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

> Source: <https://dev.to/aubakirovarman/im-building-cortexdb-an-agent-native-context-database-for-ai-agents-59fe>
> Published: 2026-06-16 23:02:22+00:00

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

Most modern RAG systems work like this:

- Split documents into chunks
- Generate embeddings
- Store them in a vector database
- Retrieve top-k similar chunks on query
- 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](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