Skip to main content
Tired of repeating the same context every time you talk to the AI? When Memory is enabled, the AI automatically remembers information learned in past conversations and uses it in subsequent ones.

​
Example

“Our team uses Python 3.11 + FastAPI, and we deploy on Azure AKS”
Memory is currently an Experimental feature. Behavior may change in the future.

​
Enabling Memory

1

Open Settings

Click the Settings icon at the top-right or in the sidebar.
2

Pick the Personalization tab

In the settings modal, go to the Personalization tab.
3

Toggle Memory on

Enable the toggle switch in the Memory section.
Memory toggle off

Memory toggle OFF (1/2)

Memory toggle on

Memory toggle ON (2/2)

Turning Memory off disables both auto-extraction and conversation injection. Existing stored memories are preserved, not deleted.

​
3 Ways Memory is Stored

Memory is categorized into three types by source, each with different retention.

​
Manual Memory

Users directly enter information they want the AI to remember. Example inputs:
  • “User is a data engineer who primarily uses Snowflake”
  • “User prefers camelCase for variable names in code reviews”
  • “User’s team holds sprint reviews every Wednesday”
Memory is more effective in third person. Use “User…” form instead of “I…”.

​
Auto Memory

When chatting with Memory enabled, the AI auto-extracts key facts in the background after a response and saves them.
  • Up to 100 auto-memories stored per user
  • Duplicate content is auto-merged to avoid bloat
  • Consecutive messages within 5 minutes in the same chat skip extraction

​
Profile Summary

When a certain number of auto/manual memories accumulate, the AI integrates them into a structured profile document. The profile includes:
  • Role and work area
  • Tech stack preferences
  • Active projects
  • Communication style
Only 1 profile is kept — auto-updated as new memories accumulate.

​
How Memory is Reflected in Conversations

When you ask a question with Memory ON, the AI auto-references relevant memories before answering. The AI auto-adjusts strategy based on memory volume:
When 20+ memories accumulate, the AI picks high-relevance memories based on the question content. For example, Python-related questions prioritize Python-related memories.

​
Memory Management

Click Personalization in Settings to open the memory management screen.
Memory management screen

Memory management modal — view stored memories and the profile summary

​
Add Memory

Add memory modal

Write in third person — 'User...' form is most effective

  1. Click Add Memory button
  2. Enter text (e.g., “User writes code comments in Korean”)
  3. Click Add

​
Edit Memory

Click the pencil icon on each memory to edit content. Auto-extracted memories can also be edited.

​
Delete Memory

  • Per-item: Click the trash icon on each item
  • Bulk: Clear memory button at the bottom (with confirmation dialog)
Deleted memories don’t disappear immediately — they’re fully removed after a 30-day grace period. During this time, they’re not reflected in conversations.

​
Profile Summary View

A Profile Summary section appears at the top of the memory management screen (when a profile exists). Click to view the AI-generated user profile.
Profile summary screen

AI-generated profile summary — role, tech stack, projects, etc.

​
Organization Memory (Admin-only)

Admins can configure memory shared across the entire organization. Path: Admin Panel > Settings > Memory tab > Organization Memory Organization memory is auto-injected into all conversations of all users in the organization.

​
Use Cases

  • “Our company must always comply with PIPA when handling customer data”
  • “In internal terminology, ‘Sprint’ means a 2-week development cycle”
  • “Use the company’s official template (Template A) when writing reports”
Organization memory is included in every member’s conversation, so enter only essential, concise info. Too much organization memory can affect AI response quality.

​
Admin Settings

Admin memory settings

Admin Panel > Settings > Memory — configure extraction model, confidence, and retention policies

In Admin Panel > Settings > Memory tab, manage the entire memory system.

​
Extraction Settings

​
Retention Policies

​
Audit Log

All memory creation, modification, deletion, and setting change events are recorded. Filter by event type and user.

​
Per-User Memory Management

Pick a specific user to view their memory list and delete entries as needed.

​
Tips for Effective Memory

Effective memories:
  • Role and area of expertise (“User is a backend developer”)
  • Tech stack (“User’s project uses Python + FastAPI”)
  • Preferred working style (“User requires type hints in code”)
  • Project context (“User is currently working on payment system migration”)
Less effective memories:
  • Temporary info (“Meeting at 3pm today”) → leave to auto-memory
  • Too generic info (“User does programming”)
  • Very long text → keep concise, only the essence
Auto-extracted memories are AI-generated by analyzing conversations, so occasional inaccuracies are possible.
  • Periodically review items with the Auto badge in the Manage screen
  • Edit or delete inaccurate memories
  • Admins can raise the Confidence Threshold to make extraction stricter (default 0.8)
When Memory toggle is OFF:
  • Auto-extraction is stopped
  • Memories aren’t injected into conversations
  • Existing stored memories are preserved, not deleted
  • Turning back ON immediately uses existing memories again

​
Personal vs. Organization Memory