simple-agent-memory

AI agents retrieve stored project knowledge quickly using a summary-first metadata approach with standardized frontmatter. This allows agents to scan and identify relevant information without reading entire memory files.

This memory system requires ripgrep for efficient searching and standard file system access for managing memory categories. It relies entirely on local file infrastructure rather than external databases or specialized services.

AI agents lose context between conversations because they lack persistent storage. Implementing a structured memory system solves this by saving research findings and architectural choices in a local directory that persists across different sessions.

The best way to manage work-in-progress status is using a persistent knowledge base that stores project-specific knowledge locally. This ensures you can recall exact context and tricky bug details when resuming work weeks later.

This file-based agent memory system does not require external databases, relying solely on standard file system access to manage memory categories. It uses ripgrep to enable efficient searching across the stored local knowledge files.