Claude API Multi-Agent Orchestration Framework Guide

What "multi-agent orchestration" actually means for Claude

A multi-agent orchestration framework is a system where several independent Claude calls — each with its own role, prompt, and sometimes its own tools — work together to complete a task that a single prompt can't handle well. One agent might plan, another might research, another might write code, and a fourth might review the output before it ships. The "framework" part is the orchestration layer: the code that decides which agent runs next, what gets passed between them, and how failures or disagreements are handled.

There is no single official "Claude multi-agent SDK" — orchestration is something you build on top of the Claude Messages API using plain application code (state machines, queues, or a lightweight library like LangGraph or CrewAI). This guide covers the architecture patterns that actually work in production and the pieces you need to build yourself.

Why split work across multiple agents at all

A single long prompt with many instructions tends to degrade: Claude starts ignoring earlier constraints as the context grows, or it blends steps that should be separate (e.g., researching and writing at the same time, producing shallow results for both). Splitting the task into focused agents with narrow responsibilities gives each call a smaller, cleaner context window and a more specific job description — which consistently produces better output quality than one mega-prompt.

The tradeoff is complexity: more API calls, more latency, more places for state to get lost. A good orchestration framework exists specifically to manage that tradeoff.

Core orchestration patterns

1. Orchestrator–worker (hub and spoke)

One "manager" agent breaks a task into subtasks and dispatches them to specialized worker agents, then synthesizes the results.

async function orchestrate(task) {
  const plan = await callAgent("planner", task); // returns subtasks as JSON
  const results = await Promise.all(
    plan.subtasks.map(st => callAgent(st.role, st.instructions))
  );
  return callAgent("synthesizer", { task, results });
}

This is the most common pattern because the manager can run subtasks in parallel and the failure domain is contained — if one worker fails, you retry just that branch.

2. Pipeline (sequential handoff)

Agents run in a fixed order, each consuming the previous agent's output: research → draft → edit → fact-check. Pipelines are simpler to reason about and debug than hub-and-spoke designs, but they're strictly sequential, so total latency adds up across every stage.

3. Debate / consensus

Two or more agents independently answer the same question, then a judge agent compares the answers and picks or merges the best one. This pattern is useful for high-stakes outputs (classification decisions, code review, content moderation) where a single model pass has meaningful error rates.

4. Router

A lightweight classifier agent decides which specialized downstream agent should handle a given input — support ticket triage, intent classification, or picking between a "coding agent" and a "writing agent." Routers should use small, fast, cheap calls since their only job is a decision, not content generation.

Building blocks every framework needs

Regardless of which pattern you pick, you need the same four pieces of infrastructure:

• Role definitions: a system prompt per agent that fixes its scope tightly. Vague roles ("be helpful") cause agents to drift into each other's territory.
• A shared state object: something that travels between calls — task description, intermediate results, tool outputs — usually plain JSON, not raw conversation history.
• Structured handoffs: agents should output JSON or another parseable format when their result is consumed by another agent, not free-form prose. This avoids brittle string parsing downstream.
• Failure and retry logic: a worker agent can time out, return malformed output, or hit a rate limit. The orchestrator needs to catch that and decide whether to retry, skip, or fall back to a default.

async function callAgent(role, input, retries = 2) {
  try {
    const res = await fetch("https://api.subtoapi.app/v1/messages", {
      method: "POST",
      headers: {
        "Authorization": `Bearer ${process.env.SUBTOAPI_KEY}`,
        "Content-Type": "application/json"
      },
      body: JSON.stringify({
        model: "claude-sonnet-4-5",
        system: ROLES[role].system,
        max_tokens: 1024,
        messages: [{ role: "user", content: JSON.stringify(input) }]
      })
    });
    if (!res.ok) throw new Error(`Agent ${role} failed: ${res.status}`);
    return await res.json();
  } catch (err) {
    if (retries > 0) return callAgent(role, input, retries - 1);
    throw err;
  }
}

Where tool use fits in

Many agents in an orchestration need tools — a research agent calling a search API, a coding agent reading files, a scheduling agent hitting a calendar. Keep tool definitions scoped per agent rather than giving every agent every tool; a planner agent doesn't need file write access, and a reviewer agent shouldn't be able to execute code. See the Claude API docs on tool use for how to define and handle tool calls in a single request — the same mechanics apply inside each agent, you just repeat them per role.

Practical constraints to plan for

• Latency compounds. A four-stage pipeline with 3-second calls is a 12-second minimum response. Parallelize whatever you can in a hub-and-spoke layout instead of chaining everything sequentially.
• Cost compounds too. Five agents on one task means five billed calls. Use smaller, cheaper models for routing and classification steps, and reserve the strongest model for the step that actually needs deep reasoning.
• Rate limits apply across the whole framework, not per agent. If your orchestration fires off ten parallel calls per task and you have real user traffic, you'll hit limits fast. This is one of the more common operational headaches with multi-agent systems — it's not a single long request, it's a burst of short ones.

If you're running this as a product rather than a one-off script, a hosted layer like SubToAPI gives you a stable API key, streaming support, and usage metadata per call, so you can see exactly how many tokens each agent in the pipeline is consuming instead of debugging that from raw logs. The quickstart and Messages API docs cover the request format each agent call in your orchestration will use.

Getting started without over-engineering

Start with the simplest pattern that solves your actual problem. Most teams reach for orchestrator–worker before they need debate/consensus, and most "multi-agent" problems turn out to be a 2-stage pipeline (generate, then critique) rather than a five-agent swarm. Build the state-passing and retry logic first — that's the part that breaks in production — and only add parallel workers once you've confirmed the task genuinely benefits from being split up.

questions

Does Anthropic provide an official multi-agent SDK for Claude? No. Anthropic ships the Claude Messages API and tool-use primitives; orchestration across multiple agents is application-level code you write yourself, often with a framework like LangGraph, CrewAI, or a custom state machine.

What's the simplest multi-agent pattern to start with? A two-stage pipeline: one agent generates an output, a second agent critiques or fact-checks it. It's easy to debug and catches a large share of quality issues before you need more complex routing or debate patterns.

How do I keep API costs under control with multiple agents? Use cheaper, faster models for routing and classification steps, run independent workers in parallel instead of sequentially, and track per-call token usage so you can identify which agent in the pipeline is driving cost.