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-oss-to-platformgit 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-oss-to-platform)<a href="https://agentmods.dev/skills/mem0ai/mem0/mem0-oss-to-platform"><img src="https://agentmods.dev/badge/skills/mem0ai/mem0/mem0-oss-to-platform/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-oss-to-platform"><img src="https://agentmods.dev/badge/skills/mem0ai/mem0/mem0-oss-to-platform.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- 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 | $0.00273 | $0.02050 |
| Opus 5 | $0.00137 | $0.01025 |
| Sonnet 5 | $0.00055 | $0.00410 |
| Haiku 4.5 | $0.00027 | $0.00205 |
Grade A, and why
mem0-oss-to-platform 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Migrate mem0 OSS → mem0 Platform (hosted)
This skill migrates a project's memory layer from the self-hosted mem0 OSS SDK to the hosted mem0 Platform SDK, working for any project shape — an agent, a RAG pipeline, an API service, a chatbot, a background worker. You discover where mem0 is actually used, write a plan the developer reviews, and then execute it on approval.
The mental model (read this first — it's why the migration is shaped the way it is)
OSS mem0 means the developer runs the whole memory stack themselves: a vector store
(Qdrant/pgvector/Chroma/…), an embedder, an LLM for fact extraction, and a local history DB. All of
that is wired up in a config object passed to Memory.
The Platform means mem0 runs that stack for them. The developer just holds an API key. So
the migration is mostly subtraction: the local infrastructure config collapses into a single
MemoryClient(api_key=...). The method calls stay recognizable (add/search/get_all/…), but a
few parameter conventions tighten up and the return values are server responses.
So the core of every migration is:
Memory/Memory.from_config({...})→MemoryClient()(reads the API key from the env).- Delete the local
vector_store/llm/embedder/graph_store/history_db_pathconfig. - Fix up each call site to the hosted call convention (entity IDs into
filters, pagination, etc.). - Flag everything that isn't a clean 1:1 so the developer can decide (see
references/gotchas.md).
Scope discipline: touch only mem0-related code, config, dependencies, and env. Preserve the project's existing behavior, structure, and style. Do not rename things, "tidy" nearby code, or change the app's logic. The developer asked to swap a backend, not to refactor their project.
Workflow
Work through these phases in order. Phases 1–4 produce the plan; phase 5 runs only after approval.
Phase 0 — Prerequisite check
The hosted SDK needs a mem0 API key (MEM0_API_KEY, obtainable at https://app.mem0.ai). Confirm
the developer has one. You don't need the key value to write the plan, but flag in the plan that it
must be set (in .env / secrets manager, never hardcoded) before execution and verification.
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 · 121 lines · 273 tokens per session scan A 2fb695051ada
mem0-oss-to-platform is a skill published in the GitHub repository mem0ai/mem0 (65,043 stars, last pushed today), licensed Apache-2.0. It adds 273 tokens to every session and 2,050 once invoked, about $0.0014 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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