Best Multi-Agent Frameworks in 2026: 11 Frameworks for Orchestrating Agents That Hand Off Work

The best multi-agent framework in 2026 depends on which coordination pattern your workflow actually needs, not which framework has the most stars: LangGraph is the only one that ships both a supervisor and a swarm as separate, maintained libraries so you pick deliberately, CrewAI is still the fastest way to get a role-based crew running today, and Microsoft Agent Framework now bundles the widest set of built-in patterns (sequential, concurrent, handoff, group chat, and Magentic) since it absorbed AutoGen and Semantic Kernel. This guide ranks 11 frameworks built specifically to coordinate several agents that hand work to each other, evaluated on their actual orchestration mechanics against vendor docs, GitHub activity, and pricing pages in August 2026.

A single agent calling tools in a loop is not what this list covers. These frameworks exist for the harder problem: a supervisor routing tasks to specialists, a swarm of peers passing control directly, or a fixed pipeline where one agent's output becomes the next agent's input. For the vendor-neutral blueprint on how that coordination works under the hood, see multi-agent systems; for the broader buy-side view across no-code, managed, and framework classes, start with best AI agent platforms. If licence terms and self-hosting rights matter more to you than orchestration pattern, our best open-source AI agent frameworks roundup ranks several of the same frameworks by that lens instead. One honest number before you pick a favorite here: Anthropic's own engineering team measured its multi-agent research system burning roughly 15 times the tokens of a single chat turn, so the harder question usually isn't which framework, it's whether your task is valuable enough to justify running more than one agent at all.

Updated August 2026.

Key Facts

• UC Berkeley's MAST study examined 1,642 real execution traces across 7 popular multi-agent frameworks and found failure rates between 41% and 86.7%, with specification and system-design issues (unclear roles, ambiguous handoffs, missing verification) causing roughly 41.8% of all failures on their own, the single largest category (arXiv:2503.13657).
• Anthropic's multi-agent research system, a lead agent coordinating parallel subagents, used about 15 times the tokens of a single chat interaction, yet outperformed a single Claude Opus 4 agent by 90.2% on internal research evaluations, with token usage alone explaining roughly 80% of that performance variance (Anthropic).
• Anthropic's own guidance names "most coding tasks" as a poor fit for multi-agent coordination specifically because they require dense shared context between agents, the opposite of the parallel, independent research work multi-agent systems are built for (Anthropic).
• Gartner predicts more than 40% of agentic AI projects will be canceled by the end of 2027 over unclear ROI and inadequate risk controls, a risk that compounds specifically in multi-agent systems where a failure can originate several hops upstream of where it surfaces (Gartner).
• Microsoft's unified agent-framework repository, merging AutoGen and Semantic Kernel into one multi-agent SDK, has already drawn more than 12,800 GitHub stars in its first 16 months since its April 2025 launch, a fast climb for a framework built specifically around orchestration patterns rather than a single-agent loop (GitHub).

What Changed in Multi-Agent Orchestration This Year

• Microsoft merged AutoGen and Semantic Kernel into Microsoft Agent Framework, generally available since April 3, 2026, and its five orchestration patterns (Sequential, Concurrent, Handoff, Group Chat, Magentic) reached stable 1.0 status this year, all supporting streaming, checkpointing, and human-in-the-loop approval.
• LangGraph's supervisor and swarm patterns matured into their own maintained libraries, langgraph-supervisor and langgraph-swarm, turning "who's in charge" into a pluggable choice instead of something every team hand-rolled from primitives.
• MetaGPT's commercial product MGX rebranded to Atoms on January 13, 2026. The open-source MetaGPT repository itself has gone quiet since, with no push since late January, while DeepWisdom's attention shifted to the hosted product.
• AG2 kept building independently under Apache-2.0 after its 2024 governance split from AutoGen, and remains the one framework on this list with genuinely open, non-corporate governance for the classic shared-conversation pattern.
• Anthropic's published engineering post on its own multi-agent research system has become the reference case study the rest of the industry cites for both the performance upside and the token cost, more than a year after it first shipped.
• A naming collision worth knowing before you search: OpenAI's original experimental "Swarm" project is retired in favor of the Agents SDK's Handoffs primitive, LangGraph's langgraph-swarm library is a different, unrelated thing, and Swarms (from Kye Gomez and swarms.ai) is a third, much larger framework. None of the three share code.

