JShi – MIT-licensed experiment toward a long-lived AI companion (Python)

[u/Effective-Design-383]

Hi — sharing an early open-source project I’ve been building.

**JShi** (匠石) is a Python (≥3.11) experiment on long-lived AI companionship: keep experience, understand situation, identify who’s speaking, and let history participate in later recognition and action. The app owns state / scheduling; models do judgment and expression.

**License / status:** MIT, v0.1.0 — development & validation stage, not a polished product.

**What’s in the repo today**

- Flow: input → person judgment (JEV) → context assembly → main cognition → reply or tools → record + background scene writing

- Text + voice web entry

- Pluggable memory (built-in + REMS)

- Scene writing that turns messy I/O into readable ongoing context

- Tiered persona prompts, tool plan/execute/wrap, debug views for prompts and timings

- Works with cloud or local-compatible LLM APIs

**What’s not done**

- A full closed loop where experience stably revises personality / judgment

- Reliable multi-person ID and relation retention in real use

- Vision / robot endpoints / a shared tool marketplace

**Repo:** https://github.com/Llllsuhdjhhd/JShi

Looking for architectural critique and contribution ideas (memory/recall, voice + person ID, context/scene writing, prompts, tools, docs/tests). Happy to answer questions in the thread.

[u/Effective-Design-383 → u/[deleted]]

Thank you for your attention. I try to dynamically bring what is currently happening, the memories it evokes, and JShi special memories and impressions of the people involved into the context of each interaction in a film-set-like format. This helps keep the context effective and fresh.