12 Best Multi-Agent Frameworks for Production in 2026

LangGraph is the best multi-agent framework for production in 2026: stateful graphs, durable execution, and human-in-the-loop checkpoints, with Anthropic, Replit, LinkedIn, and Uber already shipping on it. CrewAI is the fastest path to a working crew of role-based agents. Microsoft Agent Framework is the new default for Azure and .NET teams (AutoGen is now in maintenance mode), the OpenAI Agents SDK fits OpenAI-first stacks, the Claude Agent SDK fits teams building on Claude, and AWS Multi-Agent Orchestrator (renamed Agent Squad) fits Bedrock shops.

The trap: many "frameworks" are wrappers around a single LLM call with a planner prompt. Real orchestrators handle concurrency, retries, durable state, and human-in-the-loop checkpoints, and the names in marketing decks are not always the names that show up in stable production stacks. In 2026, the framework you pick decides whether the system survives production or stays a prototype.

This guide compares the 12 best multi-agent frameworks for production in 2026. Real capabilities, honest pricing where it is publicly known, pros and cons, who should skip each one, and 4 questions that pick the right one for your stack.

How we ranked them. Production readiness counts most: durable state, human-in-the-loop, and tracing. Then the orchestration patterns each framework ships, then how active the maintainers are, then lock-in. Facts come from each project's official docs and GitHub repo. The two entries added in this update (Microsoft Agent Framework and the Claude Agent SDK) were checked against their docs on 2026-09-26. We did not run a benchmark for this list, so no hands-on testing is claimed.

Best multi-agent frameworks: a brief overview

• LangGraph: Best for production-grade stateful agent graphs with human-in-the-loop checkpoints, used by Anthropic, Replit, LinkedIn, Uber.
• CrewAI: Best for role-based agent crews with the lowest barrier to entry; the fastest path from idea to working swarm.
• Microsoft Agent Framework: Best for Azure and .NET teams that want graph workflows with sequential, concurrent, handoff, group chat, and manager-led patterns built in.
• AutoGen (Microsoft): Best for conversational multi-agent research on existing AutoGen code; new projects should start on Microsoft Agent Framework.
• OpenAI Swarm / Agents SDK: Best for teams already deep in the OpenAI ecosystem who want lightweight handoffs.
• Claude Agent SDK: Best for Claude-based agents that delegate to parallel subagents with their own tools, prompts, and models.
• Multi-Agent Orchestrator (now Agent Squad): Best for AWS-native production deployments with Bedrock and Lambda.
• MetaGPT: Best for simulating software engineering teams (PM, architect, dev, QA) end-to-end.
• AgentScope (Alibaba): Best for high-throughput distributed agent systems with strong observability.
• AGiXT: Best for self-hosted, plugin-heavy agent platforms with a UI for non-engineers.
• SuperAGI: Best for autonomous agent workflows with built-in GUI, memory, and toolkits.
• Google ADK: Best for Gemini-native production agents with graph workflows and native MCP tools.

1. LangGraph, best for production-grade stateful agents

LangGraph is the orchestration framework from the LangChain team, and by 2026 it is the de facto standard for serious multi-agent systems. Unlike LangChain itself, LangGraph is purpose-built for graph-structured agent flows with persistent state, conditional edges, human-in-the-loop checkpoints, and durable execution. It is the framework behind agents shipped by Anthropic, Replit, LinkedIn, Uber, Elastic, and a growing list of Fortune 500 deployments documented in LangChain case studies.

The reason LangGraph wins production over flashier alternatives is mundane: it treats agents as state machines, not chat loops. You define nodes, edges, and a shared state schema. The runtime handles persistence, replay, time-travel debugging, and interruption, the unglamorous primitives that decide whether your agent survives the first 10,000 real users. Pair LangGraph with LangSmith for tracing and you get the closest thing to a production-ready multi-agent stack the open ecosystem currently ships.

