Murph

Murph is the small request classifier built into openTARS. For each request it decides the kind of task, so the app can pick the right model and tools before any language model runs.

It is not a language model. It is a softmax logistic regression over word pieces (2 to 5 letters), words and word pairs, with TF-IDF weights. It runs in plain Python on the CPU in about 0.2 ms per request, uses no GPU, and the whole model is one JSON file of about 4 MB.

Classes

Class	Meaning
visao	needs to look at the screen ("what is on this screen?")
codigo	writing or fixing code
tecnico	technical or system questions
acao	do something on the computer (open a program, create a folder)
busca	search the web
simples	greetings and simple chat
geral	everything else

Files

• murph.json: the trained model (SHA-256 f1752425196a67c4baa28de5f6564a8dbccb6d41d12f44a0d0459a888c078f45).
• tars_murph.py: the loader and classifier. It uses only the Python standard library.

Use

import tars_murph

murph = tars_murph.Murph.carregar("murph.json")
tarefa, confianca = murph.classificar("abre a calculadora")
print(tarefa, round(confianca, 2))   # acao 0.98

The model also stores a confidence threshold and a coverage minimum, so the app can fall back to another method when the request is unlike anything it saw in training.

Training and results

• Trained on 3,034 request phrases in five languages: examples from the openTARS project plus phrases written by a larger model. Phrases similar to the evaluation phrases were removed from training.
• Cross-validation accuracy stored in the file: 88.9%.
• openTARS evaluation sets, measured on 18 October 2026: 170 of 175 across languages and 85 of 86 on the check set. The evaluation phrases were seen during development, so treat these as a sanity check, not as a benchmark.

Limits

• Five languages only, and short requests work best.
• It picks one class per request. A request that mixes two tasks gets the more likely one.
• The class names are Portuguese because they come from the app code.

License: MIT.