LangGraph vs. CrewAI vs. Microsoft Agent Framework vs. Google ADK: Which Multi-Agent Framework Should You Actually Learn in 2026?

Ask five AI engineers which multi-agent framework to learn and you’ll get five different answers, three of which are now technically wrong. AutoGen — the framework half the internet’s tutorials are still written against — went into maintenance mode in 2026. Microsoft folded it into a new SDK. Semantic Kernel, its enterprise sibling, went with it. If you bookmarked a “getting started with AutoGen” guide sometime in the last two years, it’s describing a framework Microsoft itself no longer wants you to start new projects on.

That’s the problem with this space: the frameworks move faster than the content written about them. So instead of another feature-matrix post, this is a decision guide — what each of these four actually is, how they differ architecturally (not just in syntax), and a direct verdict on which one fits your situation. If you want the deeper technical background on any one of them, we’ve covered each in dedicated guides linked throughout — this is the piece that ties them together.

The Decision Table

If you only read one row, read “core mental model” — it predicts almost everything else about how a framework will feel to build in.

LangGraph: The Low-Level Orchestration Runtime

LangGraph is not, strictly speaking, an “agent framework” in the same sense as the other three — it’s a low-level orchestration runtime that agent frameworks (including LangChain’s own create_agent) get built on top of. LangChain’s own documentation is explicit about this: “LangGraph is very low-level, and focused entirely on agent orchestration,” recommending that if you want a higher-level abstraction, you start with LangChain’s prebuilt agent loops instead and drop into LangGraph when you need explicit control. It’s trusted in production by Klarna, Uber, and J.P. Morgan — the kind of adopters who needed the durable execution and checkpointing that a request-response chatbot doesn’t. (LangChain, LangGraph Overview)

For multi-agent systems specifically, LangGraph (via LangChain’s agent abstractions) supports several distinct patterns rather than one fixed structure:

• Subagents — a main agent coordinates specialist subagents as tools; all routing passes through the main agent.
• Handoffs — agents dynamically hand off control to each other via state-changing tool calls (the pattern our HTS classification agent tutorial uses for escalation).
• Skills — a single agent stays in control while loading specialized prompts/knowledge on demand.
• Router — a classification step directs input to one or more specialist agents, then synthesizes the results.
• Custom workflow — you build the graph by hand, mixing deterministic logic with agentic steps.

Notably, LangChain has been migrating away from the older langgraph-supervisor package (which modeled worker agents as separate graph nodes routed by a supervisor) toward wrapping each worker as a callable tool on a coordinating agent — a subtle but real architectural shift worth knowing if you’re following an older tutorial. (LangChain, Supervisor Migration Guide)

The trade-off is honest: nothing here is “20 lines to a working crew.” You’re building a state machine, and you own every edge. That’s exactly what makes it the right choice when correctness, auditability, and long-running state matter more than speed of first prototype — which is why it’s the framework we reach for in UiPath deployment tutorials where an audit trail is non-negotiable.

CrewAI: Role-Based Teams, Fast to Build

CrewAI’s mental model is the most intuitive of the four: you define agents with roles (“Research Analyst,” “Content Writer”), give them goals and backstories, assign them tasks, and CrewAI’s crew orchestration handles delegation between them. It consistently comes up as the framework with the lowest learning curve — a working multi-agent crew in roughly 20 lines is a realistic claim, not a marketing one. (DataCamp, CrewAI vs LangGraph vs AutoGen)

We’ve covered CrewAI’s memory architecture and Knowledge feature in depth elsewhere — see Unlocking CrewAI Memory Types and Unlocking CrewAI Knowledge Feature — and both remain accurate descriptions of an actively developed framework. That matters more than it sounds: unlike AutoGen, CrewAI hasn’t been folded into anything else, and community momentum has genuinely shifted toward it as an AutoGen alternative since Microsoft’s announcement.

CrewAI’s ceiling is lower than LangGraph’s for genuinely complex, branching, long-running workflows — the role/task/crew abstraction starts to feel restrictive once you need fine-grained control over exactly when and how agents communicate. But for the common case — “I need three or four specialized agents to divide up a task and hand back a result” — it gets you there fastest.

