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GitHub - sauravGit/open-llm-observability: A vendor-neutr...
packydarn · 2026-05-06 · via Hacker News - Newest: "AI"

A vendor-neutral, OpenTelemetry-compatible semantic convention and SDK layer for standardizing LLM observability across any provider, framework, or platform.


The Problem

Every LLM platform emits observability data differently.
Tokens are named differently. Latency is measured differently. Cost is tracked differently — or not at all.
Developers re-instrument for every new backend.

The Solution

One canonical schema. One SDK. Export everywhere.

open-llm-observability defines:

  • A universal set of metric names, span attributes, and resource tags for LLM workloads
  • A lightweight instrumentation SDK (Python + TypeScript) that you add once
  • Pluggable exporters to Prometheus, Grafana, Datadog, GCP, and any OTEL-compatible backend

Canonical Metric Names (excerpt)

Metric Type Description
gen_ai.requests.total Counter Total inference requests
gen_ai.latency Histogram End-to-end latency (ms)
gen_ai.time_to_first_token Histogram Streaming first-token latency
gen_ai.usage.total_tokens Histogram Total tokens consumed
gen_ai.usage.cost Histogram Estimated USD cost
gen_ai.requests.errors.total Counter Failed requests

Full schema: RFC.md


Quick Start (Python)

from open_llm_obs import instrument

instrument(provider="openai", export_to="otlp")

# Your existing OpenAI calls are now fully instrumented

Architecture

Your LLM App
    └── open-llm-observability SDK
            ├── Canonical metric names & span attributes (RFC.md)
            ├── OTLP exporter (traces + metrics + logs)
            └── Pluggable adapters
                    ├── Prometheus
                    ├── Grafana
                    ├── Datadog
                    └── GCP Cloud Monitoring

Semantic Convention (summary)

Resource Attributes

  • service.name, service.version, deployment.environment
  • gen_ai.provider, gen_ai.model, gen_ai.app.name, gen_ai.region

Span Names

  • gen_ai.request, gen_ai.stream, gen_ai.tool_call, gen_ai.retrieval, gen_ai.guardrail, gen_ai.embedding

Derived KPIs

  • Success rate, error rate by model, average cost per request, P95 latency by route, token efficiency, retrieval yield

See RFC.md for the full specification.


Status

  • RFC v0.1 draft
  • Python SDK (in progress)
  • TypeScript SDK
  • Adapters: Prometheus, Grafana, Datadog, GCP
  • Default dashboards

Contributing

This project is in active RFC phase. Open a Discussion to provide feedback on:

  • Metric naming conventions
  • Scope of the mandatory core vs. optional domain extension packs
  • OTEL semantic convention mapping
  • Backend adapter priorities

License

Apache 2.0 — see LICENSE