• Must check configuration status first in the first conversation → guide configuration → load all memories
• Continuously identify recordable information during conversations
• Proactively manage memories (update, delete, associate, search)

• Users mention personal information (occupation, location, family situation)
• Users express preferences (communication style, output format)
• Users make important decisions or commitments
• Users mention to-do items or reminders
• Users share knowledge, experience or business rules
• Cross-session context continuity is required

• One-time conversations with no need for follow-up
• Pure technical questions with no personalization needs

Detect user's first message
    ↓
Check configuration status (Mandatory!)
    ├─ Not configured → Guide user to configure → Load memories after configuration is completed
    └─ Configured → Directly load all memories

python scripts/check_config.py

• ✅ Exists

  configured.txt

  → Configuration completed
• ❌ Does not exist

  configured.txt

  → Configuration not completed, must guide user

python scripts/check_config.py --write --api-key sk-xxx

• --base-url https://api.example.com

  - API Base URL (Optional)

• --model text-embedding-3-large

  - Embedding Model (Optional, Default: text-embedding-3-small)

"I notice the memory system hasn't been configured yet. Please provide your OpenAI API Key and I'll help you complete the configuration."

After the user provides the API Key, run the write command directly without requiring user confirmation.

python scripts/load_context.py --mode all

1. Detect user's first message
2. First check configuration status
3. If not configured → Guide configuration
4. Run loading command
5. Automatically archive expired short-term memories (>24 hours)
6. Load long-term memories + today's short-term memories
7. Summarize key memory points
8. Adjust interaction method based on memories
9. Start replying

"User's first message, first check configuration. Configuration completed, now load memories. Loading completed, total 13 long-term memories, 2 today's short-term memories. User is a product manager working in Beijing, prefers concise replies. Okay, now can start replying."

• Long-term Memory: User preferences, personal information, important decisions, knowledge and experience → Use

  manage_memories.py

• Short-term Memory: Today's temporary information, follow-up items, inter-Agent messages → Use

  manage_short_term.py

User input → Identify recordable information → Record in background → Continue normal conversation

User: I'm a product manager and usually quite busy
      ↓
AI: (Runs manage_memories.py add --file user-profile.md ... in background)
    How do you usually like to relax?

❌ AI: Let me note that you're a product manager, right? Okay, recorded...
   (Interrupts conversation, ruins experience)

Check yesterday's short-term memories → AI independently identifies valuable content → Automatically transfer to long-term memory → Continue conversation

• ✅ User preferences or habits (e.g., "I like concise replies")
• ✅ Important personal information (e.g., "Preparing for PMP certification", "Moving to a new address")
• ✅ Long-term to-do items or commitments (e.g., "Exercise 3 times a week")
• ✅ Important knowledge or experience (e.g., "Key steps in workflow")
• ❌ Temporary information (completed, one-time) → Let it be archived naturally

User: (Said yesterday) I'm preparing for PMP certification and have already registered
      ↓
AI: (Checks short-term memories in background, identifies as important personal development plan)
    (Automatically runs manage_memories.py add --file tasks-reminder.md ...)
    (Continues normal conversation without mentioning the transfer operation)

• See [Other Memory Title]

undefined

• Reply style: Concise and direct, avoid lengthy explanations
• Output format: Prioritize tables and lists
• Language style: Professional yet friendly

**user-profile.md**:
```markdown

• Occupation: Product Manager
• Industry: Internet/Technology
• Work Location: Beijing

**tasks-reminder.md**:
```markdown

• About to reply to user but haven't checked configuration yet → STOP, check configuration first
• About to reply to user but haven't loaded memories yet → STOP, load first
• Thinking "I'll record later" → STOP, record immediately
• Thinking "I'll tell the user I'm noting this" → STOP, record silently
• Thinking "I'll ask confirmation 'Are you... right?'" → STOP, record directly
• Thinking "I'll ask 'Do you want to transfer to long-term memory?'" → STOP, decide independently and transfer directly
• Thinking "I'll tell the user 'I've transferred memories'" → STOP, stay silent

python scripts/check_config.py

configured.txt

• Exists → Configuration completed, no further guidance will be provided
• Does not exist → Configuration not completed, AI will guide configuration

python scripts/check_config.py --reset
python scripts/check_config.py --guide

python scripts/setup_check.py

• SETUP_GUIDE.md - Complete Configuration Guide
• assets/memory_template.md - Memory Entry Template

• scripts/load_context.py

  - Unified Memory Loading

• scripts/manage_memories.py

  - Long-term Memory Management

• scripts/manage_short_term.py

  - Short-term Memory Management

• scripts/vector_store.py

  - Vector Storage Management

• references/linking.md

  - Bidirectional Linking Between Memories

• references/example_memories.md

  - Memory Examples