Key features

• Stateful agent graphs with typed shared state
• Durable execution with checkpointers (Postgres, Redis, SQLite)
• Human-in-the-loop interrupts and time-travel debugging
• Streaming, tool calling, and subgraph composition
• First-class TypeScript and Python SDKs

Best for

• Teams shipping agents to real users at scale
• Workflows that need human approval mid-execution
• Long-running agents that must survive process restarts

Pricing

• Framework itself is open source (MIT)
• LangSmith observability is paid (Plus tier from $39/user/month, Enterprise custom)

Pros

• Production-tested by Anthropic, Replit, LinkedIn, Uber
• The strongest debugging story on this list via the LangSmith trace UI
• Durable state means agents survive crashes
• Strong community and frequent releases

Cons

• Steeper learning curve than CrewAI or Swarm
• Tight coupling to LangChain ecosystem (some teams want a leaner stack)

2. CrewAI, best for role-based agent crews

CrewAI hit the multi-agent scene in late 2023 and by 2026 it is the most popular framework for teams who want a working multi-agent system in an afternoon, not a quarter. Its core abstraction, "crews" of role-based agents with goals, backstories, and assigned tasks, maps cleanly to how non-engineers think about delegating work. You define a Researcher, an Analyst, a Writer; CrewAI handles the handoffs.

CrewAI's enterprise tier added in 2024-2025 has pulled it into real production use beyond demos. Teams use it for content pipelines, sales research, internal ops bots, and customer support triage. It is not as low-level or as debuggable as LangGraph, but for 70% of real business workflows that gap does not matter.

Key features

• Role/goal/backstory agent definitions
• Sequential and hierarchical crew processes
• Built-in tool integrations (Serper, browsing, RAG)
• CrewAI Enterprise for hosted execution and monitoring
• Python-first with a growing CLI

Best for

• Business workflows with clear role separation
• Teams without deep ML/agent engineering experience
• Rapid prototyping and demoable proofs

Pricing

• Open source core (MIT)
• CrewAI Enterprise tier with hosted runtime and observability (custom pricing)

Pros

• Fastest path from idea to working crew
• Excellent docs and templates
• Active community on YouTube and GitHub
• Enterprise tier closes the production gap

Cons

• Less control over low-level execution than LangGraph
• Hierarchical mode can be opaque to debug at scale

3. Microsoft Agent Framework, best for Azure and .NET multi-agent workflows

Microsoft Agent Framework is the direct successor to both AutoGen and Semantic Kernel, built by the same teams. Microsoft's docs describe it as combining AutoGen's simple agent abstractions with Semantic Kernel's enterprise features (session-based state, type safety, middleware, telemetry) and adding graph-based workflows for explicit multi-agent orchestration. If you are starting a new Microsoft-stack agent project today, this is where Microsoft points you.

The multi-agent story is concrete. Workflows ship five built-in orchestration patterns: sequential, concurrent, handoff, group chat, and Magentic, where a manager agent coordinates specialists. Orchestrations can pause on approval-required tools for human review. The GitHub README lists checkpointing, streaming, human-in-the-loop, and time-travel, plus built-in OpenTelemetry tracing, which covers most of what separates a production system from a demo.

Key features

• Graph-based workflows with sequential, concurrent, handoff, group chat, and Magentic orchestration
• Checkpointing, streaming, human-in-the-loop, and time-travel
• Built-in OpenTelemetry integration for tracing
• MCP client support for tools
• Python and C#/.NET with consistent APIs; Go in public preview
• Model providers include Microsoft Foundry, Azure OpenAI, OpenAI, Anthropic, and Ollama

Best for

• Azure and .NET shops building new multi-agent systems
• Teams migrating off AutoGen or Semantic Kernel (official migration guides exist for both)
• Workflows that need explicit, auditable execution paths between agents

Pricing

• Open source (MIT)
• You pay for model usage and any Azure services you deploy on

Pros

• Official successor to AutoGen and Semantic Kernel, so it is where Microsoft's agent investment now goes
• Five orchestration patterns out of the box instead of hand-rolled routing
• OpenTelemetry tracing built in, not bolted on
• Same concepts in Python and .NET

Cons

• Young as a merged framework: some .NET packages still install with a prerelease flag, and the Go SDK is in public preview without functional workflows
• Documentation and samples lean on Microsoft Foundry and Azure, even though other providers are supported

Skip it if you have no Microsoft footprint and already run LangGraph in production. You would trade a proven checkpointing story for a newer one without gaining much.

