Microsoft Agent Framework vs LangGraph vs CrewAI: Which to Use Now That AutoGen Is Dead (2026)

AutoGen is now in maintenance mode. Microsoft merged it and Semantic Kernel into the new Microsoft Agent Framework, which reached 1.0 GA in 2026 and was reaffirmed as the official successor at Build 2026 (June 2-3). That kills the old "LangGraph vs CrewAI vs AutoGen" question. The 2026 answer: pick LangGraph for stateful, production-grade control; CrewAI for fast role-based prototyping; Microsoft Agent Framework if you live on Azure/.NET and want the supported AutoGen replacement with A2A and MCP built in. If you already have AutoGen code, the migration is real but small — AssistantAgent becomes ChatAgent.

By Rohit Raj — Founding Engineer · 10+ yrs MVP shipping · LinkedIn

If you bookmarked a "best multi-agent framework" comparison from last year, throw it out. The whole shape of the question changed in 2026 when Microsoft stopped shipping AutoGen and Semantic Kernel as separate products and folded both into one supported SDK: the Microsoft Agent Framework. AutoGen — which was on nearly every "top 3 frameworks" list — is now in maintenance mode. So is Semantic Kernel.

That means a lot of advice floating around Google is quietly wrong. Tutorials still tell you to pip install autogen-agentchat and build an AssistantAgent, and that code still runs, but you're building on a framework Microsoft has stopped investing in. The real 2026 decision is a three-way: LangGraph, CrewAI, or Microsoft Agent Framework.

This is the builder's read, not a feature-matrix dump. I'll write the *same* agent in all three so you can see the ergonomics side by side, show where each one genuinely wins, give you an honest comparison table, walk the AutoGen migration in actual code, and — the part the listicles skip — tell you when to skip the Microsoft stack entirely and what I'd actually wire into a production app.

Here's the concrete shift. Microsoft Agent Framework is a single open-source SDK for building, orchestrating, and deploying agents, with a unified programming model across Python and .NET. It is the declared successor to *both* AutoGen and Semantic Kernel. The repo at github.com/microsoft/agent-framework crossed ~9.4K stars with 125+ contributors, and the official Microsoft Learn migration guides for both predecessors are now published — a strong signal that the merge is final, not experimental.

Why fold two frameworks into one? AutoGen was the research-y multi-agent conversation framework; Semantic Kernel was the enterprise plugin/orchestration SDK. They overlapped badly and confused every team that asked "which Microsoft thing do I use?" The Agent Framework answer is: one model. AutoGen's AssistantAgent maps to the new ChatAgent, and Semantic Kernel's Kernel + plugin pattern maps to Agent + Tool abstractions. You also get first-class A2A (agent-to-agent) protocol and MCP tool support baked in — the same Model Context Protocol that I covered for server auth here.

The honest caveat: "GA" does not mean "battle-tested everywhere." A 1.0 that shipped this year has far fewer production war stories than LangGraph, which teams have been running in anger for two years. So the framework being *official* and being the *safe* choice are not the same thing — which is exactly why this is still a real comparison and not just "Microsoft won."

Before the code, the one-line mental model for each:

• LangGraph — agents as an explicit state graph. You define nodes (steps), edges (transitions), and shared state. Every transition is visible in code. Best when you need control, conditional routing, retries, human-in-the-loop, and durable runs. The most production-hardened of the three; safe defaults in 2026 are LangGraph 0.4+.
• CrewAI — agents as a team of roles. You give each agent a role, goal, and backstory, hand the crew tasks, and it figures out the collaboration. Lowest barrier to entry; fastest to a working prototype. Transitions are *implicit* — expressed through agent descriptions, not code. Safe baseline is CrewAI 0.105+.
• Microsoft Agent Framework — agents as a unified ChatAgent + Tool model with graph-based workflows underneath, plus Azure AI Foundry guardrails, A2A, and the only one of the three with native .NET support. Best if you're already on the Microsoft stack.

The big axis is *explicit vs implicit control*. LangGraph makes you spell out the wiring; CrewAI hides it; Agent Framework sits in between with a workflow layer you opt into when you need it.

Here's the part the comparison posts skip — actual code. Same trivial task in each: an agent that summarizes a metric in plain English. Read these for *ergonomics*, not completeness.

LangGraph — you build the graph explicitly:

from langgraph.graph import StateGraph, END
from typing import TypedDict

class State(TypedDict):
    query: str
    result: str

def analyze(state: State) -> State:
    return {"result": call_llm(state["query"])}  # your LLM call

graph = StateGraph(State)
graph.add_node("analyze", analyze)
graph.set_entry_point("analyze")
graph.add_edge("analyze", END)
app = graph.compile()

print(app.invoke({"query": "Summarize Q3 churn drivers"}))

CrewAI — you describe roles, it orchestrates:

from crewai import Agent, Task, Crew

analyst = Agent(
    role="Data Analyst",
    goal="Explain churn drivers in plain English",
    backstory="You turn raw metrics into decisions.",
)
task = Task(description="Summarize Q3 churn drivers", agent=analyst)
crew = Crew(agents=[analyst], tasks=[task])

print(crew.kickoff())

Microsoft Agent Framework — a single ChatAgent:

from agent_framework import ChatAgent
from agent_framework.azure import AzureOpenAIChatClient

agent = ChatAgent(
    chat_client=AzureOpenAIChatClient(deployment_name="gpt-4o"),
    instructions="You turn raw metrics into decisions.",
)

print(await agent.run("Summarize Q3 churn drivers"))

Notice the philosophy leaking through the syntax: LangGraph wants nodes and edges, CrewAI wants personas, Agent Framework wants a chat client plus instructions. For one agent the differences look cosmetic. They stop being cosmetic the moment you have five agents that need to hand work to each other under failure — that's where LangGraph's explicit state earns its verbosity. Check the official docs for current signatures; APIs on a young 1.0 still move.

