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 10 August 2026
Observe an LLM application
Open in Tools →- 01Langfuse
Langfuse is an open-source LLM engineering platform that provides observability, prompt management, evaluation, and experimentation tools for AI applications. It helps teams trace every LLM call, monitor cost and latency, run evaluations, and continuously improve their AI products from prototype to production. It integrates with 100+ frameworks and model providers with no vendor lock-in.
Developer Tools - 02Helicone
Helicone is an AI Gateway and LLM observability platform that lets developers route, monitor, and debug AI applications across 100+ models with a single API key. It provides real-time cost and latency tracking, prompt management, agent tracing, and one-line integration with OpenAI, Anthropic, and other major providers. It's backed by Y Combinator and used by fast-growing AI companies to build more reliable AI products.
Developer Tools - 03Laminar
Laminar is an open-source observability platform built specifically for AI agents. It lets you trace every agent run, set up plain-English alerts for failure conditions, and run evaluations to catch regressions — all in one place. It integrates with Vercel AI SDK, LangChain, OpenAI, Anthropic, and more with just a line of code.
AI Agents - 04OpenLLMetry
OpenLLMetry by Traceloop is a set of open-source OpenTelemetry extensions that add tracing and observability to LLM apps and agents, exporting to any OTel-compatible backend.
Developer Tools - 05LangSmith
LangSmith is a platform for LLM observability, tracing, evaluation, and monitoring from the LangChain team — framework-agnostic, works with or without LangChain.
Developer Tools - 06AgentScope Studio
Development-oriented visualization toolkit for AgentScope. Trace, debug, and monitor multi-agent LLM systems with an OpenTelemetry-aware UI.
AI Agents
Ordered by the VibeLeaderboard index, re-surveyed each edition. 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, agents, coding, and cost.
- Evaluate an LLM applicationBuild test sets, score outputs, and catch quality regressions.
- Run models locallyUse local inference runtimes and model managers on your own hardware.
- Add vector searchStore embeddings and retrieve relevant context for AI applications.