This package provides supermemory tools for AI SDK, OpenAI, and Mastra through dedicated submodule exports, each with function-based architectures optimized for their respective use cases.

Installation

npm install @supermemory/tools

Usage

The package provides three submodule imports:

@supermemory/tools/ai-sdk - For use with the AI SDK framework (includes withSupermemory middleware)
@supermemory/tools/openai - For use with OpenAI SDK (includes withSupermemory middleware and function calling tools)
@supermemory/tools/mastra - For use with Mastra AI agents (includes withSupermemory wrapper and processors)

AI SDK Usage

import { supermemoryTools, searchMemoriesTool, addMemoryTool } from "@supermemory/tools/ai-sdk"
import { createOpenAI } from "@ai-sdk/openai"
import { generateText } from "ai"

const openai = createOpenAI({
  apiKey: process.env.OPENAI_API_KEY!,
})

// Create all tools
const tools = supermemoryTools(process.env.SUPERMEMORY_API_KEY!, {
  containerTags: ["your-user-id"],
})

// Use with AI SDK
const result = await generateText({
  model: openai("gpt-5"),
  messages: [
    {
      role: "user",
      content: "What do you remember about my preferences?",
    },
  ],
  tools,
})

// Or create individual tools
const searchTool = searchMemoriesTool(process.env.SUPERMEMORY_API_KEY!, {
  projectId: "your-project-id",
})

const addTool = addMemoryTool(process.env.SUPERMEMORY_API_KEY!, {
  projectId: "your-project-id",
})

AI SDK Middleware with Supermemory

withSupermemory will take advantage supermemory profile v4 endpoint personalized based on container tag
You can provide the Supermemory API key via the apiKey option to withSupermemory (recommended for browser usage), or fall back to SUPERMEMORY_API_KEY in the environment for server usage.
Per-turn caching: Memory injection is cached for tool-call continuations within the same user turn. The middleware detects when the AI SDK is continuing a multi-step flow (e.g., after a tool call) and reuses the cached memories instead of making redundant API calls. A fresh fetch occurs on each new user message turn.

import { generateText } from "ai"
import { withSupermemory } from "@supermemory/tools/ai-sdk"
import { openai } from "@ai-sdk/openai"

const modelWithMemory = withSupermemory(openai("gpt-5"), "user_id_life")

const result = await generateText({
	model: modelWithMemory,
	messages: [{ role: "user", content: "where do i live?" }],
})

console.log(result.text)

Conversation Grouping

Use the conversationId option to group messages into a single document for contextual memory generation:

import { generateText } from "ai"
import { withSupermemory } from "@supermemory/tools/ai-sdk"
import { openai } from "@ai-sdk/openai"

const modelWithMemory = withSupermemory(openai("gpt-5"), "user_id_life", {
	conversationId: "conversation-456"
})

const result = await generateText({
	model: modelWithMemory,
	messages: [{ role: "user", content: "where do i live?" }],
})

console.log(result.text)

Verbose Mode

Enable verbose logging to see detailed information about memory search and transformation:

import { generateText } from "ai"
import { withSupermemory } from "@supermemory/tools/ai-sdk"
import { openai } from "@ai-sdk/openai"

const modelWithMemory = withSupermemory(openai("gpt-5"), "user_id_life", {
	verbose: true
})

const result = await generateText({
	model: modelWithMemory,
	messages: [{ role: "user", content: "where do i live?" }],
})

console.log(result.text)

When verbose mode is enabled, you'll see console output like:

[supermemory] Searching memories for container: user_id_life
[supermemory] User message: where do i live?
[supermemory] System prompt exists: false
[supermemory] Found 3 memories
[supermemory] Memory content: You live in San Francisco, California. Your address is 123 Main Street...
[supermemory] Creating new system prompt with memories

Memory Search Modes

The middleware supports different modes for memory retrieval:

Profile Mode (Default) - Retrieves user profile memories without query filtering:

import { generateText } from "ai"
import { withSupermemory } from "@supermemory/tools/ai-sdk"
import { openai } from "@ai-sdk/openai"

// Uses profile mode by default - gets all user profile memories
const modelWithMemory = withSupermemory(openai("gpt-4"), "user-123")

