LangWatch is built for testing and observability for AI agents and large language models. It helps teams monitor agent behavior, catch regressions, and investigate problematic conversations down to individual prompts and responses.
Test agents with simulated users
Run agents against repeatable scenarios with “virtual” users to validate new versions without exposing real customers. Because scenarios are consistent, it’s easier to compare results across releases and pinpoint quality drops.
LLM quality evaluation and regression analysis
LangWatch collects response metrics such as accuracy, instruction adherence, and stability. Use these signals to compare different LLM versions and prompt configurations. Regressions can be traced to specific cases, not just aggregate scores.
Observability and conversation debugging
LangWatch stores interaction history from real users or simulations in a structured log. You can follow call chains and inspect context, prompts, and model outputs, which supports debugging complex agents, finding systemic issues, and improving prompt engineering.
