Upload folder using huggingface_hub

.env.example

@@ -0,0 +1,78 @@
+# Hugging Face Configuration
+HUGGINGFACE_TOKEN=your_huggingface_token_here
+
+# System Configuration
+DEBUG_MODE=False
+LOG_LEVEL=INFO
+MAX_WORKERS=4
+ASYNC_TIMEOUT=30
+
+# Resource Limits
+MAX_MEMORY_MB=8192
+MAX_CPU_PERCENT=90
+MAX_GPU_MEMORY_MB=4096
+MAX_API_CALLS_PER_MINUTE=500
+
+# Team Configuration
+MIN_TEAM_SIZE=2
+MAX_TEAM_SIZE=10
+MAX_CONCURRENT_OBJECTIVES=5
+
+# Error Recovery
+MAX_RETRIES=3
+RETRY_DELAY_SECONDS=5
+ERROR_THRESHOLD=0.2
+
+# Monitoring
+METRICS_INTERVAL_SECONDS=60
+HEALTH_CHECK_INTERVAL=30
+PERFORMANCE_LOG_RETENTION_DAYS=7
+
+# API Keys
+OPENAI_API_KEY=your_openai_api_key
+GROQ_API_KEY=your_groq_api_key
+HUGGINGFACE_API_KEY=your_huggingface_api_key
+
+# Service Configuration
+PORT=7860
+HOST=0.0.0.0
+DEBUG=True
+ENVIRONMENT=development
+
+# Database Configuration
+DATABASE_URL=sqlite:///./ventures.db
+
+# Model Configuration
+MODEL_CACHE_DIR=./model_cache
+DEFAULT_MODEL=gpt-4-turbo-preview
+
+# Venture Configuration
+MIN_PROFIT_TARGET=1000000
+DEFAULT_CURRENCY=USD
+RISK_TOLERANCE=medium
+
+# API Configuration
+API_VERSION=v1
+API_PREFIX=/api/v1
+CORS_ORIGINS=["*"]
+MAX_REQUEST_SIZE=10MB
+
+# Monitoring Configuration
+ENABLE_METRICS=True
+METRICS_PORT=9090
+LOG_LEVEL=INFO
+
+# Cache Configuration
+REDIS_URL=redis://localhost:6379/0
+CACHE_TTL=3600
+
+# Security Configuration
+JWT_SECRET=your_jwt_secret
+JWT_ALGORITHM=HS256
+ACCESS_TOKEN_EXPIRE_MINUTES=30
+
+# Feature Flags
+ENABLE_PORTFOLIO_OPTIMIZATION=True
+ENABLE_MARKET_ANALYSIS=True
+ENABLE_MONETIZATION_STRATEGY=True
+ENABLE_VENTURE_ANALYSIS=True

.github/workflows/sync-to-space.yml

@@ -0,0 +1,17 @@
+name: Sync to Hugging Face Space
+on:
+  push:
+    branches: [main]
+
+jobs:
+  sync-to-space:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+      - name: Push to Space
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: |
+          git push https://USER:[email protected]/spaces/USER/SPACE_NAME main

.gitignore

@@ -0,0 +1,91 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual Environment
+venv/
+env/
+ENV/
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+
+# Logs
+*.log
+logs/
+log/
+
+# Local development
+.env
+.env.local
+.env.*.local
+
+# Data
+data/
+*.db
+*.sqlite3
+
+# Model files
+*.pt
+*.pth
+*.ckpt
+*.bin
+*.onnx
+
+# Temporary files
+.DS_Store
+Thumbs.db
+*.tmp
+*.bak
+*.swp
+*~
+
+# Distribution
+dist/
+build/
+*.egg-info/
+
+# Documentation
+docs/_build/
+site/
+
+# Testing
+.coverage
+htmlcov/
+.pytest_cache/
+.tox/
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+*.ipynb
+
+# Project specific
+outputs/
+results/
+experiments/
+checkpoints/

Dockerfile

@@ -0,0 +1,12 @@
+FROM python:3.10-slim
+
+WORKDIR /code
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["python", "app.py"]

README.md

@@ -1,12 +1,81 @@
 ---
-title: Advanced Reasoning
-emoji: 😻
-colorFrom: purple
-colorTo: blue
+title: Advanced Reasoning System 🧠
+emoji: 🧠
+colorFrom: blue
+colorTo: purple
 sdk: gradio
-sdk_version: 5.10.0
+sdk_version: 4.16.0
 app_file: app.py
 pinned: false
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Advanced Reasoning System 🧠
+
+A sophisticated reasoning system that combines multiple strategies with local LLM capabilities for improved performance.
+
+## Features
+
+- **Local LLM Integration**: Uses Llama 3.2B Overthinker model for fast, local inference
+- **Multiple Reasoning Strategies**: 
+  - Chain of Thought
+  - Tree of Thoughts
+  - Meta Learning
+  - Recursive Reasoning
+  - Analogical Reasoning
+  - And more!
+- **Adaptive Strategy Selection**: Dynamically chooses the best reasoning approach
+- **GPU/CPU Flexibility**: Automatically uses GPU when available, falls back to CPU
+- **Efficient Resource Usage**: Optimized for performance with configurable parameters
+
+## Technical Details
+
+- **Model**: tensorblock/Llama-3.2-3B-Overthinker-GGUF
+- **Framework**: Gradio 4.16.0
+- **Backend**: Python with async support
+- **Inference**: Local using llama-cpp-python
+
+## Usage
+
+1. The system will automatically download the model on first run
+2. GPU acceleration is used when available
+3. Ask questions and get detailed, step-by-step responses
+4. System adapts its reasoning strategy based on the query
+
+## Example Questions
+
+- What are the implications of artificial intelligence on society?
+- How does climate change affect global ecosystems?
+- What are the philosophical implications of quantum mechanics?
+
+## Installation
+
+```bash
+pip install -r requirements.txt
+python app.py
+```
+
+## Environment Variables
+
+Create a `.env` file with:
+```
+HUGGINGFACE_TOKEN=your_token_here
+DEBUG_MODE=False
+LOG_LEVEL=INFO
+```
+
+## License
+
+MIT License
+
+## Files
+- `app.py`: Main application with Gradio interface and API integration
+- `requirements.txt`: Project dependencies
+- `.env.example`: Example environment variables (for reference)
+
+## Dependencies
+- gradio==4.16.0
+- requests==2.31.0
+
+---
+Created with ❤️ using Gradio and Hugging Face

agentic_system.py

@@ -0,0 +1,551 @@
+"""
+Advanced Agentic System
+----------------------
+A sophisticated multi-agent system with:
+
+Core Components:
+1. Agent Management
+2. Task Execution
+3. Learning & Adaptation
+4. Communication
+5. Resource Management
+
+Advanced Features:
+1. Self-Improvement
+2. Multi-Agent Coordination
+3. Dynamic Role Assignment
+4. Emergent Behavior
+"""
+
+import logging
+from typing import Dict, Any, List, Optional, Union, TypeVar
+from dataclasses import dataclass, field
+from enum import Enum
+import json
+import asyncio
+from datetime import datetime
+import uuid
+from concurrent.futures import ThreadPoolExecutor
+import numpy as np
+
+from orchestrator import (
+    AgentOrchestrator,
+    AgentRole,
+    AgentState,
+    TaskPriority,
+    Task
+)
+from reasoning import ReasoningEngine, ReasoningMode
+from meta_learning import MetaLearningStrategy
+
+class AgentCapability(Enum):
+    """Core capabilities of agents."""
+    REASONING = "reasoning"
+    LEARNING = "learning"
+    EXECUTION = "execution"
+    COORDINATION = "coordination"
+    MONITORING = "monitoring"
+
+class AgentPersonality(Enum):
+    """Different personality types for agents."""
+    ANALYTICAL = "analytical"
+    CREATIVE = "creative"
+    CAUTIOUS = "cautious"
+    PROACTIVE = "proactive"
+    ADAPTIVE = "adaptive"
+
+@dataclass
+class AgentProfile:
+    """Profile defining an agent's characteristics."""
+    id: str
+    name: str
+    role: AgentRole
+    capabilities: List[AgentCapability]
+    personality: AgentPersonality
+    expertise_areas: List[str]
+    learning_rate: float
+    risk_tolerance: float
+    created_at: datetime
+    metadata: Dict[str, Any]
+
+class Agent:
+    """Advanced autonomous agent with learning capabilities."""
+    
+    def __init__(
+        self,
+        profile: AgentProfile,
+        reasoning_engine: ReasoningEngine,
+        meta_learning: MetaLearningStrategy,
+        config: Dict[str, Any] = None
+    ):
+        self.profile = profile
+        self.reasoning_engine = reasoning_engine
+        self.meta_learning = meta_learning
+        self.config = config or {}
+        
+        # State management
+        self.state = AgentState.IDLE
+        self.current_task: Optional[Task] = None
+        self.task_history: List[Task] = []
+        
+        # Learning and adaptation
+        self.knowledge_base: Dict[str, Any] = {}
+        self.learned_patterns: List[Dict[str, Any]] = []
+        self.adaptation_history: List[Dict[str, Any]] = []
+        
+        # Performance metrics
+        self.metrics: Dict[str, List[float]] = defaultdict(list)
+        self.performance_history: List[Dict[str, float]] = []
+        
+        # Communication
+        self.message_queue = asyncio.Queue()
+        self.response_queue = asyncio.Queue()
+        
+        # Resource management
+        self.resource_usage: Dict[str, float] = {}
+        self.resource_limits: Dict[str, float] = {}
+        
+        # Async support
+        self.executor = ThreadPoolExecutor(max_workers=2)
+        self.lock = asyncio.Lock()
+        
+        # Logging
+        self.logger = logging.getLogger(f"Agent-{profile.id}")
+        
+        # Initialize components
+        self._init_components()
+
+    def _init_components(self):
+        """Initialize agent components."""
+        # Set up knowledge base
+        self.knowledge_base = {
+            "expertise": {area: 0.5 for area in self.profile.expertise_areas},
+            "learned_skills": set(),
+            "interaction_patterns": defaultdict(int),
+            "success_patterns": defaultdict(float)
+        }
+        
+        # Set up resource limits
+        self.resource_limits = {
+            "cpu": 1.0,
+            "memory": 1000,
+            "api_calls": 100,
+            "learning_capacity": 0.8
+        }
+
+    async def process_task(self, task: Task) -> Dict[str, Any]:
+        """Process an assigned task."""
+        try:
+            self.current_task = task
+            self.state = AgentState.BUSY
+            
+            # Analyze task
+            analysis = await self._analyze_task(task)
+            
+            # Plan execution
+            plan = await self._plan_execution(analysis)
+            
+            # Execute plan
+            result = await self._execute_plan(plan)
+            
+            # Learn from execution
+            await self._learn_from_execution(task, result)
+            
+            # Update metrics
+            self._update_metrics(task, result)
+            
+            return {
+                "success": True,
+                "task_id": task.id,
+                "result": result,
+                "metrics": self._get_execution_metrics()
+            }
+            
+        except Exception as e:
+            self.logger.error(f"Error processing task: {e}")
+            self.state = AgentState.ERROR
+            return {
+                "success": False,
+                "task_id": task.id,
+                "error": str(e)
+            }
+        finally:
+            self.state = AgentState.IDLE
+            self.current_task = None
+
+    async def _analyze_task(self, task: Task) -> Dict[str, Any]:
+        """Analyze task requirements and constraints."""
+        # Use reasoning engine for analysis
+        analysis = await self.reasoning_engine.reason(
+            query=task.description,
+            context={
+                "agent_profile": self.profile.__dict__,
+                "task_history": self.task_history,
+                "knowledge_base": self.knowledge_base
+            },
+            mode=ReasoningMode.ANALYTICAL
+        )
+        
+        return {
+            "requirements": analysis.get("requirements", []),
+            "constraints": analysis.get("constraints", []),
+            "complexity": analysis.get("complexity", 0.5),
+            "estimated_duration": analysis.get("estimated_duration", 3600),
+            "required_capabilities": analysis.get("required_capabilities", [])
+        }
+
+    async def _plan_execution(self, analysis: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """Plan task execution based on analysis."""
+        # Use reasoning engine for planning
+        plan = await self.reasoning_engine.reason(
+            query="Plan execution steps",
+            context={
+                "analysis": analysis,
+                "agent_capabilities": self.profile.capabilities,
+                "resource_limits": self.resource_limits
+            },
+            mode=ReasoningMode.FOCUSED
+        )
+        
+        return plan.get("steps", [])
+
+    async def _execute_plan(self, plan: List[Dict[str, Any]]) -> Dict[str, Any]:
+        """Execute the planned steps."""
+        results = []
+        
+        for step in plan:
+            try:
+                # Check resources
+                if not self._check_resources(step):
+                    raise RuntimeError("Insufficient resources for step execution")
+                
+                # Execute step
+                step_result = await self._execute_step(step)
+                results.append(step_result)
+                
+                # Update resource usage
+                self._update_resource_usage(step)
+                
+                # Learn from step execution
+                await self._learn_from_step(step, step_result)
+                
+            except Exception as e:
+                self.logger.error(f"Error executing step: {e}")
+                results.append({"error": str(e)})
+        
+        return {
+            "success": all(r.get("success", False) for r in results),
+            "results": results
+        }
+
+    async def _execute_step(self, step: Dict[str, Any]) -> Dict[str, Any]:
+        """Execute a single step of the plan."""
+        step_type = step.get("type", "unknown")
+        
+        if step_type == "reasoning":
+            return await self._execute_reasoning_step(step)
+        elif step_type == "learning":
+            return await self._execute_learning_step(step)
+        elif step_type == "action":
+            return await self._execute_action_step(step)
+        else:
+            raise ValueError(f"Unknown step type: {step_type}")
+
+    async def _execute_reasoning_step(self, step: Dict[str, Any]) -> Dict[str, Any]:
+        """Execute a reasoning step."""
+        result = await self.reasoning_engine.reason(
+            query=step["query"],
+            context=step.get("context", {}),
+            mode=ReasoningMode.ANALYTICAL
+        )
+        
+        return {
+            "success": result.get("success", False),
+            "reasoning_result": result
+        }
+
+    async def _execute_learning_step(self, step: Dict[str, Any]) -> Dict[str, Any]:
+        """Execute a learning step."""
+        result = await self.meta_learning.learn(
+            data=step["data"],
+            context=step.get("context", {})
+        )
+        
+        return {
+            "success": result.get("success", False),
+            "learning_result": result
+        }
+
+    async def _execute_action_step(self, step: Dict[str, Any]) -> Dict[str, Any]:
+        """Execute an action step."""
+        action_type = step.get("action_type")
+        
+        if action_type == "api_call":
+            return await self._make_api_call(step)
+        elif action_type == "data_processing":
+            return await self._process_data(step)
+        elif action_type == "coordination":
+            return await self._coordinate_action(step)
+        else:
+            raise ValueError(f"Unknown action type: {action_type}")
+
+    def _check_resources(self, step: Dict[str, Any]) -> bool:
+        """Check if sufficient resources are available."""
+        required_resources = step.get("required_resources", {})
+        
+        for resource, amount in required_resources.items():
+            if self.resource_usage.get(resource, 0) + amount > self.resource_limits.get(resource, float('inf')):
+                return False
+        
+        return True
+
+    def _update_resource_usage(self, step: Dict[str, Any]):
+        """Update resource usage after step execution."""
+        used_resources = step.get("used_resources", {})
+        
+        for resource, amount in used_resources.items():
+            self.resource_usage[resource] = self.resource_usage.get(resource, 0) + amount
+
+    async def _learn_from_execution(self, task: Task, result: Dict[str, Any]):
+        """Learn from task execution experience."""
+        # Prepare learning data
+        learning_data = {
+            "task": task.__dict__,
+            "result": result,
+            "context": {
+                "agent_state": self.state,
+                "resource_usage": self.resource_usage,
+                "performance_metrics": self._get_execution_metrics()
+            }
+        }
+        
+        # Learn patterns
+        patterns = await self.meta_learning.learn(
+            data=learning_data,
+            context=self.knowledge_base
+        )
+        
+        # Update knowledge base
+        self._update_knowledge_base(patterns)
+        
+        # Record adaptation
+        self.adaptation_history.append({
+            "timestamp": datetime.now(),
+            "patterns": patterns,
+            "metrics": self._get_execution_metrics()
+        })
+
+    async def _learn_from_step(self, step: Dict[str, Any], result: Dict[str, Any]):
+        """Learn from individual step execution."""
+        if result.get("success", False):
+            # Update success patterns
+            pattern_key = f"{step['type']}:{step.get('action_type', 'none')}"
+            self.knowledge_base["success_patterns"][pattern_key] += 1
+            
+            # Learn from successful execution
+            await self.meta_learning.learn(
+                data={
+                    "step": step,
+                    "result": result
+                },
+                context={"pattern_key": pattern_key}
+            )
+
+    def _update_knowledge_base(self, patterns: Dict[str, Any]):
+        """Update knowledge base with new patterns."""
+        # Update expertise levels
+        for area, pattern in patterns.get("expertise_patterns", {}).items():
+            if area in self.knowledge_base["expertise"]:
+                current = self.knowledge_base["expertise"][area]
+                self.knowledge_base["expertise"][area] = current * 0.9 + pattern * 0.1
+        
+        # Add new learned skills
+        new_skills = patterns.get("learned_skills", set())
+        self.knowledge_base["learned_skills"].update(new_skills)
+        
+        # Update interaction patterns
+        for pattern, count in patterns.get("interaction_patterns", {}).items():
+            self.knowledge_base["interaction_patterns"][pattern] += count
+
+    def _update_metrics(self, task: Task, result: Dict[str, Any]):
+        """Update performance metrics."""
+        metrics = {
+            "success": float(result.get("success", False)),
+            "duration": (datetime.now() - task.created_at).total_seconds(),
+            "resource_efficiency": self._calculate_resource_efficiency(),
+            "learning_progress": self._calculate_learning_progress()
+        }
+        
+        for key, value in metrics.items():
+            self.metrics[key].append(value)
+        
+        self.performance_history.append({
+            "timestamp": datetime.now(),
+            "metrics": metrics
+        })
+
+    def _calculate_resource_efficiency(self) -> float:
+        """Calculate resource usage efficiency."""
+        if not self.resource_limits:
+            return 1.0
+            
+        efficiencies = []
+        for resource, usage in self.resource_usage.items():
+            limit = self.resource_limits.get(resource, float('inf'))
+            if limit > 0:
+                efficiencies.append(1 - (usage / limit))
+                
+        return sum(efficiencies) / len(efficiencies) if efficiencies else 1.0
+
+    def _calculate_learning_progress(self) -> float:
+        """Calculate learning progress."""
+        if not self.knowledge_base["expertise"]:
+            return 0.0
+            
+        return sum(self.knowledge_base["expertise"].values()) / len(self.knowledge_base["expertise"])
+
+    def _get_execution_metrics(self) -> Dict[str, float]:
+        """Get current execution metrics."""
+        return {
+            key: sum(values[-10:]) / len(values[-10:])
+            for key, values in self.metrics.items()
+            if values
+        }
+
+class AgenticSystem:
+    """Advanced multi-agent system with orchestration."""
+    
+    def __init__(self, config: Dict[str, Any] = None):
+        self.config = config or {}
+        
+        # Initialize orchestrator
+        self.orchestrator = AgentOrchestrator(config)
+        
+        # Initialize components
+        self.agents: Dict[str, Agent] = {}
+        self.reasoning_engine = ReasoningEngine(
+            model_manager=None,  # Will be injected
+            max_depth=5,
+            beam_width=3
+        )
+        self.meta_learning = MetaLearningStrategy()
+        
+        # System state
+        self.state = "initialized"
+        self.metrics: Dict[str, List[float]] = defaultdict(list)
+        
+        # Async support
+        self.executor = ThreadPoolExecutor(max_workers=4)
+        self.lock = asyncio.Lock()
+        
+        # Logging
+        self.logger = logging.getLogger("AgenticSystem")
+
+    async def create_agent(
+        self,
+        name: str,
+        role: AgentRole,
+        capabilities: List[AgentCapability],
+        personality: AgentPersonality,
+        expertise_areas: List[str]
+    ) -> str:
+        """Create a new agent."""
+        # Create agent profile
+        profile = AgentProfile(
+            id=str(uuid.uuid4()),
+            name=name,
+            role=role,
+            capabilities=capabilities,
+            personality=personality,
+            expertise_areas=expertise_areas,
+            learning_rate=0.1,
+            risk_tolerance=0.5,
+            created_at=datetime.now(),
+            metadata={}
+        )
+        
+        # Create agent instance
+        agent = Agent(
+            profile=profile,
+            reasoning_engine=self.reasoning_engine,
+            meta_learning=self.meta_learning,
+            config=self.config.get("agent_config", {})
+        )
+        
+        # Register with orchestrator
+        agent_id = await self.orchestrator.register_agent(
+            role=role,
+            capabilities=[c.value for c in capabilities]
+        )
+        
+        # Store agent
+        async with self.lock:
+            self.agents[agent_id] = agent
+        
+        return agent_id
+
+    async def submit_task(
+        self,
+        description: str,
+        priority: TaskPriority = TaskPriority.MEDIUM,
+        deadline: Optional[datetime] = None
+    ) -> str:
+        """Submit a task to the system."""
+        return await self.orchestrator.submit_task(
+            description=description,
+            priority=priority,
+            deadline=deadline
+        )
+
+    async def get_task_status(self, task_id: str) -> Dict[str, Any]:
+        """Get status of a task."""
+        return await self.orchestrator.get_task_status(task_id)
+
+    async def get_agent_status(self, agent_id: str) -> Dict[str, Any]:
+        """Get status of an agent."""
+        agent = self.agents.get(agent_id)
+        if not agent:
+            raise ValueError(f"Unknown agent: {agent_id}")
+            
+        return {
+            "profile": agent.profile.__dict__,
+            "state": agent.state,
+            "current_task": agent.current_task.__dict__ if agent.current_task else None,
+            "metrics": agent._get_execution_metrics(),
+            "resource_usage": agent.resource_usage
+        }
+
+    async def get_system_status(self) -> Dict[str, Any]:
+        """Get overall system status."""
+        return {
+            "state": self.state,
+            "agent_count": len(self.agents),
+            "active_tasks": len([a for a in self.agents.values() if a.state == AgentState.BUSY]),
+            "performance_metrics": self._calculate_system_metrics(),
+            "resource_usage": self._calculate_resource_usage()
+        }
+
+    def _calculate_system_metrics(self) -> Dict[str, float]:
+        """Calculate overall system metrics."""
+        metrics = defaultdict(list)
+        
+        for agent in self.agents.values():
+            agent_metrics = agent._get_execution_metrics()
+            for key, value in agent_metrics.items():
+                metrics[key].append(value)
+        
+        return {
+            key: sum(values) / len(values)
+            for key, values in metrics.items()
+            if values
+        }
+
+    def _calculate_resource_usage(self) -> Dict[str, float]:
+        """Calculate overall resource usage."""
+        usage = defaultdict(float)
+        
+        for agent in self.agents.values():
+            for resource, amount in agent.resource_usage.items():
+                usage[resource] += amount
+        
+        return dict(usage)

api/venture_api.py

@@ -0,0 +1,194 @@
+"""API endpoints for venture strategies and analysis."""
+
+from fastapi import APIRouter, HTTPException, Depends
+from typing import List, Dict, Any, Optional
+from pydantic import BaseModel, Field
+from datetime import datetime
+
+from reasoning.venture_strategies import (
+    AIStartupStrategy, SaaSVentureStrategy, AutomationVentureStrategy,
+    DataVentureStrategy, APIVentureStrategy, MarketplaceVentureStrategy,
+    AIInfrastructureStrategy, AIConsultingStrategy, AIProductStrategy,
+    FinTechStrategy, HealthTechStrategy, EdTechStrategy,
+    BlockchainStrategy, AIMarketplaceStrategy
+)
+from reasoning.market_analysis import MarketAnalyzer
+from reasoning.portfolio_optimization import PortfolioOptimizer
+from reasoning.monetization import MonetizationOptimizer
+
+router = APIRouter(prefix="/api/ventures", tags=["ventures"])
+
+# Models
+class VentureRequest(BaseModel):
+    """Venture analysis request."""
+    venture_type: str
+    query: str
+    context: Dict[str, Any] = Field(default_factory=dict)
+
+class MarketRequest(BaseModel):
+    """Market analysis request."""
+    segment: str
+    context: Dict[str, Any] = Field(default_factory=dict)
+
+class PortfolioRequest(BaseModel):
+    """Portfolio optimization request."""
+    ventures: List[str]
+    context: Dict[str, Any] = Field(default_factory=dict)
+
+class MonetizationRequest(BaseModel):
+    """Monetization optimization request."""
+    venture_type: str
+    context: Dict[str, Any] = Field(default_factory=dict)
+
+# Strategy mapping
+VENTURE_STRATEGIES = {
+    "ai_startup": AIStartupStrategy(),
+    "saas": SaaSVentureStrategy(),
+    "automation": AutomationVentureStrategy(),
+    "data": DataVentureStrategy(),
+    "api": APIVentureStrategy(),
+    "marketplace": MarketplaceVentureStrategy(),
+    "ai_infrastructure": AIInfrastructureStrategy(),
+    "ai_consulting": AIConsultingStrategy(),
+    "ai_product": AIProductStrategy(),
+    "fintech": FinTechStrategy(),
+    "healthtech": HealthTechStrategy(),
+    "edtech": EdTechStrategy(),
+    "blockchain": BlockchainStrategy(),
+    "ai_marketplace": AIMarketplaceStrategy()
+}
+
+# Endpoints
+@router.post("/analyze")
+async def analyze_venture(request: VentureRequest):
+    """Analyze venture opportunity."""
+    try:
+        strategy = VENTURE_STRATEGIES.get(request.venture_type)
+        if not strategy:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Invalid venture type: {request.venture_type}"
+            )
+        
+        result = await strategy.reason(request.query, request.context)
+        return {
+            "success": True,
+            "result": result,
+            "timestamp": datetime.now().isoformat()
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/market")
+async def analyze_market(request: MarketRequest):
+    """Analyze market opportunity."""
+    try:
+        analyzer = MarketAnalyzer()
+        result = await analyzer.analyze_market(request.segment, request.context)
+        return {
+            "success": True,
+            "result": result,
+            "timestamp": datetime.now().isoformat()
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/portfolio")
+async def optimize_portfolio(request: PortfolioRequest):
+    """Optimize venture portfolio."""
+    try:
+        optimizer = PortfolioOptimizer()
+        result = await optimizer.optimize_portfolio(request.ventures, request.context)
+        return {
+            "success": True,
+            "result": result,
+            "timestamp": datetime.now().isoformat()
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/monetization")
+async def optimize_monetization(request: MonetizationRequest):
+    """Optimize venture monetization."""
+    try:
+        optimizer = MonetizationOptimizer()
+        result = await optimizer.optimize_monetization(
+            request.venture_type, request.context)
+        return {
+            "success": True,
+            "result": result,
+            "timestamp": datetime.now().isoformat()
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.get("/strategies")
+async def list_strategies():
+    """List available venture strategies."""
+    return {
+        "success": True,
+        "strategies": list(VENTURE_STRATEGIES.keys()),
+        "timestamp": datetime.now().isoformat()
+    }
+
+@router.get("/metrics/{venture_type}")
+async def get_venture_metrics(venture_type: str):
+    """Get venture performance metrics."""
+    try:
+        strategy = VENTURE_STRATEGIES.get(venture_type)
+        if not strategy:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Invalid venture type: {venture_type}"
+            )
+        
+        metrics = strategy.get_venture_metrics()
+        return {
+            "success": True,
+            "metrics": metrics,
+            "timestamp": datetime.now().isoformat()
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.get("/insights")
+async def get_market_insights():
+    """Get comprehensive market insights."""
+    try:
+        analyzer = MarketAnalyzer()
+        insights = analyzer.get_market_insights()
+        return {
+            "success": True,
+            "insights": insights,
+            "timestamp": datetime.now().isoformat()
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.get("/portfolio/insights")
+async def get_portfolio_insights():
+    """Get comprehensive portfolio insights."""
+    try:
+        optimizer = PortfolioOptimizer()
+        insights = optimizer.get_portfolio_insights()
+        return {
+            "success": True,
+            "insights": insights,
+            "timestamp": datetime.now().isoformat()
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.get("/monetization/metrics")
+async def get_monetization_metrics():
+    """Get comprehensive monetization metrics."""
+    try:
+        optimizer = MonetizationOptimizer()
+        metrics = optimizer.get_monetization_metrics()
+        return {
+            "success": True,
+            "metrics": metrics,
+            "timestamp": datetime.now().isoformat()
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

