goodvibes-memory

You implement persistent memory by defining protocols to read and write structured data, such as decisions and failures, to JSON files. This allows AI agents to retain cross-session knowledge, preventing repeated errors and ensuring consistent decision-making.

AI agents repeat errors across sessions without persistent memory. By consulting a structured failures log, an agent can find previous resolutions and prevention strategies, avoiding the error entirely in future runs.

The best way to log AI agent activity is maintaining human-readable Markdown logs alongside structured JSON files. This records decisions, activity, and errors, enabling agents to reuse proven approaches and adhere to architectural conventions.

To start a task using an AI agent memory system, run the validate-memory-usage script with your session transcript and memory directory. This reads stored patterns and preferences from JSON files before execution, ensuring past knowledge is applied.

Yes, you can use JSON files and Markdown logs to store AI agent preferences and patterns. The system writes structured data to JSON for machine reading and human-readable Markdown for activity logging, supporting cross-session learning.

You should avoid a local JSON file system for agent memory when handling highly concurrent writes or needing centralized, remote access. This system targets local, structured logging for individual agent consistency and error handling.