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tools for AI SDK, OpenAI, Voltagent and Mastra with supermemory","maintainers":[{"name":"dhravya","email":"dhravyashah@gmail.com"}],"readme":"# @supermemory/tools\n\nMemory tools for AI SDK, OpenAI, and Mastra with supermemory\n\nThis 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.\n\n## Installation\n\n```bash\nnpm install @supermemory/tools\n```\n\n## Usage\n\nThe package provides three submodule imports:\n- `@supermemory/tools/ai-sdk` - For use with the AI SDK framework (includes `withSupermemory` middleware)\n- `@supermemory/tools/openai` - For use with OpenAI SDK (includes `withSupermemory` middleware and function calling tools)\n- `@supermemory/tools/mastra` - For use with Mastra AI agents (includes `withSupermemory` wrapper and processors)\n\n### AI SDK Usage\n\n```typescript\nimport { supermemoryTools, searchMemoriesTool, addMemoryTool } from \"@supermemory/tools/ai-sdk\"\nimport { createOpenAI } from \"@ai-sdk/openai\"\nimport { generateText, stepCountIs } from \"ai\"\n\nconst openai = createOpenAI({\n  apiKey: process.env.OPENAI_API_KEY!,\n})\n\n// Create all tools\nconst tools = supermemoryTools(process.env.SUPERMEMORY_API_KEY!, {\n  containerTags: [\"your-user-id\"],\n})\n\n// Use with AI SDK\nconst result = await generateText({\n  model: openai(\"gpt-5\"),\n  messages: [\n    {\n      role: \"user\",\n      content: \"What do you remember about my preferences?\",\n    },\n  ],\n  tools,\n  stopWhen: stepCountIs(5),\n})\n\n// Or create individual tools\nconst searchTool = searchMemoriesTool(process.env.SUPERMEMORY_API_KEY!, {\n  projectId: \"your-project-id\",\n})\n\nconst addTool = addMemoryTool(process.env.SUPERMEMORY_API_KEY!, {\n  projectId: \"your-project-id\",\n})\n```\n\n#### AI SDK Middleware with Supermemory\n\n- `withSupermemory` will take advantage supermemory profile v4 endpoint personalized based on container tag\n- 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.\n- **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.\n\n```typescript\nimport { generateText } from \"ai\"\nimport { withSupermemory } from \"@supermemory/tools/ai-sdk\"\nimport { openai } from \"@ai-sdk/openai\"\n\nconst modelWithMemory = withSupermemory(openai(\"gpt-5\"), {\n\tcontainerTag: \"user_id_life\",\n\tcustomId: \"conversation-456\",\n})\n\nconst result = await generateText({\n\tmodel: modelWithMemory,\n\tmessages: [{ role: \"user\", content: \"where do i live?\" }],\n})\n\nconsole.log(result.text)\n```\n\n#### Verbose Mode\n\nEnable verbose logging to see detailed information about memory search and transformation:\n\n```typescript\nimport { generateText } from \"ai\"\nimport { withSupermemory } from \"@supermemory/tools/ai-sdk\"\nimport { openai } from \"@ai-sdk/openai\"\n\nconst modelWithMemory = withSupermemory(openai(\"gpt-5\"), {\n\tcontainerTag: \"user_id_life\",\n\tcustomId: \"conversation-456\",\n\tverbose: true,\n})\n\nconst result = await generateText({\n\tmodel: modelWithMemory,\n\tmessages: [{ role: \"user\", content: \"where do i live?\" }],\n})\n\nconsole.log(result.text)\n```\n\nWhen verbose mode is enabled, you'll see console output like:\n```\n[supermemory] Searching memories for container: user_id_life\n[supermemory] User message: where do i live?\n[supermemory] System prompt exists: false\n[supermemory] Found 3 memories\n[supermemory] Memory content: You live in San Francisco, California. Your address is 123 Main Street...