LangGraph vs CrewAI vs AutoGen
All three build multi-agent systems and all three have a demo running in twenty minutes. The difference shows up the day a step fails halfway through and you need to resume without repeating what already happened.
Key takeaways
LangGraph models the flow as an explicit graph with durable state: if the process dies at step seven, it resumes at seven. It is the one that holds up for processes with consequences.
CrewAI assembles teams of agents with roles and tasks. It reaches a first result faster and controls the path taken less.
AutoGen organises work as a conversation between agents. It is the most flexible for exploration and the hardest to bound when cost matters.
The decision is rarely about the framework. It is about what an error costs: if a step moves money, changes a record or writes to a customer, you need durable state and human approval, and that rules out the options that lack them.
The three, on what matters in production
| Dimension | LangGraph | CrewAI | AutoGen |
|---|---|---|---|
| Mental model | Graph of nodes and edges | Team with roles and tasks | Conversation between agents |
| Flow control | Explicit, you draw it | Semi-automatic per task | Emergent from the conversation |
| Durable state | Yes, with checkpoints | Limited | Limited |
| Resuming after a failure | From the node that failed | Task retry | Usually redoes the thread |
| Human approval | Interrupt at the node | Per tool | Via a user proxy agent |
| Learning curve | Steep | Gentle | Moderate |
| Where it shines | Processes with consequences and audit | Prototypes and content flows | Research and exploration |
Not which is best. What an error costs.
An agent that summarises news can be wrong a hundred times without anything serious happening. An agent that negotiates a payment date with a customer and records the agreement cannot be wrong once. That distinction decides the framework far more than any benchmark.
When a step carries consequences you need three things: durable state, so the process can resume where it stopped; human approval before the irreversible step; and an audit trail that lets you reconstruct what the agent did and why. LangGraph ships all three as framework primitives. In the other two you can build them, but you build them.
On the other side, drawing an explicit graph for a three-step flow with no consequences is wasted work. CrewAI gets to a first result sooner, and for plenty of cases that is exactly what is needed.
When the light framework stops being enough
- The process touches money, inventory, a calendar or a record another system will read later.
- Someone will ask why the agent did what it did, and the answer has to be reconstructable.
- A run takes minutes, and a full retry costs as much as the original attempt.
- A person needs to approve one specific step, not the whole process.
- Cost per run started varying tenfold between executions with no explanation.
The framework does not fix the underlying problem
All three share the same blind spot: none measures whether the agent did its job well. They ship traces, not evaluation. You can see exactly which tool the agent called and with what arguments, and still not know whether the final answer was any good.
That gets solved outside the framework, with a set of test cases and their expected outcomes, run on every change. It is boring and it is the difference between an agent you can modify and one nobody dares to touch.
Observability helps but does not replace it. LangSmith, Langfuse or your own traces show what happened; the evaluation set says whether what happened was correct.
Frequently asked questions
Which one should I use for a first agent project?
If the agent only reads and answers, CrewAI gets you to something working sooner, and that is enough to learn what the case actually needs. If the agent will write to a real system from the start, beginning with LangGraph saves the migration: durable state and human approval are hard to retrofit.
Can they be combined?
Technically yes, and it is almost never worth it. Two orchestration frameworks in the same system double the mental model, the observability surface and the failure points. When a flow needs something the chosen framework lacks, writing that piece by hand is usually cheaper than adding a second framework.
What is durable state and why does it matter?
It is the ability to persist progress at every step so that a failure can resume from there. Without it, a twelve-step process that fails at step eleven starts again at one: you pay the full model spend a second time and repeat actions already executed, which is the serious part when those actions write to real systems.
What does it cost to run a multi-agent system?
The dominant cost is almost never the framework: it is model spend, which grows with the number of steps and with how much context gets dragged along. A three-agent system that passes the whole thread between agents can cost ten times the same system passing only the result. Measuring cost per run from day one avoids that surprise.
Do I need an agent framework at all?
For many cases, no. If the flow is three model calls in a fixed order, a script with error handling is easier to read and maintain. A framework starts paying off when there is branching, conditional retries, parallelism, or steps that require approval.