Dictionary
LLM Observability
Tracing every prompt, tool call, and token in production.
1 min readupdated 2026-07-04
/ quick answer
LLM observability (Langfuse, LangSmith, Helicone, Arize) captures inputs, outputs, latency, cost, and errors per call. Non-negotiable once you have real users. Tracing every prompt, tool call, and token in production.
Tracing every prompt, tool call, and token in production. LLM observability (Langfuse, LangSmith, Helicone, Arize) captures inputs, outputs, latency, cost, and errors per call. Non-negotiable once you have real users. In practice: A trace shows the exact chunks retrieved, the prompt sent, and the answer scored by an LLM-judge. This dictionary node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Definition
LLM observability (Langfuse, LangSmith, Helicone, Arize) captures inputs, outputs, latency, cost, and errors per call. Non-negotiable once you have real users.
Example
A trace shows the exact chunks retrieved, the prompt sent, and the answer scored by an LLM-judge.
Related Tool Stacks
/ frequently asked
What is LLM Observability?
LLM observability (Langfuse, LangSmith, Helicone, Arize) captures inputs, outputs, latency, cost, and errors per call. Non-negotiable once you have real users.
What is an example of LLM Observability?
A trace shows the exact chunks retrieved, the prompt sent, and the answer scored by an LLM-judge.
Why does LLM Observability matter for AI and automation?
Tracing every prompt, tool call, and token in production. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
↳ connected nodes
Tool Stack↳ linked
AI Ops Observability Stack
Monitoring layer for agent runs, workflow health, cost, errors, and review queues.
Workflow↳ linked
Implement AI Cost Monitoring System
This workflow guides the establishment of a robust system to track, visualize, and alert on AI-related expenditures, particularly LLM token usage.
Dictionary↳ linked
Guardrails
Runtime checks that constrain LLM inputs and outputs to keep behavior safe and on-spec.
Dictionary↳ linked
AI Evals
Reproducible test suites that measure LLM output quality across model, prompt and code changes.
Dictionary↳ linked
Prompt Versioning
Treating prompts as code: tracked, diffed, rollback-able.
Tool Stack↳ linked
Agent Economics Observability Stack
This stack provides tools to monitor, analyze, and optimize the economic performance of AI agents, focusing on token costs, performance, and ROI.