Pick the wrong framework, and you find out three months in.
Not because the docs were bad. Because the framework's whole philosophy fought your actual use case the entire time.
That's the real cost of this decision. Not learning curve. Architectural fit.
Four frameworks dominate this conversation right now: LangGraph, CrewAI, Microsoft Agent Framework (the AutoGen is the legacy, but Microsoft Agent Framework is what it became, and what you'd actually build on today), and the OpenAI Agents SDK.
Why This Comparison Changed Recently
AutoGen isn't quite AutoGen anymore.
Microsoft merged it with Semantic Kernel into a new project called Microsoft Agent Framework, reaching general availability in April 2026.
The original AutoGen repo hit 50,400 GitHub stars and 559 contributors before the merge, per Microsoft's own announcement. It still exists. It's just in maintenance mode now: bug fixes and security patches, no new features.
That single change reshaped this whole comparison. We'll get into what it means for you further down.
Four Philosophies, Four Sentences
LangGraph treats an agent system as a graph. Nodes, edges, explicit state. You control every transition.
CrewAI treats it as a team. You define roles and goals; the framework figures out coordination.
AutoGen (now folding into Microsoft Agent Framework) treats it as a conversation. Agents talk to each other, debate, and reach consensus.
The OpenAI Agents SDK treats it as a handoff chain. A small set of primitives, minimal ceremony, agents passing work to each other directly.
None of these is wrong. They're just optimized for different shapes of problem.
Architecture: How Each Framework Actually Structures Work
LangGraph's architecture is explicit by design. You define a state graph: nodes are steps, edges are transitions, and you control exactly what happens when.
That gives you cycles, conditional branches, and retries baked into the structure itself. It's also why LangGraph has more boilerplate than the others.
CrewAI's architecture sits one level up. You describe agents by role and goal, assign tasks, and the framework's Crews handle coordination.
For more control, CrewAI adds Flows: event-driven workflows that can orchestrate Crews and regular code together with explicit state management.
AutoGen's original architecture ran on conversational turn-taking. Agents exchanged messages in a group chat pattern, and a manager agent decided who spoke next.
Microsoft Agent Framework replaces that with typed, graph-based workflows. If you're starting fresh today, you're building on the new model, not the old GroupChat pattern.
The OpenAI Agents SDK strips things down further. Agents are just LLMs with instructions and tools. Handoffs let one agent delegate to another, and they're represented as tools the model can call directly.
Memory Management Across the Four
Memory is where these frameworks diverge sharply, and it's worth checking closely before you commit.
LangGraph has a built-in persistence layer through checkpoints. That's what enables human-in-the-loop review, time-travel debugging, and long-running workflows that survive a crash.
CrewAI added a memory backend abstraction in recent releases, letting crews retain context across task runs without you wiring it by hand.
Microsoft Agent Framework carries over Semantic Kernel's session state management, which is more enterprise-grade than AutoGen's original conversation history model.
OpenAI Agents SDK uses Sessions: a persistent memory layer that keeps conversation history across agent runs, plus automatic context compaction so long tool-calling chains don't overflow the context window.
If your build needs durable, resumable state, LangGraph's checkpointing is a strong choice. We don't have head-to-head benchmarks across all four frameworks to call one definitively 'the best.' It's the one most production teams reach for today, based on what's publicly documented.
Orchestration and Multi-Agent Coordination
This is the category most people actually mean when they ask "which framework is best."
CrewAI's role-based delegation is the fastest way to get multiple agents cooperating with almost no boilerplate. Assign roles, assign tasks, done.
LangGraph's orchestration is more manual, but far more precise. You decide exactly which agent runs next, under what condition, with what fallback.
AutoGen's original strength was multi-party conversation: agents debating, reaching consensus, or handing off through a group chat. Microsoft Agent Framework keeps that pattern but rebuilds it on structured workflows instead of implicit chat management.
The OpenAI Agents SDK keeps orchestration lightweight through handoffs. One agent hands a task to another, and the loop continues until the run finishes. By default, the new agent gets full context. Developers can filter or change what gets passed during a handoff. It's not fixed.
Not sure which orchestration model actually fits the workflow you're trying to automate?