\n[supermemory] Creating new system prompt with memories\n```\n\n#### Memory Search Modes\n\nThe middleware supports different modes for memory retrieval:\n\n**Profile Mode (Default)** - Retrieves user profile memories without query filtering:\n```typescript\nimport { generateText } from \"ai\"\nimport { withSupermemory } from \"@supermemory/tools/ai-sdk\"\nimport { openai } from \"@ai-sdk/openai\"\n\n// Uses profile mode by default - gets all user profile memories\nconst modelWithMemory = withSupermemory(openai(\"gpt-4\"), {\n  containerTag: \"user-123\",\n  customId: \"conversation-456\",\n})\n\n// Explicitly specify profile mode\nconst modelWithProfile = withSupermemory(openai(\"gpt-4\"), {\n  containerTag: \"user-123\",\n  customId: \"conversation-456\",\n  mode: \"profile\",\n})\n\nconst result = await generateText({\n  model: modelWithMemory,\n  messages: [{ role: \"user\", content: \"What do you know about me?\" }],\n})\n```\n\n**Query Mode** - Searches memories based on the user's message:\n```typescript\nimport { generateText } from \"ai\"\nimport { withSupermemory } from \"@supermemory/tools/ai-sdk\"\nimport { openai } from \"@ai-sdk/openai\"\n\nconst modelWithQuery = withSupermemory(openai(\"gpt-4\"), {\n  containerTag: \"user-123\",\n  customId: \"conversation-456\",\n  mode: \"query\",\n})\n\nconst result = await generateText({\n  model: modelWithQuery,\n  messages: [{ role: \"user\", content: \"What's my favorite programming language?\" }],\n})\n```\n\n**Full Mode** - Combines both profile and query results:\n```typescript\nimport { generateText } from \"ai\"\nimport { withSupermemory } from \"@supermemory/tools/ai-sdk\"\nimport { openai } from \"@ai-sdk/openai\"\n\nconst modelWithFull = withSupermemory(openai(\"gpt-4\"), {\n  containerTag: \"user-123\",\n  customId: \"conversation-456\",\n  mode: \"full\",\n})\n\nconst result = await generateText({\n  model: modelWithFull,\n  messages: [{ role: \"user\", content: \"Tell me about my preferences\" }],\n})\n```\n\n#### Automatic Memory Capture\n\nThe middleware can automatically save user messages as memories:\n\n**Always Save Memories** - Automatically stores every user message as a memory:\n```typescript\nimport { generateText } from \"ai\"\nimport { withSupermemory } from \"@supermemory/tools/ai-sdk\"\nimport { openai } from \"@ai-sdk/openai\"\n\nconst modelWithAutoSave = withSupermemory(openai(\"gpt-4\"), {\n  containerTag: \"user-123\",\n  customId: \"conversation-456\",\n  addMemory: \"always\",\n})\n\nconst result = await generateText({\n  model: modelWithAutoSave,\n  messages: [{ role: \"user\", content: \"I prefer React with TypeScript for my projects\" }],\n})\n// This message will be automatically saved as a memory\n```\n\n**Never Save Memories** - Only retrieves memories without storing new ones:\n```typescript\nconst modelWithNoSave = withSupermemory(openai(\"gpt-4\"), {\n  containerTag: \"user-123\",\n  customId: \"conversation-456\",\n  addMemory: \"never\",  // explicit since default is now \"always\"\n})\n```\n\n**Combined Options** - Use verbose logging with specific modes and memory storage:\n```typescript\nconst modelWithOptions = withSupermemory(openai(\"gpt-4\"), {\n  containerTag: \"user-123\",\n  customId: \"conversation-456\",\n  mode: \"profile\",\n  addMemory: \"always\",\n  verbose: true,\n})\n```\n\n#### Custom Prompt Templates\n\nCustomize how memories are formatted and injected into the system prompt using the `promptTemplate` option. This is useful for:\n- Using XML-based prompting (e.g., for Claude models)\n- Custom branding (removing \"supermemories\" references)\n- Controlling how your agent describes where information comes from\n\n```typescript\nimport { generateText } from \"ai\"\nimport { withSupermemory, type MemoryPromptData } from \"@supermemory/tools/ai-sdk\"\nimport { openai } from \"@ai-sdk/openai\"\n\nconst customPrompt = (data: MemoryPromptData) => `\n<user_memories>\nHere is some information about your past conversations with the user:\n${data.userMemories}\n${data.generalSearchMemories}\n</user_memories>\n`.trim()\n\nconst modelWithCustomPrompt = withSupermemory(openai(\"gpt-4\"), {\n  containerTag: \"user-123\",\n  customId: \"conversation-456\",\n  mode: \"full\",\n  promptTemplate: customPrompt,\n})\n\nconst result = await generateText({\n  model: modelWithCustomPrompt,\n  messages: [{ role: \"user\", content: \"What do you know about me?\" }],\n})\n```\n\nThe `MemoryPromptData` object provides:\n- `userMemories`: Pre-formatted markdown combining static profile facts (name, preferences, goals) and dynamic context (current projects, recent interests)\n- `generalSearchMemories`: Pre-formatted search results based on semantic similarity to the current query\n- `searchResults`: Raw search results array for traversing, filtering, or selectively including results based on metadata\n\n### OpenAI SDK Usage\n\n#### OpenAI Middleware with Supermemory\n\nThe `withSupermemory` function creates an OpenAI client with SuperMemory middleware automatically injected:\n\n```typescript\nimport { withSupermemory } from \"@supermemory/tools/openai\"\n\n// Create OpenAI client with supermemory middleware\nconst openaiWithSupermemory = withSupermemory(openai, {\n  containerTag: \"user-123\",      // Required: identifies the user/container\n  customId: \"conversation-456\",  // Required: groups messages into the same document \n  apiKey: process.env.SUPERMEMORY_API_KEY, // Optional env fallback\n  baseUrl: process.env.SUPERMEMORY_BASE_URL,\n  mode: \"full\",\n  addMemory: \"always\",           // Default: \"always\"\n  verbose: true,\n})\n\n// Use directly with chat completions - memories are automatically injected\nconst completion = await openaiWithSupermemory.chat.completions.create({\n  model: \"gpt-4o-mini\",\n  messages: [\n    { role: \"user\", content: \"What do you remember about my preferences?\" }\n  ],\n})\n\nconsole.log(completion.choices[0]?.message?.content)\n```\n\n#### OpenAI Middleware Options\n\nThe middleware supports the same configuration options as the AI SDK version:\n\n```typescript\nconst openaiWithSupermemory = withSupermemory(openai, {\n  containerTag: \"user-123\",      // Required: identifies the user/container\n  customId: \"conversation-456\",  // Required: groups messages for contextual memory\n  apiKey: process.env.SUPERMEMORY_API_KEY, // Optional; captured per client\n  baseUrl: process.env.SUPERMEMORY_BASE_URL,\n  mode: \"full\",                  // \"profile\" | \"query\" | \"full\"\n  addMemory: \"always\",           // \"always\" (default) | \"never\"\n  verbose: true,                 // Enable detailed logging\n})\n```\n\n#### Next.js API Route Example\n\nHere's a complete example for a Next.js API route:\n\n```typescript\n// app/api/chat/route.ts\nimport { withSupermemory } from \"@supermemory/tools/openai\"\nimport type { OpenAI as OpenAIType } from \"openai\"\n\nexport async function POST(req: Request) {\n  const { messages, conversationId } = (await req.json()) as {\n    messages: OpenAIType.Chat.Completions.ChatCompletionMessageParam[]\n    conversationId: string\n  }\n\n  const openaiWithSupermemory = withSupermemory(openai, {\n    containerTag: \"user-123\",\n    customId: conversationId,\n    apiKey: process.env.SUPERMEMORY_API_KEY,\n    baseUrl: process.env.SUPERMEMORY_BASE_URL,\n    mode: \"full\",\n    addMemory: \"always\",\n    verbose: true,\n  })\n\n  const completion = await openaiWithSupermemory.chat.completions.create({\n    model: \"gpt-4o-mini\",\n    messages,\n  })\n\n  const message = completion.choices?.