Show HN: Core – open source memory graph for LLMs – shareable, user owned
github.comI keep running in the same problem of each AI app “remembers” me in its own silo. ChatGPT knows my project details, Cursor forgets them, Claude starts from zero… so I end up re-explaining myself dozens of times a day across these apps.
The deeper problem
1. Not portable – context is vendor-locked; nothing travels across tools.
2. Not relational – most memory systems store only the latest fact (“sticky notes”) with no history or provenance.
3. Not yours – your AI memory is sensitive first-party data, yet you have no control over where it lives or how it’s queried.
Demo video: https://youtu.be/iANZ32dnK60
Repo: https://github.com/RedPlanetHQ/core
What we built
- CORE (Context Oriented Relational Engine): An open source, shareable knowledge graph (your memory vault) that lets any LLM (ChatGPT, Cursor, Claude, SOL, etc.) share and query the same persistent context.
- Temporal + relational: Every fact gets a full version history (who, when, why), and nothing is wiped out when you change it—just timestamped and retired.
- Local-first or hosted: Run it offline in Docker, or use our hosted instance. You choose which memories sync and which stay private.
Try it
- Hosted free tier (HN launch): https://core.heysol.ai
So, this is cool and a per-user memory is obviously relevant for effective LLM use. And major props for the temporal focus.
However, keeping a tight, constrained context turns out to actually be pretty important for correct LLM results (https://www.dbreunig.com/2025/06/22/how-contexts-fail-and-ho...).
Do you have a take on how we reconcile the tension between these objectives? How to make sure the model has access to relevant info, while explicitly excluding irrelevant or confounding factors from the context?
For those asking how this is different from a simple text based memory archive, I think that is answered here:
--- Unlike most memory systems—which act like basic sticky notes, only showing what’s true right now. C.O.R.E is built as a dynamic, living temporal knowledge graph:
Every fact is a first-class “Statement” with full history, not just a static edge between entities. Each statement includes what was said, who said it, when it happened, and why it matters. You get full transparency: you can always trace the source, see what changed, and explore why the system “believes” something. ---
I'm not sure the graph offers any clear advantage in the demonstrated use case.
It's overhead in coding.
The source is the doc. Raw text is as much of a fact as an abstracted data structure derived from that text (which is done by an external LLM - provenance seems to break here btw, what other context is used to support that transcription, why is it more reliable than a doc within the actual codebase?).
Hey - i agree that the demonstrated use can be solved with simple plan.md file in the codebase itself.
With use-case we wanted to showcase the shareable aspect of CORE more. The main problem statement we wanted to address was "take your memory to every AI" and not repeating yourself again and again anymore.
The relational graph based aspect of CORE architecture is an overkill for simple fact recalling. But if you want an intelligent memory layer about you that can answer What, When, Why and also is accessible in all the major AI tools that you use, then CORE would make more sense.
> Local Setup > Prerequisites > OpenAI API Key
This does not seem to be local and additionally appears to be tied to one SaaS LLM provider?
Hey we are actively working on improving support for Llama models. At the moment, CORE does not provide optimal results with Llama-based models, but we are making progress to ensure better compatibility and output in the near future.
Also we build core first internally for our main project SOL - AI personal assistant. Along the journey of building a better memory for our assistant we realised it's importance and are of the opinion that memory should not be vendor locked. It should be pluggable and belong to the user. Hence build it as a separate service.
I definitely would not recommend llama models, they were mostly outdated by the time they released, but the likes of Qwen, deepseek etc are much more useful.
Hey we started with llama but since llama was not giving good results hence fall backed to using gpt and launch it.
We will evaluate qwen and deepseek going forward, thanks for mentioning.
You can run OpenAI compatible servers locally. vLLM, ollama, LMStudio and others.
https://news.ycombinator.com/item?id=44436031
I've been building a memory system myself, so I have some thoughts...
Why use a knowledge graph/triples? I have not been able to come up with any use for the predicate or reason to make these associations. Simple flat statements seem entirely sufficient and more accurate to the source material.
... OK, looking a little more, I'm guessing it is a way to see when a memory should be updated; you can match on the first two items of the predicate. In a sense you are normalizing the input and hoping that shows an update or duplicate memory.
I would be curious how well this works in practice. I've spent a fair amount of effort trying to merge and deduplicate memories in a more ad hoc way, generally using the LLM for this process (giving it a new memory and a list of old memories). It would feel much more deterministic and understandable to do this in a structured way. On the other hand I'm not sure how stable these triples would be. Would they all end up attached to the user? And will the predicate be helpful to establish meaningful relationships, or could the memories simply be attached to an entity?
For instance I could list a bunch of facts related to my house: the address, which room I sleep in, upcoming and past repairs, observations on the yard, etc. Many (but not all) of these could be best represented as one "about my house" memory, with all the details embedded in one string of natural language text. It would be great to structure repairs... but how will that work? (my house, needs repair, attic bath)? Or (my house, has room, attic bathroom) and (attic bathroom, needs repair, bath)? Will the system pick one somewhat arbitrarily then, being able to see that past memory, replicate its structure?
Another representation that occurs to for detecting duplicates and updates is simply "is related to entities". This creates a flatter database where there's less ambiguity in how memories are represented.
Anyway, that's one area that stuck out to me. It wasn't clear to me where the schema for memories is in the codebase, I think that would be very useful to understanding the system.
I don't see the advantage over a simple text file accessible by MCP. Could you elaborate?
Hey - agreed that for basic fact recall, a simple text file + MCP works fine.
We designed CORE for complex, evolving memory where text files break down.
Example: Health conversations across ChatGPT, Claude, etc. where your parameters change over time.
A text file can't give you: "What medications have I tried, why did I stop each one, and when?" or "Show me how my symptoms evolved over 6 months."
For timeline and relational memory, CORE wins. For static facts, text files are enough i guess.
I don't know this project, but the is probably simplicity/performance benefits to using a proxy over MCP as in theory there is less overhead.
Why do open source projects do not version control their CLAUDE.md?
It certainly looks interesting, how does this differ from a plan.md?
Hey plan.md mostly will be a static file that you manually have to maintain. It won't be relational and not be able to form connections between info. You can't recall or query intelligently? (When did my preference change?)
CORE lets you - Automatically extracts and stores facts from conversations - Builds intelligent connections between related information - Answers complex queries ("What did I say about something and when?") - Detects contradictions and explains changes with full context
For simple fact recall, plan.md should work but for complex systems a relational memory should be able to help better.
how would you say you compare to graphiti from zep?
Hi,
There are 3 major differences between Zep and CORE 1. Market: Zep is B2B focused, CORE indvidual users 2. Portablity: Zep is locked to their platform , CORE works across claude, cursor, windsurf 3. Architecture: Zep is Temporal based vs CORE is Reified + Temporal based graph
What this means:
Zep remembers what happened when CORE remembers what happened when + why we should believe it + how facts relate
Example: You say "I love Thai food" → Later: "Actually, I hate Thai food"
Zep: "You hate Thai food" (old preference vanishes) CORE: "You currently hate Thai food. This contradicts your earlier statement from [date/source]. The change came from your correction today."
Bottom line: CORE provides full explainability and audit trails that Zep cannot.
I love how we have come full circle. Anybody remembers the "semantic web" (RDF-based knowledge graph)? It didn't take off because building and maintaining such a graph requires extensive knowledge engineering work and tools. Fast forward a couple of decades and we have LLMs, which is basically auto-complete on steroids based on general knowledge, with the downside that it doesn't "remember" any facts unless you spoon-feed it with the right context. We're now back to: "let's encode context knowledge as a graph and plug it into LLMs". Fun times :)