Memories Search (/v4/search)

Memories search (POST /v4/search) provides minimal-latency search optimized for real-time interactions. This endpoint prioritizes speed over extensive control, making it perfect for chatbots, Q&A systems, and any application where users expect immediate responses.

Basic Search

TypeScript
Python
cURL

import Supermemory from 'supermemory';

const client = new Supermemory({
  apiKey: process.env.SUPERMEMORY_API_KEY!
});

const results = await client.search.memories({
  q: "machine learning applications",
  limit: 5
});

console.log(results)

from supermemory import Supermemory
import os

client = Supermemory(api_key=os.environ.get("SUPERMEMORY_API_KEY"))

results = client.search.memories(
    q="machine learning applications",
    limit=5
)

console.log(results)

curl -X POST "https://api.supermemory.ai/v4/search" \
  -H "Authorization: Bearer $SUPERMEMORY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "q": "machine learning applications",
    "limit": 5
  }'

Sample Output:

{
  "results": [
    {
      "id": "mem_ml_apps_2024",
      "memory": "Machine learning applications span numerous industries including healthcare (diagnostic imaging, drug discovery), finance (fraud detection, algorithmic trading), autonomous vehicles (computer vision, path planning), and natural language processing (chatbots, translation services).",
      "similarity": 0.92,
      "title": "Machine Learning Industry Applications",
      "type": "text",
      "metadata": {
        "topic": "machine-learning",
        "industry": "technology",
        "created": "2024-01-10"
      }
    },
    {
      "id": "mem_ml_healthcare",
      "memory": "In healthcare, machine learning enables early disease detection through medical imaging analysis, personalized treatment recommendations, and drug discovery acceleration by predicting molecular behavior.",
      "similarity": 0.89,
      "title": "ML in Healthcare",
      "type": "text"
    }
  ],
  "total": 8,
  "timing": 87
}

Container Tag Filtering

Filter by user, project, or organization:

TypeScript
Python
cURL

const results = await client.search.memories({
  q: "project updates",
  containerTag: "user_123",  // Note: singular, not plural
  limit: 10
});

results = client.search.memories(
    q="project updates",
    container_tag="user_123",  # Note: singular, not plural
    limit=10
)

curl -X POST "https://api.supermemory.ai/v4/search" \
  -H "Authorization: Bearer $SUPERMEMORY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "q": "project updates",
    "containerTag": "user_123",
    "limit": 10
  }'

Threshold Control

Control result quality with similarity threshold:

TypeScript
Python
cURL

const results = await client.search.memories({
  q: "artificial intelligence research",
  threshold: 0.7,  // Higher = fewer, more similar results
  limit: 10
});

results = client.search.memories(
    q="artificial intelligence research",
    threshold=0.7,  # Higher = fewer, more similar results
    limit=10
)

curl -X POST "https://api.supermemory.ai/v4/search" \
  -H "Authorization: Bearer $SUPERMEMORY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "q": "artificial intelligence research",
    "threshold": 0.7,
    "limit": 10
  }'

Reranking

Improve result quality with secondary ranking:

TypeScript
Python
cURL

const results = await client.search.memories({
  q: "quantum computing breakthrough",
  rerank: true,  // Better relevance, slight latency increase
  limit: 5
});

results = client.search.memories(
    q="quantum computing breakthrough",
    rerank=True,  # Better relevance, slight latency increase
    limit=5
)

curl -X POST "https://api.supermemory.ai/v4/search" \
  -H "Authorization: Bearer $SUPERMEMORY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "q": "quantum computing breakthrough",
    "rerank": true,
    "limit": 5
  }'

Query Rewriting

Improve search accuracy with automatic query expansion:

TypeScript
Python
cURL

const results = await client.search.memories({
  q: "How do neural networks learn?",
  rewriteQuery: true,  // +400ms latency but better results
  limit: 5
});

results = client.search.memories(
    q="How do neural networks learn?",
    rewrite_query=True,  # +400ms latency but better results
    limit=5
)

curl -X POST "https://api.supermemory.ai/v4/search" \
  -H "Authorization: Bearer $SUPERMEMORY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "q": "How do neural networks learn?",
    "rewriteQuery": true,
    "limit": 5
  }'

Include documents, related memories, and summaries:

TypeScript
Python
cURL

const results = await client.search.memories({
  q: "machine learning trends",
  include: {
    documents: true,        // Include source documents
    relatedMemories: true,  // Include related memory entries
    summaries: true         // Include memory summaries
  },
  limit: 5
});

results = client.search.memories(
    q="machine learning trends",
    include={
        "documents": True,        # Include source documents
        "relatedMemories": True,  # Include related memory entries
        "summaries": True         # Include memory summaries
    },
    limit=5
)

curl -X POST "https://api.supermemory.ai/v4/search" \
  -H "Authorization: Bearer $SUPERMEMORY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "q": "machine learning trends",
    "include": {
      "documents": true,
      "relatedMemories": true,
      "summaries": true
    },
    "limit": 5
  }'

