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The Mem0 AI SDK Provider is a library developed by Mem0 to integrate with the Vercel AI SDK. This library brings enhanced AI interaction capabilities to your applications by introducing persistent memory functionality.
Mem0 AI SDK now supports Vercel AI SDK V5.

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Overview

  1. Offers persistent memory storage for conversational AI
  2. Enables smooth integration with the Vercel AI SDK
  3. Ensures compatibility with multiple LLM providers
  4. Supports structured message formats for clarity
  5. Facilitates streaming response capabilities

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Setup and Configuration

Install the SDK provider using npm: 
npm install @mem0/vercel-ai-provider

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Getting Started

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Setting Up Mem0

  1. Get your Mem0 API Key from the Mem0 Dashboard.
  2. Initialize the Mem0 Client in your application: 
    import { createMem0 } from "@mem0/vercel-ai-provider";

const mem0 = createMem0({
  provider: "openai",
  mem0ApiKey: "m0-xxx",
  apiKey: "provider-api-key",
  config: {
    // Options for LLM Provider
  },
  // Optional Mem0 Global Config
  mem0Config: {
    user_id: "mem0-user-id",
  },
});
    Note: The openai provider is set as default. Consider using MEM0_API_KEY and OPENAI_API_KEY as environment variables for security.
    Note: The mem0Config is optional. It is used to set the global config for the Mem0 Client (eg. user_id, agent_id, app_id, run_id etc).
  3. Add Memories to Enhance Context: 
    import { LanguageModelV2Prompt } from "@ai-sdk/provider";
import { addMemories } from "@mem0/vercel-ai-provider";

const messages: LanguageModelV2Prompt = [
  { role: "user", content: [{ type: "text", text: "I love red cars." }] },
];

await addMemories(messages, { user_id: "borat" });

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Standalone Features:

await addMemories(messages, { user_id: "borat", mem0ApiKey: "m0-xxx" });
await retrieveMemories(prompt, { user_id: "borat", mem0ApiKey: "m0-xxx" });
await getMemories(prompt, { user_id: "borat", mem0ApiKey: "m0-xxx" });
For standalone features, such as addMemories, retrieveMemories, and getMemories, you must either set MEM0_API_KEY as an environment variable or pass it directly in the function call.
getMemories will return raw memories in the form of an array of objects, while retrieveMemories will return a response in string format with a system prompt ingested with the retrieved memories.
getMemories returns an array of memory objects.

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1. Basic Text Generation with Memory Context

import { generateText } from "ai";
import { createMem0 } from "@mem0/vercel-ai-provider";

const mem0 = createMem0();

const { text } = await generateText({
  model: mem0("gpt-4-turbo", { user_id: "borat" }),
  prompt: "Suggest me a good car to buy!",
});

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2. Combining OpenAI Provider with Memory Utils

import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";
import { retrieveMemories } from "@mem0/vercel-ai-provider";

const prompt = "Suggest me a good car to buy.";
const memories = await retrieveMemories(prompt, { user_id: "borat" });

const { text } = await generateText({
  model: openai("gpt-4-turbo"),
  prompt: prompt,
  system: memories,
});

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3. Structured Message Format with Memory

import { generateText } from "ai";
import { createMem0 } from "@mem0/vercel-ai-provider";

const mem0 = createMem0();

const { text } = await generateText({
  model: mem0("gpt-4-turbo", { user_id: "borat" }),
  messages: [
    {
      role: "user",
      content: [
        { type: "text", text: "Suggest me a good car to buy." },
        { type: "text", text: "Why is it better than the other cars for me?" },
      ],
    },
  ],
});