Quick Comparison Table

Multi-Agent Orchestration Topologies, Explained

Most roundups list features. The choice that actually determines whether your multi-agent system works is the topology: the shape control takes as it moves between agents. Every framework below implements at least one of these, and several implement three or four, which is the real differentiator once you look past the marketing.

Swarms is the outlier worth flagging here: it ships nearly all six patterns in one package (12 named structures in total), which is either exactly what you want or more surface area than a small team needs to learn. Most teams are better served picking the one or two topologies their actual workflow needs and choosing the framework built around those, not the framework with the longest list.

How Handoffs Actually Pass Context

The topology tells you who talks to whom. It doesn't tell you what actually travels when they do, and that detail decides whether your multi-agent system stays coherent or quietly loses information three hops in.

The practical trade-off repeats across every framework here. Full-history handoffs (LangGraph's swarm, the OpenAI Agents SDK) are simple to reason about but bloat context, and cost, the longer a chain of handoffs runs, since every downstream agent re-reads everything upstream agents said. Summary-only handoffs (Claude Agent SDK subagents) protect the lead agent's context window, but the lead never sees the detail the subagent worked through, only its conclusion. Shared-topic models (AutoGen and AG2's group chat) skip the handoff question entirely by making every agent pay to read everything, relevant or not, which is simple until the group grows past a handful of participants.

Shared State and Memory Between Agents

A handoff is a single moment of transfer. Shared state is the ongoing store multiple agents read and write across a whole run, and it's what determines whether a crashed multi-agent job can resume or has to restart from zero.

If your agents need to survive a server restart mid-task, that durability column is the one to read closely, not the topology diagram. LangGraph, Microsoft Agent Framework, and Google ADK all treat state as a first-class, checkpointed object. Several others treat it as something you're expected to wire yourself once you outgrow a single session.

The Real Cost of Multi-Agent: Token Multiplication

Every product page in this category shows a demo where a crew of agents solves something a single prompt couldn't. What most skip is the bill. Anthropic's own engineering team, describing the system behind Claude's research feature, measured multi-agent orchestration at roughly 15 times the token volume of a single chat turn, and about 4 times a single agent working alone. That number isn't a worst case; it's what a well-built orchestrator-worker system costs by design, because a lead agent's context accumulates on top of what every subagent already spent reaching its own answer.

The table below is an illustrative estimate, not a benchmark or a vendor-published figure: it applies Claude Sonnet 5's real, current API pricing ($2 per million input tokens, $10 per million output tokens, as of August 2026) to a plausible token range for each pattern, so you can see the shape of the cost curve rather than treat "multi-agent" as a single line item.

The gap is real, and it compounds with every hop a full-history handoff design adds. Anthropic's own guidance is specific about when that's worth paying: multi-agent systems earn their cost on tasks with heavy parallelization, information that exceeds a single context window, and many independent tool calls, the kind of work where a human team would also divide labor. The same guidance calls out "most coding tasks" as a poor fit for multi-agent coordination specifically, because edits to a shared codebase create dense dependencies between agents rather than clean, parallel, independent work, which is part of why the strongest entries in our best AI coding agents roundup default to one capable agent instead of a crew.

Debugging and Tracing a Multi-Agent Run

Single-agent debugging means reading one transcript top to bottom. Multi-agent debugging means reconstructing which agent said what, in what order, across parallel branches and handoffs that can interleave unpredictably, and MAST's own failure taxonomy exists precisely because that reconstruction is hard enough that most teams get it wrong. A failure surfacing in agent four's output can originate in a bad handoff from agent one, and without step-level tracing, you're debugging the symptom instead of the cause.

Pick the tracing tool before you scale usage, not after a failed run makes you wish you had one. A framework with a free licence and no visibility into which agent did what is more expensive in debugging hours than a framework with a modest paid tracing tier and a team that actually uses it.

