Orchestrator-Workers Pattern
A manager who hands out jobs on the fly
Think of a head chef during a busy dinner. An order comes in, and the chef decides on the spot who does what: "You grill the steak, you make the sauce, you plate the salad." Then the chef combines it all into one dish. Orchestrator-Workers is this idea for LLMs: a central orchestrator looks at the task, decides the subtasks at runtime , sends them to worker LLMs, and then stitches the results together.
Key points
- An orchestrator LLM is the 'manager' that plans subtasks.
- The subtasks are decided at RUNTIME, not fixed in advance.
- Workers do the subtasks; the orchestrator combines their output.
The one-line definition
Orchestrator-Workers is a workflow where a central orchestrator LLM dynamically breaks a task into subtasks, delegates each subtask to a worker LLM, and then synthesises the workers' results into a final answer.
Note: A manager LLM splits the work on the fly, hands it out, then merges it back.
The orchestrator plans, delegates, and synthesises
COMPLEX TASK (e.g. 'build a small website') │ ▼ ┌───────────────────┐ │ ORCHESTRATOR 🧑✈️ │ │ reads the task │ │ decides subtasks │ (decided at RUNTIME) └──┬──────┬──────┬──┘ 'write HTML'│ 'CSS'│ 'write JS' ▼ ▼ ▼ ┌────────┐┌────────┐┌────────┐ │WORKER 1││WORKER 2││WORKER 3│ │ LLM ││ LLM ││ LLM │ └───┬────┘└───┬────┘└───┬────┘ │ html │ css │ js └─────────┼─────────┘ ▼ ┌───────────────────┐ │ ORCHESTRATOR 🧑✈️ │ │ SYNTHESISE all │ │ parts into one │ └─────────┬─────────┘ ▼ ✅ FINISHED WEBSITE
Parallelization vs Orchestrator-Workers (key difference)
PARALLELIZATION ORCHESTRATOR-WORKERS (subtasks fixed by YOU) (subtasks decided by the LLM) ─────────────────────── ────────────────────────────
You write code that Orchestrator LLM reads the ALWAYS splits into task and INVENTS the right the same N parts. number & kind of subtasks.
Task A ─► [p1][p2][p3] Task A ─► maybe 2 subtasks Task B ─► [p1][p2][p3] Task B ─► maybe 5 subtasks (split is hard-coded) (split adapts per task)
A tiny code example (read it like English)
The big idea is the first call: the orchestrator LLM itself returns the list of subtasks. Your code didn't hard-code them. Then workers handle each, and the orchestrator merges everything.
import json
def solve(task):
# 1) ORCHESTRATOR decides the subtasks AT RUNTIME
plan = llm(
f"Break this task into a JSON list of subtasks:\n{task}"
)
subtasks = json.loads(plan) # e.g. ["write html", "write css"]
# 2) WORKERS each handle one subtask
results = [llm(f"Do this subtask: {s}") for s in subtasks]
# 3) ORCHESTRATOR synthesises the pieces into one answer
return llm(
f"Combine these parts into a final result:\n{results}"
)
When should you use orchestrator-workers?
| Scenario | Recommendation | Why |
|---|---|---|
| You can't predict the subtasks until you see the actual input | ✅ Use it | The orchestrator decides the breakdown at runtime. |
| Complex tasks like coding changes across many unknown files | ✅ Use it | Number and type of subtasks vary per request. |
| The subtasks are always the same fixed set | ❌ Use parallelization | No need for dynamic planning; just hard-code the split. |
| A single straightforward prompt handles it | ❌ Single call | Orchestration adds cost and complexity you don't need. |
Orchestrator-Workers mistakes beginners make
| Mistake | Consequence | Fix |
|---|---|---|
| Using it when the subtasks are actually fixed and predictable. | You add an extra planning LLM call (cost + latency) for nothing. | If you can hard-code the split, use parallelization instead. |
| Not validating the orchestrator's plan before running workers. | A malformed or silly plan sends workers off in wrong directions. | Parse and sanity-check the plan (and cap the number of subtasks). |
| Skipping the synthesis step. | You end up with disconnected worker outputs and no coherent result. | Always have the orchestrator combine the pieces into one final answer. |
Remember these lines
- Orchestrator-Workers = a manager LLM that plans, delegates, and synthesises.
- Subtasks are decided at RUNTIME — that's what separates it from parallelization.
- Always validate the plan and always synthesise at the end.
Key takeaways
- An orchestrator LLM dynamically breaks a task into subtasks at runtime.
- It delegates each subtask to a worker LLM, then synthesises their results.
- Unlike parallelization, the subtasks are not fixed in advance — they adapt per task.
- Validate the orchestrator's plan and always finish with a synthesis step.
Frequently Asked Questions
What is Orchestrator-Workers Pattern?
Think of a head chef during a busy dinner. An order comes in, and the chef decides on the spot who does what: "You grill the steak, you make the sauce, you plate the salad." Then the chef combines it all into one dish.
How does Orchestrator-Workers Pattern work?
Orchestrator-Workers is a workflow where a central orchestrator LLM dynamically breaks a task into subtasks , delegates each subtask to a worker LLM , and then synthesises the workers' results into a final answer.
What are the key takeaways about Orchestrator-Workers Pattern?
An orchestrator LLM dynamically breaks a task into subtasks at runtime. It delegates each subtask to a worker LLM, then synthesises their results. Unlike parallelization, the subtasks are not fixed in advance — they adapt per task. Validate the orchestrator's plan and always finish with a synthesis step.
Related topics
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