# Memory
URL: /docs/framework/memory
Source: /app/src/content/docs/framework/memory.mdx

import { DocImage } from '@/components/DocImage';

<DocImage title="Memory" />

**Persistent conversation memory with vector search and automatic context integration**

## Overview

The Memory system provides agents with long-term memory capabilities, enabling them to remember past conversations, learn from interactions, and maintain context across sessions. When memory is enabled, agents automatically store and retrieve relevant information from previous conversations, creating a more personalized and context-aware experience.

## Enabling Memory

Enable memory for an agent by setting the `memory` option to `true`:

```typescript
import { Agent } from '@astreus-ai/astreus';

const agent = await Agent.create({
  name: 'MemoryAgent',
  model: 'gpt-4o',
  memory: true  // Enable persistent memory
});
```

## Basic Usage

Here's a complete example showing how memory works across conversations:

```typescript
import { Agent } from '@astreus-ai/astreus';

// Create an agent with memory
const agent = await Agent.create({
  name: 'PersonalAssistant',
  model: 'gpt-4o',
  memory: true,
  systemPrompt: 'You are a helpful personal assistant who remembers user preferences.'
});

// First conversation
const response1 = await agent.ask('My name is John and I love TypeScript');
console.log(response1);
// Output: "Nice to meet you, John! It's great that you love TypeScript..."

// Later conversation - agent remembers
const response2 = await agent.ask('What programming language do I like?');
console.log(response2);
// Output: "You mentioned that you love TypeScript, John!"

// Memory persists even after restarting
const sameAgent = await Agent.create({
  name: 'PersonalAssistant', // Same name retrieves existing memories
  model: 'gpt-4o',
  memory: true
});

const response3 = await sameAgent.ask('Do you remember my name?');
console.log(response3);
// Output: "Yes, your name is John!"
```

## Memory Methods

When memory is enabled, agents have access to these memory management methods:

```typescript
// Add a memory manually
const memory = await agent.addMemory(
  'Important project information: Budget is $50k',
  { type: 'project', category: 'budget' }
);

// Remember conversation with role context
const userMemory = await agent.rememberConversation(
  'I prefer TypeScript over JavaScript',
  'user'
);

// Get a specific memory by ID
const existingMemory = await agent.getMemory(memory.id);

// Search memories by content (semantic search with embeddings)
const budgetMemories = await agent.searchMemories('budget', {
  limit: 5,
  startDate: new Date('2024-01-01')
});

// Vector similarity search for semantic matching
const happyMemories = await agent.searchMemoriesBySimilarity('joyful moments', {
  similarityThreshold: 0.7,  // Minimum similarity score
  limit: 10
});

// List all memories with options
const allMemories = await agent.listMemories({
  limit: 20,
  orderBy: 'createdAt',
  order: 'desc'
});

// Update a memory
const updatedMemory = await agent.updateMemory(memory.id, {
  content: 'Updated budget: $75k',
  metadata: { type: 'project', category: 'budget', updated: true }
});

// Delete a specific memory
const deleted = await agent.deleteMemory(memory.id);

// Generate embedding for existing memory (migration/repair)
const result = await agent.generateEmbeddingForMemory(memory.id);
if (result.success) {
  console.log('✅ Embedding generated successfully');
}

// Clear all memories
const deletedCount = await agent.clearMemories();
```

## Similarity Search Mathematics

When searching memories using vector similarity, the system calculates similarity scores between query and memory embeddings:

### Cosine Similarity Score

$\text{similarity} = \frac{\vec{q} \cdot \vec{m}}{||\vec{q}|| \cdot ||\vec{m}||} \in [0, 1]$

Where:

* $\vec{q}$ is the query embedding vector
* $\vec{m}$ is the memory embedding vector
* Result ranges from 0 (completely different) to 1 (identical)

### Distance-Based Score

For distance metrics, the similarity score is calculated as:
$\text{score} = \frac{1}{1 + d(\vec{q}, \vec{m})}$

Where $d$ is the Euclidean distance between vectors.

### Threshold Filtering

Memories are returned only if:
$\text{similarity} \geq \theta$

Where $\theta$ is the `similarityThreshold` parameter (default: 0.7).

## Memory Object Structure

```typescript
interface Memory {
  id?: number;              // Unique memory identifier
  agentId: number;          // ID of the owning agent
  content: string;          // Memory content
  embedding?: number[];     // Vector embedding (auto-generated)
  metadata?: MetadataObject; // Custom metadata
  createdAt?: Date;         // When memory was created
  updatedAt?: Date;         // Last update time
}

interface MemorySearchOptions {
  limit?: number;           // Max results (default: 10 for search, 100 for list)
  offset?: number;          // Skip results (default: 0)
  orderBy?: 'createdAt' | 'updatedAt' | 'relevance'; // Sort field
  order?: 'asc' | 'desc';   // Sort order (default: 'desc')
  startDate?: Date;         // Filter from date
  endDate?: Date;           // Filter to date
  similarityThreshold?: number; // Similarity threshold (0-1, default: 0.7)
  useEmbedding?: boolean;   // Use embedding search (default: true)
}
```