persistent-memory

To preserve AI context across coding sessions, use a file-based memory protocol that records decisions, context, and learnings in structured local files. This allows AI agents to resume work exactly where they left off without losing previous session history.

Yes, persistent memory works with Cursor, Claude Code, Antigravity, and Gemini CLI. It uses a portable file-based protocol that any AI agent capable of reading and writing local files can automatically access and update.

The best way to track AI agent decisions between sessions is logging them in a dedicated decisions.md file. This creates a recoverable, structured decision log that any compatible agent can read to restore historical context.

Keep AI session memory manageable by using progressive summary and compression techniques. This automatically condenses older context while preserving critical learnings, ensuring the memory file stays under control for future sessions.

Yes, you can perform session handoffs without a database by using a local file structure with dedicated handoffs and sessions directories. This stores recoverable context directly in your project for agents to retrieve.

To initialize file-based memory for an AI coding project, run the memory protocol setup to create the standard directory structure, including MEMORY.md, context folders, and session logs. This enables automatic persistent context capture.