Five-minute demo  ·  The checks  ·  The judge's exam  ·  Newsletter

No API keys, ever, by default. No platform. One markdown report.

A performance review for your AI agent: the checks that matter before it touches production. One CLI, one YAML test file, one markdown report.

Point it at your RAG bot's HTTP endpoint with a hand-written YAML test file, and it writes report.md: a one-page verdict a stakeholder can read without installing anything, with the failing answers quoted verbatim and a line per failure saying whether retrieval or generation broke. The report commits to git and diffs in pull requests. The judge is a local Ollama model, and the tool grades its own judge before it grades you.

agent-report-card run --tests board_questions.yaml --endpoint http://localhost:8000
→ accuracy 84% (16/19) · hallucination 6% (1/16) · 3 failures · NOT READY → report.md

Those numbers are real output from the bundled demo (a fixture bot with deliberately planted flaws; see below). CI re-runs the deterministic numbers (the accuracy and the failure count) on every push; the judged 6% is pinned to the committed judged report in reports/, which CI checks this README against.

🧭 Why this exists

Every eval tool hands you floats, a DataFrame, a web grid, or a SaaS share link. Nothing hands a non-engineer a one-page markdown verdict with the failures quoted, which is the document teams hand-write before every "is it safe to ship" meeting. This tool treats that document as the product. And with hosted eval platforms being acquired and shut down, your quality bar should not be someone else's product: this is MIT, local, and its output is a text file that cannot be deprecated.

⚡ Five minutes, no model, no keys

pip install agent-report-card
agent-report-card demo --judge none

Python 3.10 or newer (stock macOS python3 may be 3.9; use a python3.11, pyenv, or uv interpreter in that case). To work on the tool itself, clone it and pip install -e . instead.

That boots a bundled fixture bot (a fake support bot for the fictional Northstar Telecom, with nine flaws planted on purpose), runs the bundled 21-question suite against it, and writes report.md. Fully offline, and every report prints its own wall clock in the header (the committed deterministic run measured under a second, plus interpreter startup). With Ollama running, drop the --judge none and the judge checks light up too; the committed judged run's wall clock is in its own header, because judged runs on a 27B local model are minutes, not seconds, and pretending otherwise would break house rules.

To try the promised command exactly as written above:

agent-report-card demo --keep-serving --port 8000
# in another terminal, from the same directory:
agent-report-card run --tests board_questions.yaml --endpoint http://localhost:8000 --judge none

(--port 8000 errors clearly if something else owns the port; without the flag, demo falls back to a free port and tells you. Drop --judge none when Ollama is up; without Ollama the run exits 2 with a message naming the fallback, because run never silently downgrades.)

See a report before installing anything — one committed example per verdict, all real runs against the bundled bot:

Each log is the exact command and per-case console stream that produced its report, ending with the exit code (0 for both PASS levels, 1 for NOT READY: what CI keys on). scripts/make_gallery.py regenerates all eight files.

🔌 What your endpoint must return

The tool speaks HTTP: one POST per question, JSON in and out. The default contract is:

POST {endpoint}/ask          {"question": "..."}
  -> {"answer": "...",
      "contexts": [{"text": "...", "source": "doc.md"}, ...]}   # optional

contexts unlocks the grounding and retrieval-localization checks; source fields unlock the citation checks; without them those checks render as n/a with the reason, never as passes. The mappings above are the code's actual defaults (set contexts: null in the YAML to disable one), and different shapes map via dot-paths (answer: choices.0.message.content covers any OpenAI-compatible server), so the system under test can be written in any language. agent-report-card init writes a commented starter YAML showing every supported field.

Test files are strictly validated: unknown keys, duplicate ids, and contradictory fields fail fast with the line number, the case id, and a one-line fix, before a single request is sent. And because JSON is a subset of YAML, --tests suite.json works through the same loader with the same validation.

📋 The checks that matter

14 deterministic checks (pure Python) and 5 judge checks (local LLM, binary verdicts only), fixed by design: no plugin API, no 50-metric buffet. The full catalog with what each check catches is in CHECKS.md, generated from the same table the code runs. Accuracy is always computed by both routes, deterministic and judge, and shown side by side; when they disagree, the case lands in a "needs human review" section instead of being averaged away. The deterministic route wins the verdict, and the judge is never the sole authority on a number.

The verdict's hard gates come only from the thresholds in your YAML, so the exit code gates CI honestly: 0 pass (with or without warnings), 1 gate failed, 2 tool error. Warnings (route disagreements, judge errors, an unevaluable judge-fed gate) soften PASS to PASS WITH WARNINGS without touching the exit code, and the banner's failure count is defined as answerable cases whose correctness verdict failed; the report spells that out next to the full quoted-failure list.

⚖️ The judge takes the exam it grades

Every report names its judge, its temperature, and its prompt hash, and embeds the judge's score on a 30-pair hand-labeled exam (agent-report-card judge-check), including five answers that are wrong by less than 1%, the kind of error judges miss most. On this machine, qwen3.6:27b scored 30/30 (zero false passes, zero false fails, 5/5 on the subtle numerics); the committed result is in reports/. Thirty items supports "measured error rate on this labeled set", not "calibrated", and below 80% agreement the verdict line itself tells you to trust the deterministic column.

One honest war story from building this: the first exam run used the judge's default thinking mode, and 12 of 30 calls blew the per-call timeout (reports/calibration_first_run_thinking_enabled.log, the captured output: agreement 18/30, 4824s). The tool recorded every timeout as judge_error rather than guessing, exactly as designed, and the fix (judge calls now ask Ollama to skip thinking, with an automatic fallback for models that reject the field) took the same exam to 233.6s at 30/30, per the committed calibration JSON. The judge's own report card caught the judge's own failure before it graded anything real.

🌏 Languages

Everything is UTF-8. The deterministic checks are substring- and number-based with NFC normalization, which makes them well-defined for unsegmented Thai text (tested with Thai fixtures; note a substring can match across Thai word boundaries). Refusal detection extends to any language via the patterns block. The judge handles whatever languages your local model handles; the calibration exam is English-only in v0.1 and every report says so.

🚫 What this deliberately is not

No dashboard, no web UI, no cloud sync, no comparison matrices, no synthetic test generation, no plugin API, no security scanning. Those exist elsewhere and most of them are the reason this tool exists. CI on the free GitHub runner covers everything except live-judge calls (no Ollama there); judged artifacts are produced locally and committed, and scripts/verify_readme_claims.py fails CI when the load-bearing numbers this README quotes (the banner's accuracy and failure count, the judged hallucination fraction, the calibration score and subtle-numeric recall) drift from a fresh run or from the committed artifact behind them.

🗺️ Roadmap

• v0.1.1 (this, on PyPI): RAG QA mode, the report, the judge's own report card, documented --help, and trusted publishing.
• Next, if users ask: static single-file HTML export of the same report, OpenAI-compatible judge URLs, multi-part completeness check.
• v0.2: agent-trace mode, per-step scoring of multi-step agent runs (see agent-failure-lab).
• v0.3: CI mode, baseline diffing, fail-the-build on regression.

Written by Satsawat Natakarnkitkul, author of Why Your AI Agent Will Fail. Companion article: "Why Your AI Agent Needs a Performance Review (Literally)" at satsawat.ai · Newsletter: AI in Practice

License: MIT