[0]?.message\n  return Response.json({ message, usage: completion.usage })\n}\n```\n\n### OpenAI Function Calling Usage\n\n```typescript\nimport { supermemoryTools, getToolDefinitions, createToolCallExecutor } from \"@supermemory/tools/openai\"\nimport OpenAI from \"openai\"\n\nconst client = new OpenAI({\n  apiKey: process.env.OPENAI_API_KEY!,\n})\n\n// Get tool definitions for OpenAI\nconst toolDefinitions = getToolDefinitions()\n\n// Create tool executor\nconst executeToolCall = createToolCallExecutor(process.env.SUPERMEMORY_API_KEY!, {\n  projectId: \"your-project-id\",\n})\n\n// Use with OpenAI Chat Completions\nconst completion = await client.chat.completions.create({\n  model: \"gpt-5\",\n  messages: [\n    {\n      role: \"user\",\n      content: \"What do you remember about my preferences?\",\n    },\n  ],\n  tools: toolDefinitions,\n})\n\n// Execute tool calls if any\nif (completion.choices[0]?.message.tool_calls) {\n  for (const toolCall of completion.choices[0].message.tool_calls) {\n    const result = await executeToolCall(toolCall)\n    console.log(result)\n  }\n}\n\n// Or create individual function-based tools\nconst tools = supermemoryTools(process.env.SUPERMEMORY_API_KEY!, {\n  containerTags: [\"your-user-id\"],\n})\n\nconst searchResult = await tools.searchMemories({\n  informationToGet: \"user preferences\",\n  limit: 10,\n})\n\nconst addResult = await tools.addMemory({\n  memory: \"User prefers dark roast coffee\",\n})\n```\n\n### Mastra Usage\n\nAdd persistent memory to [Mastra](https://mastra.ai) AI agents. The integration provides processors that:\n- **Input Processor**: Fetches relevant memories and injects them into the system prompt before LLM calls\n- **Output Processor**: Saves conversations to Supermemory after responses (enabled by default)\n\n#### Quick Start with `withSupermemory` Wrapper\n\nThe simplest way to add memory to a Mastra agent - wrap your config before creating the Agent:\n\n```typescript\nimport { Agent } from \"@mastra/core/agent\"\nimport { withSupermemory } from \"@supermemory/tools/mastra\"\nimport { openai } from \"@ai-sdk/openai\"\n\n// Create agent with memory-enhanced config\nconst agent = new Agent(withSupermemory(\n  {\n    id: \"my-assistant\",\n    name: \"My Assistant\",\n    model: openai(\"gpt-4o\"),\n    instructions: \"You are a helpful assistant.\",\n  },\n  {\n    containerTag: \"user-123\",  // Required: scopes memories to this user\n    customId: \"conv-456\",      // Required: groups messages for contextual memory\n    mode: \"full\",\n  }\n))\n\nconst response = await agent.generate(\"What do you know about me?\")\nconsole.log(response.text)\n```\n\n#### Direct Processor Usage\n\nFor fine-grained control, use processors directly:\n\n```typescript\nimport { Agent } from \"@mastra/core/agent\"\nimport { createSupermemoryProcessors } from \"@supermemory/tools/mastra\"\nimport { openai } from \"@ai-sdk/openai\"\n\nconst { input, output } = createSupermemoryProcessors({\n  containerTag: \"user-123\",\n  customId: \"conv-456\",\n  mode: \"full\",\n  verbose: true, // Enable logging\n})\n\nconst agent = new Agent({\n  id: \"my-assistant\",\n  name: \"My Assistant\",\n  model: openai(\"gpt-4o\"),\n  instructions: \"You are a helpful assistant with memory.\",\n  inputProcessors: [input],\n  outputProcessors: [output],\n})\n\nconst response = await agent.generate(\"What's my favorite programming language?