4. AutoGen (Microsoft), best for conversational multi-agent research

AutoGen, originally from Microsoft Research, popularized the "agents that talk to each other" paradigm in 2023 and matured with its v0.4 architecture rewrite. It is the framework many academic papers and Microsoft-internal projects build on, and it ships with deep Azure OpenAI integration out of the box. One change matters for any production decision: the AutoGen GitHub README now says the project "is now in maintenance mode" and "will not receive new features or enhancements," and it points new users to Microsoft Agent Framework (entry 3).

The v0.4 architecture introduced an event-driven core with actor-style messaging, making AutoGen more suitable for distributed deployments than its earlier conversation-loop design. For research teams and Azure-native enterprises it remains a top pick, particularly for scenarios involving code execution, group chat patterns, and tool-using assistants that need to negotiate.

Key features

• Event-driven, actor-style agent runtime (v0.4+)
• Group chat, nested chat, and sequential patterns
• AutoGen Studio low-code UI for prototyping
• Strong code-execution sandbox support
• Tight integration with Azure OpenAI and Semantic Kernel

Best for

• Research teams exploring agent communication patterns
• Azure-native enterprises
• Code-generation and code-review multi-agent setups

Pricing

• Fully open source (MIT/CC-BY-4.0)
• LLM costs via Azure OpenAI or other providers

Pros

• Built by Microsoft Research, with a large body of papers and samples
• Excellent for code-executing agents
• AutoGen Studio lowers the bar for non-coders
• Official migration guide to Microsoft Agent Framework when you outgrow it

Cons

• In maintenance mode: no new features, community managed going forward
• Breaking changes between v0.2 and v0.4 hurt some teams
• Less opinionated than LangGraph, so production patterns vary

5. OpenAI Agents SDK (formerly Swarm), best for OpenAI-native handoffs

OpenAI shipped Swarm as an experimental framework in late 2024 and graduated it into the production-ready OpenAI Agents SDK in 2025. The design philosophy is intentionally minimalist: agents, tools, and handoffs. Nothing else. No graph DSL, no orchestration ceremony, just a clean Python API that mirrors how the OpenAI Assistants and Responses APIs already think about delegation.

For teams already deep in the OpenAI ecosystem (using GPT-4.1, the Responses API, function calling, and the assistants platform) the Agents SDK is the path of least resistance. It does not try to be a universal orchestrator. It tries to be the most ergonomic way to build agent swarms on top of OpenAI's own runtime, and it succeeds at that.

Key features

• Minimal API: agents, tools, handoffs
• Native Responses API integration
• Built-in tracing in the OpenAI dashboard
• Guardrails and structured outputs
• Python SDK with a JS port maturing

Best for

• OpenAI-first teams
• Lightweight agent handoff use cases
• Teams allergic to framework bloat

Pricing

• SDK is free and open source
• Pay-as-you-go OpenAI API usage

Pros

• Cleanest API of any framework in this list
• Zero migration cost if you already use OpenAI
• Official OpenAI tracing dashboard
• Production-ready as of late 2025

Cons

• Single-provider lock-in (OpenAI only by default)
• No durable state; long-running agents need external persistence

6. Claude Agent SDK, best for Claude-based agents that delegate to subagents

The Claude Agent SDK is Anthropic's library for building agents on the same agent loop, built-in tools, and context management that run Claude Code, programmable in Python and TypeScript. It replaced the earlier Claude Code SDK packages (Anthropic publishes a migration guide). Its multi-agent model is subagents: a main agent spawns separate agent instances for focused subtasks, and each one runs in its own conversation.

That design solves the problem that kills most multi-agent builds, which is context bloat. Per Anthropic's docs, only a subagent's final message returns to the parent, so a research subagent can read dozens of files without flooding the main agent. Subagents can run concurrently, each with its own system prompt, tool allowlist, and model. You can cap nesting depth, concurrent subagents, and total spend per query, and a Workflow tool in the TypeScript SDK moves larger runs of many agents into a script.

