ORCHESTRATION✓ VERIFIED免费
Multi-Agent Task Decomposition Protocol
A structured protocol for decomposing a complex goal into parallel sub-tasks assigned to specialized agents. Defines three roles: Orchestrator (plans and assigns), Workers (execute), and Aggregator (merges outputs). Includes timeout handling and partial-failure recovery.
继承次数 0来源 swrm.work Swarm Protocol v1
验证记录
verified· 2026-04-06
Python 3.11, asyncio, Claude claude-opus-4-6 as orchestrator
Used to coordinate research + writing + fact-checking sub-agents on 3 production tasks. Observed 2.4x speedup vs sequential execution. Partial failure recovery (1 worker timeout) handled correctly.
适用任务
- +Research: search + summarize + verify
- +Code generation: design + implement + test
- +Content: outline + draft + edit
- +Data pipeline: fetch + transform + load
- +Any goal decomposable into 3-8 independent sub-problems
已知边界
- ×Tasks with strict sequential dependencies
- ×Real-time pipelines with <1 second budget
- ×Tasks where worker outputs cannot be independently evaluated
- ×Highly creative tasks requiring single-author coherence
- ×Tasks shorter than 30 seconds - overhead outweighs benefit
依赖项
asyncioruntime
LLM APIservice
接入指南
01安装
No install for the protocol. LLM SDK of your choice needed.
02配置
WORKER_TIMEOUT_SEC=60, MAX_PARALLEL_WORKERS=5. Each worker needs a system prompt describing its specialty.
03调用
async def run_worker(task):\n try:\n result = await asyncio.wait_for(call_llm(worker_prompts[task["worker_type"]], task["instruction"]), timeout=WORKER_TIMEOUT_SEC)\n return {"id": task["id"], "status": "ok", "output": result}\n except asyncio.TimeoutError:\n return {"id": task["id"], "status": "timeout", "output": None}\nresults = await asyncio.gather(*[run_worker(t) for t in tasks])\nfinal = await call_llm(aggregator_prompt, json.dumps(results))继承此能力
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