\")\n```\n\n#### Complete Example\n\nHere's a full example showing a multi-turn conversation with memory:\n\n```typescript\nimport { Agent } from \"@mastra/core/agent\"\nimport { createSupermemoryProcessors } from \"@supermemory/tools/mastra\"\nimport { openai } from \"@ai-sdk/openai\"\n\nasync function main() {\n  const userId = \"user-alex-123\"\n  const customId = `thread-${Date.now()}`\n\n  const { input, output } = createSupermemoryProcessors({\n    containerTag: userId,\n    customId,\n    mode: \"profile\",      // Fetch user profile memories\n    verbose: true,\n  })\n\n  const agent = new Agent({\n    id: \"memory-assistant\",\n    name: \"Memory Assistant\",\n    instructions: `You are a helpful assistant with memory.\nUse the memories provided to personalize your responses.`,\n    model: openai(\"gpt-4o-mini\"),\n    inputProcessors: [input],\n    outputProcessors: [output],\n  })\n\n  // First conversation - introduce yourself\n  console.log(\"User: Hi! I'm Alex, a TypeScript developer.\")\n  const r1 = await agent.generate(\"Hi! I'm Alex, a TypeScript developer.\")\n  console.log(\"Assistant:\", r1.text)\n\n  // Second conversation - the agent should remember\n  console.log(\"\\nUser: What do you know about me?\")\n  const r2 = await agent.generate(\"What do you know about me?\")\n  console.log(\"Assistant:\", r2.text)\n}\n\nmain()\n```\n\n#### Memory Search Modes\n\n- **`profile`** (default): Fetches user profile memories (static facts + dynamic context)\n- **`query`**: Searches memories based on the user's message\n- **`full`**: Combines both profile and query results\n\n```typescript\n// Profile mode - good for general personalization\nconst { input } = createSupermemoryProcessors({\n  containerTag: \"user-123\",\n  customId: \"conv-456\",\n  mode: \"profile\",\n})\n\n// Query mode - good for specific lookups\nconst { input } = createSupermemoryProcessors({\n  containerTag: \"user-123\",\n  customId: \"conv-456\",\n  mode: \"query\",\n})\n\n// Full mode - comprehensive context\nconst { input } = createSupermemoryProcessors({\n  containerTag: \"user-123\",\n  customId: \"conv-456\",\n  mode: \"full\",\n})\n```\n\n#### Custom Prompt Templates\n\nCustomize how memories are formatted in the system prompt:\n\n```typescript\nimport { createSupermemoryProcessors, type MemoryPromptData } from \"@supermemory/tools/mastra\"\n\nconst customTemplate = (data: MemoryPromptData) => `\n<user_context>\n${data.userMemories}\n${data.generalSearchMemories}\n</user_context>\n`.trim()\n\nconst { input, output } = createSupermemoryProcessors({\n  containerTag: \"user-123\",\n  customId: \"conv-456\",\n  mode: \"full\",\n  promptTemplate: customTemplate,\n})\n```\n\n#### Using RequestContext for Dynamic Thread IDs\n\nFor server setups where one agent instance handles multiple concurrent conversations, use Mastra's `RequestContext` to provide per-request thread IDs. **RequestContext takes precedence** over the construction-time `customId`:\n\n```typescript\nimport { Agent } from \"@mastra/core/agent\"\nimport { RequestContext, MASTRA_THREAD_ID_KEY } from \"@mastra/core/request-context\"\nimport { createSupermemoryProcessors } from \"@supermemory/tools/mastra\"\n\nconst { input, output } = createSupermemoryProcessors({\n  containerTag: \"user-123\",\n  customId: \"fallback-conv\",  // Used only when RequestContext doesn't provide a threadId\n  mode: \"profile\",\n})\n\nconst agent = new Agent({\n  id: \"my-assistant\",\n  name: \"My Assistant\",\n  model: openai(\"gpt-4o\"),\n  inputProcessors: [input],\n  outputProcessors: [output],\n})\n\n// Per-request threadId takes precedence over customId\nconst ctx = new RequestContext()\nctx.set(MASTRA_THREAD_ID_KEY, \"user-456-session-789\")\n\nconst response = await agent.generate(\"Hello!