// Explicitly specify profile mode
const modelWithProfile = withSupermemory(openai("gpt-4"), "user-123", { 
  mode: "profile" 
})

const result = await generateText({
  model: modelWithMemory,
  messages: [{ role: "user", content: "What do you know about me?" }],
})

Query Mode - Searches memories based on the user's message:

import { generateText } from "ai"
import { withSupermemory } from "@supermemory/tools/ai-sdk"
import { openai } from "@ai-sdk/openai"

const modelWithQuery = withSupermemory(openai("gpt-4"), "user-123", { 
  mode: "query" 
})

const result = await generateText({
  model: modelWithQuery,
  messages: [{ role: "user", content: "What's my favorite programming language?" }],
})

Full Mode - Combines both profile and query results:

import { generateText } from "ai"
import { withSupermemory } from "@supermemory/tools/ai-sdk"
import { openai } from "@ai-sdk/openai"

const modelWithFull = withSupermemory(openai("gpt-4"), "user-123", { 
  mode: "full" 
})

const result = await generateText({
  model: modelWithFull,
  messages: [{ role: "user", content: "Tell me about my preferences" }],
})

Automatic Memory Capture

The middleware can automatically save user messages as memories:

Always Save Memories - Automatically stores every user message as a memory:

import { generateText } from "ai"
import { withSupermemory } from "@supermemory/tools/ai-sdk"
import { openai } from "@ai-sdk/openai"

const modelWithAutoSave = withSupermemory(openai("gpt-4"), "user-123", {
  addMemory: "always"
})

const result = await generateText({
  model: modelWithAutoSave,
  messages: [{ role: "user", content: "I prefer React with TypeScript for my projects" }],
})
// This message will be automatically saved as a memory

Never Save Memories (Default) - Only retrieves memories without storing new ones:

const modelWithNoSave = withSupermemory(openai("gpt-4"), "user-123")

Combined Options - Use verbose logging with specific modes and memory storage:

const modelWithOptions = withSupermemory(openai("gpt-4"), "user-123", {
  mode: "profile",
  addMemory: "always",
  verbose: true
})

Custom Prompt Templates

Customize how memories are formatted and injected into the system prompt using the promptTemplate option. This is useful for:

Using XML-based prompting (e.g., for Claude models)
Custom branding (removing "supermemories" references)
Controlling how your agent describes where information comes from

import { generateText } from "ai"
import { withSupermemory, type MemoryPromptData } from "@supermemory/tools/ai-sdk"
import { openai } from "@ai-sdk/openai"

const customPrompt = (data: MemoryPromptData) => `
<user_memories>
Here is some information about your past conversations with the user:
${data.userMemories}
${data.generalSearchMemories}
</user_memories>
`.trim()

const modelWithCustomPrompt = withSupermemory(openai("gpt-4"), "user-123", {
  mode: "full",
  promptTemplate: customPrompt,
})

const result = await generateText({
  model: modelWithCustomPrompt,
  messages: [{ role: "user", content: "What do you know about me?" }],
})

The MemoryPromptData object provides:

userMemories: Pre-formatted markdown combining static profile facts (name, preferences, goals) and dynamic context (current projects, recent interests)
generalSearchMemories: Pre-formatted search results based on semantic similarity to the current query
searchResults: Raw search results array for traversing, filtering, or selectively including results based on metadata

OpenAI SDK Usage

OpenAI Middleware with Supermemory

The withSupermemory function creates an OpenAI client with SuperMemory middleware automatically injected:

import { withSupermemory } from "@supermemory/tools/openai"

// Create OpenAI client with supermemory middleware
const openaiWithSupermemory = withSupermemory("user-123", {
  conversationId: "conversation-456",
  mode: "full",
  addMemory: "always",
  verbose: true,
})

// Use directly with chat completions - memories are automatically injected
const completion = await openaiWithSupermemory.chat.completions.create({
  model: "gpt-4o-mini",
  messages: [
    { role: "user", content: "What do you remember about my preferences?" }
  ],
})

console.log(completion.choices[0]?.message?.content)

OpenAI Middleware Options

The middleware supports the same configuration options as the AI SDK version:

const openaiWithSupermemory = withSupermemory("user-123", {
  conversationId: "conversation-456", // Group messages for contextual memory
  mode: "full",                       // "profile" | "query" | "full"
  addMemory: "always",                // "always" | "never"
  verbose: true,                      // Enable detailed logging
})