LangGraph wins on production control. If your workflow has branching logic, needs to pause for human approval, must survive a crash and resume mid-run, or you want fine-grained observability over every step — LangGraph is built for exactly this. It's the one I reach for when the agent is doing something I'd get paged about at 3am. Two years of production use means most failure modes already have a documented answer.

CrewAI wins on speed-to-first-demo. When a task decomposes naturally into roles ("a researcher, a writer, an editor") and you want something working this afternoon, CrewAI's readability is unmatched. Non-engineers can read a Crew definition and understand it. The trade is that implicit orchestration gets hard to debug as it grows — teams routinely *prototype in CrewAI, then migrate to LangGraph* when they need real state management.

Microsoft Agent Framework wins on the Microsoft stack. If you need .NET (no other framework here has first-class .NET), deep Azure integration, A2A so agents from different vendors share tool surfaces, or enterprise guardrails through Azure AI Foundry, this is the obvious pick — and it's the only supported path off AutoGen. For a regulated, Azure-hosted enterprise, the compliance story alone decides it.

### Is LangGraph or CrewAI better than Microsoft Agent Framework?

Wrong question — they optimize for different things. If you're *not* on Azure and you want maximum control, LangGraph beats Agent Framework today on production maturity. If you want fast prototypes, CrewAI is simpler. Agent Framework "wins" specifically when .NET, Azure, or the AutoGen-successor path matters to you. Outside that, its main draw is being Microsoft-supported.

If you only remember one row: languages. Need .NET, the choice is made for you. Pure Python with a control bias, LangGraph. Pure Python with a speed bias, CrewAI.

If you have AutoGen code today, you don't have to rewrite the world — the mapping is mechanical. The headline change: AutoGen's AssistantAgent becomes the Agent Framework ChatAgent, and you gain checkpointing and simpler messaging in the process.

# BEFORE — AutoGen (now maintenance mode)
from autogen_agentchat.agents import AssistantAgent

agent = AssistantAgent(
    name="analyst",
    model_client=client,
    system_message="You turn raw metrics into decisions.",
)

# AFTER — Microsoft Agent Framework (supported successor)
from agent_framework import ChatAgent

agent = ChatAgent(
    chat_client=client,
    instructions="You turn raw metrics into decisions.",
)

Multi-agent orchestration maps too: AutoGen's group-chat patterns move to Agent Framework workflows, and Semantic Kernel users swap Kernel + plugins for Agent + Tool. Microsoft ships a migration assistant and step-by-step migration guides on Microsoft Learn for both. My advice: migrate when you're already touching that code for another reason — there's no fire, AutoGen still runs, but every new agent should start on the successor, not the deprecated one.

### Is AutoGen deprecated?

Effectively yes. AutoGen is in maintenance mode — it still works and is still installable, but active development moved into Microsoft Agent Framework. Don't start new projects on AutoGen in 2026; start on the framework that replaced it (or LangGraph/CrewAI if you're not on the Microsoft stack).

Being the official Microsoft successor doesn't make it the right default for everyone. Skip it — for now — if:

• You're not on Azure or .NET. The biggest draws (Foundry guardrails, .NET, deep Azure wiring) don't help a Python team deploying to AWS or a couple of GPUs. You'd be adopting a young 1.0 for benefits you won't use.
• You need a deep bench of production examples today. LangGraph's two-year head start means almost every weird failure mode already has a Stack Overflow answer and a battle-tested pattern. A 2026 GA doesn't yet.
• You're prototyping and want to move fast. CrewAI gets you to a working multi-agent demo faster, full stop.
• Your "agent" is one model call with two tools. Then you may not need *any* of these frameworks — a plain LLM SDK call with a tool loop is less to maintain. Reach for an orchestration framework when you genuinely have multi-step state or multiple agents, not before.

That last one is the lazy-but-correct move more often than framework comparisons admit: the cheapest agent is the one you didn't wrap in a framework.

Here's the wiring the READMEs leave out. Whichever framework you pick, the framework is maybe 20% of a real agent system — the other 80% is the plumbing around it.

Pick by constraint, not hype. Most of my client builds are Python on non-Azure infra shipping in a 6-week MVP window, so I default to LangGraph for anything stateful and skip frameworks entirely for single-call agents. If a client is an Azure/.NET shop, Agent Framework becomes the obvious pick — the compliance and .NET story outweighs LangGraph's maturity edge for them.

Put the model behind a seam. Don't hardcode one provider. Wrap the LLM call so you can swap models without touching agent logic — the same discipline that lets you route cheap models for easy steps and expensive ones for hard steps. Agent frameworks make this easy to forget because the client is baked into the agent constructor.

Budget for the boring 80%: retries with backoff on tool calls, a hard timeout and step cap so a looping agent can't burn your token budget, structured logging of every step (input, tool, output) because you *will* need to debug a weird run, and a kill switch. None of the three frameworks gives you all of this for free; LangGraph gets closest with durable runs.

The failure mode I'd worry about with Agent Framework specifically: betting a long-lived production system on a 1.0 that's still moving. Pin your versions, read the changelog before upgrading, and keep your agent logic thin enough that a breaking API change is a small diff. If you want a second pair of hands wiring a multi-agent system without learning these trade-offs the hard way, that's the kind of build I do.