app.py

@@ -0,0 +1,110 @@
+"""
+Advanced Agentic System Interface
+-------------------------------
+Provides an interface to interact with the autonomous agent system
+using local LLM for improved performance.
+"""
+
+import gradio as gr
+import asyncio
+from typing import Dict, Any, List
+import json
+from datetime import datetime
+import logging
+
+from agentic_system import AgenticSystem
+from team_management import TeamManager
+from orchestrator import AgentOrchestrator
+from reasoning.unified_engine import UnifiedReasoningEngine
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class AgentInterface:
+    """Interface for the agentic system."""
+    
+    def __init__(self):
+        """Initialize the interface components."""
+        self.orchestrator = AgentOrchestrator()
+        self.reasoning_engine = UnifiedReasoningEngine(
+            min_confidence=0.7,
+            parallel_threshold=3,
+            learning_rate=0.1
+        )
+        
+    async def process_query(self, message: str) -> str:
+        """Process user query through the reasoning system."""
+        try:
+            # Prepare context
+            context = {
+                'timestamp': datetime.now().isoformat(),
+                'objective': 'Provide helpful and accurate responses',
+                'mode': 'analytical'
+            }
+            
+            # Get response from reasoning engine
+            result = await self.reasoning_engine.reason(
+                query=message,
+                context=context
+            )
+            
+            if result.success:
+                return result.answer
+            else:
+                return f"Error: Unable to process query. Please try again."
+                
+        except Exception as e:
+            logger.error(f"Error processing query: {e}")
+            return f"Error: {str(e)}"
+
+# Initialize interface
+interface = AgentInterface()
+
+# Create Gradio interface
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # AI Reasoning System
+    This system uses advanced reasoning strategies including local LLM for improved performance.
+    
+    Note: First query might take a few seconds as the model loads.
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=4):
+            input_text = gr.Textbox(
+                label="Your question",
+                placeholder="Ask me anything...",
+                lines=2
+            )
+            output_text = gr.Textbox(
+                label="Response",
+                lines=10,
+                interactive=False
+            )
+            submit_btn = gr.Button("Ask")
+            clear_btn = gr.Button("Clear")
+            
+        with gr.Column(scale=1):
+            gr.Markdown("""
+            ### Example Questions:
+            - What are the implications of artificial intelligence on society?
+            - How does climate change affect global ecosystems?
+            - What are the philosophical implications of quantum mechanics?
+            """)
+    
+    # Set up event handlers
+    submit_btn.click(
+        fn=interface.process_query,
+        inputs=input_text,
+        outputs=output_text
+    )
+    clear_btn.click(
+        lambda: ("", ""),
+        inputs=None,
+        outputs=[input_text, output_text]
+    )
+
+# Launch the interface
+if __name__ == "__main__":
+    demo.launch()

app.py.backup

@@ -0,0 +1,338 @@
+"""
+Advanced Agentic System Interface
+-------------------------------
+Provides a chat interface to interact with the autonomous agent teams:
+- Team A: Coders (App/Software Developers)
+- Team B: Business (Entrepreneurs)
+- Team C: Research (Deep Online Research)
+- Team D: Crypto & Sports Trading
+"""
+
+import gradio as gr
+import asyncio
+from typing import Dict, Any, List
+import json
+from datetime import datetime
+
+from agentic_system import AgenticSystem
+from team_management import TeamManager, TeamType, TeamObjective
+from orchestrator import AgentOrchestrator
+from reasoning import ReasoningEngine
+
+class ChatInterface:
+    def __init__(self):
+        # Initialize core components
+        self.orchestrator = AgentOrchestrator()
+        self.agentic_system = AgenticSystem()
+        self.team_manager = TeamManager(self.orchestrator)
+        self.chat_history = []
+        self.active_objectives = {}
+        
+        # Initialize teams
+        asyncio.run(self.team_manager.initialize_team_agents())
+
+    async def process_message(
+        self,
+        message: str,
+        history: List[List[str]]
+    ) -> str:
+        """Process incoming chat message."""
+        try:
+            # Analyze message intent
+            intent = await self._analyze_intent(message)
+            
+            if intent["type"] == "query":
+                response = await self._handle_query(message)
+            elif intent["type"] == "objective":
+                response = await self._handle_objective(message)
+            elif intent["type"] == "status":
+                response = await self._handle_status_request(message)
+            else:
+                response = await self._handle_general_chat(message)
+            
+            # Update chat history
+            self.chat_history.append({
+                "role": "user",
+                "content": message,
+                "timestamp": datetime.now()
+            })
+            self.chat_history.append({
+                "role": "assistant",
+                "content": response,
+                "timestamp": datetime.now()
+            })
+            
+            return response
+            
+        except Exception as e:
+            return f"Error processing message: {str(e)}"
+
+    async def _analyze_intent(self, message: str) -> Dict[str, Any]:
+        """Analyze user message intent."""
+        # Use reasoning engine to analyze intent
+        analysis = await self.orchestrator.reasoning_engine.reason(
+            query=message,
+            context={
+                "chat_history": self.chat_history,
+                "active_objectives": self.active_objectives
+            }
+        )
+        
+        return {
+            "type": analysis.get("intent_type", "general"),
+            "confidence": analysis.get("confidence", 0.5),
+            "entities": analysis.get("entities", []),
+            "action_required": analysis.get("action_required", False)
+        }
+
+    async def _handle_query(self, message: str) -> str:
+        """Handle information queries."""
+        # Get relevant teams for the query
+        recommended_teams = await self.team_manager.get_team_recommendations(message)
+        
+        # Get responses from relevant teams
+        responses = []
+        for team_type in recommended_teams:
+            team_response = await self._get_team_response(team_type, message)
+            responses.append(team_response)
+        
+        # Combine and format responses
+        combined_response = self._format_team_responses(responses)
+        
+        return combined_response
+
+    async def _handle_objective(self, message: str) -> str:
+        """Handle new objective creation."""
+        # Analyze objective requirements
+        analysis = await self.orchestrator.reasoning_engine.reason(
+            query=f"Analyze objective requirements: {message}",
+            context={"teams": self.team_manager.teams}
+        )
+        
+        # Determine required teams
+        required_teams = [
+            TeamType[team.upper()]
+            for team in analysis.get("required_teams", [])
+        ]
+        
+        # Create cross-team objective
+        objective_id = await self.team_manager.create_cross_team_objective(
+            objective=message,
+            required_teams=required_teams
+        )
+        
+        self.active_objectives[objective_id] = {
+            "description": message,
+            "teams": required_teams,
+            "status": "initiated",
+            "created_at": datetime.now()
+        }
+        
+        return self._format_objective_creation(objective_id)
+
+    async def _handle_status_request(self, message: str) -> str:
+        """Handle status check requests."""
+        # Get system status
+        system_status = await self.agentic_system.get_system_status()
+        
+        # Get team status
+        team_status = {}
+        for team_id, team in self.team_manager.teams.items():
+            team_status[team.name] = await self.team_manager.monitor_objective_progress(team_id)
+        
+        # Get objective status
+        objective_status = {}
+        for obj_id, obj in self.active_objectives.items():
+            objective_status[obj_id] = await self.team_manager.monitor_objective_progress(obj_id)
+        
+        return self._format_status_response(system_status, team_status, objective_status)
+
+    async def _handle_general_chat(self, message: str) -> str:
+        """Handle general chat interactions."""
+        # Use reasoning engine for response generation
+        response = await self.orchestrator.reasoning_engine.reason(
+            query=message,
+            context={
+                "chat_history": self.chat_history,
+                "system_state": await self.agentic_system.get_system_status()
+            }
+        )
+        
+        return response.get("response", "I'm not sure how to respond to that.")
+
+    async def _get_team_response(self, team_type: TeamType, query: str) -> Dict[str, Any]:
+        """Get response from a specific team."""
+        team_id = next(
+            (tid for tid, team in self.team_manager.teams.items() 
+             if team.type == team_type),
+            None
+        )
+        
+        if not team_id:
+            return {
+                "team": team_type.value,
+                "response": "Team not available",
+                "confidence": 0.0
+            }
+        
+        # Get team agents
+        team_agents = self.team_manager.agents[team_id]
+        
+        # Aggregate responses from team agents
+        responses = []
+        for agent in team_agents.values():
+            agent_response = await agent.process_query(query)
+            responses.append(agent_response)
+        
+        # Combine responses
+        combined_response = self._combine_agent_responses(responses)
+        
+        return {
+            "team": team_type.value,
+            "response": combined_response,
+            "confidence": sum(r.get("confidence", 0) for r in responses) / len(responses)
+        }
+
+    def _combine_agent_responses(self, responses: List[Dict[str, Any]]) -> str:
+        """Combine multiple agent responses into a coherent response."""
+        # Sort by confidence
+        valid_responses = [
+            r for r in responses 
+            if r.get("success", False) and r.get("response")
+        ]
+        
+        if not valid_responses:
+            return "No valid response available"
+            
+        sorted_responses = sorted(
+            valid_responses,
+            key=lambda x: x.get("confidence", 0),
+            reverse=True
+        )
+        
+        # Take the highest confidence response
+        best_response = sorted_responses[0]
+        
+        return best_response.get("response", "No response available")
+
+    def _format_team_responses(self, responses: List[Dict[str, Any]]) -> str:
+        """Format team responses into a readable message."""
+        formatted = []
+        
+        for response in responses:
+            if response.get("confidence", 0) > 0.3:  # Confidence threshold
+                formatted.append(
+                    f"Team {response['team'].title()}:\n"
+                    f"{response['response']}\n"
+                )
+        
+        if not formatted:
+            return "No team was able to provide a confident response."
+            
+        return "\n".join(formatted)
+
+    def _format_objective_creation(self, objective_id: str) -> str:
+        """Format objective creation response."""
+        objective = self.active_objectives[objective_id]
+        
+        return (
+            f"Objective created successfully!\n\n"
+            f"Objective ID: {objective_id}\n"
+            f"Description: {objective['description']}\n"
+            f"Assigned Teams: {', '.join(t.value for t in objective['teams'])}\n"
+            f"Status: {objective['status']}\n"
+            f"Created: {objective['created_at'].strftime('%Y-%m-%d %H:%M:%S')}"
+        )
+
+    def _format_status_response(
+        self,
+        system_status: Dict[str, Any],
+        team_status: Dict[str, Any],
+        objective_status: Dict[str, Any]
+    ) -> str:
+        """Format status response."""
+        # Format system status
+        status = [
+            "System Status:",
+            f"- State: {system_status['state']}",
+            f"- Active Agents: {system_status['agent_count']}",
+            f"- Active Tasks: {system_status['active_tasks']}",
+            "\nTeam Status:"
+        ]
+        
+        # Add team status
+        for team_name, team_info in team_status.items():
+            status.extend([
+                f"\n{team_name}:",
+                f"- Active Agents: {team_info['active_agents']}",
+                f"- Completion Rate: {team_info['completion_rate']:.2%}",
+                f"- Collaboration Score: {team_info['collaboration_score']:.2f}"
+            ])
+        
+        # Add objective status
+        if objective_status:
+            status.append("\nActive Objectives:")
+            for obj_id, obj_info in objective_status.items():
+                obj = self.active_objectives[obj_id]
+                status.extend([
+                    f"\n{obj['description']}:",
+                    f"- Status: {obj['status']}",
+                    f"- Teams: {', '.join(t.value for t in obj['teams'])}",
+                    f"- Progress: {sum(t['completion_rate'] for t in obj_info.values())/len(obj_info):.2%}"
+                ])
+        
+        return "\n".join(status)
+
+class VentureUI:
+    def __init__(self, app):
+        self.app = app
+
+    def create_interface(self):
+        return gr.Interface(
+            fn=self.app,
+            inputs=[
+                gr.Textbox(
+                    label="Message",
+                    placeholder="Chat with the Agentic System...",
+                    lines=2
+                ),
+                gr.State([])  # For chat history
+            ],
+            outputs=gr.Textbox(
+                label="Response",
+                lines=10
+            ),
+            title="Advanced Agentic System Chat Interface",
+            description="""
+            Chat with our autonomous agent teams:
+            - Team A: Coders (App/Software Developers)
+            - Team B: Business (Entrepreneurs)
+            - Team C: Research (Deep Online Research)
+            - Team D: Crypto & Sports Trading
+            
+            You can:
+            1. Ask questions
+            2. Create new objectives
+            3. Check status of teams and objectives
+            4. Get insights and recommendations
+            """,
+            theme="default",
+            allow_flagging="never"
+        )
+
+def create_chat_interface() -> gr.Interface:
+    """Create Gradio chat interface."""
+    chat = ChatInterface()
+    ui = VentureUI(chat.process_message)
+    
+    return ui.create_interface()
+
+# Create and launch the interface
+interface = create_chat_interface()
+
+if __name__ == "__main__":
+    interface.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=True
+    )

app.yaml

@@ -0,0 +1,9 @@
+title: Advanced Reasoning System
+emoji: 🧠
+colorFrom: indigo
+colorTo: purple
+sdk: gradio
+sdk_version: 4.16.0
+app_file: app.py
+pinned: false
+license: mit

check_space_status.py

@@ -0,0 +1,55 @@
+from huggingface_hub import HfApi
+import time
+import os
+import requests
+
+def check_space_status():
+    api = HfApi()
+    space_name = "nananie143/Agentic_llm"
+    
+    try:
+        # First try direct API request
+        response = requests.get(
+            f"https://huggingface.co/api/spaces/{space_name}/runtime",
+            headers={"Authorization": f"Bearer {os.environ['HUGGINGFACE_TOKEN']}"}
+        )
+        print(f"\nAPI Response Status: {response.status_code}")
+        if response.ok:
+            data = response.json()
+            print(f"Space Info: {data}")
+            return data.get("stage")
+            
+        # Fallback to HF API
+        space_info = api.space_info(space_name)
+        print(f"\nSpace Info via HF API: {space_info}")
+        
+        if hasattr(space_info, 'runtime'):
+            status = space_info.runtime.stage
+            print(f"Status: {status}")
+            return status
+        
+        print("No status information available")
+        return None
+        
+    except Exception as e:
+        print(f"Error checking status: {e}")
+        return None
+
+print("Starting Space status check...")
+print("Will check every 30 seconds until the Space is running...")
+
+while True:
+    status = check_space_status()
+    print(f"Current status: {status}")
+    
+    if status == "RUNNING":
+        print("\nSpace is now running! ")
+        print(f"Access your Space at: https://huggingface.co/spaces/nananie143/Agentic_llm")
+        break
+    elif status == "FAILED":
+        print("\nSpace build failed! Please check the logs for details.")
+        break
+    elif status is None:
+        print("\nCouldn't determine status. Will try again...")
+    
+    time.sleep(30)

config.py

@@ -0,0 +1,321 @@
+"""
+System Configuration
+------------------
+Central configuration for the Agentic System including:
+1. Local Model Settings
+2. Team Settings
+3. System Parameters
+4. Resource Limits
+5. Free API Configurations
+"""
+
+import os
+from typing import Dict, Any
+from pathlib import Path
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+class SystemConfig:
+    """System-wide configuration."""
+    
+    # Base Paths
+    BASE_DIR = Path(__file__).parent.absolute()
+    CACHE_DIR = BASE_DIR / "cache"
+    LOG_DIR = BASE_DIR / "logs"
+    DATA_DIR = BASE_DIR / "data"
+    MODEL_DIR = BASE_DIR / "models"
+    
+    # System Parameters
+    DEBUG_MODE = os.getenv("DEBUG_MODE", "False").lower() == "true"
+    LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO")
+    MAX_WORKERS = int(os.getenv("MAX_WORKERS", "4"))
+    ASYNC_TIMEOUT = int(os.getenv("ASYNC_TIMEOUT", "30"))
+    
+    # Local Model Configurations
+    MODEL_CONFIG = {
+        "quick_coder": {
+            "name": "tugstugi/Qwen2.5-Coder-0.5B-QwQ-draft",
+            "type": "transformers",
+            "description": "Fast code completion and simple tasks",
+            "temperature": 0.2,
+            "max_tokens": 1000,
+            "timeout": 30
+        },
+        "deep_coder": {
+            "name": "YorkieOH10/deepseek-coder-6.7B-kexer-Q8_0-GGUF",
+            "type": "gguf",
+            "description": "Complex code generation and refactoring",
+            "temperature": 0.3,
+            "max_tokens": 2000,
+            "timeout": 45
+        },
+        "text_gen": {
+            "name": "Orenguteng/Llama-3-8B-Lexi-Uncensored",
+            "type": "transformers",
+            "description": "General text generation and reasoning",
+            "temperature": 0.7,
+            "max_tokens": 1500,
+            "timeout": 40
+        },
+        "workflow": {
+            "name": "deepseek-ai/JanusFlow-1.3B",
+            "type": "transformers",
+            "description": "Task planning and workflow management",
+            "temperature": 0.5,
+            "max_tokens": 1000,
+            "timeout": 30
+        }
+    }
+    
+    # Team Configurations
+    TEAM_CONFIG = {
+        "coders": {
+            "min_agents": 3,
+            "max_agents": 7,
+            "capabilities": [
+                "full_stack_development",
+                "cloud_architecture",
+                "ai_ml",
+                "blockchain",
+                "mobile_development"
+            ],
+            "resource_limits": {
+                "cpu_percent": 80,
+                "memory_mb": 4096,
+                "gpu_memory_mb": 2048
+            }
+        },
+        "business": {
+            "min_agents": 2,
+            "max_agents": 5,
+            "capabilities": [
+                "market_analysis",
+                "business_strategy",
+                "digital_transformation",
+                "startup_innovation",
+                "product_management"
+            ],
+            "resource_limits": {
+                "cpu_percent": 60,
+                "memory_mb": 2048,
+                "api_calls_per_minute": 100
+            }
+        },
+        "research": {
+            "min_agents": 2,
+            "max_agents": 6,
+            "capabilities": [
+                "deep_research",
+                "data_analysis",
+                "trend_forecasting",
+                "competitive_analysis",
+                "technology_assessment"
+            ],
+            "resource_limits": {
+                "cpu_percent": 70,
+                "memory_mb": 3072,
+                "api_calls_per_minute": 150
+            }
+        },
+        "traders": {
+            "min_agents": 2,
+            "max_agents": 5,
+            "capabilities": [
+                "crypto_trading",
+                "sports_betting",
+                "risk_management",
+                "market_timing",
+                "portfolio_optimization"
+            ],
+            "resource_limits": {
+                "cpu_percent": 60,
+                "memory_mb": 2048,
+                "api_calls_per_minute": 200
+            }
+        }
+    }
+    
+    # Resource Management
+    RESOURCE_LIMITS = {
+        "total_cpu_percent": 90,
+        "total_memory_mb": 8192,
+        "total_gpu_memory_mb": 4096,
+        "max_api_calls_per_minute": 500,
+        "max_concurrent_tasks": 20
+    }
+    
+    # Collaboration Settings
+    COLLABORATION_CONFIG = {
+        "min_confidence_threshold": 0.6,
+        "max_team_size": 10,
+        "max_concurrent_objectives": 5,
+        "objective_timeout_minutes": 60,
+        "team_sync_interval_seconds": 30
+    }
+    
+    # Error Recovery
+    ERROR_RECOVERY = {
+        "max_retries": 3,
+        "retry_delay_seconds": 5,
+        "error_threshold": 0.2,
+        "recovery_timeout": 300
+    }
+    
+    # Monitoring
+    MONITORING = {
+        "metrics_interval_seconds": 60,
+        "health_check_interval": 30,
+        "performance_log_retention_days": 7,
+        "alert_threshold": {
+            "cpu": 85,
+            "memory": 90,
+            "error_rate": 0.1
+        }
+    }
+    
+    # Free API Configurations (No API Keys Required)
+    API_CONFIG = {
+        "search": {
+            "duckduckgo": {
+                "base_url": "https://api.duckduckgo.com",
+                "rate_limit": 100,
+                "requires_auth": False,
+                "method": "GET"
+            },
+            "wikipedia": {
+                "base_url": "https://en.wikipedia.org/w/api.php",
+                "rate_limit": 200,
+                "requires_auth": False,
+                "method": "GET"
+            },
+            "arxiv": {
+                "base_url": "http://export.arxiv.org/api/query",
+                "rate_limit": 60,
+                "requires_auth": False,
+                "method": "GET"
+            },
+            "crossref": {
+                "base_url": "https://api.crossref.org/works",
+                "rate_limit": 50,
+                "requires_auth": False,
+                "method": "GET"
+            },
+            "unpaywall": {
+                "base_url": "https://api.unpaywall.org/v2",
+                "rate_limit": 100,
+                "requires_auth": False,
+                "method": "GET"
+            }
+        },
+        "crypto": {
+            "coincap": {
+                "base_url": "https://api.coincap.io/v2",
+                "rate_limit": 200,
+                "requires_auth": False,
+                "method": "GET",
+                "endpoints": {
+                    "assets": "/assets",
+                    "rates": "/rates",
+                    "markets": "/markets"
+                }
+            },
+            "blockchair": {
+                "base_url": "https://api.blockchair.com",
+                "rate_limit": 30,
+                "requires_auth": False,
+                "method": "GET"
+            }
+        },
+        "news": {
+            "wikinews": {
+                "base_url": "https://en.wikinews.org/w/api.php",
+                "rate_limit": 200,
+                "requires_auth": False,
+                "method": "GET"
+            },
+            "reddit": {
+                "base_url": "https://www.reddit.com/r/news/.json",
+                "rate_limit": 60,
+                "requires_auth": False,
+                "method": "GET"
+            },
+            "hackernews": {
+                "base_url": "https://hacker-news.firebaseio.com/v0",
+                "rate_limit": 100,
+                "requires_auth": False,
+                "method": "GET"
+            }
+        },
+        "market_data": {
+            "yahoo_finance": {
+                "base_url": "https://query1.finance.yahoo.com/v8/finance",
+                "rate_limit": 100,
+                "requires_auth": False,
+                "method": "GET"
+            },
+            "marketstack_free": {
+                "base_url": "https://api.marketstack.com/v1",
+                "rate_limit": 100,
+                "requires_auth": False,
+                "method": "GET"
+            }
+        },
+        "sports": {
+            "football_data": {
+                "base_url": "https://www.football-data.org/v4",
+                "rate_limit": 10,
+                "requires_auth": False,
+                "method": "GET",
+                "free_endpoints": [
+                    "/competitions",
+                    "/matches"
+                ]
+            },
+            "nhl": {
+                "base_url": "https://statsapi.web.nhl.com/api/v1",
+                "rate_limit": 50,
+                "requires_auth": False,
+                "method": "GET"
+            },
+            "mlb": {
+                "base_url": "https://statsapi.mlb.com/api/v1",
+                "rate_limit": 50,
+                "requires_auth": False,
+                "method": "GET"
+            }
+        },
+        "web_scraping": {
+            "web_archive": {
+                "base_url": "https://archive.org/wayback/available",
+                "rate_limit": 40,
+                "requires_auth": False,
+                "method": "GET"
+            },
+            "metahtml": {
+                "base_url": "https://html.spec.whatwg.org/multipage",
+                "rate_limit": 30,
+                "requires_auth": False,
+                "method": "GET"
+            }
+        }
+    }
+
+    @classmethod
+    def get_team_config(cls, team_name: str) -> Dict[str, Any]:
+        """Get configuration for a specific team."""
+        return cls.TEAM_CONFIG.get(team_name, {})
+
+    @classmethod
+    def get_model_config(cls, model_type: str) -> Dict[str, Any]:
+        """Get configuration for a specific model type."""
+        return cls.MODEL_CONFIG.get(model_type, {})
+
+    @classmethod
+    def get_api_config(cls, api_name: str) -> Dict[str, Any]:
+        """Get configuration for a specific API."""
+        for category in cls.API_CONFIG.values():
+            if api_name in category:
+                return category[api_name]
+        return {}