Advanced Usage with Custom OpenAI Options

You can also pass custom OpenAI client options:

import { withSupermemory } from "@supermemory/tools/openai"

const openaiWithSupermemory = withSupermemory(
  "user-123", 
  {
    mode: "profile",
    addMemory: "always",
  },
  {
    baseURL: "https://api.openai.com/v1",
    organization: "org-123",
  },
  "custom-api-key" // Optional: custom API key
)

const completion = await openaiWithSupermemory.chat.completions.create({
  model: "gpt-4o-mini",
  messages: [{ role: "user", content: "Tell me about my preferences" }],
})

Next.js API Route Example

Here's a complete example for a Next.js API route:

// app/api/chat/route.ts
import { withSupermemory } from "@supermemory/tools/openai"
import type { OpenAI as OpenAIType } from "openai"

export async function POST(req: Request) {
  const { messages, conversationId } = (await req.json()) as {
    messages: OpenAIType.Chat.Completions.ChatCompletionMessageParam[]
    conversationId: string
  }

  const openaiWithSupermemory = withSupermemory("user-123", {
    conversationId,
    mode: "full",
    addMemory: "always",
    verbose: true,
  })

  const completion = await openaiWithSupermemory.chat.completions.create({
    model: "gpt-4o-mini",
    messages,
  })

  const message = completion.choices?.[0]?.message
  return Response.json({ message, usage: completion.usage })
}

OpenAI Function Calling Usage

import { supermemoryTools, getToolDefinitions, createToolCallExecutor } from "@supermemory/tools/openai"
import OpenAI from "openai"

const client = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY!,
})

// Get tool definitions for OpenAI
const toolDefinitions = getToolDefinitions()

// Create tool executor
const executeToolCall = createToolCallExecutor(process.env.SUPERMEMORY_API_KEY!, {
  projectId: "your-project-id",
})

// Use with OpenAI Chat Completions
const completion = await client.chat.completions.create({
  model: "gpt-5",
  messages: [
    {
      role: "user",
      content: "What do you remember about my preferences?",
    },
  ],
  tools: toolDefinitions,
})

// Execute tool calls if any
if (completion.choices[0]?.message.tool_calls) {
  for (const toolCall of completion.choices[0].message.tool_calls) {
    const result = await executeToolCall(toolCall)
    console.log(result)
  }
}

// Or create individual function-based tools
const tools = supermemoryTools(process.env.SUPERMEMORY_API_KEY!, {
  containerTags: ["your-user-id"],
})

const searchResult = await tools.searchMemories({
  informationToGet: "user preferences",
  limit: 10,
})

const addResult = await tools.addMemory({
  memory: "User prefers dark roast coffee",
})

Mastra Usage

Add persistent memory to Mastra AI agents. The integration provides processors that:

Input Processor: Fetches relevant memories and injects them into the system prompt before LLM calls
Output Processor: Optionally saves conversations to Supermemory after responses

Quick Start with `withSupermemory` Wrapper

The simplest way to add memory to a Mastra agent - wrap your config before creating the Agent:

import { Agent } from "@mastra/core/agent"
import { withSupermemory } from "@supermemory/tools/mastra"
import { openai } from "@ai-sdk/openai"

// Create agent with memory-enhanced config
const agent = new Agent(withSupermemory(
  {
    id: "my-assistant",
    name: "My Assistant",
    model: openai("gpt-4o"),
    instructions: "You are a helpful assistant.",
  },
  "user-123",  // containerTag - scopes memories to this user
  {
    mode: "full",
    addMemory: "always",
    threadId: "conv-456",
  }
))

const response = await agent.generate("What do you know about me?")
console.log(response.text)

Direct Processor Usage

For fine-grained control, use processors directly:

import { Agent } from "@mastra/core/agent"
import { createSupermemoryProcessors } from "@supermemory/tools/mastra"
import { openai } from "@ai-sdk/openai"

const { input, output } = createSupermemoryProcessors("user-123", {
  mode: "full",
  addMemory: "always",
  threadId: "conv-456",
  verbose: true, // Enable logging
})

const agent = new Agent({
  id: "my-assistant",
  name: "My Assistant",
  model: openai("gpt-4o"),
  instructions: "You are a helpful assistant with memory.",
  inputProcessors: [input],
  outputProcessors: [output],
})

const response = await agent.generate("What's my favorite programming language?")

