The first decision in any agent project is also the most expensive one to get wrong.
Pick a framework, build for three months, discover it can’t express your state machine — and you’re not swapping a library, you’re rewriting the agent. That’s why agent-framework selection debates never die on the internet, and why this guide exists: a same-task, same-tools comparison of the four frameworks that matter in 2026, plus an honest answer to the question nobody’s docs will give you — when to skip frameworks entirely.
We’ll keep one thing fixed across every section: the framework is the orchestration layer, not the model layer. All four accept any OpenAI-compatible endpoint, which means model choice and framework choice are separable decisions. That separation is the backbone of this comparison.
Before You Pick: Selection vs Construction vs Protocol
Takeaway: framework selection is one of three different decisions — and most comparison guides conflate them.
- Construction is about how agents work internally: tool-calling loops, memory, orchestration patterns. We have a full architecture guide for that layer, and it argues — correctly, we think — that you should understand the loop before you adopt a framework.
- Protocol is how agents and tools talk: function calling, MCP, A2A. That’s a separate decision entirely, covered in our protocol comparison.
- Selection — this article — is which framework, if any, wraps your construction layer.
One more filter before you read a single option section: when you don’t need a framework. A single tool-calling loop, one model, no persistence — that’s maybe 50 lines of Python with the SDK directly, and our quickstart shows the base call. Every framework below is a solution to problems that appear after that 50-line prototype stops being enough.
Option 1: LangGraph — Graph Orchestration & Production Ecosystem
Takeaway: LangGraph is the production-default choice for complex stateful agents — at the cost of the steepest learning curve.
LangGraph models agents as graphs: nodes are steps, edges are transitions, and the graph has real state that persists between runs. That design buys three things the other frameworks struggle with: checkpointing (a crashed agent resumes where it stopped), human-in-the-loop interruptions as a first-class primitive, and durable state for long-running workflows.
The 1.0 rewrite (late 2025) cleaned up the API substantially — the “LangChain is bloated” complaints that dominated 2023-2024 are largely about the older abstraction stack, not the graph core. The ecosystem around it (tracing, deployment, testing utilities) is the most mature of the four; the LangGraph overview docs are the authoritative reference for the current API surface.
The cost side. The learning curve is real: graphs, reducers, checkpointers, and the mental overhead of “where does my state live” is a concept you didn’t have before. Teams that adopt LangGraph without a concrete state-management need pay that tax for nothing.
Multi-provider reality check. LangGraph talks to any OpenAI-compatible endpoint through the model layer. Point it at your unified API endpoint and the graph runs on whatever model routing you configure — custom routing can send cheap models to cheap nodes and frontier models to the critical ones. The framework locks your orchestration, not your models.
Who it fits: teams with complex, stateful, long-running workflows; anyone who needs checkpoint/resume; organizations that will outgrow simpler abstractions within a year.
Option 2: CrewAI — Role-Based Collaboration, Fastest Start
Takeaway: CrewAI is the fastest way to ship a multi-agent prototype — and the fastest way to hit a ceiling on complex state.
CrewAI’s bet is that agent systems are teams: you define Agents with roles and goals, Tasks with descriptions, and a Crew that orchestrates them. The abstraction is legible to non-experts — a product manager can review a CrewAI definition file and understand it. That’s a real advantage for teams where the agent design isn’t purely an engineering artifact.
The ceiling. CrewAI’s process model (sequential and hierarchical execution) covers linear and lightly branched workflows well. The moment your agent needs conditional loops, dynamic re-planning, or fine-grained state recovery, you’re fighting the abstraction instead of using it. The honest guidance: prototype in CrewAI, and budget for a LangGraph or hand-rolled migration if the workflow gets genuinely stateful.
The setup cost is correspondingly low. A CrewAI definition reads like a spec: agents with role, goal, and backstory; tasks with expected outputs; a crew that runs them sequentially or hierarchically. You can have a working three-agent system before lunch — which is exactly why it’s the default recommendation for product teams validating an idea.
Who it fits: teams shipping their first multi-agent system, business-process-style automation, and anyone optimizing for time-to-first-working-agent over architectural headroom.
Option 3: AutoGen — Maintenance Mode, and What to Do About It
Takeaway: AutoGen is now in maintenance mode — start new Microsoft-ecosystem projects on Microsoft Agent Framework instead.
