Give Chatbots a Conversation Memory That Never Resets
A chatbot that can't recall last week's conversation feels broken — no matter how clever its replies. MemoryLake's conversation memory API stores every chatbot interaction as compressed, searchable, structured memory that survives sessions, channels, and model upgrades.
Give Chatbots a Conversation Memory That Never Resets
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The problem: chatbots forget the conversation between sessions
A user spent twenty minutes explaining their situation to your support bot on Monday. On Tuesday they come back and have to start over. Or worse — they switch from your web widget to WhatsApp and lose everything. Conversation memory is the difference between a chatbot that earns trust and one users abandon.
How MemoryLake solves conversation memory for chatbots
Permanent, searchable conversation memory
Every turn is compressed and stored as Conversation Memory. The bot can recall a chat from three months ago in milliseconds.
Cross-channel continuity
The same user across web, mobile, WhatsApp, and email sees one continuous conversation memory.
Reflection memory captures user patterns
Beyond raw transcripts, MemoryLake extracts how the user thinks, what they value, and what they've already rejected.
End-to-end encrypted
AES-256 with three-party encryption. Even MemoryLake cannot read the contents. Critical for chatbots handling PII or healthcare data.
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How it works for chatbots
- Connect — Pipe every chatbot turn into MemoryLake through the SDK or REST endpoint.
- Structure — MemoryLake compresses, dedupes, and classifies each turn. Long sessions become compact memory blocks.
- Reuse — Before responding, the bot retrieves relevant prior conversations, facts, and user preferences.
Before vs. after: chatbot conversation memory
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Returning user opens a new chat | "Hi, how can I help?" again | Picks up where last conversation ended |
| User switches from web to mobile | Two disconnected histories | One continuous memory |
| Conversation from six months ago | Lost or stored in cold logs | Retrieved in milliseconds |
| Compliance / data export request | Manual log scraping | One-click memory export |
Who this is for
Product teams shipping customer-facing chatbots — support, sales, coaching, healthcare triage, onboarding — where users return repeatedly and conversation continuity is the product, not a nice-to-have.
Try this use case on your own account
A family-assistant chatbot remembers 65 facts about one user. In tonight's chat only the first question names who it is about: "What is Leo allergic to?" — "And Maya?" — "Who's her doctor?". The demo sends the same seven questions to the same model three ways and shows which way of bringing memory in survives the follow-ups.
What you will see
- Lookup on the latest message (
search→ top 3 into the prompt): "And Maya?" brings back Maya's age and birthday, not her allergy, and the bot says it doesn't know. 45 of 70 answers right over 10 runs - Lookup on the last few turns: the follow-ups work, but the old subject drags into the next one and the bot makes answers up ("the dog won't eat carrots"). 34 of 49
- MemoryRouter with the conversation in the body (
router chat --body … --boundary): its retrieval reads the conversation. 56 of 56 - A checker (Claude copying out what it was given) shows what MemoryRouter attached on each follow-up. Plus
chat --arm, and a local web UI where your own follow-ups are answered both ways
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/followup-chat-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
Watch it run · real, unedited recordings
Frequently asked questions
How much chat history can MemoryLake store per user?
How much chat history can MemoryLake store per user?
There's no practical cap. Production users store millions of turns per user with millisecond retrieval. Old turns compress; nothing gets deleted unless you delete it.
Is the data encrypted?
Is the data encrypted?
Yes — AES-256 with end-to-end three-party encryption. The MemoryLake team cannot read your users' conversations.
How does the bot decide what memory to retrieve?
How does the bot decide what memory to retrieve?
You can use semantic retrieval, type-filtered queries, or hybrid. MemoryLake returns a ranked, token-budgeted block ready to drop into your prompt.