/mem0-oss-to-platform

name: mem0-oss-to-platform
description: >-
  Plan and then execute a migration of a project from the mem0 open-source / self-hosted SDK
  (the local `Memory` class) to the mem0 Platform / hosted / managed SDK (the `MemoryClient`
  class). Use this whenever a developer wants to move, switch, or migrate their mem0 usage off
  OSS/self-hosted to the hosted API — e.g. "migrate my mem0 setup to the platform", "switch from
  self-hosted mem0 to MemoryClient", "use my mem0 API key instead of a local Qdrant", "move mem0
  to the cloud/hosted/managed service", or "replace my local mem0 vector store + embedder config
  with the platform". Applies to Python (`from mem0 import Memory` → `from mem0 import MemoryClient`)
  and TypeScript/JavaScript (`import { Memory } from "mem0ai/oss"` → `import MemoryClient from "mem0ai"`).
  Trigger even when the user doesn't say the word "migrate" but clearly wants their existing mem0
  integration to run against the hosted platform. It first produces a reviewable migration plan,
  then executes it after the developer approves. Strictly scoped to the mem0 integration — it does
  not refactor, restructure, or "improve" any unrelated code.

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: 1. `Memory` / `Memory.from_config({...})` → `MemoryClient()` (reads the API key from the env). 2. Delete the local `vector_store` / `llm` / `embedder` / `graph_store` / `history_db_path` config. 3. Fix up each call site to the hosted call convention (entity IDs into `filters`, pagination, etc.). 4. 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.

Phase 1 — Discover the mem0 footprint

Do not assume the layout. Find every place mem0 appears. Detect the language and the **installed** version first, then sweep for usage. Concretely, search for:

• **Imports / instantiation:** `from mem0 import Memory`, `Memory.from_config`, `Memory(`,

`import ... from "mem0ai"`, `from "mem0ai/oss"`, `new Memory(`.

• **Config blocks:** keys like `vector_store`/`vectorStore`, `embedder`, `llm`, `graph_store`/

`graphStore`, `history_db_path`, `historyStore`, `custom_fact_extraction_prompt`, `custom_update_memory_prompt`, `enable_graph`.

• **Every call site:** `.add(`, `.search(`, `.get_all(`/`.getAll(`, `.delete_all(`/`.deleteAll(`,

`.get(`, `.update(`, `.delete(`, `.reset(`, `.history(`.

• **Dependencies & env:** `requirements.txt`/`pyproject.toml`/`package.json` for `mem0ai` and any

local-infra deps that exist *only* for mem0 (e.g. `qdrant-client`, `chromadb`); `.env`/config for things like `OPENAI_API_KEY` used by the local embedder/LLM; any docker-compose service (e.g. a Qdrant container) that exists only to back mem0.

Use Grep/Glob broadly; a single missed call site is a runtime break later. Record `file:line` for each finding — the plan's inventory is built from this.

Phase 2 — Verify the API against the installed SDK (don't guess)

Versions drift, and the OSS and hosted classes have subtly different signatures. Before mapping, confirm the **real** signatures of the installed package rather than trusting memory:

• **Python:** `python -c "import inspect; from mem0 import MemoryClient; print(inspect.signature(MemoryClient.search))"`

for each method you'll touch, and read the installed source under `site-packages/mem0/client/main.py` if anything is ambiguous (e.g. whether a method *rejects* top-level entity params). Also check the OSS side the project currently uses.

• **TypeScript:** read the installed types/dist under `node_modules/mem0ai/` to confirm option names

(`limit` vs `topK`, `userId` vs a nested `filters`) and the default vs `mem0ai/oss` export.

This verification step is the single most important habit — it's what keeps the plan correct across mem0 versions. Then consult `references/api-mapping.md` for the OSS→hosted translation of each method (Python and TypeScript), and the official guide at https://docs.mem0.ai/migration/oss-v2-to-v3.

Phase 3 — Map each site and flag the gaps

For every call site and config block from Phase 1, determine the hosted equivalent using the mapping. Most calls map cleanly. Some don't — and those matte