Alibaba Team Open-Sources Copa: A High-Performance Personal Agent Workstation for Developers to Scale Multi-Channel AI Workflows and Memory

As the industry moves from simple Large Language Model (LLM) inference to autonomous agentic systems, the challenge for developers has changed. It’s not just about the models anymore; It’s about the environment in which that model operates. A team of researchers from Alibaba released CoPawAn open-source framework designed to address this by providing a standardized workstation for deploying and managing individual AI agents.

CoPaw is built on a technology stack agentscope, agentscope runtimeAnd supreme. It acts as a bridge between high-level agent logic and the practical needs of the personal assistant, such as persistent memory, multi-channel connectivity, and task scheduling.

Architecture: AgentScope and Remi integration

CoPaw is not a standalone bot but a workstation that orchestrates multiple components to create a cohesive ‘agentic app’.

The system relies on three primary layers:

1. AgentScope: The underlying framework that handles agent communication and logic.
2. AgentScope Runtime: Execution environment that ensures stable operation and resource management.
3. REMI (Memory Management): A special module that handles both local and cloud-based memory. This allows agents to maintain ‘long-lived experience’ while solving the statelessness problem inherent in standard LLM APIs.

taking advantage supremeCoPaw allows users to control their data privacy, while ensuring that the agent maintains context across different sessions and platforms. This persistent memory is what enables the workstation to adapt to the user’s specific workflow over time.

Expandability through skill system

A key feature of CoPaw Workstation is its skill expansion Capacity. In this framework, a ‘skill’ is a discrete unit of functionality – essentially a tool that the agent can apply to interact with the external world.

Adding capabilities to CoPaw does not require modifying the core engine. Instead, CoPaw supports a Custom Skills Directory Where engineers can leave out Python-based functions. These skills follow a standardized specification (influenced by anthropics/skills), allows the agent to:

• Perform web scraping (for example, summarizing Reddit threads or YouTube videos).
• Interact with local files and desktop environments.
• Question individual knowledge bases stored in the workstation.
• Manage calendars and emails through natural language.

It allows the creation of designs agent apps-Complex workflow where the agent uses a combination of built-in skills and scheduled tasks to autonomously achieve a goal.

Multi-channel connectivity (all-domain access)

One of the primary technical barriers to personal AI is deployment on fragmented communication platforms. CoPaw addresses this through all-domain access Layer that standardizes how agents interact with different messaging protocols.

Currently, CoPaw supports integration with:

• Enterprise Platform: Dingtock and Lark (Feshu).
• Social/Developer Platform: Discord, QQ, and iMessage.

This multi-channel support means that a developer can initiate a single CoPaw instance and interact with it from any of these endpoints. The workstation handles the translation of messages between the agent’s logic and the specific channel’s API, maintaining a consistent state and memory regardless of where the interaction occurs.

key takeaways

• Change from Model to Workstation: CoPaw shifts the focus from just large language models (LLMs) to a structured workplace architecture. It acts as a middleware layer that orchestrates agentscope framework, agentscope runtimeand external communication channels to transform raw LLM capabilities into a functional, consistent assistant.
• Long Term Memory via ReMe: Unlike standard stateless LLM interactions, CoPaw integrates REMI (Memory Management) module. This allows agents to maintain ‘long-term experiences’ by storing user preferences and past action data locally or in the cloud, allowing personalized evolution of agent behavior over time.
• Extensible Python-based ‘skills’: The framework uses a decoupled skill extension system based on anthropics/skills Specification. Developers can extend the usefulness of an agent by simply adding Python functions to a custom skills directory, allowing the agent to perform specific tasks such as web scraping, file manipulation, or API integration without modifying the core codebase.
• All-Domain Multi-Channel Access: Provides a unified interface for CoPaw Cross-platform deployment. A single Workstation instance can be connected to enterprise tools (Lark, DingTalk) and social/developer platforms (Discord, QQ, iMessage), allowing the same agent and its memory to be accessed across different environments.
• Automated Agent Workflows: by mixing scheduled tasks With the Skills system, CoPaw transitions from reactive chat to proactive automation. Dave can program ‘agentic apps’ that perform background operations – such as daily research synthesis or automated repository monitoring – and send the results over the user’s preferred communication channel.

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