Openclaw Swarm

What This Skill Does

OpenClaw Swarm is a powerful orchestration framework designed to extend OpenClaw with sophisticated subagent management capabilities. At its core, the skill enables developers and power users to spawn multiple subagents, share contextual data between them, and manage complex, multi-step workflows with ease. By utilizing persistent storage and event-driven communication, this skill transforms isolated agents into a cohesive, collaborative swarm capable of handling parallelized tasks, pipeline processing, and asynchronous coordination.

Installation

To integrate OpenClaw Swarm into your environment, use the OpenClaw Hub command line interface. Open your terminal and execute the following command:

clawhub install openclaw/skills/skills/heldinhow/openclaw-swarm

Ensure your OpenClaw instance is updated to the latest version to maintain compatibility with the advanced orchestration hooks provided by this package.

Use Cases

OpenClaw Swarm is ideal for complex task execution requiring high-level coordination. Common use cases include:

1. Parallel Research: Triggering simultaneous web searches and internal document analysis to aggregate research data rapidly.
2. Pipeline Processing: Passing data through a chain of agents where each agent performs a specific transformation, storing results in the central context_store.
3. Asynchronous Monitoring: Utilizing event-driven context_publish mechanisms to trigger downstream processes only when a subagent completes a specific goal.
4. Scalable Batch Workloads: Managing heavy lifting by breaking large tasks into manageable sub-tasks executed in parallel with shared history.

Example Prompts

1. "Spawn three subagents to research the latest developments in AI ethics from three different news sources and summarize the key trends for me."
2. "Use the swarm orchestration to first summarize the project requirements in the context store, then trigger an agent to write the technical specification based on that summary."
3. "Launch a parallel scan of the local directories for configuration files and notify me via the orchestrator when the index is complete."

Tips & Limitations

• Context Strategy: Be mindful of the contextSharing parameter. Using full for large histories can increase token usage; prefer recent or summary for optimal performance.
• Event Handling: The auto-announce feature eliminates the need for manual status polling, making it perfect for long-running processes.
• Race Conditions: When using parallel_spawn with the race strategy, ensure that downstream tasks are idempotent, as multiple agents might attempt to write to the same context_store key.