unified-brain

System One Coding Agents: code-level answers at the same score as Claude Fable 5.1 on the same 74-point xz benchmark (69 / 74, 93.2 % each), at 1/20 of the cost and 3.5× faster. No Context Loss and Memory Limits on sessions since every answer starts from the same, updatable, SQLite index.

27 ★ on GitHub

What it is

Persistent memory for System One coding agents, by DevLoops.

The homepage listed on the repository, devloops.ai, puts it like this: DevLoops — Persistent memory for coding agents

Getting started

From the README:

git clone https://github.com/DevLoops-ai/unified-brain.git
cd unified-brain
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

Where the decision sits

unified-brain runs its own weights or its own runtime rather than calling a hosted API, so its decisions do not depend on a vendor being up or on a key being funded. The trade is that the model is now yours to keep current.

Models it references: jev.

How active it is

Created 2026-10-15, last pushed 2026-11-01, 27 stars and 8 forks when we read it on 2026-11-02. Everything in this category is weeks old at most, so a stale push date means something different here than it would on a mature project. Check the repository before you depend on it.

Find out what your data actually supports

We turn your historical decisions into a frozen test set, then tell you which model, which threshold, and how much of it you can safely automate.

Request a benchmark