fix_indentation.patch

@@ -0,0 +1,42 @@
+--- reasoning.py
++++ reasoning.py
+@@ -2796,297 +2796,297 @@
+     async def _cross_modal_alignment(self, modalities: Dict[str, List[Dict[str, Any]]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
+         """Align information across different modalities."""
+         try:
+             # Extract modality types
+             modal_types = list(modalities.keys())
+             
+             # Initialize alignment results
+             alignments = []
+             
+             # Process each modality pair
+             for i in range(len(modal_types)):
+                 for j in range(i + 1, len(modal_types)):
+                     type1, type2 = modal_types[i], modal_types[j]
+                     
+                     # Get items from each modality
+                     items1 = modalities[type1]
+                     items2 = modalities[type2]
+                     
+                     # Find alignments between items
+                     for item1 in items1:
+                         for item2 in items2:
+                             similarity = self._calculate_similarity(item1, item2)
+                             if similarity > 0.5:  # Threshold for alignment
+                                 alignments.append({
+                                     "type1": type1,
+                                     "type2": type2,
+                                     "item1": item1,
+                                     "item2": item2,
+                                     "similarity": similarity
+                                 })
+             
+             # Sort alignments by similarity
+             alignments.sort(key=lambda x: x["similarity"], reverse=True)
+             
+             return alignments
+             
+         except Exception as e:
+             logging.error(f"Error in cross-modal alignment: {str(e)}")
+             return []

meta_learning.py

@@ -0,0 +1,406 @@
+"""
+Meta-Learning System
+------------------
+Implements meta-learning capabilities for improved learning and adaptation.
+"""
+
+from typing import Dict, Any, List, Optional, Tuple
+import numpy as np
+from dataclasses import dataclass, field
+import logging
+from datetime import datetime
+from enum import Enum
+import json
+from .quantum_learning import QuantumLearningSystem, Pattern, PatternType
+
+class LearningStrategy(Enum):
+    GRADIENT_BASED = "gradient_based"
+    MEMORY_BASED = "memory_based"
+    EVOLUTIONARY = "evolutionary"
+    REINFORCEMENT = "reinforcement"
+    QUANTUM = "quantum"
+
+@dataclass
+class MetaParameters:
+    """Meta-parameters for learning strategies"""
+    learning_rate: float = 0.01
+    memory_size: int = 1000
+    evolution_rate: float = 0.1
+    exploration_rate: float = 0.2
+    quantum_interference: float = 0.5
+    adaptation_threshold: float = 0.7
+
+@dataclass
+class LearningMetrics:
+    """Metrics for learning performance"""
+    accuracy: float
+    convergence_rate: float
+    adaptation_speed: float
+    resource_usage: float
+    timestamp: str = field(default_factory=lambda: datetime.now().isoformat())
+
+class MetaLearningSystem:
+    """Meta-learning system for optimizing learning strategies"""
+    
+    def __init__(self):
+        self.logger = logging.getLogger(__name__)
+        self.quantum_system = QuantumLearningSystem()
+        self.strategies = {}
+        self.performance_history = []
+        self.meta_parameters = MetaParameters()
+        
+    async def optimize_learning(
+        self,
+        observation: Dict[str, Any],
+        current_strategy: LearningStrategy
+    ) -> Tuple[Dict[str, Any], LearningMetrics]:
+        """Optimize learning strategy based on observation"""
+        try:
+            # Process with quantum system
+            quantum_result = await self.quantum_system.process_observation(observation)
+            
+            # Evaluate current strategy
+            current_metrics = self._evaluate_strategy(
+                current_strategy,
+                observation,
+                quantum_result
+            )
+            
+            # Update performance history
+            self._update_performance_history(current_metrics)
+            
+            # Adapt meta-parameters
+            self._adapt_meta_parameters(current_metrics)
+            
+            # Select optimal strategy
+            optimal_strategy = self._select_optimal_strategy(
+                observation,
+                current_metrics
+            )
+            
+            # Apply selected strategy
+            result = await self._apply_strategy(
+                optimal_strategy,
+                observation,
+                quantum_result
+            )
+            
+            return result, current_metrics
+            
+        except Exception as e:
+            self.logger.error(f"Failed to optimize learning: {str(e)}")
+            raise
+            
+    def _evaluate_strategy(
+        self,
+        strategy: LearningStrategy,
+        observation: Dict[str, Any],
+        quantum_result: Dict[str, Any]
+    ) -> LearningMetrics:
+        """Evaluate performance of current learning strategy"""
+        # Calculate accuracy
+        accuracy = self._calculate_accuracy(
+            strategy,
+            observation,
+            quantum_result
+        )
+        
+        # Calculate convergence rate
+        convergence_rate = self._calculate_convergence_rate(
+            strategy,
+            self.performance_history
+        )
+        
+        # Calculate adaptation speed
+        adaptation_speed = self._calculate_adaptation_speed(
+            strategy,
+            observation
+        )
+        
+        # Calculate resource usage
+        resource_usage = self._calculate_resource_usage(strategy)
+        
+        return LearningMetrics(
+            accuracy=accuracy,
+            convergence_rate=convergence_rate,
+            adaptation_speed=adaptation_speed,
+            resource_usage=resource_usage
+        )
+        
+    def _update_performance_history(
+        self,
+        metrics: LearningMetrics
+    ) -> None:
+        """Update performance history with new metrics"""
+        self.performance_history.append(metrics)
+        
+        # Maintain history size
+        if len(self.performance_history) > self.meta_parameters.memory_size:
+            self.performance_history.pop(0)
+            
+    def _adapt_meta_parameters(
+        self,
+        metrics: LearningMetrics
+    ) -> None:
+        """Adapt meta-parameters based on performance metrics"""
+        # Adjust learning rate
+        if metrics.convergence_rate < self.meta_parameters.adaptation_threshold:
+            self.meta_parameters.learning_rate *= 0.9
+        else:
+            self.meta_parameters.learning_rate *= 1.1
+            
+        # Adjust memory size
+        if metrics.resource_usage > 0.8:
+            self.meta_parameters.memory_size = int(
+                self.meta_parameters.memory_size * 0.9
+            )
+        elif metrics.resource_usage < 0.2:
+            self.meta_parameters.memory_size = int(
+                self.meta_parameters.memory_size * 1.1
+            )
+            
+        # Adjust evolution rate
+        if metrics.adaptation_speed < self.meta_parameters.adaptation_threshold:
+            self.meta_parameters.evolution_rate *= 1.1
+        else:
+            self.meta_parameters.evolution_rate *= 0.9
+            
+        # Adjust exploration rate
+        if metrics.accuracy < self.meta_parameters.adaptation_threshold:
+            self.meta_parameters.exploration_rate *= 1.1
+        else:
+            self.meta_parameters.exploration_rate *= 0.9
+            
+        # Adjust quantum interference
+        if metrics.accuracy > 0.8:
+            self.meta_parameters.quantum_interference *= 1.1
+        else:
+            self.meta_parameters.quantum_interference *= 0.9
+            
+        # Ensure parameters stay within reasonable bounds
+        self._normalize_parameters()
+        
+    def _normalize_parameters(self) -> None:
+        """Normalize meta-parameters to stay within bounds"""
+        self.meta_parameters.learning_rate = np.clip(
+            self.meta_parameters.learning_rate,
+            0.001,
+            0.1
+        )
+        self.meta_parameters.memory_size = np.clip(
+            self.meta_parameters.memory_size,
+            100,
+            10000
+        )
+        self.meta_parameters.evolution_rate = np.clip(
+            self.meta_parameters.evolution_rate,
+            0.01,
+            0.5
+        )
+        self.meta_parameters.exploration_rate = np.clip(
+            self.meta_parameters.exploration_rate,
+            0.1,
+            0.9
+        )
+        self.meta_parameters.quantum_interference = np.clip(
+            self.meta_parameters.quantum_interference,
+            0.1,
+            0.9
+        )
+        
+    def _select_optimal_strategy(
+        self,
+        observation: Dict[str, Any],
+        metrics: LearningMetrics
+    ) -> LearningStrategy:
+        """Select optimal learning strategy"""
+        strategies = list(LearningStrategy)
+        scores = []
+        
+        for strategy in strategies:
+            # Calculate strategy score
+            score = self._calculate_strategy_score(
+                strategy,
+                observation,
+                metrics
+            )
+            scores.append((strategy, score))
+            
+        # Select strategy with highest score
+        optimal_strategy = max(scores, key=lambda x: x[1])[0]
+        
+        return optimal_strategy
+        
+    async def _apply_strategy(
+        self,
+        strategy: LearningStrategy,
+        observation: Dict[str, Any],
+        quantum_result: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Apply selected learning strategy"""
+        if strategy == LearningStrategy.GRADIENT_BASED:
+            return await self._apply_gradient_strategy(
+                observation,
+                quantum_result
+            )
+        elif strategy == LearningStrategy.MEMORY_BASED:
+            return await self._apply_memory_strategy(
+                observation,
+                quantum_result
+            )
+        elif strategy == LearningStrategy.EVOLUTIONARY:
+            return await self._apply_evolutionary_strategy(
+                observation,
+                quantum_result
+            )
+        elif strategy == LearningStrategy.REINFORCEMENT:
+            return await self._apply_reinforcement_strategy(
+                observation,
+                quantum_result
+            )
+        else:  # QUANTUM
+            return quantum_result
+            
+    def _calculate_accuracy(
+        self,
+        strategy: LearningStrategy,
+        observation: Dict[str, Any],
+        quantum_result: Dict[str, Any]
+    ) -> float:
+        """Calculate accuracy of learning strategy"""
+        if "patterns" not in quantum_result:
+            return 0.0
+            
+        patterns = quantum_result["patterns"]
+        if not patterns:
+            return 0.0
+            
+        # Calculate pattern confidence
+        confidence_sum = sum(pattern.confidence for pattern in patterns)
+        return confidence_sum / len(patterns)
+        
+    def _calculate_convergence_rate(
+        self,
+        strategy: LearningStrategy,
+        history: List[LearningMetrics]
+    ) -> float:
+        """Calculate convergence rate of learning strategy"""
+        if not history:
+            return 0.0
+            
+        # Calculate rate of improvement
+        accuracies = [metrics.accuracy for metrics in history[-10:]]
+        if len(accuracies) < 2:
+            return 0.0
+            
+        differences = np.diff(accuracies)
+        return float(np.mean(differences > 0))
+        
+    def _calculate_adaptation_speed(
+        self,
+        strategy: LearningStrategy,
+        observation: Dict[str, Any]
+    ) -> float:
+        """Calculate adaptation speed of learning strategy"""
+        if not self.performance_history:
+            return 0.0
+            
+        # Calculate time to reach adaptation threshold
+        threshold = self.meta_parameters.adaptation_threshold
+        for i, metrics in enumerate(self.performance_history):
+            if metrics.accuracy >= threshold:
+                return 1.0 / (i + 1)
+                
+        return 0.0
+        
+    def _calculate_resource_usage(
+        self,
+        strategy: LearningStrategy
+    ) -> float:
+        """Calculate resource usage of learning strategy"""
+        # Simulate resource usage based on strategy
+        base_usage = {
+            LearningStrategy.GRADIENT_BASED: 0.4,
+            LearningStrategy.MEMORY_BASED: 0.6,
+            LearningStrategy.EVOLUTIONARY: 0.7,
+            LearningStrategy.REINFORCEMENT: 0.5,
+            LearningStrategy.QUANTUM: 0.8
+        }
+        
+        return base_usage[strategy]
+        
+    def _calculate_strategy_score(
+        self,
+        strategy: LearningStrategy,
+        observation: Dict[str, Any],
+        metrics: LearningMetrics
+    ) -> float:
+        """Calculate score for learning strategy"""
+        # Weight different factors
+        weights = {
+            "accuracy": 0.4,
+            "convergence": 0.2,
+            "adaptation": 0.2,
+            "resources": 0.2
+        }
+        
+        score = (
+            weights["accuracy"] * metrics.accuracy +
+            weights["convergence"] * metrics.convergence_rate +
+            weights["adaptation"] * metrics.adaptation_speed +
+            weights["resources"] * (1 - metrics.resource_usage)
+        )
+        
+        # Add exploration bonus
+        if np.random.random() < self.meta_parameters.exploration_rate:
+            score += 0.1
+            
+        return score
+        
+    async def _apply_gradient_strategy(
+        self,
+        observation: Dict[str, Any],
+        quantum_result: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Apply gradient-based learning strategy"""
+        return {
+            "result": "gradient_optimization",
+            "quantum_enhanced": quantum_result,
+            "meta_parameters": self.meta_parameters.__dict__
+        }
+        
+    async def _apply_memory_strategy(
+        self,
+        observation: Dict[str, Any],
+        quantum_result: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Apply memory-based learning strategy"""
+        return {
+            "result": "memory_optimization",
+            "quantum_enhanced": quantum_result,
+            "meta_parameters": self.meta_parameters.__dict__
+        }
+        
+    async def _apply_evolutionary_strategy(
+        self,
+        observation: Dict[str, Any],
+        quantum_result: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Apply evolutionary learning strategy"""
+        return {
+            "result": "evolutionary_optimization",
+            "quantum_enhanced": quantum_result,
+            "meta_parameters": self.meta_parameters.__dict__
+        }
+        
+    async def _apply_reinforcement_strategy(
+        self,
+        observation: Dict[str, Any],
+        quantum_result: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Apply reinforcement learning strategy"""
+        return {
+            "result": "reinforcement_optimization",
+            "quantum_enhanced": quantum_result,
+            "meta_parameters": self.meta_parameters.__dict__
+        }

multimodal_reasoning.py

@@ -0,0 +1,273 @@
+"""
+Multi-Modal Reasoning Implementation
+----------------------------------
+Implements reasoning across different types of information.
+"""
+
+import logging
+from typing import Dict, Any, List
+from datetime import datetime
+import json
+import numpy as np
+from .reasoning import ReasoningStrategy
+
+class MultiModalReasoning(ReasoningStrategy):
+    """Implements multi-modal reasoning across different types of information."""
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        try:
+            # Process different modalities
+            modalities = await self._process_modalities(query, context)
+            
+            # Align across modalities
+            alignment = await self._cross_modal_alignment(modalities, context)
+            
+            # Integrated analysis
+            integration = await self._integrated_analysis(alignment, context)
+            
+            # Generate final response
+            response = await self._generate_response(integration, context)
+            
+            return {
+                "success": True,
+                "answer": response["conclusion"],
+                "modalities": modalities,
+                "alignment": alignment,
+                "integration": integration,
+                "confidence": response["confidence"]
+            }
+        except Exception as e:
+            logging.error(f"Error in multi-modal reasoning: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def _process_modalities(self, query: str, context: Dict[str, Any]) -> Dict[str, List[Dict[str, Any]]]:
+        """Process query across different modalities."""
+        prompt = f"""
+        Process query across modalities:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        For each modality extract:
+        1. [Type]: Modality type
+        2. [Content]: Relevant content
+        3. [Features]: Key features
+        4. [Quality]: Content quality
+        
+        Format as:
+        [M1]
+        Type: ...
+        Content: ...
+        Features: ...
+        Quality: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_modalities(response["answer"])
+
+    async def _cross_modal_alignment(self, modalities: Dict[str, List[Dict[str, Any]]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """Align information across different modalities."""
+        try:
+            # Extract modality types
+            modal_types = list(modalities.keys())
+            
+            # Initialize alignment results
+            alignments = []
+            
+            # Process each modality pair
+            for i in range(len(modal_types)):
+                for j in range(i + 1, len(modal_types)):
+                    type1, type2 = modal_types[i], modal_types[j]
+                    
+                    # Get items from each modality
+                    items1 = modalities[type1]
+                    items2 = modalities[type2]
+                    
+                    # Find alignments between items
+                    for item1 in items1:
+                        for item2 in items2:
+                            similarity = self._calculate_similarity(item1, item2)
+                            if similarity > 0.5:  # Threshold for alignment
+                                alignments.append({
+                                    "type1": type1,
+                                    "type2": type2,
+                                    "item1": item1,
+                                    "item2": item2,
+                                    "similarity": similarity
+                                })
+            
+            # Sort alignments by similarity
+            alignments.sort(key=lambda x: x["similarity"], reverse=True)
+            
+            return alignments
+            
+        except Exception as e:
+            logging.error(f"Error in cross-modal alignment: {str(e)}")
+            return []
+
+    def _calculate_similarity(self, item1: Dict[str, Any], item2: Dict[str, Any]) -> float:
+        """Calculate similarity between two items from different modalities."""
+        try:
+            # Extract content from items
+            content1 = str(item1.get("content", ""))
+            content2 = str(item2.get("content", ""))
+            
+            # Calculate basic similarity (can be enhanced with more sophisticated methods)
+            common_words = set(content1.lower().split()) & set(content2.lower().split())
+            total_words = set(content1.lower().split()) | set(content2.lower().split())
+            
+            if not total_words:
+                return 0.0
+                
+            return len(common_words) / len(total_words)
+            
+        except Exception as e:
+            logging.error(f"Error calculating similarity: {str(e)}")
+            return 0.0
+
+    async def _integrated_analysis(self, alignment: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
+        prompt = f"""
+        Perform integrated multi-modal analysis:
+        Alignment: {json.dumps(alignment)}
+        Context: {json.dumps(context)}
+        
+        For each insight:
+        1. [Insight]: Key finding
+        2. [Sources]: Contributing modalities
+        3. [Support]: Supporting evidence
+        4. [Confidence]: Confidence level
+        
+        Format as:
+        [I1]
+        Insight: ...
+        Sources: ...
+        Support: ...
+        Confidence: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_integration(response["answer"])
+
+    async def _generate_response(self, integration: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]:
+        prompt = f"""
+        Generate unified multi-modal response:
+        Integration: {json.dumps(integration)}
+        Context: {json.dumps(context)}
+        
+        Provide:
+        1. Main conclusion
+        2. Modal contributions
+        3. Integration benefits
+        4. Confidence level (0-1)
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_response(response["answer"])
+
+    def _parse_modalities(self, response: str) -> Dict[str, List[Dict[str, Any]]]:
+        """Parse modalities from response."""
+        modalities = {}
+        current_modality = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[M'):
+                if current_modality:
+                    if current_modality["type"] not in modalities:
+                        modalities[current_modality["type"]] = []
+                    modalities[current_modality["type"]].append(current_modality)
+                current_modality = {
+                    "type": "",
+                    "content": "",
+                    "features": "",
+                    "quality": ""
+                }
+            elif current_modality:
+                if line.startswith('Type:'):
+                    current_modality["type"] = line[5:].strip()
+                elif line.startswith('Content:'):
+                    current_modality["content"] = line[8:].strip()
+                elif line.startswith('Features:'):
+                    current_modality["features"] = line[9:].strip()
+                elif line.startswith('Quality:'):
+                    current_modality["quality"] = line[8:].strip()
+        
+        if current_modality:
+            if current_modality["type"] not in modalities:
+                modalities[current_modality["type"]] = []
+            modalities[current_modality["type"]].append(current_modality)
+        
+        return modalities
+
+    def _parse_integration(self, response: str) -> List[Dict[str, Any]]:
+        """Parse integration from response."""
+        integration = []
+        current_insight = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[I'):
+                if current_insight:
+                    integration.append(current_insight)
+                current_insight = {
+                    "insight": "",
+                    "sources": "",
+                    "support": "",
+                    "confidence": 0.0
+                }
+            elif current_insight:
+                if line.startswith('Insight:'):
+                    current_insight["insight"] = line[8:].strip()
+                elif line.startswith('Sources:'):
+                    current_insight["sources"] = line[8:].strip()
+                elif line.startswith('Support:'):
+                    current_insight["support"] = line[8:].strip()
+                elif line.startswith('Confidence:'):
+                    try:
+                        current_insight["confidence"] = float(line[11:].strip())
+                    except:
+                        pass
+        
+        if current_insight:
+            integration.append(current_insight)
+        
+        return integration
+
+    def _parse_response(self, response: str) -> Dict[str, Any]:
+        """Parse response from response."""
+        response_dict = {
+            "conclusion": "",
+            "modal_contributions": [],
+            "integration_benefits": [],
+            "confidence": 0.0
+        }
+        
+        mode = None
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('Conclusion:'):
+                response_dict["conclusion"] = line[11:].strip()
+            elif line.startswith('Modal Contributions:'):
+                mode = "modal"
+            elif line.startswith('Integration Benefits:'):
+                mode = "integration"
+            elif line.startswith('Confidence:'):
+                try:
+                    response_dict["confidence"] = float(line[11:].strip())
+                except:
+                    response_dict["confidence"] = 0.5
+                mode = None
+            elif mode == "modal" and line.startswith('- '):
+                response_dict["modal_contributions"].append(line[2:].strip())
+            elif mode == "integration" and line.startswith('- '):
+                response_dict["integration_benefits"].append(line[2:].strip())
+        
+        return response_dict

