Grounded vLLM operational knowledge for AI SRE

Schema.ai is a grounding layer for vLLM source code and official docs that lets AI tools reason more reliably about how vLLM works in production.

Try querying the Schema.ai API for vLLM knowledge — the same search an AI agent runs.

Topics

Topics are a high-level map of what Schema.ai knows about vLLM. Each groups related claims, surfaces, and known gaps into an operational area, so coverage — and its limits — is visible before you drill in.

Topic	Summary	Claims	Surfaces	Comprehensiveness
Prefix cache effectiveness metrics	Coverage for the vLLM v0.23.0 first-party prefix-cache counters and how to compute and interpret the prefix cache hit rate.	5	0	seed slice
Scheduler and KV pressure metrics	Coverage for a small vLLM v0.23.0 metric slice that exposes scheduler queue state, KV cache usage, and preemption signals.	13	1	seed slice
vLLM v0.23.0 metric identity	Identity facts for the complete v0.23.0 Prometheus metric inventory — which metrics exist, their instrument kind, their label names, and their literal histogra…	94	1	broad

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Use Cases

Use cases are concrete operator question shapes and an honest record of whether the current corpus can answer them — including the ones that fail today.

Use case	What it targets	Questions
Prometheus exposition names differ from declared metric names	prometheus_client renames samples at exposition: counters gain _total and histograms emit _bucket/_count/_sum series, so the names operators see in s…	3
Prefix cache effectiveness is the derived hit rate, not the exposed counters	The vLLM design docs record prefix-cache activity as two token-denominated counters but state the metric of interest is the derived hit rate. The rat…	3

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Explore the catalog

Claims105 Sources7 Metrics45 Settings20 Surfaces2