Vercel AI SDK

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 Provider v3.0.0 supports Vercel AI SDK v6 (LanguageModelV3 / ProviderV3). If you are upgrading from v2.x, see the AI SDK v6 migration guide.

Overview

Offers persistent memory storage for conversational AI
Enables smooth integration with the Vercel AI SDK v6
Ensures compatibility with multiple LLM providers (OpenAI, Anthropic, Google, Groq, Cohere)
Supports structured message formats for clarity
Facilitates streaming response capabilities
Attaches Mem0 memories as sources in responses for programmatic access

Setup and Configuration

Install the SDK provider and AI SDK:

npm install @mem0/vercel-ai-provider ai@^6

Peer Dependencies

@mem0/vercel-ai-provider v3.0.0 requires:

ai v6+ (^6.0.199)
@ai-sdk/provider v3+ (^3.0.10)
Provider packages at v3+: @ai-sdk/openai@^3, @ai-sdk/anthropic@^3, @ai-sdk/google@^3, @ai-sdk/groq@^3, @ai-sdk/cohere@^3

Getting Started

Setting Up Mem0

Get your Mem0 API Key from the Mem0 Dashboard.

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 the upstream LLM provider (e.g. baseURL)
  },
  // 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).

Add Memories to Enhance Context:

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

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

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

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.

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-5-mini", { user_id: "borat" }),
  prompt: "Suggest me a good car to buy!",
});

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-5-mini"),
  prompt: prompt,
  system: memories,
});

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-5-mini", { 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?" },
      ],
    },
  ],
});

4. 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-5-mini", {
    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);
}

5. Generate Responses with Tools Call

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

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

const result = await generateText({
  model: mem0('claude-sonnet-4-20250514'),
  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: "What the temperature in the city that I live in?",
});

console.log(result);

6. Get Sources from Memory

generateText and streamText responses include Mem0 memories as a source, giving you programmatic access to the memories that influenced the response:

const { text, sources } = await generateText({
  model: mem0("gpt-5-mini", { user_id: "borat" }),
  prompt: "Suggest me a good car to buy!",
});

// sources[0].title === "Mem0 Memories"
// sources[0].providerMetadata.mem0.memories — array of memory objects
console.log(sources);

The same can be done for streamText as well.

7. 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() {
  const filePath = join(process.cwd(), 'my_pdf.pdf');
  const fileBuffer = readFileSync(filePath);

  const arrayBuffer = fileBuffer.buffer.slice(fileBuffer.byteOffset, fileBuffer.byteOffset + fileBuffer.byteLength);
  const uint8Array = new Uint8Array(arrayBuffer);
  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.

Supported LLM Providers

Provider	Configuration Value
OpenAI	`openai`
Anthropic	`anthropic`
Google / Gemini	`google` or `gemini`
Groq	`groq`
Cohere	`cohere`

Note: You can use either google or gemini as the provider value for Google Gemini models. Both map to the @ai-sdk/google package internally.

Configuration Options

Mem0ConfigSettings

These options can be passed per-request when creating a model instance:

Option	Type	Description
`user_id`	`string`	User identifier for memory scoping
`agent_id`	`string`	Agent identifier
`app_id`	`string`	Application identifier
`run_id`	`string`	Run/session identifier
`metadata`	`object`	Custom metadata for memories
`filters`	`object`	Filters for memory search
`infer`	`boolean`	Enable inference-based retrieval
`top_k`	`number`	Number of memories to retrieve (default: 10)
`threshold`	`number`	Relevance threshold for search
`rerank`	`boolean`	Enable reranking of results
`page`	`number`	Page number for pagination
`page_size`	`number`	Results per page

Key Features

createMem0(): Initializes a new Mem0 provider instance implementing ProviderV3.
retrieveMemories(): Retrieves memory context for prompts as a formatted system prompt string.
getMemories(): Get memories from your profile in array format.
addMemories(): Adds user memories to enhance contextual responses.

Migrating from v2.x

If you’re upgrading from @mem0/vercel-ai-provider v2.x:

Upgrade AI SDK: npm install ai@^6 and update all @ai-sdk/* provider packages to ^3.x
Remove deprecated params: Remove org_id, project_id, output_format, filter_memories, async_mode, enable_graph from your config
Remove graph memory: All graph-related options (enable_graph, graph prompts) have been removed. Graph memory is now a project-level setting on the Mem0 Platform
Update imports: LanguageModelV2Prompt is now LanguageModelV3Prompt if you import types directly

Best Practices

User Identification: Use a unique user_id for consistent memory retrieval.
Memory Cleanup: Regularly clean up unused memory data.
Sources: Access result.sources to inspect which memories influenced the response.
Note: We also have support for agent_id, app_id, and run_id. Refer Docs.

Conclusion

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

OpenAI Agents SDK

Build agents with OpenAI SDK and Mem0

Mastra Integration

Create intelligent agents with Mastra framework

​Overview

​Setup and Configuration

​Peer Dependencies

​Getting Started

​Setting Up Mem0

​Standalone Features

​1. Basic Text Generation with Memory Context

​2. Combining OpenAI Provider with Memory Utils

​3. Structured Message Format with Memory

​4. Streaming Responses with Memory Context

​5. Generate Responses with Tools Call

​6. Get Sources from Memory

​7. File Support with Memory Context

​Supported LLM Providers

​Configuration Options

​Mem0ConfigSettings

​Key Features

​Migrating from v2.x

​Best Practices

​Conclusion

OpenAI Agents SDK

Mastra Integration

Overview

Setup and Configuration

Peer Dependencies

Getting Started

Setting Up Mem0

Standalone Features

1. Basic Text Generation with Memory Context

2. Combining OpenAI Provider with Memory Utils

3. Structured Message Format with Memory

4. Streaming Responses with Memory Context

5. Generate Responses with Tools Call

6. Get Sources from Memory

7. File Support with Memory Context

Supported LLM Providers

Configuration Options

Mem0ConfigSettings

Key Features

Migrating from v2.x

Best Practices

Conclusion