AutoGen’s v0.4 rewrite introduced an actor-based architecture with typed messages and was genuinely influential — multi-agent conversation patterns, group chat orchestration, and the research lineage it brought are all over the current agent ecosystem. If you have a working AutoGen system, it keeps working; the v0.4 actor model doesn’t rot overnight.
But the 2026 status is unambiguous: Microsoft consolidated its agent story, AutoGen moved to maintenance, and the successor is Microsoft Agent Framework (Python and .NET). Maintenance mode means bug fixes and security updates, not new capabilities.
The practical rule: don’t start a new project on a framework whose vendor has publicly moved on. If you’re Microsoft-stack, evaluate Agent Framework directly; if you’re not, the conversation-multi-agent pattern it inspired is well-served by the other three options here.
What AutoGen taught the ecosystem. Before you dismiss the lineage entirely, its legacy is worth studying. The conversation-as-computation model — agents exchanging structured messages, group chat as an orchestration primitive — is now ambient in the industry. If your design calls for free-form multi-agent conversation with message-level control, you’re implementing an AutoGen idea; Microsoft Agent Framework and LangGraph both carry the concept forward in their own idioms, but the original v0.4 actor model remains a clean mental model for message-passing systems.
Who it fits: teams with existing AutoGen investments (stay, plan a migration window), and nobody else starting fresh.
Option 4: OpenAI Agents SDK — Official Lightweight Primitives
Takeaway: the Agents SDK is the least-framework framework — three primitives, no DSL — and it’s the best default for OpenAI-ecosystem teams.
Agents, Handoffs, Guardrails. That’s the entire surface area. An Agent wraps a model plus tools; Handoffs let one agent delegate to another; Guardrails run input and output validation outside the model loop. There’s no graph DSL, no crew definition file — it’s Python objects and async functions, which means the code reads like your codebase, not like a framework’s.
The SDK sits on the Responses API (itself the successor to Chat Completions for agentic work), and 2026 has been busy on the platform side: model-native harness improvements and tighter integration with OpenAI’s agent tooling — see the OpenAI Agents SDK docs for the current feature set. For teams already committed to OpenAI models, it’s the lowest-friction production path available: official maintenance, sane defaults, and no third-party dependency in your agent core.
The tradeoff: the primitives are deliberately small. Complex state machines still want a graph; heavy multi-agent role design still wants something with more structure. And the SDK’s default affinity to OpenAI models is a feature until it isn’t — which is exactly why the model-layer separation matters: the same Agent objects can target an OpenAI-compatible unified endpoint with whatever model your routing decides.
Who it fits: OpenAI-ecosystem teams, production-minded developers who distrust DSLs, and anyone who wants the smallest possible framework surface.
Same Task, Four Frameworks: A Production Agent
Takeaway: the frameworks differ less in what they can do than in what they make easy — measure them on production dimensions, not demo videos.
Take one task: a support agent with tool access (ticket lookup, refund eligibility), memory of the conversation, and a human-approval step for refunds over a threshold. Roughly 150-250 lines per framework. The structural differences:
| Dimension | LangGraph | CrewAI | AutoGen | Agents SDK |
|---|---|---|---|---|
| State & persistence | first-class (checkpointers) | session-scoped | actor-based messages | session-scoped |
| Human-in-the-loop | interrupt primitives | task-level | conversation-level | guardrails only |
| Debugging & tracing | mature ecosystem | basic | basic | official + third-party |
| Error recovery | resume from checkpoint | restart task | replay messages | retry wrapper |
| Model portability | OpenAI-compatible endpoint | same | same | same (defaults to OpenAI) |
| Learning curve | steep | gentle | moderate | gentle |
The column that decides your project: state and recovery. If a crashed overnight job must resume mid-graph, LangGraph is the only framework where that’s a designed feature. If your agent is stateless request-response with tools, the Agents SDK does the job with a fraction of the machinery.
Two things the table can’t show, and both matter more than the rows: debugging experience and team familiarity. Every framework here is debuggable; none is easy to debug once the agent is doing real work. Trace a failed multi-step run in LangGraph and you walk the graph; in the Agents SDK you read a trace of primitives. Both are usable. The team-familiarity axis is the one that actually decides: a framework your team already half-knows beats a technically superior one nobody can review. That’s a hiring and training decision masquerading as a technology decision.
