Summary：Exciting New Tool 'eval-doctor' Now Available on PyPI for Developers **Introduction** Developers b

referrerpolicy="no-referrer"
style="max-width:100%;height:auto;display:block;margin:0 auto;">

Exciting New Tool 'eval-doctor' Now Available on PyPI for Developers

**Introduction**
Developers building retrieval‑augmented generation (RAG) pipelines and autonomous agent systems often face a blind spot: they ship code without knowing which parts of their evaluation suite are actually exercised. A new open‑source utility, **eval-doctor**, has just landed on the Python Package Index (PyPI) to fill that gap. By auditing test coverage specifically for evaluation logic, the tool helps teams spot untested branches before they reach production, reducing the risk of silent failures in AI‑driven applications.

**Key Developments**
Eval-doctor operates as a lightweight command‑line wrapper that instruments evaluation functions during test runs. When a developer executes their usual pytest suite, the tool records which evaluation paths—such as relevance scoring, hallucination checks, or agent decision logs—are invoked. After the run, it generates a clear HTML report highlighting uncovered segments, complete with line‑level annotations and suggestions for additional test cases.

The package integrates seamlessly with existing CI/CD pipelines. A single line added to a `tox.ini` or GitHub Actions workflow triggers the audit, and the resulting artifact can be published as a build summary. Because eval-doctor is pure Python and has no heavy dependencies, installation is as simple as `pip install eval-doctor`. Early adopters have reported a 30 % increase in detected evaluation gaps within the first week of use, translating into fewer post‑release bugs related to inaccurate model outputs or misguided agent behavior.

**Industry Analysis**
The rise of RAG and agent‑based architectures has shifted testing priorities from unit‑level correctness to end‑to‑end validation of model interactions. Traditional coverage tools, such as coverage.py, measure code execution but ignore the semantic nuances of evaluation metrics—precisely the area where AI systems are most fragile. Industry analysts note that as enterprises scale generative AI products, the demand for specialized testing aids will grow at a compound annual rate exceeding 20 % through 2028. Eval-doctor addresses a niche that larger testing frameworks have overlooked, positioning itself as a complementary tool rather than a replacement for existing solutions