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 matter more than the mechanical edits. Read references/gotchas.md and flag anything that needs a human decision: self-hosted/data- residency setups, local model choices moving server-side, graph-memory usage, custom prompts, hot- path calls that now make network round-trips, and existing locally-stored memories not carrying over (data migration is out of scope unless the developer asks — note it, don't silently attempt it).

Phase 4 — Write the plan and stop

Write the full plan to MEM0_MIGRATION_PLAN.md at the repo root, following the structure in references/plan-template.md. It must be concrete enough to execute from and honest about the gaps. Then stop and present it for review. Do not start editing code in the same turn — the whole point is that the developer reads and approves the plan first.

Phase 5 — Execute on approval (guided)

Once the developer approves (they may ask for changes first — incorporate them), execute the plan:

• Make the edits file by file, staying strictly within mem0 scope.
• Update dependencies and env (MEM0_API_KEY; remove now-dead local-infra deps/services only if they exist solely for mem0 and you're confident).
• Verify, mirroring how you'd confirm any backend swap:
  • It imports / type-checks / byte-compiles.
  • A smoke test exercises add → search/get_all → delete_all against the hosted API with a real MEM0_API_KEY, and the app's own entry point still runs.
  • No local mem0 storage directory gets created anymore (e.g. a .mem0/, local Qdrant path) — proof the memory really lives on the platform now.
• Report what changed, what was verified, and any flagged concerns the developer still needs to act on (e.g. configuring custom instructions in the dashboard, migrating old data).

Reference files

• references/api-mapping.md — exact OSS→hosted method/param/return mapping for Python and TypeScript, plus dependency and env changes. Read during Phase 2–3.
• references/gotchas.md — the things that aren't a clean 1:1 and need a human decision. Read during Phase 3 so the plan's "Concerns" section is complete.
• references/plan-template.md — the exact structure for MEM0_MIGRATION_PLAN.md. Use in Phase 4.