/mem0-vercel-ai-sdk

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: "2.0.0"
  category: ai-memory
  tags: "vercel, ai-sdk, memory, nextjs, typescript, provider"
  mem0_tested_versions: "@mem0/vercel-ai-provider (npm) >=3.0.0,<4.0.0; ai (npm) >=6.0.0,<7.0.0; mem0ai (npm) >=3.0.0,<4.0.0"
compatibility: Node.js 18+, npm install @mem0/vercel-ai-provider, Vercel AI SDK v6 (ai package ^6), MEM0_API_KEY + 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@^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.

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 stores the conversation and retrieves memories before streaming begins.

Supported Providers

| Provider | Config value | Required env var | |----------|-------------|------------------| | OpenAI (default) | `"openai"` | `OPENAI_API_KEY` | | Anthropic | `"anthropic"` | `ANTHROPIC_API_KEY` | | Google | `"google"` (alias `"gemini"`) | `GOOGLE_GENERATIVE_AI_API_KEY` | | Groq | `"groq"` | `GROQ_API_KEY` | | Cohere | `"cohere"` | `COHERE_API_KEY` |

Select a provider when creating the Mem0 instance:

const mem0 = createMem0({ provider: "anthropic" });
const { text } = await generateText({
  model: mem0("claude-sonnet-4-20250514", { user_id: "alice" }),
  prompt: "Hello!",
});

How It Works Internally

Wrapped model flow

User prompt
  --> processMemories: addMemories (POST /v3/memories/add/, awaited)
  --> processMemories: getMemories (POST /v3/memories/search/)
  --> memories (if any) injected as system message at start of prompt
  --> underlying LLM generates response (doGenerate or doStream)
  --> response returned to caller (doGenerate also attaches a "Mem0 Memories" source)

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 Functions

| Function | Returns | U