fanout

name	fanout
description	Fan one task out to multiple independent lionagi workers through `fanout.submit`, optionally synthesizing their answers. Use when several parallel perspectives can work without dependency handoffs.
allowed-tools	["Bash","Read","Write","Glob","Grep"]

Running a Fanout

Use fanout.submit when every worker can receive the same task independently. Use flow when one worker needs another's output or the run should discover reactive follow-up work.

Required call sequence

Ask for the catalog first, in its own call, and confirm fanout.submit is available:

mcp__plugin_orchestrate_lion__request(help=true)

Then fetch the verb's full current schema and fingerprint:

mcp__plugin_orchestrate_lion__request(help="fanout.submit")

Do not combine help and ops. Copy the returned fingerprint beside args, never inside it.

Worked example

Run four independent analyses and synthesize them into one recommendation:

mcp__plugin_orchestrate_lion__request(ops=[
  {"op": "fanout.submit",
   "args": {
     "query": ["claude"],
     "prompt": "Identify the main risks in migrating a Python service from threads to asyncio.",
     "num_workers": 4,
     "with_synthesis": true,
     "synthesis_prompt": "Merge the analyses into a prioritized migration checklist."
   },
   "schema_fingerprint": "<fingerprint from help=\"fanout.submit\">"}
])

The response returns a run_id immediately while the run continues in the background. It is not the synthesized answer. Observe and retrieve it with separate calls:

mcp__plugin_orchestrate_lion__request(ops=[
  {"op": "job.status", "args": {"run_id": "<run_id>"}}
])

mcp__plugin_orchestrate_lion__request(ops=[
  {"op": "job.output", "args": {"run_id": "<run_id>"}}
])

job.list finds recent runs, and job.kill stops one by run_id.

Capacity and boundaries

• num_workers caps the initial independent assignments; the planner may produce fewer.
• fanout.submit is a fixed fanout. Its current schema has no reactive, max_ops, or dry_run argument, so do not pass those names.
• For reactive discovery, use flow.submit. Its spawn capacity is max_ops - initial assignment count when max_ops is nonzero. A plan that fills max_ops has no capacity left for follow-ups; reserve headroom and verify the initial plan with dry_run: true.
• Ops in one request run in order. A failed op returns ok=false without stopping its siblings, so inspect every op result.

Checkout-local alternative

Inside a lionagi checkout with li on PATH:

li o fanout claude "Identify the main risks in migrating a Python service from threads to asyncio." \
  -n 4 --with-synthesis --synthesis-prompt "Produce a prioritized migration checklist."