agent-activity-log

To keep AI agent memories alive across multiple sessions, you bootstrap a persistent memory.md file in your project. This enforces a read-before-act and write-after-change protocol to maintain context and history.

The read-before-act protocol for agent memory persistence requires an AI agent to read the memory.md file at the start of a session. It pairs with write-after-change discipline to ensure structured logging of decisions and coherent multi-session workflows.

You create a persistent activity log for AI agents by initializing a memory.md file in your project directory. This file serves as an append-only history where agents record meaningful changes and open threads across sessions.

Yes, you can use a structured session log to track AI agent decisions over time. By maintaining a memory.md file with session protocol guidance, you keep decisions, changes, and open threads fully traceable across multiple sessions.

You do not need any external dependencies to maintain multi-session agent memory. The approach relies entirely on creating and updating a local memory.md file within your existing project structure without additional tools.

You should implement an append-only history for AI agent session tracking when you need to preserve a continuous context across disconnected workflows. This ensures past decisions and open threads remain traceable without losing historical data.