let Jev score OpenTelemetry logs before a bigger LLM sees them

An independent developer released Jev Logs, an MIT-licensed open-source layer that scores and routes OpenTelemetry logs with TypeSafe's Jev model before they reach a more expensive reasoning LLM. The tool assigns each record a 0–100 diagnostic-value score, priority, and actionable probability, then routes it to either deeper analysis or retention, with an annotation mode that preserves every log and attaches jev.* attributes. It ships as an npm package with a TypeScript API, an OpenTelemetry exporter wrapper, and a local OTLP receiver, requiring Node.js 22+.

Health checks. Cache hits. A payment failure hiding in the middle. If every OpenTelemetry log goes into a reasoning model, you pay for noise before the investigation starts. Jev Logs is a small open-source layer that puts TypeSafe’s Jev https://typesafe.ai/ in front of those logs. Jev makes the first decision: how useful is this record, how urgent is it, and does it deserve a more expensive model? I wrote it. MIT licensed. Independent not a TypeSafe, Vercel, or OpenTelemetry product. A little intelligence between your logs and your LLM bill. Score, prioritize, and route OpenTelemetry logs with Jev. Keep the signal. Keep your stack Website https://jevlogs.com · Guide https://jevlogs.com/guide/ · llms.txt https://jevlogs.com/llms.txt · npm https://www.npmjs.com/package/jevlogs · Feedback Health checks. Cache hits. A payment failure hiding in the middle. Sending every event to a reasoning model adds cost before the investigation even starts. Jev Logs makes the first decision: how useful is this log, how urgent is it, and does it deserve deeper analysis? It uses TypeSafe’s Jev https://typesafe.ai/ through the Vercel AI SDK, with a small TypeScript API and an OpenTelemetry exporter wrapper. | A small layer | What you get | |---|---| | Score the signal | A 0–100 diagnostic-value score, priority, and actionable probability. | | Keep your pipeline | Wrap your existing exporter; preserve resource, scope, timestamps, and trace context. | | Start with visibility | Annotation mode keeps every record and attaches jev. attributes. | | Spend | | Jev is built for structured choices, not paragraphs. For each log, Jev Logs asks it for: critical , high , normal , low analyze or retain Your archive still gets every record. The analysis branch only needs the ones Jev or a rule, or a conservative fallback says are worth it. A log may skip deeper analysis only when all three are true: priority is low , value is 25 or below, and actionable probability is under 0.1 . Errors, jev.protected records, timeouts, and provider failures stay eligible. Nothing in the SDK deletes your logs. Offline demo. No key. No network. npx jevlogs 0 / 100 low RETAIN GET /health returned 200 in 2ms 25 / 100 low RETAIN Cache hit for product:482 100 / 100 critical ANALYZE Payment capture failed after three retries 75 / 100 high ANALYZE Database connection pool at 94% capacity That walkthrough uses fixed answers so you can see the shape. It does not call Jev. Real Jev, still on your machine: export AI GATEWAY API KEY=your-vercel-ai-gateway-key npx jevlogs --live --sample npx jevlogs --live --file ./app.log --limit 20 --live alone starts a local OTLP HTTP/JSON receiver on http://127.0.0.1:4318/v1/logs . Point your app at it; Jev Logs prints one JSON decision per record and can forward annotated batches to the collector you already run. Node.js 22+. npm install jevlogs js import { createJevLogs } from "jevlogs"; const jev = createJevLogs ; const decision = await jev.triage { body: "Database connection pool at 94% capacity", severityText: "WARN", } ; console.log decision ; // value · priority · route · actionableProbability · reason Skip health checks without spending a Jev call: js const jev = createJevLogs { rules: { name: "health", match: "^GET /health", route: "retain" } , } ; Wrap the exporter you have. Annotation mode keeps every log and attaches jev. attributes. js import { LoggerProvider, BatchLogRecordProcessor, ConsoleLogRecordExporter, } from "@opentelemetry/sdk-logs"; import { JevLogExporter } from "jevlogs"; const provider = new LoggerProvider { processors: new BatchLogRecordProcessor { exporter: new JevLogExporter { exporter: new ConsoleLogRecordExporter , mode: "annotate", } , maxExportBatchSize: 16, } , , } ; Keep that archive processor. Add a second exporter with mode: "analysis-only" when you actually want to drop low-value records from the LLM path. Annotation alone does not cut the bill — the downstream pipeline has to honor route . A second giant completion per log is the thing this is trying to avoid. Jev’s published rate is cheap structured evaluation TypeSafe lists $0.042/M input , free output . You pay Jev for a small decision, then pay GPT-class analysis only for the selected slice. The README has an illustrative table: 1M logs/month, if 10% still need analysis, a $1,000 GPT-4.1-style bill models down to about $129 including Jev triage. That is not a measured production result. Measure incident recall on your logs before you filter. No hosted dashboard. No log storage. No Collector plugin. No root-cause write-up. Preview software: jevlogs on npm, TypeScript first, CI on the repo. Jev itself is a hosted model via Vercel AI Gateway; this repo is the integration . jevlogs If you try it, I want feedback on the Jev scoring/routing shape and whether wrapping an exporter is the right split vs the local receiver. Issues with sanitized examples are welcome.