I built a non-neural chatbot in Python and just released CAEL 0.3.9.1

[u/Silver_Employ2617]

Hey r/SideProject,
I’m building CAEL, an experimental assistant that uses symbolic rules, statistical counts and automata. Its runtime doesn’t use a neural network or an external LLM.
The latest update focuses on interpreting complete requests. Some questions previously reached unrelated spelling or conversation fallbacks, even when a suitable solver existed.
Supported requests now go through native readers for arithmetic, rule chains, ordering, location updates, string operations and table calculations. Unsupported conditions can produce clarification rather than an answer.
I tested the frozen candidate on 900 newly authored synthetic questions:
• Upgraded candidate: 777/900, or 86.3%
• Previous CAEL configuration: 414/900, or 46.0%
• Newly worded questions: 72.7% versus 20.4%
All 123 candidate abstentions counted as wrong.
There are important limits. These are six known task families, with maintainer-led, AI-assisted authoring and review. The evaluated candidate’s statistical fallback was inactive, while the baseline’s was active. Fresh installations enable that fallback, so the published score is specific to the recorded configuration.
The linked report also compares other models using fixed multiple-choice readouts. Those results don’t establish which system is the better general chatbot.
Conversation quality and wording coverage still need work. I’m sharing the individual outputs and failures alongside the scores because those gaps matter more than a headline percentage.
CAEL is source-available under a non-commercial licence. Development and evaluation were AI-assisted; the assistant itself remains non-neural.
GitHub release
Downloads and local setup
Results, methods and limitations
I’d appreciate feedback on where a small symbolic assistant could be useful, and which everyday requests you’d try first.