1. LangGraph: Supervisor and Swarm as Pluggable Libraries, Not a Hand-Rolled Pattern

LangGraph earns the top spot for one specific reason: it's the only framework here that ships both major topologies as separate, maintained libraries instead of forcing a team to commit to one pattern and hand-code the rest. langgraph-supervisor gives you a central router that calls each worker, reads the result, and decides what happens next. langgraph-swarm removes the router entirely: agents hand off directly to each other through a create_handoff_tool that passes the full message history, and the graph remembers which agent was last active so the next turn resumes with it. Both sit on LangGraph's native checkpointing, so a run can pause, resume, and time-travel back to an earlier state without losing progress.

The trade-off is the one that shows up across LangGraph generally: more control means more to learn. For a hands-on build, see build an AI agent with LangGraph.

Licence: MIT. Pricing: Framework free, including langgraph-swarm and langgraph-supervisor. LangSmith Plus $39/seat/month (10,000 free traces/month), Enterprise custom. Best for: Teams that want to choose supervisor or swarm deliberately, and replay exactly which agent held which state at each step.

2. CrewAI: Role-Based Crews, Sequential or Hierarchical

CrewAI defines agents by role, goal, and backstory, then runs them through a Process: sequential, where each task's output feeds the next, or hierarchical, where a manager agent (one CrewAI generates for you, or one you supply) assigns work across the crew. That structure, plus the largest tutorial and course base in this category, is why CrewAI is usually the fastest path from zero to a working multi-agent demo, even for a team that has never built one before. Flows add a more deterministic, code-first layer on top for teams that outgrow pure autonomous crews.

For a hands-on setup, see build an AI agent with CrewAI.

Licence: MIT. Pricing: Basic free (50 workflow executions/month, visual editor, AI copilot); Enterprise custom (adds SSO, RBAC, workload identity, PII redaction). Best for: Teams that want a role-based crew running in an afternoon, sequential or hierarchical.

3. Microsoft Agent Framework: Every Built-In Pattern in One SDK

Microsoft placed AutoGen in maintenance mode and merged it with Semantic Kernel into Microsoft Agent Framework, generally available since April 3, 2026. What makes it distinct for this specific list is breadth: it ships five stable orchestration patterns rather than one. Sequential and Concurrent cover the predictable cases. Handoff dynamically transfers control based on context, built for escalation and expert routing. Group Chat gives you the shared-topic pattern AutoGen popularized. Magentic, modeled on the Magentic-One research system, is the most flexible: a dedicated manager agent selects who acts next based on evolving context and progress, rather than a fixed script.

That flexibility has a cost signature worth knowing upfront: sequential and handoff patterns invoke agents individually, which limits concurrent resource use but accumulates cost across steps, while Magentic's iterate-until-viable-plan design makes total cost the hardest of the five to predict in advance.

Licence: MIT. Pricing: Free and open source, no licence fee. Cost is the model API usage you connect it to, typically Azure OpenAI Service token pricing. Best for: Teams that want to pick a different orchestration pattern per workflow without adopting a second framework.

4. AutoGen and AG2: The Original Shared-Conversation Pattern, Two Paths Now

AutoGen popularized multi-agent orchestration as a group conversation: agents publish to and read from one shared topic, and a GroupChatManager uses an LLM-based selector to pick the next speaker, tracking the previous speaker so the same agent doesn't dominate the thread. That shared-topic model is genuinely different from a handoff: nothing is "transferred," because every participant already sees everything.

As of 2026, Microsoft's AutoGen is officially in maintenance mode, its development redirected into Microsoft Agent Framework. AG2, the community fork created after a 2024 governance split, kept building independently under Apache-2.0, with an active roadmap and the only genuinely open, non-corporate governance model in this list. For the underlying pattern either way, see multi-agent systems.

Licence: AutoGen: MIT (code). AG2: Apache-2.0. Pricing: Both free and open source; no official hosted tier for either. Best for: Teams that want the classic shared-conversation multi-agent pattern, with AG2 as the actively developed choice.

