Agent Framework Benchmarks What to Measure

Agent framework benchmarks mislead because they measure the wrong things — throughput on a synthetic task that doesn't match yours. The benchmarks that matter are the ones on your workflow.

Public agent benchmarks measure what's easy to measure: throughput, latency on a standard task, star count. They rarely measure what decides your decision: how the framework behaves on your workflow's shape, state, and failure modes.

Benchmark on your workflow, not the standard task:

• **Latency on your workflow.** Not a synthetic task.
• **State representation fit.** Does the framework represent your state cleanly?
• **Failure handling.** What happens when a call fails, in each framework?
• **Maintenance cost.** How much of the framework do you have to learn and own?

The right benchmark is an ROI question: which framework produces the most value per unit of your engineering time? A framework that's faster on a synthetic benchmark but harder to maintain is a net loss. The benchmark that captures this is the one that compares your workflow, your state, your failure modes — across two frameworks, in a prototype.

• Prototype your workflow in two frameworks.
• Measure latency, state fit, failure handling, and your time.
• Choose on the prototype, not the comparison table.

• They measure synthetic tasks, not your workflow.
• They measure throughput, not the cost of your time.
• They ignore failure handling, which is where frameworks diverge in production.
• They ignore maintenance, which is where the real cost lives.

• Choosing on a public benchmark.
• Choosing on star count or GitHub activity.
• Choosing without a prototype in two frameworks.
• Choosing without measuring your own time.

Agent framework benchmarks mislead when they measure synthetic tasks. The benchmark that matters is a prototype of your workflow in two frameworks, measured on latency, state fit, failure handling, and your time. Choose on that.