{"slug": "i-m-building-cortexdb-an-agent-native-context-database-for-ai-agents", "title": "I'm building CortexDB — an agent-native context database for AI agents", "summary": "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.", "body_md": "#\nI'm building CortexDB — an agent-native context database for AI agents\n\nMost modern RAG systems work like this:\n\n- Split documents into chunks\n- Generate embeddings\n- Store them in a vector database\n- Retrieve top-k similar chunks on query\n- Send them to an LLM\n\nIt works for simple use cases. But as AI agents become more autonomous and complex, a clear problem appears:\n\nAgents don’t just need similar text chunks.\n\nThey need **bounded, permission-safe, evidence-aware, and verifiable context**.\n\nThis is why I started building **CortexDB**.\n\n**GitHub:** [https://github.com/AubakirovArman/CortexDB](https://github.com/AubakirovArman/CortexDB)\n\n##\nWhat is CortexDB?\n\n**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.\n\nInstead of returning raw chunks, it returns a ready-to-use package that includes:\n\n- Source citations\n- Explanation of why each piece was selected\n- Token usage information\n- Anomaly and conflict detection\n- Permission and scope awareness\n\n##\nKey Features\n\n-\n**ContextPack** — structured output format with citations and token control\n-\n**VERIFY FACT** — deterministic fact verification (including numerical conflicts)\n-\n**AQL** — custom declarative query language designed for agents\n-\n**Tool Registry** + **Typed Knowledge Graph**\n- Durable single-node storage (WAL + MVCC)\n- Published SDKs for\n**Python**, **TypeScript**, and **Rust**\n\n##\nExample: ContextPack", "url": "https://wpnews.pro/news/i-m-building-cortexdb-an-agent-native-context-database-for-ai-agents", "canonical_source": "https://dev.to/aubakirovarman/im-building-cortexdb-an-agent-native-context-database-for-ai-agents-59fe", "published_at": "2026-06-16 23:02:22+00:00", "updated_at": "2026-06-16 23:21:22.029714+00:00", "lang": "en", "topics": ["large-language-models", "ai-agents", "developer-tools", "ai-infrastructure", "natural-language-processing"], "entities": ["CortexDB", "GitHub", "Python", "TypeScript", "Rust", "AQL", "ContextPack"], "alternates": {"html": "https://wpnews.pro/news/i-m-building-cortexdb-an-agent-native-context-database-for-ai-agents", "markdown": "https://wpnews.pro/news/i-m-building-cortexdb-an-agent-native-context-database-for-ai-agents.md", "text": "https://wpnews.pro/news/i-m-building-cortexdb-an-agent-native-context-database-for-ai-agents.txt", "jsonld": "https://wpnews.pro/news/i-m-building-cortexdb-an-agent-native-context-database-for-ai-agents.jsonld"}}