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Replay fixtures let you rerun an agent evaluation using recorded model responses instead of making a fresh live API call for every case. That reduces outages and response variation as confounders—but the resulting score describes the recorded fixture and evaluation harness, not every behavior or external effect of a live run.
What replay changes in an agent evaluation
A live evaluation calls its target for each test case. A replay evaluation loads recorded fixtures instead, so the model-facing inputs and responses can be held constant while the evaluation runs again. The agent-eval-kit Core Concepts documentation describes live, replay, and judge-only modes; it characterizes replay as running from fixtures without API calls.
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This helps answer a focused question: did a code, orchestration, or grading change alter the result when the recorded model interaction stayed the same? It also reduces the chance that a transient API outage or a changed response is mistaken for a change in agent quality.
Replay and judge-only are different
Replay runs the evaluation against recorded interactions. Judge-only mode re-grades an existing run with updated graders. Neither is the same as a new live evaluation, which calls the target again.
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What a replay score proves—and what it does not
A replay score is evidence about the fixture, the harness, and the graders used for that run. It is not a universal measure of agent performance, and it does not certify external effects that were not recorded.
An OpenAI Agents Python repository issue proposes capturing normalized model requests and responses, then replaying them through a scripted model while the actual runner and orchestration logic execute again. The proposal initially leaves external tools and sandbox side effects outside the fixture boundary. Under that design, a replay can help isolate model-boundary interactions and scoring logic, but a reproduced call sequence does not show that a real tool action succeeded.
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- Held steady: the recorded model-boundary requests and responses, to the extent represented by the fixture.
- Still exercised: orchestration and grading logic that the replay harness runs again.
- Not established by replay alone: uncaptured tool behavior, sandbox effects, or whether an external action would succeed in a live environment.
How to build a useful replay workflow
- Capture deliberately. Record a representative live interaction when there is a reason to preserve it. The OpenAI Agents issue proposal calls for opt-in recording because fixtures may include prompts, tool arguments, and model output.
- Redact before writing fixtures. Use a redaction or transformation step to remove sensitive content before it is saved. Treat fixtures as potentially sensitive data, not harmless test artifacts.
- Version the fixture and harness. Store a stable fixture identity and record the relevant target, suite, orchestration, and grader versions. Agent-eval-kit documents a configuration hash based on suite name and targetVersion; the OpenAI proposal describes versioned deterministic JSON fixtures.
- Replay with the intended grading setup. Run the same orchestration and graders you want to assess, and report the fixture identity with the score so readers can tell what was replayed.
- Test external boundaries separately. Stub or exercise tools and sandbox effects independently, and state which behavior the fixture does not capture.
- Refresh fixtures when behavior changes. A fixture can become stale when the target changes. Agent-eval-kit documents a configurable fixture TTL with a 14-day default in that project; verify the setting in the version you use rather than treating it as a general standard.
Make the score interpretable
Keep grading criteria visible alongside the result. Agent-eval-kit documents deterministic graders, LLM graders, weighted scoring, and optional gates for pass rate, maximum cost, and p95 latency. These are project capabilities, not evidence that a replay score automatically captures every quality, cost, or latency dimension.
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Separate deterministic checks—such as exact output or schema requirements—from judge-based assessments, where a model evaluates an answer against a rubric. If a judge is part of the workflow, identify the rubric and judge setup; a replayed target response does not by itself make every grading component deterministic.
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What to compare when choosing a replay approach
| Question | Why it matters |
|---|---|
| What is captured and replayed? | Defines whether the fixture covers only model requests and responses or additional boundaries. |
| How are fixtures versioned and invalidated? | Helps identify stale recordings and connect a score to the target and harness versions. |
| Which graders determine the score? | Distinguishes deterministic checks from LLM judging and mixed grading. |
| Can an existing run be re-graded? | Judge-only workflows can update grading without calling the target again. |
| Which gates and metrics are reported? | Clarifies whether quality, cost, or latency thresholds are part of the evaluation. |
Where the documented examples stand
Agent-eval-kit documents replay and grading capabilities as part of its own project. The OpenAI Agents Python issue describes a proposed capture-and-replay approach; it should not be read as confirmation that the proposal shipped. The evidence supports replay as a way to control recorded model interactions for evaluation, not as a method that removes every API, vendor, or external-system effect from testing.
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