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

A developer is building CortexDB, an agent-native context database designed to provide AI agents with bounded, permission-safe, evidence-aware, and verifiable context. Instead of returning raw text chunks like traditional RAG systems, CortexDB compiles ContextPacks—structured bundles that include source citations, token usage, anomaly detection, and permission scoping. The project is open-source on GitHub and offers SDKs for Python, TypeScript, and Rust.

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