orchestrator.py

@@ -0,0 +1,522 @@
+"""
+Agentic Orchestrator for Advanced AI System
+-----------------------------------------
+Manages and coordinates multiple agentic components:
+1. Task Planning & Decomposition
+2. Resource Management
+3. Agent Communication
+4. State Management
+5. Error Recovery
+6. Performance Monitoring
+"""
+
+import logging
+from typing import Dict, Any, List, Optional, Union, TypeVar, Generic
+from dataclasses import dataclass, field
+from enum import Enum
+import json
+import asyncio
+from datetime import datetime
+import uuid
+from concurrent.futures import ThreadPoolExecutor
+import networkx as nx
+from collections import defaultdict
+import numpy as np
+
+from reasoning import ReasoningEngine, ReasoningMode
+from meta_learning import MetaLearningStrategy
+
+T = TypeVar('T')
+
+class AgentRole(Enum):
+    """Different roles an agent can take."""
+    PLANNER = "planner"
+    EXECUTOR = "executor"
+    MONITOR = "monitor"
+    COORDINATOR = "coordinator"
+    LEARNER = "learner"
+
+class AgentState(Enum):
+    """Possible states of an agent."""
+    IDLE = "idle"
+    BUSY = "busy"
+    ERROR = "error"
+    LEARNING = "learning"
+    TERMINATED = "terminated"
+
+class TaskPriority(Enum):
+    """Task priority levels."""
+    LOW = 0
+    MEDIUM = 1
+    HIGH = 2
+    CRITICAL = 3
+
+@dataclass
+class AgentMetadata:
+    """Metadata about an agent."""
+    id: str
+    role: AgentRole
+    capabilities: List[str]
+    state: AgentState
+    load: float
+    last_active: datetime
+    metrics: Dict[str, float]
+
+@dataclass
+class Task:
+    """Represents a task in the system."""
+    id: str
+    description: str
+    priority: TaskPriority
+    dependencies: List[str]
+    assigned_to: Optional[str]
+    state: str
+    created_at: datetime
+    deadline: Optional[datetime]
+    metadata: Dict[str, Any]
+
+class AgentOrchestrator:
+    """Advanced orchestrator for managing agentic system."""
+    
+    def __init__(self, config: Dict[str, Any] = None):
+        self.config = config or {}
+        
+        # Core components
+        self.agents: Dict[str, AgentMetadata] = {}
+        self.tasks: Dict[str, Task] = {}
+        self.task_graph = nx.DiGraph()
+        
+        # State management
+        self.state_history: List[Dict[str, Any]] = []
+        self.global_state: Dict[str, Any] = {}
+        
+        # Resource management
+        self.resource_pool: Dict[str, Any] = {}
+        self.resource_locks: Dict[str, asyncio.Lock] = {}
+        
+        # Communication
+        self.message_queue = asyncio.Queue()
+        self.event_bus = asyncio.Queue()
+        
+        # Performance monitoring
+        self.metrics = defaultdict(list)
+        self.performance_log = []
+        
+        # Error handling
+        self.error_handlers: Dict[str, callable] = {}
+        self.recovery_strategies: Dict[str, callable] = {}
+        
+        # Async support
+        self.executor = ThreadPoolExecutor(max_workers=4)
+        self.lock = asyncio.Lock()
+        
+        # Logging
+        self.logger = logging.getLogger(__name__)
+        
+        # Initialize components
+        self._init_components()
+
+    def _init_components(self):
+        """Initialize orchestrator components."""
+        # Initialize reasoning engine
+        self.reasoning_engine = ReasoningEngine(
+            model_manager=None,  # Will be injected
+            max_depth=5,
+            beam_width=3,
+            config=self.config.get("reasoning", {})
+        )
+        
+        # Initialize meta-learning
+        self.meta_learning = MetaLearningStrategy()
+        
+        # Register basic error handlers
+        self._register_error_handlers()
+
+    async def register_agent(
+        self,
+        role: AgentRole,
+        capabilities: List[str]
+    ) -> str:
+        """Register a new agent with the orchestrator."""
+        agent_id = str(uuid.uuid4())
+        
+        agent = AgentMetadata(
+            id=agent_id,
+            role=role,
+            capabilities=capabilities,
+            state=AgentState.IDLE,
+            load=0.0,
+            last_active=datetime.now(),
+            metrics={}
+        )
+        
+        async with self.lock:
+            self.agents[agent_id] = agent
+            self.logger.info(f"Registered new agent: {agent_id} with role {role}")
+            
+        return agent_id
+
+    async def submit_task(
+        self,
+        description: str,
+        priority: TaskPriority = TaskPriority.MEDIUM,
+        dependencies: List[str] = None,
+        deadline: Optional[datetime] = None,
+        metadata: Dict[str, Any] = None
+    ) -> str:
+        """Submit a new task to the orchestrator."""
+        task_id = str(uuid.uuid4())
+        
+        task = Task(
+            id=task_id,
+            description=description,
+            priority=priority,
+            dependencies=dependencies or [],
+            assigned_to=None,
+            state="pending",
+            created_at=datetime.now(),
+            deadline=deadline,
+            metadata=metadata or {}
+        )
+        
+        async with self.lock:
+            self.tasks[task_id] = task
+            self._update_task_graph(task)
+            
+        # Trigger task planning
+        await self._plan_task_execution(task_id)
+        
+        return task_id
+
+    async def _plan_task_execution(self, task_id: str) -> None:
+        """Plan the execution of a task."""
+        task = self.tasks[task_id]
+        
+        # Check dependencies
+        if not await self._check_dependencies(task):
+            self.logger.info(f"Task {task_id} waiting for dependencies")
+            return
+        
+        # Find suitable agent
+        agent_id = await self._find_suitable_agent(task)
+        if not agent_id:
+            self.logger.warning(f"No suitable agent found for task {task_id}")
+            return
+        
+        # Assign task
+        await self._assign_task(task_id, agent_id)
+
+    async def _check_dependencies(self, task: Task) -> bool:
+        """Check if all task dependencies are satisfied."""
+        for dep_id in task.dependencies:
+            if dep_id not in self.tasks:
+                return False
+            if self.tasks[dep_id].state != "completed":
+                return False
+        return True
+
+    async def _find_suitable_agent(self, task: Task) -> Optional[str]:
+        """Find the most suitable agent for a task."""
+        best_agent = None
+        best_score = float('-inf')
+        
+        for agent_id, agent in self.agents.items():
+            if agent.state != AgentState.IDLE:
+                continue
+                
+            score = await self._calculate_agent_suitability(agent, task)
+            if score > best_score:
+                best_score = score
+                best_agent = agent_id
+                
+        return best_agent
+
+    async def _calculate_agent_suitability(
+        self,
+        agent: AgentMetadata,
+        task: Task
+    ) -> float:
+        """Calculate how suitable an agent is for a task."""
+        # Base score on capabilities match
+        capability_score = sum(
+            1 for cap in task.metadata.get("required_capabilities", [])
+            if cap in agent.capabilities
+        )
+        
+        # Consider agent load
+        load_score = 1 - agent.load
+        
+        # Consider agent's recent performance
+        performance_score = sum(agent.metrics.values()) / len(agent.metrics) if agent.metrics else 0.5
+        
+        # Weighted combination
+        weights = self.config.get("agent_selection_weights", {
+            "capabilities": 0.5,
+            "load": 0.3,
+            "performance": 0.2
+        })
+        
+        return (
+            weights["capabilities"] * capability_score +
+            weights["load"] * load_score +
+            weights["performance"] * performance_score
+        )
+
+    async def _assign_task(self, task_id: str, agent_id: str) -> None:
+        """Assign a task to an agent."""
+        async with self.lock:
+            task = self.tasks[task_id]
+            agent = self.agents[agent_id]
+            
+            task.assigned_to = agent_id
+            task.state = "assigned"
+            agent.state = AgentState.BUSY
+            agent.load += 1
+            agent.last_active = datetime.now()
+            
+            self.logger.info(f"Assigned task {task_id} to agent {agent_id}")
+            
+            # Notify agent
+            await self.message_queue.put({
+                "type": "task_assignment",
+                "task_id": task_id,
+                "agent_id": agent_id,
+                "timestamp": datetime.now()
+            })
+
+    def _update_task_graph(self, task: Task) -> None:
+        """Update the task dependency graph."""
+        self.task_graph.add_node(task.id, task=task)
+        for dep_id in task.dependencies:
+            self.task_graph.add_edge(dep_id, task.id)
+
+    async def _monitor_system_state(self):
+        """Monitor overall system state."""
+        while True:
+            try:
+                # Collect agent states
+                agent_states = {
+                    agent_id: {
+                        "state": agent.state,
+                        "load": agent.load,
+                        "metrics": agent.metrics
+                    }
+                    for agent_id, agent in self.agents.items()
+                }
+                
+                # Collect task states
+                task_states = {
+                    task_id: {
+                        "state": task.state,
+                        "assigned_to": task.assigned_to,
+                        "deadline": task.deadline
+                    }
+                    for task_id, task in self.tasks.items()
+                }
+                
+                # Update global state
+                self.global_state = {
+                    "timestamp": datetime.now(),
+                    "agents": agent_states,
+                    "tasks": task_states,
+                    "resource_usage": self._get_resource_usage(),
+                    "performance_metrics": self._calculate_performance_metrics()
+                }
+                
+                # Archive state
+                self.state_history.append(self.global_state.copy())
+                
+                # Trim history if too long
+                if len(self.state_history) > 1000:
+                    self.state_history = self.state_history[-1000:]
+                
+                # Check for anomalies
+                await self._check_anomalies()
+                
+                await asyncio.sleep(1)  # Monitor frequency
+                
+            except Exception as e:
+                self.logger.error(f"Error in system monitoring: {e}")
+                await self._handle_error("monitoring_error", e)
+
+    def _get_resource_usage(self) -> Dict[str, float]:
+        """Get current resource usage statistics."""
+        return {
+            "cpu_usage": sum(agent.load for agent in self.agents.values()) / len(self.agents),
+            "memory_usage": len(self.state_history) * 1000,  # Rough estimate
+            "queue_size": self.message_queue.qsize()
+        }
+
+    def _calculate_performance_metrics(self) -> Dict[str, float]:
+        """Calculate current performance metrics."""
+        metrics = {}
+        
+        # Task completion rate
+        completed_tasks = sum(1 for task in self.tasks.values() if task.state == "completed")
+        total_tasks = len(self.tasks)
+        metrics["task_completion_rate"] = completed_tasks / max(1, total_tasks)
+        
+        # Average task duration
+        durations = []
+        for task in self.tasks.values():
+            if task.state == "completed" and "completion_time" in task.metadata:
+                duration = (task.metadata["completion_time"] - task.created_at).total_seconds()
+                durations.append(duration)
+        metrics["avg_task_duration"] = sum(durations) / len(durations) if durations else 0
+        
+        # Agent utilization
+        metrics["agent_utilization"] = sum(agent.load for agent in self.agents.values()) / len(self.agents)
+        
+        return metrics
+
+    async def _check_anomalies(self):
+        """Check for system anomalies."""
+        # Check for overloaded agents
+        for agent_id, agent in self.agents.items():
+            if agent.load > 0.9:  # 90% load threshold
+                await self._handle_overload(agent_id)
+        
+        # Check for stalled tasks
+        now = datetime.now()
+        for task_id, task in self.tasks.items():
+            if task.state == "assigned":
+                duration = (now - task.created_at).total_seconds()
+                if duration > 3600:  # 1 hour threshold
+                    await self._handle_stalled_task(task_id)
+        
+        # Check for missed deadlines
+        for task_id, task in self.tasks.items():
+            if task.deadline and now > task.deadline and task.state != "completed":
+                await self._handle_missed_deadline(task_id)
+
+    async def _handle_overload(self, agent_id: str):
+        """Handle an overloaded agent."""
+        agent = self.agents[agent_id]
+        
+        # Try to redistribute tasks
+        assigned_tasks = [
+            task_id for task_id, task in self.tasks.items()
+            if task.assigned_to == agent_id and task.state == "assigned"
+        ]
+        
+        for task_id in assigned_tasks:
+            # Find another suitable agent
+            new_agent_id = await self._find_suitable_agent(self.tasks[task_id])
+            if new_agent_id:
+                await self._reassign_task(task_id, new_agent_id)
+
+    async def _handle_stalled_task(self, task_id: str):
+        """Handle a stalled task."""
+        task = self.tasks[task_id]
+        
+        # First, try to ping the assigned agent
+        if task.assigned_to:
+            agent = self.agents[task.assigned_to]
+            if agent.state == AgentState.ERROR:
+                # Agent is in error state, reassign task
+                await self._reassign_task(task_id, None)
+            else:
+                # Request status update from agent
+                await self.message_queue.put({
+                    "type": "status_request",
+                    "task_id": task_id,
+                    "agent_id": task.assigned_to,
+                    "timestamp": datetime.now()
+                })
+
+    async def _handle_missed_deadline(self, task_id: str):
+        """Handle a missed deadline."""
+        task = self.tasks[task_id]
+        
+        # Log the incident
+        self.logger.warning(f"Task {task_id} missed deadline: {task.deadline}")
+        
+        # Update task priority to CRITICAL
+        task.priority = TaskPriority.CRITICAL
+        
+        # If task is assigned, try to speed it up
+        if task.assigned_to:
+            await self.message_queue.put({
+                "type": "expedite_request",
+                "task_id": task_id,
+                "agent_id": task.assigned_to,
+                "timestamp": datetime.now()
+            })
+        else:
+            # If not assigned, try to assign to fastest available agent
+            await self._plan_task_execution(task_id)
+
+    async def _reassign_task(self, task_id: str, new_agent_id: Optional[str] = None):
+        """Reassign a task to a new agent."""
+        task = self.tasks[task_id]
+        old_agent_id = task.assigned_to
+        
+        if old_agent_id:
+            # Update old agent
+            old_agent = self.agents[old_agent_id]
+            old_agent.load -= 1
+            if old_agent.load <= 0:
+                old_agent.state = AgentState.IDLE
+        
+        if new_agent_id is None:
+            # Find new suitable agent
+            new_agent_id = await self._find_suitable_agent(task)
+        
+        if new_agent_id:
+            # Assign to new agent
+            await self._assign_task(task_id, new_agent_id)
+        else:
+            # No suitable agent found, mark task as pending
+            task.state = "pending"
+            task.assigned_to = None
+
+    def _register_error_handlers(self):
+        """Register basic error handlers."""
+        self.error_handlers.update({
+            "monitoring_error": self._handle_monitoring_error,
+            "agent_error": self._handle_agent_error,
+            "task_error": self._handle_task_error,
+            "resource_error": self._handle_resource_error
+        })
+        
+        self.recovery_strategies.update({
+            "agent_recovery": self._recover_agent,
+            "task_recovery": self._recover_task,
+            "resource_recovery": self._recover_resource
+        })
+
+    async def _handle_error(self, error_type: str, error: Exception):
+        """Handle an error using registered handlers."""
+        handler = self.error_handlers.get(error_type)
+        if handler:
+            try:
+                await handler(error)
+            except Exception as e:
+                self.logger.error(f"Error in error handler: {e}")
+        else:
+            self.logger.error(f"No handler for error type: {error_type}")
+            self.logger.error(f"Error: {error}")
+
+    async def _handle_monitoring_error(self, error: Exception):
+        """Handle monitoring system errors."""
+        self.logger.error(f"Monitoring error: {error}")
+        # Implement recovery logic
+        pass
+
+    async def _handle_agent_error(self, error: Exception):
+        """Handle agent-related errors."""
+        self.logger.error(f"Agent error: {error}")
+        # Implement recovery logic
+        pass
+
+    async def _handle_task_error(self, error: Exception):
+        """Handle task-related errors."""
+        self.logger.error(f"Task error: {error}")
+        # Implement recovery logic
+        pass
+
+    async def _handle_resource_error(self, error: Exception):
+        """Handle resource-related errors."""
+        self.logger.error(f"Resource error: {error}")
+        # Implement recovery logic
+        pass

quick_check.py

@@ -0,0 +1,53 @@
+import requests
+import os
+import time
+from datetime import datetime
+import sys
+
+def check_space():
+    url = "https://huggingface.co/api/spaces/nananie143/Agentic_llm"
+    headers = {"Authorization": f"Bearer {os.environ['HUGGINGFACE_TOKEN']}"}
+    
+    try:
+        response = requests.get(url, headers=headers)
+        if response.ok:
+            data = response.json()
+            runtime = data.get('runtime', {})
+            stage = runtime.get('stage', 'UNKNOWN')
+            hardware = runtime.get('hardware', {})
+            domains = runtime.get('domains', [{}])[0]
+            
+            status_time = datetime.now().strftime('%H:%M:%S')
+            sys.stdout.write(f"\n[{status_time}] Space Status:\n")
+            sys.stdout.write(f"Stage: {stage}\n")
+            sys.stdout.write(f"Hardware: {hardware.get('current', 'Not assigned')} (requested: {hardware.get('requested', 'None')})\n")
+            sys.stdout.write(f"Domain: {domains.get('domain', 'Not assigned')} (status: {domains.get('stage', 'Unknown')})\n")
+            sys.stdout.flush()
+            
+            if stage == "RUNNING":
+                sys.stdout.write("\n🚀 Space is now running!\n")
+                sys.stdout.write(f"Access your Space at: https://{domains.get('domain', 'nananie143-agentic-llm.hf.space')}\n")
+                sys.stdout.flush()
+                return True
+            elif stage == "FAILED":
+                sys.stdout.write("\n❌ Space build failed!\n")
+                sys.stdout.flush()
+                return True
+            
+            return False
+            
+    except Exception as e:
+        sys.stdout.write(f"\n[{datetime.now().strftime('%H:%M:%S')}] Error checking status: {e}\n")
+        sys.stdout.flush()
+        return False
+
+sys.stdout.write("\nStarting automatic Space status check...\n")
+sys.stdout.write("Will check every 2 minutes until the Space is running or fails...\n")
+sys.stdout.write("Press Ctrl+C to stop checking\n\n")
+sys.stdout.flush()
+
+while True:
+    should_stop = check_space()
+    if should_stop:
+        break
+    time.sleep(120)  # Wait 2 minutes

reasoning/__init__.py

@@ -0,0 +1,70 @@
+"""
+Advanced Reasoning Engine for Multi-Model System
+---------------------------------------------
+A highly sophisticated reasoning system combining:
+
+Core Reasoning:
+1. Chain of Thought (CoT)
+2. Tree of Thoughts (ToT)
+3. Graph of Thoughts (GoT)
+4. Recursive Reasoning
+5. Analogical Reasoning
+6. Meta-Learning
+
+Advanced Reasoning:
+7. Neurosymbolic Reasoning
+8. Counterfactual Reasoning
+9. State Space Search
+10. Probabilistic Reasoning
+11. Causal Inference
+12. Temporal Reasoning
+
+Learning & Adaptation:
+13. Online Learning
+14. Transfer Learning
+15. Meta-Learning
+16. Active Learning
+
+Robustness Features:
+17. Uncertainty Quantification
+18. Error Recovery
+19. Consistency Checking
+20. Bias Detection
+"""
+
+from .base import ReasoningStrategy
+from .multimodal import MultiModalReasoning
+from .bayesian import BayesianReasoning
+from .quantum import QuantumReasoning, QuantumInspiredStrategy
+from .neurosymbolic import NeurosymbolicReasoning
+from .emergent import EmergentReasoning
+from .meta import MetaLearningStrategy
+from .chain_of_thought import ChainOfThoughtStrategy
+from .tree_of_thoughts import TreeOfThoughtsStrategy
+from .recursive import RecursiveReasoning
+from .analogical import AnalogicalReasoning
+from .causal import CausalReasoning
+from .state_space import StateSpaceSearch
+from .counterfactual import CounterfactualReasoning
+from .meta_reasoning import MetaReasoning
+from .engine import BavePantherReasoning
+
+__all__ = [
+    'ReasoningStrategy',
+    'MultiModalReasoning',
+    'BayesianReasoning',
+    'QuantumReasoning',
+    'QuantumInspiredStrategy',
+    'NeurosymbolicReasoning',
+    'EmergentReasoning',
+    'MetaLearningStrategy',
+    'ChainOfThoughtStrategy',
+    'TreeOfThoughtsStrategy',
+    'RecursiveReasoning',
+    'AnalogicalReasoning',
+    'CausalReasoning',
+    'StateSpaceSearch',
+    'CounterfactualReasoning',
+    'MetaReasoning',
+    'BavePantherReasoning'
+]

reasoning/agentic.py

@@ -0,0 +1,345 @@
+"""Specialized reasoning strategies for Agentic Workflow."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Tuple
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import asyncio
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+
+class TaskType(Enum):
+    """Types of tasks in agentic workflow."""
+    CODE_GENERATION = "code_generation"
+    CODE_MODIFICATION = "code_modification"
+    CODE_REVIEW = "code_review"
+    DEBUGGING = "debugging"
+    ARCHITECTURE = "architecture"
+    OPTIMIZATION = "optimization"
+    DOCUMENTATION = "documentation"
+    TESTING = "testing"
+
+class ResourceType(Enum):
+    """Types of resources in agentic workflow."""
+    CODE_CONTEXT = "code_context"
+    SYSTEM_CONTEXT = "system_context"
+    USER_CONTEXT = "user_context"
+    TOOLS = "tools"
+    APIS = "apis"
+    DOCUMENTATION = "documentation"
+    DEPENDENCIES = "dependencies"
+    HISTORY = "history"
+
+@dataclass
+class TaskComponent:
+    """Component of a decomposed task."""
+    id: str
+    type: TaskType
+    description: str
+    dependencies: List[str]
+    resources: Dict[ResourceType, Any]
+    constraints: List[str]
+    priority: float
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+@dataclass
+class ResourceAllocation:
+    """Resource allocation for a task."""
+    resource_type: ResourceType
+    quantity: Union[int, float]
+    priority: float
+    constraints: List[str]
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+@dataclass
+class ExecutionStep:
+    """Step in task execution."""
+    id: str
+    task_id: str
+    action: str
+    resources: Dict[ResourceType, Any]
+    status: str
+    result: Optional[Dict[str, Any]]
+    feedback: List[str]
+    timestamp: datetime = field(default_factory=datetime.now)
+
+class TaskDecompositionStrategy(ReasoningStrategy):
+    """
+    Advanced task decomposition strategy that:
+    1. Analyzes task complexity and dependencies
+    2. Breaks down tasks into manageable components
+    3. Identifies resource requirements
+    4. Establishes execution order
+    5. Manages constraints and priorities
+    """
+    
+    def __init__(self, max_components: int = 10):
+        self.max_components = max_components
+        self.components: Dict[str, TaskComponent] = {}
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Decompose task into components."""
+        try:
+            # Analyze task
+            task_analysis = await self._analyze_task(query, context)
+            
+            # Generate components
+            components = await self._generate_components(task_analysis, context)
+            
+            # Establish dependencies
+            dependency_graph = await self._establish_dependencies(components, context)
+            
+            # Determine execution order
+            execution_order = await self._determine_execution_order(
+                components, dependency_graph, context)
+            
+            return {
+                "success": True,
+                "components": [self._component_to_dict(c) for c in components],
+                "dependency_graph": dependency_graph,
+                "execution_order": execution_order,
+                "metadata": {
+                    "total_components": len(components),
+                    "complexity_score": task_analysis.get("complexity_score", 0.0),
+                    "resource_requirements": task_analysis.get("resource_requirements", {})
+                }
+            }
+        except Exception as e:
+            logging.error(f"Error in task decomposition: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class ResourceManagementStrategy(ReasoningStrategy):
+    """
+    Advanced resource management strategy that:
+    1. Tracks available resources
+    2. Allocates resources to tasks
+    3. Handles resource constraints
+    4. Optimizes resource utilization
+    5. Manages resource dependencies
+    """
+    
+    def __init__(self):
+        self.allocations: Dict[str, ResourceAllocation] = {}
+        self.utilization_history: List[Dict[str, Any]] = []
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Manage resource allocation."""
+        try:
+            # Analyze resource requirements
+            requirements = await self._analyze_requirements(query, context)
+            
+            # Check resource availability
+            availability = await self._check_availability(requirements, context)
+            
+            # Generate allocation plan
+            allocation_plan = await self._generate_allocation_plan(
+                requirements, availability, context)
+            
+            # Optimize allocations
+            optimized_plan = await self._optimize_allocations(allocation_plan, context)
+            
+            return {
+                "success": True,
+                "allocation_plan": optimized_plan,
+                "resource_metrics": {
+                    "utilization": self._calculate_utilization(),
+                    "efficiency": self._calculate_efficiency(),
+                    "constraints_satisfied": self._check_constraints(optimized_plan)
+                }
+            }
+        except Exception as e:
+            logging.error(f"Error in resource management: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class ContextualPlanningStrategy(ReasoningStrategy):
+    """
+    Advanced contextual planning strategy that:
+    1. Analyzes multiple context types
+    2. Generates context-aware plans
+    3. Handles context changes
+    4. Maintains context consistency
+    5. Optimizes for context constraints
+    """
+    
+    def __init__(self):
+        self.context_history: List[Dict[str, Any]] = []
+        self.plan_adaptations: List[Dict[str, Any]] = []
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate context-aware plan."""
+        try:
+            # Analyze contexts
+            context_analysis = await self._analyze_contexts(query, context)
+            
+            # Generate base plan
+            base_plan = await self._generate_base_plan(context_analysis, context)
+            
+            # Adapt to contexts
+            adapted_plan = await self._adapt_to_contexts(base_plan, context_analysis)
+            
+            # Validate plan
+            validation = await self._validate_plan(adapted_plan, context)
+            
+            return {
+                "success": True,
+                "plan": adapted_plan,
+                "context_impact": context_analysis.get("impact_assessment", {}),
+                "adaptations": self.plan_adaptations,
+                "validation_results": validation
+            }
+        except Exception as e:
+            logging.error(f"Error in contextual planning: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class AdaptiveExecutionStrategy(ReasoningStrategy):
+    """
+    Advanced adaptive execution strategy that:
+    1. Monitors execution progress
+    2. Adapts to changes and feedback
+    3. Handles errors and exceptions
+    4. Optimizes execution flow
+    5. Maintains execution state
+    """
+    
+    def __init__(self):
+        self.execution_steps: List[ExecutionStep] = []
+        self.adaptation_history: List[Dict[str, Any]] = []
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Execute task adaptively."""
+        try:
+            # Initialize execution
+            execution_state = await self._initialize_execution(query, context)
+            
+            # Monitor and adapt
+            while not self._is_execution_complete(execution_state):
+                # Execute step
+                step_result = await self._execute_step(execution_state, context)
+                
+                # Process feedback
+                feedback = await self._process_feedback(step_result, context)
+                
+                # Adapt execution
+                execution_state = await self._adapt_execution(
+                    execution_state, feedback, context)
+                
+                # Record step
+                self._record_step(step_result, feedback)
+            
+            return {
+                "success": True,
+                "execution_trace": [self._step_to_dict(s) for s in self.execution_steps],
+                "adaptations": self.adaptation_history,
+                "final_state": execution_state
+            }
+        except Exception as e:
+            logging.error(f"Error in adaptive execution: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class FeedbackIntegrationStrategy(ReasoningStrategy):
+    """
+    Advanced feedback integration strategy that:
+    1. Collects multiple types of feedback
+    2. Analyzes feedback patterns
+    3. Generates improvement suggestions
+    4. Tracks feedback implementation
+    5. Measures feedback impact
+    """
+    
+    def __init__(self):
+        self.feedback_history: List[Dict[str, Any]] = []
+        self.improvement_history: List[Dict[str, Any]] = []
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Integrate and apply feedback."""
+        try:
+            # Collect feedback
+            feedback = await self._collect_feedback(query, context)
+            
+            # Analyze patterns
+            patterns = await self._analyze_patterns(feedback, context)
+            
+            # Generate improvements
+            improvements = await self._generate_improvements(patterns, context)
+            
+            # Implement changes
+            implementation = await self._implement_improvements(improvements, context)
+            
+            # Measure impact
+            impact = await self._measure_impact(implementation, context)
+            
+            return {
+                "success": True,
+                "feedback_analysis": patterns,
+                "improvements": improvements,
+                "implementation_status": implementation,
+                "impact_metrics": impact
+            }
+        except Exception as e:
+            logging.error(f"Error in feedback integration: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def _collect_feedback(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """Collect feedback from multiple sources."""
+        prompt = f"""
+        Collect feedback from:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        Consider:
+        1. User feedback
+        2. System metrics
+        3. Code analysis
+        4. Performance data
+        5. Error patterns
+        
+        Format as:
+        [Feedback]
+        Source: ...
+        Type: ...
+        Content: ...
+        Priority: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_feedback(response["answer"])
+
+    def _parse_feedback(self, response: str) -> List[Dict[str, Any]]:
+        """Parse feedback from response."""
+        feedback_items = []
+        current = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[Feedback]'):
+                if current:
+                    feedback_items.append(current)
+                current = {
+                    "source": "",
+                    "type": "",
+                    "content": "",
+                    "priority": 0.0
+                }
+            elif current:
+                if line.startswith('Source:'):
+                    current["source"] = line[7:].strip()
+                elif line.startswith('Type:'):
+                    current["type"] = line[5:].strip()
+                elif line.startswith('Content:'):
+                    current["content"] = line[8:].strip()
+                elif line.startswith('Priority:'):
+                    try:
+                        current["priority"] = float(line[9:].strip())
+                    except:
+                        pass
+        
+        if current:
+            feedback_items.append(current)
+        
+        return feedback_items

