name: mem0-vercel-ai-sdk

description: >

Mem0 provider for Vercel AI SDK (@mem0/vercel-ai-provider).

TRIGGER when: user mentions "vercel ai sdk", "@mem0/vercel-ai-provider",

"createMem0", "retrieveMemories", "addMemories", "getMemories",

"searchMemories", "mem0 vercel", "AI SDK provider", "AI SDK memory",

or is using generateText/streamText with mem0. Also triggers for Next.js

apps needing memory-augmented AI.

DO NOT TRIGGER when: user asks about direct Python/TS SDK calls without Vercel

(use mem0 skill), or CLI terminal commands (use mem0-cli skill).

license: Apache-2.0

metadata:

author: mem0ai

version: "1.1.0"

category: ai-memory

tags: "vercel, ai-sdk, memory, nextjs, typescript, provider"

compatibility: Node.js 18+, npm install @mem0/vercel-ai-provider, Vercel AI SDK v5 (ai package), MEM0APIKEY + LLM provider API key

Mem0 Vercel AI SDK Provider

Memory-enhanced AI provider for Vercel AI SDK. Automatically retrieves and stores memories during LLM calls.

Step 1: Install

npm install @mem0/vercel-ai-provider ai

Step 2: Set up environment variables

export MEM0_API_KEY="m0-xxx"
export OPENAI_API_KEY="sk-xxx"   # or ANTHROPIC_API_KEY, GOOGLE_API_KEY, etc.

Get a Mem0 API key at: https://app.mem0.ai/dashboard/api-keys?utmsource=oss&utmmedium=skill-mem0-vercel-ai-sdk

Pattern 1: Wrapped Model

The wrapped model approach is the simplest. createMem0 returns a provider that wraps any supported LLM with automatic memory retrieval and storage.

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: "alice" }),
  prompt: "Recommend a restaurant",
});

What happens under the hood:

1. The prompt is sent to Mem0 search (POST /v3/memories/search/) to retrieve relevant memories
2. Retrieved memories are injected as a system message at the start of the prompt
3. The underlying LLM (e.g., OpenAI gpt-5-mini) generates a response using the enriched prompt
4. The conversation is stored back to Mem0 (POST /v3/memories/add/) as a fire-and-forget async call (no await)

Pattern 2: Standalone Utilities

Use standalone utilities when you want full control over the memory retrieve/store cycle, or you want to use a provider that is already configured separately.

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

const prompt = "Recommend a restaurant";

// Retrieve memories -- returns a formatted system prompt string
const memories = await retrieveMemories(prompt, {
  user_id: "alice",
  mem0ApiKey: "m0-xxx",
});

// Generate using any provider with injected memories
const { text } = await generateText({
  model: openai("gpt-5-mini"),
  prompt,
  system: memories,
});

// Optionally store the conversation back
await addMemories(
  [
    { role: "user", content: [{ type: "text", text: prompt }] },
    { role: "assistant", content: [{ type: "text", text }] },
  ],
  { user_id: "alice", mem0ApiKey: "m0-xxx" }
);

Pattern 3: Streaming

Use streamText for streaming responses with memory augmentation:

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

const mem0 = createMem0();
const result = streamText({
  model: mem0("gpt-5-mini", { user_id: "alice" }),
  prompt: "What should I cook for dinner?",
});

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

The wrapped model handles memory retrieval before streaming begins and stores the conversation after.

Supported Providers

Select a provider when creating the Mem0 instance:

const mem0 = createMem0({ provider: "anthropic" });
const { text } = await generateText({
  model: mem0("gpt-5-mini", { user_id: "alice" }),
  prompt: "Hello!",
});

How It Works Internally

Wrapped model flow

User prompt
  --> searchInternalMemories (POST /v3/memories/search/)
  --> memories injected as system message at start of prompt
  --> underlying LLM generates response (doGenerate or doStream)
  --> processMemories fires addMemories as fire-and-forget (no await)
  --> response returned to caller

Standalone flow

User controls each step:
  1. retrieveMemories / getMemories / searchMemories -> fetch memories
  2. inject into system prompt manually
  3. call generateText / streamText with any provider
  4. addMemories -> store new conversation to Mem0

Key Differences Between the 4 Utility