OpenTelemetry sink

import { meter, multiSink } from "@amit641/llmmeter";
import { sqliteSink } from "@amit641/llmmeter/sqlite";
import { otelSink } from "llmmeter-otel";
import { trace } from "@opentelemetry/api";

const ai = meter(openai, {
  sink: multiSink(
    sqliteSink({ filePath: "./.llmmeter/llmmeter.db" }),
    otelSink({ tracer: trace.getTracer("my-app") }),
  ),
});

Emits spans with Gen-AI semantic conventions so your existing Jaeger / Tempo / Honeycomb / Datadog backend gets them automatically.

Span attributes

Attribute	Source
`gen_ai.system`	`record.provider`
`gen_ai.operation.name`	`record.operation`
`gen_ai.request.model`	`record.model`
`gen_ai.usage.input_tokens`	`record.tokens.input`
`gen_ai.usage.output_tokens`	`record.tokens.output`
`gen_ai.usage.cached_input_tokens`	`record.tokens.cachedInput` (if present)
`gen_ai.conversation.id`	`record.conversationId`
`enduser.id`	`record.userId`
`llmmeter.cost_usd`	`record.costUsd`
`llmmeter.feature`	`record.feature`
`llmmeter.ttft_ms`	`record.ttftMs`

Plus two histograms: gen_ai.client.token.usage and gen_ai.client.operation.duration.

Span attributes​

Span attributes