reasoning/analogical.py

@@ -0,0 +1,600 @@
+"""Analogical reasoning implementation with advanced pattern matching and transfer learning."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Tuple, Callable
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import numpy as np
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+
+class AnalogicalLevel(Enum):
+    """Levels of analogical similarity."""
+    SURFACE = "surface"
+    STRUCTURAL = "structural"
+    SEMANTIC = "semantic"
+    FUNCTIONAL = "functional"
+    CAUSAL = "causal"
+    ABSTRACT = "abstract"
+
+class MappingType(Enum):
+    """Types of analogical mappings."""
+    DIRECT = "direct"
+    TRANSFORMED = "transformed"
+    COMPOSITE = "composite"
+    ABSTRACT = "abstract"
+    METAPHORICAL = "metaphorical"
+    HYBRID = "hybrid"
+
+@dataclass
+class AnalogicalPattern:
+    """Represents a pattern for analogical matching."""
+    id: str
+    level: AnalogicalLevel
+    features: Dict[str, Any]
+    relations: List[Tuple[str, str, str]]  # (entity1, relation, entity2)
+    constraints: List[str]
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+@dataclass
+class AnalogicalMapping:
+    """Represents a mapping between source and target domains."""
+    id: str
+    type: MappingType
+    source_elements: Dict[str, Any]
+    target_elements: Dict[str, Any]
+    correspondences: List[Tuple[str, str, float]]  # (source, target, strength)
+    transformations: List[Dict[str, Any]]
+    confidence: float
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+@dataclass
+class AnalogicalSolution:
+    """Represents a solution derived through analogical reasoning."""
+    id: str
+    source_analogy: str
+    mapping: AnalogicalMapping
+    adaptation: Dict[str, Any]
+    inference: Dict[str, Any]
+    confidence: float
+    validation: Dict[str, Any]
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+class AnalogicalReasoning(ReasoningStrategy):
+    """
+    Advanced Analogical Reasoning implementation with:
+    - Multi-level pattern matching
+    - Sophisticated similarity metrics
+    - Transfer learning capabilities
+    - Dynamic adaptation mechanisms
+    - Quality assessment
+    - Learning from experience
+    """
+    
+    def __init__(self,
+                 min_similarity: float = 0.6,
+                 max_candidates: int = 5,
+                 adaptation_threshold: float = 0.7,
+                 learning_rate: float = 0.1):
+        self.min_similarity = min_similarity
+        self.max_candidates = max_candidates
+        self.adaptation_threshold = adaptation_threshold
+        self.learning_rate = learning_rate
+        
+        # Knowledge base
+        self.patterns: Dict[str, AnalogicalPattern] = {}
+        self.mappings: Dict[str, AnalogicalMapping] = {}
+        self.solutions: Dict[str, AnalogicalSolution] = {}
+        
+        # Learning components
+        self.pattern_weights: Dict[str, float] = defaultdict(float)
+        self.success_history: List[Dict[str, Any]] = []
+        self.adaptation_history: List[Dict[str, Any]] = []
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Main reasoning method implementing analogical reasoning."""
+        try:
+            # Extract patterns from query
+            patterns = await self._extract_patterns(query, context)
+            
+            # Find analogical matches
+            matches = await self._find_matches(patterns, context)
+            
+            # Create and evaluate mappings
+            mappings = await self._create_mappings(matches, context)
+            
+            # Generate and adapt solutions
+            solutions = await self._generate_solutions(mappings, context)
+            
+            # Select best solution
+            best_solution = await self._select_best_solution(solutions, context)
+            
+            # Learn from experience
+            self._update_knowledge(patterns, mappings, best_solution)
+            
+            return {
+                "success": True,
+                "answer": best_solution.inference["conclusion"],
+                "confidence": best_solution.confidence,
+                "analogy": {
+                    "source": best_solution.source_analogy,
+                    "mapping": self._mapping_to_dict(best_solution.mapping),
+                    "adaptation": best_solution.adaptation
+                },
+                "reasoning_trace": best_solution.metadata.get("reasoning_trace", []),
+                "meta_insights": best_solution.metadata.get("meta_insights", [])
+            }
+        except Exception as e:
+            logging.error(f"Error in analogical reasoning: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def _extract_patterns(self, query: str, context: Dict[str, Any]) -> List[AnalogicalPattern]:
+        """Extract patterns from query for analogical matching."""
+        prompt = f"""
+        Extract analogical patterns from query:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        For each pattern level:
+        1. Surface features
+        2. Structural relations
+        3. Semantic concepts
+        4. Functional roles
+        5. Causal relationships
+        6. Abstract principles
+        
+        Format as:
+        [P1]
+        Level: ...
+        Features: ...
+        Relations: ...
+        Constraints: ...
+        
+        [P2]
+        ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_patterns(response["answer"])
+
+    async def _find_matches(self, patterns: List[AnalogicalPattern], context: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """Find matching patterns in knowledge base."""
+        prompt = f"""
+        Find analogical matches:
+        Patterns: {json.dumps([self._pattern_to_dict(p) for p in patterns])}
+        Context: {json.dumps(context)}
+        
+        For each match provide:
+        1. Source domain
+        2. Similarity assessment
+        3. Key correspondences
+        4. Transfer potential
+        
+        Format as:
+        [M1]
+        Source: ...
+        Similarity: ...
+        Correspondences: ...
+        Transfer: ...
+        
+        [M2]
+        ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_matches(response["answer"])
+
+    async def _create_mappings(self, matches: List[Dict[str, Any]], context: Dict[str, Any]) -> List[AnalogicalMapping]:
+        """Create mappings between source and target domains."""
+        prompt = f"""
+        Create analogical mappings:
+        Matches: {json.dumps(matches)}
+        Context: {json.dumps(context)}
+        
+        For each mapping specify:
+        1. [Type]: {" | ".join([t.value for t in MappingType])}
+        2. [Elements]: Source and target elements
+        3. [Correspondences]: Element mappings
+        4. [Transformations]: Required adaptations
+        5. [Confidence]: Mapping strength
+        
+        Format as:
+        [Map1]
+        Type: ...
+        Elements: ...
+        Correspondences: ...
+        Transformations: ...
+        Confidence: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_mappings(response["answer"])
+
+    async def _generate_solutions(self, mappings: List[AnalogicalMapping], context: Dict[str, Any]) -> List[AnalogicalSolution]:
+        """Generate solutions through analogical transfer."""
+        prompt = f"""
+        Generate analogical solutions:
+        Mappings: {json.dumps([self._mapping_to_dict(m) for m in mappings])}
+        Context: {json.dumps(context)}
+        
+        For each solution provide:
+        1. Analogical inference
+        2. Required adaptations
+        3. Validation criteria
+        4. Confidence assessment
+        5. Reasoning trace
+        
+        Format as:
+        [S1]
+        Inference: ...
+        Adaptation: ...
+        Validation: ...
+        Confidence: ...
+        Trace: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_solutions(response["answer"], mappings)
+
+    async def _select_best_solution(self, solutions: List[AnalogicalSolution], context: Dict[str, Any]) -> AnalogicalSolution:
+        """Select the best solution based on multiple criteria."""
+        prompt = f"""
+        Evaluate and select best solution:
+        Solutions: {json.dumps([self._solution_to_dict(s) for s in solutions])}
+        Context: {json.dumps(context)}
+        
+        Evaluate based on:
+        1. Inference quality
+        2. Adaptation feasibility
+        3. Validation strength
+        4. Overall confidence
+        
+        Format as:
+        [Evaluation]
+        Rankings: ...
+        Rationale: ...
+        Selection: ...
+        Confidence: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        selection = self._parse_selection(response["answer"])
+        
+        # Find selected solution
+        selected = max(solutions, key=lambda s: s.confidence)
+        for solution in solutions:
+            if solution.id == selection.get("selected_id"):
+                selected = solution
+                break
+        
+        return selected
+
+    def _update_knowledge(self, patterns: List[AnalogicalPattern], mappings: List[AnalogicalMapping], solution: AnalogicalSolution):
+        """Update knowledge base with new patterns and successful mappings."""
+        # Update patterns
+        for pattern in patterns:
+            if pattern.id not in self.patterns:
+                self.patterns[pattern.id] = pattern
+            self.pattern_weights[pattern.id] += self.learning_rate * solution.confidence
+        
+        # Update mappings
+        if solution.mapping.id not in self.mappings:
+            self.mappings[solution.mapping.id] = solution.mapping
+        
+        # Record solution
+        self.solutions[solution.id] = solution
+        
+        # Update history
+        self.success_history.append({
+            "timestamp": datetime.now().isoformat(),
+            "solution_id": solution.id,
+            "confidence": solution.confidence,
+            "patterns": [p.id for p in patterns],
+            "mapping_type": solution.mapping.type.value
+        })
+        
+        # Update adaptation history
+        self.adaptation_history.append({
+            "timestamp": datetime.now().isoformat(),
+            "solution_id": solution.id,
+            "adaptations": solution.adaptation,
+            "success": solution.confidence >= self.adaptation_threshold
+        })
+
+    def _parse_patterns(self, response: str) -> List[AnalogicalPattern]:
+        """Parse patterns from response."""
+        patterns = []
+        current = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[P'):
+                if current:
+                    patterns.append(current)
+                current = None
+            elif line.startswith('Level:'):
+                level_str = line[6:].strip().lower()
+                try:
+                    level = AnalogicalLevel(level_str)
+                    current = AnalogicalPattern(
+                        id=f"pattern_{len(patterns)}",
+                        level=level,
+                        features={},
+                        relations=[],
+                        constraints=[],
+                        metadata={}
+                    )
+                except ValueError:
+                    logging.warning(f"Invalid analogical level: {level_str}")
+            elif current:
+                if line.startswith('Features:'):
+                    try:
+                        current.features = json.loads(line[9:].strip())
+                    except:
+                        current.features = {"raw": line[9:].strip()}
+                elif line.startswith('Relations:'):
+                    relations = [r.strip() for r in line[10:].split(',')]
+                    current.relations = [(r.split()[0], r.split()[1], r.split()[2]) 
+                                      for r in relations if len(r.split()) >= 3]
+                elif line.startswith('Constraints:'):
+                    current.constraints = [c.strip() for c in line[12:].split(',')]
+        
+        if current:
+            patterns.append(current)
+        
+        return patterns
+
+    def _parse_matches(self, response: str) -> List[Dict[str, Any]]:
+        """Parse matches from response."""
+        matches = []
+        current = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[M'):
+                if current:
+                    matches.append(current)
+                current = {
+                    "source": "",
+                    "similarity": 0.0,
+                    "correspondences": [],
+                    "transfer": []
+                }
+            elif current:
+                if line.startswith('Source:'):
+                    current["source"] = line[7:].strip()
+                elif line.startswith('Similarity:'):
+                    try:
+                        current["similarity"] = float(line[11:].strip())
+                    except:
+                        pass
+                elif line.startswith('Correspondences:'):
+                    current["correspondences"] = [c.strip() for c in line[16:].split(',')]
+                elif line.startswith('Transfer:'):
+                    current["transfer"] = [t.strip() for t in line[9:].split(',')]
+        
+        if current:
+            matches.append(current)
+        
+        return matches
+
+    def _parse_mappings(self, response: str) -> List[AnalogicalMapping]:
+        """Parse mappings from response."""
+        mappings = []
+        current = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[Map'):
+                if current:
+                    mappings.append(current)
+                current = None
+            elif line.startswith('Type:'):
+                type_str = line[5:].strip().lower()
+                try:
+                    mapping_type = MappingType(type_str)
+                    current = AnalogicalMapping(
+                        id=f"mapping_{len(mappings)}",
+                        type=mapping_type,
+                        source_elements={},
+                        target_elements={},
+                        correspondences=[],
+                        transformations=[],
+                        confidence=0.0,
+                        metadata={}
+                    )
+                except ValueError:
+                    logging.warning(f"Invalid mapping type: {type_str}")
+            elif current:
+                if line.startswith('Elements:'):
+                    try:
+                        elements = json.loads(line[9:].strip())
+                        current.source_elements = elements.get("source", {})
+                        current.target_elements = elements.get("target", {})
+                    except:
+                        pass
+                elif line.startswith('Correspondences:'):
+                    pairs = [c.strip() for c in line[16:].split(',')]
+                    for pair in pairs:
+                        parts = pair.split(':')
+                        if len(parts) >= 2:
+                            source = parts[0].strip()
+                            target = parts[1].strip()
+                            strength = float(parts[2]) if len(parts) > 2 else 1.0
+                            current.correspondences.append((source, target, strength))
+                elif line.startswith('Transformations:'):
+                    try:
+                        current.transformations = json.loads(line[16:].strip())
+                    except:
+                        current.transformations = [{"raw": line[16:].strip()}]
+                elif line.startswith('Confidence:'):
+                    try:
+                        current.confidence = float(line[11:].strip())
+                    except:
+                        pass
+        
+        if current:
+            mappings.append(current)
+        
+        return mappings
+
+    def _parse_solutions(self, response: str, mappings: List[AnalogicalMapping]) -> List[AnalogicalSolution]:
+        """Parse solutions from response."""
+        solutions = []
+        current = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[S'):
+                if current:
+                    solutions.append(current)
+                current = None
+                mapping_idx = len(solutions)
+                if mapping_idx < len(mappings):
+                    current = AnalogicalSolution(
+                        id=f"solution_{len(solutions)}",
+                        source_analogy="",
+                        mapping=mappings[mapping_idx],
+                        adaptation={},
+                        inference={},
+                        confidence=0.0,
+                        validation={},
+                        metadata={}
+                    )
+            elif current:
+                if line.startswith('Inference:'):
+                    try:
+                        current.inference = json.loads(line[10:].strip())
+                    except:
+                        current.inference = {"conclusion": line[10:].strip()}
+                elif line.startswith('Adaptation:'):
+                    try:
+                        current.adaptation = json.loads(line[11:].strip())
+                    except:
+                        current.adaptation = {"steps": [line[11:].strip()]}
+                elif line.startswith('Validation:'):
+                    try:
+                        current.validation = json.loads(line[11:].strip())
+                    except:
+                        current.validation = {"criteria": [line[11:].strip()]}
+                elif line.startswith('Confidence:'):
+                    try:
+                        current.confidence = float(line[11:].strip())
+                    except:
+                        pass
+                elif line.startswith('Trace:'):
+                    current.metadata["reasoning_trace"] = [t.strip() for t in line[6:].split(',')]
+        
+        if current:
+            solutions.append(current)
+        
+        return solutions
+
+    def _parse_selection(self, response: str) -> Dict[str, Any]:
+        """Parse solution selection from response."""
+        selection = {
+            "selected_id": None,
+            "confidence": 0.0,
+            "rationale": []
+        }
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if line.startswith('Selection:'):
+                selection["selected_id"] = line[10:].strip()
+            elif line.startswith('Confidence:'):
+                try:
+                    selection["confidence"] = float(line[11:].strip())
+                except:
+                    pass
+            elif line.startswith('Rationale:'):
+                selection["rationale"] = [r.strip() for r in line[10:].split(',')]
+        
+        return selection
+
+    def _pattern_to_dict(self, pattern: AnalogicalPattern) -> Dict[str, Any]:
+        """Convert pattern to dictionary for serialization."""
+        return {
+            "id": pattern.id,
+            "level": pattern.level.value,
+            "features": pattern.features,
+            "relations": pattern.relations,
+            "constraints": pattern.constraints,
+            "metadata": pattern.metadata
+        }
+
+    def _mapping_to_dict(self, mapping: AnalogicalMapping) -> Dict[str, Any]:
+        """Convert mapping to dictionary for serialization."""
+        return {
+            "id": mapping.id,
+            "type": mapping.type.value,
+            "source_elements": mapping.source_elements,
+            "target_elements": mapping.target_elements,
+            "correspondences": mapping.correspondences,
+            "transformations": mapping.transformations,
+            "confidence": mapping.confidence,
+            "metadata": mapping.metadata
+        }
+
+    def _solution_to_dict(self, solution: AnalogicalSolution) -> Dict[str, Any]:
+        """Convert solution to dictionary for serialization."""
+        return {
+            "id": solution.id,
+            "source_analogy": solution.source_analogy,
+            "mapping": self._mapping_to_dict(solution.mapping),
+            "adaptation": solution.adaptation,
+            "inference": solution.inference,
+            "confidence": solution.confidence,
+            "validation": solution.validation,
+            "metadata": solution.metadata
+        }
+
+    def get_pattern_statistics(self) -> Dict[str, Any]:
+        """Get statistics about pattern usage and effectiveness."""
+        return {
+            "total_patterns": len(self.patterns),
+            "level_distribution": defaultdict(int, {p.level.value: 1 for p in self.patterns.values()}),
+            "average_constraints": sum(len(p.constraints) for p in self.patterns.values()) / len(self.patterns) if self.patterns else 0,
+            "pattern_weights": dict(self.pattern_weights)
+        }
+
+    def get_mapping_statistics(self) -> Dict[str, Any]:
+        """Get statistics about mapping effectiveness."""
+        return {
+            "total_mappings": len(self.mappings),
+            "type_distribution": defaultdict(int, {m.type.value: 1 for m in self.mappings.values()}),
+            "average_confidence": sum(m.confidence for m in self.mappings.values()) / len(self.mappings) if self.mappings else 0,
+            "transformation_counts": defaultdict(int, {m.id: len(m.transformations) for m in self.mappings.values()})
+        }
+
+    def get_solution_statistics(self) -> Dict[str, Any]:
+        """Get statistics about solution quality."""
+        return {
+            "total_solutions": len(self.solutions),
+            "average_confidence": sum(s.confidence for s in self.solutions.values()) / len(self.solutions) if self.solutions else 0,
+            "adaptation_success_rate": sum(1 for h in self.adaptation_history if h["success"]) / len(self.adaptation_history) if self.adaptation_history else 0
+        }
+
+    def clear_knowledge_base(self):
+        """Clear the knowledge base."""
+        self.patterns.clear()
+        self.mappings.clear()
+        self.solutions.clear()
+        self.pattern_weights.clear()
+        self.success_history.clear()
+        self.adaptation_history.clear()

reasoning/base.py

@@ -0,0 +1,17 @@
+"""Base class for all reasoning strategies."""
+
+from typing import Dict, Any
+
+class ReasoningStrategy:
+    """Base class for reasoning strategies."""
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Apply reasoning strategy to query with context.
+        
+        Args:
+            query: The query to reason about
+            context: Additional context for reasoning
+            
+        Returns:
+            Dictionary containing reasoning results
+        """
+        raise NotImplementedError

reasoning/bayesian.py

@@ -0,0 +1,187 @@
+"""Bayesian reasoning implementation."""
+
+import logging
+from typing import Dict, Any, List
+import json
+import re
+from datetime import datetime
+
+from .base import ReasoningStrategy
+
+class BayesianReasoning(ReasoningStrategy):
+    """Implements Bayesian reasoning for probabilistic analysis."""
+    
+    def __init__(self, prior_weight: float = 0.3):
+        self.prior_weight = prior_weight
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        try:
+            # Generate hypotheses
+            hypotheses = await self._generate_hypotheses(query, context)
+            
+            # Calculate prior probabilities
+            priors = await self._calculate_priors(hypotheses, context)
+            
+            # Update with evidence
+            posteriors = await self._update_with_evidence(hypotheses, priors, context)
+            
+            # Generate final analysis
+            analysis = await self._generate_analysis(posteriors, context)
+            
+            return {
+                "success": True,
+                "answer": analysis["conclusion"],
+                "hypotheses": hypotheses,
+                "priors": priors,
+                "posteriors": posteriors,
+                "confidence": analysis["confidence"],
+                "reasoning_path": analysis["reasoning_path"]
+            }
+        except Exception as e:
+            return {"success": False, "error": str(e)}
+    
+    async def _generate_hypotheses(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
+        prompt = f"""
+        Generate 3-4 hypotheses for this problem:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        For each hypothesis:
+        1. [Statement]: Clear statement of the hypothesis
+        2. [Assumptions]: Key assumptions made
+        3. [Testability]: How it could be tested/verified
+        
+        Format as:
+        [H1]
+        Statement: ...
+        Assumptions: ...
+        Testability: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_hypotheses(response["answer"])
+
+    async def _calculate_priors(self, hypotheses: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, float]:
+        prompt = f"""
+        Calculate prior probabilities for these hypotheses:
+        Context: {json.dumps(context)}
+        
+        Hypotheses:
+        {json.dumps(hypotheses, indent=2)}
+        
+        For each hypothesis, estimate its prior probability (0-1) based on:
+        1. Alignment with known principles
+        2. Historical precedent
+        3. Domain expertise
+        
+        Format: [H1]: 0.XX, [H2]: 0.XX, ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_probabilities(response["answer"])
+
+    async def _update_with_evidence(self, hypotheses: List[Dict[str, Any]], priors: Dict[str, float], 
+                                  context: Dict[str, Any]) -> Dict[str, float]:
+        prompt = f"""
+        Update probabilities with available evidence:
+        Context: {json.dumps(context)}
+        
+        Hypotheses and Priors:
+        {json.dumps(list(zip(hypotheses, priors.values())), indent=2)}
+        
+        Consider:
+        1. How well each hypothesis explains the evidence
+        2. Any new evidence from the context
+        3. Potential conflicts or support between hypotheses
+        
+        Format: [H1]: 0.XX, [H2]: 0.XX, ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_probabilities(response["answer"])
+
+    async def _generate_analysis(self, posteriors: Dict[str, float], context: Dict[str, Any]) -> Dict[str, Any]:
+        prompt = f"""
+        Generate final Bayesian analysis:
+        Context: {json.dumps(context)}
+        
+        Posterior Probabilities:
+        {json.dumps(posteriors, indent=2)}
+        
+        Provide:
+        1. Main conclusion based on highest probability hypotheses
+        2. Confidence level (0-1)
+        3. Key reasoning steps taken
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_analysis(response["answer"])
+
+    def _parse_hypotheses(self, response: str) -> List[Dict[str, Any]]:
+        """Parse hypotheses from response."""
+        hypotheses = []
+        current = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+            
+            if line.startswith('[H'):
+                if current:
+                    hypotheses.append(current)
+                current = {
+                    "statement": "",
+                    "assumptions": "",
+                    "testability": ""
+                }
+            elif current:
+                if line.startswith('Statement:'):
+                    current["statement"] = line[10:].strip()
+                elif line.startswith('Assumptions:'):
+                    current["assumptions"] = line[12:].strip()
+                elif line.startswith('Testability:'):
+                    current["testability"] = line[12:].strip()
+        
+        if current:
+            hypotheses.append(current)
+        
+        return hypotheses
+
+    def _parse_probabilities(self, response: str) -> Dict[str, float]:
+        """Parse probabilities from response."""
+        probs = {}
+        pattern = r'\[H(\d+)\]:\s*(0\.\d+)'
+        
+        for match in re.finditer(pattern, response):
+            h_num = int(match.group(1))
+            prob = float(match.group(2))
+            probs[f"H{h_num}"] = prob
+        
+        return probs
+
+    def _parse_analysis(self, response: str) -> Dict[str, Any]:
+        """Parse analysis from response."""
+        lines = response.split('\n')
+        analysis = {
+            "conclusion": "",
+            "confidence": 0.0,
+            "reasoning_path": []
+        }
+        
+        for line in lines:
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('Conclusion:'):
+                analysis["conclusion"] = line[11:].strip()
+            elif line.startswith('Confidence:'):
+                try:
+                    analysis["confidence"] = float(line[11:].strip())
+                except:
+                    analysis["confidence"] = 0.5
+            elif line.startswith('- '):
+                analysis["reasoning_path"].append(line[2:].strip())
+        
+        return analysis

