Keep Your AI Context Intact, From One Conversation to the Next
You've built up context over a conversation: your preferences are established, your project is explained, your constraints are on the table. Then the session ends. Next time, none of it is there. MemoryLake makes AI context persistent — not just across sessions, but across models, tools, and team members.
Keep Your AI Context Intact, From One Conversation to the Next
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The Memory Problem
The context you establish in a conversation doesn't transfer anywhere. Your AI doesn't write it down, doesn't remember it, and doesn't carry it forward. Every conversation starts from zero. If you work with AI daily, you're rebuilding context constantly — the same background, the same constraints, the same preferences — at the cost of time you should be spending on the actual work.
What MemoryLake Does Differently
Permanent conversation archives
Conversation Memory stores every AI session as a permanent, searchable record. Not just the output — the full exchange, the reasoning, the questions you asked, the answers you got. You can pick up any thread from any past conversation in natural language, instantly.
Stable context that loads automatically
Background Memory holds the facts that don't change: your role, your organization, your project parameters, your preferences. It loads at the start of every session as read-only context. Your AI always knows the basics without you explaining them.
Recurring workflows that don't need re-explaining
Skill Memory stores the processes you run repeatedly: your weekly review format, your standard analysis workflow, your document structure. Load any of them in seconds, in any session, with any model.
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How It Works
- Connect — Connect MemoryLake to your AI tools via MCP protocol or REST API. Works with ChatGPT, Claude, Gemini, Perplexity, and any LLM accessible via API.
- Structure — Stable facts go into Background Memory (loads every session). Conversation records go into Conversation Memory (permanent, searchable). Reusable processes go into Skill Memory. Specific facts and decisions go into versioned Fact Memory.
- Reuse — Open a new conversation with your context already present. Resume a prior conversation without re-reading history. Switch models without losing a thread. Your AI context becomes a persistent asset rather than a temporary state.
Before & After
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Continuing work across sessions | Paste in prior conversation summary manually | Conversation Memory is searchable and auto-loaded for relevant context |
| Switching between AI models | Re-establish all context in the new model | Background and Conversation Memory are model-agnostic |
| Recurring weekly processes | Reconstruct the prompt and format from memory each week | Load the process directly from Skill Memory |
| Sharing context with a colleague | Export chat history, write summary, explain verbally | Colleague accesses shared Conversation Memory directly |
Built For
Professionals and teams who use AI tools continuously and need their accumulated context to behave like a persistent asset rather than a session-level artifact. This includes knowledge workers who use multiple AI tools interchangeably, teams that need shared AI context, and anyone managing ongoing projects or client relationships with AI assistance.
Try this use case on your own account
Save a team channel the moment each message is posted, and replies in a thread don't repeat what they are about: "It goes ahead on 2 February." Read on its own, that reply is matched to whatever memory already holds, so Porto's date overwrites the Leeds move's date ("previously 9 March") and a week later the assistant gives the wrong date for Leeds without a hint of doubt. Keeping each thread in its own conversation, or writing the thread's first message in front of each reply, keeps every value on its own move.
What you will see
- The same ten Slack messages, each saved as it is posted (
conv msg append, read one at a time), into three memories: A the channel as one conversation, B one conversation per thread, C one conversation with each reply tagged with its thread's first message - Over 9 runs A stored Porto's date under Leeds every time and the late Leeds reply under Austin 7 times; values stored under the right move A 33 / 63, B 61 / 63, C 62 / 63
- Through MemoryRouter another move's value was given as the answer A 56 / 192 times, B 0, C 0; right answers B 203 / 216, C 203 / 216, no memory 12 / 216
- Eight questions (
router chat --boundary) with an empty-attachment baseline, a no-memory arm, a reverse question nobody answered in the channel, a grader with offline cases (python3 demo.py selftest), plusmemoryandasksubcommands 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/thread-reply-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
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Frequently asked questions
If I switch from ChatGPT to Claude mid-project, does my context come with me?
If I switch from ChatGPT to Claude mid-project, does my context come with me?
Yes. MemoryLake is model-agnostic. Your Background Memory, Conversation Memory, and any other stored memory is available in any connected model. The context you built in one AI is available in any other.
Can I search my conversation history in natural language?
Can I search my conversation history in natural language?
Yes. Conversation Memory supports natural language queries. You can ask "What did we decide about the pricing model in March?" and retrieve the relevant exchange — not a document search, but the actual conversation record, at millisecond latency.
How is Conversation Memory different from just saving chat exports?
How is Conversation Memory different from just saving chat exports?
Chat exports are flat files — you can read them, but your AI can't query them without you pasting them back in. Conversation Memory is structured, indexed, and instantly retrievable by your AI in any future session. It's the difference between a filing cabinet and a database.