langwatch: a platform for LLM evaluations and AI agent testing with end-to-end simulations and production observability

What it solves

LangWatch is designed to help teams build more reliable LLM-powered agents by providing a unified platform for testing, simulation, evaluation, and production monitoring. It eliminates the need for custom-built internal tooling for regression testing and observability, allowing developers to pinpoint exactly where agents break and why.

How it works

LangWatch integrates into the AI stack via OpenTelemetry/OTLP-native tracing, making it framework- and LLM-provider agnostic. It creates a continuous loop of tracing production data, converting those traces into datasets for offline evaluation, and using those results to optimize prompts and models before re-testing.

Who it’s for

It is built for development teams creating AI agents that require systematic reliability, performance, and cost control, particularly those who need to avoid vendor lock-in and support self-hosting or hybrid data residency requirements.

Highlights

• End-to-end agent simulations: Run realistic scenarios against the full stack (tools, state, user simulator, and judge) to identify failure points.
• AI Gateway: An OpenAI/Anthropic-compatible proxy that provides virtual keys, hierarchical budgets, inline guardrails, and automatic provider fallbacks.
• Integrated Eval Loop: A seamless workflow that connects tracing, dataset creation, evaluation, and prompt optimization in one place.
• Extensive Integrations: Out-of-the-box support for frameworks like LangChain, LangGraph, CrewAI, and Vercel AI SDK, as well as major model providers.
• Open Standards: Built on OpenTelemetry to ensure no lock-in and compatibility with any OTLP-compatible library.