Tracing got you started. Simulation is what ships.
Beyond logging. Ship agents that actually work.
Langfuse is great at capturing what happened. LangWatch tests what your agent will do, before a single user touches it. Simulations, evaluations, observability, and prompt optimization in one platform that domain experts can use too.
Join thousands of AI developers shipping reliable agents with LangWatch.
How LangWatch compares to Langfuse.
Five things teams care about when picking a quality layer for agents. Each row shows what Langfuse ships today and what LangWatch gives you on day one.
Scenario, the open-source simulation framework, runs thousands of multi-turn conversations with tools, persistent state, a virtual user, and a judge.
Langfuse started as a tracing tool. Strong on capture, thin on agent-level testing before code hits production.
OpenTelemetry is the first-class data model. Drop in any framework and trace lines map cleanly out of the box.
Functions as an OTEL backend but requires manual property mapping for trace visualization to line up.
Scenarios in code or in the UI. Quality gates that legal, support, and product can read and edit.
Built for devs. Onboarding non-technical stakeholders into the loop is heavy lifting.
Automated optimization with MIPROv2, ChainOfThought, and few-shot, version controlled alongside the rest of your repo.
Prompt management exists, but optimization is on you to drive iteration cycle by cycle.
Traces, cost, latency next to funnels, session patterns, and conversion. One view for AI quality and AI outcomes.
Strong on trace analysis and cost, light on user behavior and product analytics for the agent itself.
Three reasons agent teams choose LangWatch.
Multi-step workflows, tool calls, and multi-modal flows tested against realistic scenarios. Release confidence, not crossed fingers.
Systematic generation, scoring, and selection of prompt variants. Real algorithmic optimization, not yet another diff viewer.
Domain experts design scenarios in the UI. Engineers build workflows in code. Both edit the same source of truth.
“It felt like ML evals from the classic era, but built for agents. We finally had a way to ship without holding our breath.”
Ship agents with confidence, not crossed fingers.
Connect in five minutes. Any framework, any model. Agent simulation included on day one.