I think everyone has already read Karpathy's Post about LLM Knowledge Bases. Actually for recent weeks I am already working on agent-native knowledge base for complex research (DocMason). And it is purely running in Codex/Claude Code. I call this paradigm is: The repo is the app. Codex is the runtime.During my daily working life, I have tons of office documents with knowledge from all teams, and as an IT Architect, I need to combine them altogether to handle complex deep research (which normal LLM definitely could not help). That is the originally reason I built DocMason, and I am using it in everyday which support me on lots of complex topics.I have already open-sourced this repo. And I think it takes Karpathy's concept a step further for real-world usage in three ways:
1. It could handle most kinds of office docs (pptx, docx, excels, even .eml). And really extract multimodal information from all IT architecture diagram or excel sheets.
2. It is running as a Real APP but not a naive RAG tool. DocMason could run smoothly and intelligently to prepare environment, auto update, and auto incrementally sync Knowledge base.
3. Most importantly it is running in Native AI Agents, which could leverage powerful AI Agents engine (e.g. Codex or Claude Code)View detail architecture diagram in DocMason Readme, and then download have a try :) You will find it could help a lot during daily work. Would love to hear your feedback and issues in Github!
Show HN: DocMason – Agent Knowledge Base for local complex office files
A real-world, advanced LLM knowledge base running in native AI agents (Codex/Claude Code), capable of extracting multimodal information from diverse office documents, going beyond naive RAG tools.
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A real-world, advanced LLM knowledge base running in native AI agents (Codex/Claude Code), capable of extracting multimodal information from diverse office documents, going beyond naive RAG tools.
DocMason addresses a critical enterprise pain point: extracting and synthesizing knowledge from disparate, complex internal documents, a task traditional LLMs struggle with. Its positioning as an 'agent-native knowledge base' running within AI agent engines like Codex/Claude Code signifies a significant evolution beyond basic RAG implementations. The ability to handle diverse office formats and extract multimodal information, including from diagrams and spreadsheets, offers substantial value for IT architects and researchers. This project highlights a strong market demand for sophisticated, AI-powered knowledge management solutions that integrate deeply into enterprise workflows, automating complex information synthesis and reducing manual research overhead. The 'repo is the app, Codex is the runtime' paradigm suggests a new model for deploying AI-driven enterprise applications.
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