mem0-vercel-ai-sdk

Q: How do I install mem0-vercel-ai-sdk?

Run npx skills add https://github.com/mem0ai/mem0 --skill mem0-vercel-ai-sdk in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/mem0-vercel-ai-sdk/ (personal) or .claude/skills/mem0-vercel-ai-sdk/ (project).

by mem0aiAI & Agents

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).

Installs: 514
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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/mem0ai/mem0 --skill mem0-vercel-ai-sdk

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads mem0-vercel-ai-sdk automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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:

The prompt is stored to Mem0 (POST /v3/memories/add/), awaited before the LLM call (a failed write is logged and ignored)
The prompt is sent to Mem0 search (POST /v3/memories/search/) to retrieve relevant memories
If any memories are found, they 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

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	Use when
`retrieveMemories`	Formatted system prompt string	Injecting directly into `system` parameter
`getMemories`	Raw memory array	Processing memories programmatically
`searchMemories`	Raw search response (as returned by the API)	Need scores and full metadata
`addMemories`	API response	Storing new messages to Mem0

retrieveMemories, getMemories, and searchMemories accept LanguageModelV3Prompt | string as the first argument; addMemories is typed as LanguageModelV3Prompt (a string also works at runtime). All four take optional Mem0ConfigSettings as the second argument.

Common Edge Cases and Tips

Always provide user_id (or agent_id/app_id/run_id) for consistent memory retrieval. The search endpoint requires at least one entity ID in filters; the provider places these IDs there for you.
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 v6 (LanguageModelV3 / ProviderV3 interfaces, @mem0/vercel-ai-provider 3.x). Provider 2.x targeted AI SDK v5. It is not compatible with AI SDK v4 or earlier.
processMemories awaits addMemories before searching and calling the LLM, so each wrapped call includes one memory write and one memory search. If either request fails, the error is logged and the LLM call proceeds without memories.
"google" and "gemini" are both accepted and map to @ai-sdk/google.
Removed in 3.0.0: org_id, project_id, org_name, project_name, output_format, filter_memories, async_mode, enable_graph, version, api_version. Graph memory is now a Mem0 Platform project setting, not a provider option.
Default top_k is 10. threshold and rerank are only sent when set (the API default for rerank is false; threshold is a server-side cutoff, not a floor on the returned score).
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

FAQ

What does the mem0-vercel-ai-sdk skill do?

How do I install mem0-vercel-ai-sdk?

Run npx skills add https://github.com/mem0ai/mem0 --skill mem0-vercel-ai-sdk in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/mem0-vercel-ai-sdk/ (personal) or .claude/skills/mem0-vercel-ai-sdk/ (project).

Which AI agents can use mem0-vercel-ai-sdk?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is mem0-vercel-ai-sdk safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains mem0-vercel-ai-sdk?

It is published by mem0ai in mem0ai/mem0. It has 514 recorded installs.

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mem0-vercel-ai-sdk

Install

Install

Describe the task

Review the result

SKILL.md

Mem0 Vercel AI SDK Provider

Step 1: Install

Step 2: Set up environment variables

Pattern 1: Wrapped Model

Pattern 2: Standalone Utilities

Pattern 3: Streaming

Supported Providers

How It Works Internally

Wrapped model flow

Standalone flow

Key Differences Between the 4 Utility Functions

Common Edge Cases and Tips

References

Related Mem0 Skills

FAQ

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