Add Memory

`memory.add`

Saves a new memory with automatic embedding generation for semantic search.

Parameters

string

required

User identifier for memory isolation. Must be a stable, unique ID for each user.

userId: 'user-123'

string

required

The text content to save. Should be a complete, self-contained statement.

content: 'User prefers TypeScript over JavaScript for type safety'

Write memories as complete sentences that make sense out of context.

object

Optional metadata for categorization and filtering.

metadata: {
  category: 'preference',
  tags: ['programming', 'languages'],
  importance: 'high',
  customField: 'value'
}

Response

string

required

Unique identifier (UUID) for the memory

string

required

The saved text content

string

required

User identifier

string

required

Tenant identifier (API key owner)

object

Custom metadata object

string

required

ISO 8601 timestamp of creation

string

required

ISO 8601 timestamp of last update

Examples

import { createTRPCClient, httpBatchLink } from '@trpc/client';
import type { AppRouter } from '@satori/server';

const client = createTRPCClient<AppRouter>({
  links: [
    httpBatchLink({
      url: 'https://api.usesatori.sh/trpc',
      headers: {
        'x-api-key': process.env.SATORI_API_KEY!,
      },
    }),
  ],
});

const memory = await client.memory.add.mutate({
  userId: 'user-123',
  content: 'User prefers dark mode in all applications',
  metadata: {
    category: 'preference',
    tags: ['ui', 'theme'],
  },
});

console.log('Memory saved:', memory.id);

const memory = await client.memory.add.mutate({
  userId: 'user-123',
  content: 'User prefers dark mode in all applications',
  metadata: {
    category: 'preference',
    tags: ['ui', 'theme'],
  },
});

curl -X POST 'https://api.usesatori.sh/trpc/memory.add' \
  -H 'x-api-key: sk_satori_...' \
  -H 'Content-Type: application/json' \
  -d '{
    "userId": "user-123",
    "content": "User prefers dark mode in all applications",
    "metadata": {
      "category": "preference",
      "tags": ["ui", "theme"]
    }
  }'

{
  "userId": "user-123",
  "content": "User prefers TypeScript over JavaScript for type safety",
  "metadata": {
    "category": "preference",
    "tags": ["programming", "languages"],
    "importance": "high"
  }
}

{
  "id": "550e8400-e29b-41d4-a716-446655440000",
  "content": "User prefers TypeScript over JavaScript for type safety",
  "userId": "user-123",
  "clerkUserId": "user_372Icb...",
  "metadata": {
    "category": "preference",
    "tags": ["programming", "languages"],
    "importance": "high"
  },
  "createdAt": "2024-01-15T10:30:00.000Z",
  "updatedAt": "2024-01-15T10:30:00.000Z"
}

{
  "error": {
    "message": "Unauthorized - Invalid API key",
    "code": "UNAUTHORIZED"
  }
}

{
  "error": {
    "message": "Invalid input: content is required",
    "code": "BAD_REQUEST"
  }
}

Embedding Generation

When you save a memory, Satori automatically:

Sends the content to OpenAI’s text-embedding-3-small model
Receives a 1536-dimensional vector
Stores both the text and embedding in PostgreSQL

This enables semantic search without any additional work on your part.

Embedding generation typically takes 50-100ms. The API call returns after the memory is fully saved with its embedding.

Best Practices

Write complete, self-contained memories

// ✅ Good: Complete sentence with context
await client.memory.add.mutate({
  userId: 'user-123',
  content: 'User prefers TypeScript over JavaScript for type safety',
});

// ❌ Bad: Too vague
await client.memory.add.mutate({
  userId: 'user-123',
  content: 'likes TS',
});

Use metadata for organization

await client.memory.add.mutate({
  userId: 'user-123',
  content: 'User works at Acme Corp as a senior engineer',
  metadata: {
    category: 'professional',
    subcategory: 'employment',
    verified: true,
    source: 'user-provided',
  },
});

Avoid duplicate memories

// Search first to avoid duplicates
const existing = await client.memory.search.query({
  userId: 'user-123',
  query: 'TypeScript preference',
  threshold: 0.9,
});

if (existing.length === 0) {
  await client.memory.add.mutate({
    userId: 'user-123',
    content: 'User prefers TypeScript',
  });
}

Error Handling

try {
  const memory = await client.memory.add.mutate({
    userId: 'user-123',
    content: 'Important information',
  });
  
  console.log('Memory saved:', memory.id);
} catch (error) {
  if (error.message.includes('Unauthorized')) {
    console.error('Invalid API key');
  } else if (error.message.includes('rate limit')) {
    console.error('Rate limit exceeded, retry later');
  } else {
    console.error('Failed to save memory:', error);
  }
}

Search Memories

Find relevant memories using semantic search

Get All Memories

Retrieve all memories for a user

Delete Memory

Remove a specific memory

Integration Guide

See this endpoint in action

Overview

Memory Operations

API Key Management

`memory.add`

Parameters

Response

Examples

Embedding Generation

Best Practices

Error Handling

Search Memories

Get All Memories

Delete Memory

Integration Guide

​memory.add

​Parameters

​Response

​Examples

​Embedding Generation

​Best Practices

​Error Handling

​Related Endpoints

Search Memories

Get All Memories

Delete Memory

Integration Guide

`memory.add`

Parameters

Response

Examples

Embedding Generation

Best Practices

Error Handling

Related Endpoints