What is multi-agent orchestration?

Multi-agent orchestration is the coordination of multiple specialised AI agents to complete work that a single agent cannot reliably do alone. The orchestrator decides which agent runs when, how state is shared, how outputs are combined, and how failures are handled. It is the difference between five smart agents in a room and a team that ships.

What is the difference between LangGraph, CrewAI, and AutoGen?

LangGraph models agents as nodes in a graph with explicit state and control flow — best for complex production workflows that need precise control. CrewAI models agents as crew members with roles and goals — best for fast prototyping of role-based teams. AutoGen models agents as conversational participants — best for research and code-execution scenarios. Different abstractions for different problems.

Which framework is most production-ready?

LangGraph is widely cited as the most production-mature, with first-class checkpointing for state persistence, LangSmith observability, and streaming support. CrewAI is faster to assemble but typically requires bolt-on infrastructure (Redis, Celery) to match LangGraph's reliability. AutoGen and its successor AG2 are still consolidating after the rewrite.

What are the common multi-agent patterns?

Three patterns dominate. Supervisor — a central orchestrator decomposes the task and delegates to specialist agents. Sequential — agents pass work down a pipeline (research → write → review → publish). Hierarchical — supervisors of supervisors, used for very large workflows. Most production systems start supervisor and add hierarchy only when one supervisor cannot reason about the whole graph.

Should I use a framework or build my own?

Use a framework. The orchestration plumbing — state, retries, checkpointing, observability, streaming — is the boring 90% that has already been solved. Build only the agent skills, prompts, and tools that encode your domain. The minority of teams that build orchestration themselves do so because their workload has unusual constraints, not because the frameworks are inadequate.

Are multi-agent systems more accurate than single agents?

Sometimes. Specialisation can improve accuracy on tasks that span distinct sub-skills (research, code, write, review). But multi-agent systems also multiply token cost, latency, and the surface area for failure. The right design starts as a single agent with tools and decomposes into multi-agent only when measurable accuracy or throughput gains justify the orchestration tax.

How do agents share context across a multi-agent run?

Three approaches. Shared state — a structured object the orchestrator updates and every agent reads. Message passing — agents communicate through a queue with explicit handoffs. Memory — a persistent store (vector DB, document store) every agent can read and write. Production systems typically combine all three: shared state for orchestration, messages for handoffs, persistent memory for facts that outlive a single run.

How does BYOM work with multi-agent frameworks?

Each agent in the system can route to a different model — a fast cheap model for triage, a frontier model for reasoning, a coding-specialised model for code generation, an open-weight model for sensitive workloads. All major frameworks (LangGraph, CrewAI, AutoGen, OpenAI Agents SDK) support per-agent model selection. This is BYOM — see our companion guide for the architectural principle.

Multi-Agent Orchestration: LangGraph vs CrewAI vs AutoGen (2026)

The agent framework wars settled into something useful in 2026. The five frameworks worth knowing — LangGraph, CrewAI, AutoGen (now consolidating into AG2), OpenAI Agents SDK, and Google's Agent Development Kit (ADK) — each pick a different abstraction, and the right one for your workload depends on what you are actually trying to coordinate.

This guide explains what multi-agent orchestration actually is, the three patterns underneath every framework, the side-by-side tradeoffs of the major options, when a multi-agent system is the right design (and when it is not), and how the orchestration layer fits with BYOM, MCP, and the broader agentic stack.

What "Orchestration" Actually Means

An orchestrator is the runtime that owns the answers to four questions every multi-step agent system has to answer:

Who runs next? — agent selection given current state
What do they see? — context shaping for the next agent
How is progress saved? — checkpointing, state persistence, recovery
When do we stop? — termination conditions, escalation paths, budget caps

You can answer these in 200 lines of glue code; you can also use a framework that has answered them for you across thousands of production deployments. For most enterprises, the second is the right call — see the build-vs-buy logic in our agent skills piece.

The Three Patterns Every Framework Implements

Supervisor (most common)

A central orchestrator agent receives the task, decomposes it into sub-tasks, delegates each to a specialist agent, and synthesises the results. Easy to reason about, easy to audit, easy to add specialists. The default starting point for almost every production multi-agent system.

Sequential pipeline

Agents pass work down a fixed sequence — research → outline → write → fact-check → publish. Predictable, low-latency for the right shape of task, but inflexible when work needs to loop back. Good for content generation and document processing.

Hierarchical (supervisor of supervisors)

For very large workflows, supervisors at the top delegate to mid-level supervisors that delegate to specialists. Used when one supervisor's reasoning would not fit the full graph in its context window. Adds coordination overhead; only justifies its complexity at scale.

A fourth pattern — peer-to-peer conversational — exists in AutoGen-style frameworks where agents debate or collaborate without a central orchestrator. It is interesting for research; it is rare in production because the reasoning trace is hard to audit and termination is hard to guarantee.

The Major Frameworks, Compared

LangGraph (LangChain team)

Models a multi-agent system as a graph with explicit nodes (agents, tools, conditions) and edges (transitions). State is first-class — every node reads from and writes to a shared, typed state object. Checkpointing is built in: a run can be paused, resumed, or replayed from any saved checkpoint. Observability through LangSmith. Streaming, human-in-the-loop interrupts, and long-running workflows are well supported.