Complete Example

Here's a full example showing a multi-turn conversation with memory:

import { Agent } from "@mastra/core/agent"
import { createSupermemoryProcessors } from "@supermemory/tools/mastra"
import { openai } from "@ai-sdk/openai"

async function main() {
  const userId = "user-alex-123"
  const threadId = `thread-${Date.now()}`

  const { input, output } = createSupermemoryProcessors(userId, {
    mode: "profile",      // Fetch user profile memories
    addMemory: "always",  // Save all conversations
    threadId,
    verbose: true,
  })

  const agent = new Agent({
    id: "memory-assistant",
    name: "Memory Assistant",
    instructions: `You are a helpful assistant with memory.
Use the memories provided to personalize your responses.`,
    model: openai("gpt-4o-mini"),
    inputProcessors: [input],
    outputProcessors: [output],
  })

  // First conversation - introduce yourself
  console.log("User: Hi! I'm Alex, a TypeScript developer.")
  const r1 = await agent.generate("Hi! I'm Alex, a TypeScript developer.")
  console.log("Assistant:", r1.text)

  // Second conversation - the agent should remember
  console.log("\nUser: What do you know about me?")
  const r2 = await agent.generate("What do you know about me?")
  console.log("Assistant:", r2.text)
}

main()

Memory Search Modes

profile (default): Fetches user profile memories (static facts + dynamic context)
query: Searches memories based on the user's message
full: Combines both profile and query results

// Profile mode - good for general personalization
const { input } = createSupermemoryProcessors("user-123", { mode: "profile" })

// Query mode - good for specific lookups
const { input } = createSupermemoryProcessors("user-123", { mode: "query" })

// Full mode - comprehensive context
const { input } = createSupermemoryProcessors("user-123", { mode: "full" })

Custom Prompt Templates

Customize how memories are formatted in the system prompt:

import { createSupermemoryProcessors, type MemoryPromptData } from "@supermemory/tools/mastra"

const customTemplate = (data: MemoryPromptData) => `
<user_context>
${data.userMemories}
${data.generalSearchMemories}
</user_context>
`.trim()

const { input, output } = createSupermemoryProcessors("user-123", {
  mode: "full",
  promptTemplate: customTemplate,
})

Using RequestContext for Dynamic Thread IDs

Instead of hardcoding threadId, use Mastra's RequestContext for dynamic values:

import { Agent } from "@mastra/core/agent"
import { RequestContext, MASTRA_THREAD_ID_KEY } from "@mastra/core/request-context"
import { createSupermemoryProcessors } from "@supermemory/tools/mastra"

const { input, output } = createSupermemoryProcessors("user-123", {
  mode: "profile",
  addMemory: "always",
  // threadId not set here - will be read from RequestContext
})

const agent = new Agent({
  id: "my-assistant",
  name: "My Assistant",
  model: openai("gpt-4o"),
  inputProcessors: [input],
  outputProcessors: [output],
})

// Set threadId dynamically per request
const ctx = new RequestContext()
ctx.set(MASTRA_THREAD_ID_KEY, "dynamic-thread-123")

const response = await agent.generate("Hello!", { requestContext: ctx })

Mastra Configuration Options

interface SupermemoryMastraOptions {
  apiKey?: string              // Supermemory API key (or use SUPERMEMORY_API_KEY env var)
  baseUrl?: string             // Custom API endpoint
  mode?: "profile" | "query" | "full"  // Memory search mode (default: "profile")
  addMemory?: "always" | "never"       // Auto-save conversations (default: "never")
  threadId?: string            // Conversation ID for grouping messages
  verbose?: boolean            // Enable debug logging (default: false)
  promptTemplate?: (data: MemoryPromptData) => string  // Custom memory formatting
}

Configuration

Both modules accept the same configuration interface:

interface SupermemoryToolsConfig {
  baseUrl?: string
  containerTags?: string[]
  projectId?: string
  strict?: boolean
}

baseUrl: Custom base URL for the supermemory API
containerTags: Array of custom container tags (mutually exclusive with projectId)
projectId: Project ID which gets converted to container tag format (mutually exclusive with containerTags)
strict: Enable strict schema mode for OpenAI strict validation. When true, all schema properties are required (satisfies OpenAI strict mode). When false (default), optional fields remain optional for maximum compatibility with all models.