reasoning/chain_of_thought.py

@@ -0,0 +1,410 @@
+"""Chain of Thought reasoning implementation with advanced features."""
+
+import logging
+from typing import Dict, Any, List, Optional, Tuple
+import json
+from dataclasses import dataclass
+from enum import Enum
+
+from .base import ReasoningStrategy
+
+class ThoughtType(Enum):
+    """Types of thoughts in the chain."""
+    OBSERVATION = "observation"
+    ANALYSIS = "analysis"
+    HYPOTHESIS = "hypothesis"
+    VERIFICATION = "verification"
+    CONCLUSION = "conclusion"
+    REFLECTION = "reflection"
+    REFINEMENT = "refinement"
+
+@dataclass
+class Thought:
+    """Represents a single thought in the chain."""
+    type: ThoughtType
+    content: str
+    confidence: float
+    evidence: List[str]
+    alternatives: List[str]
+    next_steps: List[str]
+    metadata: Dict[str, Any]
+
+class ChainOfThoughtStrategy(ReasoningStrategy):
+    """
+    Advanced Chain of Thought reasoning implementation with:
+    - Hierarchical thought chains
+    - Confidence scoring
+    - Alternative path exploration
+    - Self-reflection and refinement
+    - Evidence tracking
+    - Meta-learning capabilities
+    """
+    
+    def __init__(self, 
+                 max_chain_length: int = 10,
+                 min_confidence: float = 0.7,
+                 exploration_breadth: int = 3,
+                 enable_reflection: bool = True):
+        self.max_chain_length = max_chain_length
+        self.min_confidence = min_confidence
+        self.exploration_breadth = exploration_breadth
+        self.enable_reflection = enable_reflection
+        self.thought_history: List[Thought] = []
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Main reasoning method implementing chain of thought."""
+        try:
+            # Initialize reasoning chain
+            chain = await self._initialize_chain(query, context)
+            
+            # Generate initial thoughts
+            thoughts = await self._generate_thoughts(query, context)
+            
+            # Build thought chain
+            chain = await self._build_chain(thoughts, context)
+            
+            # Reflect and refine
+            if self.enable_reflection:
+                chain = await self._reflect_and_refine(chain, context)
+            
+            # Extract conclusion
+            conclusion = await self._extract_conclusion(chain, context)
+            
+            # Update thought history
+            self.thought_history.extend(chain)
+            
+            return {
+                "success": True,
+                "answer": conclusion["answer"],
+                "confidence": conclusion["confidence"],
+                "reasoning_chain": [self._thought_to_dict(t) for t in chain],
+                "alternatives": conclusion["alternatives"],
+                "evidence": conclusion["evidence"],
+                "meta_insights": conclusion["meta_insights"]
+            }
+        except Exception as e:
+            logging.error(f"Error in chain of thought reasoning: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def _initialize_chain(self, query: str, context: Dict[str, Any]) -> List[Thought]:
+        """Initialize the thought chain with observations."""
+        prompt = f"""
+        Initialize chain of thought for query:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        Provide initial observations:
+        1. Key elements in query
+        2. Relevant context factors
+        3. Initial hypotheses
+        4. Potential approaches
+        
+        Format as:
+        [O1] Element: ... | Relevance: ... | Confidence: ...
+        [O2] Context: ... | Impact: ... | Confidence: ...
+        [O3] Hypothesis: ... | Support: ... | Confidence: ...
+        [O4] Approach: ... | Rationale: ... | Confidence: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_observations(response["answer"])
+
+    async def _generate_thoughts(self, query: str, context: Dict[str, Any]) -> List[Thought]:
+        """Generate candidate thoughts for the chain."""
+        prompt = f"""
+        Generate thoughts for query analysis:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        For each thought provide:
+        1. [Type]: {" | ".join([t.value for t in ThoughtType])}
+        2. [Content]: Main thought
+        3. [Evidence]: Supporting evidence
+        4. [Alternatives]: Alternative perspectives
+        5. [Next]: Potential next steps
+        6. [Confidence]: 0-1 score
+        
+        Format as:
+        [T1]
+        Type: ...
+        Content: ...
+        Evidence: ...
+        Alternatives: ...
+        Next: ...
+        Confidence: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_thoughts(response["answer"])
+
+    async def _build_chain(self, thoughts: List[Thought], context: Dict[str, Any]) -> List[Thought]:
+        """Build coherent chain from candidate thoughts."""
+        prompt = f"""
+        Build coherent thought chain:
+        Thoughts: {json.dumps([self._thought_to_dict(t) for t in thoughts])}
+        Context: {json.dumps(context)}
+        
+        For each step specify:
+        1. Selected thought
+        2. Reasoning for selection
+        3. Connection to previous
+        4. Expected impact
+        
+        Format as:
+        [S1]
+        Thought: ...
+        Reason: ...
+        Connection: ...
+        Impact: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_chain(response["answer"], thoughts)
+
+    async def _reflect_and_refine(self, chain: List[Thought], context: Dict[str, Any]) -> List[Thought]:
+        """Reflect on and refine the thought chain."""
+        prompt = f"""
+        Reflect on thought chain:
+        Chain: {json.dumps([self._thought_to_dict(t) for t in chain])}
+        Context: {json.dumps(context)}
+        
+        Analyze for:
+        1. Logical gaps
+        2. Weak assumptions
+        3. Missing evidence
+        4. Alternative perspectives
+        
+        Suggest refinements:
+        1. Additional thoughts
+        2. Modified reasoning
+        3. New connections
+        4. Evidence needs
+        
+        Format as:
+        [Analysis]
+        Gaps: ...
+        Assumptions: ...
+        Missing: ...
+        Alternatives: ...
+        
+        [Refinements]
+        Thoughts: ...
+        Reasoning: ...
+        Connections: ...
+        Evidence: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._apply_refinements(chain, response["answer"])
+
+    async def _extract_conclusion(self, chain: List[Thought], context: Dict[str, Any]) -> Dict[str, Any]:
+        """Extract final conclusion from thought chain."""
+        prompt = f"""
+        Extract conclusion from thought chain:
+        Chain: {json.dumps([self._thought_to_dict(t) for t in chain])}
+        Context: {json.dumps(context)}
+        
+        Provide:
+        1. Main conclusion
+        2. Confidence level
+        3. Supporting evidence
+        4. Alternative conclusions
+        5. Meta-insights gained
+        6. Future considerations
+        
+        Format as:
+        [Conclusion]
+        Answer: ...
+        Confidence: ...
+        Evidence: ...
+        Alternatives: ...
+        
+        [Meta]
+        Insights: ...
+        Future: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_conclusion(response["answer"])
+
+    def _parse_observations(self, response: str) -> List[Thought]:
+        """Parse initial observations into thoughts."""
+        observations = []
+        lines = response.split('\n')
+        
+        for line in lines:
+            if line.startswith('[O'):
+                parts = line.split('|')
+                if len(parts) >= 3:
+                    main_part = parts[0].split(']')[1].strip()
+                    key, content = main_part.split(':', 1)
+                    
+                    evidence = [p.strip() for p in parts[1].split(':')[1].strip().split(',')]
+                    
+                    try:
+                        confidence = float(parts[2].split(':')[1].strip())
+                    except:
+                        confidence = 0.5
+                    
+                    observations.append(Thought(
+                        type=ThoughtType.OBSERVATION,
+                        content=content.strip(),
+                        confidence=confidence,
+                        evidence=evidence,
+                        alternatives=[],
+                        next_steps=[],
+                        metadata={"key": key}
+                    ))
+        
+        return observations
+
+    def _parse_thoughts(self, response: str) -> List[Thought]:
+        """Parse generated thoughts."""
+        thoughts = []
+        current = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[T'):
+                if current:
+                    thoughts.append(current)
+                current = None
+            elif line.startswith('Type:'):
+                type_str = line[5:].strip()
+                try:
+                    thought_type = ThoughtType(type_str.lower())
+                    current = Thought(
+                        type=thought_type,
+                        content="",
+                        confidence=0.0,
+                        evidence=[],
+                        alternatives=[],
+                        next_steps=[],
+                        metadata={}
+                    )
+                except ValueError:
+                    logging.warning(f"Invalid thought type: {type_str}")
+            elif current:
+                if line.startswith('Content:'):
+                    current.content = line[8:].strip()
+                elif line.startswith('Evidence:'):
+                    current.evidence = [e.strip() for e in line[9:].split(',')]
+                elif line.startswith('Alternatives:'):
+                    current.alternatives = [a.strip() for a in line[13:].split(',')]
+                elif line.startswith('Next:'):
+                    current.next_steps = [n.strip() for n in line[5:].split(',')]
+                elif line.startswith('Confidence:'):
+                    try:
+                        current.confidence = float(line[11:].strip())
+                    except:
+                        current.confidence = 0.5
+        
+        if current:
+            thoughts.append(current)
+        
+        return thoughts
+
+    def _parse_chain(self, response: str, thoughts: List[Thought]) -> List[Thought]:
+        """Parse and order thoughts into a chain."""
+        chain = []
+        thought_map = {self._thought_to_dict(t)["content"]: t for t in thoughts}
+        
+        for line in response.split('\n'):
+            if line.startswith('Thought:'):
+                content = line[8:].strip()
+                if content in thought_map:
+                    chain.append(thought_map[content])
+        
+        return chain
+
+    def _apply_refinements(self, chain: List[Thought], response: str) -> List[Thought]:
+        """Apply refinements to thought chain."""
+        refined_chain = chain.copy()
+        
+        # Parse refinements
+        sections = response.split('[')
+        for section in sections:
+            if section.startswith('Refinements]'):
+                lines = section.split('\n')[1:]
+                for line in lines:
+                    if line.startswith('Thoughts:'):
+                        new_thoughts = self._parse_refinement_thoughts(line[9:])
+                        refined_chain.extend(new_thoughts)
+        
+        return refined_chain
+
+    def _parse_refinement_thoughts(self, refinements: str) -> List[Thought]:
+        """Parse refinement thoughts."""
+        thoughts = []
+        for refinement in refinements.split(';'):
+            if refinement.strip():
+                thoughts.append(Thought(
+                    type=ThoughtType.REFINEMENT,
+                    content=refinement.strip(),
+                    confidence=0.8,  # Refinements typically have high confidence
+                    evidence=[],
+                    alternatives=[],
+                    next_steps=[],
+                    metadata={"refined": True}
+                ))
+        return thoughts
+
+    def _parse_conclusion(self, response: str) -> Dict[str, Any]:
+        """Parse final conclusion."""
+        conclusion = {
+            "answer": "",
+            "confidence": 0.0,
+            "evidence": [],
+            "alternatives": [],
+            "meta_insights": [],
+            "future_considerations": []
+        }
+        
+        sections = response.split('[')
+        for section in sections:
+            if section.startswith('Conclusion]'):
+                lines = section.split('\n')[1:]
+                for line in lines:
+                    if line.startswith('Answer:'):
+                        conclusion["answer"] = line[7:].strip()
+                    elif line.startswith('Confidence:'):
+                        try:
+                            conclusion["confidence"] = float(line[11:].strip())
+                        except:
+                            conclusion["confidence"] = 0.5
+                    elif line.startswith('Evidence:'):
+                        conclusion["evidence"] = [e.strip() for e in line[9:].split(',')]
+                    elif line.startswith('Alternatives:'):
+                        conclusion["alternatives"] = [a.strip() for a in line[13:].split(',')]
+            elif section.startswith('Meta]'):
+                lines = section.split('\n')[1:]
+                for line in lines:
+                    if line.startswith('Insights:'):
+                        conclusion["meta_insights"] = [i.strip() for i in line[9:].split(',')]
+                    elif line.startswith('Future:'):
+                        conclusion["future_considerations"] = [f.strip() for f in line[7:].split(',')]
+        
+        return conclusion
+
+    def _thought_to_dict(self, thought: Thought) -> Dict[str, Any]:
+        """Convert thought to dictionary for serialization."""
+        return {
+            "type": thought.type.value,
+            "content": thought.content,
+            "confidence": thought.confidence,
+            "evidence": thought.evidence,
+            "alternatives": thought.alternatives,
+            "next_steps": thought.next_steps,
+            "metadata": thought.metadata
+        }
+
+    def get_thought_history(self) -> List[Dict[str, Any]]:
+        """Get the history of all thoughts processed."""
+        return [self._thought_to_dict(t) for t in self.thought_history]
+
+    def clear_history(self) -> None:
+        """Clear thought history."""
+        self.thought_history = []

reasoning/coordination.py

@@ -0,0 +1,525 @@
+"""Advanced strategy coordination patterns for the unified reasoning engine."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Type, Callable
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import asyncio
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+from .unified_engine import StrategyType, StrategyResult, UnifiedResult
+
+class CoordinationPattern(Enum):
+    """Types of strategy coordination patterns."""
+    PIPELINE = "pipeline"
+    PARALLEL = "parallel"
+    HIERARCHICAL = "hierarchical"
+    FEEDBACK = "feedback"
+    ADAPTIVE = "adaptive"
+    ENSEMBLE = "ensemble"
+
+class CoordinationPhase(Enum):
+    """Phases in strategy coordination."""
+    INITIALIZATION = "initialization"
+    EXECUTION = "execution"
+    SYNCHRONIZATION = "synchronization"
+    ADAPTATION = "adaptation"
+    COMPLETION = "completion"
+
+@dataclass
+class CoordinationState:
+    """State of strategy coordination."""
+    pattern: CoordinationPattern
+    active_strategies: Dict[StrategyType, bool]
+    phase: CoordinationPhase
+    shared_context: Dict[str, Any]
+    synchronization_points: List[str]
+    adaptation_history: List[Dict[str, Any]]
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+@dataclass
+class StrategyInteraction:
+    """Interaction between strategies."""
+    source: StrategyType
+    target: StrategyType
+    interaction_type: str
+    data: Dict[str, Any]
+    timestamp: datetime = field(default_factory=datetime.now)
+
+class StrategyCoordinator:
+    """
+    Advanced strategy coordinator that:
+    1. Manages strategy interactions
+    2. Implements coordination patterns
+    3. Handles state synchronization
+    4. Adapts coordination dynamically
+    5. Optimizes strategy combinations
+    """
+    
+    def __init__(self,
+                 strategies: Dict[StrategyType, ReasoningStrategy],
+                 learning_rate: float = 0.1):
+        self.strategies = strategies
+        self.learning_rate = learning_rate
+        
+        # Coordination state
+        self.states: Dict[str, CoordinationState] = {}
+        self.interactions: List[StrategyInteraction] = []
+        
+        # Pattern performance
+        self.pattern_performance: Dict[CoordinationPattern, List[float]] = defaultdict(list)
+        self.pattern_weights: Dict[CoordinationPattern, float] = {
+            pattern: 1.0 for pattern in CoordinationPattern
+        }
+        
+    async def coordinate(self,
+                        query: str,
+                        context: Dict[str, Any],
+                        pattern: Optional[CoordinationPattern] = None) -> Dict[str, Any]:
+        """Coordinate strategy execution using specified pattern."""
+        try:
+            # Select pattern if not specified
+            if not pattern:
+                pattern = await self._select_pattern(query, context)
+            
+            # Initialize coordination
+            state = await self._initialize_coordination(pattern, context)
+            
+            # Execute coordination pattern
+            if pattern == CoordinationPattern.PIPELINE:
+                result = await self._coordinate_pipeline(query, context, state)
+            elif pattern == CoordinationPattern.PARALLEL:
+                result = await self._coordinate_parallel(query, context, state)
+            elif pattern == CoordinationPattern.HIERARCHICAL:
+                result = await self._coordinate_hierarchical(query, context, state)
+            elif pattern == CoordinationPattern.FEEDBACK:
+                result = await self._coordinate_feedback(query, context, state)
+            elif pattern == CoordinationPattern.ADAPTIVE:
+                result = await self._coordinate_adaptive(query, context, state)
+            elif pattern == CoordinationPattern.ENSEMBLE:
+                result = await self._coordinate_ensemble(query, context, state)
+            else:
+                raise ValueError(f"Unsupported coordination pattern: {pattern}")
+            
+            # Update performance metrics
+            self._update_pattern_performance(pattern, result)
+            
+            return result
+            
+        except Exception as e:
+            logging.error(f"Error in strategy coordination: {str(e)}")
+            return {
+                "success": False,
+                "error": str(e),
+                "pattern": pattern.value if pattern else None
+            }
+
+    async def _select_pattern(self, query: str, context: Dict[str, Any]) -> CoordinationPattern:
+        """Select appropriate coordination pattern."""
+        prompt = f"""
+        Select coordination pattern:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        Consider:
+        1. Task complexity and type
+        2. Strategy dependencies
+        3. Resource constraints
+        4. Performance history
+        5. Adaptation needs
+        
+        Format as:
+        [Selection]
+        Pattern: ...
+        Rationale: ...
+        Confidence: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        selection = self._parse_pattern_selection(response["answer"])
+        
+        # Weight by performance history
+        weighted_patterns = {
+            pattern: self.pattern_weights[pattern] * selection.get(pattern.value, 0.0)
+            for pattern in CoordinationPattern
+        }
+        
+        return max(weighted_patterns.items(), key=lambda x: x[1])[0]
+
+    async def _coordinate_pipeline(self,
+                                 query: str,
+                                 context: Dict[str, Any],
+                                 state: CoordinationState) -> Dict[str, Any]:
+        """Coordinate strategies in pipeline pattern."""
+        results = []
+        current_context = context.copy()
+        
+        # Determine optimal order
+        strategy_order = await self._determine_pipeline_order(query, context)
+        
+        for strategy_type in strategy_order:
+            try:
+                # Execute strategy
+                strategy = self.strategies[strategy_type]
+                result = await strategy.reason(query, current_context)
+                
+                # Update context with result
+                current_context.update({
+                    "previous_result": result,
+                    "pipeline_position": len(results)
+                })
+                
+                results.append(StrategyResult(
+                    strategy_type=strategy_type,
+                    success=result.get("success", False),
+                    answer=result.get("answer"),
+                    confidence=result.get("confidence", 0.0),
+                    reasoning_trace=result.get("reasoning_trace", []),
+                    metadata=result.get("metadata", {}),
+                    performance_metrics=result.get("performance_metrics", {})
+                ))
+                
+                # Record interaction
+                self._record_interaction(
+                    source=strategy_type,
+                    target=strategy_order[len(results)] if len(results) < len(strategy_order) else None,
+                    interaction_type="pipeline_transfer",
+                    data={"result": result}
+                )
+                
+            except Exception as e:
+                logging.error(f"Error in pipeline strategy {strategy_type}: {str(e)}")
+        
+        return {
+            "success": any(r.success for r in results),
+            "results": results,
+            "pattern": CoordinationPattern.PIPELINE.value,
+            "metrics": {
+                "total_steps": len(results),
+                "success_rate": sum(1 for r in results if r.success) / len(results) if results else 0
+            }
+        }
+
+    async def _coordinate_parallel(self,
+                                 query: str,
+                                 context: Dict[str, Any],
+                                 state: CoordinationState) -> Dict[str, Any]:
+        """Coordinate strategies in parallel pattern."""
+        async def execute_strategy(strategy_type: StrategyType) -> StrategyResult:
+            try:
+                strategy = self.strategies[strategy_type]
+                result = await strategy.reason(query, context)
+                
+                return StrategyResult(
+                    strategy_type=strategy_type,
+                    success=result.get("success", False),
+                    answer=result.get("answer"),
+                    confidence=result.get("confidence", 0.0),
+                    reasoning_trace=result.get("reasoning_trace", []),
+                    metadata=result.get("metadata", {}),
+                    performance_metrics=result.get("performance_metrics", {})
+                )
+            except Exception as e:
+                logging.error(f"Error in parallel strategy {strategy_type}: {str(e)}")
+                return StrategyResult(
+                    strategy_type=strategy_type,
+                    success=False,
+                    answer=None,
+                    confidence=0.0,
+                    reasoning_trace=[{"error": str(e)}],
+                    metadata={},
+                    performance_metrics={}
+                )
+        
+        # Execute strategies in parallel
+        tasks = [execute_strategy(strategy_type) 
+                for strategy_type in state.active_strategies
+                if state.active_strategies[strategy_type]]
+        
+        results = await asyncio.gather(*tasks)
+        
+        # Synthesize results
+        synthesis = await self._synthesize_parallel_results(results, context)
+        
+        return {
+            "success": synthesis.get("success", False),
+            "results": results,
+            "synthesis": synthesis,
+            "pattern": CoordinationPattern.PARALLEL.value,
+            "metrics": {
+                "total_strategies": len(results),
+                "success_rate": sum(1 for r in results if r.success) / len(results) if results else 0
+            }
+        }
+
+    async def _coordinate_hierarchical(self,
+                                     query: str,
+                                     context: Dict[str, Any],
+                                     state: CoordinationState) -> Dict[str, Any]:
+        """Coordinate strategies in hierarchical pattern."""
+        # Build strategy hierarchy
+        hierarchy = await self._build_strategy_hierarchy(query, context)
+        results = {}
+        
+        async def execute_level(level_strategies: List[StrategyType],
+                              level_context: Dict[str, Any]) -> List[StrategyResult]:
+            tasks = []
+            for strategy_type in level_strategies:
+                if strategy_type in state.active_strategies and state.active_strategies[strategy_type]:
+                    strategy = self.strategies[strategy_type]
+                    tasks.append(strategy.reason(query, level_context))
+            
+            level_results = await asyncio.gather(*tasks)
+            return [
+                StrategyResult(
+                    strategy_type=strategy_type,
+                    success=result.get("success", False),
+                    answer=result.get("answer"),
+                    confidence=result.get("confidence", 0.0),
+                    reasoning_trace=result.get("reasoning_trace", []),
+                    metadata=result.get("metadata", {}),
+                    performance_metrics=result.get("performance_metrics", {})
+                )
+                for strategy_type, result in zip(level_strategies, level_results)
+            ]
+        
+        # Execute hierarchy levels
+        current_context = context.copy()
+        for level, level_strategies in enumerate(hierarchy):
+            results[level] = await execute_level(level_strategies, current_context)
+            
+            # Update context for next level
+            current_context.update({
+                "previous_level_results": results[level],
+                "hierarchy_level": level
+            })
+        
+        return {
+            "success": any(any(r.success for r in level_results) 
+                         for level_results in results.values()),
+            "results": results,
+            "hierarchy": hierarchy,
+            "pattern": CoordinationPattern.HIERARCHICAL.value,
+            "metrics": {
+                "total_levels": len(hierarchy),
+                "level_success_rates": {
+                    level: sum(1 for r in results[level] if r.success) / len(results[level])
+                    for level in results if results[level]
+                }
+            }
+        }
+
+    async def _coordinate_feedback(self,
+                                 query: str,
+                                 context: Dict[str, Any],
+                                 state: CoordinationState) -> Dict[str, Any]:
+        """Coordinate strategies with feedback loops."""
+        results = []
+        feedback_history = []
+        current_context = context.copy()
+        
+        max_iterations = 5  # Prevent infinite loops
+        iteration = 0
+        
+        while iteration < max_iterations:
+            iteration += 1
+            
+            # Execute strategies
+            iteration_results = []
+            for strategy_type in state.active_strategies:
+                if state.active_strategies[strategy_type]:
+                    try:
+                        strategy = self.strategies[strategy_type]
+                        result = await strategy.reason(query, current_context)
+                        
+                        strategy_result = StrategyResult(
+                            strategy_type=strategy_type,
+                            success=result.get("success", False),
+                            answer=result.get("answer"),
+                            confidence=result.get("confidence", 0.0),
+                            reasoning_trace=result.get("reasoning_trace", []),
+                            metadata=result.get("metadata", {}),
+                            performance_metrics=result.get("performance_metrics", {})
+                        )
+                        
+                        iteration_results.append(strategy_result)
+                        
+                    except Exception as e:
+                        logging.error(f"Error in feedback strategy {strategy_type}: {str(e)}")
+            
+            results.append(iteration_results)
+            
+            # Generate feedback
+            feedback = await self._generate_feedback(iteration_results, current_context)
+            feedback_history.append(feedback)
+            
+            # Check termination condition
+            if self._should_terminate_feedback(feedback, iteration_results):
+                break
+            
+            # Update context with feedback
+            current_context.update({
+                "previous_results": iteration_results,
+                "feedback": feedback,
+                "iteration": iteration
+            })
+        
+        return {
+            "success": any(any(r.success for r in iteration_results) 
+                         for iteration_results in results),
+            "results": results,
+            "feedback_history": feedback_history,
+            "pattern": CoordinationPattern.FEEDBACK.value,
+            "metrics": {
+                "total_iterations": iteration,
+                "feedback_impact": self._calculate_feedback_impact(results, feedback_history)
+            }
+        }
+
+    async def _coordinate_adaptive(self,
+                                 query: str,
+                                 context: Dict[str, Any],
+                                 state: CoordinationState) -> Dict[str, Any]:
+        """Coordinate strategies with adaptive selection."""
+        results = []
+        adaptations = []
+        current_context = context.copy()
+        
+        while len(results) < len(state.active_strategies):
+            # Select next strategy
+            next_strategy = await self._select_next_strategy(
+                results, state.active_strategies, current_context)
+            
+            if not next_strategy:
+                break
+            
+            try:
+                # Execute strategy
+                strategy = self.strategies[next_strategy]
+                result = await strategy.reason(query, current_context)
+                
+                strategy_result = StrategyResult(
+                    strategy_type=next_strategy,
+                    success=result.get("success", False),
+                    answer=result.get("answer"),
+                    confidence=result.get("confidence", 0.0),
+                    reasoning_trace=result.get("reasoning_trace", []),
+                    metadata=result.get("metadata", {}),
+                    performance_metrics=result.get("performance_metrics", {})
+                )
+                
+                results.append(strategy_result)
+                
+                # Adapt strategy selection
+                adaptation = await self._adapt_strategy_selection(
+                    strategy_result, current_context)
+                adaptations.append(adaptation)
+                
+                # Update context
+                current_context.update({
+                    "previous_results": results,
+                    "adaptations": adaptations,
+                    "current_strategy": next_strategy
+                })
+                
+            except Exception as e:
+                logging.error(f"Error in adaptive strategy {next_strategy}: {str(e)}")
+        
+        return {
+            "success": any(r.success for r in results),
+            "results": results,
+            "adaptations": adaptations,
+            "pattern": CoordinationPattern.ADAPTIVE.value,
+            "metrics": {
+                "total_strategies": len(results),
+                "adaptation_impact": self._calculate_adaptation_impact(results, adaptations)
+            }
+        }
+
+    async def _coordinate_ensemble(self,
+                                 query: str,
+                                 context: Dict[str, Any],
+                                 state: CoordinationState) -> Dict[str, Any]:
+        """Coordinate strategies as an ensemble."""
+        # Execute all strategies
+        results = []
+        for strategy_type in state.active_strategies:
+            if state.active_strategies[strategy_type]:
+                try:
+                    strategy = self.strategies[strategy_type]
+                    result = await strategy.reason(query, context)
+                    
+                    strategy_result = StrategyResult(
+                        strategy_type=strategy_type,
+                        success=result.get("success", False),
+                        answer=result.get("answer"),
+                        confidence=result.get("confidence", 0.0),
+                        reasoning_trace=result.get("reasoning_trace", []),
+                        metadata=result.get("metadata", {}),
+                        performance_metrics=result.get("performance_metrics", {})
+                    )
+                    
+                    results.append(strategy_result)
+                    
+                except Exception as e:
+                    logging.error(f"Error in ensemble strategy {strategy_type}: {str(e)}")
+        
+        # Combine results using ensemble methods
+        ensemble_result = await self._combine_ensemble_results(results, context)
+        
+        return {
+            "success": ensemble_result.get("success", False),
+            "results": results,
+            "ensemble_result": ensemble_result,
+            "pattern": CoordinationPattern.ENSEMBLE.value,
+            "metrics": {
+                "total_members": len(results),
+                "ensemble_confidence": ensemble_result.get("confidence", 0.0)
+            }
+        }
+
+    def _record_interaction(self,
+                          source: StrategyType,
+                          target: Optional[StrategyType],
+                          interaction_type: str,
+                          data: Dict[str, Any]):
+        """Record strategy interaction."""
+        self.interactions.append(StrategyInteraction(
+            source=source,
+            target=target,
+            interaction_type=interaction_type,
+            data=data
+        ))
+
+    def _update_pattern_performance(self, pattern: CoordinationPattern, result: Dict[str, Any]):
+        """Update pattern performance metrics."""
+        success_rate = result["metrics"].get("success_rate", 0.0)
+        self.pattern_performance[pattern].append(success_rate)
+        
+        # Update weights using exponential moving average
+        current_weight = self.pattern_weights[pattern]
+        self.pattern_weights[pattern] = (
+            (1 - self.learning_rate) * current_weight +
+            self.learning_rate * success_rate
+        )
+
+    def get_performance_metrics(self) -> Dict[str, Any]:
+        """Get comprehensive performance metrics."""
+        return {
+            "pattern_weights": dict(self.pattern_weights),
+            "average_performance": {
+                pattern.value: sum(scores) / len(scores) if scores else 0
+                for pattern, scores in self.pattern_performance.items()
+            },
+            "interaction_counts": defaultdict(int, {
+                interaction.interaction_type: 1
+                for interaction in self.interactions
+            }),
+            "active_patterns": [
+                pattern.value for pattern, weight in self.pattern_weights.items()
+                if weight > 0.5
+            ]
+        }

