What LangSmith does well: if you're building on LangChain or LangGraph, LangSmith is the most tightly integrated tracing and evaluation option available — automatic run trees, prompt playground, dataset-based evaluators, annotation queues, and monitoring dashboards, all native to the framework you're already using.
What Dunetrace adds: LangSmith is a tracer first — it records what happened so you can inspect and evaluate it, whether after the fact or via its online evaluators. Nothing in a tracing SDK runs a check inside your agent's process that can stop a run before it finishes; that requires instrumentation built for it from the start, which is what Dunetrace's runtime prevention policies are. Dunetrace is also framework-agnostic — same detectors and policies whether you're on LangChain, CrewAI, AutoGen, a custom Python agent, or TypeScript.
Side by side
| Dunetrace | LangSmith | |
|---|---|---|
| Framework coverage | Framework-agnostic (LangChain, CrewAI, AutoGen, custom, TypeScript) | Best with LangChain/LangGraph; other frameworks via manual instrumentation |
| Can stop a run mid-flight | Yes — runtime prevention policies | No — tracing and eval, not in-path |
| Structural failure detection | 23 detectors, zero LLM calls, automatic | Not built for this — evaluators run against traces/datasets |
| Prompt playground & versioning | No | Yes |
| Dataset-based evaluation | No — semantic evaluation is sampling-based against live runs, not curated datasets | Yes — this is a core strength |
| Proactive alerting | Slack / webhook / Linear, <15s | Alerting exists but is not the primary design center |
Use both
Dunetrace can pull LangSmith's evaluation results in directly, correlated to the same runs via trace_id. Keep LangSmith for prompt iteration and dataset evaluation during development; let Dunetrace watch production for structural failures and enforce runtime guardrails you can't get from a tracer. See the LangSmith integration docs.