Key features

• Subagents defined in code or as markdown files in .claude/agents/
• Per-subagent system prompt, tool allowlist, and model override
• Parallel subagents with isolated context; subagents can spawn their own
• Caps on subagent depth, concurrency, and dollar spend per query
• Built-in tools, hooks, permissions, sessions, and MCP connections

Best for

• Coding, code review, and repo-level ops agents
• Teams already standardized on Claude
• Research and audit agents that read far more than they return

Pricing

• No separate license fee; usage is billed through the Claude API
• Governed by Anthropic's Commercial Terms of Service rather than an open-source license

Pros

• Same agent loop and tools that run Claude Code, so you are not assembling a harness from scratch
• Context isolation per subagent keeps the parent agent's window small
• Spend and concurrency caps give you a hard ceiling on runaway agent trees
• Tool allowlists per subagent limit what each agent can touch

Cons

• Built around Claude models, so it is a single-vendor choice like the OpenAI Agents SDK
• Delegation is model-driven: Claude decides when to spawn subagents, so you get less explicit routing than a LangGraph graph or a Microsoft Agent Framework workflow

Skip it if your agents must run on several model vendors, or your process needs fixed, auditable step order. A graph framework fits that better. For a deeper comparison with the CLI, see Claude Agent SDK vs Claude Code.

7. AWS Multi-Agent Orchestrator (now Agent Squad), best for AWS-native deployments

AWS released the Multi-Agent Orchestrator in 2024 as an open-source framework for routing user queries across specialized agents on Amazon Bedrock. It has since been renamed Agent Squad, and its GitHub README now says the project moved from awslabs to 2fastlabs/agent-squad, where it is maintained today. It is still the most natural fit for teams already running on AWS who want multi-agent systems without leaving the AWS ecosystem.

The framework's intent classification layer, which routes incoming requests to the right specialized agent, is what sets it apart. It plugs directly into Bedrock, Lambda, and Amazon API Gateway, and ships Python and TypeScript implementations (the current README adds Swift). A SupervisorAgent lets a lead agent direct specialist agents in parallel. For enterprises with strict data residency and AWS-only mandates, this is the most natural multi-agent stack on the market.

Key features

• Intent classifier routes queries to specialized agents
• Native Bedrock, Lambda, and Anthropic Claude integration
• Conversation memory in DynamoDB
• Built-in Lex, Bedrock Knowledge Bases, and Lambda agent types
• Open source (Apache 2.0)

Best for

• AWS-native enterprises
• Teams using Bedrock for Claude, Llama, or Titan
• Regulated industries with AWS-only data policies

Pricing

• Framework is free and open source
• Pay AWS usage for Bedrock, Lambda, DynamoDB

Pros

• Deep AWS integration, originally built by AWS
• Intent classifier removes a lot of routing boilerplate
• Works with Bedrock Agents out of the box
• Ships in Python, TypeScript, and Swift

Cons

• Strong AWS lock-in by design
• No longer under the awslabs GitHub org, so check the new maintainer's release cadence before you commit
• Smaller community than LangGraph or CrewAI

8. MetaGPT, best for simulated software engineering teams

MetaGPT takes a different approach from the general-purpose frameworks above. It encodes a software development SOP (product manager, architect, project manager, engineer, QA) into the agent graph and runs the entire pipeline end-to-end given a single product brief. It hit GitHub virality in 2023 and by 2026 is the most-starred multi-agent framework on GitHub.

For research into agent-generated software, internal tooling, and "build me a prototype" use cases, MetaGPT is the cleanest implementation of the simulated-team idea. It is a focused product rather than a general orchestration framework. That focus is its strength and its limitation.

Key features

• Pre-built PM, architect, engineer, QA agent roles
• SOP-driven workflow inspired by software-team processes
• Document generation (PRDs, system designs, code)
• Self-improving via reflection loops
• Active research community

Best for

• Code generation and software prototyping
• Research into agent-driven SDLC
• Internal-tool generation pipelines

Pricing

• Fully open source (MIT)
• LLM costs via any provider

Pros

• Strongest opinionated SOP for software work
• Generates structured artifacts (PRDs, diagrams, code)
• Easy to demo and adapt
• Large, engaged community

Cons

• Narrow scope; not a general multi-agent runtime
• Output quality is highly model-dependent

9. AgentScope (Alibaba), best for distributed high-throughput agents

AgentScope, open-sourced by Alibaba in 2024, is one of the most production-minded multi-agent frameworks built outside the US ecosystem. It targets distributed deployments with built-in fault tolerance, asynchronous messaging, and a Studio UI for debugging large agent topologies.

