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:

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

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

Related Mem0 Skills

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