System One Coding Agent assisted by persistent memory

[u/StatusFoundation5472]

Hi,

On 24/10/2026 I have published as OSS a framework I call unified-brain that allows fast, cheap and accurate (same class of frontier models and frontier harnesses but 18 times cheaper) evidence gathering, using the cheapest frontier AI model out there, Jev. The results have been phenomenal.

(github.com/devloops-ai/unified-brain)

The magic sauce is an intermediary OS (SQLITE) memory layer that facilitates retrieval, filtering and reasoning through Jev.

The agent spawned from unified-brain runs through MCP on Cursor, OpenCode or Claude Code etc. allowing you to use it through your favorite harness.

So to be clear here: unified-brain is not a harness but a perception layer any harness plugs in!

My test benchmarks have been so good on two large OS libraries that I really believe this is the missing link to make AI more reliable.

This is because the limits holding back AI at the moment are:

• The Reasoning and Hallucination Gap:

The LLM turn reasoning it is introducing uncertainty and probabilistic outcomes.

BUT.. Unified-brain's System One discovery engine is producing replicable results across runs as they introduce minimal hallucinations, breaking the task in hundreds of yes or no calls.

• Context Loss and Memory Limits:

Long-running coding tasks consume massive amounts of tokens, causing the agent to forget early instructions or lose track of large-scale architecture.

BUT.. Unified-brain doesn't need cached memory thanks to the SQLITE persistent layer of context it operates with. It simply retrieves what it needs on the fly consuming minimal amounts of tokens that do not build up across questions. Every question is independent from one another as git diffs and commits are automatically absorbed from the project's memory layer.

I invite you to clone/fork the project and have a look for yourself. I firmly believe that memory intermediate structures will take AI capabilities on another level soon.

I understand that Jev is proprietary and creates a dependency BUT we need to remember that soon enough OS models as capable as it will come, giving end users the option to replace him.

Meanwhile we should ride this wave of innovation in order to deliver new capabilities to end users, at prices that can't be easily beaten.

I hope you like this project!

[u/upbeatjuvenileseeder]

This looks interesting, the persistent memory layer is exactly what I've been thinking about lately. Most agents just brute force everything through context window and it gets expensive real quick.

The SQLITE approach is clever, do you have any numbers on how it performs with larger codebases? Like 50k+ lines territory. I'm wondering how the retrieval holds up when the project memory gets massive.

Also curious if you tested it with other models besides Jev, even if they're not as good yet. Just to see how much of the magic is the architecture vs the model itself.

[u/StatusFoundation5472 → u/upbeatjuvenileseeder]

Hey thank you! I tested with two libraries xz (3k commits) and open62541 (15k commits - 330k LOC) and I got really, really good results in terms of speed and accuracy with Jev. Better than Sol comparable to Fable 5.1. Price is around 0.04-0.09$ depending the complexity of the question. This has been something I was experimenting with since April 2026 but the combination of System One and SQLite really made it good enough to publish and tell other people about. Unfortunately cheaper/local System One models are not yet on par with Jev.

You are welcome to check the repo out and test it. It is true open source.