A context engine for AI agents. It replaces vector DBs, search pipelines, and RAG glue with one system that ingests data, structures it, keeps it current, and serves the right context at inference time.

RAG retrieves similar fragments. It can't decide what's current, what conflicts, or what actually belongs in the working set. Cilow handles all three.

How is Cilow different from a vector DB or GraphRAG?

Vector DBs return similarity. GraphRAG adds relationships. Cilow goes further: ingestion, structuring, updating, conflict resolution, and context assembly in one layer. The goal isn't more data. It's the right data.

What data can Cilow use?

Documents, chats, code, APIs, product data, tickets, notes, structured records. If your agent depends on it, Cilow turns it into context.

Does it support continual learning?

Yes. Cilow updates context without retraining. As facts change, what the model sees changes - no weight updates, no brittle prompt hacks.

How does it fit into my stack?

Cilow replaces your retrieval and context layer. One API in front of your models - no separate vector DB, search service, or RAG pipeline to maintain.

DOCS

Documentation

API references, SDK guides, and integration tutorials for the Cilow memory layer. Everything you need to give your agents persistent context.

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