# SemanticRecall

The `SemanticRecall` is a **hybrid processor** that enables semantic search over conversation history using vector embeddings. On input, it performs semantic search to find relevant historical messages. On output, it creates embeddings for new messages to enable future semantic retrieval.

## Usage example

```typescript
import { SemanticRecall } from '@mastra/core/processors'
import { openai } from '@ai-sdk/openai'

const processor = new SemanticRecall({
  storage: memoryStorage,
  vector: vectorStore,
  embedder: openai.embedding('text-embedding-3-small'),
  topK: 5,
  messageRange: 2,
  scope: 'resource',
})
```

## Constructor parameters

**options** (`SemanticRecallOptions`): Configuration options for the semantic recall processor

**options.storage** (`MemoryStorage`): Storage instance for retrieving messages

**options.vector** (`MastraVector`): Vector store for semantic search

**options.embedder** (`MastraEmbeddingModel<string>`): Embedder for generating query embeddings

**options.topK** (`number`): Number of most similar messages to retrieve

**options.messageRange** (`number | { before: number; after: number }`): Number of context messages to include before/after each match. Can be a single number (same for both) or an object with separate values

**options.scope** (`'thread' | 'resource'`): Scope of semantic search. 'thread' searches within the current thread only. 'resource' searches across all threads for the resource

**options.threshold** (`number`): Minimum similarity score threshold (0-1). Messages below this threshold are filtered out

**options.indexName** (`string`): Index name for the vector store. If not provided, auto-generated based on embedder model

**options.logger** (`IMastraLogger`): Optional logger instance for structured logging

## Returns

**id** (`string`): Processor identifier set to 'semantic-recall'

**name** (`string`): Processor display name set to 'SemanticRecall'

**processInput** (`(args: { messages: MastraDBMessage[]; messageList: MessageList; abort: (reason?: string) => never; tracingContext?: TracingContext; requestContext?: RequestContext }) => Promise<MessageList | MastraDBMessage[]>`): Performs semantic search on historical messages and adds relevant context to the message list

**processOutputResult** (`(args: { messages: MastraDBMessage[]; messageList?: MessageList; abort: (reason?: string) => never; tracingContext?: TracingContext; requestContext?: RequestContext }) => Promise<MessageList | MastraDBMessage[]>`): Creates embeddings for new messages to enable future semantic search

## Extended usage example

```typescript
import { Agent } from '@mastra/core/agent'
import { SemanticRecall, MessageHistory } from '@mastra/core/processors'
import { PostgresStorage } from '@mastra/pg'
import { PgVector } from '@mastra/pg'
import { openai } from '@ai-sdk/openai'

const storage = new PostgresStorage({
  id: 'pg-storage',
  connectionString: process.env.DATABASE_URL,
})

const vector = new PgVector({
  id: 'pg-vector',
  connectionString: process.env.DATABASE_URL,
})

const semanticRecall = new SemanticRecall({
  storage,
  vector,
  embedder: openai.embedding('text-embedding-3-small'),
  topK: 5,
  messageRange: { before: 2, after: 1 },
  scope: 'resource',
  threshold: 0.7,
})

export const agent = new Agent({
  name: 'semantic-memory-agent',
  instructions: 'You are a helpful assistant with semantic memory recall',
  model: 'openai/gpt-5.5',
  inputProcessors: [semanticRecall, new MessageHistory({ storage, lastMessages: 50 })],
  outputProcessors: [semanticRecall, new MessageHistory({ storage })],
})
```

## Behavior

### Input processing

1. Extracts the user query from the last user message
2. Generates embeddings for the query
3. Performs vector search to find semantically similar messages
4. Retrieves matched messages along with surrounding context (based on `messageRange`)
5. For `scope: 'resource'`, formats cross-thread messages as a system message with timestamps
6. Adds recalled messages with `source: 'memory'` tag

### Output processing

1. Extracts text content from new user and assistant messages
2. Generates embeddings for each message
3. Stores embeddings in the vector store with metadata (message ID, thread ID, resource ID, role, content, timestamp)
4. Uses LRU caching for embeddings to avoid redundant API calls

### Cross-thread recall

When `scope` is set to `'resource'`, the processor can recall messages from other threads. These cross-thread messages are formatted as a system message with timestamps and conversation labels to provide context about when and where the conversation occurred.

## Related

- [Guardrails](https://mastra.ai/docs/agents/guardrails)