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.
Quick Install
{
"mcpServers": {
"hidai25-eval-view": {
"command": "npx",
"args": [
"-y",
"hidai25-eval-view"
]
}
}
}Using an AI coding agent (Claude Code, Cursor, etc.)? Copy a ready-made prompt that tells it to fetch the setup instructions and install this server for you.
Documentation Overview
Snapshot testing for AI agents.
Record what your agent does today. Get told when it silently changes.
Your agent returns 200 and looks fine. But a model update, a provider change, or a one-line prompt edit just made it skip a clarification, call the wrong tool, or quietly drop output quality. Your tests still pass. Your users notice before you do.
EvalView snapshots your agent's behavior — the tools it calls, in what order, with what output — and tells you the moment that behavior changes. Like Jest snapshots, but for tool-calling, multi-turn agents.
↑ 30-second live demo — no API key needed
Quick Start
pip install evalview
evalview snapshot # Record your agent's current behavior as the baseline
evalview check # After any change, diff against the baseline
That's the whole loop. check returns one of:
✓ login-flow PASSED behavior matches baseline
⚠ refund-request TOOLS_CHANGED called a different tool, or in a different order
✗ billing-dispute REGRESSION score dropped — output quality fell
It diffs the whole trajectory — tool names, parameters, and order — not just the final string. The deterministic tool + sequence diff runs offline, with no API key. Add an LLM judge only when you want output-quality scoring.
No agent yet? See it work in 30 seconds:
evalview demo
Why snapshot testing (and not assertions)?
Most eval tools ask you to write down what "good" looks like — assertions, metrics, rubrics. That's a lot of upfront work, and you can only catch the failures you thought to assert.
EvalView inverts it: it records what your agent actually does now, and flags any drift from that. You catch regressions you never anticipated, with zero assertions written. When the new behavior is correct, evalview snapshot accepts it as the new baseline — same as updating a snapshot in Jest.
| EvalView | Assertion-based eval tools | |
|---|---|---|
| Setup | Record current behavior | Write assertions/metrics first |
| Catches | Any drift from baseline | Only what you asserted |
| Non-determinism | Multi-variant baselines (up to 5 valid paths) | You handle it |
| Unit of comparison | Full tool-call trajectory | Usually final output |
This makes EvalView a merge-time regression gate, which is a different job from observability (Langfuse, LangSmith) or metric scoring (promptfoo, DeepEval, Braintrust). Many teams run one of those for visibility and EvalView as the gate. Honest comparisons →
EvalView tests itself in public, every day
The badge at the top is live. Every day at 09:00 UTC, a GitHub Action runs EvalView against EvalView — including a regression check where the tool snapshots a live agent and diffs it with the same snapshot / check loop this README asks you to trust. It also runs the full test suite, type checks, evalview demo, the end-to-end flows, an evalview monitor smoke test, and chat-mode self-tests.
When something breaks, the run opens a single rolling 🐕 dogfood issue and keeps updating it until the tool is green again — so failures are public, not quietly patched.
Live dogfood runs → · How it works →
CI: block regressions in every PR
# .github/workflows/evalview.yml
name: EvalView
on: [pull_request]
jobs:
agent-check:
runs-on: ubuntu-latest
permissions: { pull-requests: write }
steps:
- uses: actions/checkout@v4
- uses: hidai25/eval-view@v0.8.1
with:
openai-api-key: ${{ secrets.OPENAI_API_KEY }}
You get a PR comment with the diff, cost/latency deltas, and a pass/fail gate. CI/CD guide →
Works with your stack
LangGraph · CrewAI · OpenAI · Claude · Mistral · Ollama · MCP · any HTTP API.
evalview check --agent http://localhost:8000/invoke
Use it as a library
from evalview import gate
result = gate(test_dir="tests/")
result.passed # bool
result.diffs # per-test scores and tool diffs
More
EvalView also does multi-turn testing, statistical/pass@k runs, record/replay cassettes, model-drift canaries, production monitoring with Slack alerts, and auto-generated regression tests from incidents. These are power-user features — start with snapshot and check, reach for the rest when you need them.
→ Full feature reference · Getting Started · FAQ
Contributing
This is a young project built mostly by one developer. Issues, PRs, and "I tried it and X was confusing" feedback are all genuinely valuable.
License: Apache 2.0