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LLM Observability

Tracing, logging, evaluation, and cost monitoring across every LLM call in a production AI app, so you can debug, measure quality, and control spend.

What Is LLM Observability?

Tools include Langfuse, LangSmith, Arize Phoenix, Helicone, and Honeycomb AI mode. They capture each prompt, response, latency, token usage, and tool call as a trace. Production AI apps need this because LLMs fail in subtle ways (drift, hallucination, bad tool calls) that traditional logging misses.

How Groovy Web Uses This

We instrument every production agent we deliver with Langfuse or LangSmith out of the box. Clients see token spend, eval scores, and failure traces from day one.

Related Terms

AI Agent Inference LLM Evals

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