Search your memories by meaning (semantic search). Finds relevant memories from any session.

Query agent memories using semantic search. Returns relevant memories based on the query text.

Export AIS memories to an external platform. Translates memories to the target format and pushes them.

Import memories from an external platform (e.g., Mem0, Claude) into AIS. Connects to the platform, fetches memories, deduplicates, and stores with provenance.

List recent captured session memories. Requires Memory plan.

Recall relevant information. Uses hybrid search to find relevant memories, documents, and connections. Args: query: What to search for source_type: Optional filter ('memory', 'document', or None for all) tags: Optional list of tags to filter by (OR logic - memories with ANY of these tags) start_date: Optional filter for memories created on or after this date (ISO 8601: '2025-01-01') end_date: Optional filter for memories created on or before this date (ISO 8601: '2025-01-09') limit: Maximum results to return (default 10, max 100) include_inactive: Include superseded/contradicted memories (default: false) ctx: MCP context (automatically provided) Returns: Dict with success status, query, found count, and memories list Examples: >>> await recall("Python error handling") {'success': True, 'found': 3, 'memories': [...]} >>> await recall("documentation", source_type="document", limit=5) {'success': True, 'found': 2, 'memories': [...]} >>> await recall("debugging", tags=["python"]) {'success': True, 'found': 2, 'memories': [...]} # Only memories tagged with 'python' >>> await recall("project updates", start_date="2025-01-01", end_date="2025-01-07") {'success': True, 'found': 5, 'memories': [...]} # Only memories from that week Note: Document chunks include surrounding context automatically (2 chunks before/after). Document results also include source_type="document", filename, document_title, and document_id when available, making it easy to identify which document a result came from.