Failures only show up in production
Teams running voice agents keep saying the same thing: the calls that break are never the ones in the test script. They have to be turned into offline evals by hand.
CallReplay turns the voice-agent calls that went wrong into regression tests that run in CI before every prompt or model change, and tells you what the change does to quality, cost and latency. Open source, one command, any OpenAI-compatible model.
This change breaks 1 case that passed before and makes each call 74% more expensive.
callreplay demo) with a built-in fake model.Teams running voice agents keep saying the same thing: the calls that break are never the ones in the test script. They have to be turned into offline evals by hand.
One team reviews its 100 worst calls by hand every week. Then a prompt change ships and nobody checks whether those calls still go right.
One team took 11 months to get its LLM judge to agree with humans. Meanwhile a "friendlier" prompt can double reply length, and the pass rate does not show it.
Load a transcript from Vapi, Retell, LiveKit Agents or plain JSON. Say which agent turn went wrong and what a good reply does.
CallReplay sends the same conversation to your new prompt or model, then runs deterministic checks and an LLM judge.
Compared with the baseline: regressions, fixes, cost per call and latency. Exit code 1 on any regression or on a cost increase you set.
# try it offline, no API key pip install -e . # from the repo: source code coming soon callreplay demo # turn a bad call into a test callreplay show call.json callreplay capture call.json --at-turn 5 \ --tool transfer_to_human --expect "Transfers the caller to a person" # in CI, on every pull request callreplay run --agent agent.json --cases evals \ --baseline .callreplay/baseline.json --fail-on-cost-increase 15 FAILED: 1 regression(s): human-handoff
The CLI, forever. Apache-2.0.
Hosted dashboard for one team.
Evals on the audio, not just the transcript.
Prices are provisional launch prices and may change.
We are looking for 2–3 teams with voice agents in production to try it on their own calls.
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