hidai25/eval-view — Regression testing for AI agents.

Description and use

**Brief Summary (2‑3 sentences)**
hidai25/eval‑view is an open‑source regression‑testing framework for AI agents that snapshots their behavior, diffs tool calls, and flags regressions in CI pipelines. It plugs into popular orchestration stacks such as LangGraph, CrewAI, OpenAI, and Anthropic, turning ad‑hoc prompts and tool invocations into repeatable, testable workflows. With over 100 stars, recent commits, and active community interest, it’s ready for pilot projects in production environments.

**Value**

• **Reliability:** By automatically capturing agent outputs and tool‑call traces, teams can detect subtle regressions before they reach users, reducing costly debugging cycles.
• **Standardisation:** Provides a unified way to define, run, and compare multi‑agent pipelines, making it easier to onboard new developers and maintain consistent memory/knowledge handling across projects.
• **Ecosystem Compatibility:** Native integrations with LangGraph, CrewAI, OpenAI, and Anthropic mean you can adopt it without rewriting existing agents or tooling.

**Practical Adoption Path**

1. **Prototype:** Add the eval‑view Python package to a sandbox repo and instrument a single LangGraph or CrewAI workflow with the provided decorators/CLI.
2. **CI Integration:** Configure a GitHub Actions (or equivalent) step that runs eval-view test on each PR, storing snapshots as artifacts and failing the build on diff mismatches.
3. **Scale:** Extend instrumentation to all critical agent pipelines, define baseline snapshots for each major version, and incorporate custom diff rules for domain‑specific tolerances (e.g., numeric drift, ordering).
4. **Governance:** Use the generated reports to create a regression‑review checklist for the data‑science or ML ops team, ensuring that any intentional behavior change is accompanied by an updated snapshot.

**Production Readiness**

• **Activity:** Last commit on 2026‑05‑10, 102 stars, 20 forks, and active issue discussions indicate a healthy, maintained project.
• **Integration Simplicity:** Exposes a clear API/CLI and emits language‑agnostic metadata, making it straightforward to embed in existing Python‑based agent stacks.
• **Risk Profile:** No immediate licensing or security red flags, though a final audit of the license and maintainer commitment is advisable.

Overall, eval‑view meets the criteria for a serious pilot: it is mature enough for early‑stage production use, integrates cleanly with leading agent frameworks, and provides tangible safeguards against regression‑related failures.

Problem

hidai25/eval-view helps turn isolated prompts and tools into repeatable agent workflows.

Outcome

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.

Workflow

• coordinate multi-agent workflows
• add tool-use pipelines
• standardize agent memory

Evidence

Regression testing for AI agents. Snapshot behavior,diff tool calls,catch regressions in CI. Works with LangGraph, CrewAI, OpenAI, Anthropic.

Source