ai-eval-forge 0.1.0

Zero-dependency eval harness for LLM and agent regression testing. Scores outputs with exact, contains, regex, JSON, citation, and token-F1 checks.

ai-eval-forge

Zero-dependency eval harness for LLM and agent regression testing. Score outputs with exact, contains, regex, token_f1, json_valid, json_field, and citation_coverage checks. Ships a CLI and a small library API. No runtime dependencies — pure stdlib.

Python port of the @mukundakatta/ai-eval-forge npm package. Same check types, same output shape — cases files you wrote for the npm version work here unchanged.

Install

pip install ai-eval-forge

Run the CLI

aef score cases.jsonl
# or
ai-eval-forge score cases.jsonl --format markdown

Exits 0 on all pass, 1 on any failures, 2 on bad input.

Case file format

Each case is a JSON object. The file can be either a JSON array or JSONL (one object per line).

{"id": "greeting", "actual": "hello world", "expected": "hello world"}
{"id": "json-output", "actual": "{\"user\":{\"name\":\"Alice\"}}", "checks":[{"type":"json_field","path":"user.name","value":"Alice"}]}
{"id": "cited", "actual": "See [src1] and [src2].", "sources":[{"id":"src1"},{"id":"src2"}], "checks":[{"type":"citation_coverage","min":1}]}

Check types

Type	What it does
exact	Normalized (lowercase, whitespace-collapsed) string equality.
contains	All listed substrings present in actual. Optional caseSensitive.
regex	Python regex match against actual. flags accepts i, m, s.
token_f1	F1 over lowercase alphanumeric tokens. Default check if none specified.
json_valid	actual parses as valid JSON.
json_field	Parse JSON, drill into path, deep-equal against value.
citation_coverage	Fraction of source IDs from sources that appear inside actual.

Every check accepts required (default true) and min (default 1). The case passes iff every required check has score >= min. The case's overall score is the average of all checks.

Library API

from ai_eval_forge import evaluate_suite, parse_cases, render_markdown
from pathlib import Path

cases = parse_cases(Path("cases.jsonl").read_text())
suite = evaluate_suite(cases)
print(render_markdown(suite))
print(f"Pass rate: {suite.summary.passRate:.0%}")

Output shape (JSON)

{
  "summary": {
    "total": 2,
    "passed": 1,
    "failed": 1,
    "passRate": 0.5,
    "averageScore": 0.82,
    "totalCostUsd": 0.0,
    "averageLatencyMs": 0
  },
  "cases": [
    {
      "id": "greeting",
      "passed": true,
      "score": 1.0,
      "checks": [{"type": "token_f1", "required": true, "passed": true, "score": 1.0, "min": 0.65, "detail": "token_f1=1.0"}],
      "meta": {"input": null, "tags": [], "costUsd": 0, "latencyMs": 0}
    }
  ]
}

Differences from the npm version

• js_expression check type is dropped. The JS version lets you run a JavaScript expression against case context. Python's equivalent (eval) is harder to sandbox, so the Python port omits this check type rather than ship a half-sandbox. If you need custom logic, use regex or json_field — or extend the library via your own run_check wrapper.

Everything else matches the npm package 1:1: same check types, same scoring formulas, same summary fields, same exit codes, same CLI flags.

Development

pip install -e '.[dev]'
pytest

License

MIT.