Verify before you commit
Use this page to sanity-check Openlit quickly. We surface the evidence tier, freshness, and failure posture here, then put the official links where you can actually act on them, especially on mobile.
Evidence
Assessed
Docs reviewed · Mar 28, 2026
Freshness
Updated 2026-03-28T14:13:31.109+00:00
Mar 28, 2026
Failures
Clear
No active failures listed
| Dimension | Score | Bar |
|---|---|---|
| Execution Score Measures reliability, idempotency, error ergonomics, latency distribution, and schema stability. | 8.5 | |
| Access Readiness Score Measures how easily an agent can onboard, authenticate, and start using this service autonomously. | 8.4 | |
| Aggregate AN Score Composite score: 70% execution + 30% access readiness. | 8.5 | |
No active failure modes reported.
Published review summaries with trust provenance attached to each card.
Docs-backed Built from public docs and product materials.
Test-backed Backed by guided testing or evaluator-run checks.
Runtime-verified Verified from authenticated runtime evidence.
Open-source LLM observability platform auto-instrumenting 20+ providers (OpenAI, Anthropic, Cohere, Groq, LangChain, LlamaIndex, etc.) via OpenTelemetry. Per-request cost and token tracking. GPU utilization monitoring. Prompt hub for version management. Self-hosted via Docker or OpenLIT Cloud. Confidence is docs-derived.
Python SDK: openlit.init() one-line instrumentation; OTLP exporter endpoint configurable for any OpenTelemetry backend (Jaeger, Grafana Tempo, OpenLIT native); REST API for trace ingestion (OTLP HTTP); prompt hub REST API for prompt CRUD and versioning; evaluation API for LLM-as-judge scoring; dashboard accessible via web UI.
No credentials required for local OTLP export; OpenLIT Cloud uses API key for cloud ingestion; self-hosted deployment controls its own access; prompt hub supports access control per team; environment token for automatic deployment grouping; no PII redaction by default — configurable via disable_metrics flag.
Auto-instrumentation wraps provider SDK calls and emits spans; cost tracking uses per-model pricing lookup table (updated with provider changes); GPU metric collection requires nvidia-smi access; trace export is async and non-blocking to avoid adding latency to LLM calls; OTLP HTTP export with configurable batch size and flush interval.
Documentation covers quickstart (pip install + one-line init), provider auto-instrumentation list, OTLP exporter configuration, GPU monitoring setup, prompt hub guide, and evaluation API. Docker Compose for self-hosted deployment. Active GitHub project with fast iteration. Confidence is docs-derived.
Trust shortcuts
This score is documentation-derived. Treat it as a docs-based evaluation of API design, auth, error handling, and documentation quality.
Read how the score works, how disputes are handled, and how Rhumb scored itself before launch.
Overall tier
8.5 / 10.0
No alternatives captured yet.