Recipe: Conductor

The Conductor orchestrator delegates work to a pool of worker providers keyed by role name. A coordinator LLM emits structured dispatch JSON; workers run in parallel under a configurable fanout cap.

Minimal Conductor

import tako

conductor = tako.Conductor(
    coordinator=tako.providers.Anthropic(model="claude-opus-4-7", api_key="..."),
    workers={
        "code": tako.providers.OpenAI(model="gpt-5", api_key="..."),
        "math": tako.providers.Anthropic(model="claude-sonnet-4-6", api_key="..."),
    },
    max_fanout=4,
    max_steps=8,
    worker_timeout_secs=60,
    fail_fast=False,
)

result = await conductor.run("Implement merge sort and verify its O(n log n) bound.")
print(result.text)

Coordinator output schema

The coordinator gets a system prompt containing this schema and is expected to emit JSON matching it:

{
  "workers": [
    {"name": "code", "task": "Implement merge sort in Python.", "tools": ["fs"]},
    {"name": "math", "task": "Verify the bound."}
  ],
  "join_strategy": "all",
  "next_step": "summarise"
}

join_strategy is "all" or "any". next_step is "summarise" or "halt". Markdown ```json fences are stripped before parsing. Malformed plans are fed back to the coordinator as a one-turn retry (capped at max_steps).

Knobs

Knob	Default	What it does
max_steps	8	Maximum coordinator+worker rounds
max_fanout	4	Concurrent workers per round (Semaphore-bounded)
worker_timeout_secs	60	Per-worker hard timeout
fail_fast	false	Abort on first worker error vs collect partial results

Cross-cloud

conductor = tako.Conductor(
    coordinator=tako.providers.Anthropic(model="claude-opus-4-7", api_key="..."),
    workers={
        "code": tako.providers.OpenAI(model="gpt-5", api_key="..."),
        "long": tako.providers.AzureOpenAI(
            endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
            deployment="gpt-4o-prod",
            api_key=os.environ["AZURE_OPENAI_API_KEY"],
        ),
        "math": tako.providers.Vertex(
            project_id="my-proj",
            model="gemini-2.0-pro",
            access_token=os.environ["VERTEX_ACCESS_TOKEN"],
        ),
    },
)

The provider ids show up as OTel span attributes (worker.provider.id), so you can break down latency and cost by worker role across vendors.