LangWatch

Test, simulate, and monitor autonomous AI agents through multi-turn conversational scenarios

Pricing model

Free Tier, Paid

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use LangWatch to test, evaluate, and monitor autonomous AI agents before and after deploying them to production. The software simulates real text and voice conversations to test how agents respond across multiple conversation turns. You can run automated red-teaming simulations to detect jailbreaks, security risks, and broken workflows. It also traces every execution step, tool call, and token cost with OpenTelemetry support. When failures occur, you can convert production traces into repeatable test cases and generate pull requests to fix prompt issues.

What LangWatch does

Run automated multi-turn conversation tests against AI agentsSimulate adversarial jailbreak attempts to test agent securityTrace and debug model execution steps using OpenTelemetryConvert production conversation traces into repeatable test casesScore agent outputs using custom code or LLM-based evaluatorsTrack token usage, response latency, and operational expenses per requestCluster user conversations automatically by topic and intentSimulates text and voice conversations to test agent resilience before deploymentAutomates red teaming simulations to probe for jailbreaks and unsafe tool callsProvides an LLM-as-a-judge system that reviews entire execution traces with reasoningTraces tool calls, skills, and MCP servers with mocking support for deterministic runsSupports OpenTelemetry natively for framework-agnostic tracing and cost trackingConverts production bug traces directly into automated test scenariosGenerates prompt pull requests automatically from PM specifications and failed runs