LangWatch

About LangWatch

LangWatch tests AI agents by simulating users against them rather than asserting on fixed input-output pairs. The premise is that an agent can reach the same goal down a hundred different paths, so hand-written tests only ever cover a handful — and the ones that break in production are the paths nobody imagined. A LangWatch scenario describes the behaviour you want in plain language; a simulated user then pushes the agent turn after turn, in text or in voice, the way a real user would. The same scenarios run locally while you build and on every pull request in CI, with no separate setup. Evaluation is not a thumbs-up score: the judge reads the entire trace, expands each step, and returns a verdict with the reasoning attached. Tool calls, skills and MCP servers are all traced, and each can be mocked or fixtured so a run is deterministic. Around that sit the pieces you would otherwise assemble yourself — LLM observability with per-step cost and latency, prompt versioning with GitHub sync and A/B tests, red-teaming that probes for jailbreaks and unsafe tool calls, and an AI governance layer issuing virtual keys with budgets, routing policies and an audit trail. A production trace can be converted into a simulation, which is the fastest honest way to prove a bug is actually fixed. It also traces coding-agent usage — Claude Code, Codex and others — for token spend visibility. Self-hosting takes about 15 minutes.

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