Alignment with DF goals (BGI, Platform growth, community)

Functional Graph AGI is an experimental reasoning engine made of callable, memory-aware modules. Each module carries self-description, trust scoring, and explainability. The system integrates user-guided teaching, semantic abstraction, and curiosity-based learning. It uses a local LLM to generate hypotheses and safely builds a symbolic reasoning graph that evolves over time. The goal is to make AGI transparent, controllable, and open for experimentation across domains like math, logic, education, and simulations.

Live demo (UI walkthrough): https://limitgraph.com  
Public API (real-time reasoning): http://api.limitgraph.com:8000/docs  
Research paper (Zenodo): https://zenodo.org/records/15611501

Problem description

AI systems today are opaque, hard to teach, and not built for explainable growth. AGI research often focuses on large models, not composable and controllable reasoning systems. There's a gap between symbolic clarity and modern learning.

Proposed Solutions

We built a modular AGI where each function is a cognitive block. It supports teaching by examples, conflict detection, and abstraction. It uses a local LLM to hypothesize missing logic and builds a functional graph of growing intelligence. This system is already live and ready for next steps.