Verify before you commit
Use this page to sanity-check Ollama 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. | 8.7 | |
| Access Readiness Score Measures how easily an agent can onboard, authenticate, and start using this service autonomously. | 8.5 | |
| Aggregate AN Score Composite score: 70% execution + 30% access readiness. | 8.8 | |
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.
Local LLM runner for macOS, Linux, and Windows. Pull open-weight models (Llama 3, Mistral, Gemma, Phi, Qwen, etc.) from the Ollama library and run them locally via a REST API. OpenAI-compatible endpoint enables drop-in use with existing tooling. Zero cloud dependency for inference. Confidence is docs-derived.
REST API: /api/generate (completion), /api/chat (chat completion), /api/embeddings (embeddings), /api/pull (model download), /api/push, /api/list, /api/show, /api/delete; OpenAI-compatible /v1/chat/completions, /v1/completions, /v1/embeddings; streaming via NDJSON; Modelfile for custom system prompts, parameters, and base model selection.
No auth by default (local binding on 127.0.0.1:11434); OLLAMA_HOST env var for external binding; when exposed externally, access control is the caller's responsibility (reverse proxy with auth); no built-in API key or user-level access control in core; intended for local/internal developer use.
REST API is synchronous for non-streaming and streaming NDJSON for streamed responses; model pull is resumable and tracks progress; /api/show returns model metadata for validation before use; structured JSON error responses; keepalive parameter controls model unload timeout after last request; graceful handling of VRAM/memory exhaustion with clear error messages.
Documentation covers installation (macOS/Linux/Windows/Docker), model library browsing, Modelfile syntax, REST API reference, OpenAI compatibility, and GPU configuration. Large active community. CLI for interactive chat and model management. 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
8.8 / 10.0
No alternatives captured yet.