recall_memories
Search and retrieve saved memories using semantic ranking and optional filters for precise results. Find relevant information from past conversations quickly.
Instructions
Search and retrieve saved memories with intelligent semantic ranking.
🎯 BASIC SEARCH: recall_memories(query="authentication") → Returns all memories about authentication, ranked by semantic relevance
🔍 FILTERED SEARCH (Phase 2 Knowledge Graph Intelligence): Use filters when you need PRECISION over semantic similarity:
✓ entity="name" - Find memories mentioning specific people/projects/technologies Example: entity="purmemo" → Only memories discussing purmemo
✓ has_observations=true - Find substantial, fact-dense conversations Example: has_observations=true → Only high-quality technical discussions
✓ initiative="project" - Scope to specific initiatives/goals Example: initiative="Q1 OKRs" → Only Q1-related memories
✓ intent="type" - Filter by conversation purpose Options: decision, learning, question, blocker Example: intent="blocker" → Only conversations about blockers
💡 WHEN TO FILTER:
Use entity when user asks about specific person/project by name
Use has_observations for "detailed" or "substantial" requests
Use initiative/stakeholder for project-specific searches
Use intent when user asks for decisions, learnings, or blockers
📝 COMBINED EXAMPLES: recall_memories(query="auth", entity="purmemo", has_observations=true) → Find detailed technical discussions about purmemo authentication
recall_memories(query="blockers", intent="blocker", stakeholder="Engineering") → Find engineering team blockers
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query - can be keywords, topics, or specific content | |
| includeChunked | No | Include chunked/multi-part conversations in results | |
| limit | No | Maximum number of memories to return | |
| entity | No | Filter by entity name (people, projects, technologies). Use when user asks about a specific person, project, or technology by name. Example: entity="Alice" finds only memories mentioning Alice. More precise than semantic search. Supports partial matching. | |
| initiative | No | Filter by initiative/project name from conversation context. Use when user scopes search to specific project or goal. Example: initiative="Q1 OKRs" finds only Q1-related memories. Supports partial matching (ILIKE). | |
| stakeholder | No | Filter by stakeholder (person or team) from conversation context. Use when user asks about specific person's or team's involvement. Example: stakeholder="Engineering Team" finds memories where Engineering Team was mentioned as stakeholder. Supports partial matching (ILIKE). | |
| deadline | No | Filter by deadline date from conversation context (YYYY-MM-DD format). Use when user asks about time-sensitive memories or specific deadlines. Example: deadline="2025-03-31" finds memories with March 31, 2025 deadline. Exact match only. | |
| intent | No | Filter by conversation intent/purpose. Options: "decision" (decisions made), "learning" (knowledge gained), "question" (open questions), "blocker" (obstacles/issues). Use when user asks specifically for one of these types. Example: intent="decision" finds only conversations where decisions were made. Exact match only. | |
| has_observations | No | Filter by conversation quality based on extracted observations (atomic facts). Set to true to find substantial, structured conversations with extracted knowledge (high-quality technical discussions, detailed planning). Set to false for lightweight chats. Omit to return all memories regardless of observation count. Use when user asks for "detailed", "substantial", or "in-depth" information. |