memory-search

To recall past conversations across sessions, use semantic memory search to index and retrieve relevant past discussions and decisions. This maintains continuity in multi-step tasks by surfacing prior interactions and rationale.

Semantic memory search works by indexing past conversations and decisions using vector storage, then matching current queries against that index to retrieve relevant context. This surfaces prior discussions to maintain project continuity.

Yes, you need an AI Maestro instance with vector storage to index and retrieve memory. This environment is required to perform semantic search across past conversations and surface relevant decisions for current tasks.

The best way to onboard a team member using past project decisions is to run a semantic memory search query about the project. This summarizes prior decisions and rationale by surfacing relevant past discussions instantly.

Yes, you can use keyword and symbol matching to locate specific discussions and code references. This works alongside semantic search to pinpoint exact technical context retrieved from past conversations.

Run a memory search query about your current task to surface relevant past discussions and decisions. This provides immediate context from previous sessions, ensuring continuity when starting new multi-step tasks.