---
title: Vercel AI SDK
---

The [**Mem0 AI SDK Provider**](https://www.npmjs.com/package/@mem0/vercel-ai-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.

<Note type="info">
  🎉 Exciting news! Mem0 AI SDK now supports **OpenAI**, **Anthropic**, **Cohere**, and **Groq** providers.
</Note>

## Overview

In this guide, we'll create a Travel Agent AI that:
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

## Setup and Configuration

Install the SDK provider using npm:

```bash
npm install @mem0/vercel-ai-provider
```

## Getting Started

### Setting Up Mem0

1. Get your **Mem0 API Key** from the [Mem0 Dashboard](https://app.mem0.ai/dashboard/api-keys).

2. Initialize the Mem0 Client in your application:

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

    const mem0 = createMem0({
      provider: "openai",
      mem0ApiKey: "m0-xxx",
      apiKey: "provider-api-key",
      config: {
        compatibility: "strict",
      },
    });
    ```

    > **Note**: The `openai` provider is set as default. Consider using `MEM0_API_KEY` and `OPENAI_API_KEY` as environment variables for security.

3. Add Memories to Enhance Context:

    ```typescript
    import { LanguageModelV1Prompt } from "ai";
    import { addMemories } from "@mem0/vercel-ai-provider";

    const messages: LanguageModelV1Prompt = [
      { role: "user", content: [{ type: "text", text: "I love red cars." }] },
    ];

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

### Standalone Features:

    ```typescript
    await addMemories(messages, { user_id: "borat", mem0ApiKey: "m0-xxx", org_id: "org_xx", project_id: "proj_xx" });
    await retrieveMemories(prompt, { user_id: "borat", mem0ApiKey: "m0-xxx", org_id: "org_xx", project_id: "proj_xx" });
    await getMemories(prompt, { user_id: "borat", mem0ApiKey: "m0-xxx", org_id: "org_xx", project_id: "proj_xx" });
    ```
     > 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

    ```typescript
    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!",
    });
    ```

### 2. Combining OpenAI Provider with Memory Utils

    ```typescript
    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,
    });
    ```

### 3. Structured Message Format with Memory

    ```typescript
    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?" },
          ],
        },
      ],
    });
    ```

### 3. Streaming Responses with Memory Context

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

    const mem0 = createMem0();

    const { textStream } = await 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);
    }
    ```

## 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.

## 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](/api-reference/memory/add-memories).

## Conclusion

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

## Help

- For more details on Vercel AI SDK, visit the [Vercel AI SDK documentation](https://sdk.vercel.ai/docs/introduction).
- For Mem0 documentation, refer to the [Mem0 Platform](https://app.mem0.ai/).
- If you need further assistance, please feel free to reach out to us through following methods:

<Snippet file="get-help.mdx" />