What are the best LLM observability and tracing tools?
The one thing to require is full prompt and response capture on every call. Aggregate latency and token charts are easy; being able to open the exact conversation that produced a bad output is what you actually need at 2am.
Surveyed 17 September 2026
Observe an LLM application
Open in Tools →- 01Langfuse
Open-source LLM engineering platform with tracing, prompt management, evaluations, and cost monitoring for AI apps.
Developer Tools - 02LangSmith
Tracing, evaluation, and monitoring for LLM apps, framework-agnostic and usable without LangChain.
Developer Tools - 03Helicone
Routes, monitors, and debugs LLM apps across 100+ models behind one API key, with tracing and prompt management.
Developer Tools - 04Laminar
Open-source observability platform for AI agents with run tracing, plain-English failure alerts, and evaluations.
AI Agents - 05OpenLLMetry
OpenTelemetry extensions that add tracing to LLM apps and agents, exporting to any OTel-compatible backend.
Developer Tools - 06AgentScope Studio
Traces, debugs, and monitors multi-agent LLM systems through an OpenTelemetry-aware visual interface.
AI Agents
A curated selection in editorial order. Use the fit and evidence to judge it for your task. Something missing?
What to look for
- 01Does it store the full prompt and response, or only metrics? Metrics alone cannot explain a bad answer.
- 02Can you trace a multi-step agent run as one tree, or only individual calls?
- 03Does it attribute cost per feature and per user, not just in total?
Common questions
- What should I log for an LLM application?
- Full prompt, full response, model and version, token counts, latency, and a request ID linking to the user action. Redact personal data at the boundary rather than logging selectively.
- How do I control runaway model costs?
- Attribute spend per feature so you can see which path is expensive, set hard per-request token caps, and alert on cost per user rather than only on the monthly total.
More in Understand the AI landscape
- Find AI benchmarksBrowse the evaluations used to compare models and agents — coding, tool use, cost, and the domain-specific long tail.
- Evaluate an LLM applicationBuild test sets, score outputs, and catch quality regressions.
- Choose an inference providerCompare model routers, inference clouds, cloud catalogs, and direct lab APIs without collapsing them into one category.
- Run models locallyUse local inference runtimes and model managers on your own hardware.
- Add vector searchStore embeddings and retrieve relevant context for AI applications.