Lock-in, honestly. Every framework here is a lock-in at the orchestration layer — that’s what adopting a framework means. The mitigation isn’t “pick the least-locking one,” it’s keeping the model layer separate: all four target OpenAI-compatible endpoints, so switching models — or providers, when pricing shifts — is a configuration change, not a rewrite. That’s the pattern our production optimization guide calls “models as configuration,” and it’s the one lock-in you can actually avoid.
Decision Matrix: Team Size × Complexity
Takeaway: default to the smallest framework that expresses your state — and when in doubt, no framework at all.
| Simple workflows | Complex stateful workflows | |
|---|---|---|
| Solo / small team | Agents SDK (or raw SDK) | LangGraph, only if state is real |
| Product team | CrewAI (fastest to ship) | LangGraph |
| Microsoft stack | Microsoft Agent Framework | Microsoft Agent Framework |
Three defaults, stated plainly:
- No framework yet? Start with the raw SDK and the unified-client pattern — most prototypes don’t need a framework, and prototypes that don’t need one teach you what you actually need.
- Need state or resume? LangGraph. Nothing else in this comparison treats persistence as a core feature.
- OpenAI-committed, want production with minimal ceremony? Agents SDK. It’s official, it’s small, and it gets out of your way.
One warning to close: every framework’s docs are written by the framework’s vendor, and every vendor’s docs assume you’ve chosen them. The protocol layer and the construction layer we linked above stay useful no matter which framework — or no-framework — you land on.
A 30-minute evaluation test. Take one real task from your backlog and build it twice: once with the raw SDK, once with your top framework candidate. Compare four things — lines of code, how a crashed run behaves, how you’d add a human-approval step, and how you’d switch the model. Four questions, one afternoon. If the framework doesn’t win on at least two of them, you don’t need it.
FAQ
Which framework should I learn in 2026?
Learn the raw tool-calling loop first — it’s ~50 lines and it’s the same loop inside every framework. Then learn LangGraph if your work involves state, or the OpenAI Agents SDK if you’re on OpenAI’s stack. Learning order matters more than the framework choice.
Will a framework lock me in?
Yes, at the orchestration layer — and that’s fine. What you must avoid is model lock-in: all four frameworks accept OpenAI-compatible endpoints, so keep the model layer behind a unified endpoint and provider changes stay configuration-level, not rewrite-level.
Can CrewAI handle production workloads?
Yes, for linear and lightly branched workflows — thousands of teams run CrewAI-style orchestration in production. The moment you need conditional loops, dynamic re-planning, or checkpoint recovery, migrate to LangGraph or a hand-rolled graph before the workflow fights you.
What’s the difference between a framework and MCP?
They answer different questions. MCP standardizes how agents talk to tools and servers; frameworks standardize how you orchestrate agents. You can use MCP servers from any framework — our protocol comparison covers where they overlap and where they don’t.
Is AgentKit the same as the OpenAI Agents SDK?
No. The Agents SDK is the Python and TypeScript library for building agents with primitives. AgentKit is OpenAI’s higher-level agent builder aimed at product teams assembling agents from a UI. If you’re writing code, start with the Agents SDK; if you’re assembling from a dashboard, AgentKit is the path. They share the Responses API underneath, so the model layer stays compatible either way.
How do I evaluate a framework in an afternoon?
Run the four-question test: build one backlog task with the raw SDK, then with the candidate framework, and compare lines of code, crash behavior, how you’d add a human-approval step, and how you’d switch models. A framework that wins on fewer than two of those isn’t earning its abstraction — and the cheapest fix is not adopting it.
Does using a framework make my agent slower?
The abstraction overhead is small — single-digit percentage on most workloads — and it’s usually dwarfed by model latency anyway. Frameworks don’t make agents slow; bad model routing makes agents slow. Route cheap models to cheap nodes and the framework tax becomes noise.
Can I use two frameworks in one project?
Don’t mix them inside one agent — you inherit both abstractions and neither’s debugging story. Running different frameworks in different projects (or different services) is fine; a shared model endpoint keeps the costs comparable across both.
Summary
When it comes to AI agent frameworks, LangGraph owns complex state, CrewAI owns fast shipping, the Agents SDK owns minimal ceremony, and AutoGen is a maintenance-mode legacy you shouldn’t start on. The frameworks differ less in capability than in what they make easy — so pick the smallest one that expresses your state, and keep the model layer behind a unified endpoint so the framework decision stays a framework decision and nothing more.
Pick a framework after you’ve measured, not before. Get your TokSpan API key — your first $5 in credits is free — and run the same workload against a few models; the graphs settle the debate the internet can’t.