Hey luca, heavy obsidian user here and went through your website and github. Def will try it out. Connecting codex with Tolaria to manage your knowledgebase is something i'm looking forward to try.
Hey we already had PostgreSQL so no new infrastructure to manage, it was easy way to see if vector database change has any value.
It also has good enough performance - handles 10M vectors with HNSW indexes adequately
open source - leverages existing infrastructure
for future migration. we've created a vector service, easy to swap later if needed
Author here. We've been building CORE (open source) for the past year. Happy to answer questions about the architecture, reification approach, or what broke at scale.
You’re right dumping all memory into the context window doesn’t scale. But with CORE, we don’t do that.
We use a reified knowledge graph for memory, where:
Each fact is a first-class node (with timestamp, source, certainty, etc.)
- Nodes are typed (Person, Tool, Issue, etc.) and richly linked
- Activity (e.g. a Slack message) is decomposed and connected to relevant context
This structure allows precise subgraph retrieval based on semantic, temporal, or relational filters—so only what’s relevant is pulled into the context window.
It’s not just RAG over documents. It’s graph traversal over structured memory. The model doesn’t carry memory—it queries what it needs.
So yes, the memory problem is real—but reified graphs actually make it tractable.
Claude is incredibly powerful but it's limitation is no persistent memory hence you have to repeat yourself again and again.
I integrated Claude with CORE memory MCP, making it an assistant that remembers everything and have a better memory than Cursor or chatgpt.
Before CORE : "Hey Claude, I need to know the pros and cons of hosting my project on cloudfare vs AWS, here is the detailed spec about my project...."
And i have to REPEAT MYSELF again and again regarding my preferences and my tech stack and project details.
After CORE: "Hey Claude, tell me pros n cons of hosting my project on cloudfare vs AWS."
Claude instantly knows everything from my memory context.
What This Means
- Persistent Context: You Never repeat yourself again
- Continuous Learning: Claude gets smarter with every interaction it ingest and recall from memory
- Personalized Responses: Tailored to your specific workflow and preferences
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.
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.
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.
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.
Hey! Manik here, one of the co-founders of Tegon. Would love to know what are the sources whose context is generally missing in issues in your experience?
I'm recently started using YNAB via my friends recommendation. The only problem i feel in YNAB is i need to manually do all the entries since I am from India and it only supports US and Europe bank accounts.
Tried gpt5.5 and so far good. Zapier also shared an automation benchmark where 5.5 came on top in the leaderboard https://zapier.com/benchmarks