The benchmark repository reports LangGraph ahead of CrewAI and AutoGen on the three success-rate categories shown, as well as average token use and latency. That is a result for this repository’s test harness, not proof that LangGraph is generally better or scales better for production data engineering. 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 published material does not resolve.
What the benchmark tested—and what “107 tasks” means
The agent-framework-benchmark repository says it ran LangGraph, CrewAI, and AutoGen on the same LLM, Groq Llama 3.3 70B, with the same prompts and timeout conditions. It says it measured success rate, token cost, latency, and boilerplate lines. These are the repository’s claims about its own experiment; they are not an independent replication.
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The README’s task counts need careful reading. It describes 24 unique tasks across six categories, with 107 task instances in total. The category counts it lists sum to 108:
The Tool Desk
Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →| Category listed in the README | Count listed |
|---|---|
| SQL generation | 24 |
| Pipeline debugging | 19 |
| Data quality | 17 |
| ETL orchestration | 16 |
| Transformation | 16 |
| Metadata generation | 16 |
Those figures should not be silently reconciled: the published description does not establish which total is correct. In particular, “107 tasks” can misleadingly suggest 107 distinct task types when the README says 24 unique tasks and 107 instances.
#1 Best Overall
What results does the repository report?
The README’s visible results table reports success rates for only three categories, alongside average tokens and average latency. The values below are repository-reported results, accessed in 2026; they are not independently verified measurements.
| Framework | SQL generation success | Pipeline debugging success | Transformation success | Average tokens | Average latency |
|---|---|---|---|---|---|
| LangGraph | 87.5% | 79.0% | 75.0% | ~2,700 | ~12.7 s |
| CrewAI | 82.6% | 73.7% | 68.8% | ~5,005 | ~20.0 s |
| AutoGen | 82.6% | 79.0% | 56.3% | ~5,678 | ~17.9 s |
On the three displayed success-rate measures, LangGraph has the highest reported figure in SQL generation and transformation, while it ties AutoGen in pipeline debugging. It also has the lowest reported average token use and latency. The README’s broader summary says LangGraph leads on accuracy, token cost, and latency, but the visible table does not show detailed scores for data quality, ETL orchestration, or metadata generation. Its token figures are counts, not dollar costs; the table does not establish the financial cost of a workload.
Rank #2
Does this show that LangGraph scales better?
No. The numbers suggest that LangGraph performed better on several reported measures in this particular harness. They do not establish performance as task volume, data size, concurrency, or production traffic grows. A task-instance count alone is not a scale test, and an average latency does not show how response times behave under load.
The repository identifies a shared model, prompts, and timeout conditions, but the accessible README does not fully substantiate hardware, pinned framework versions, repetitions per framework, uncertainty intervals, detailed scoring criteria, or run-level data. Without those details, readers cannot reliably determine how much variation a rerun might produce, whether the scoring treated partial correctness consistently, or how results would transfer to a different model or workload.
How the frameworks describe their intended roles
Documentation can help identify what to evaluate in a project, but it is not comparative performance evidence.
- CrewAI: Its documentation describes agents, crews, and flows, and lists capabilities including flow state management, persistence and resumption for long-running workflows, guardrails, callbacks, and human-in-the-loop triggers. Those features may matter when a pipeline needs explicit state, recovery, or review points.
- AutoGen: Microsoft describes AgentChat as a programming framework for conversational single- and multi-agent applications, and AutoGen Core as an event-driven framework for scalable multi-agent systems. These are documented roles, not evidence that AutoGen outperforms another framework on a given data-engineering job.
- LangGraph: The repository includes LangGraph in this benchmark, but the documentation information available for this comparison does not support additional feature claims. Assess its current documentation and implementation directly against your requirements rather than inferring capabilities from this table.
How to choose for your own data-engineering workload
Which is better for data engineering—LangGraph, CrewAI, or AutoGen—depends on the work you need to automate and the operational constraints around it. Use the repository’s results as a reason to include LangGraph in an evaluation, not as a substitute for one.
Rank #4
- Select representative tasks. Include the SQL generation, debugging, transformation, orchestration, data-quality, or metadata work your team actually performs. Test ordinary cases and failure-prone edge cases; do not assume the benchmark’s task mix represents your systems.
- Fix the comparison conditions. Use the same model, prompts, timeout, input data, and evaluation criteria for each framework. Record framework versions, model settings, hardware, and any tool or retry configuration so another run can reproduce the setup.
- Define correctness before running. Specify what counts as a successful result for each task—for example, whether generated SQL must execute and return the expected result, or whether a debugging answer must identify the fault and propose a valid repair. Keep human review consistent across frameworks.
- Repeat runs and retain individual outcomes. Report success rate with the denominator and show run-to-run variation. For latency, retain the distribution or percentiles as well as an average; a single mean can hide slow or unstable cases.
- Measure operational cost and effort. Track token use and model charges separately, along with retries, timeouts, failure recovery, and implementation work. Count boilerplate only if you define what qualifies and apply the same rule to all three.
- Inspect observability and recovery. Check whether engineers can trace decisions, diagnose failed steps, resume interrupted work, and safely handle human approval where needed. These can decide production suitability even when task accuracy is similar.
- Test the scale you expect. Vary workload size and concurrency, then measure throughput, latency, resource use, and failure behavior. This is the evidence needed to answer whether a framework scales for your system.
How to read the verdict
The repository’s displayed results favor LangGraph on the measurements shown, but the count mismatch, partial category table, and limited public methodology prevent a strong general conclusion. For a team choosing a framework, the useful takeaway is to reproduce a controlled comparison on representative tasks and include reliability, observability, recovery, and load behavior—not accuracy and average speed alone.
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