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Get Started Free →claw-multi-agent
Multi-agent parallel orchestration for OpenClaw. Spawn AI agents as a team — parallel research, multi-model comparison, code pipelines. Proven 50-65% time savings. Trigger words: multi-agent, parallel agents, swarm, spawn multiple agents, parallel research, compare models, deep research, comprehensive research, detailed investigation, thorough analysis, research multiple topics, 多智能体, 多个Agent, 并行调研, 并行搜索, 同时搜索, 同时调研, 深度调研, 详细调研, 全面调研, 深度研究, 详细检索, 多角度分析, 全面分析, 多个模型, 让多个AI, 分别搜索, 同时搜索, 组建团队, Agent
Passed 14 of 23 test cases with the skill, and 0 of 23 without it.
14 fixed0 passed either way9 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
The instructions your model reads when the skill loads.
Replace one AI with a team of AIs. Turn serial into parallel. Turn hours into minutes.
| Scenario | Example | Speedup |
|---|---|---|
| Parallel research | Search 5 frameworks simultaneously, each writes a report | ~65% ⚡ |
| Multi-model compare | Ask Claude, Gemini, Kimi the same question at the same time | ~50% ⚡ |
| Code pipeline | Plan → Code → Review, auto hand-off in sequence | Quality ↑ |
| Batch processing | Translate / analyze / summarize multiple docs in parallel | Scales linearly |
Just say something like:
This is the recommended pattern. Every multi-agent run must follow this interaction pattern.
⚠️ Iron rule: The activation announcement must be your FIRST reply after receiving the task — before reading any files, before investigating, before spawning.
Why this matters: Reading files, researching background, and spawning all take time. If you do those first, users see long silence. Worse: context compression can happen during that time, and the announcement will never be sent.
Correct order: Receive task → Send announcement immediately → Then read files / spawn / wait
test cases passed, vs 0 / 23 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.