Search through your memories and documents with a single API call.
Use searchMode: "hybrid" for best results. It searches both memories and document chunks, returning the most relevant content.
Quick Start import Supermemory from 'supermemory' ; const client = new Supermemory (); const results = await client . search . memories ({ q: "machine learning" , containerTag: "user_123" , searchMode: "hybrid" , limit: 5 }); results . results . forEach ( result => { console . log ( result . memory || result . chunk , result . similarity ); });
from supermemory import Supermemory client = Supermemory() results = client.search.memories( q = "machine learning" , container_tag = "user_123" , search_mode = "hybrid" , limit = 5 ) for result in results.results: print (result.memory or result.chunk, result.similarity)
curl -X POST "https://api.supermemory.ai/v4/search" \ -H "Authorization: Bearer $SUPERMEMORY_API_KEY " \ -H "Content-Type: application/json" \ -d '{ "q": "machine learning", "containerTag": "user_123", "searchMode": "hybrid", "limit": 5 }'
Response: { "results" : [ { "id" : "mem_xyz" , "memory" : "User is interested in machine learning for product recommendations" , "similarity" : 0.91 , "metadata" : { "topic" : "interests" }, "updatedAt" : "2024-01-15T10:30:00.000Z" , "version" : 1 }, { "id" : "chunk_abc" , "chunk" : "Machine learning enables personalized experiences at scale..." , "similarity" : 0.87 , "metadata" : { "source" : "onboarding_doc" }, "updatedAt" : "2024-01-14T09:15:00.000Z" , "version" : 1 } ], "timing" : 92 , "total" : 5 }
In hybrid mode, results contain either a memory field (extracted facts) or a chunk field (document content), depending on the source.
Parameters Parameter Type Default Description qstring required Search query containerTagstring — Filter by user/project searchModestring "hybrid""hybrid" (recommended) or "memories"limitnumber 10 Max results threshold0-1 0.5 Similarity cutoff (higher = fewer, better results) rerankboolean false Re-score for better relevance (+100ms) filtersobject — Metadata filters (AND/OR structure)
Search Modes
hybridmemories
// Hybrid: memories + document chunks (recommended) await client . search . memories ({ q: "quarterly goals" , containerTag: "user_123" , searchMode: "hybrid" }); // Memories only: just extracted facts await client . search . memories ({ q: "user preferences" , containerTag: "user_123" , searchMode: "memories" });
Filtering Filter by containerTag to scope results to a user or project:
const results = await client . search . memories ({ q: "project updates" , containerTag: "user_123" , searchMode: "hybrid" });
Use filters for metadata-based filtering:
const results = await client . search . memories ({ q: "meeting notes" , containerTag: "user_123" , filters: { AND: [ { key: "type" , value: "meeting" }, { key: "year" , value: "2024" } ] } });
String equality: { key: "status", value: "active" }String contains: { filterType: "string_contains", key: "title", value: "react" }Numeric: { filterType: "numeric", key: "priority", value: "5", numericOperator: ">=" }Array contains: { filterType: "array_contains", key: "tags", value: "important" }Negate: { key: "status", value: "draft", negate: true } See Organizing & Filtering for full syntax. Query Optimization Reranking Re-scores results for better relevance. Adds ~100ms latency.
const results = await client . search . memories ({ q: "complex technical question" , containerTag: "user_123" , rerank: true });
Threshold Control result quality vs quantity:
// Broad search — more results await client . search . memories ({ q: "..." , threshold: 0.3 }); // Precise search — fewer, better results await client . search . memories ({ q: "..." , threshold: 0.8 });
Chatbot Example Optimal configuration for conversational AI:
async function getContext ( userId : string , message : string ) { const results = await client . search . memories ({ q: message , containerTag: userId , searchMode: "hybrid" , threshold: 0.6 , limit: 5 }); return results . results . map ( r => r . memory || r . chunk ) . join ( ' \n\n ' ); }
interface SearchResult { id : string ; memory ?: string ; // Present for memory results chunk ?: string ; // Present for document chunk results similarity : number ; // 0-1 metadata : object | null ; updatedAt : string ; version : number ; } interface SearchResponse { results : SearchResult []; timing : number ; // ms total : number ; }
Next Steps 
