Building Persistent AI Systems Without a Traditional Database
Year: 2026
Type: article
Abstract: This paper presents a practical architecture for persistent AI assistants in which long-term memory, skill metadata, and agent operating context are stored as ordinary Markdown files rather than rows in a relational, document, or vector database. In OctaMind, the canonical state for each assistant lives in human-readable files such as working_memory.md, episodic_memory.md, semantic_memory.md, personality.md, habits.md, self_reflection.md, skills.md, and skill_context.md. Semantic retrieval is provided by lightweight FAISS indexes built on demand from these files using SentenceTransformer embeddings, while the files themselves remain the sole source of truth. The implementation couples a six-layer memory system, periodic background consolidation, semantic episodic recall, and semantic tool selection without introducing a traditional database tier. This work argues that Markdown-native architectures offer strong advantages for small-to-medium AI systems, including inspectability, debuggability, versionability, portability, and low operational overhead.
Author: Maluskar, Hrishikesh
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7135408289
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