Memory System Guide Personalization

Long-term memory lets the AI remember user preferences and historical information for a more personalized conversation experience.

🧠 Memory System

The memory system automatically extracts user information from conversations and uses it in later conversations. Summary results use structured output: each line is [category] content (categories limited to profile/preference/event/relation/topic/custom; [category:subtype] is also supported).

Quick Start

Enable Memory

1. In the Web panel, go to Config → Memory
2. Turn on Enable long-term memory
3. Save the config

Or in the config file:

memory:
  enabled: true

Basic Usage

Once memory is enabled, the AI will automatically:

• Extract information: recognize the user's name, preferences, important dates, etc. from conversations
• Store memories: save the extracted information by category
• Personalize replies: use the remembered information in later conversations

Example Conversation

User: My name is Xiao Ming, and today is my birthday

AI: Happy birthday, Xiao Ming! 🎂 Hope you have a great day!

(in the next conversation)

User: Do you remember me?

AI: Of course I do, Xiao Ming! By the way, is your birthday coming up?

Memory Categories

The system divides memories into six categories:

Category	Icon	Description	Example
Basic Information	👤	Name, age, occupation, etc.	"User is Xiao Ming, 25, a programmer"
Preferences & Habits	❤️	Likes, dislikes, habits	"Likes games, dislikes coriander"
Important Events	📅	Birthdays, anniversaries, plans	"Birthday is March 15"
Relationships	👥	Family, friends, colleagues	"Xiao Hong is the user's good friend"
Topic Interests	💬	Topics of interest	"Interested in AI technology"
Custom	🏷️	Other information	Any custom content

Memory Summaries and Structured Output

The memory summary endpoint combines "merge & deduplicate + LLM summary + (optional) low-quality cleanup" in one call:

POST /api/memories/user/:userId/summarize

Request Body

{
  "useLLM": true,
  "cleanup": true
}

Parameter	Type	Default	Description
useLLM	boolean	true	Whether to use an LLM for the summary
cleanup	boolean	true	Whether to run low-quality memory cleanup after summarizing (skipped when cleanup === false / 'false', preserving the existing call semantics)
groupId	string	-	Restrict to a group
model	string	-	Specify the summary model

Structured Output Format

LLM summary results are parsed line by line into the store; each line strictly follows [category] content (category whitelist: profile / preference / event / relation / topic / custom):

[profile] User is a software engineer
[preference] Likes iced Americano
[event] Joined a new company in March 2026

Parsing rules:

• Normalized lines with a [category] prefix are stored directly (categories validated against the whitelist);
• Free-text lines are only stored after stripping old-format prefixes and passing a triple filter (leading reasoning-word list / reasoning-word density / meta-narrative sentence patterns). Reasoning and explanatory text output by the model never enters memories.

Cleanup Endpoint

POST /api/memories/user/:userId/cleanup

Cleans up the user's low-quality memories (low confidence / expired / too old / too short). Can be called separately without triggering an LLM summary.

Managing Memories

Commands

View Memories

#ai查看记忆

Clear Memories

#ai清除记忆

Add Memory (master)

#ai添加记忆 @user likes pizza

Web Panel

1. Open the group management page
2. Select the target group
3. Click the Memory Management tab

From there you can:

• View the user memory list
• Filter memories by category
• Edit or delete individual memories
• Batch-clean memories

Group Chat Context

The group chat context feature automatically collects and analyzes group chat information:

memory:
  groupContext:
    enabled: true
    collectInterval: 10       # Collect every 10 minutes
    analyzeThreshold: 20      # Trigger analysis after 20 messages
    extractUserInfo: true     # Extract user info
    extractTopics: true       # Extract discussion topics
    extractRelations: true    # Extract user relations

How It Works

Extracted Content

• User info: nickname preferences, speaking style, active hours
• Discussion topics: trending topics in the group, user interests
• User relations: who is friends with whom, who interacts often

Summary Push

Scheduled group chat summaries help members catch up on what they missed:

memory:
  summaryPush:
    enabled: true
    defaultPushHour: 22       # Push at 22:00 every day
    maxMessages: 300          # Analyze at most 300 messages
    useLLM: true              # Use AI to generate the summary

Example Output

📊 Today's Group Chat Summary

📌 Main Topics
• Discussed the plan for releasing the new version
• Shared photos from the weekend activity
• Technical discussion: Python async programming

👥 Active Members
Xiao Ming (50 msgs), Xiao Hong (35 msgs), Xiao Hua (28 msgs)

💬 Highlights
"This approach is 3x more efficient than the previous one" - Xiao Ming

Memory Model

You can specify a model dedicated to memory extraction:

memory:
  model: "gpt-4o-mini"  # Use a cheaper model for memory processing

Suggestion

Memory extraction does not need the strongest model; gpt-4o-mini or claude-3-haiku is enough and saves cost.

Privacy & Security

Memory Scope

• Personal memories: only the user's own conversations are remembered
• Group memories: group chat information is only used in that group
• Data isolation: memories of different groups/users are fully isolated

User Control

Users can at any time:

• View their own memories
• Delete specific memories
• Clear all memories

Sensitive Information

The system does not extract or store:

• Passwords, API keys, etc.
• Bank card numbers, ID card numbers
• Private chat content

Config Reference

Full Configuration

memory:
  # Basic settings
  enabled: true
  storage: database
  maxMemories: 50

  # Auto extraction
  autoExtract: true
  pollInterval: 5
  minPollInterval: 30   # Minimum interval (minutes) between two polled summaries for a conversation target; defaults to 30 when unset
  model: ""

  # Group chat context
  groupContext:
    enabled: true
    collectInterval: 10
    maxMessagesPerCollect: 50
    analyzeThreshold: 20
    extractUserInfo: true
    extractTopics: true
    extractRelations: true

  # Summary push
  summaryPush:
    enabled: false
    checkInterval: 58
    defaultInterval: 1
    defaultPushHour: 22
    maxMessages: 300
    useLLM: true
    groups: {}
    intervalType: hour

  # Summary model
  summaryModel: ""

Parameter Reference

Parameter	Type	Default	Description
enabled	boolean	false	Enable the memory system
storage	string	database	Storage method
maxMemories	number	50	Max memories per user
autoExtract	boolean	true	Extract memories automatically
pollInterval	number	5	Extraction interval (minutes)
minPollInterval	number	30	Minimum interval (minutes) between two polled summaries for a conversation target; read dynamically in code
model	string	""	Extraction model (empty = default)

Best Practices

1. Set a Reasonable Memory Count

memory:
  maxMemories: 30  # Too many can hurt conversation quality

2. Use an Economical Model

memory:
  model: "gpt-4o-mini"
  summaryModel: "gpt-4o-mini"

3. Enable Features Selectively

memory:
  groupContext:
    extractUserInfo: true   # Enable
    extractTopics: false    # Disable what you don't need
    extractRelations: false

Troubleshooting

Memory Not Working

1. Check whether it is enabled: memory.enabled: true
2. Check whether an API channel is available
3. Check the logs for errors

Inaccurate Extraction

1. Try a different extraction model
2. Check whether the conversation content is clear enough
3. Manually add important memories

Too Many Memories

# Clean up old memories
#ai清除记忆

Next Steps

• Memory Config - Detailed configuration options
• Memory Architecture - Technical implementation
• Trigger Guide - Configure trigger methods