This release adds 3 notable features for engineering teams evaluating rollout.
✓ No known CVEs patched in this version
Topics
+12 more
Summary
AI summaryAdded evalview model-check for closed-model drift detection with new internals and extensive tests.
Full changelog
What's new
evalview model-check — closed-model drift detection
Detect silent drift in closed-weight models (Anthropic in v1; OpenAI/Mistral/Cohere in v1.1) by running a small structural canary suite directly against the provider.
- Two-anchor comparison (reference + previous)
- Dry-run cost estimation
- Per-provider fingerprint strength labeling
- Custom suites via
--suite - Suite-hash enforcement for rotation safety
- Pinned
temperature=0.0/top_p=1.0for stable drift signal
Bundled canary suite
15 structural prompts across four scorer families: tool choice, JSON schema, refusal, exact match. Versioned, hash-pinned, rotated via held-out companion suite.
New internals
DriftKind+DriftConfidenceenums — unified drift taxonomymodel_snapshots— timestamped store with auto-pin first-run reference and pruningmodel_check_scoring— pure-function structural scorers (no LLM judge dependency)model_provider_runner— single-shot completions with per-provider fingerprint captureanthropicadapter registered inadapter_factoryTraceDiffgainsdrift_kindanddrift_confidencefields
Tests
80 net new tests covering snapshot store (16), structural scorers (29), canary suite loader (13), and command integration (22) — all mocked, no real API calls in CI.
Install / upgrade:
pip install evalview==0.6.2
Full changelog: https://github.com/hidai25/eval-view/blob/main/CHANGELOG.md
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About hidai25/eval-view
Regression testing framework for AI agents. Save golden baselines, detect behavioral drift, and block regressions in CI. Works with LangGraph, CrewAI, OpenAI, Claude, and any HTTP API.
Beta — feedback welcome: [email protected]