For teams running multi-agent systems at meaningful scale (thousands of concurrent conversations, distributed worker pools, mixed-model routing) AgentScope's design holds up better than most alternatives. The English documentation has improved significantly through 2025, making it a credible global option rather than a China-domestic one.

Key features

• Distributed message-passing runtime
• Fault tolerance with retry and rollback
• AgentScope Studio for visual debugging
• Pre-built agent and tool library
• Multi-model and multi-provider routing

Best for

• High-concurrency production agent systems
• Distributed deployments across regions
• Teams on Alibaba Cloud or PAI

Pricing

• Fully open source (Apache 2.0)
• LLM and infra costs separate

Pros

• Strong distributed-system primitives
• Visual studio shortens debug cycles
• Active development from Alibaba team
• Genuinely battle-tested at scale

Cons

• Documentation still catching up to LangGraph-tier polish
• Smaller English-speaking community

10. AGiXT, best for self-hosted plugin-heavy platforms

AGiXT is a self-hosted, dockerized AI agent platform with a web UI, plugin system, and provider-agnostic LLM routing. It blurs the line between framework and product: instead of only writing agent code, you spin up the platform and configure agents in the UI.

For teams that want a self-hosted alternative to managed agent platforms, without writing a full app around LangGraph or CrewAI, AGiXT fills a real gap. It is especially popular in self-hosted homelab, defense, and privacy-sensitive deployments where data cannot leave the perimeter. If you would rather configure agents than code them, our AI workflow automation platforms guide compares 13 no-code and self-hosted options.

Key features

• Self-hosted Docker deployment
• Web UI for agent and chain configuration
• Plugin/extension marketplace
• Provider-agnostic (OpenAI, Anthropic, local models)
• Built-in memory, vector store, and task scheduling

Best for

• Self-hosted, privacy-sensitive deployments
• Teams wanting a UI-driven agent platform
• Non-engineers configuring agents

Pricing

• Fully open source (MIT)
• Hosting cost is your own infra

Pros

• One-command self-hosted deployment
• UI-driven configuration reduces engineering load
• Plugin model encourages extensibility
• Privacy-first design

Cons

• Less code-level control than framework-only options
• UI introduces moving parts to maintain

11. SuperAGI, best for autonomous agent workflows with GUI

SuperAGI launched in 2023 as one of the first attempts to package autonomous agents into a production-leaning product. By 2026 it has stabilized into a respected open-source platform with a GUI, agent marketplace, memory systems, and toolkit support across browsing, code, and integrations.

It sits in the same category as AGiXT (framework plus platform) but leans more toward autonomous "set a goal, let it run" workflows. For teams exploring autonomous research, lead generation, and continuous-monitoring agents with a UI rather than raw code, SuperAGI is a strong option.

Key features

• Web GUI for agent management
• Toolkits for browsing, code, integrations, social
• Vector memory (Pinecone, Weaviate, Chroma)
• Multi-agent and concurrent execution
• Open-source core with a cloud tier

Best for

• Autonomous research and monitoring agents
• Teams wanting a managed UI experience
• Workflow automation across multiple SaaS tools

Pricing

• Open source (MIT)
• SuperAGI Cloud paid tier (custom)

Pros

• Polished GUI lowers adoption friction
• Strong toolkit ecosystem
• Active development and community
• Cloud tier removes hosting burden

Cons

• Less production-hardened than LangGraph at high scale
• Autonomous-agent reliability still highly model-dependent

12. Google ADK, best for Gemini-native production agents with graph workflows

Google's Agent Development Kit (ADK) is the company's own open-source framework for building, evaluating, and deploying multi-agent systems. It is code-first and Python-primary, with official SDKs in Java, Kotlin, Go, and TypeScript, and while it is optimized for Gemini, the framework itself is model-agnostic and deployment-agnostic.

ADK's core is a graph-based Workflow Runtime: routing, fan-out/fan-in, loops, retries, state management, and human-in-the-loop checkpoints, plus a structured Task API for agent-to-agent delegation. It ships first-party MCP tool support, so an ADK agent can call the same MCP servers a Claude Code or OpenCode agent would use. Deployment targets Cloud Run or Vertex AI Agent Engine out of the box.

