Pick LangGraph if the thing you are building has to be correct twice in a row. Pick CrewAI if you need something demoable this week and can live with not fully understanding why it did what it did. Pick AutoGen if your problem is genuinely a conversation between participants rather than a pipeline wearing a costume. And pick none of them — write a plain state machine that calls a model — far more often than the framework marketing suggests.
That last option is the one nobody selling an agent framework will mention.
The axis that actually separates these frameworks
Every comparison of open source AI agent frameworks gets written as a feature grid: memory, tools, streaming, human-in-the-loop, observability. All three have all of it, or will by next quarter. The grids are useless because these projects are converging on features and diverging on philosophy.
The real axis is control versus convenience.
Convenience means the framework decides the control flow for you. You describe who is doing the work — a researcher agent, a writer agent, a reviewer agent — and the framework decides when each one runs and what they see. You get to a working prototype fast. You also get a system whose behaviour lives inside the library rather than inside your code.
Control means you define the control flow yourself: the states, the transitions, and what data crosses each boundary. It is slower and less fun on day one. On day thirty, when a run fails in production and someone asks "why did it call the refund tool twice," it is the difference between an answer and a shrug.
Neither is correct in the abstract, and most teams pick based on which tutorial they read first. Before you commit, it is worth reading our take on where agent capability ends and the marketing begins, because the framework choice matters far less than whether the task is agent-shaped at all.
CrewAI: fast to a demo, opaque when it breaks
CrewAI's primitive is the role. You define agents with a goal and a backstory, hand them tools, group them into a crew, and give the crew a list of tasks. The framework handles the orchestration — sequential by default, with a hierarchical mode where a manager agent delegates.
The appeal is real. Role-based decomposition maps onto how people already talk about work, so a non-specialist can read the code and follow it. You can stand up a research-then-summarise-then-critique pipeline in an afternoon.
Where it breaks down: the graph is implicit. When a crew produces a wrong answer, you are debugging an emergent property of prompts, delegation rules, and task ordering — three things that interact in ways that are hard to hold in your head. And "the manager agent will figure out the delegation" is a lovely sentence in a README and an expensive one in production.
Who should use CrewAI: teams prototyping, internal tools where a wrong answer is embarrassing rather than costly, and anyone who wants to find out whether an agent approach is viable before investing in one.
Who should not: anyone with a compliance surface, a payment surface, or an SLA. If a wrong action costs real money, you want the transitions written down.
LangGraph: painful on day one, invaluable on day thirty
LangGraph makes you say the quiet part out loud. You define a graph: nodes are functions, edges are transitions, and there is an explicit shared state object that every node reads and writes. Cycles are first-class, which is the whole point — a loop that re-plans until a condition is met is a graph, not a chain.
This is annoying at first. You will write more code to do less, and spend the first hour arguing with a state schema. Then three useful things happen. Conditional edges make branching legible instead of prompt-dependent. Checkpointing lets you pause a run, get a human to approve a step, and resume — the single most requested feature in any real deployment. And when something goes wrong, you can point at the node.
Where it breaks down: it comes out of the LangChain ecosystem and inherits some of that ecosystem's abstraction density. The learning curve is real, the API surface has moved over time, and there is a persistent temptation to model everything as a graph including things that are obviously a for loop.
Who should use LangGraph: anyone shipping to users, anyone who needs human approval steps, anyone whose finance team will eventually ask what the token bill was spent on.
Who should not: a solo builder validating an idea over a weekend. The ceremony will outweigh the payoff before you know whether the idea works.
AutoGen: conversation as the primitive
AutoGen, out of Microsoft Research, treats the multi-agent system as a chat. Agents send messages; a group chat manager decides who speaks next; a user proxy agent can execute code and represent the human.
Where it shines is when the problem really is conversational: a coder agent and a critic agent going back and forth, or a debate pattern where disagreement is the mechanism that produces quality. The code-execution loop — agent writes code, proxy runs it, error comes back, agent fixes it — remains one of the most convincing demonstrations of the whole agent idea.
Where it breaks down: conversation is a leaky abstraction for a pipeline. If your task is "fetch, transform, validate, write," modelling it as agents chatting introduces nondeterminism you did not need and did not ask for. Termination conditions are a recurring source of pain — chats that will not stop are the classic AutoGen bug. And the project has been through significant architectural revision, so tutorials and Stack Overflow answers age badly.
Who should use AutoGen: research teams, anyone building a genuinely multi-participant system, anyone whose core loop is generate-execute-critique.
Who should not: anyone who describes their problem as "a pipeline." You have a pipeline. Build a pipeline.