5. OpenAI Agents SDK: Native Handoffs From the Model Vendor Itself

The OpenAI Agents SDK's signature multi-agent pattern is the Handoffs primitive: a cheap triage agent classifies an incoming request and transfers it to the right specialist using the handoff() function, with the full conversation history moving to the new agent by default and optional structured metadata (a reason, a priority) attached along the way. It's the production successor to OpenAI's experimental "Swarm" project, a different thing entirely from LangGraph's langgraph-swarm library or the separate Swarms framework covered below.

Despite the name, it isn't locked to OpenAI models; an official LiteLLM extension reaches more than 100 providers.

Licence: MIT. Pricing: Free SDK; cost is model token usage, with flagship pricing around $5.00 per million input tokens and $30.00 per million output tokens per OpenAI's published API rates. Best for: Teams building a triage-and-specialist pattern natively on OpenAI's models.

6. Google ADK: Hierarchical Sub-Agents Built for Vertex AI Deployment

Google's Agent Development Kit models multi-agent systems as a hierarchy: a parent agent owns a list of sub_agents it can delegate to, and three workflow agent types handle the mechanical patterns without custom orchestration code. SequentialAgent runs children in a fixed order. ParallelAgent fans work out to run concurrently. LoopAgent repeats a sub-agent until a condition is met. All three compose, so a real ADK system is usually a tree of these wired together.

ADK is Apache-2.0 and free to self-host. Where it earns its keep is deployment: it's built to ship straight to Vertex AI Agent Engine, Google's managed runtime, which bills on consumption (compute, memory, and session storage) rather than a flat subscription, the same model AWS Bedrock AgentCore uses for its own agent runtime.

Licence: Apache-2.0. Pricing: Framework free. Vertex AI Agent Engine hosting bills on consumption, no flat subscription; foundation model tokens billed separately. Best for: Teams deploying a hierarchical multi-agent system on Google Cloud infrastructure.

7. Claude Agent SDK: Orchestrator-Worker With Isolated Subagent Context

The Claude Agent SDK exposes the same lead-agent-plus-subagents architecture that powers Claude Code. A lead agent delegates a task to a subagent, which runs in its own isolated context window, a clean slate containing only its system prompt, the delegation message, and whatever it reads or calls, with no visibility into the main conversation's history. When the subagent finishes, only its final summary returns to the lead; the verbose intermediate work (searches, file reads, tool output) never reaches the parent context. Subagents can spawn their own subagents up to a configurable depth (3 levels by default), and up to 20 can run concurrently before hitting the default limit.

That isolation is a deliberate design choice, not a limitation: it's what keeps a lead agent's context window from filling up with exploration detail it doesn't need, at the cost of the lead never seeing what it didn't ask a subagent to summarize.

Licence: MIT (SDK). Pricing: Free, open source SDK; cost is standard Claude API token usage, currently $2/$10 per million input/output tokens for Sonnet 5, $5/$25 for Opus 5, $1/$5 for Haiku 4.5. Best for: Coding and research agents that need clean context isolation between a lead agent and its specialists.

8. LlamaIndex Workflows: Handoffs Inside a Retrieval-Heavy Pipeline

LlamaIndex Workflows' AgentWorkflow orchestrates multiple agents through a canHandoffTo parameter: each agent declares which other agents it's allowed to delegate to, and when it calls the built-in handoff tool, control and the full workflow Context object transfer to the named agent. That grew directly out of LlamaIndex's retrieval and data-connector heritage, which shows in how naturally it handles a multi-agent system whose core job is pulling from documents and structured sources rather than pure conversation.

Licence: MIT. Pricing: Framework free. LlamaCloud (hosted parsing and indexing) is usage-based and separate. Best for: Multi-agent systems where handing off between retrieval and reasoning specialists is the core job.

