para-memory-files

Persistent memory for AI agents is achieved by implementing a file-based storage system that organizes knowledge using the PARA methodology. This approach structures information into Projects, Areas, Resources, and Archives, ensuring context survives across session boundaries.

The PARA method for AI knowledge management organizes files into Projects, Areas, Resources, and Archives. It structures a multi-layered memory system containing atomic facts, daily timeline logs, and tacit user knowledge repositories.

Yes, you need qmd to enable semantic search for AI agent memory files. The system requires qmd to perform advanced vector-based retrieval and keyword search operations over stored markdown and yaml files.

To store atomic facts and daily timeline logs in an AI memory system, you save them as structured markdown and yaml files within a PARA directory structure. This creates a chronological timeline and a knowledge graph of facts.

The best way to retrieve past project context and user preferences is by using a file-based memory system with semantic search. It manages tacit user knowledge and retrieves past plans through vector and keyword-based queries.

Yes, you can use yaml and markdown files for AI agent knowledge management. The system stores atomic facts in items.yaml files and logs daily events, enabling structured knowledge persistence across sessions.