Key features

• Graph-based Workflow Runtime (routing, fan-out/fan-in, loops, retries, HITL)
• Task API for structured multi-turn agent-to-agent delegation
• Native MCP tool support
• Official SDKs: Python, Java, Kotlin, Go, TypeScript
• Deploys to Cloud Run or Vertex AI Agent Engine

Best for

• Teams already building on Google Cloud or Gemini
• Multi-language orgs that need the same framework across Python and TypeScript or Java
• Agents that need first-class MCP tool access without extra glue

Pricing

• Fully open source (Apache 2.0)
• No framework license cost; Vertex AI Agent Engine and Cloud Run billing apply if you deploy there

Pros

• Backed and actively maintained by Google (21,000-plus GitHub stars, commits shipped within the last day as of this writing)
• Genuinely model-agnostic despite the Gemini optimization
• Containerized deploy story that works beyond Google Cloud, not locked to Vertex AI

Cons

• Younger production track record than LangGraph; the ADK 2.0 release shipped breaking changes from 1.x, so the API surface is still settling
• Most tutorials and samples assume a GCP-adjacent workflow even though the framework itself does not require one

How to choose the best multi-agent framework

1) Are you optimizing for production reliability or speed of prototyping?

If you need agents that survive production (durable state, human-in-the-loop, replay, debugging) pick LangGraph and pair it with LangSmith. It is the most production-tested framework on this list and is what serious AI teams ship in 2026. If you need a working swarm by Friday and the workflow tolerates some opacity, CrewAI is faster to first value. For teams already building serious agent products, working with a partner like an AI agent development agency often shortcuts the framework-selection debate entirely.

2) Are you locked into a cloud or LLM provider?

If your stack is AWS-mandated, AWS Multi-Agent Orchestrator (now Agent Squad) is the natural answer: it speaks Bedrock and Lambda natively. If you are OpenAI-first, the OpenAI Agents SDK removes friction. If you are Azure-native, start new work on Microsoft Agent Framework, the successor Microsoft now points AutoGen and Semantic Kernel users to. If you are standardized on Claude, the Claude Agent SDK gives you the Claude Code agent loop with subagents. If you are Google Cloud or Gemini-first, Google ADK is the native answer, with first-party MCP support and multi-language SDKs. For teams building on Claude specifically, framework choice often goes hand-in-hand with hiring a Claude Code agency that understands the Anthropic ecosystem end-to-end.

3) Do you need a framework, a platform, or a product?

A framework is code you write against. A platform is a runtime you deploy. A product is something a non-engineer can configure. LangGraph, CrewAI, Microsoft Agent Framework, AutoGen, Swarm, the Claude Agent SDK, AWS Orchestrator, MetaGPT, AgentScope, Google ADK are frameworks. AGiXT and SuperAGI are closer to platforms with GUIs. If your team includes non-engineers configuring agents, the platform-style options reduce friction. If your team is engineering-heavy, frameworks give you more control. Whichever you pick, the topology matters as much as the library; our guide to multi-agent system architecture patterns covers orchestrator-worker, hierarchical, blackboard, and event-driven layouts. For a deeper look at the underlying Python toolkits beneath these orchestrators, see our guide on the best Python AI agent frameworks. For TypeScript-specific framework options and comparisons, see best TypeScript AI agent frameworks.

4) What is your scale and observability requirement?

For sub-100-user prototypes, almost any framework here works. Past that, observability and durability decide the survivors. LangGraph + LangSmith has the strongest debug story. AgentScope has the strongest distributed-systems story. CrewAI Enterprise and SuperAGI Cloud give you managed observability if you do not want to run your own. Picking a framework without an observability plan is the single most common reason multi-agent systems fail in production. Microsoft Agent Framework ships OpenTelemetry tracing built in, and the Claude Agent SDK lets you cap subagent depth, concurrency, and spend per query. Before launch, run the system through an AI agent production readiness checklist.

Build your multi-agent system with AY Automate

We help companies design, build, and ship production multi-agent systems on the frameworks above: LangGraph and the Claude Agent SDK most often, CrewAI and the OpenAI Agents SDK where they fit. Whether you need a Claude Code agency to ship a coding agent for your engineering org, or a full AI agent development team to orchestrate research, support, and ops agents end-to-end, we move from idea to deployed system in weeks, not quarters. Book a free consultation and we will sketch the framework and architecture that fits your stack.

