Operations, HR & Teams

The AI That Actually Knows You

Every AI tool asks you to start over. Your communication style, your priorities, your recurring context — you re-explain them every session, to every model. MemoryLake stores who you are once, and loads that context into every AI session you run, regardless of which model you're using.

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The Memory Problem

You've explained your role dozens of times. You've described your working style, your preferences for tone, the specific context of your projects. Every time you open a new session — or switch to a different AI tool — you start over. The models are getting smarter. The memory problem isn't getting better.

What MemoryLake Does Differently

Background Memory as your AI identity layer

Store your role, working style, communication preferences, recurring project context, and any constraints that should always apply. Background Memory is read-only for the AI: it shapes every session without you having to prompt it.

Skill Memory for your frameworks and workflows

The thinking frameworks, templates, and processes you've developed aren't generic — they're yours. Store them in Skill Memory and invoke them by name in any session, with any model.

Consistent identity across every model

Whether you're in Claude, ChatGPT, Grok, or Gemini, your Background Memory loads first. Every AI session starts knowing who you are, what you're working on, and how you prefer to work.

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How It Works

Connect — Set up MemoryLake and connect it to the AI tools you use. Takes under ten minutes.
Structure — Define your Background Memory: role, style, recurring context, constraints. Add Skill Memory for your go-to frameworks and workflows.
Reuse — Open any connected AI. Your context loads automatically. You spend your session on the work, not the setup.

Before & After

	Without MemoryLake	With MemoryLake
Session start	Re-explain role, style, context	Background Memory loads automatically
Switching models	Start over with each model	Same identity available everywhere
Recurring frameworks	Copy-paste or re-prompt each time	Stored in Skill Memory, invoked by name
Preference evolution	Informal, inconsistent	Versioned and auditable

Built For

Knowledge workers, consultants, researchers, founders, and anyone who uses AI tools as a core part of their work and wants those tools to behave like they actually know them. If you've ever written a long system prompt and wished it could follow you from tool to tool, Background Memory is what you were asking for.

Runnable demoabout 3 minutes

Try this use case on your own account

A fictional operations lead talks to an AI assistant in Claude one day and in ChatGPT the next, and both sessions land on one memory: hers. On the built-in default, the first session also keeps her family, her diet and her knee surgery. So she writes her own fact instruction for what her memory may record and removes what it already kept. The next session, in the other tool, records her new work rules and updates her answer length in place. The demo checks each excluded category, and the profile then goes byte for byte into a Claude, an OpenAI and a Gemini request.

What you will see

Sessions from two tools as conversations with the same person, so every fact lands on one memory
fact instruction draft shows what a person's memory records by default; fact instruction set --file replaces it with her own Include / Exclude rules
conv fact-actions as a per-session receipt: rules added, the changed preference updated with its old value, each excluded category judged ✓/✗ (37 of 42 sessions clean in our runs)
One fact list --actors block written into Anthropic, OpenAI and Gemini payloads with the same sha256. Plus a local web UI

You need: a MemoryLake API key · the memorylake CLI · Python 3.9+

Run it

git clone https://github.com/memorylake-ai/memorylake-usecases.git
cd memorylake-usecases/preference-memory
export MEMORYLAKE_API_KEY=sk-…
python3 demo.py          # or: python3 web/server.py

Open on GitHub Read the walkthrough

Watch it run · real, unedited recordings

CLI demo3:45 Web demo4:02

Related use cases

Engineering & DeveloperAdd Persistent Memory to GrokGrok resets after every session. MemoryLake adds persistent Conversation, Fact, and Skill Memory to Grok — and carries it to every other AI you use.

Research & AnalyticsAdd Persistent Memory to PerplexityPerplexity is excellent for real-time research — but every session starts cold. MemoryLake preserves your findings so research compounds instead of resets.

Research & AnalyticsAI Memory Beyond NotionNotion AI is powerful inside Notion. The moment you open Claude or ChatGPT, that context is gone. MemoryLake is the cross-tool memory layer Notion wasn't built to be.

Engineering & DeveloperAdd Persistent Memory to Cursor AICursor loses your codebase context at every session boundary. MemoryLake replaces static .cursorrules with living Background, Skill, and Conversation Memory.

Frequently asked questions

What's the difference between Background Memory and a system prompt?

A system prompt has to be written fresh for each session and each model. Background Memory is stored once, maintained over time with versioning, and loads automatically into every connected AI session. You maintain one source of truth instead of many copies.

Can I have different preferences for different AI tools?

Yes. You can configure context that applies globally and context that applies only to specific models. If you use Claude for writing and ChatGPT for analysis, each can have relevant context layered on top of your shared identity.

How does MemoryLake handle preferences that change over time?

All memory entries are versioned. When your preferences evolve, you update them — MemoryLake records the change and when it was made. You can see how your working style has shifted and roll back to a prior version if something doesn't stick.

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