Skill

mem0-vercel-ai-sdk

Name: mem0-vercel-ai-sdk
Author: Mem0

integrate Mem0 with Vercel AI SDK

Published by Mem0 Updated Jul 13

Covers Vercel Memory 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).

SKILL.md

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?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:

The prompt is sent to Mem0 search (POST /v3/memories/search/) to retrieve relevant memories
Retrieved memories are injected as a system message at the start of the prompt
The underlying LLM (e.g., OpenAI gpt-5-mini) generates a response using the enriched prompt
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

Provider	Config value	Required env var
OpenAI (default)	`"openai"`	`OPENAI_API_KEY`
Anthropic	`"anthropic"`	`ANTHROPIC_API_KEY`
Google	`"google"`	`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("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 Functions

Function	Returns	Use when
`retrieveMemories`	Formatted system prompt string	Injecting directly into `system` parameter
`getMemories`	Raw memory array	Processing memories programmatically
`searchMemories`	Full search response (results + relations)	Need relations, scores, metadata
`addMemories`	API response	Storing new messages to Mem0

All four accept LanguageModelV2Prompt | string as the first argument and optional Mem0ConfigSettings as the second.

Common Edge Cases and Tips

Always provide user_id (or agent_id/app_id/run_id) for consistent memory retrieval. Without an entity identifier, memories cannot be scoped.
Standalone utilities require explicit API key: pass mem0ApiKey in the config object, or set the MEM0_API_KEY environment variable.
This uses Vercel AI SDK v5 (LanguageModelV2 / ProviderV2 interfaces). It is not compatible with AI SDK v3 or v4.
processMemories fires addMemories as fire-and-forget (.then() without await). Memory storage happens asynchronously and does not block the LLM response.
The "gemini" alias exists in the provider switch but is NOT in the supportedProviders list. Use "google" instead.
Custom host: set host in the config to point to a different Mem0 API endpoint (default: https://api.mem0.ai).

References

Topic	File
Provider API (`createMem0`, `Mem0Provider`, types)	local / GitHub
Memory utilities (`addMemories`, `retrieveMemories`, etc.)	local / GitHub
Usage patterns and examples	local / GitHub

Skill	When to use	Link
mem0	Python/TypeScript SDK, REST API, framework integrations	local / GitHub
mem0-cli	Terminal commands, scripting, CI/CD, agent tool loops	local / GitHub

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