Open-source framework for persistent AI agent memory with local storage and graph-based retrieval

What this is

A developer is building an open-source framework for persistent AI agent memory that operates entirely locally without cloud dependencies. The system stores data as Markdown files on disk and uses wiki-links to create graph edges between notes.

Key technical details

The framework implements several specific features extracted from the source:

• Storage architecture: Local Markdown files on disk with Git for version control
• Graph structure: Wiki-links serve as graph edges connecting notes
• Retrieval system: Four-signal fusion approach combining:
  • Semantic embedding
  • Keyword matching
  • PageRank graph importance
  • Associative warmth
• Memory management: Graph-aware forgetting system based on ACT-R cognitive science where:
  • Notes decay over time unless accessed
  • Used notes remain alive and relevant
  • Graph and semantic neighbors stay relevant through spreading activation
• Spreading activation: When a note is accessed, connected notes get "warmer" too, helping the agent anticipate relevance before tasks begin
• Performance: 22 MB total storage after three months of use, described as "extremely efficient"
• Development status: First two GitHub issues filed, small community forming around keeping AI memory free and decentralized

This type of local memory system is useful for AI coding agents that need persistent context across sessions without relying on cloud services or external APIs.