When your agents
get complex

Simulation-based AI agent testing and evaluation that turns unpredictable agents into reliable production systems.

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improve agent · vibe-eval loop

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simulation — qualified senior candidate

11/11 · 100% · 50.46s

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Trusted in production by

AI agents are still tested by hand, breaking in production.
LangWatch brings loop engineering to agent testing and evaluation.

An agent can take a hundred paths to the same goal, testing them by hand catches only a few.

The best teams run agent simulations as continuous testing and evaluation, so reliability climbs every release.

Spec-driven agent building

Turn your requirements into agent tests automatically.

Speed up development

Set up a self-improving agent loop.

Replicate and fix issues from production

Turn a production trace into a simulation and prove the fix.

Specs

Simulations

Agent
Improvement

Simulate

Real users, in text and voice, pushing your agent the way production will, before it does.

Simulate real users

Text and voice conversations from a simulated user that pushes your agent turn after turn, like the real world does.

Run a scenario

Write scenarios in Claude Code

Describe the behavior you want to test in plain language, right from your editor, and Scenario writes the test.

Write a scenario

Local and CI

The same scenarios on your machine while you build and on every pull request, with no separate setup.

Add to CI

Red teaming

Adversarial simulations probe for jailbreaks, policy breaks, and unsafe tool calls before your users find them.

Red team it

More than a judge

The judge reads the whole trace like you would, expanding each step, so a verdict comes with the reasoning behind it.

Meet the judge

Tools, skills, MCP

Every tool call, skill, and MCP server is traced, and mockable or fixtured for deterministic runs.

Trace tool calls

Whitebox and blackbox

Test through the API or hook into internals. Works with every agent framework, no rewrite required.

See integrations

Evaluate

Score everything, from a single output to a whole conversation, offline and live in production.

LLM as a judge

Score with an LLM, custom code, or a full workflow, over a single output or an entire conversation.

Build an eval

Notebook or UI

Run evals from your Jupyter notebook while you experiment, or from the UI for your whole team.

Open the workbench

Online evaluations

Evaluate production traffic in real time, capture any signal, and turn it into insights.

Go online

Pairwise

Compare two outputs side by side to pick the better model, prompt, or version with confidence.

Compare outputs

Multimodal

Evaluate images and mixed media, not just text, with the same scoring surface.

Evaluate images

Observe

Every trace, token, and cost, searchable at the speed of thought, from any framework.

OpenTelemetry native

Full GenAI spec support, so your traces work with any framework and any OTel-compatible stack.

Instrument in minutes

At the speed of thought

Jump to any trace, filter, or view with Cmd+K. Blazing fast, however much you log.

Try Cmd K

Every agent, every token

Claude Code, Codex, opencode and more, with tokens, cost, and cache accounted for on every span.

See the breakdown

Custom views and AI search

Save the views your team lives in, search in plain language, and filter smartly across millions of traces.

Build a view

See it every way

Waterfall, flame graph, topology, and sequence diagram, whichever way you need to read a run.

Open a trace

Topic clustering

Every conversation is clustered by topic and subtopic automatically, so you see what your users actually ask.

Explore topics

Plot any metric

Build custom analytics graphs over any metric, cost, latency, scores, whatever you track.

Build a graph

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.