Microsoft Agent Framework: AutoGen and Semantic Kernel, Merged

This is the framework most likely to surprise readers who last looked at this space a year ago. On April 3, 2026, Microsoft shipped Microsoft Agent Framework 1.0 — a production-ready unification of AutoGen (Microsoft Research’s multi-agent conversation framework) and Semantic Kernel (Microsoft’s enterprise-grade agent SDK) into a single framework, following a public preview in October 2025. Microsoft’s own documentation calls it “the direct successor” to both, built by the same teams. (Microsoft Agent Framework 1.0 announcement, Visual Studio Magazine)

Architecturally, it takes Semantic Kernel’s enterprise plumbing — session-based state management, type safety, filters, telemetry, and its connector ecosystem — and combines it with the multi-agent orchestration patterns that made AutoGen interesting: sequential, concurrent, handoff, group chat, and the more sophisticated Magentic-One pattern for open-ended, dynamically planned tasks. On top of that sits a graph-based workflow engine with type-safe routing and checkpointing. It supports both MCP (for tool discovery) and Google’s A2A protocol (for cross-runtime agent-to-agent collaboration) natively. (Microsoft Agent Framework migration guide)

Two practical implications:

1. If you’re on AutoGen today, you have a migration to plan. It’s not abandoned — bug fixes and security patches continue — but new feature investment is going into Agent Framework, and Microsoft is actively publishing migration guides.
2. If you’re a Microsoft-stack shop, this is now the natural on-ramp — it sits next to Microsoft Copilot and Azure AI Foundry in the same ecosystem, with .NET and Python parity from day one.

It’s genuinely new as a unified 1.0 product, though — “production-ready” per Microsoft’s release notes doesn’t mean “battle-tested at the scale LangGraph or CrewAI have accumulated.” Weigh that if you’re choosing for a large existing team versus a greenfield Microsoft-native project.

Google ADK: Multi-Language, Multi-Pattern, Cloud-Native

Google’s Agent Development Kit stands out for two reasons: it’s genuinely multi-language (Python, TypeScript, Go, Java, and Kotlin SDKs, with Go reaching 2.0 GA in 2026), and it offers the widest range of explicit workflow structures of any framework here. ADK gives you four distinct ways to compose multi-agent systems: template workflows (SequentialAgent, ParallelAgent, and LoopAgent — pre-built patterns extending a common BaseAgent), graph-based workflows (ADK 2.0+, mixing AI agents and deterministic nodes with branching), dynamic workflows (full programmatic control), and collaborative workflows (a single coordinator agent dynamically directing a set of sub-agents). (Google ADK, Workflows)

A common ADK pattern looks structurally similar to AutoGen’s GroupChat or a CrewAI crew — a planner dispatches to a retrieval sub-agent, then a drafting sub-agent, then a grading sub-agent — but ADK’s distinguishing trait is that workflow agents are themselves agents, so they nest: a SequentialAgent can contain a ParallelAgent, which can contain another SequentialAgent. (Google Developers Blog, Multi-Agent Patterns in ADK)

We covered building a production multi-agent system with ADK in detail in Building Multi-Agent Systems with Google ADK — that guide holds up well against ADK’s current architecture. Where ADK pulls ahead of the other three is deployment flexibility if you’re already committed to Google Cloud: first-class paths to Vertex AI Agent Engine, Cloud Run, and GKE, plus a built-in evaluation framework and both MCP and A2A protocol support in both directions (exposing your agents to other systems and consuming external agents).

Orchestration Patterns, Side by Side

The four frameworks converge on a similar set of underlying patterns even though the terminology differs — worth knowing so you can translate a tutorial written for one framework into another:

Production Readiness: What to Actually Pin

If you’re starting a new project in mid-to-late 2026, the safe version floors are LangGraph 0.4 or later and CrewAI 0.105 or later — anything older is missing checkpointing, observability, or current API surfaces you’ll want within six months. (Tensoria, Multi-Agent Orchestration Comparison) For Microsoft Agent Framework, start at 1.0 — there’s no meaningful pre-1.0 version worth building on now that AutoGen and Semantic Kernel are both deprecated in favor of it. For Google ADK, use 2.0+ if you want graph-based workflows; the template workflow agents (Sequential/Parallel/Loop) have been stable for longer and are the lower-risk starting point if you don’t need graph branching yet.

The Verdict

Use LangGraph when you’re building a production agent that needs to run for minutes, hours, or days; needs durable state and human-in-the-loop review; and where you’re willing to trade a steeper learning curve for explicit control over every transition. This is our default recommendation for anything deploying into a governed environment like UiPath Orchestrator, where auditability matters as much as capability — see our LangGraph + UiPath deployment tutorial for exactly that pattern.

Use CrewAI when you want the fastest path from idea to a working multi-agent prototype, your task decomposes naturally into clear roles, and you don’t yet need fine-grained control over exactly how and when agents communicate. It’s also a reasonable default if your team is newer to agent development and needs to build intuition before graduating to something lower-level.

