Verify before you commit
Use this page to sanity-check Litellm 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. | 9.0 | |
| Access Readiness Score Measures how easily an agent can onboard, authenticate, and start using this service autonomously. | 8.9 | |
| Aggregate AN Score Composite score: 70% execution + 30% access readiness. | 9.0 | |
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.
Unified LLM proxy translating 100+ provider APIs (OpenAI, Anthropic, Gemini, Cohere, Groq, etc.) into a single OpenAI-compatible interface. Deploy as a standalone proxy server or use as a Python SDK. Load balancing, automatic fallbacks, virtual API keys per team, usage budget enforcement, and prompt caching. Confidence is docs-derived.
Proxy REST API: OpenAI-compatible /v1/chat/completions, /v1/completions, /v1/embeddings, /v1/models; /health for provider health check; /model/info for deployment metadata; management API for virtual keys and team budgets; Python SDK: litellm.completion() with model='provider/model-name' format; async support; streaming.
Virtual API key management with per-key and per-team budget limits; master key required to generate virtual keys; model access control lists restrict which models a key can call; IP allowlisting per virtual key; audit logs per API call; JWT passthrough auth for end-user attribution; secrets managed via environment variables per provider.
Automatic retry with exponential backoff on provider errors; load balancing across multiple deployments of the same model; automatic fallback to secondary models on rate limit or error; budget enforcement blocks requests when per-key or per-team limit is reached; OpenTelemetry tracing for per-request latency and token usage; callback hooks for custom logging.
Comprehensive documentation covering proxy setup, virtual key management, load balancing, fallback configuration, budget controls, provider-specific configuration, and SDK usage. LiteLLM Proxy quick-start and Docker deployment guides. Active community on Discord. 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
9.0 / 10.0
No alternatives captured yet.