Side by side
| Framework | Core primitive | Best for | The honest catch |
|---|---|---|---|
| CrewAI | Roles and tasks | Fast prototypes, internal tools | The control flow is implicit; debugging is archaeology |
| LangGraph | Explicit state graph | Production systems, human-in-the-loop, anything auditable | Steep first day; ecosystem abstraction fatigue |
| AutoGen | Multi-agent conversation | Code-execution loops, critique/debate patterns | Termination conditions; conversation is the wrong shape for pipelines |
| A plain state machine | Your own functions | Most tasks people call "agentic" | You maintain it — but you also understand it |
| Hosted (AgentGPT, Lindy) | A configured product | Non-engineers, business workflows | Less control, vendor dependency, per-run pricing |
Swipe the table sideways to see every column →
The uncomfortable part: most agent problems are a state machine
Here is the reframing that saves teams the most time. Take the workflow you want to automate and write it on a whiteboard as boxes and arrows. Now count the arrows whose destination genuinely depends on a model's judgment rather than a value you could check with an if statement.
For most business workflows, that count is small — often one or two. Classify this ticket. Decide whether this document is relevant. Everything else is retrieval, formatting, validation, and API calls, all of which a language model does worse and more expensively than ordinary code.
An agent framework earns its cost when the graph is genuinely dynamic — when the model must decide what to do next and the space of next steps is too large to enumerate. When the graph is fixed and only the node contents are fuzzy, you want ordinary software with a few model calls inside it. That is not a lesser architecture. It is the one still running in a year.
The tell is simple: if you can draw your agent's behaviour as a flowchart with no "it depends what the model decides" arrows, you do not need an agent. You need a script. Developers scoping the surrounding toolchain will get more from the developer tools category than from another orchestration library, and our use-case guide for developers covers what earns its keep in a working stack.
When not to write code at all
If the workflow is a business process — route inbound leads, triage support email, summarise a meeting into a CRM record — there is a decent chance a hosted product already does it, and the build-versus-buy maths is not close.
AgentGPT is the easiest way to see autonomous task decomposition without installing anything; treat it as a demo of the concept rather than infrastructure. Lindy sits closer to the business end, building agents around triggers and integrations rather than around Python. And much of what gets pitched as agentic is served perfectly well by conventional automation with a model call bolted on — the automation tools category is full of options that are cheaper to run and easier to explain to your team.
Buy when the workflow is common and the value is in the integrations. Build when the workflow is your differentiator.
A ten-minute decision rule
- Draw the graph. Boxes and arrows, on paper. If you cannot draw it, you do not understand the problem yet, and no framework will fix that.
- Count the model-decides arrows. Zero or one? Write a script. Many, and unpredictable? You have an agent.
- Ask what a wrong action costs. If the answer includes the word "refund," "email the customer," or "delete," go straight to LangGraph and build the approval step first, not last.
- Prototype in the convenient one, ship in the controlled one. CrewAI to learn the shape of the problem, LangGraph to run it. Throwing away the prototype is the plan, not a failure.
FAQ
Is CrewAI or LangGraph better for beginners?
CrewAI, without much argument. Roles and tasks are an intuitive model and you will get something running the same day. The caveat is that the intuitions CrewAI gives you do not transfer cleanly to production systems, so treat it as a learning environment rather than a foundation. Beginners who already know they are building something durable are usually better off paying the LangGraph learning tax immediately.
Are open source agent frameworks free to run?
The framework is free. The inference is not. Agent loops make many model calls per task, they re-send accumulated context on each turn, and failure modes tend to be expensive ones — a retry loop that will not terminate bills you for every attempt. Budget for tokens as an operating cost from day one, cap iterations in code, and log per-run spend before you scale anything.
What about the OpenAI Agents SDK, LlamaIndex Workflows, or smolagents?
They are real options and the space is still churning. The choice among them still reduces to the same control-versus-convenience axis, so evaluate them on how explicit the control flow is and how well you can inspect a failed run. Do not pick a framework because it is new. Pick it because you can debug it at 2am.
Do I need a vector database to build an agent?
Usually not at the start. Retrieval matters when the agent must reason over a corpus too large for the context window, and plenty of useful agents never touch one. Adding a vector store early is a common way to spend two weeks on infrastructure before learning anything about your actual problem. Our glossary covers the terminology if the jargon is the blocker.
How do I stop an agent from looping forever?
In code, not in the prompt. Set a hard maximum on iterations, a hard cap on total tokens, and a wall-clock timeout, and make exceeding any of them a terminal state that returns partial output. Prompt instructions like "stop when you are finished" are suggestions, and a model that has lost the thread will not follow them.
Where to go next
The framework debate absorbs more attention than it deserves. Spend an hour drawing your graph and the answer usually picks itself — and occasionally the answer is a cron job.
If you would rather assemble a working stack than a research project, browse the AI agents category for products that already do the common workflows, or the tool directory if you are scoping a build. Running something in production? Submit it.