mem0 is memory infrastructure that lets AI agents and applications store and retrieve information across interactions. It supports agents and developers who need persistent context for AI systems.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mem0ai/mem0 --skill mem0-vercel-ai-sdkgit clone --depth 1 https://github.com/mem0ai/mem0Wrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mem0ai/mem0/mem0-vercel-ai-sdk)<a href="https://agentmods.dev/skills/mem0ai/mem0/mem0-vercel-ai-sdk"><img src="https://agentmods.dev/badge/skills/mem0ai/mem0/mem0-vercel-ai-sdk/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mem0ai/mem0/mem0-vercel-ai-sdk"><img src="https://agentmods.dev/badge/skills/mem0ai/mem0/mem0-vercel-ai-sdk.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk fail
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00146 | $0.01995 |
| Opus 5 | $0.00073 | $0.00997 |
| Sonnet 5 | $0.00029 | $0.00399 |
| Haiku 4.5 | $0.00015 | $0.00199 |
Grade A, and why
mem0-vercel-ai-sdk scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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" }
);
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 193 lines · 146 tokens per session scan A 0745976ea6d0
mem0-vercel-ai-sdk is a skill published in the GitHub repository mem0ai/mem0 (65,043 stars, last pushed today), licensed Apache-2.0. It adds 146 tokens to every session and 1,995 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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