agent-os

What This Skill Does

The Agent OS skill transforms OpenClaw from a single-task executor into a persistent, multi-agent operating system. It provides a robust framework for managing complex, long-running projects by maintaining state, memory, and task coordination across agent sessions. Unlike transient AI interactions, Agent OS ensures that your AI coworkers learn from every task they complete, improving their success rates and efficiency over time. It utilizes a central TaskRouter to decompose high-level objectives into granular sequences, mapping these tasks to specialized agents based on their registered capabilities, such as research, design, or coding. With built-in state persistence, your projects are saved to disk, allowing you to stop, restart, or power down your machine without losing progress or context.

Installation

To integrate Agent OS into your OpenClaw environment, execute the following command in your terminal:

clawhub install openclaw/skills/skills/cryptocana/agent-os

Once installed, you can import the AgentOS class from the agent-os package and begin registering agents by defining their names, descriptions, and functional capabilities. Ensure your project directory has appropriate read/write permissions, as the skill will generate data/ files to manage memory and project states.

Use Cases

• Agile Software Development: Manage full-stack feature builds where one agent handles research, another handles UI design, and a third manages codebase implementation.
• Content Marketing Engines: Coordinate a sequence where one agent researches trending topics, another writes the article, and a third manages publication formatting.
• Automated Research Pipelines: Deploy agents that perform multi-step data collection and synthesis, where subsequent tasks depend on the findings of previous ones.

Example Prompts

1. "AgentOS, register a new research agent with web access and begin the 'Market Trend' project using my existing development workflow."
2. "Show me the current progress of the 'Website Redesign' project and tell me if any agents are currently blocked or awaiting input."
3. "Resume the 'Q3 Feature Launch' project exactly where the development agent left off yesterday, and summarize the last three tasks performed."

Tips & Limitations

• Memory Management: Regularly audit the [agent-id]-memory.json files. While persistent memory is powerful, agents can accumulate irrelevant data over time; pruning these files can keep performance snappy.
• Dependency Tracking: When defining project workflows, ensure that task dependencies are clearly mapped. The TaskRouter relies on logical sequences to prevent circular dependencies that could hang your project.
• Limitations: Agent OS currently requires defined schemas for agents. It is not designed for unpredictable, purely creative tasks without clear, repeatable goal structures. Always ensure your environment has sufficient disk space for the persistent JSON state logs.