9. Swarms: Every Topology in One Enterprise-Grade Framework

Swarms, built by Kye Gomez and the Swarm Corporation, takes a different bet than the rest of this list: instead of picking one or two topologies, it ships 12 named multi-agent structures in one package, including sequential and concurrent workflows, a director-worker hierarchical pattern, an asynchronous group chat, a mixture-of-agents pattern that runs parallel experts and aggregates their answers, and a graph-based DAG orchestrator. It also advertises backward compatibility with LangChain, AutoGen, and CrewAI agents, positioning itself as an orchestration layer over other frameworks rather than a replacement for them.

That breadth is the whole pitch, and also the learning curve: a small team building one workflow will use a fraction of what's here.

Licence: Apache-2.0. Pricing: Framework free. Swarms Cloud: Free ($0, 100 requests/minute); Pro $19.99/month; Premium $100/month ($1,020/year); Enterprise custom, with a separate $9,999/year on-premise licence. Best for: Teams that want every major topology available in one framework without switching tools per pattern.

10. CAMEL: The Role-Playing Framework Behind Multi-Agent Research

CAMEL started from a research question, "what happens when you let two AI agents talk to each other with only a starting role and goal," and its role_playing module still shows that origin: two agents, an "AI user" and an "AI assistant," carry out a structured, turn-taking dialogue toward a task, with no external orchestrator directing the conversation. CAMEL has since scaled that idea into "societies" and "workforces" of many agents, positioning itself as a framework for studying how agent populations behave and coordinate at scale, not just a tool for shipping one workflow.

The commercial product built on top, Eigent, is a separate desktop multi-agent product with its own subscription pricing, distinct from the open-source CAMEL framework itself.

Licence: Apache-2.0. Pricing: Framework free. Eigent, the team's separate commercial desktop product, is priced independently; see eigent.ai/pricing for current tiers. Best for: Teams and researchers studying how agent populations coordinate, not just teams that need one workflow shipped.

11. MetaGPT: A Fixed Pipeline That Simulates a Software Team

MetaGPT takes a one-line requirement and runs it through a fixed sequence of specialized role agents, product manager, architect, project manager, engineer, each producing a structured document (user stories, a design, tasks, code) that becomes the next role's input, following SOPs meant to mirror how a real software team hands off work. That fixed, sequential, role-based pipeline is a genuinely different topology from the other frameworks here, closer to an assembly line than a conversation.

Worth stating plainly: the open-source MetaGPT repository has gone quiet, with no push since late January 2026, while its creator, DeepWisdom, moved commercial focus to Atoms (rebranded from MGX in January 2026), a separate hosted product with its own pricing. That's not disqualifying, the framework still works and remains MIT-licensed, but it's a real signal to weigh against a framework where the team's attention has visibly shifted.

Licence: MIT. Pricing: Framework free. Atoms (the commercial, rebranded product): Free ($0/month, 15 credits/day); Pro from $20/month (100 credits); Max from $100/month (500 credits). Best for: Reproducing a fixed, sequential software-team handoff pipeline, with eyes open about the framework's current maintenance pace.

When Multi-Agent Is the Wrong Choice

Every framework above works. The harder discipline is knowing when none of them should be used yet. Anthropic's own guidance, from the team that published the clearest data on multi-agent performance and cost, is to find the simplest solution that works, and to treat a single LLM call or a single tool-using agent as sufficient for most tasks.

If your team is choosing a platform for procurement, compliance, or security-review reasons rather than orchestration pattern, that's a different decision entirely; see best enterprise AI agent platforms for that lens.

How to Choose: Decision Framework

Choose the control topology first, then use the matrix to pick the framework that implements it with the durability and deployment model you need.

What to Do Next

Before you commit to a framework, write down the handoff contract for your actual workflow: which agent owns the final decision, what exactly transfers at each handoff, and what happens when two agents disagree. Then build the smallest version that tests it, one supervisor and two workers, or a single sequential handoff, in whichever framework matches your language and ecosystem. Time-box it to a week and measure token cost against a single well-tooled agent doing the same job. If the single agent gets you most of the way there, you likely don't need the others yet.

If you do need them, choosing an AI agent platform covers the broader buy-versus-build decision, and how to build an AI agent walks through the fundamentals regardless of which framework you land on.