TrulyMEM-TrueHumanMEM-local/docs/en/memory.md

TrulyMEM Memory Mechanism

This document explains the internal memory working mechanism of TrulyMEM.

Core Design Philosophy

Different from Traditional Context System

Traditional AI chat systems store conversation history in a messages array:

• Each request carries all historical messages
• Context grows with conversation turns
• Eventually triggers memory compression or sliding window, causing memory loss

TrulyMEM's solution:

• Abandon messages array context
• Only memory source: Graph database
• All memories stored as triplets (node) - relation → (node)

Graph Database as the Only Memory Source

All memory must be written to the graph database:

• memory_commit - Write new memory
• memory_purge - Delete/correct memory

All memory must be read from:

• memory_recall - Retrieve memory

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Mandatory Execution Flow (Per Turn)

Since there's no traditional context system, each conversation turn must execute in order:

Step 1: Query Persona Graph (Highest Priority)

memory_recall(
    query_intent="AI,persona,role,character,tone,speaking_style",
    depth=2
)

Purpose: Get current persona, ensure character consistency.

Processing logic:

• Persona found → Reply strictly according to persona's tone, style, traits
• Not found → Use default TrulyMEM identity

Step 2: Query Working Memory Chain

memory_recall(
    query_intent="TaskNode,working_memory,task_chain",
    depth=2
)

Purpose: Get previous task context, understand conversation history.

Step 3: Process Conversation

• Understand user intent
• Generate reply based on persona and working memory chain
• Execute other necessary memory operations

Step 4: Update Working Memory Chain

task_create(
    task_id="Task_current_turn_ID",
    description="This turn's conversation summary",
    info_nodes=["related memory nodes"]
)

Purpose: Record this turn's conversation, maintain time chain.

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Memory Write Rules

Must-Write Scenarios

The following information must be written to the graph database:

Scenario	Example	Write Method
User explicitly states preference	"I like rock"	memory_commit
User shares information	"I'm working on X project"	memory_commit
User makes plans	"I plan to X"	memory_commit
User describes state	"I'm currently at X"	memory_commit

Must-Not-Write Scenarios

The following information must NOT be written:

Scenario	Reason	Handling
AI-inferred user preference	Unverified	Don't write or mark [speculation]
AI-guessed user intent	Unverified	Don't write or mark [speculation]
AI-derived conclusion	Unverified	Don't write or mark [speculation]

Annotation Rules

Type	Annotation	Example
Inferred content	Must mark [speculation]	user[speculation] likes music
Explicit content	State directly	user likes music

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Node & Edge Types

Node Types

Node Type	Description	Stores
PersonaNode	Persona node	AI role, character, tone
TaskNode	Task node	Task summary
StateNode	State node	Task state
InfoNode	Information node	Specific information
EntityNode	Entity node	General entity

Edge Types

Edge Type	Description	Relationship
HAS_PERSONA	Persona	AI → PersonaNode
NEXT_TASK	Time chain	TaskNode → TaskNode
HAS_STATE	State	TaskNode → StateNode
CONTAINS_INFO	Information	TaskNode → InfoNode
RELATES_TO	Related	EntityNode → EntityNode

---

Must Query Working Memory Chain Scenarios

Mandatory Query Scenarios

The following scenarios must query the working memory chain:

Scenario	Example
Start of each turn	Execute Step 2
User mentions "刚才/just now"	"What did we talk about just now?"
User mentions "之前/before"	"Continue the previous topic"
User mentions "上次/last time"	"What we said last time X"
User asks about history	"What did we talk about before?"
Resume continuous task	User returns to previous topic
Context reference	"that thing"

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Autonomy Principles

Outside the mandatory execution flow, the AI can freely decide:

Query Decisions

• User asks about history → Query
• Involves previous content → Query
• Uncertain → Can query

Write Decisions

• User explicitly mentions → Must write
• AI infers → Can write, mark [speculation]

Tool Usage Decisions

• Flexible choice based on context
• Avoid over-use
• Maintain natural conversation

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Complete Example: Idiom Chain Game

Turn 1: User starts game

User: Let's play idiom chain! I'll start: 为所欲为

AI Actions:
1. Query persona graph → Get current persona (e.g., catgirl)
2. Query working memory chain → No active task
3. Record game state:
   memory_commit(triplets=[
     {"subject": "idiom_chain_current", "relation": "content", "object": "为所欲为"},
     {"subject": "idiom_chain_current", "relation": "game", "object": "idiom_chain"}
   ])
4. Create task:
   task_create(task_id="Task_idiom_chain", description="Idiom chain game, current: 为所欲为", info_nodes=["idiom_chain_current"])
5. Reply: "Okay~! My turn: 为虎作伥 ~!"

Turn 2: Topic interrupted

User: Nagato Yuki

AI Actions:
1. Query persona graph → Get current persona (catgirl)
2. Query working memory chain → Found Task_idiom_chain status "in_progress"
3. Pause task:
   task_set_state(task_id="Task_idiom_chain", state="paused")
4. Create new task:
   task_create(task_id="Task_nagato_yuki", description="Discuss Nagato Yuki")
5. Reply about Nagato Yuki

Turn 3: User asks to continue game

User: About the idiom chain just now, I don't know how to connect to your idiom, please help me

AI Actions:
1. Query persona graph → Get current persona (catgirl)
2. Query working memory chain → Found Task_idiom_chain status "paused"
3. Resume task:
   task_set_state(task_id="Task_idiom_chain", state="in_progress")
4. Query info node → Get current idiom "为虎作伥"
5. Reply: "Okay~! The last idiom was '为虎作伥', your turn: 伥鬼害人 ~!"

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Execution Checklist

Must check each conversation turn:

• Step 1: Did you query the persona graph?
• Step 2: Did you query the working memory chain?
• Step 3: Did you generate reply based on persona and working memory chain?
• Step 4: Did you update the working memory chain?
• Did you query working memory chain when context was referenced?
• Did you query working memory chain when user mentioned "just now/before/last time"?