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

Most modern RAG systems work like this:

1. Split documents into chunks
2. Generate embeddings
3. Store them in a vector database
4. Retrieve top-k similar chunks on query
5. Send them to an LLM

It works for simple use cases. But as AI agents become more autonomous and complex, a clear problem appears:

Agents don’t just need similar text chunks.

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

This is why I started building CortexDB.

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

What is CortexDB?

CortexDB is a single-node, agent-native context database. Its main goal is to compile ContextPacks — structured, citation-rich, token-budgeted bundles of context for AI agents.

Instead of returning raw chunks, it returns a ready-to-use package that includes:

• Source citations
• Explanation of why each piece was selected
• Token usage information
• Anomaly and conflict detection
• Permission and scope awareness

Key Features

• ContextPack — structured output format with citations and token control
• VERIFY FACT — deterministic fact verification (including numerical conflicts)
• AQL — custom declarative query language designed for agents
• Tool Registry + Typed Knowledge Graph
• Durable single-node storage (WAL + MVCC)
• Published SDKs for Python, TypeScript, and Rust

Example: ContextPack

json
{
  "token_budget_tokens": 4000,
  "estimated_tokens": 2500,
  "truncated": false,
  "citations_required": true,
  "cells": [...],
  "anomalies": [...]
}