memory-contract

To persist project memory between AI agent sessions, you can use a structured memory system that writes decisions, constraints, and learnings to markdown files with YAML frontmatter under a centralized directory, ensuring long-term context continuity and traceability.

Agent memory retrieval is the process of searching and reading structured memory entries to inform current decisions. It preserves project context by enforcing a retrieve-before-action commitment, allowing agents to access past learnings and constraints during active workflows.

Yes, you can use markdown with YAML frontmatter to store structured agent memory. This approach enforces consistent indexing and search by applying defined fields such as type, topic, status, and date directly within the memory file format.

You need to capture memory entries at specific decision points, constraints, and learnings within project workflows. Adhering to a store-before-reasoning commitment ensures that critical context is recorded immediately after a task is completed for future audits.

The best way to index AI agent memory files for auditing is to enforce a standardized file format using YAML frontmatter fields like type, topic, status, and date, which enables consistent semantic retrieval and search across all stored project memory entries.

No, this memory system requires no external dependencies to manage project context. It operates independently by writing structured markdown files with YAML frontmatter to a local directory, ensuring memory capture and retrieval works without additional software installations.