mrsalty

Yes it makes more sense now. One thing I wonder, as most of memory systems this one also requires an LLM API key, what's the expected pricing involved? What does the LLM do here? I was looking into Cognee, one of the main players in the field, and they use YOUR tokens to summarise and chunk all your company data, which I expect to generate a hell of a token bill.

can you give a bit more details on how it works? what makes this different/better from main AI agent memory out there? the website is also quite cryptic

awesome, thanks

This is very interesting. Question: I am building an LLM-free memory system, does the gpt-4o-mini full evaluation requirement from the docs mean that systems using an LLM must standardize on gpt-4o-mini, or are LLM-free Add/Search systems ineligible? Thanks

Interesting, I'm building something somehow similar (purely latent-space though, no LLM in the loop) and I'm curious about how did you define supersession/contradiction? is it purely via cosine similarity threshold (as per readme) or do you leverage LLM on top-k docs to actually track them? If yes did you dogfood it and found it working correctly?

Cheers

go to https://huggingface.co/settings/hardware?next=%2Fmodels and add your hardware, it will show models you can run on it

Really nice! I always had in mind to build something similar but on the geographical axes: given a long/lat param show all historical events related in the surrounding area.

thanks, I will try that

that's my current setup, but it requires quite some manual work. Especially when I want to release. ie I commit and push to private repo, create PR with summary, merge. Then I run the script to copy over files to public repo (skipping files listed in a custom .publicignore file), then manually commit to public repo (I have to copy over commit messages and PR summaries from private repo), then push and release on public. It's a bit exahusting, and when something drifts it becomes a hell to keep them both in sync.

>Does security matter? Real-time performance? Never losing a transaction? Never leaking memory? What matters for the code you're trying to write? Future maintainability?

The assumption I believe is: if you are experienced enough you will instruct your agent(s) to address all of these during your iterations.

Antirez's take is bold, and probably very scary for most programmers (and especially for their managers), but I think he's mostly right. I'd even go further: people who don't adapt to this change will be soon left behind, because today things are moving blazingly fast.

This doesn't mean AI-generated code is safe. The key point here (which AR explains well in his latest YT video on his way to the gym :D) is that you still need to master the CS fundamentals. Which means:

- People who master the fundamentals will use AI to move much faster.

- People who don't master the fundamentasl and vibe code will evetually hit a wall,

but most importantly

- People who master the fundamentals but review all the code will move much slower.

Innit?

the problem you are addressing is interesting, but given this is NOT an OSS I found the post a bit difficult to evaluate because the core mechanics of the product are not very clear.

You provide a lot of implementation details (authentication providers, database choices, frontend stack, etc.), but those are quite unrelevant to understand your tool. The key questions for a memory systems to me are:

- What exactly is a "memory" or "context record" in ContextVault?

- How is information added, updated, or removed over time?

- How does retrieval work (vector search, ranking, rules, something else)?

- How do you handle stale information, contradictions, or superseded knowledge?

- What makes this different from a shared RAG knowledge base or a document store with MCP access?

A few concrete examples or an architecture overview would probably make the product much easier to understand. For example, showing a before/after workflow of an agent using ContextVault would help clarify where the intelligence of the system actually is.

Cheers

nice article(s), AI agent memory is still deeply unexplored, IMO it will become a critical space very soon (if it's not already). The biggest issue it's benchmarking memory systems: benchmarks are still quite immature because testing memory systems is intrinsically complex. Locomo, LongMemEval, etc do not cover all the use cases you listed there.