Greensighter's guide to AI agent orchestration breaks down how that handoff logic gets designed before you touch a framework at all.
Scalability: Where Each One Holds Up
LangGraph scales the way production infrastructure should. Durable execution, checkpointing, and a deployment platform built specifically for long-running, stateful agents.
CrewAI scales through its Flows layer and an enterprise suite (CrewAI AMP) for managed deployment, observability, and governance once a prototype needs to go live.
Microsoft Agent Framework inherits Semantic Kernel's enterprise posture: middleware, telemetry, type safety. That's the whole reason Microsoft merged the two projects instead of just picking one.
OpenAI Agents SDK scales primarily by staying thin. Fewer abstractions means fewer places for scale problems to hide, but you'll write more of the orchestration logic yourself as complexity grows.
Developer Experience: What It's Actually Like to Build
CrewAI wins on day one. A working multi-agent crew in under 30 minutes is a realistic claim, and its getting-started docs are the most approachable of the four.
LangGraph's documentation is the most exhaustive, but that's also the complaint. There's a lot to navigate before you find the specific pattern you need.
Microsoft Agent Framework's documentation is mid-transition. Some pages still point to old AutoGen docs, some to the new framework. Expect some friction until that settles.
OpenAI Agents SDK keeps things minimal by design: agents, handoffs, guardrails, sessions, function tools. If you already know the OpenAI API, the learning curve is short.
Building something more complex than a single agent calling one tool is really where any of these frameworks start earning their keep. For a single-purpose agent, the vendor SDK alone is often faster than reaching for a framework at all.
So, Which One Should You Actually Use?
Reach for LangGraph if your workflow needs cycles, retries, or a human approval step, and you're building something that has to survive production for years, not months.
Reach for CrewAI if the work splits naturally into specialist roles and you want to validate the idea fast before hardening it.
Reach for Microsoft Agent Framework if your stack is already Microsoft-heavy, or you specifically need multi-agent conversational patterns like debate or consensus-building.
Reach for the OpenAI Agents SDK if you're building on OpenAI's models already and want the thinnest possible layer between you and a working agent.
One common pattern: prototype in CrewAI, then move production-critical paths to LangGraph once the architecture is proven.
That's not the only direction. CrewAI now publishes its own official guide for moving the other way, from LangGraph to CrewAI. Neither direction is universally correct. It depends on whether you need more speed or more control as your build matures.
Source: CrewAI's official migration guide.
Not sure which of these actually fits what you're building? Greensighter's AI agent development team has shipped across multiple frameworks and can help you pick before you write a line of orchestration code. Start a scoping conversation.
Migration and Vendor Lock-In
Switching frameworks later is possible. It's not free.
What usually carries over: your tool definitions. Most of these frameworks call tools in a similar way, so the functions your agents use rarely need a full rewrite.
What usually needs rewriting: your orchestration logic. CrewAI's role-based coordination and LangGraph's explicit graph state work on different mental models. Moving between them means re-expressing how agents hand off work, not just swapping a library.
What sits in between: memory and state. LangGraph's checkpointer, CrewAI's memory backend, and the OpenAI SDK's Sessions are structured differently enough that migrating state usually means redesigning it, not moving it as-is.
CrewAI's own docs now include an official guide for moving from LangGraph to CrewAI, and the reverse direction is just as common. Neither path is a light lift. Plan for it before you commit to a framework, not after.
In our own builds, the framework choice rarely comes first. The workflow does. We map the actual process, then pick whichever framework fits it, not the other way around.
Greensighter's breakdown of real-world AI agent use cases by industry covers what that looks like once you've picked a framework and need to choose the first thing to actually automate.
Bottom Line
There's no universal winner among these four AI agent frameworks. There's only a better or worse fit for the specific system you're building.
LangGraph for control and durability. CrewAI for speed and simplicity. Microsoft Agent Framework for conversational, enterprise-anchored builds. OpenAI Agents SDK for a thin, fast path when you're already committed to OpenAI's stack.
Pick based on your actual workflow's shape, not on which framework has the most GitHub stars this month.
Tell us what you're building, and we'll tell you honestly which framework fits. Start the conversation.
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