mem0-vercel-ai-sdk

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@^6

ai and the @ai-sdk/* provider packages ship as regular dependencies of @mem0/vercel-ai-provider, so nothing else needs installing. The only peer dependency is zod (optional, ^3.0.0).

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?utm_source=oss&utm_medium=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 stored to Mem0 (POST /v3/memories/add/), awaited before the LLM call (a failed write is logged and ignored)
2. The prompt is sent to Mem0 search (POST /v3/memories/search/) to retrieve relevant memories
3. If any memories are found, they are injected as a system message at the start of the prompt
4. The underlying LLM (e.g., OpenAI gpt-5-mini) generates a response using the enriched prompt

For generateText, the retrieved memories are also attached to the result as a source:

const { text, sources } = await generateText({
  model: mem0("gpt-5-mini", { user_id: "alice" }),
  prompt: "Recommend a restaurant",
});

console.log(sources.find((s) => s.title === "Mem0 Memories")?.providerMetadata?.mem0);

The source has title: "Mem0 Memories" and providerMetadata.mem0 holds memories (array of memory objects) and memoriesText. It is only present when at least one memory was retrieved.