What Langfuse does well: deep observability. Full trace trees with every prompt and completion, prompt versioning and management, dataset-based evaluation, and a mature UI for inspecting exactly what happened in a run. If you need to answer "what did this agent actually see and say at every step," Langfuse is built for that.
What Dunetrace adds: Langfuse is a tracer — it records what happened so you can inspect it later. Nothing in a tracer runs inside your agent's process while it executes, so nothing in a tracer can act before a bad run finishes. Dunetrace's structural detectors do run in-path, which is what makes runtime prevention — stopping, redirecting, or downgrading a run mid-flight — possible at all.
Side by side
| Dunetrace | Langfuse | |
|---|---|---|
| When it fires | In-path (policies) or within 15s of run completion (structural) | You query it after you notice a problem |
| Can stop a run mid-flight | Yes — runtime prevention policies | No — passive tracer, no in-path hook |
| Structural failure detection | 23 detectors, zero LLM calls, automatic | Not built for this — manual trace inspection |
| LLM-based evaluation | Built in (semantic evaluation), or pull Langfuse's own results in | Yes — datasets, scores, LLM-as-judge |
| Full trace inspection | Step graph + event log | Yes — this is Langfuse's core strength |
| Prompt management/versioning | No | Yes |
| Proactive alerting | Slack / webhook / Linear, <15s | No — passive by design |
Use both
Dunetrace can pull your Langfuse evaluation results directly into its own dashboard, correlated to the same runs via trace_id — one alert channel, one place to look, instead of switching tools mid-incident. Get the alert (and, where possible, the automatic stop) from Dunetrace; drill into the full trace and prompt history in Langfuse when you need to go deep. See the Langfuse integration docs.