Framework comparisons usually list features. We did something more useful: built the same agent three times and kept notes on what hurt.
The test task
A support-triage agent. It reads a ticket, looks up the customer’s plan in a mock database, searches a small knowledge base, assigns a category and priority, and drafts a reply. Four tools, one clear output format, and a 60-ticket test set with known-good answers.
Results at a glance
| LangGraph | CrewAI | Claude Agent SDK | |
|---|---|---|---|
| Time to first working run | 3 h | 50 min | 1.5 h |
| Lines of code | 310 | 140 | 180 |
| Accuracy on 60 tickets | 55/60 | 49/60 | 56/60 |
| Debugging a failed run | Easy | Hard | Medium |
| Human-in-the-loop support | Built in | Manual | Built in |
LangGraph
LangGraph makes you draw the agent as a graph: nodes for each step, edges for the transitions. That upfront design cost is why it took three hours to get running.
It pays off the moment something fails. Every run is checkpointed, so you can replay from any node, and adding a “pause for human approval” step is one line. If your agent will run unattended in production, this is the framework we would pick.
Downsides: steep learning curve, a lot of boilerplate, and the documentation assumes you already know the rest of the LangChain ecosystem.
CrewAI
CrewAI was the fastest to a demo by a wide margin. You describe agents by role (“Support Analyst”, “Reply Writer”), give them tools and a task, and it orchestrates the handoffs.
The trouble came in debugging. When a run went wrong we had to read a long transcript to work out which agent made the mistake, and the fix was usually prompt-tweaking rather than a structural change. Accuracy was also the lowest of the three, mostly from the analyst and writer disagreeing.
Best for: prototypes, demos and internal tools where a human reviews every output.
Claude Agent SDK
The Claude Agent SDK sits in the middle. It gives you the loop, tool handling, file access and subagents out of the box, with opinionated defaults that are mostly right. We spent most of the 90 minutes writing tools, not plumbing.
It scored highest on accuracy, partly because the built-in verification step caught two kinds of formatting mistake the other two let through. Debugging is decent thanks to structured tool-call logs, though not as good as LangGraph’s checkpoint replay.
Downsides: you are committed to Claude models, and complex branching logic is less explicit than a graph.
Our recommendation
- Prototyping or a weekend project: CrewAI.
- Production agent that must recover from failures and pause for approvals: LangGraph.
- Production agent on Claude where you want less code: Claude Agent SDK.
Whichever you pick, keep your tools framework-agnostic. Plain functions with their own tests were the only part of this experiment we did not have to rewrite three times.
See our tools page for ratings and pricing on all three, and our agent fundamentals post if the loop itself is still fuzzy.
Questions, answered
Which AI agent framework is best for beginners?
CrewAI. Its role-based API maps to how people naturally describe a team, and a working demo takes under an hour. You will outgrow it for complex state, but it is the fastest start.
Which AI agent framework is best for production?
LangGraph if you need explicit control over state and recovery, or the Claude Agent SDK if you are committed to Claude models and want sensible defaults with less code.
Can I switch frameworks later?
Mostly. Keep your tools as plain functions with their own tests, and keep prompts in separate files. The framework-specific code is then a thin layer you can rewrite in a day or two.
