memory-recall

Semantic memory search retrieves shared agent memories across sessions by querying the ourmem persistent memory layer with natural language. It returns relevance-ranked results, eliminating manual context lookup and the need to restate past task notes or user preferences.

Agents recall historical context by querying the ourmem shared memory store using natural language. This semantic search mechanism retrieves relevant stored memories ranked by relevance score, directly eliminating manual context lookup across unstructured memory data.

Yes, you can use natural language to search persistent AI agent memory. The memory-recall capability queries the ourmem v1 memories search API to find and rank relevant past task notes, team knowledge, and historical context based on semantic relevance.

Yes, you need an API key for authenticated access to the ourmem shared memory store. The memory retrieval process uses this key to securely query the ourmem v1 memories search API and return relevance-ranked stored memories.

The best way to find user preferences saved previously is to perform a semantic memory search across the ourmem shared memory store. This retrieves specific stored notes from past interactions without requiring the user to repeat information.

A limitation of semantic search for agent context retrieval is that it depends entirely on the data already stored in the ourmem persistent memory layer. It cannot retrieve external context or memories that were not explicitly saved during previous agent sessions.