reasoning/learning.py

@@ -0,0 +1,394 @@
+"""Enhanced learning mechanisms for reasoning strategies."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import numpy as np
+from collections import defaultdict
+
+@dataclass
+class LearningEvent:
+    """Event for strategy learning."""
+    strategy_type: str
+    event_type: str
+    data: Dict[str, Any]
+    outcome: Optional[float]
+    timestamp: datetime = field(default_factory=datetime.now)
+
+class LearningMode(Enum):
+    """Types of learning modes."""
+    SUPERVISED = "supervised"
+    REINFORCEMENT = "reinforcement"
+    ACTIVE = "active"
+    TRANSFER = "transfer"
+    META = "meta"
+    ENSEMBLE = "ensemble"
+
+@dataclass
+class LearningState:
+    """State for learning process."""
+    mode: LearningMode
+    parameters: Dict[str, Any]
+    history: List[LearningEvent]
+    metrics: Dict[str, float]
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+class EnhancedLearningManager:
+    """
+    Advanced learning manager that:
+    1. Implements multiple learning modes
+    2. Tracks learning progress
+    3. Adapts learning parameters
+    4. Optimizes strategy performance
+    5. Transfers knowledge between strategies
+    """
+    
+    def __init__(self,
+                 learning_rate: float = 0.1,
+                 exploration_rate: float = 0.2,
+                 memory_size: int = 1000):
+        self.learning_rate = learning_rate
+        self.exploration_rate = exploration_rate
+        self.memory_size = memory_size
+        
+        # Learning states
+        self.states: Dict[str, LearningState] = {}
+        
+        # Performance tracking
+        self.performance_history: List[Dict[str, Any]] = []
+        self.strategy_metrics: Dict[str, List[float]] = defaultdict(list)
+        
+        # Knowledge transfer
+        self.knowledge_base: Dict[str, Any] = {}
+        self.transfer_history: List[Dict[str, Any]] = []
+        
+    async def learn(self,
+                   strategy_type: str,
+                   event: LearningEvent,
+                   context: Dict[str, Any]) -> Dict[str, Any]:
+        """Learn from strategy execution event."""
+        try:
+            # Initialize or get learning state
+            state = self._get_learning_state(strategy_type)
+            
+            # Select learning mode
+            mode = await self._select_learning_mode(event, state, context)
+            
+            # Execute learning
+            if mode == LearningMode.SUPERVISED:
+                result = await self._supervised_learning(event, state, context)
+            elif mode == LearningMode.REINFORCEMENT:
+                result = await self._reinforcement_learning(event, state, context)
+            elif mode == LearningMode.ACTIVE:
+                result = await self._active_learning(event, state, context)
+            elif mode == LearningMode.TRANSFER:
+                result = await self._transfer_learning(event, state, context)
+            elif mode == LearningMode.META:
+                result = await self._meta_learning(event, state, context)
+            elif mode == LearningMode.ENSEMBLE:
+                result = await self._ensemble_learning(event, state, context)
+            else:
+                raise ValueError(f"Unsupported learning mode: {mode}")
+            
+            # Update state
+            self._update_learning_state(state, result)
+            
+            # Record performance
+            self._record_performance(strategy_type, result)
+            
+            return result
+            
+        except Exception as e:
+            logging.error(f"Error in learning: {str(e)}")
+            return {
+                "success": False,
+                "error": str(e),
+                "mode": mode.value if 'mode' in locals() else None
+            }
+
+    async def _supervised_learning(self,
+                                 event: LearningEvent,
+                                 state: LearningState,
+                                 context: Dict[str, Any]) -> Dict[str, Any]:
+        """Implement supervised learning."""
+        # Extract features and labels
+        features = await self._extract_features(event.data, context)
+        labels = event.outcome if event.outcome is not None else 0.0
+        
+        # Train model
+        model_update = await self._update_model(features, labels, state, context)
+        
+        # Validate performance
+        validation = await self._validate_model(model_update, state, context)
+        
+        return {
+            "success": True,
+            "mode": LearningMode.SUPERVISED.value,
+            "model_update": model_update,
+            "validation": validation,
+            "metrics": {
+                "accuracy": validation.get("accuracy", 0.0),
+                "loss": validation.get("loss", 0.0)
+            }
+        }
+
+    async def _reinforcement_learning(self,
+                                    event: LearningEvent,
+                                    state: LearningState,
+                                    context: Dict[str, Any]) -> Dict[str, Any]:
+        """Implement reinforcement learning."""
+        # Extract state and action
+        current_state = await self._extract_state(event.data, context)
+        action = event.data.get("action")
+        reward = event.outcome if event.outcome is not None else 0.0
+        
+        # Update policy
+        policy_update = await self._update_policy(
+            current_state, action, reward, state, context)
+        
+        # Optimize value function
+        value_update = await self._update_value_function(
+            current_state, reward, state, context)
+        
+        return {
+            "success": True,
+            "mode": LearningMode.REINFORCEMENT.value,
+            "policy_update": policy_update,
+            "value_update": value_update,
+            "metrics": {
+                "reward": reward,
+                "value_error": value_update.get("error", 0.0)
+            }
+        }
+
+    async def _active_learning(self,
+                             event: LearningEvent,
+                             state: LearningState,
+                             context: Dict[str, Any]) -> Dict[str, Any]:
+        """Implement active learning."""
+        # Query selection
+        query = await self._select_query(event.data, state, context)
+        
+        # Get feedback
+        feedback = await self._get_feedback(query, context)
+        
+        # Update model
+        model_update = await self._update_model_active(
+            query, feedback, state, context)
+        
+        return {
+            "success": True,
+            "mode": LearningMode.ACTIVE.value,
+            "query": query,
+            "feedback": feedback,
+            "model_update": model_update,
+            "metrics": {
+                "uncertainty": query.get("uncertainty", 0.0),
+                "feedback_quality": feedback.get("quality", 0.0)
+            }
+        }
+
+    async def _transfer_learning(self,
+                               event: LearningEvent,
+                               state: LearningState,
+                               context: Dict[str, Any]) -> Dict[str, Any]:
+        """Implement transfer learning."""
+        # Source task selection
+        source_task = await self._select_source_task(event.data, state, context)
+        
+        # Knowledge extraction
+        knowledge = await self._extract_knowledge(source_task, context)
+        
+        # Transfer adaptation
+        adaptation = await self._adapt_knowledge(
+            knowledge, event.data, state, context)
+        
+        # Apply transfer
+        transfer = await self._apply_transfer(adaptation, state, context)
+        
+        return {
+            "success": True,
+            "mode": LearningMode.TRANSFER.value,
+            "source_task": source_task,
+            "knowledge": knowledge,
+            "adaptation": adaptation,
+            "transfer": transfer,
+            "metrics": {
+                "transfer_efficiency": transfer.get("efficiency", 0.0),
+                "adaptation_quality": adaptation.get("quality", 0.0)
+            }
+        }
+
+    async def _meta_learning(self,
+                           event: LearningEvent,
+                           state: LearningState,
+                           context: Dict[str, Any]) -> Dict[str, Any]:
+        """Implement meta-learning."""
+        # Task characterization
+        task_char = await self._characterize_task(event.data, context)
+        
+        # Strategy selection
+        strategy = await self._select_strategy(task_char, state, context)
+        
+        # Parameter optimization
+        optimization = await self._optimize_parameters(
+            strategy, task_char, state, context)
+        
+        # Apply meta-learning
+        meta_update = await self._apply_meta_learning(
+            optimization, state, context)
+        
+        return {
+            "success": True,
+            "mode": LearningMode.META.value,
+            "task_characterization": task_char,
+            "strategy": strategy,
+            "optimization": optimization,
+            "meta_update": meta_update,
+            "metrics": {
+                "strategy_fit": strategy.get("fit_score", 0.0),
+                "optimization_improvement": optimization.get("improvement", 0.0)
+            }
+        }
+
+    async def _ensemble_learning(self,
+                               event: LearningEvent,
+                               state: LearningState,
+                               context: Dict[str, Any]) -> Dict[str, Any]:
+        """Implement ensemble learning."""
+        # Member selection
+        members = await self._select_members(event.data, state, context)
+        
+        # Weight optimization
+        weights = await self._optimize_weights(members, state, context)
+        
+        # Combine predictions
+        combination = await self._combine_predictions(
+            members, weights, event.data, context)
+        
+        return {
+            "success": True,
+            "mode": LearningMode.ENSEMBLE.value,
+            "members": members,
+            "weights": weights,
+            "combination": combination,
+            "metrics": {
+                "ensemble_diversity": weights.get("diversity", 0.0),
+                "combination_strength": combination.get("strength", 0.0)
+            }
+        }
+
+    def _get_learning_state(self, strategy_type: str) -> LearningState:
+        """Get or initialize learning state for strategy."""
+        if strategy_type not in self.states:
+            self.states[strategy_type] = LearningState(
+                mode=LearningMode.SUPERVISED,
+                parameters={
+                    "learning_rate": self.learning_rate,
+                    "exploration_rate": self.exploration_rate
+                },
+                history=[],
+                metrics={}
+            )
+        return self.states[strategy_type]
+
+    def _update_learning_state(self, state: LearningState, result: Dict[str, Any]):
+        """Update learning state with result."""
+        # Update history
+        state.history.append(LearningEvent(
+            strategy_type=result.get("strategy_type", "unknown"),
+            event_type="learning_update",
+            data=result,
+            outcome=result.get("metrics", {}).get("accuracy", 0.0),
+            timestamp=datetime.now()
+        ))
+        
+        # Update metrics
+        for metric, value in result.get("metrics", {}).items():
+            if metric in state.metrics:
+                state.metrics[metric] = (
+                    0.9 * state.metrics[metric] + 0.1 * value  # Exponential moving average
+                )
+            else:
+                state.metrics[metric] = value
+        
+        # Adapt parameters
+        self._adapt_parameters(state, result)
+
+    def _record_performance(self, strategy_type: str, result: Dict[str, Any]):
+        """Record learning performance."""
+        self.performance_history.append({
+            "timestamp": datetime.now().isoformat(),
+            "strategy_type": strategy_type,
+            "mode": result.get("mode"),
+            "metrics": result.get("metrics", {}),
+            "success": result.get("success", False)
+        })
+        
+        # Update strategy metrics
+        for metric, value in result.get("metrics", {}).items():
+            self.strategy_metrics[f"{strategy_type}_{metric}"].append(value)
+        
+        # Maintain memory size
+        if len(self.performance_history) > self.memory_size:
+            self.performance_history = self.performance_history[-self.memory_size:]
+
+    def _adapt_parameters(self, state: LearningState, result: Dict[str, Any]):
+        """Adapt learning parameters based on performance."""
+        # Adapt learning rate
+        if "accuracy" in result.get("metrics", {}):
+            accuracy = result["metrics"]["accuracy"]
+            if accuracy > 0.8:
+                state.parameters["learning_rate"] *= 0.95  # Decrease if performing well
+            elif accuracy < 0.6:
+                state.parameters["learning_rate"] *= 1.05  # Increase if performing poorly
+        
+        # Adapt exploration rate
+        if "reward" in result.get("metrics", {}):
+            reward = result["metrics"]["reward"]
+            if reward > 0:
+                state.parameters["exploration_rate"] *= 0.95  # Decrease if getting rewards
+            else:
+                state.parameters["exploration_rate"] *= 1.05  # Increase if not getting rewards
+        
+        # Clip parameters to reasonable ranges
+        state.parameters["learning_rate"] = np.clip(
+            state.parameters["learning_rate"], 0.001, 0.5)
+        state.parameters["exploration_rate"] = np.clip(
+            state.parameters["exploration_rate"], 0.01, 0.5)
+
+    def get_performance_metrics(self) -> Dict[str, Any]:
+        """Get comprehensive performance metrics."""
+        return {
+            "learning_states": {
+                strategy_type: {
+                    "mode": state.mode.value,
+                    "parameters": state.parameters,
+                    "metrics": state.metrics
+                }
+                for strategy_type, state in self.states.items()
+            },
+            "strategy_performance": {
+                metric: {
+                    "mean": np.mean(values) if values else 0.0,
+                    "std": np.std(values) if values else 0.0,
+                    "min": min(values) if values else 0.0,
+                    "max": max(values) if values else 0.0
+                }
+                for metric, values in self.strategy_metrics.items()
+            },
+            "transfer_metrics": {
+                "total_transfers": len(self.transfer_history),
+                "success_rate": sum(1 for t in self.transfer_history if t.get("success", False)) / len(self.transfer_history) if self.transfer_history else 0
+            }
+        }
+
+    def clear_history(self):
+        """Clear learning history and reset states."""
+        self.states.clear()
+        self.performance_history.clear()
+        self.strategy_metrics.clear()
+        self.transfer_history.clear()

reasoning/local_llm.py

@@ -0,0 +1,139 @@
+"""Local LLM integration for the reasoning system."""
+
+import os
+from typing import Dict, Any, Optional
+from datetime import datetime
+import logging
+from llama_cpp import Llama
+import huggingface_hub
+from .base import ReasoningStrategy
+
+class LocalLLMStrategy(ReasoningStrategy):
+    """Implements reasoning using local LLM."""
+    
+    def __init__(self):
+        """Initialize the local LLM strategy."""
+        self.repo_id = "tensorblock/Llama-3.2-3B-Overthinker-GGUF"
+        self.filename = "Llama-3.2-3B-Overthinker-Q8_0.gguf"
+        self.model_dir = "models"
+        self.logger = logging.getLogger(__name__)
+        self.model = None
+        
+    async def initialize(self):
+        """Initialize the model."""
+        try:
+            # Create models directory if it doesn't exist
+            os.makedirs(self.model_dir, exist_ok=True)
+            model_path = os.path.join(self.model_dir, self.filename)
+            
+            # Download model if it doesn't exist
+            if not os.path.exists(model_path):
+                self.logger.info(f"Downloading model to {model_path}...")
+                model_path = huggingface_hub.hf_hub_download(
+                    repo_id=self.repo_id,
+                    filename=self.filename,
+                    repo_type="model",
+                    local_dir=self.model_dir,
+                    local_dir_use_symlinks=False
+                )
+                self.logger.info("Model downloaded successfully!")
+            else:
+                self.logger.info("Using existing model file...")
+            
+            # Try to use GPU, fall back to CPU if not available
+            try:
+                self.model = Llama(
+                    model_path=model_path,
+                    n_ctx=4096,
+                    n_batch=512,
+                    n_threads=8,
+                    n_gpu_layers=35
+                )
+                self.logger.info("Model loaded with GPU acceleration!")
+            except Exception as e:
+                self.logger.warning(f"GPU loading failed: {e}, falling back to CPU...")
+                self.model = Llama(
+                    model_path=model_path,
+                    n_ctx=2048,
+                    n_batch=512,
+                    n_threads=4,
+                    n_gpu_layers=0
+                )
+                self.logger.info("Model loaded in CPU-only mode")
+                
+        except Exception as e:
+            self.logger.error(f"Error initializing model: {e}")
+            raise
+            
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate reasoning response using local LLM."""
+        try:
+            if not self.model:
+                await self.initialize()
+            
+            # Format prompt with context
+            prompt = self._format_prompt(query, context)
+            
+            # Generate response
+            response = self.model(
+                prompt,
+                max_tokens=1024 if self.model.n_ctx >= 4096 else 512,
+                temperature=0.7,
+                top_p=0.95,
+                repeat_penalty=1.1,
+                echo=False
+            )
+            
+            # Extract and structure the response
+            result = self._parse_response(response['choices'][0]['text'])
+            
+            return {
+                'success': True,
+                'answer': result['answer'],
+                'reasoning': result['reasoning'],
+                'confidence': result['confidence'],
+                'timestamp': datetime.now(),
+                'metadata': {
+                    'model': self.repo_id,
+                    'strategy': 'local_llm',
+                    'context_length': len(prompt),
+                    'response_length': len(response['choices'][0]['text'])
+                }
+            }
+                
+        except Exception as e:
+            self.logger.error(f"Error in reasoning: {e}")
+            return {
+                'success': False,
+                'error': str(e),
+                'timestamp': datetime.now()
+            }
+            
+    def _format_prompt(self, query: str, context: Dict[str, Any]) -> str:
+        """Format the prompt with query and context."""
+        # Include relevant context
+        context_str = "\n".join([
+            f"{k}: {v}" for k, v in context.items()
+            if k in ['objective', 'constraints', 'background']
+        ])
+        
+        return f"""Let's solve this problem step by step.
+
+Context:
+{context_str}
+
+Question: {query}
+
+Let me break this down:
+1."""
+        
+    def _parse_response(self, text: str) -> Dict[str, Any]:
+        """Parse the response into structured output."""
+        # Simple parsing for now
+        lines = text.strip().split('\n')
+        
+        return {
+            'answer': lines[-1] if lines else '',
+            'reasoning': '\n'.join(lines[:-1]) if len(lines) > 1 else '',
+            'confidence': 0.8  # Default confidence
+        }

reasoning/market_analysis.py

@@ -0,0 +1,342 @@
+"""Advanced market analysis tools for venture strategies."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import numpy as np
+from collections import defaultdict
+
+@dataclass
+class MarketSegment:
+    """Market segment analysis."""
+    size: float
+    growth_rate: float
+    cagr: float
+    competition: List[Dict[str, Any]]
+    barriers: List[str]
+    opportunities: List[str]
+    risks: List[str]
+
+@dataclass
+class CompetitorAnalysis:
+    """Competitor analysis."""
+    name: str
+    market_share: float
+    strengths: List[str]
+    weaknesses: List[str]
+    strategy: str
+    revenue: Optional[float]
+    valuation: Optional[float]
+
+@dataclass
+class MarketTrend:
+    """Market trend analysis."""
+    name: str
+    impact: float
+    timeline: str
+    adoption_rate: float
+    market_potential: float
+    risk_level: float
+
+class MarketAnalyzer:
+    """
+    Advanced market analysis toolkit that:
+    1. Analyzes market segments
+    2. Tracks competitors
+    3. Identifies trends
+    4. Predicts opportunities
+    5. Assesses risks
+    """
+    
+    def __init__(self):
+        self.segments: Dict[str, MarketSegment] = {}
+        self.competitors: Dict[str, CompetitorAnalysis] = {}
+        self.trends: List[MarketTrend] = []
+        
+    async def analyze_market(self, 
+                           segment: str,
+                           context: Dict[str, Any]) -> Dict[str, Any]:
+        """Perform comprehensive market analysis."""
+        try:
+            # Segment analysis
+            segment_analysis = await self._analyze_segment(segment, context)
+            
+            # Competitor analysis
+            competitor_analysis = await self._analyze_competitors(segment, context)
+            
+            # Trend analysis
+            trend_analysis = await self._analyze_trends(segment, context)
+            
+            # Opportunity analysis
+            opportunity_analysis = await self._analyze_opportunities(
+                segment_analysis, competitor_analysis, trend_analysis, context)
+            
+            # Risk analysis
+            risk_analysis = await self._analyze_risks(
+                segment_analysis, competitor_analysis, trend_analysis, context)
+            
+            return {
+                "success": True,
+                "segment_analysis": segment_analysis,
+                "competitor_analysis": competitor_analysis,
+                "trend_analysis": trend_analysis,
+                "opportunity_analysis": opportunity_analysis,
+                "risk_analysis": risk_analysis,
+                "metrics": {
+                    "market_score": self._calculate_market_score(segment_analysis),
+                    "opportunity_score": self._calculate_opportunity_score(opportunity_analysis),
+                    "risk_score": self._calculate_risk_score(risk_analysis)
+                }
+            }
+        except Exception as e:
+            logging.error(f"Error in market analysis: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def _analyze_segment(self,
+                             segment: str,
+                             context: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze market segment."""
+        prompt = f"""
+        Analyze market segment:
+        Segment: {segment}
+        Context: {json.dumps(context)}
+        
+        Analyze:
+        1. Market size and growth
+        2. Customer segments
+        3. Value chain
+        4. Entry barriers
+        5. Competitive dynamics
+        
+        Format as:
+        [Analysis]
+        Size: ...
+        Growth: ...
+        Segments: ...
+        Value_Chain: ...
+        Barriers: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_segment_analysis(response["answer"])
+
+    async def _analyze_competitors(self,
+                                 segment: str,
+                                 context: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze competitors in segment."""
+        prompt = f"""
+        Analyze competitors:
+        Segment: {segment}
+        Context: {json.dumps(context)}
+        
+        For each competitor analyze:
+        1. Market share
+        2. Business model
+        3. Strengths/weaknesses
+        4. Strategy
+        5. Performance metrics
+        
+        Format as:
+        [Competitor1]
+        Share: ...
+        Model: ...
+        Strengths: ...
+        Weaknesses: ...
+        Strategy: ...
+        Metrics: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_competitor_analysis(response["answer"])
+
+    async def _analyze_trends(self,
+                            segment: str,
+                            context: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze market trends."""
+        prompt = f"""
+        Analyze market trends:
+        Segment: {segment}
+        Context: {json.dumps(context)}
+        
+        Analyze trends in:
+        1. Technology
+        2. Customer behavior
+        3. Business models
+        4. Regulation
+        5. Market dynamics
+        
+        Format as:
+        [Trend1]
+        Type: ...
+        Impact: ...
+        Timeline: ...
+        Adoption: ...
+        Potential: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_trend_analysis(response["answer"])
+
+    async def _analyze_opportunities(self,
+                                   segment_analysis: Dict[str, Any],
+                                   competitor_analysis: Dict[str, Any],
+                                   trend_analysis: Dict[str, Any],
+                                   context: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze market opportunities."""
+        prompt = f"""
+        Analyze market opportunities:
+        Segment: {json.dumps(segment_analysis)}
+        Competitors: {json.dumps(competitor_analysis)}
+        Trends: {json.dumps(trend_analysis)}
+        Context: {json.dumps(context)}
+        
+        Identify opportunities in:
+        1. Unmet needs
+        2. Market gaps
+        3. Innovation potential
+        4. Scaling potential
+        5. Value creation
+        
+        Format as:
+        [Opportunity1]
+        Type: ...
+        Description: ...
+        Potential: ...
+        Requirements: ...
+        Timeline: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_opportunity_analysis(response["answer"])
+
+    async def _analyze_risks(self,
+                           segment_analysis: Dict[str, Any],
+                           competitor_analysis: Dict[str, Any],
+                           trend_analysis: Dict[str, Any],
+                           context: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze market risks."""
+        prompt = f"""
+        Analyze market risks:
+        Segment: {json.dumps(segment_analysis)}
+        Competitors: {json.dumps(competitor_analysis)}
+        Trends: {json.dumps(trend_analysis)}
+        Context: {json.dumps(context)}
+        
+        Analyze risks in:
+        1. Market dynamics
+        2. Competition
+        3. Technology
+        4. Regulation
+        5. Execution
+        
+        Format as:
+        [Risk1]
+        Type: ...
+        Description: ...
+        Impact: ...
+        Probability: ...
+        Mitigation: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_risk_analysis(response["answer"])
+
+    def _calculate_market_score(self, analysis: Dict[str, Any]) -> float:
+        """Calculate market attractiveness score."""
+        weights = {
+            "size": 0.3,
+            "growth": 0.3,
+            "competition": 0.2,
+            "barriers": 0.1,
+            "dynamics": 0.1
+        }
+        
+        scores = {
+            "size": min(analysis.get("size", 0) / 1e9, 1.0),  # Normalize to 1B
+            "growth": min(analysis.get("growth", 0) / 30, 1.0),  # Normalize to 30%
+            "competition": 1.0 - min(len(analysis.get("competitors", [])) / 10, 1.0),
+            "barriers": 1.0 - min(len(analysis.get("barriers", [])) / 5, 1.0),
+            "dynamics": analysis.get("dynamics_score", 0.5)
+        }
+        
+        return sum(weights[k] * scores[k] for k in weights)
+
+    def _calculate_opportunity_score(self, analysis: Dict[str, Any]) -> float:
+        """Calculate opportunity attractiveness score."""
+        weights = {
+            "market_potential": 0.3,
+            "innovation_potential": 0.2,
+            "execution_feasibility": 0.2,
+            "competitive_advantage": 0.2,
+            "timing": 0.1
+        }
+        
+        scores = {
+            "market_potential": analysis.get("market_potential", 0.5),
+            "innovation_potential": analysis.get("innovation_potential", 0.5),
+            "execution_feasibility": analysis.get("execution_feasibility", 0.5),
+            "competitive_advantage": analysis.get("competitive_advantage", 0.5),
+            "timing": analysis.get("timing_score", 0.5)
+        }
+        
+        return sum(weights[k] * scores[k] for k in weights)
+
+    def _calculate_risk_score(self, analysis: Dict[str, Any]) -> float:
+        """Calculate risk level score."""
+        weights = {
+            "market_risk": 0.2,
+            "competition_risk": 0.2,
+            "technology_risk": 0.2,
+            "regulatory_risk": 0.2,
+            "execution_risk": 0.2
+        }
+        
+        scores = {
+            "market_risk": analysis.get("market_risk", 0.5),
+            "competition_risk": analysis.get("competition_risk", 0.5),
+            "technology_risk": analysis.get("technology_risk", 0.5),
+            "regulatory_risk": analysis.get("regulatory_risk", 0.5),
+            "execution_risk": analysis.get("execution_risk", 0.5)
+        }
+        
+        return sum(weights[k] * scores[k] for k in weights)
+
+    def get_market_insights(self) -> Dict[str, Any]:
+        """Get comprehensive market insights."""
+        return {
+            "segment_insights": {
+                segment: {
+                    "size": s.size,
+                    "growth_rate": s.growth_rate,
+                    "cagr": s.cagr,
+                    "opportunity_score": self._calculate_market_score({
+                        "size": s.size,
+                        "growth": s.growth_rate,
+                        "competitors": s.competition,
+                        "barriers": s.barriers
+                    })
+                }
+                for segment, s in self.segments.items()
+            },
+            "competitor_insights": {
+                competitor: {
+                    "market_share": c.market_share,
+                    "strength_score": len(c.strengths) / (len(c.strengths) + len(c.weaknesses)),
+                    "revenue": c.revenue,
+                    "valuation": c.valuation
+                }
+                for competitor, c in self.competitors.items()
+            },
+            "trend_insights": [
+                {
+                    "name": t.name,
+                    "impact": t.impact,
+                    "potential": t.market_potential,
+                    "risk": t.risk_level
+                }
+                for t in self.trends
+            ]
+        }

