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Use this page to sanity-check Weave Wandb 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. | 8.6 | |
| 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. | 8.6 | |
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.
W&B Weave provides LLM tracing, evaluation, and dataset management on top of Weights & Biases infrastructure. Auto-trace LLM calls from OpenAI, Anthropic, and other providers using the @weave.op() decorator. Build evaluation pipelines with composable scorers. Manage datasets with versioning. Confidence is docs-derived.
Python SDK: weave.init() for project initialization, @weave.op() decorator for automatic call tracing, weave.Evaluation class for evaluation pipelines, weave.Dataset for versioned dataset management; REST API for trace retrieval and dataset access; W&B dashboard for trace visualization, evaluation comparison, and prompt iteration.
W&B API key for authentication (stored in ~/.netrc or WANDB_API_KEY env var); project-level and entity-level access control inheriting from W&B platform; no additional credential management beyond W&B account; self-hosted W&B (W&B Server) for air-gapped deployments.
@weave.op() decorator wraps function calls and captures inputs, outputs, and exceptions asynchronously without blocking; evaluation pipelines are reproducible with pinned dataset and scorer versions; trace export via REST API for downstream analysis; rate limits per W&B plan; offline mode for local tracing without network.
Documentation covers quickstart (weave.init + @weave.op()), evaluation pipeline guide with scorer examples, dataset management guide, and REST API reference. Tightly integrated with W&B Runs for correlating LLM traces with model training experiments. Active community. 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
8.6 / 10.0
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