Strengths: production maturity, state and checkpoint discipline, the richest ecosystem of pre-built tools (via LangChain), explicit control flow that experienced engineers find easy to reason about. Weaknesses: steeper learning curve than role-based frameworks, more code to set up a simple workflow.

CrewAI

Models agents as crew members with roles, goals, and backstories. Sequential and hierarchical task execution. Fast to assemble — a working multi-agent prototype in a few dozen lines. Tool ecosystem covers the common needs out of the box.

Strengths: shortest path from idea to running prototype, role-based abstraction maps cleanly to business workflows, growing production tooling. Weaknesses: state persistence and checkpointing typically need bolt-on infrastructure (Redis, Celery) for production reliability; community benchmarks have noted moderate token overhead per task compared to LangGraph for the same workflow.

AutoGen (Microsoft Research) / AG2

Models multi-agent systems as conversations between agents. Strong support for code-writing and code-execution patterns. The original AutoGen project has consolidated into AG2 with a maturing API; production readiness is medium and improving.

Strengths: research-grade flexibility, excellent for agents that write and execute code, strong for experimental architectures. Weaknesses: conversational pattern can be hard to audit and terminate predictably in production; the rewrite into AG2 means picking the right version matters.

OpenAI Agents SDK

A deliberately minimal framework. An agent is a model, a set of tools, and a loop. Native MCP support. Safety constraints expressed at the model level. Designed to make the simple case easy and the complex case still possible.

Strengths: small surface area, MCP-native, idiomatic for OpenAI-centric estates, fast onboarding. Weaknesses: less opinionated about state and orchestration patterns — teams end up implementing those themselves; less mature for non-OpenAI models in some integrations.

Google Agent Development Kit (ADK)

Production framework with deep Google Cloud integration. Strong observability, scalability primitives, and enterprise deployment patterns. Targets teams that have committed to Vertex AI and Google's broader AI stack.

Strengths: enterprise-grade observability and deployment, deep Google Cloud integration, comprehensive primitives. Weaknesses: most idiomatic inside Google Cloud — out-of-cloud deployment is possible but loses some integration value.

Side-by-Side Comparison

Framework	Abstraction	Production maturity	Best for
LangGraph	Stateful graph	High	Stateful production workflows; human-in-the-loop
CrewAI	Role-based crew	Medium	Fast prototyping; team-of-specialists workflows
AutoGen / AG2	Conversational	Medium	Research; code-writing and code-executing agents
OpenAI Agents SDK	Model + tools + loop	Medium-High	OpenAI-centric estates; MCP-first integrations
Google ADK	Production agent runtime	High (in Google Cloud)	Vertex AI estates; enterprise observability

How to Choose

Start with the workload, not the framework:

Long-running, stateful, audit-heavy production workflow? — LangGraph
Fast prototype of a "team of specialists" pattern that maps to a business process? — CrewAI
Agents that need to write and execute code, or research-grade flexibility? — AutoGen / AG2
Already on OpenAI, want native MCP, prefer small frameworks? — OpenAI Agents SDK
On Vertex AI, need enterprise-grade observability and deployment in Google Cloud? — Google ADK

And start single-agent. The cost of multi-agent orchestration — token overhead, latency, debugging complexity, eval surface — is meaningful. Decompose into multiple agents only when measurements show the single-agent design is the bottleneck.

Patterns That Reduce Production Pain

Pin the supervisor's planning step. The supervisor's first call decides the whole run. Make that prompt deterministic, evaluated, and versioned. Treat it as a critical path.
Budget caps per run. A multi-agent loop can chain 30+ LLM calls. Hard caps on tokens, dollars, and wall-clock time per run prevent runaway loops.
Fail fast, escalate cleanly. Agents should surface uncertainty and escalate to humans rather than guess. Define the escalation path before you ship.
Replayability. Every run captures full traces — prompts, tool calls, outputs, model versions, latencies. The eval and incident-response loop depends on it. See LLMOps in Production.
Per-agent eval suites. Each specialist agent has its own ground-truth eval set. The supervisor has its own eval set focused on planning and delegation quality. Combine into end-to-end task evals. See AI Agent Evaluation.

How Frameworks Fit With MCP and BYOM

The orchestration framework is one layer; the integration layer (MCP) is another; the model layer (BYOM) is a third. The mature enterprise pattern is to keep these layers cleanly separated:

Frameworks (LangGraph et al.) own orchestration, state, and the agent loop
MCP owns access to tools, data, and external services
BYOM means each agent in the framework can target whichever model is cheapest and accurate enough for that agent's specific task

This separation lets you swap any layer independently — change frameworks, add or remove MCP servers, route specific agents to a different model — without rewriting the others.

What Will Change Through 2026

Three trends worth tracking. First, frameworks are converging on similar primitives — typed state, checkpointing, MCP integration, observability — even as they keep distinct abstractions. Second, OpenAI's Agents SDK and Google's ADK are pulling agent runtimes deeper into vendor platforms, raising the bar on observability and lowering the bar on initial setup. Third, evaluation tooling (LangSmith, Braintrust, Arize Phoenix, OpenAI Evals) is becoming framework-agnostic — you can evaluate an agent regardless of where it runs.

Pick a framework that fits your team and your workload today; assume you will be re-evaluating in 12–18 months as the layer matures further. The cost of switching is mostly orchestration code; agent skills, MCP servers, and evaluation suites travel.

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Multi-Agent Orchestration — Framework Guide.