Claw Brain Skill 🧠

Personal AI Memory System with Soul, Bonding, and Learning for ClawDBot.

Features

• 🎭 Soul/Personality - 6 evolving traits (humor, empathy, curiosity, creativity, helpfulness, honesty)
• 👤 User Profile - Learns user preferences, interests, communication style
• 💭 Conversation State - Real-time mood detection and context tracking
• 📚 Learning Insights - Continuously learns from interactions and corrections
• 🧠 get_full_context() - Everything for personalized responses

Installation

Option 1: Git Clone (Recommended for ClawDBot)

# Clone into ClawDBot workspace
git clone https://github.com/clawcolab/clawbrain.git ClawBrain

Option 2: pip install

pip install git+https://github.com/clawcolab/clawbrain.git

ClawDBot Setup

1. Install the Skill

# Clone to your ClawDBot workspace
cd /path/to/your/clawdbot
git clone https://github.com/clawcolab/clawbrain.git ClawBrain

2. Import in Your Bot

Add to your bot's main file (e.g., main.py):

import sys
sys.path.insert(0, "ClawBrain")

from clawbrain import Brain

# Initialize the brain
brain = Brain()

# Make it available globally or pass to handlers
app.brain = brain

3. Use in Message Handlers

def handle_message(message, channel="telegram"):
    # Get user context
    context = app.brain.get_full_context(
        session_key=f"{channel}_{message.chat.id}",
        user_id=str(message.chat.id),
        agent_id="jarvis",
        message=message.text
    )
    
    # Generate personalized response
    response = generate_response(context)
    
    # Store conversation
    app.brain.remember(
        agent_id="jarvis",
        memory_type="conversation",
        content=message.text,
        key=f"last_message_{message.chat.id}"
    )
    
    return response

Configuration

Environment Variables

# PostgreSQL (optional - auto-detected)
export POSTGRES_HOST=192.168.4.176
export POSTGRES_PORT=5432
export POSTGRES_DB=clawcolab
export POSTGRES_USER=postgres
export POSTGRES_PASSWORD=postgres

# Redis (optional - auto-detected)
export REDIS_HOST=192.168.4.175
export REDIS_PORT=6379

Force Storage Backend

# Force SQLite (zero setup)
brain = Brain({"storage_backend": "sqlite"})

# Force PostgreSQL
brain = Brain({"storage_backend": "postgresql"})

# Auto-detect (default)
brain = Brain()

API Reference

Brain Class

from clawbrain import Brain

brain = Brain()

Methods

get_full_context()

context = brain.get_full_context(
    session_key="telegram_12345",  # Unique session ID
    user_id="username",              # User identifier
    agent_id="jarvis",             # Bot identifier
    message="Hey, how's it going?" # Current message
)

Returns:

{
    "user_profile": {...},        # User preferences, interests
    "mood": {"mood": "happy", ...},  # Current mood
    "intent": "question",         # Detected intent
    "memories": [...],            # Relevant memories
    "personality": "...",         # Personality guidance
    "suggested_responses": [...]  # Response suggestions
}

detect_user_mood()

mood = brain.detect_user_mood("I'm so excited about this!")
# Returns: {"mood": "happy", "confidence": 0.9, "emotions": ["joy", "anticipation"]}

detect_user_intent()

intent = brain.detect_user_intent("How does AI work?")
# Returns: "question"

intent = brain.detect_user_intent("Set a reminder for 3pm")
# Returns: "command"

intent = brain.detect_user_intent("I had a great day today")
# Returns: "casual"

Example: Full Integration

import sys
sys.path.insert(0, "ClawBrain")

from clawbrain import Brain

class JarvisBot:
    def __init__(self):
        self.brain = Brain()
    
    def handle_message(self, message, chat_id):
        # Get context
        context = self.brain.get_full_context(
            session_key=f"telegram_{chat_id}",
            user_id=str(chat_id),
            agent_id="jarvis",
            message=message
        )
        
        # Generate response using context
        response = self.generate_response(context)
        
        # Learn from interaction
        self.brain.learn_user_preference(
            user_id=str(chat_id),
            pref_type="interest",
            value="AI"
        )
        
        return response
    
    def generate_response(self, context):
        # Use user preferences
        name = context["user_profile"].name or "there"
        mood = context["mood"]["mood"]
        
        # Personalized response
        if mood == "frustrated":
            return f"Hey {name}, I'm here to help. Let me assist you."
        else:
            return f"Hi {name}! How can I help you today?"
    
    def shutdown(self):
        self.brain.close()

Storage Backends

SQLite (Default - Zero Setup)

No configuration needed. Data stored in local SQLite database.

brain = Brain({"storage_backend": "sqlite"})

Best for: Development, testing, single-user deployments

PostgreSQL + Redis (Production)

Requires PostgreSQL and Redis servers.

brain = Brain()  # Auto-detects

Requirements:

• PostgreSQL 14+
• Redis 6+
• Python packages: psycopg2-binary, redis

pip install psycopg2-binary redis

Best for: Production, multi-user, high-concurrency

Files

• clawbrain.py - Main Brain class with all features
• __init__.py - Module exports
• SKILL.md - This documentation
• skill.json - ClawdHub metadata
• README.md - Quick start guide

Troubleshooting

ImportError: No module named 'clawbrain'

# Ensure ClawBrain folder is in your path
sys.path.insert(0, "ClawBrain")

PostgreSQL connection failed

# Check environment variables
echo $POSTGRES_HOST
echo $POSTGRES_PORT

# Verify PostgreSQL is running
pg_isready -h $POSTGRES_HOST -p $POSTGRES_PORT

Redis connection failed

# Check Redis is running
redis-cli ping

Using SQLite (fallback)

If PostgreSQL/Redis are unavailable, Claw Brain automatically falls back to SQLite:

brain = Brain({"storage_backend": "sqlite"})

Learn More

• Repository: https://github.com/clawcolab/clawbrain
• README: See README.md for quick start
• Issues: Report bugs at GitHub Issues