Add Memory

Store user preferences, conversation history, and facts in your AI application’s memory system for intelligent recall and personalization. Store information and conversations in your memory system.

Basic Usage

Python SDK
JavaScript SDK

from gravixlayer import GravixLayer

client = GravixLayer()

# Initialize memory with all required parameters
memory = client.memory(
    embedding_model="baai/bge-large-en-v1.5",
    inference_model="google/gemma-3-12b-it",
    index_name="my_memories",
    cloud_provider="AWS",
    region="us-east-1"
)

# Add simple text
result = memory.add("I love pizza", user_id="alice")
print(f"Added memory: {result['results'][0]['memory']}")
print(f"Memory ID: {result['results'][0]['id']}")

# Add with metadata
result = memory.add("User prefers dark mode", user_id="alice", metadata={"type": "preference"})
print(f"Added preference: {result['results'][0]['memory']}")
print(f"Metadata: {result['results'][0]['metadata']}")

# Get all memories to verify
all_memories = memory.get_all(user_id="alice")
print(f"\nTotal memories for alice: {len(all_memories['results'])}")
for i, mem in enumerate(all_memories['results'], 1):
    print(f"{i}. {mem['memory']}")
    if mem.get('metadata'):
        print(f"   Metadata: {mem['metadata']}")

Expected Output:

Added memory: I love pizza
Memory ID: b355d0d2-3eaa-4bc6-a61b-48ee615279bf
Added preference: User prefers dark mode
Metadata: {'type': 'preference'}

Total memories for alice: 2
1. I love pizza
2. User prefers dark mode
   Metadata: {'type': 'preference'}

import { GravixLayer, Memory } from 'gravixlayer';

const client = new GravixLayer();

// Initialize memory with all required parameters
const memory = new Memory(
    client,
    'baai/bge-large-en-v1.5',
    'google/gemma-3-12b-it',
    'my_memories',
    'AWS',
    'us-east-1'
);

async function addMemories() {
    // Add simple text
    const result1 = await memory.add("I love pizza", "alice");
    console.log(`Added memory: ${result1.results[0].memory}`);
    console.log(`Memory ID: ${result1.results[0].id}`);

    // Add with metadata
    const result2 = await memory.add("User prefers dark mode", "alice", {
        metadata: {type: "preference"}
    });
    console.log(`Added preference: ${result2.results[0].memory}`);
    console.log(`Metadata: ${JSON.stringify(result2.results[0].metadata)}`);

    // Get all memories to verify
    const allMemories = await memory.getAll("alice");
    console.log(`\nTotal memories for alice: ${allMemories.results.length}`);
    allMemories.results.forEach((mem, index) => {
        console.log(`${index + 1}. ${mem.memory}`);
        if (mem.metadata) {
            console.log(`   Metadata: ${JSON.stringify(mem.metadata)}`);
        }
    });
}

addMemories().catch(console.error);

Expected Output:

Added memory: I love pizza
Memory ID: b355d0d2-3eaa-4bc6-a61b-48ee615279bf
Added preference: User prefers dark mode
Metadata: {"type":"preference"}

Total memories for alice: 2
1. I love pizza
2. User prefers dark mode
   Metadata: {"type":"preference"}

Add Conversations

Store entire conversations and let AI extract key memories:

Python SDK
JavaScript SDK

from gravixlayer import GravixLayer

client = GravixLayer()

# Initialize memory
memory = client.memory(
    embedding_model="baai/bge-large-en-v1.5",
    inference_model="google/gemma-3-12b-it",
    index_name="conversations",
    cloud_provider="AWS",
    region="us-east-1"
)

# Store a conversation with AI inference
conversation = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]

result = memory.add(conversation, user_id="alice", infer=True, metadata={"type": "conversation"})
print(f"AI extracted {len(result['results'])} memories from conversation:")

for i, extracted_memory in enumerate(result['results'], 1):
    print(f"{i}. {extracted_memory['memory']}")
    print(f"   ID: {extracted_memory['id']}")
    if extracted_memory.get('metadata'):
        print(f"   Metadata: {extracted_memory['metadata']}")

# Verify by searching for movie preferences
search_results = memory.search("movie preferences", user_id="alice")
print(f"\nFound {len(search_results['results'])} movie-related memories:")
for result in search_results['results']:
    print(f"- {result['memory']}")

Expected Output:

AI extracted 2 memories from conversation:
1. User prefers sci-fi movies
   ID: c455d0d2-3eaa-4bc6-a61b-48ee615279bf
   Metadata: {'type': 'conversation'}
2. User dislikes thriller movies
   ID: d755d0d2-3eaa-4bc6-a61b-48ee615279bf
   Metadata: {'type': 'conversation'}

Found 2 movie-related memories:
- User prefers sci-fi movies
- User dislikes thriller movies

import { GravixLayer, Memory } from 'gravixlayer';

const client = new GravixLayer();

// Initialize memory
const memory = new Memory(
    client,
    'baai/bge-large-en-v1.5',
    'google/gemma-3-12b-it',
    'conversations',
    'AWS',
    'us-east-1'
);

async function addConversation() {
    // Store a conversation with AI inference
    const conversation = [
        {role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?"},
        {role: "assistant", content: "How about thriller movies? They can be quite engaging."},
        {role: "user", content: "I'm not a big fan of thriller movies but I love sci-fi movies."},
        {role: "assistant", content: "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
    ];

    const result = await memory.add(conversation, "alice", {
        infer: true,
        metadata: {type: "conversation"}
    });
    
    console.log(`AI extracted ${result.results.length} memories from conversation:`);
    
    result.results.forEach((extractedMemory, index) => {
        console.log(`${index + 1}. ${extractedMemory.memory}`);
        console.log(`   ID: ${extractedMemory.id}`);
        if (extractedMemory.metadata) {
            console.log(`   Metadata: ${JSON.stringify(extractedMemory.metadata)}`);
        }
    });

