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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

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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

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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

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Example: ContextPack