Memory Best Practices

MemoryLake extracts memories automatically — you don’t write them by hand. Best practice is therefore less about authoring and more about feeding the system well and keeping it honest. Here’s what works.

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Feeding Memory Well

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Say facts explicitly when they matter

Extraction is good at picking up clear statements and decisions, and appropriately skips small talk. When something must be remembered, state it plainly:

• ✅ “Remember: the budget cap for Q1 is $75K, hard limit.”
• ✅ “We decided to use PostgreSQL — better JSON support and the team knows it.”
• ⚠️ “Yeah let’s maybe go with the second option I guess” — ambiguous references extract poorly. Name the thing.

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Include the why, not just the what

A decision with rationale is a far more useful memory than a bare conclusion. “We chose PostgreSQL over MySQL for JSON support and team expertise” lets a future assistant answer “why Postgres?” — not just “which database?”

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Upload the source document, don’t paraphrase it

If knowledge lives in a file, upload the file. Document content is parsed and indexed with higher fidelity than a chat summary of it, and memories extracted from it carry a trace to the actual source.

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Correct out loud

When something changes, say so explicitly — “The launch moved from October 15 to November 1” — rather than just mentioning the new date in passing. Explicit corrections update or conflict-flag the old memory instead of leaving two versions in play.

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Scoping: the Highest-Leverage Decision

Retrieval is scoped to projects. Well-scoped projects mean relevant recall; kitchen-sink projects mean noise.

• One project per context — per client, per initiative, per life domain. Not one giant “Everything” project.
• Use per-agent config for multi-client work — the OpenClaw plugin’s per-directory .memorylake/config.json pins each working folder to its own project, so client A’s facts never surface in client B’s session. See Coding Agents.
• Split when retrieval gets noisy — if answers start citing irrelevant context, your project is probably covering two contexts. Split it.

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Keeping Memory Honest

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Review conflicts promptly

Conflicts are memory’s immune system — but only if someone resolves them. Check the project’s Conflicts tab when the assistant seems uncertain about a fact, and after any burst of corrections. In teams, make sure someone holds the resolve permission and owns the habit.

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Audit with traces

Before trusting a surprising assertion, open the memory’s Source Trace — you’ll see exactly which conversation or document produced it. This is also the fastest way to debug why an assistant believes something wrong: find the memory, trace it, then edit or forget it.

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Prune deliberately

• Edit memories whose details drifted (titles, numbers, names)
• Forget memories that are obsolete or should never have been captured — deletion is permanent and takes effect everywhere
• Don’t over-prune: dedup and merging already handle repetition; delete for wrongness, not tidiness

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For Teams

• Establish what belongs in shared memory — decisions, policies, client facts: yes. Personal working notes: keep in personal space. The workspace boundary is the individual/organizational memory line.
• Assign conflict ownership — a project without a conflict-resolver accumulates stale facts silently.
• Lean on permissions, not caution — if only leads should delete memories, remove mem_delete from other roles rather than relying on convention. See the Permission Reference.

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For Builders (API)

• Send full conversation turns to add-memory (infer: true) and let extraction decide what’s durable — pre-filtering usually loses context that improves extraction quality.
• Use user_id so memories attribute to the right end user.
• Set infer: false only when you’re storing an already-curated fact verbatim.
• Expose trace and forget to your users — “why do you know this?” and “forget this” build trust and cover compliance.
• Check conflicts programmatically if your product asserts facts — see Conflicts API.

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Common Mistakes

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Next Steps