I'm building CortexDB — an agent-native context database for AI agents

Most modern RAG systems follow the same pattern:

1. Split documents into chunks


2. Compute embeddings


3. Store them in a vector database


4. Retrieve top-k similar chunks


5. Send them to an LLM

It works. But as AI agents become more autonomous, a clear problem emerges:

Agents don't just need similar chunks.


They need bounded, permission-safe, evidence-aware, and verifiable context.

That's why I'm building CortexDB.

GitHub: https://github.com/AubakirovArman/CortexDB

What is CortexDB?

CortexDB is an experimental agent-native context database.

It's not a traditional vector database.


It's not a key-value store.


It's not just another memory layer on top of embeddings.

The core idea is to store knowledge and agent memory in a way that allows the system to compile a structured Context Pack — a ready-to-use, evidence-aware package of context.

Why classic RAG is often not enough

Classic retrieval often returns raw chunks. This leads to several problems:

• Duplication


• Weak provenance


• Token budget overruns


• Potential data leakage


• Ignored contradictions

Example:

• Document 1: Solar Plant budget is 1.2B KZT
  


• Document 2: Solar Plant budget was updated to 1.4B KZT
  

A classic pipeline may return only the first document, and the agent confidently answers with an outdated number.

CortexDB is designed to handle such conflicts properly.

Core Feature: ContextPack

The main output of CortexDB is a ContextPack — a structured context package:

json

{

  "token_budget_tokens": 4000,

  "estimated_tokens": 2500,

  "truncated": false,

  "citations_required": true,

  "cells": [...],

  "anomalies": [...]

}