thinkfleetai/memmesh/memmesh-migrate

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Migrate an existing memory setup ONTO MemMesh — either from another vendor (Mem0, Zep, Letta/MemGPT, a raw vector store) or from MemMesh Local (SQLite) up to MemMesh Hosted. Audits the current usage, produces a reviewable migration plan (API mapping + data export/import), and executes it on approval. Maps add→observe, search→search, and shows what MemMesh adds that the source lacked (prediction, calibration, bi-temporal graph). TRIGGER when: user says "migrate from mem0", "switch from zep to memmesh", "move my memory to memmesh", "replace mem0 with memmesh", or "move local memmesh to the hosted platform". DO NOT TRIGGER for: a fresh integration with no incumbent (use memmesh-integrate) or general SDK questions (use memmesh-sdk).

Install

npx vskill@latest install thinkfleetai/memmesh/memmesh-migrate

SKILL.md

memmesh-migrate

Move an existing memory setup onto MemMesh with a reviewable, reversible plan.

Canonical sources (fetch first)

  • Docs / API mapping: https://docs.memmesh.ai/llms.txt
  • Delegate call-site code to: memmesh-sdk (hosted) / memmesh-cli (local)

Step 1 — audit the incumbent

Detect what's in use and record .memmesh-migration/audit.md:

  • Vendor & SDK (Mem0 MemoryClient / Memory, Zep, Letta, LangChain memory,

bare Qdrant/pgvector, …).

  • Call sites — every add / search / get_all / update / delete.
  • Scoping — how user_id / agent_id / run_id / session map today.
  • Data volume — roughly how many memories, and where they live.

Step 2 — API mapping (cite in the plan)

Incumbent (e.g. Mem0)MemMesh equivalentNotes
client.add(text, user_id=…)memory.observe({ text, userId })MemMesh's engine decides what to save — you can stop pre-filtering.
client.search(q, user_id=…)memory.search({ query, userId })Same shape; MemMesh adds scope hierarchy + status lifecycle.
client.get_all(user_id=…)memory.list({ userId })
client.update(id, text)memory.observe(new) + memory.supersede(oldId, newId)Correction keeps provenance instead of destructive overwrite.
client.delete(id)memory.delete({ id }) (soft) / hard:true (GDPR)
user / agent / run scopinguserId / agentId / sessionId (+ projectId, platformId)6-level hierarchy.
(no equivalent)lattice.predict / predictTarget, behaviors.discover, context.queryGraphThis is why you're migrating — calibrated prediction the source can't do.

Step 3 — data migration

  1. Export from the incumbent (its export API or a get_all dump to JSONL).
  2. Transform each record to a MemMesh observe (preferred — lets the engine

re-extract and build the graph) OR a memory.save with an explicit id when you must preserve exact rows.

  1. Import in batches; keep a checkpoint file so a re-run is idempotent.
  2. Reconcile — count source vs destination, sample-search for known facts,

write .memmesh-migration/reconcile.md.

For Local → Hosted: set sync.url in ~/.thinkfleet-memory/config.toml and let the CRDT-style bi-temporal sync push; or export the SQLite items and observe them into the hosted tenant. Reconcile the same way.

Step 4 — cutover (gated)

Keep the incumbent behind the old flag; bring MemMesh up behind MEMMESH_ENABLED. Run both in shadow (dual-write) for a window, compare retrieval quality, then flip the default. Never hard-delete the source until reconciliation passes.

Step 5 — show the upgrade

After parity, add one prediction call at a real decision point so the user sees what they gained: a calibrated confidence + provenance + honest abstention that their previous vendor could not produce. Consider running the benchmark skill to put numbers on the retrieval-quality / cost delta.

Definition of done

Reconciled counts match, known facts retrievable on MemMesh, dual-write window clean, and a rollback note (how to fall back to the incumbent) in the plan.

Security

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  • Model reviewPassed

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Quality evaluation

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Source

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