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Show HN: Yet another memory system for LLMs

165 points| blackmanta | 6 months ago |github.com

Built this for my LLM workflows - needed searchable, persistent memory that wouldn't blow up storage costs. I also wanted to use it locally for my research. It's a content-addressed storage system with block-level deduplication (saves 30-40% on typical codebases). I have integrated the CLI tool into most of my workflows in Zed, Claude Code, and Cursor, and I provide the prompt I'm currently using in the repo.

The project is in C++ and the build system is rough around the edges but is tested on macOS and Ubuntu 24.04.

46 comments

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[+] retreatguru|6 months ago|reply
How do you use this in your workflow? Please give some examples because it’s not clear to me what this is for.
[+] blackmanta|6 months ago|reply
I have been using it for task tracking, research, and code search. When using CLI tools, I found that the LLM's were able to find code in less tool calls when I stored my codebase in the tool. I had to wrangle the LLMs to use the tool verse native rgrep or find.

I am also trying to stabilize PDF text extraction to improve knowledge retrieval when I want to revisit a paper I read but cannot remember which one it was. Most of these use cases come from my personal use and updates to the tool but I am trying to make it as general as possible.

[+] threecheese|6 months ago|reply
Reviewing the prompts, looks like you are using this CAS tool as a global context data manager, supporting primarily a code use case. There are a number of extant MCP-capable code understanding tools (Serena and others), but what I am lacking in my CLI toolchain is non-code memory. You even called this out in another thread, mentioning task management- I find that the type of memory I need is not scoped to a code module, but an agent session - specifically to the orchestration of many agent sessions. What we have today are techniques, using a bunch of hacked together context files for sessions (tasks.md, changes.md), for agents (roles.md), for tech (architecture.md), etc etc, hoping that our prompts guide the agent to use them, and this is IMO a natural place for some abstraction over memory that can provide rigor.

I am observing in my professional (non-Claude Max) life that context is a real limiter, from both the “too much is confusing the agent” and “I’m hitting limits doing basic shit” perspectives (looking at you, Bedrock and Github), and having a tool that will help me give an agent only what it needs would be really valuable. I could do more with the tools, spend less time trying to manually intervene, and spend less of my token budget.

[+] blackmanta|6 months ago|reply
While the examples and provided prompt lean toward code (since that's my personal use case), YAMS is fundamentally a generic content-addressed storage system.

I will attempt to run some small agents with custom prompts and report back.

[+] elpocko|6 months ago|reply
>block-level deduplication (saves 30-40% on typical codebases)

How is savings of 40% on a typical codebase possible with block-level deduplication? What kind of blocks are you talking about? Blocks as in the filesystem?

[+] blackmanta|6 months ago|reply
I am working to improve the CLI tools to make getting this information easier but I have stored the yam repo in yams with multiple snapshots and metadata tags and I am seeing about 32% storage savings.
[+] rkunnamp|6 months ago|reply
Thank you for sharing this. Sorry for a possible noob question. How are embedding generated? Does it use a hosted embedding model? (I was trying to understand how is semantic search implemented)
[+] jerpint|6 months ago|reply
I also developed yet another memory system !

https://github.com/jerpint/context-llemur

Although I developed it explicitly without search, and catered it to the latest agents which are all really good at searching and reading files. Instead you and LLMs cater your context to be easily searchable (folders and files). It’s meant for dev workflows (i.e a projects context, a user context)

I made a video showing how easy it is to pull in context to whatever IDE/desktop app/CLI tool you use

https://m.youtube.com/watch?v=DgqlUpnC3uw

[+] mempko|6 months ago|reply
Wicked cool. Useful for single users. Any plans to build support for multiple users? Would be useful for an LLM project that requires per user sandboxing.
[+] sitkack|6 months ago|reply
How would you use the built in functionality to enable graph functionality? Metadata or another document used as the link or collection of links?
[+] blackmanta|6 months ago|reply
The graph functionality is exposed through the retrieval functionality. I may improve this later but the idea was to maximize getting the best results when looking for stored data.
[+] huqedato|6 months ago|reply
In my RAG I use qdrant w/ Redis. Very successfully. I don't really see the use of "another memory system for LLM", perhaps I'm missing something.
[+] A4ET8a8uTh0_v2|6 months ago|reply
I like it and I will be perusing your code for what could be used in my 'not yet working' variant.
[+] yukukotani|6 months ago|reply
Cool! Any plan to support shared storage like cloud RDBs or S3?
[+] blackmanta|6 months ago|reply
I will look into adding this in a future update.
[+] marcofiocco|6 months ago|reply
What about versioning of files?
[+] blackmanta|6 months ago|reply
The tool has built-in versioning. Each file gets a unique SHA-256 hash on storage (automatic versioning), you can update metadata to track version info, and use collections/snapshots to group versions together. I have been using the metadata to track progress and link code snippets.
[+] ActorNightly|6 months ago|reply
>MCP server (requires Boost)

I see stuff like this, and I really have to wonder if people just write software with bloat for the sake of using a particular library.

[+] pessimizer|6 months ago|reply
Boost is a nearly 30 year old open source library that provides stuff for C++ that most standard libraries for other languages already have out of the box. You seem to think that it is hipster bullshit rather than almost a dinosaur itself.
[+] SJC_Hacker|6 months ago|reply
Blame the committee for refusing to include basic functionality like regular expressions , networking and threads as part of the STL
[+] noodletheworld|6 months ago|reply
? Are you complaining about MCP or boost?

It’s an optional component.

What do you want the OP to do?

MCP may not be strictly necessary but it’s straight in line with the intent of the library.

Are you going to take shots at llama.cpp for having an http server and a template library next?

Come on. This uses conan, it has a decent cmake file. The code is ok.

This is pretty good work. Dont be a dick. (Yeah, ill eat the down votes, it deserves to be said)

[+] menaerus|6 months ago|reply
The reason for depending on Boost in this repo is just few search characters away - he needs HTTP/WebSocket implementation and Boost.Beast provides it. The actual bloat here in this repo is conan.
[+] airstrike|6 months ago|reply
This feels like a shallow dismissal, which is frowned upon per the HN guidelines
[+] JSR_FDED|6 months ago|reply
Thanks, I learned a lot from this.
[+] vira28|6 months ago|reply
How does this compare to Letta?
[+] skyzouwdev|6 months ago|reply
That sounds like a practical take on LLM memory — especially the block-level deduplication part.

Most “memory” layers I’ve seen for AI are either overly complex or end up ballooning storage costs over time, so a content-addressed approach makes a lot of sense.

Also curious — have you benchmarked retrieval speed compared to more traditional vector DB setups? That could be a big selling point for devs running local research workflow

[+] blackmanta|6 months ago|reply
I have not, but that is something I plan to do when I have time.
[+] winterrx|6 months ago|reply
The domain listed on the GitHub repo redirects too many times.
[+] blackmanta|6 months ago|reply
That should be fixed now. It was a misconfiguration of CloudFlare SSL with GitHub Pages.