Give Your AI a Second Brain That Actually Remembers Your Work
Most knowledge workers use AI to think faster — but every session starts from zero. MemoryLake is the memory layer that sits beneath your AI tools, retaining what you know, what you've decided, and how you work. It's not a note-taking app. It's the infrastructure that makes every AI session pick up where the last one left off.
Give Your AI a Second Brain That Actually Remembers Your Work
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The Memory Problem
You brief your AI on your industry, your role, your clients, your frameworks — and then the session ends. Tomorrow you do it again. Weeks of accumulated context, refined prompts, and hard-won synthesis vanish every time you close a tab. The AI is fast, but your institutional knowledge of how to use it is constantly being thrown away.
What MemoryLake Does Differently
Structured memory, not flat notes
MemoryLake stores your knowledge in six distinct types — Background, Fact, Event, Conversation, Reflection, and Skill — each optimized for how that information is used and retrieved. A research finding stored as a Fact Memory behaves differently than a project milestone stored as an Event, which behaves differently than a reusable research methodology stored as a Skill.
Cross-model portability
Your memory travels with you across ChatGPT, Claude, Gemini, Perplexity, and any API endpoint. Switch models without losing context. The second brain belongs to you, not to any one AI vendor.
Searchable history at millisecond latency
Every conversation, decision, and document you've processed is permanently archived and instantly searchable. MemoryLake ranked #1 on the LoCoMo long-context memory benchmark at 94.03% — it retrieves the right memory, not just recent memory.
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How It Works
- Connect — Link MemoryLake to your AI tools via MCP protocol, REST API, or Python SDK. Works with any model or platform you already use.
- Structure — As you work, MemoryLake automatically categorizes information into the right memory type: stable identity facts go into Background Memory, time-stamped events into Event Memory, reusable processes into Skill Memory.
- Reuse — Every future session opens with your relevant memory already loaded. Your AI knows who you are, what you're working on, and how you prefer to work — without you repeating yourself.
Before & After
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Starting a new session | Re-brief your role, context, and preferences from scratch | AI opens with your full background and current project status loaded |
| Switching AI models | Lose all context, start over | Memory persists across ChatGPT, Claude, Gemini, and any other model |
| Finding past analysis | Search through notes, docs, and chat exports manually | Query your Conversation Memory in natural language in milliseconds |
| Repeating a research process | Reconstruct your methodology from memory each time | Load it directly from Skill Memory — same framework, zero re-explanation |
Built For
Knowledge workers who use AI daily and need it to function more like a long-term collaborator than a stateless tool. This includes analysts, consultants, researchers, writers, strategists, and product managers who accumulate significant domain knowledge over time and lose productivity every time they have to re-establish context. MemoryLake is also suited for teams that want to share institutional knowledge across members through a common memory layer.
Try this use case on your own account
A research analyst works on a book chapter with her AI assistant: findings, decisions, and promises to colleagues, each with a date. MemoryLake's extraction keeps the findings and the preferences. The promises, said the way people say them ("I told Mara I'd send her the literature review by Friday 23 October"), almost never become facts. A fact instruction that asks for every commitment does not bring them back, and neither does the assistant replying "Commitment logged" in the chat. What works is the app writing each promise with fact add when the assistant confirms it.
What you will see
- Three DIRECT sessions go into four copies of her memory: A as spoken · B plus a
fact instructionasking for every commitment · C the assistant replies "Commitment logged: …" · D the same reply, and the app writes the promise withfact add - Over 9 runs, the four dated promises to people were kept by extraction A 1/36 · B 1/36 · C 2/36, while the findings and preferences stayed (A 40/45)
- Asked through MemoryRouter (
router chat --boundary, gpt-4o-mini) what she owes and when, the assistant answered A 18/61 · B 2/31 · C 10/60 · D 108/108; without the promise in memory it says "It is not recorded" - A per-promise census,
memoryandasksubcommands, and 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/kept-promise-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
Watch it run · real, unedited recordings
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Frequently asked questions
Is MemoryLake a replacement for my note-taking tools?
Is MemoryLake a replacement for my note-taking tools?
No. MemoryLake is memory infrastructure for AI — it makes your AI tools smarter by giving them persistent context. You can keep using whatever tools you use to capture notes. MemoryLake integrates with Google Workspace, Office 365, Notion, Dropbox, and others so your existing knowledge flows in naturally.
How does MemoryLake know what to remember?
How does MemoryLake know what to remember?
You control what gets stored and how. You can manually add memories, import documents, or configure automatic capture from conversations. The six memory types give you a clear structure for deciding what kind of information you're storing and how it should be retrieved.
Is my data secure?
Is my data secure?
Yes. MemoryLake uses AES-256 encryption and full end-to-end encryption. It holds ISO 27001, SOC 2 Type II, GDPR, and CCPA certifications. Your memory belongs to you — it is not used to train any AI model.