPaw can auto-load, avoiding modifications to the core engine.
• Multi-channel chat access: MarkTechPost lists connectivity across DingTalk, Lark/Feishu, Discord, QQ, iMessage, plus a local console.
• Scheduling: The GitHub README highlights built-in cron for scheduled reminders/digests delivered to a selected channel.
• Quick start: The README shows a pip-based path: pip install copaw, copaw init --defaults, copaw app, with a local console at http://127.0.0.1:8088/.
• License: The repository is published under Apache-2.0.

Source links
https://www.marktechpost.com/2026/03/01/alibaba-team-open-sources-copaw-a-high-performance-personal-agent-workstation-for-developers-to-scale-multi-channel-ai-workflows-and-memory/
https://github.com/agentscope-ai/CoPaw

Closing
CoPaw’s most interesting bet is that the “agent era” won’t be won by clever prompts—it’ll be won by the unglamorous plumbing: memory you can trust, connectors users already live in, and a reliable runtime that can schedule work while you sleep.

—

Want to learn how to USE AI technology to make money and/or your life easier? Join our FREE AI community here: https://www.skool.com/ai-with-apex/about