Overview

Overview

EvalCI is a GitHub Action that runs your @eval_case suites on every pull request and blocks merge if quality drops below threshold.

No infrastructure. No backend. 2-minute setup.

→ Full docs: https://synapsekit.github.io/synapsekit-docs/docs/evalci/overview

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Why EvalCI

LLM applications degrade silently. A prompt change, a model update, a retrieval tweak — any of these can drop quality by 10–20% without a single test failure. EvalCI gives you a quality gate that catches this before it ships.

Without EvalCI	With EvalCI
Quality regressions ship to production	Blocked at PR review
Manual eval runs, inconsistent	Automatic on every PR
No visibility into cost/latency trends	Score, cost, latency per case on every PR
Requires external tooling	Works in your existing GitHub Actions

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How it works

1. pip install synapsekit[{extras}] on the runner
2. synapsekit test {path} --format json --threshold {threshold} — discovers all @eval_case functions, runs them, outputs JSON
3. Parse JSON results
4. Post results table as a PR comment
5. Set Action outputs: passed, failed, total, mean-score
6. Exit 0 (all pass) or 1 (any failure)

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PR comment

## EvalCI Results

|   | Test                   | Score | Cost    | Latency |
|---|------------------------|-------|---------|---------|
| ✅ | eval_rag_relevancy     | 0.850 | $0.0050 | 1200ms  |
| ❌ | eval_rag_faithfulness  | 0.650 | $0.0120 | 2500ms  |

**1/2 passed** · Threshold: `0.80`

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File discovery

EvalCI discovers files matching eval_*.py or *_eval.py recursively under path.

tests/
└── evals/
    ├── eval_rag.py        ✅ discovered
    ├── eval_agents.py     ✅ discovered
    ├── rag_eval.py        ✅ discovered
    └── test_rag.py        ❌ not discovered

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Next steps

• Quickstart — set up in 5 minutes
• Writing-Eval-Cases — write @eval_case functions
• Action-Reference — all inputs and outputs
• Examples — real-world workflows