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3. Streaming Responses with Memory Context

import { streamText } from "ai";
import { createMem0 } from "@mem0/vercel-ai-provider";

const mem0 = createMem0();

const { textStream } = streamText({
    model: mem0("gpt-4-turbo", {
        user_id: "borat",
    }),
    prompt: "Suggest me a good car to buy! Why is it better than the other cars for me? Give options for every price range.",
});

for await (const textPart of textStream) {
    process.stdout.write(textPart);
}

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4. Generate Responses with Tools Call

import { generateText } from "ai";
import { createMem0 } from "@mem0/vercel-ai-provider";
import { z } from "zod";

const mem0 = createMem0({
  provider: "anthropic",
  apiKey: "anthropic-api-key",
  mem0Config: {
    // Global User ID
    user_id: "borat"
  }
});

const prompt = "What the temperature in the city that I live in?"

const result = await generateText({
  model: mem0('claude-3-5-sonnet-20240620'),
  tools: {
    weather: tool({
      description: 'Get the weather in a location',
      parameters: z.object({
        location: z.string().describe('The location to get the weather for'),
      }),
      execute: async ({ location }) => ({
        location,
        temperature: 72 + Math.floor(Math.random() * 21) - 10,
      }),
    }),
  },
  prompt: prompt,
});

console.log(result);

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5. Get sources from memory

const { text, sources } = await generateText({
    model: mem0("gpt-4-turbo"),
    prompt: "Suggest me a good car to buy!",
});

console.log(sources);
The same can be done for streamText as well.

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6. File Support with Memory Context

Mem0 AI SDK supports file processing with memory context. Here’s an example of analyzing a PDF file: 
import { streamText } from "ai";
import { createMem0 } from "@mem0/vercel-ai-provider";
import { readFileSync } from 'fs';
import { join } from 'path';

const mem0 = createMem0({
  provider: "google",
  mem0ApiKey: "m0-xxx",
  config: {
    apiKey: "google-api-key"
  },
  mem0Config: {
    user_id: "alice",
  },
});

async function main() {
  // Read the PDF file
  const filePath = join(process.cwd(), 'my_pdf.pdf');
  const fileBuffer = readFileSync(filePath);

  // Convert the file's arrayBuffer to a Base64 data URL
  const arrayBuffer = fileBuffer.buffer.slice(fileBuffer.byteOffset, fileBuffer.byteOffset + fileBuffer.byteLength);
  const uint8Array = new Uint8Array(arrayBuffer);

  // Convert Uint8Array to an array of characters
  const charArray = Array.from(uint8Array, byte => String.fromCharCode(byte));
  const binaryString = charArray.join('');
  const base64Data = Buffer.from(binaryString, 'binary').toString('base64');
  const fileDataUrl = `data:application/pdf;base64,${base64Data}`;

  const { textStream } = streamText({
    model: mem0("gemini-2.5-flash"),
    messages: [
      {
        role: 'user',
        content: [
          {
            type: 'text',
            text: 'Analyze the following PDF and generate a summary.',
          },
          {
            type: 'file',
            data: fileDataUrl,
            mediaType: 'application/pdf',
          },
        ],
      },
    ],
  });

  for await (const textPart of textStream) {
    process.stdout.write(textPart);
  }
}

main();
Note: File support is available with providers that support multimodal capabilities like Google’s Gemini models. The example shows how to process PDF files, but you can also work with images, text files, and other supported formats.

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Supported LLM Providers

ProviderConfiguration Value
OpenAIopenai
Anthropicanthropic
Googlegoogle
Groqgroq
Note: You can use google as provider for Gemini (Google) models. They are same and internally they use @ai-sdk/google package.

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Key Features

  • createMem0(): Initializes a new Mem0 provider instance.
  • retrieveMemories(): Retrieves memory context for prompts.
  • getMemories(): Get memories from your profile in array format.
  • addMemories(): Adds user memories to enhance contextual responses.

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Best Practices

  1. User Identification: Use a unique user_id for consistent memory retrieval.
  2. Memory Cleanup: Regularly clean up unused memory data.
    Note: We also have support for agent_id, app_id, and run_id. Refer Docs.

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Conclusion

Mem0’s Vercel AI SDK enables the creation of intelligent, context-aware applications with persistent memory and seamless integration.

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