Analysis updated 2026-05-18
Grade a voice or chat AI agent's responses against strict, rule-based readback requirements.
Run randomized aviation-style test scenarios so an agent cannot memorize the answers.
Replay a graded session as a visual animation to see where an agent succeeded or failed.
| tanisha-byte/roger-that | 0xallam/my-recipe | 0xhassaan/nn-from-scratch | |
|---|---|---|---|
| Stars | 0 | — | 0 |
| Language | Python | Python | Python |
| Last pushed | — | 2022-11-22 | — |
| Maintenance | — | Dormant | — |
| Setup difficulty | moderate | moderate | moderate |
| Complexity | 4/5 | 2/5 | 4/5 |
| Audience | developer | general | developer |
Figures from each repo's GitHub metadata at analysis time.
Live phone testing requires a Bolna API key, grading a real language model requires an OpenAI-compatible API key.
Roger That is a testing tool for AI voice and chat agents that uses real air traffic control radio procedures as its exam. In real aviation, every instruction a controller gives has a required, exact readback, so a pilot cannot just say something that sounds close, it has to match specific rules. This project takes that same strict structure and uses it to grade AI agents without needing another AI to act as the judge. Instead, a parser checks the agent's response against the rules directly, which makes scoring repeatable and fair. The project includes six scenario types, such as a descent instruction, a rapid sequence of commands, a callsign mix-up trap, a case where the agent mishears something, and an emergency situation, and each one is randomized with a seed so an agent cannot just memorize answers. A scorer module reads the transcript, converts spoken numbers and the phonetic alphabet into a standard form, checks whether the agent caught any traps or made any hearing errors, and produces a weighted score. The scorer agrees with human graders on a hand-labeled test set at least 99% of the time. You can run this two ways: place a real outbound phone call to a live Bolna voice agent and grade the actual conversation, or run a text-based campaign against any OpenAI-compatible language model. Results are stored in a database and viewable through a dashboard with two views, one that replays a session as a pixel-art radar animation showing successes and mistakes, and one that shows a leaderboard, learning curves over time, and full transcripts. Setup involves creating a Python virtual environment, installing dependencies, and running the test suite before starting the dashboard with uvicorn. Live phone testing needs a Bolna API key, and testing language models directly needs an OpenAI-compatible API key. The project also includes an experimental learning-loop feature for helping agents improve between sessions, though the README notes this part has not been tested against a live system yet.
A grading tool that tests AI voice and chat agents using real air traffic control procedures, scoring them with a parser instead of another AI judge.
Mainly Python. The stack also includes Python, FastAPI, SQLite.
Setup difficulty is rated moderate, with roughly 1h+ to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.