Where AY Automate fits

We are not one of the tools on this list. We place AI engineers who use tools like these to generate revenue for your business, that is the offer. For multi-agent systems, that means an engineer who designs the orchestration layer and ships it in weeks, not quarters. See AI engineer placement or explore our related service.

FAQ

What is a multi-agent framework?

A multi-agent framework is software that orchestrates two or more LLM-powered agents working together on a task, handling message passing, state, tool use, handoffs, and retries. Without a framework, you would hand-roll all of that orchestration logic yourself.

How is a multi-agent framework different from a single-agent framework?

A single-agent framework runs one LLM in a loop with tools. A multi-agent framework coordinates multiple agents with distinct roles, memories, or models, and adds primitives like handoffs, group chat, and shared state that single-agent libraries do not need.

Is AutoGen still maintained in 2026?

AutoGen is in maintenance mode. Its GitHub README says it will not receive new features or enhancements and is now community managed. Microsoft points new users to Microsoft Agent Framework, which it calls the enterprise-ready successor to AutoGen, and publishes a migration guide. Existing AutoGen code still runs, but new builds should start elsewhere.

What is the difference between Microsoft Agent Framework and AutoGen?

Microsoft Agent Framework merges AutoGen's agent abstractions with Semantic Kernel's enterprise features, such as session-based state, type safety, middleware, and telemetry. It adds graph-based workflows with sequential, concurrent, handoff, group chat, and Magentic orchestration, plus checkpointing and human-in-the-loop. AutoGen gets no new features, so the framework is where Microsoft's agent work continues.

Which multi-agent framework works best with Claude?

If you only use Claude, the Claude Agent SDK is the most direct option. It gives you Claude Code's agent loop and tools, plus subagents with their own prompts, tools, and models. If you need to mix Claude with other models, LangGraph, CrewAI, Microsoft Agent Framework, and Google ADK all support several model providers.

What happened to AWS Multi-Agent Orchestrator?

It was renamed Agent Squad. Its GitHub README says the project moved from the awslabs organization to 2fastlabs/agent-squad, which maintains it now. It is still Apache 2.0 licensed, keeps its intent classifier for routing, and adds a SupervisorAgent for coordinating specialist agents, with Python, TypeScript, and Swift versions.

How do I verify a multi-agent framework is production-ready?

Look for public case studies from real companies (not demos), durable state and checkpointing, observability integrations, and active release cadence. LangGraph, CrewAI Enterprise, and AWS Multi-Agent Orchestrator all clear this bar in 2026.

How much do multi-agent frameworks cost in 2026?

Most frameworks on this list are open source and free; the Claude Agent SDK has no license fee but runs under Anthropic's commercial terms. Your cost is LLM API spend (OpenAI, Anthropic, Bedrock) plus optional paid tiers like LangSmith ($39/user/month and up) or CrewAI Enterprise (custom). Real production deployments typically spend more on LLM tokens than on framework tooling.

How long does it take to ship a production multi-agent system?

A working prototype on CrewAI or the OpenAI Agents SDK is achievable in days. A hardened production system on LangGraph with observability, evals, and human-in-the-loop checkpoints typically takes 4-12 weeks depending on scope and integration surface.

Is LangGraph or CrewAI better?

LangGraph wins on production reliability, durability, and debugging. CrewAI wins on time-to-first-demo and accessibility for non-specialists. Many teams prototype on CrewAI and migrate to LangGraph when reliability becomes the bottleneck. For deeper Python tooling comparisons see our best Python AI agent frameworks post. For a direct comparison of LangChain and LangGraph, see LangChain vs LangGraph.

Can a multi-agent framework train my internal team?

The frameworks themselves are just code. Internal training usually happens through workshops, paired implementation, or working with an AI agent development partner who hands off knowledge as part of the engagement.

Should I use a framework at all or build orchestration from scratch?

Build from scratch only if your requirements are genuinely outside the design space of every framework on this list, which is rare. The frameworks here represent years of pattern discovery. Reinventing them is almost always a tax, not a feature.

How do I monitor a multi-agent system in production? Multi-agent orchestration adds failure modes a single-agent setup does not have: which agent made a bad call, where a task got stuck in a loop, and how errors cascade across a crew. See our best AI agent observability tools roundup for the tracing and monitoring platforms built for exactly this.