This release adds 3 notable features for engineering teams evaluating rollout.
Published 2mo
Developer Productivity
✓ No known CVEs patched
✓ No known CVEs patched in this version
Topics
agent-benchmark
agent-evaluation
agentic-ai
ai-agents
anthropic
autogen
+12 more
cli
crewai
evaluation
langchain-agent
langgraph
llm
mcp
openai-assistants
pytest
python
regression-testing
testing
Summary
AI summaryProduction‑grade cold‑start test generation now supports GPT‑5 models.
Full changelog
What's New in v0.5.2
Cold-Start Test Generation (evalview generate)
- Production-grade test generation from live agent probing — no manual YAML writing needed
- Interactive probe budget and model selection
- Multi-turn conversation tests generated as single cohesive test cases
- Domain-aware draft generation with coherence filtering
--synth-modelflag to override the synthesis model- Real-time elapsed timer during probe runs
- Delta reporting: shows changes since last generation
Improved Reports
- Model and token usage displayed in HTML reports
- Judge cost tracking surfaced in check reports
- Per-query model shown in trace cost breakdown
- Cleaner baseline metadata and timeline in check reports
- Turn-level details with clickable chevrons in multi-turn traces
Better Onboarding (evalview init)
- Remembers active test suite for plain
snapshotandcheck - Auto-approves generated drafts with scoped snapshot guidance
- Detects local agents on
/executeand/healthendpoints - Refreshes stale config when a live agent is detected
Check Command Improvements
- Shows last baseline snapshot timestamp
- Auto-generates local HTML report on failures
- Streamlined regression demo flow
Model Support
- GPT-5 family model support (gpt-5.4, gpt-5.4-mini)
- Interactive model selection from available providers
Multi-Turn & Monitoring
- Multi-turn golden baselines with per-turn tool sequences
- Cost/latency spike alerts in monitor mode
- Batch edge-case expansion for test coverage
Bug Fixes
- Fix multi-turn filter — different output is meaningful regardless of tools
- Fix probe progress for skipped follow-ups
- Predictable timing — 1 discovery, multi-turn counts against budget
- Always show agent model in run output
- Eliminate duplicate multi-turn tests
- Silence Ollama JSON fallback warnings in normal runs
Docs
- Trimmed README from 1420 to 274 lines — details moved to dedicated docs
- Comparison docs and SEO content added
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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.
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Beta — feedback welcome: [email protected]