\", { requestContext: ctx })\n// This conversation is stored under \"user-456-session-789\", not \"fallback-conv\"\n```\n\n> **Server-side usage**: Always use `RequestContext` to pass unique conversation IDs per request. Using a fixed `customId` for all requests will merge conversations from different users.\n\n#### Mastra Configuration Options\n\n```typescript\ninterface SupermemoryMastraOptions {\n  containerTag: string         // Required: User/container tag for scoping memories\n  customId: string             // Required: Groups messages into a single document for contextual memory\n  apiKey?: string              // Supermemory API key (or use SUPERMEMORY_API_KEY env var)\n  baseUrl?: string             // Custom API endpoint\n  mode?: \"profile\" | \"query\" | \"full\"  // Memory search mode (default: \"profile\")\n  addMemory?: \"always\" | \"never\"       // Auto-save conversations (default: \"always\")\n  verbose?: boolean            // Enable debug logging (default: false)\n  promptTemplate?: (data: MemoryPromptData) => string  // Custom memory formatting\n}\n```\n\n## Configuration\n\nBoth modules accept the same configuration interface:\n\n```typescript\ninterface SupermemoryToolsConfig {\n  baseUrl?: string\n  containerTags?: string[]\n  projectId?: string\n  strict?: boolean\n}\n```\n\n- **baseUrl**: Custom base URL for the supermemory API\n- **containerTags**: Non-empty array of custom container tags (mutually exclusive with `projectId`). `searchMemories`, `getProfile`, and `memoryForget` use the first tag because v4 memory APIs are single-space. Add operations attach every configured tag, while `documentList` and `documentDelete` use the configured tags as their supported union scope. `documentDelete` still refuses a document with any tag outside that scope or a nonterminal processing status.\n- **projectId**: Project ID which gets converted to container tag format (mutually exclusive with containerTags)\n- **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.\n\n### OpenAI Strict Mode Compatibility\n\nWhen 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:\n\n```typescript\nimport { searchMemoriesTool, addMemoryTool } from \"@supermemory/tools/ai-sdk\"\nimport { createOpenRouter } from \"@openrouter/ai-sdk-provider\"\nimport { streamText } from \"ai\"\n\nconst openrouter = createOpenRouter({ apiKey: process.env.OPENROUTER_API_KEY })\n\nconst tools = {\n  searchMemories: searchMemoriesTool(apiKey, { \n    containerTags: [userId],\n    strict: true  // ✅ Required for OpenAI strict mode\n  }),\n  addMemory: addMemoryTool(apiKey, { \n    containerTags: [userId],\n    strict: true\n  }),\n}\n\nconst result = streamText({\n  model: openrouter.chat(\"openai/gpt-5-nano\"),\n  messages: [...],\n  tools,\n})\n```\n\nWithout `strict: true`, optional fields like `includeFullDocs` and `limit` won't be in the `required` array, which will cause validation errors with OpenAI strict mode.\n\n### withSupermemory Middleware Options\n\nThe `withSupermemory` middleware accepts a configuration object as the second argument:\n\n```typescript\ninterface WithSupermemoryOptions {\n  containerTag: string  // Required: identifies the user/container\n  customId: string      // Required: groups messages into the same document \n  verbose?: boolean\n  mode?: \"profile\" | \"query\" | \"full\"\n  addMemory?: \"always\" | \"never\"  // Default: \"always\"\n  /** Optional Supermemory API key. Use this in browser environments. */\n  apiKey?: string\n  baseUrl?: string\n  promptTemplate?: (data: MemoryPromptData) => string\n  skipMemoryOnError?: boolean\n}\n```\n\n- **containerTag**: Required. The container tag/identifier for memory search (e.g., user ID, project ID)\n- **customId**: Required. Custom ID to group messages into a single document for contextual memory generation\n- **verbose**: Enable detailed logging of memory search and injection process (default: false)\n- **mode**: Memory search mode - \"profile\" (default), \"query\", or \"full\"\n- **addMemory**: Automatic memory storage mode - \"always\" (default) or \"never\"\n- **skipMemoryOnError**: If memory retrieval fails or hits the internal timeout, continue with the original prompt (default: true)\n\n## Available Tools\n\n### Search Memories\nRuns v4 hybrid search in the primary (first) configured container tag. Results can contain learned memories (`memory`) and source chunks (`chunk`). Only IDs on results containing `memory` can be passed to `memoryForget`; chunk-result IDs cannot.\n\n**Parameters:**\n- `informationToGet` (string): Terms to search for\n- `includeFullDocs` (boolean, optional): Deprecated compatibility input; ignored by v4 hybrid search\n- `limit` (number, optional): Maximum number of results (default: 10)\n\n### Add Memory\nAdds a new memory to the system.\n\n**Parameters:**\n- `memory` (string): The content to remember\n\n\n\n## Claude Memory Tool\n\nEnable Claude to store and retrieve persistent memory across conversations using supermemory as the backend.\n\n### Installation\n\n```bash\nnpm install @supermemory/tools @anthropic-ai/sdk\n```\n\n### Basic Usage\n\n```typescript\nimport Anthropic from '@anthropic-ai/sdk'\nimport { createClaudeMemoryTool } from '@supermemory/tools/claude-memory'\n\nconst anthropic = new Anthropic({\n  apiKey: process.env.ANTHROPIC_API_KEY!,\n})\n\nconst memoryTool = createClaudeMemoryTool(process.env.SUPERMEMORY_API_KEY!, {\n  projectId: 'my-app',\n})\n\nasync function chatWithMemory(userMessage: string) {\n  // Send message to Claude with memory tool\n  const response = await anthropic.beta.messages.create({\n    model: 'claude-sonnet-4-5',\n    max_tokens: 2048,\n    messages: [{ role: 'user', content: userMessage }],\n    tools: [{ type: 'memory_20250818', name: 'memory' }],\n    betas: ['context-management-2025-06-27'],\n  })\n\n  // Handle any memory tool calls\n  const toolResults = []\n  for (const block of response.content) {\n    if (block.type === 'tool_use' && block.name === 'memory') {\n      const toolResult = await memoryTool.handleCommandForToolResult(\n        block.input,\n        block.id\n      )\n      toolResults.push(toolResult)\n    }\n  }\n\n  return response\n}\n\n// Example usage\nconst response = await chatWithMemory(\n  \"Remember that I prefer React with TypeScript for my projects\"\n)\n```\n\n### Memory Operations\n\nClaude can perform these memory operations automatically:\n\n- **`view`** - List memory directory contents or read specific files\n- **`create`** - Create new memory files with content\n- **`str_replace`** - Find and replace text within memory files\n- **`insert`** - Insert text at specific line numbers\n- **`delete`** - Delete memory files\n- **`rename`** - Rename or move memory files\n\nAll memory files are stored in supermemory with normalized paths and can be searched and retrieved across conversations.\n\n## Environment Variables\n\n```env\nSUPERMEMORY_API_KEY=your_supermemory_api_key\nANTHROPIC_API_KEY=your_anthropic_api_key  # for Claude Memory Tool\nSUPERMEMORY_BASE_URL=https://your-custom-url  # optional\n```\n","readmeFilename":"README.md"}