AgenticFleet Memory System Guide

You can persist agent context across sessions by using a two-tier memory system that coordinates local storage and Chroma Cloud. This setup enables semantic search and saves new learnings for future reuse across different projects.

The best way to recall past debugging solutions is through semantic search across local and cloud memory. By querying the memory manager, you retrieve relevant historical context to accelerate current debugging and reasoning.

To initialize local memory context at the start of a new session, run the memory manager hydration script. This process prepares the local memory hydrator to ensure your context is ready for immediate reasoning and recall.

Yes, you need Python and uv installed for execution, along with a configured Chroma Cloud and a local memory hydrator. These dependencies are required to enable persistent context and semantic search capabilities.

An agent learns and indexes new solutions by saving them as records in the memory store. You pass a file to the memory manager, which processes and stores the learning for future semantic search and retrieval.

You should use end of session reflection to summarize activity and keep memory history clean. Running the reflection command at the end of a session ensures the persistent memory store remains organized for future use.