Use Microsoft Agent Framework when you’re already invested in Semantic Kernel, migrating off AutoGen, or building inside a Microsoft/Azure-native stack where Copilot Studio, Azure AI Foundry, and .NET tooling are already part of your architecture. Go in with eyes open that the unified 1.0 is new — validate it against your specific reliability requirements before betting a large team on it.

Use Google ADK when you need multi-language support across a polyglot team (Python, TypeScript, Go, Java, Kotlin), you’re deploying to Vertex AI or Google Cloud generally, or you want the widest built-in menu of workflow structures without hand-rolling a graph. It’s also the strongest choice if A2A cross-agent interoperability with non-Google systems is a hard requirement.

None of these are permanent commitments — MCP and A2A protocol support across all four means agents built on different frameworks can increasingly talk to each other rather than forcing an all-or-nothing bet. Pick based on where your team and infrastructure already are, not on which framework has the loudest hype cycle this quarter.

Key Takeaways

• AutoGen is in maintenance mode; Microsoft Agent Framework (shipped 1.0 in April 2026) is its and Semantic Kernel’s official successor — update your mental model and any bookmarked tutorials accordingly.
• LangGraph is a low-level orchestration runtime, not a high-level agent framework — steepest learning curve, but the most control, and the choice for long-running, auditable, production-governed agents.
• CrewAI remains the fastest path to a working multi-agent prototype and has picked up momentum as an AutoGen alternative since Microsoft’s consolidation announcement.
• Google ADK is the most language-agnostic option (Python/TypeScript/Go/Java/Kotlin) and offers the broadest built-in menu of workflow structures — template, graph-based, dynamic, and collaborative.
• All four now support MCP, and three of the four (Microsoft Agent Framework, Google ADK, and increasingly LangGraph via tool integrations) support the A2A protocol — cross-framework agent interoperability is becoming the norm, not the exception.
• Pin real version floors: LangGraph 0.4+, CrewAI 0.105+, Microsoft Agent Framework 1.0+, Google ADK 2.0+ if you need graph workflows.

FAQs

Is AutoGen dead? Not dead, but deprecated in favor of Microsoft Agent Framework. It still receives bug fixes and security patches, but new feature development and Microsoft’s strategic focus have moved to the unified framework. Don’t start a new project on AutoGen in 2026.

Can I use more than one of these frameworks together? Yes, increasingly — MCP lets any of these frameworks call the same external tools, and A2A (supported by Microsoft Agent Framework and Google ADK, with growing LangGraph support) lets agents built on different frameworks communicate directly. A common pattern is a LangGraph agent for a governed, audited core workflow calling out to an ADK-based agent for a Google-Cloud-specific task via A2A.

Which framework is easiest for someone coming from UiPath’s low-code Agent Builder? CrewAI’s role/task/crew model maps most intuitively onto how low-code agent builders think about work — named roles with goals. LangGraph maps more closely to how you’d think about a Maestro flow or process orchestration diagram, since you’re building an explicit graph.

Do I need to pick one framework for my whole organization? No — and given how fast this space moves, betting your entire agent strategy on one framework is risky. It’s reasonable to standardize per use case: LangGraph for governed, production-critical agents; CrewAI for internal tooling and fast prototypes; Google ADK or Microsoft Agent Framework depending on which cloud and enterprise stack a given team is already in.

What replaced Semantic Kernel specifically, separate from AutoGen? Semantic Kernel wasn’t replaced by a different product — it was merged into Microsoft Agent Framework alongside AutoGen. If you have existing Semantic Kernel code, Microsoft’s migration guide covers moving it into the unified framework directly.

References

• LangGraph Overview — docs.langchain.com/oss/python/langgraph/overview
• LangChain Multi-Agent Patterns — docs.langchain.com/oss/python/langchain/multi-agent
• LangGraph Supervisor Migration Guide — docs.langchain.com/oss/python/migrate/langgraph-supervisor
• Microsoft Agent Framework 1.0 announcement — devblogs.microsoft.com/agent-framework
• Microsoft ships production-ready Agent Framework 1.0 — Visual Studio Magazine, April 2026
• Migrating Semantic Kernel / AutoGen to Microsoft Agent Framework — devblogs.microsoft.com/agent-framework
• Google ADK Workflows documentation — adk.dev/workflows
• Developer’s Guide to Multi-Agent Patterns in ADK — Google Developers Blog
• CrewAI vs LangGraph vs AutoGen comparison — DataCamp
• Multi-Agent Orchestration Comparison (version guidance) — Tensoria