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Support · Intermediate

# Persistent customer context for support conversations

Track user preferences and interaction history across support chats

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Opens BotBento. Paste the prompt into a bot; nothing is preconfigured.

- When you askUser opens support chat
- ThenQuery stored history and facts
- ResultReply using customer context

Maintain longitudinal context about customers by recording key facts, past inquiries, and stated preferences during support conversations. When a user returns, retrieve prior details to resolve issues without repeating questions.

## Prompt to start with

BotBentoRecommended

```
When a customer reaches out, retrieve their recorded history and preferences from the database. Reference previous issues when relevant to minimize redundant troubleshooting. Extract new facts, stated requirements, and resolutions from the ongoing conversation and draft memory updates. Present any external message sends, profile updates, or data deletions to a human supervisor for approval before finalizing.
```

Copy

## Tools it uses

ChatDatabase

## Works with

BotBento

## Tags

- support
- memory
- customer-service
- context

## Source

[mem0: The Memory Layer for AI Agents - Drop-in memory infrastructure for AI agents and apps. Context that persists. Built for production.](https://github.com/mem0ai/mem0)
GitHub · checked 2026-10-15

Summarized in our own words. Any results described there are the authors’ own, not measured by BotBento.

By BotBento Editorial · Added 2026-10-15 · Updated 2026-10-15 · [Suggest a correction](/contact/)

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