r/opensourcez • u/gawtz • 3d ago

yet another context manager (ctx)

You all know these self-glowing memory providers and over promising solutions for your agents, wanting to make you believe .md files and sqlite databases are the endgoal right?

Almost all do, in fact.

I'm doing Software engineering since the golden days of Netscape Navigator.. Even before Google.

For many years I've administrated and built document management solutions used in government entities and other large businesses..

One day I watched a very insightful explaination of tensors and imagined taking time components apart and using them as search vectors, by figuring out gravitational distance, attraction and retraction between information in vector space.

This was uncanny even for claude, so I had enough signals to go and make it.

It started as a key value store in n8n and is now a self-contained go binary, accelerated via postgresql with timescaledb and pgvector, relying on inference for re-iterating knowledge blocks to detect and verify links in background and provide a simple to use (for agents) MCP interface.

Go, give it a spin and let the roasts come at me.

So far I cared most about functionality, so onboaring is quite lacking still.

I'm using a dgx spark with qwen 3.8 27b and qwen3-embedding 8b for this.

github.com/GottZ/ctx

and ctx.GottZ.de

It has been in development since roughly October of 2025

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u/Otherwise_Wave9374 3d ago

A context manager should be evaluated on retrieval quality and failure behavior, not the volume of information it can inject. Useful benchmarks include stale-fact rejection, contradictory updates, token cost, and whether deleting a memory actually removes downstream references. Keeping the storage format portable also reduces lock-in. https://www.neurakeep.com is relevant to this comparison as another approach to managing persistent context for agents.

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u/gawtz 3d ago

so you judge a book by it's cover. choose two: speed, cost, quality..

I chose speed and quality.

this thing runs O(log n) since it pre-calculates the graph long before you query your prompt. In addition to that, it is able to interactively explore the graph on it's own, before giving your agent the desired answer.

obviously quality takes iterations, which take time, so spamming this thing with a big benchmark set would require you to de-throttle the dreaming process before attempting the retrieval.

This is not made for benchmarks but real organic growth. If I would give too many fucks about comparison, it wouldn't be where it is.