OpenAI Strict Mode Compatibility

When using OpenAI-compatible providers with strict schema validation (e.g., OpenRouter with Azure OpenAI backend), enable strict mode to ensure all schema properties are included in the required array:

import { searchMemoriesTool, addMemoryTool } from "@supermemory/tools/ai-sdk"
import { createOpenRouter } from "@openrouter/ai-sdk-provider"
import { streamText } from "ai"

const openrouter = createOpenRouter({ apiKey: process.env.OPENROUTER_API_KEY })

const tools = {
  searchMemories: searchMemoriesTool(apiKey, { 
    containerTags: [userId],
    strict: true  // ✅ Required for OpenAI strict mode
  }),
  addMemory: addMemoryTool(apiKey, { 
    containerTags: [userId],
    strict: true
  }),
}

const result = streamText({
  model: openrouter.chat("openai/gpt-5-nano"),
  messages: [...],
  tools,
})

Without strict: true, optional fields like includeFullDocs and limit won't be in the required array, which will cause validation errors with OpenAI strict mode.

withSupermemory Middleware Options

The withSupermemory middleware accepts additional configuration options:

interface WithSupermemoryOptions {
  conversationId?: string
  verbose?: boolean
  mode?: "profile" | "query" | "full"
  addMemory?: "always" | "never"
  /** Optional Supermemory API key. Use this in browser environments. */
  apiKey?: string
}

conversationId: Optional conversation ID to group messages into a single document for contextual memory generation
verbose: Enable detailed logging of memory search and injection process (default: false)
mode: Memory search mode - "profile" (default), "query", or "full"
addMemory: Automatic memory storage mode - "always" or "never" (default: "never")

Available Tools

Search Memories

Searches through stored memories based on a query string.

Parameters:

informationToGet (string): Terms to search for
includeFullDocs (boolean, optional): Whether to include full document content (default: true)
limit (number, optional): Maximum number of results (default: 10)

Add Memory

Adds a new memory to the system.

Parameters:

memory (string): The content to remember

Claude Memory Tool

Enable Claude to store and retrieve persistent memory across conversations using supermemory as the backend.

Installation

npm install @supermemory/tools @anthropic-ai/sdk

Basic Usage

import Anthropic from '@anthropic-ai/sdk'
import { createClaudeMemoryTool } from '@supermemory/tools/claude-memory'

const anthropic = new Anthropic({
  apiKey: process.env.ANTHROPIC_API_KEY!,
})

const memoryTool = createClaudeMemoryTool(process.env.SUPERMEMORY_API_KEY!, {
  projectId: 'my-app',
})

async function chatWithMemory(userMessage: string) {
  // Send message to Claude with memory tool
  const response = await anthropic.beta.messages.create({
    model: 'claude-sonnet-4-5',
    max_tokens: 2048,
    messages: [{ role: 'user', content: userMessage }],
    tools: [{ type: 'memory_20250818', name: 'memory' }],
    betas: ['context-management-2025-06-27'],
  })

  // Handle any memory tool calls
  const toolResults = []
  for (const block of response.content) {
    if (block.type === 'tool_use' && block.name === 'memory') {
      const toolResult = await memoryTool.handleCommandForToolResult(
        block.input,
        block.id
      )
      toolResults.push(toolResult)
    }
  }

  return response
}

// Example usage
const response = await chatWithMemory(
  "Remember that I prefer React with TypeScript for my projects"
)

Memory Operations

Claude can perform these memory operations automatically:

view - List memory directory contents or read specific files
create - Create new memory files with content
str_replace - Find and replace text within memory files
insert - Insert text at specific line numbers
delete - Delete memory files
rename - Rename or move memory files

All memory files are stored in supermemory with normalized paths and can be searched and retrieved across conversations.

Environment Variables

SUPERMEMORY_API_KEY=your_supermemory_api_key
ANTHROPIC_API_KEY=your_anthropic_api_key  # for Claude Memory Tool
SUPERMEMORY_BASE_URL=https://your-custom-url  # optional

Keywords

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Package last updated on 19 Mar 2026

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@supermemory/tools