I built an open-source knowledge layer for AI agents, not a chat-memory wrapper, something your agent can actually reason over

Hi everyone. Quick disclaimer up front: this is going to sound like it's in the "agent memory" space, and I want to push back on that framing immediately, because I think the memory angle has gotten flooded and a lot of it is the same (vector-store/graph)-of-chat-messages wrapper.

What I built is closer to a knowledge layer or DB for agents than a memory system. The point isn't remembering what the user said three turns ago. The point is giving an agent a place to hold real operational information and then match, overlap, and recall it later.

Concretely, the kind of stuff it's meant to hold:

• internal processes and how things actually get done
• ticket and support history
• ecommerce and company logs
• prospects, and the patterns from previous closed clients
• competitor information
• your own personal notes

...and then surface the connections across all of it. The interesting part isn't storage, it's that an agent can ask "who looks like the deals we closed last quarter" or "where does this ticket overlap with a known process" and get something back that was actually linked, not just semantically nearby.

Under the hood it ingests structured and unstructured data through a four-stage pipeline, extracts entities and relationships, and builds an event-centric knowledge graph you can query over REST or MCP. Relationships are modeled as events, so it's not just static edges, it's what happened and when. Recall is relational: multi-hop traversal, entity neighbors, actual paths you can inspect, not just a similarity score you have to trust. It's self-hostable (Docker) and the graph is yours to inspect and edit.

The reason I'm posting here rather than just shipping it: I want to know if the framing even resonates with people building agents.

1. If you could give your agent a single knowledge layer to hold this kind of cross-domain operational info, what would you put in it first, and what would you want to ask it?
2. What are you using today to give agents access to company/operational knowledge, and where does it fall short? Vector DB, custom retrieval, dumping everything in context, something else?

Happy to get into the graph mechanics in the comments if people want.