Metadata Filtering

Simple metadata filtering for Memories search:

TypeScript
Python
cURL

const results = await client.search.memories({
  q: "research findings",
  filters: {
    AND: [
      { key: "category", value: "science", negate: false },
      { key: "status", value: "published", negate: false }
    ]
  },
  limit: 10
});

results = client.search.memories(
    q="research findings",
    filters={
        "AND": [
            {"key": "category", "value": "science", "negate": False},
            {"key": "status", "value": "published", "negate": False}
        ]
    },
    limit=10
)

curl -X POST "https://api.supermemory.ai/v4/search" \
  -H "Authorization: Bearer $SUPERMEMORY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "q": "research findings",
    "filters": {
      "AND": [
        {"key": "category", "value": "science", "negate": false},
        {"key": "status", "value": "published", "negate": false}
      ]
    },
    "limit": 10
  }'

Chatbot Example

Optimal configuration for conversational AI:

TypeScript
Python
cURL

// Optimized for chatbot responses
const results = await client.search.memories({
  q: userMessage,
  containerTag: userId,
  threshold: 0.6,     // Balanced relevance
  rerank: false,      // Skip for speed
  rewriteQuery: false, // Skip for speed
  limit: 3            // Few, relevant results
});

// Quick response for chat
const context = results.results
  .map(r => r.memory)
  .join('\n\n');

# Optimized for chatbot responses
results = client.search.memories(
    q=user_message,
    container_tag=user_id,
    threshold=0.6,     # Balanced relevance
    rerank=False,      # Skip for speed
    rewrite_query=False, # Skip for speed
    limit=3            # Few, relevant results
)

# Quick response for chat
context = '\n\n'.join([r.memory for r in results.results])

# Optimized for chatbot responses
curl -X POST "https://api.supermemory.ai/v4/search" \
  -H "Authorization: Bearer $SUPERMEMORY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "q": "user question here",
    "containerTag": "user_123",
    "threshold": 0.6,
    "rerank": false,
    "rewriteQuery": false,
    "limit": 3
  }'

Complete Memories Search Example

Combining features for comprehensive results:

TypeScript
Python
cURL

const results = await client.search.memories({
  q: "machine learning model performance",
  containerTag: "research_team",
  filters: {
    AND: [
      { key: "topic", value: "ai", negate: false }
    ]
  },
  threshold: 0.7,
  rerank: true,
  rewriteQuery: false, // Skip for speed
  include: {
    documents: true,
    relatedMemories: false,
    summaries: true
  },
  limit: 5
});

results = client.search.memories(
    q="machine learning model performance",
    container_tag="research_team",
    filters={
        "AND": [
            {"key": "topic", "value": "ai", "negate": False}
        ]
    },
    threshold=0.7,
    rerank=True,
    rewrite_query=False,  # Skip for speed
    include={
        "documents": True,
        "relatedMemories": False,
        "summaries": True
    },
    limit=5
)

curl -X POST "https://api.supermemory.ai/v4/search" \
  -H "Authorization: Bearer $SUPERMEMORY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "q": "machine learning model performance",
    "containerTag": "research_team",
    "filters": {
      "AND": [
        {"key": "topic", "value": "ai", "negate": false}
      ]
    },
    "threshold": 0.7,
    "rerank": true,
    "rewriteQuery": false,
    "include": {
      "documents": true,
      "relatedMemories": false,
      "summaries": true
    },
    "limit": 5
  }'

Comon Use Cases

Chatbots: Basic search with container tag and low threshold
Q&A Systems: Add reranking for better relevance
Knowledge Retrieval: Include documents and summaries
Real-time Search: Skip rewriting and reranking for maximum speed

Getting Started

Memory API

User Profiles

Memory Router

Integrations with no-code tools

Migration Guides

Deployment

Basic Search

Container Tag Filtering

Threshold Control

Reranking

Query Rewriting

Metadata Filtering

Chatbot Example

Complete Memories Search Example

Comon Use Cases

Getting Started

Memory API

User Profiles

Memory Router

Integrations with no-code tools

Migration Guides

Deployment

​Basic Search

​Container Tag Filtering

​Threshold Control

​Reranking

​Query Rewriting

​Include Related Content

​Metadata Filtering

​Chatbot Example

​Complete Memories Search Example

​Comon Use Cases

Basic Search

Container Tag Filtering

Threshold Control

Reranking

Query Rewriting

Include Related Content

Metadata Filtering

Chatbot Example

Complete Memories Search Example

Comon Use Cases