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# TrulyMEM Memory Mechanism
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This document explains the internal memory working mechanism of TrulyMEM.
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## Core Design Philosophy
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### Different from Traditional Context System
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Traditional AI chat systems store conversation history in a messages array:
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- Each request carries all historical messages
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- Context grows with conversation turns
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- Eventually triggers memory compression or sliding window, causing memory loss
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TrulyMEM's solution:
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- **Abandon** messages array context
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- **Only** memory source: Graph database
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- All memories stored as triplets (node) - relation → (node)
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### Graph Database as the Only Memory Source
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All memory must be written to the graph database:
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- `memory_commit` - Write new memory
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- `memory_purge` - Delete/correct memory
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All memory must be read from:
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- `memory_recall` - Retrieve memory
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---
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## Mandatory Execution Flow (Per Turn)
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Since there's no traditional context system, each conversation turn must execute in order:
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### Step 1: Query Persona Graph (Highest Priority)
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```python
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memory_recall(
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query_intent="AI,persona,role,character,tone,speaking_style",
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depth=2
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)
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```
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**Purpose**: Get current persona, ensure character consistency.
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**Processing logic**:
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- Persona found → Reply strictly according to persona's tone, style, traits
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- Not found → Use default TrulyMEM identity
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### Step 2: Query Working Memory Chain
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```python
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memory_recall(
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query_intent="TaskNode,working_memory,task_chain",
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depth=2
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)
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```
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**Purpose**: Get previous task context, understand conversation history.
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### Step 3: Process Conversation
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- Understand user intent
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- Generate reply based on persona and working memory chain
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- Execute other necessary memory operations
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### Step 4: Update Working Memory Chain
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```python
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task_create(
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task_id="Task_current_turn_ID",
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description="This turn's conversation summary",
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info_nodes=["related memory nodes"]
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)
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```
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**Purpose**: Record this turn's conversation, maintain time chain.
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---
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## Memory Write Rules
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### Must-Write Scenarios
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The following information **must** be written to the graph database:
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| Scenario | Example | Write Method |
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|----------|---------|--------------|
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| User explicitly states preference | "I like rock" | `memory_commit` |
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| User shares information | "I'm working on X project" | `memory_commit` |
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| User makes plans | "I plan to X" | `memory_commit` |
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| User describes state | "I'm currently at X" | `memory_commit` |
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### Must-Not-Write Scenarios
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The following information **must NOT** be written:
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| Scenario | Reason | Handling |
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|----------|--------|----------|
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| AI-inferred user preference | Unverified | Don't write or mark [speculation] |
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| AI-guessed user intent | Unverified | Don't write or mark [speculation] |
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| AI-derived conclusion | Unverified | Don't write or mark [speculation] |
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### Annotation Rules
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| Type | Annotation | Example |
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|------|------------|---------|
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| Inferred content | Must mark **[speculation]** | user[speculation] likes music |
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| Explicit content | State directly | user likes music |
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---
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## Node & Edge Types
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### Node Types
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| Node Type | Description | Stores |
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|-----------|-------------|--------|
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| `PersonaNode` | Persona node | AI role, character, tone |
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| `TaskNode` | Task node | Task summary |
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| `StateNode` | State node | Task state |
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| `InfoNode` | Information node | Specific information |
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| `EntityNode` | Entity node | General entity |
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### Edge Types
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| Edge Type | Description | Relationship |
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|-----------|-------------|--------------|
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| `HAS_PERSONA` | Persona | AI → PersonaNode |
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| `NEXT_TASK` | Time chain | TaskNode → TaskNode |
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| `HAS_STATE` | State | TaskNode → StateNode |
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| `CONTAINS_INFO` | Information | TaskNode → InfoNode |
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| `RELATES_TO` | Related | EntityNode → EntityNode |
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---
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## Must Query Working Memory Chain Scenarios
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### Mandatory Query Scenarios
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The following scenarios **must** query the working memory chain:
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| Scenario | Example |
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|----------|---------|
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| Start of each turn | Execute Step 2 |
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| User mentions "刚才/just now" | "What did we talk about just now?" |
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| User mentions "之前/before" | "Continue the previous topic" |
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| User mentions "上次/last time" | "What we said last time X" |
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| User asks about history | "What did we talk about before?" |
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| Resume continuous task | User returns to previous topic |
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| Context reference | "that thing" |
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---
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## Autonomy Principles
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Outside the mandatory execution flow, the AI can freely decide:
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### Query Decisions
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- User asks about history → Query
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- Involves previous content → Query
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- Uncertain → Can query
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### Write Decisions
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- User explicitly mentions → Must write
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- AI infers → Can write, mark [speculation]
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### Tool Usage Decisions
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- Flexible choice based on context
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- Avoid over-use
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- Maintain natural conversation
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---
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## Complete Example: Idiom Chain Game
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### Turn 1: User starts game
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```
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User: Let's play idiom chain! I'll start: 为所欲为
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AI Actions:
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1. Query persona graph → Get current persona (e.g., catgirl)
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2. Query working memory chain → No active task
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3. Record game state:
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memory_commit(triplets=[
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{"subject": "idiom_chain_current", "relation": "content", "object": "为所欲为"},
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{"subject": "idiom_chain_current", "relation": "game", "object": "idiom_chain"}
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])
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4. Create task:
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task_create(task_id="Task_idiom_chain", description="Idiom chain game, current: 为所欲为", info_nodes=["idiom_chain_current"])
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5. Reply: "Okay~! My turn: 为虎作伥 ~!"
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```
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### Turn 2: Topic interrupted
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```
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User: Nagato Yuki
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AI Actions:
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1. Query persona graph → Get current persona (catgirl)
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2. Query working memory chain → Found Task_idiom_chain status "in_progress"
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3. Pause task:
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task_set_state(task_id="Task_idiom_chain", state="paused")
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4. Create new task:
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task_create(task_id="Task_nagato_yuki", description="Discuss Nagato Yuki")
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5. Reply about Nagato Yuki
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```
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### Turn 3: User asks to continue game
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```
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User: About the idiom chain just now, I don't know how to connect to your idiom, please help me
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AI Actions:
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1. Query persona graph → Get current persona (catgirl)
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2. Query working memory chain → Found Task_idiom_chain status "paused"
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3. Resume task:
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task_set_state(task_id="Task_idiom_chain", state="in_progress")
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4. Query info node → Get current idiom "为虎作伥"
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5. Reply: "Okay~! The last idiom was '为虎作伥', your turn: 伥鬼害人 ~!"
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```
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---
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## Execution Checklist
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Must check each conversation turn:
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- [ ] Step 1: Did you query the persona graph?
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- [ ] Step 2: Did you query the working memory chain?
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- [ ] Step 3: Did you generate reply based on persona and working memory chain?
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- [ ] Step 4: Did you update the working memory chain?
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- [ ] Did you query working memory chain when context was referenced?
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- [ ] Did you query working memory chain when user mentioned "just now/before/last time"?
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