LangWatch

LangWatch is a specialized platform designed for testing AI agents, evaluating large language models (LLMs), and providing observability into their behavior. It enables developers and teams to simulate realistic, real-world user interactions with AI agents in controlled environments before deployment. By creating synthetic user scenarios—such as multi-turn conversations, edge cases, or high-load conditions—users can verify how their agents respond to diverse inputs and tasks. The platform records each interaction and flags pass/fail outcomes, allowing teams to detect regressions early and systematically improve agent reliability. Beyond simulation, LangWatch offers deep observability features that capture production traces and convert them into actionable evaluation metrics. Users can inspect every step of an agent’s decision-making process, including prompt inputs, model outputs, tool calls, and intermediate reasoning. This transparency helps identify where an agent fails, why it fails, and how to fix it. The platform also supports comparing different prompt versions, model configurations, or system prompts side by side, so teams can measure performance changes before rolling out updates. LangWatch’s evaluation capabilities extend to end-to-end testing of agentic systems—complex workflows where multiple LLM calls, retrievals, or external APIs are chained together. Users can define custom test suites, set pass/fail criteria based on specific outputs or behaviors, and run these tests automatically on each code or configuration change. The results are aggregated into dashboards that show overall pass rates, failure patterns, and latency metrics. This allows teams to catch regressions that might otherwise slip into production. The platform also facilitates prompt engineeri