#OPenclaw Cloud memory how to

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glacial zenith
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Is the new cloud memory feature fully built in, or do I need to provide my own cloud service/support? My claw doesnt know anything about how it works

jovial tapirBOT
chrome pagodaBOT
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Let me verify this against the docs so I don't hand-wave it.

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Short version: the default memory is fully built in, and local. You do not need your own cloud service for that.

What OpenClaw has today:

  1. Builtin memory
    Stores memory in workspace Markdown files (MEMORY.md, memory/*.md) and indexes it locally with SQLite. No extra service needed.
    Docs: https://docs.openclaw.ai/concepts/memory
    https://docs.openclaw.ai/concepts/memory-builtin

  2. Honcho memory (the cloud style / cross-session one)
    This is not built into core by default. It uses a separate service, either:

  3. Active Memory
    Separate feature. This is the “look up relevant memory before replying” layer. It is not a storage backend by itself, and it is not automatically “cloud memory.”

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Docs: https://docs.openclaw.ai/concepts/active-memory

So if you have not explicitly installed/configured Honcho, your Claw is probably using the local builtin memory only.

If you want, paste your memory-related config and I can tell you exactly which mode you're on.

glacial zenith
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I mean the new cloud memory/ lance db feature released in openclaw 4.15

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?

chrome pagodaBOT
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Ah, got it, that’s a different thing from Honcho.

For memory-lancedb in 4.15:

  • You do not need a separate cloud memory service just to use it.
  • It is a bundled, install-on-demand memory plugin.
  • By default it stores locally, with the DB path defaulting to ~/.openclaw/memory/lancedb.
  • You do need to configure an embedding provider for it (the plugin config requires embedding.apiKey, with optional model / baseUrl).
  • Cloud storage is optional, not required. The plugin supports a dbPath that can be local or a cloud URI like s3://... or gs://..., plus storageOptions for creds/endpoints.

So the practical answer is:

No, you do not need to bring your own cloud service unless you specifically want the LanceDB files stored in cloud object storage.
If you do nothing special, it can run locally.

The key bit to enable it is selecting it as the memory slot, for example:

# 
{
  plugins: {
    slots: { memory: "memory-lancedb" },
    entries: {
      "memory-lancedb": {
        enabled: true,
        config: {
          embedding: {
            apiKey: "${OPENAI_API_KEY}",
            model: "text-embedding-3-small"
          },
          autoCapture: true,
          autoRecall: true
        }
      }
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Super short version:

LanceDB memory does not require a separate cloud service.
It can run locally. You just need to enable/configure the plugin and give it an embeddings API key.

Cloud storage is optional only if you want the DB on something like S3/GS.

If you want, I can paste the smallest working local config.

glacial zenith
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my claw is current using:

So the fuller picture is:

Active memory
The current working context, recent conversation state, and whatever is being actively carried in-session.

Short-term file memory
memory/YYYY-MM-DD.md

Long-term curated memory
MEMORY.md

Semantic recall
memory_search backed by qmd

Indexed session/history recall
Prior transcripts when available

Memory promotion / “dreaming”
The process that promotes useful bits from short-term into long-term memory

Whats the best overall architecture moving forward to ensure I dont have issues

chrome pagodaBOT
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I’m going to sanity-check the docs on QMD/active-memory/dreaming before I recommend a stack.

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If your goal is “works reliably, low drama”, I’d recommend this architecture:

Recommended stack

  1. Canonical memory stays file-based

    • MEMORY.md = evergreen facts, preferences, decisions
    • memory/YYYY-MM-DD.md = short-term / daily notes

  2. Use exactly one semantic retrieval layer

    • Since you already have QMD, keep QMD as the retrieval/index layer
    • Let memory_search resolve through QMD
    • If you want transcript recall, use QMD session indexing, not multiple overlapping transcript systems

  3. Use Active Memory as a thin pre-reply layer

    • Good for personalization
    • Keep it DM/persistent-chat first
    • Only expand to channels/groups once it feels predictable
      (Docs default it to allowedChatTypes: ["direct"] for a reason)
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  1. Use Dreaming conservatively
    • Enable it, but keep it reviewable
    • Let it promote into MEMORY.md
    • Check DREAMS.md occasionally so long-term memory does not drift

What I would not do yet

I would not pile LanceDB on top of this unless you have a very specific need for it.

Why:

  • you already have file memory + QMD + active memory + dreaming
  • adding memory-lancedb gives you another memory store / recall path
  • that usually means more moving parts, more ambiguity, more “why did it remember this but not that?”

My blunt recommendation

Stay with your current file-centric architecture, keep QMD as the single search backend, and treat MEMORY.md as the source of truth.
That is the cleanest setup.

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Biggest thing to watch for

The main failure mode is scope mismatch:

  • Active Memory defaults to DM-style sessions
  • QMD can surface results in more places depending on config
  • transcript recall can come from more than one path if you enable overlapping features

So pick one intentional policy:

  • DM only first, then widen later
  • one transcript index path
  • one semantic backend
  • one canonical long-term store

When LanceDB does make sense

Use memory-lancedb if you specifically want:

  • plugin-driven auto-capture / auto-recall
  • a single vector-store-style memory backend
glacial zenith
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so really unless I have another openclaw/agent on a different machine that I want to have shared memory with my existinbg install, I really dont need lance db?

chrome pagodaBOT
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Yeah, basically yes.

If your current setup is working, and you are happy with:

  • MEMORY.md
  • daily memory files
  • QMD-backed memory_search
  • active memory
  • dreaming

then you do not need LanceDB.

LanceDB starts to make sense when you specifically want one of these:

  • shared / remote-backed memory across installs or machines
  • a more vector-store-centric memory backend
  • the plugin’s auto-capture / auto-recall behavior