Alibaba Team Open-Sources CoPaw: A High-Performance Human Agent Workspace for Developers to Scale Multi-Channel AI Workflows and Memory

As the industry moves from the simple concept of the Large Language Model (LLM) to autonomous agent systems, the challenge for devs has changed. It’s not just a model anymore; it’s about the place where that model works. A research team from Alibaba has been released CoPawis an open source framework designed to address this by providing a standardized workspace for deploying and managing AI personal agents.

CoPaw is built on an integrated technology stack AgentScope, AgentScope runtimeagain ReMe. It acts as a bridge between the logic of a high-level agent and the practical needs of a personal assistant, such as continuous memory, multi-channel connectivity, and task scheduling.

Architecture: AgentScope and ReMe Integration

CoPaw is not a stand-alone bot but a workstation that organizes multiple components to create a unified ‘Agentic App’.

The system relies on three main layers:

1. AgentScope: A basic framework that handles agent and logic communication.
2. AgentScope runtime: An implementation environment that ensures stable operation and resource management.
3. ReMe (Memory Management): A special module that manages both local and cloud-based storage. This allows agents to maintain a ‘Long Term Experience,’ solving the statelessness problem found in standard LLM APIs.

By using force ReMeCoPaw allows users to control the privacy of their data while ensuring that the agent maintains context across different time periods and platforms. This persistent memory is what enables the workspace to adapt to the user’s specific workflow over time.

Expansion with the Skill System

The main feature of the CoPaw workstation is that Extension of Skills strength. In this framework, a ‘Skill’ is a distinct unit of action—essentially a tool that an agent can use to interact with the external world.

Adding capabilities to CoPaw does not require modification of the core engine. Instead, CoPaw supports ia tradition is literary ability where developers can drop Python-based tasks. These skills follow a standard specification (influenced by anthropics/skills), which allows the agent to:

• Do web scraping (eg, scraping Reddit threads or YouTube videos).
• Connect to local files and desktop locations.
• Question the bases of personal information stored in the workplace.
• Manage calendars and email in natural language.

This design allows the creation of Agent applications-a complex workflow in which an agent uses a combination of built-in skills and programmed tasks to achieve a goal automatically.

Multi-Channel Communication (Access to All Domains)

One of the main technical barriers to personal AI is deployment across different social networks. CoPaw addresses this through its All Domain Access layer, which simulates how agents interact with different messaging protocols.

Currently, CoPaw supports integration with:

• Enterprise platforms: DingTalk and Lark (Feishu).
• Social/Developer Platforms: Discord, QQ, and iMessage.

This multi-channel support means that a developer can launch a single instance of CoPaw and interact with it on any of these endpoints. The workspace handles the translation of messages between the agent logic and the channel-specific API, maintaining consistent state and memory regardless of where the communication takes place.

Key Takeaways

• Shift from model to Workstation: CoPaw takes the focus off the Large Language Model (LLM) and onto the built-in Workplace layout. It acts as a middleware layer that organizes the AgentScope outline, AgentScope runtimeand external communication channels to turn raw LLM skills into an effective, ongoing resource.
• Long Term Memory with ReMe: Unlike typical LLM collaborations that are informal, CoPaw includes a ReMe (Memory Management) module. This allows agents to maintain a ‘Long-Term Experience’ by storing user preferences and past activity data on-premise or in the cloud, allowing for personalized adaptation of agent behavior over time.
• Extensible Python-Based ‘Skills’: The framework uses a decouple Skill Development Program based on anthropics/skills clarification. Developers can extend the agent’s utility by simply adding Python functions to a custom capabilities directory, allowing the agent to perform specific tasks such as web scraping, file manipulation, or API integration without modifying the core codebase.
• Accessing Multiple Domain Channels: CoPaw provides a unified interface cross-platform deployment. A single instance of a workstation can be connected to business tools (Lark, DingTalk) and social/developer platforms (Discord, QQ, iMessage), allowing the same agent and its memory to be accessed from different locations.
• Automated Agentic workflow: By combining Organized Activities with the skills system, CoPaw changes from active conversation to active automation. Devs can program ‘Agenttic Apps’ that perform background tasks—such as daily research integration or automated database monitoring—and push the results to the user’s preferred communication channel.

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