Teams building support and assistant bots
Support bots that remember the customer
Give a support assistant per-customer recall so it stops asking for context the customer already gave.
In short
Each customer gets a session; every interaction is stored as an exchange. When they return, Memorg retrieves the relevant history so the bot picks up where the last conversation left off.
The problem
- ✕The bot re-asks for account details and prior issues the customer already explained.
- ✕Context from last week’s ticket is gone, so every conversation restarts cold.
- ✕Important facts (a known bug, a promised follow-up) get buried under recent chatter.
With Memorg
- ✓Open a session per customer and record each interaction as an exchange.
- ✓Tag durable facts as memory items with higher importance so they surface first.
- ✓Retrieve the blended top context on each new message so the bot resumes with the right history.
How it works
- 1
Session per customer
Key a session to the customer id so all their history is scoped and portable.
- 2
Flag durable facts
Store account facts and known issues as memory items with importance so they outrank small talk.
- 3
Recall on return
On the next message, search_context() surfaces the relevant, recent, important history within the token budget.
Features it uses
See the full feature set or how it works.
FAQ
+ How do I make sure critical facts always surface?
Store them as memory items with a higher importance signal. The retrieval blend weights importance alongside similarity and recency, so durable facts rise above passing chatter.
+ Is customer memory isolated per customer?
Yes. Sessions are keyed to a user id and the whole store is a single SQLite file you control, so scoping and isolation are explicit.
Related use cases
Conversation continuity for LLM chat
Keep a chat coherent across turns, sessions, and days without stuffing the whole history into every prompt.
→Upgrade naive RAG to structured recall
Move past a bare vector store to retrieval that understands users, sessions, recency, and importance.
→A memory layer for your agent
Drop deterministic recall next to any orchestrator without adopting a whole agent runtime.
→Put it to work
Install Memorg and wire this pattern into your app in a few lines.