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In one repository-run comparison, LangGraph leads CrewAI and AutoGen on the reported success rates, average token use, and average latency. That is evidence about this benchmark, not proof that LangGraph is the best choice for data engineering or scales better in production. The README describes 24 unique tasks and 107 task instances—not 107 unique tasks—and its six category counts add up to 108, a discrepancy the repository does not resolve.
What the benchmark compared
The agent-framework-benchmark repository says it ran the same data-engineering tasks with LangGraph, CrewAI, and AutoGen, using Groq Llama 3.3 70B, common prompts, and the same timeout conditions. It says it measured success rate, token cost, latency, and boilerplate lines. These are the repository’s descriptions of its own experiment; they do not amount to an independent replication.
The README presents the workload as 24 unique tasks and 107 task instances across six categories. Its category counts, however, total 108:
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- SQL generation: 24
- Pipeline debugging: 19
- Data quality: 17
- ETL orchestration: 16
- Transformation: 16
- Metadata generation: 16
The category figures sum to 108, while the stated total is 107 task instances. The available description does not establish which figure is correct. Treat the benchmark as a suite described as 107 instances, with an unresolved count inconsistency, rather than silently treating all 108 category entries as a verified total.
#1 Best Overall
Which framework scored best in the reported results?
The README’s visible results table reports success rates for three named categories, alongside average tokens and average latency. The success-rate figures are percentages; token and latency values are approximate averages as displayed by the repository.
| Framework | SQL generation | Pipeline debugging | Transformation | Average tokens | Average latency |
|---|---|---|---|---|---|
| LangGraph | 87.5% | 79.0% | 75.0% | ~2,700 | ~12.7 seconds |
| CrewAI | 82.6% | 73.7% | 68.8% | ~5,005 | ~20.0 seconds |
| AutoGen | 82.6% | 79.0% | 56.3% | ~5,678 | ~17.9 seconds |
On the three categories shown, LangGraph has the highest reported success rate in SQL generation and transformation; LangGraph and AutoGen tie on pipeline debugging. LangGraph also has the lowest displayed average token count and latency. The repository’s summary likewise says LangGraph leads on accuracy, token cost, and latency. The table does not show individual results for data quality, ETL orchestration, or metadata generation, so it cannot support a category-by-category comparison across all six.
These are reported aggregate results, not guarantees for a new workload. The accessible README does not provide enough detail to judge how representative the tasks are of production work or how much variation sits behind the averages.
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Does this show LangGraph scales better?
No. The displayed outcomes make LangGraph the leader in this particular comparison, but they do not establish that it scales better as workload size, concurrency, data volume, or operational complexity grows. The repository describes task counts and average latency, but the accessible methodology does not fully substantiate hardware, pinned framework versions, run repetitions, uncertainty intervals, detailed scoring rules, or run-level results.
Without those details, a reader cannot tell whether the reported gaps are stable across repeated runs, whether the scoring treats partial correctness consistently, or whether latency changes under higher concurrency. Shared model, prompts, and timeout conditions improve comparability within the described setup; they do not answer those separate scaling questions.
How the frameworks’ documented roles affect the choice
Performance is only one consideration when selecting an agent framework. Official documentation describes different building blocks and operating concerns; those descriptions help frame a fit assessment but are not comparative performance evidence.
CrewAI
CrewAI describes its system in terms of agents, crews, and flows. Its documentation also covers flow state management, persistence and resumption for long-running workflows, guardrails, callbacks, and human-in-the-loop triggers. Those capabilities may matter when a data workflow needs durable state, oversight, or controlled recovery. Their presence in the documentation does not establish that CrewAI is faster, more reliable, or easier to operate than the alternatives.
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AutoGen
Microsoft describes AutoGen AgentChat as a framework for conversational single- and multi-agent applications, and AutoGen Core as an event-driven framework for scalable multi-agent systems. Those documented roles can help you assess whether your application is centered on conversational agent patterns or event-driven systems. They are not evidence that AutoGen wins on the benchmark’s task suite or on your deployment.
LangGraph
LangGraph is one of the three frameworks tested by the repository, and it leads on the displayed measures. The documentation source available for this comparison redirected to a notice that the documentation had moved, so this article makes no additional feature claims about LangGraph. Check its current official documentation when evaluating specific capabilities.
Rank #4
How to test the result against your own data work
A useful framework decision comes from running a controlled comparison on tasks that reflect your actual pipelines, failure modes, and operating constraints. Design the test so that a change in framework—not a change in model or evaluation method—explains any difference you observe.
- Select representative tasks. Include routine and difficult cases from the workflows you expect to automate, such as SQL generation, pipeline debugging, validation, transformations, orchestration, or metadata work. Define what counts as a correct and acceptable result before running the test.
- Hold the conditions constant. Pin each framework version and record the model, prompts, timeout, hardware, and relevant runtime settings. Use the same task inputs and comparable access to tools and data.
- Repeat runs and retain run-level data. Record each result rather than only an average. Compare the distribution of latency and success across repetitions, and note timeouts, retries, and failures. This helps distinguish a consistent advantage from a result sensitive to one run.
- Measure costs and implementation effort. Track token use and model costs alongside correctness. Record the code and configuration needed to build the workflow, including boilerplate, and include the effort needed to diagnose failures.
- Exercise recovery and operations. Test interruptions, retries, invalid outputs, and human review where they apply. Assess whether traces and logs let your team understand what happened and resume or safely rerun work.
- Choose against your constraints. Weigh correctness, latency distribution, cost, recoverability, visibility, and implementation effort according to what your workflow actually requires. A single aggregate score cannot substitute for those trade-offs.
Keep the benchmark repository’s result as a useful starting signal: under its stated setup, LangGraph led the measures shown. Your own controlled workload test is what can establish whether that outcome holds for your tasks and operating environment.
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