    // Verify by searching for movie preferences
    const searchResults = await memory.search("movie preferences", "alice");
    console.log(`\nFound ${searchResults.results.length} movie-related memories:`);
    searchResults.results.forEach(result => {
        console.log(`- ${result.memory}`);
    });
}

addConversation().catch(console.error);

Expected Output:

AI extracted 2 memories from conversation:
1. User prefers sci-fi movies
   ID: c455d0d2-3eaa-4bc6-a61b-48ee615279bf
   Metadata: {"type":"conversation"}
2. User dislikes thriller movies
   ID: d755d0d2-3eaa-4bc6-a61b-48ee615279bf
   Metadata: {"type":"conversation"}

Found 2 movie-related memories:
- User prefers sci-fi movies
- User dislikes thriller movies

Multilingual Support

Initialize memory with multilingual embedding model for multi-language support:

Configuration Parameters

All parameters are REQUIRED:

embedding_model - How text gets converted to searchable vectors (required)
inference_model - AI model that extracts memories from conversations (required)
index_name - Where memories are stored (like folders) (required)
cloud_provider - Where your data is hosted (required)
region - Cloud region (required)
delete_protection - Protect index from deletion (optional, default: False)

Python SDK
JavaScript SDK

from gravixlayer import GravixLayer

client = GravixLayer()

# Initialize with multilingual support
memory = client.memory(
    embedding_model="microsoft/multilingual-e5-large",  # Supports 100+ languages
    inference_model="google/gemma-3-12b-it",
    index_name="user_preferences",
    cloud_provider="GCP",
    region="us-central1",
    delete_protection=False  # Optional, defaults to False
)

# Now works with any language
result1 = memory.add("El usuario prefiere pizza", user_id="alice")
result2 = memory.add("L'utilisateur aime le café", user_id="alice")
result3 = memory.add("用户喜欢寿司", user_id="alice")

print("Added multilingual memories:")
print(f"Spanish: {result1['results'][0]['memory']}")
print(f"French: {result2['results'][0]['memory']}")
print(f"Chinese: {result3['results'][0]['memory']}")

# Check current configuration
config = memory.get_current_configuration()
print(f"\nCurrent configuration:")
print(f"Embedding model: {config['embedding_model']}")
print(f"Inference model: {config['inference_model']}")
print(f"Index name: {config['index_name']}")

# Search works across all languages
search_results = memory.search("food preferences", user_id="alice")
print(f"\nFound {len(search_results['results'])} food-related memories:")
for result in search_results['results']:
    print(f"- {result['memory']}")

Expected Output:

Added multilingual memories:
Spanish: El usuario prefiere pizza
French: L'utilisateur aime le café
Chinese: 用户喜欢寿司

Current configuration:
Embedding model: microsoft/multilingual-e5-large
Inference model: google/gemma-3-12b-it
Index name: user_preferences

Found 3 food-related memories:
- El usuario prefiere pizza
- L'utilisateur aime le café
- 用户喜欢寿司

import { GravixLayer, Memory } from 'gravixlayer';

const client = new GravixLayer();

// Initialize with multilingual support
const memory = new Memory(
    client,
    'microsoft/multilingual-e5-large',  // Supports 100+ languages
    'google/gemma-3-12b-it',
    'user_preferences',
    'GCP',
    'us-central1',
    false  // deleteProtection - optional, defaults to false
);

async function setupMultilingualMemory() {
    // Now works with any language
    const result1 = await memory.add("El usuario prefiere pizza", "alice");
    const result2 = await memory.add("L'utilisateur aime le café", "alice");
    const result3 = await memory.add("用户喜欢寿司", "alice");

    console.log("Added multilingual memories:");
    console.log(`Spanish: ${result1.results[0].memory}`);
    console.log(`French: ${result2.results[0].memory}`);
    console.log(`Chinese: ${result3.results[0].memory}`);

    // Check current configuration
    const config = memory.getCurrentConfiguration();
    console.log(`\nCurrent configuration:`);
    console.log(`Embedding model: ${config.embedding_model}`);
    console.log(`Inference model: ${config.inference_model}`);
    console.log(`Index name: ${config.index_name}`);

    // Search works across all languages
    const searchResults = await memory.search("food preferences", "alice");
    console.log(`\nFound ${searchResults.results.length} food-related memories:`);
    searchResults.results.forEach(result => {
        console.log(`- ${result.memory}`);
    });
}

setupMultilingualMemory().catch(console.error);

Expected Output:

Added multilingual memories:
Spanish: El usuario prefiere pizza
French: L'utilisateur aime le café
Chinese: 用户喜欢寿司

Current configuration:
Embedding model: microsoft/multilingual-e5-large
Inference model: google/gemma-3-12b-it
Index name: user_preferences

Found 3 food-related memories:
- El usuario prefiere pizza
- L'utilisateur aime le café
- 用户喜欢寿司

Getting Started

Core Features

Advanced Usage

Resources

Basic Usage

Add Conversations

Multilingual Support

Configuration Parameters

Getting Started

Core Features

Advanced Usage

Resources

​Basic Usage

​Add Conversations

​Multilingual Support

​Configuration Parameters

Basic Usage

Add Conversations

Multilingual Support

Configuration Parameters