When your agents
get complex

Run multi-turn simulations, surface exactly where your agent breaks, and gate every release in CI.

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claude code~/voice-agent

improve agent · vibe-eval loop

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simulation — qualified senior candidate

11/11 · 100% · 50.46s

waiting for the assistant…

Why LangWatch

Testing agents should not take all week.

Manual checks are slow, and failures are hard to see. LangWatch runs repeatable scenarios and shows every turn, tool call, verdict, and trace in one place.

Scenario flow

From scenario to trace

Repeatable scenario

Before LangWatch

Testing takes too long.

Each release turns into a manual replay of the same scenarios.

Failures stay hard to see.

Teams see the bad answer, but not the scenario, turn, or tool behind it.

With LangWatch

Scenario

real user goal

Agent simulation

multi-turn run

Step evals

judge verdicts

Find failure

wrong tool call

Open trace

see every step

Turn real conversations into scenarios, run them before release, then inspect the exact step that failed.

The platform

Test. Evaluate. Observe.

One stack for the full agent lifecycle. Open by default, OpenTelemetry-native, runs against any model.

01 / 03

Agent testing

Test agents end-to-end with multi-turn simulations across text and voice. A user simulator drives real conversations, a judge scores every turn, and adversarial runs surface the failures single-shot evals miss.

Multi-turn simulations of real users
Text and voice agent simulations
Adversarial red-teaming runs
Per-turn judge with pass/fail criteria
Powered by Scenario, MIT-licensed OSS
Runs locally or in CI

Explore Scenario

langwatch · agent-testing

simulation — qualified senior candidate

11/11 · 100% · 50.46s

0:00 / 0:17

Hello, and thank you for joining the interview. I am an AI assistant conducting this interview — the conversation may be recorded and assessed, and you can request a human at any time. Let's start: could you tell me about a recent project where you led the development of an LLM evaluation tool?

Langy

Our AI tests your AI.

Langy turns a PM's goal into a full Scenario test plan, then turns the failures into pull requests.

PMs own the spec. Devs stay in flow. Nothing slips through.

PM writes the goalno codePlain English. No code, no YAML. The brief is the spec.
Langy drafts the planlivePicks the simulator, generates the scenarios, writes the JudgeAgent rubric.
Scenario runs in parallelparallelMulti-turn conversations against your agent, concurrent across projects.
JudgeAgent scores itsignedYour rubric, audited. Faithfulness, policy adherence, de-escalation.
Regressions become PRsready to shipLangy drafts the prompt revision. Devs review and ship via Prompt Registry.