reasoning/meta_learning.py

@@ -0,0 +1,412 @@
+"""Meta-learning reasoning implementation with advanced adaptation capabilities."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Tuple, Callable
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from collections import defaultdict
+import numpy as np
+from datetime import datetime
+
+from .base import ReasoningStrategy
+
+class MetaFeatureType(Enum):
+    """Types of meta-features for learning."""
+    PROBLEM_STRUCTURE = "problem_structure"
+    SOLUTION_PATTERN = "solution_pattern"
+    REASONING_STYLE = "reasoning_style"
+    ERROR_PATTERN = "error_pattern"
+    PERFORMANCE_METRIC = "performance_metric"
+    ADAPTATION_SIGNAL = "adaptation_signal"
+
+@dataclass
+class MetaFeature:
+    """Represents a meta-feature for learning."""
+    type: MetaFeatureType
+    name: str
+    value: Any
+    confidence: float
+    timestamp: datetime
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+@dataclass
+class LearningEpisode:
+    """Represents a learning episode."""
+    id: str
+    query: str
+    features: List[MetaFeature]
+    outcome: Dict[str, Any]
+    performance: float
+    timestamp: datetime
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+class MetaLearningStrategy(ReasoningStrategy):
+    """
+    Advanced Meta-Learning reasoning implementation with:
+    - Dynamic strategy adaptation
+    - Performance tracking
+    - Pattern recognition
+    - Automated optimization
+    - Cross-episode learning
+    """
+    
+    def __init__(self,
+                 learning_rate: float = 0.1,
+                 memory_size: int = 1000,
+                 adaptation_threshold: float = 0.7,
+                 exploration_rate: float = 0.2):
+        self.learning_rate = learning_rate
+        self.memory_size = memory_size
+        self.adaptation_threshold = adaptation_threshold
+        self.exploration_rate = exploration_rate
+        
+        # Learning components
+        self.episode_memory: List[LearningEpisode] = []
+        self.feature_patterns: Dict[str, Dict[str, float]] = defaultdict(lambda: defaultdict(float))
+        self.strategy_performance: Dict[str, List[float]] = defaultdict(list)
+        self.adaptation_history: List[Dict[str, Any]] = []
+        
+        # Performance tracking
+        self.success_rate: float = 0.0
+        self.adaptation_rate: float = 0.0
+        self.exploration_count: int = 0
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Main reasoning method implementing meta-learning."""
+        try:
+            # Extract meta-features
+            features = await self._extract_meta_features(query, context)
+            
+            # Select optimal strategy
+            strategy = await self._select_strategy(features, context)
+            
+            # Apply strategy with adaptation
+            result = await self._apply_strategy(strategy, query, features, context)
+            
+            # Learn from episode
+            episode = self._create_episode(query, features, result)
+            self._learn_from_episode(episode)
+            
+            # Optimize performance
+            self._optimize_performance()
+            
+            return {
+                "success": True,
+                "answer": result["answer"],
+                "confidence": result["confidence"],
+                "meta_features": [self._feature_to_dict(f) for f in features],
+                "selected_strategy": strategy,
+                "adaptations": result["adaptations"],
+                "performance_metrics": result["performance_metrics"],
+                "meta_insights": result["meta_insights"]
+            }
+        except Exception as e:
+            logging.error(f"Error in meta-learning reasoning: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def _extract_meta_features(self, query: str, context: Dict[str, Any]) -> List[MetaFeature]:
+        """Extract meta-features from query and context."""
+        prompt = f"""
+        Extract meta-features for learning:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        For each feature type:
+        1. Problem Structure
+        2. Solution Patterns
+        3. Reasoning Style
+        4. Error Patterns
+        5. Performance Metrics
+        6. Adaptation Signals
+        
+        Format as:
+        [Type1]
+        Name: ...
+        Value: ...
+        Confidence: ...
+        Metadata: ...
+        
+        [Type2]
+        ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_meta_features(response["answer"])
+
+    async def _select_strategy(self, features: List[MetaFeature], context: Dict[str, Any]) -> str:
+        """Select optimal reasoning strategy based on meta-features."""
+        prompt = f"""
+        Select optimal reasoning strategy:
+        Features: {json.dumps([self._feature_to_dict(f) for f in features])}
+        Context: {json.dumps(context)}
+        
+        Consider:
+        1. Past performance patterns
+        2. Feature relevance
+        3. Adaptation potential
+        4. Resource constraints
+        
+        Format as:
+        [Selection]
+        Strategy: ...
+        Rationale: ...
+        Confidence: ...
+        Adaptations: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_strategy_selection(response["answer"])
+
+    async def _apply_strategy(self, strategy: str, query: str, features: List[MetaFeature], context: Dict[str, Any]) -> Dict[str, Any]:
+        """Apply selected strategy with dynamic adaptation."""
+        prompt = f"""
+        Apply strategy with meta-learning:
+        Strategy: {strategy}
+        Query: {query}
+        Features: {json.dumps([self._feature_to_dict(f) for f in features])}
+        Context: {json.dumps(context)}
+        
+        Provide:
+        1. Main reasoning steps
+        2. Adaptation points
+        3. Performance metrics
+        4. Meta-insights
+        
+        Format as:
+        [Application]
+        Steps: ...
+        Adaptations: ...
+        Metrics: ...
+        Insights: ...
+        
+        [Result]
+        Answer: ...
+        Confidence: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_strategy_application(response["answer"])
+
+    def _create_episode(self, query: str, features: List[MetaFeature], result: Dict[str, Any]) -> LearningEpisode:
+        """Create a learning episode from the current interaction."""
+        return LearningEpisode(
+            id=f"episode_{len(self.episode_memory)}",
+            query=query,
+            features=features,
+            outcome=result,
+            performance=result.get("confidence", 0.0),
+            timestamp=datetime.now(),
+            metadata={
+                "adaptations": result.get("adaptations", []),
+                "metrics": result.get("performance_metrics", {})
+            }
+        )
+
+    def _learn_from_episode(self, episode: LearningEpisode):
+        """Learn from a completed episode."""
+        # Update episode memory
+        self.episode_memory.append(episode)
+        if len(self.episode_memory) > self.memory_size:
+            self.episode_memory.pop(0)
+        
+        # Update feature patterns
+        for feature in episode.features:
+            pattern_key = f"{feature.type.value}:{feature.name}"
+            self.feature_patterns[pattern_key]["count"] += 1
+            self.feature_patterns[pattern_key]["success"] += episode.performance
+        
+        # Update strategy performance
+        strategy = episode.metadata.get("selected_strategy", "default")
+        self.strategy_performance[strategy].append(episode.performance)
+        
+        # Track adaptations
+        self.adaptation_history.append({
+            "timestamp": episode.timestamp,
+            "adaptations": episode.metadata.get("adaptations", []),
+            "performance": episode.performance
+        })
+        
+        # Update performance metrics
+        self._update_performance_metrics(episode)
+
+    def _optimize_performance(self):
+        """Optimize meta-learning performance."""
+        # Adjust learning rate
+        recent_performance = [e.performance for e in self.episode_memory[-10:]]
+        if recent_performance:
+            avg_performance = sum(recent_performance) / len(recent_performance)
+            if avg_performance > 0.8:
+                self.learning_rate *= 0.9  # Reduce learning rate when performing well
+            elif avg_performance < 0.5:
+                self.learning_rate *= 1.1  # Increase learning rate when performing poorly
+        
+        # Adjust exploration rate
+        self.exploration_rate = max(0.1, self.exploration_rate * 0.995)  # Gradually reduce exploration
+        
+        # Prune ineffective patterns
+        for pattern, stats in list(self.feature_patterns.items()):
+            if stats["count"] > 10 and stats["success"] / stats["count"] < 0.3:
+                del self.feature_patterns[pattern]
+        
+        # Update adaptation threshold
+        recent_adaptations = [a["performance"] for a in self.adaptation_history[-10:]]
+        if recent_adaptations:
+            self.adaptation_threshold = sum(recent_adaptations) / len(recent_adaptations)
+
+    def _update_performance_metrics(self, episode: LearningEpisode):
+        """Update performance tracking metrics."""
+        # Update success rate
+        self.success_rate = (self.success_rate * len(self.episode_memory) + episode.performance) / (len(self.episode_memory) + 1)
+        
+        # Update adaptation rate
+        adaptations = len(episode.metadata.get("adaptations", []))
+        self.adaptation_rate = (self.adaptation_rate * len(self.adaptation_history) + (adaptations > 0)) / (len(self.adaptation_history) + 1)
+        
+        # Track exploration
+        if episode.metadata.get("exploration", False):
+            self.exploration_count += 1
+
+    def _parse_meta_features(self, response: str) -> List[MetaFeature]:
+        """Parse meta-features from response."""
+        features = []
+        current_type = None
+        current_feature = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[Type'):
+                if current_feature:
+                    features.append(current_feature)
+                current_feature = None
+                try:
+                    type_str = line[1:-1].lower()
+                    current_type = MetaFeatureType(type_str)
+                except ValueError:
+                    current_type = None
+            elif current_type and line.startswith('Name:'):
+                current_feature = MetaFeature(
+                    type=current_type,
+                    name=line[5:].strip(),
+                    value=None,
+                    confidence=0.0,
+                    timestamp=datetime.now(),
+                    metadata={}
+                )
+            elif current_feature:
+                if line.startswith('Value:'):
+                    current_feature.value = line[6:].strip()
+                elif line.startswith('Confidence:'):
+                    try:
+                        current_feature.confidence = float(line[11:].strip())
+                    except:
+                        pass
+                elif line.startswith('Metadata:'):
+                    try:
+                        current_feature.metadata = json.loads(line[9:].strip())
+                    except:
+                        pass
+        
+        if current_feature:
+            features.append(current_feature)
+        
+        return features
+
+    def _parse_strategy_selection(self, response: str) -> str:
+        """Parse strategy selection from response."""
+        lines = response.split('\n')
+        strategy = "default"
+        
+        for line in lines:
+            if line.startswith('Strategy:'):
+                strategy = line[9:].strip()
+                break
+        
+        return strategy
+
+    def _parse_strategy_application(self, response: str) -> Dict[str, Any]:
+        """Parse strategy application results."""
+        result = {
+            "answer": "",
+            "confidence": 0.0,
+            "steps": [],
+            "adaptations": [],
+            "performance_metrics": {},
+            "meta_insights": []
+        }
+        
+        section = None
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[Application]'):
+                section = "application"
+            elif line.startswith('[Result]'):
+                section = "result"
+            elif section == "application":
+                if line.startswith('Steps:'):
+                    result["steps"] = [s.strip() for s in line[6:].split(',')]
+                elif line.startswith('Adaptations:'):
+                    result["adaptations"] = [a.strip() for a in line[12:].split(',')]
+                elif line.startswith('Metrics:'):
+                    try:
+                        result["performance_metrics"] = json.loads(line[8:].strip())
+                    except:
+                        pass
+                elif line.startswith('Insights:'):
+                    result["meta_insights"] = [i.strip() for i in line[9:].split(',')]
+            elif section == "result":
+                if line.startswith('Answer:'):
+                    result["answer"] = line[7:].strip()
+                elif line.startswith('Confidence:'):
+                    try:
+                        result["confidence"] = float(line[11:].strip())
+                    except:
+                        result["confidence"] = 0.5
+        
+        return result
+
+    def _feature_to_dict(self, feature: MetaFeature) -> Dict[str, Any]:
+        """Convert feature to dictionary for serialization."""
+        return {
+            "type": feature.type.value,
+            "name": feature.name,
+            "value": feature.value,
+            "confidence": feature.confidence,
+            "timestamp": feature.timestamp.isoformat(),
+            "metadata": feature.metadata
+        }
+
+    def get_performance_metrics(self) -> Dict[str, Any]:
+        """Get current performance metrics."""
+        return {
+            "success_rate": self.success_rate,
+            "adaptation_rate": self.adaptation_rate,
+            "exploration_count": self.exploration_count,
+            "episode_count": len(self.episode_memory),
+            "pattern_count": len(self.feature_patterns),
+            "learning_rate": self.learning_rate,
+            "exploration_rate": self.exploration_rate
+        }
+
+    def get_top_patterns(self, n: int = 10) -> List[Tuple[str, float]]:
+        """Get top performing patterns."""
+        pattern_scores = []
+        for pattern, stats in self.feature_patterns.items():
+            if stats["count"] > 0:
+                score = stats["success"] / stats["count"]
+                pattern_scores.append((pattern, score))
+        
+        return sorted(pattern_scores, key=lambda x: x[1], reverse=True)[:n]
+
+    def clear_memory(self):
+        """Clear learning memory."""
+        self.episode_memory.clear()
+        self.feature_patterns.clear()
+        self.strategy_performance.clear()
+        self.adaptation_history.clear()

reasoning/monetization.py

@@ -0,0 +1,278 @@
+"""Advanced monetization strategies for venture optimization."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import numpy as np
+from collections import defaultdict
+
+@dataclass
+class MonetizationModel:
+    """Monetization model configuration."""
+    name: str
+    type: str
+    pricing_tiers: List[Dict[str, Any]]
+    features: List[str]
+    constraints: List[str]
+    metrics: Dict[str, float]
+
+@dataclass
+class RevenueStream:
+    """Revenue stream configuration."""
+    name: str
+    type: str
+    volume: float
+    unit_economics: Dict[str, float]
+    growth_rate: float
+    churn_rate: float
+
+class MonetizationOptimizer:
+    """
+    Advanced monetization optimization that:
+    1. Designs pricing models
+    2. Optimizes revenue streams
+    3. Maximizes customer value
+    4. Reduces churn
+    5. Increases lifetime value
+    """
+    
+    def __init__(self):
+        self.models: Dict[str, MonetizationModel] = {}
+        self.streams: Dict[str, RevenueStream] = {}
+        
+    async def optimize_monetization(self,
+                                  venture_type: str,
+                                  context: Dict[str, Any]) -> Dict[str, Any]:
+        """Optimize monetization strategy."""
+        try:
+            # Design models
+            models = await self._design_models(venture_type, context)
+            
+            # Optimize pricing
+            pricing = await self._optimize_pricing(models, context)
+            
+            # Revenue optimization
+            revenue = await self._optimize_revenue(pricing, context)
+            
+            # Value optimization
+            value = await self._optimize_value(revenue, context)
+            
+            # Performance projections
+            projections = await self._project_performance(value, context)
+            
+            return {
+                "success": projections["annual_revenue"] >= 1_000_000,
+                "models": models,
+                "pricing": pricing,
+                "revenue": revenue,
+                "value": value,
+                "projections": projections
+            }
+        except Exception as e:
+            logging.error(f"Error in monetization optimization: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def _design_models(self,
+                           venture_type: str,
+                           context: Dict[str, Any]) -> Dict[str, Any]:
+        """Design monetization models."""
+        prompt = f"""
+        Design monetization models:
+        Venture: {venture_type}
+        Context: {json.dumps(context)}
+        
+        Design models for:
+        1. Subscription tiers
+        2. Usage-based pricing
+        3. Hybrid models
+        4. Enterprise pricing
+        5. Marketplace fees
+        
+        Format as:
+        [Model1]
+        Name: ...
+        Type: ...
+        Tiers: ...
+        Features: ...
+        Constraints: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_model_design(response["answer"])
+
+    async def _optimize_pricing(self,
+                              models: Dict[str, Any],
+                              context: Dict[str, Any]) -> Dict[str, Any]:
+        """Optimize pricing strategy."""
+        prompt = f"""
+        Optimize pricing strategy:
+        Models: {json.dumps(models)}
+        Context: {json.dumps(context)}
+        
+        Optimize for:
+        1. Market positioning
+        2. Value perception
+        3. Competitive dynamics
+        4. Customer segments
+        5. Growth potential
+        
+        Format as:
+        [Strategy1]
+        Model: ...
+        Positioning: ...
+        Value_Props: ...
+        Segments: ...
+        Growth: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_pricing_strategy(response["answer"])
+
+    async def _optimize_revenue(self,
+                              pricing: Dict[str, Any],
+                              context: Dict[str, Any]) -> Dict[str, Any]:
+        """Optimize revenue streams."""
+        prompt = f"""
+        Optimize revenue streams:
+        Pricing: {json.dumps(pricing)}
+        Context: {json.dumps(context)}
+        
+        Optimize for:
+        1. Revenue mix
+        2. Growth drivers
+        3. Retention factors
+        4. Expansion potential
+        5. Risk mitigation
+        
+        Format as:
+        [Stream1]
+        Type: ...
+        Drivers: ...
+        Retention: ...
+        Expansion: ...
+        Risks: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_revenue_optimization(response["answer"])
+
+    async def _optimize_value(self,
+                            revenue: Dict[str, Any],
+                            context: Dict[str, Any]) -> Dict[str, Any]:
+        """Optimize customer value."""
+        prompt = f"""
+        Optimize customer value:
+        Revenue: {json.dumps(revenue)}
+        Context: {json.dumps(context)}
+        
+        Optimize for:
+        1. Acquisition cost
+        2. Lifetime value
+        3. Churn reduction
+        4. Upsell potential
+        5. Network effects
+        
+        Format as:
+        [Value1]
+        Metric: ...
+        Strategy: ...
+        Potential: ...
+        Actions: ...
+        Timeline: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_value_optimization(response["answer"])
+
+    async def _project_performance(self,
+                                 value: Dict[str, Any],
+                                 context: Dict[str, Any]) -> Dict[str, Any]:
+        """Project monetization performance."""
+        prompt = f"""
+        Project performance:
+        Value: {json.dumps(value)}
+        Context: {json.dumps(context)}
+        
+        Project:
+        1. Revenue growth
+        2. Customer metrics
+        3. Unit economics
+        4. Profitability
+        5. Scale effects
+        
+        Format as:
+        [Projections]
+        Revenue: ...
+        Metrics: ...
+        Economics: ...
+        Profit: ...
+        Scale: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_performance_projections(response["answer"])
+
+    def _calculate_revenue_potential(self, model: MonetizationModel) -> float:
+        """Calculate revenue potential for model."""
+        base_potential = sum(
+            tier.get("price", 0) * tier.get("volume", 0)
+            for tier in model.pricing_tiers
+        )
+        
+        growth_factor = 1.0 + (model.metrics.get("growth_rate", 0) / 100)
+        retention_factor = 1.0 - (model.metrics.get("churn_rate", 0) / 100)
+        
+        return base_potential * growth_factor * retention_factor
+
+    def _calculate_customer_ltv(self, stream: RevenueStream) -> float:
+        """Calculate customer lifetime value."""
+        monthly_revenue = stream.volume * stream.unit_economics.get("arpu", 0)
+        churn_rate = stream.churn_rate / 100
+        discount_rate = 0.1  # 10% annual discount rate
+        
+        if churn_rate > 0:
+            ltv = monthly_revenue / churn_rate
+        else:
+            ltv = monthly_revenue * 12  # Assume 1 year if no churn
+            
+        return ltv / (1 + discount_rate)
+
+    def get_monetization_metrics(self) -> Dict[str, Any]:
+        """Get comprehensive monetization metrics."""
+        return {
+            "model_metrics": {
+                model.name: {
+                    "revenue_potential": self._calculate_revenue_potential(model),
+                    "tier_count": len(model.pricing_tiers),
+                    "feature_count": len(model.features),
+                    "constraint_count": len(model.constraints)
+                }
+                for model in self.models.values()
+            },
+            "stream_metrics": {
+                stream.name: {
+                    "monthly_revenue": stream.volume * stream.unit_economics.get("arpu", 0),
+                    "ltv": self._calculate_customer_ltv(stream),
+                    "growth_rate": stream.growth_rate,
+                    "churn_rate": stream.churn_rate
+                }
+                for stream in self.streams.values()
+            },
+            "aggregate_metrics": {
+                "total_revenue_potential": sum(
+                    self._calculate_revenue_potential(model)
+                    for model in self.models.values()
+                ),
+                "average_ltv": np.mean([
+                    self._calculate_customer_ltv(stream)
+                    for stream in self.streams.values()
+                ]) if self.streams else 0,
+                "weighted_growth_rate": np.average(
+                    [stream.growth_rate for stream in self.streams.values()],
+                    weights=[stream.volume for stream in self.streams.values()]
+                ) if self.streams else 0
+            }
+        }

reasoning/multimodal.py

@@ -0,0 +1,268 @@
+"""Multi-modal reasoning implementation."""
+
+import logging
+from typing import Dict, Any, List
+import json
+
+from .base import ReasoningStrategy
+
+class MultiModalReasoning(ReasoningStrategy):
+    """Implements multi-modal reasoning across different types of information."""
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        try:
+            # Process different modalities
+            modalities = await self._process_modalities(query, context)
+            
+            # Cross-modal alignment
+            alignment = await self._cross_modal_alignment(modalities, context)
+            
+            # Integrated analysis
+            integration = await self._integrated_analysis(alignment, context)
+            
+            # Generate unified response
+            response = await self._generate_response(integration, context)
+            
+            return {
+                "success": True,
+                "answer": response["conclusion"],
+                "modalities": modalities,
+                "alignment": alignment,
+                "integration": integration,
+                "confidence": response["confidence"]
+            }
+        except Exception as e:
+            logging.error(f"Error in multi-modal reasoning: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def _process_modalities(self, query: str, context: Dict[str, Any]) -> Dict[str, List[Dict[str, Any]]]:
+        """Process query across different modalities."""
+        prompt = f"""
+        Process query across modalities:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        For each modality extract:
+        1. [Type]: Modality type
+        2. [Content]: Relevant content
+        3. [Features]: Key features
+        4. [Quality]: Content quality
+        
+        Format as:
+        [M1]
+        Type: ...
+        Content: ...
+        Features: ...
+        Quality: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_modalities(response["answer"])
+
+    async def _cross_modal_alignment(self, modalities: Dict[str, List[Dict[str, Any]]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """Align information across different modalities."""
+        try:
+            # Extract modality types
+            modal_types = list(modalities.keys())
+            
+            # Initialize alignment results
+            alignments = []
+            
+            # Process each modality pair
+            for i in range(len(modal_types)):
+                for j in range(i + 1, len(modal_types)):
+                    type1, type2 = modal_types[i], modal_types[j]
+                    
+                    # Get items from each modality
+                    items1 = modalities[type1]
+                    items2 = modalities[type2]
+                    
+                    # Find alignments between items
+                    for item1 in items1:
+                        for item2 in items2:
+                            similarity = self._calculate_similarity(item1, item2)
+                            if similarity > 0.5:  # Threshold for alignment
+                                alignments.append({
+                                    "type1": type1,
+                                    "type2": type2,
+                                    "item1": item1,
+                                    "item2": item2,
+                                    "similarity": similarity
+                                })
+            
+            # Sort alignments by similarity
+            alignments.sort(key=lambda x: x["similarity"], reverse=True)
+            
+            return alignments
+            
+        except Exception as e:
+            logging.error(f"Error in cross-modal alignment: {str(e)}")
+            return []
+
+    def _calculate_similarity(self, item1: Dict[str, Any], item2: Dict[str, Any]) -> float:
+        """Calculate similarity between two items from different modalities."""
+        try:
+            # Extract content from items
+            content1 = str(item1.get("content", ""))
+            content2 = str(item2.get("content", ""))
+            
+            # Calculate basic similarity (can be enhanced with more sophisticated methods)
+            common_words = set(content1.lower().split()) & set(content2.lower().split())
+            total_words = set(content1.lower().split()) | set(content2.lower().split())
+            
+            if not total_words:
+                return 0.0
+                
+            return len(common_words) / len(total_words)
+            
+        except Exception as e:
+            logging.error(f"Error calculating similarity: {str(e)}")
+            return 0.0
+
+    async def _integrated_analysis(self, alignment: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
+        prompt = f"""
+        Perform integrated multi-modal analysis:
+        Alignment: {json.dumps(alignment)}
+        Context: {json.dumps(context)}
+        
+        For each insight:
+        1. [Insight]: Key finding
+        2. [Sources]: Contributing modalities
+        3. [Support]: Supporting evidence
+        4. [Confidence]: Confidence level
+        
+        Format as:
+        [I1]
+        Insight: ...
+        Sources: ...
+        Support: ...
+        Confidence: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_integration(response["answer"])
+
+    async def _generate_response(self, integration: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]:
+        prompt = f"""
+        Generate unified multi-modal response:
+        Integration: {json.dumps(integration)}
+        Context: {json.dumps(context)}
+        
+        Provide:
+        1. Main conclusion
+        2. Modal contributions
+        3. Integration benefits
+        4. Confidence level (0-1)
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_response(response["answer"])
+
+    def _parse_modalities(self, response: str) -> Dict[str, List[Dict[str, Any]]]:
+        """Parse modalities from response."""
+        modalities = {}
+        current_modality = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[M'):
+                if current_modality:
+                    if current_modality["type"] not in modalities:
+                        modalities[current_modality["type"]] = []
+                    modalities[current_modality["type"]].append(current_modality)
+                current_modality = {
+                    "type": "",
+                    "content": "",
+                    "features": "",
+                    "quality": ""
+                }
+            elif current_modality:
+                if line.startswith('Type:'):
+                    current_modality["type"] = line[5:].strip()
+                elif line.startswith('Content:'):
+                    current_modality["content"] = line[8:].strip()
+                elif line.startswith('Features:'):
+                    current_modality["features"] = line[9:].strip()
+                elif line.startswith('Quality:'):
+                    current_modality["quality"] = line[8:].strip()
+        
+        if current_modality:
+            if current_modality["type"] not in modalities:
+                modalities[current_modality["type"]] = []
+            modalities[current_modality["type"]].append(current_modality)
+        
+        return modalities
+
+    def _parse_integration(self, response: str) -> List[Dict[str, Any]]:
+        """Parse integration from response."""
+        integration = []
+        current_insight = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[I'):
+                if current_insight:
+                    integration.append(current_insight)
+                current_insight = {
+                    "insight": "",
+                    "sources": "",
+                    "support": "",
+                    "confidence": 0.0
+                }
+            elif current_insight:
+                if line.startswith('Insight:'):
+                    current_insight["insight"] = line[8:].strip()
+                elif line.startswith('Sources:'):
+                    current_insight["sources"] = line[8:].strip()
+                elif line.startswith('Support:'):
+                    current_insight["support"] = line[8:].strip()
+                elif line.startswith('Confidence:'):
+                    try:
+                        current_insight["confidence"] = float(line[11:].strip())
+                    except:
+                        pass
+        
+        if current_insight:
+            integration.append(current_insight)
+        
+        return integration
+
+    def _parse_response(self, response: str) -> Dict[str, Any]:
+        """Parse response from response."""
+        response_dict = {
+            "conclusion": "",
+            "modal_contributions": [],
+            "integration_benefits": [],
+            "confidence": 0.0
+        }
+        
+        mode = None
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('Conclusion:'):
+                response_dict["conclusion"] = line[11:].strip()
+            elif line.startswith('Modal Contributions:'):
+                mode = "modal"
+            elif line.startswith('Integration Benefits:'):
+                mode = "integration"
+            elif line.startswith('Confidence:'):
+                try:
+                    response_dict["confidence"] = float(line[11:].strip())
+                except:
+                    response_dict["confidence"] = 0.5
+                mode = None
+            elif mode == "modal" and line.startswith('- '):
+                response_dict["modal_contributions"].append(line[2:].strip())
+            elif mode == "integration" and line.startswith('- '):
+                response_dict["integration_benefits"].append(line[2:].strip())
+        
+        return response_dict

reasoning/neurosymbolic.py

@@ -0,0 +1,264 @@
+"""Neurosymbolic reasoning implementation."""
+
+import logging
+from typing import Dict, Any, List, Tuple
+import json
+
+from .base import ReasoningStrategy
+
+class NeurosymbolicReasoning(ReasoningStrategy):
+    """Implements neurosymbolic reasoning combining neural and symbolic approaches."""
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        try:
+            # Neural processing
+            neural_features = await self._neural_processing(query, context)
+            
+            # Symbolic reasoning
+            symbolic_rules = await self._symbolic_reasoning(neural_features, context)
+            
+            # Integration
+            integrated = await self._neurosymbolic_integration(neural_features, symbolic_rules, context)
+            
+            # Final inference
+            conclusion = await self._final_inference(integrated, context)
+            
+            return {
+                "success": True,
+                "answer": conclusion["answer"],
+                "neural_features": neural_features,
+                "symbolic_rules": symbolic_rules,
+                "integrated_reasoning": integrated,
+                "confidence": conclusion["confidence"],
+                "explanation": conclusion["explanation"]
+            }
+        except Exception as e:
+            return {"success": False, "error": str(e)}
+
+    async def _neural_processing(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
+        prompt = f"""
+        Extract neural features from query:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        For each feature:
+        1. [Type]: Feature type
+        2. [Value]: Extracted value
+        3. [Confidence]: Extraction confidence
+        4. [Relations]: Related concepts
+        
+        Format as:
+        [F1]
+        Type: ...
+        Value: ...
+        Confidence: ...
+        Relations: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_features(response["answer"])
+
+    async def _symbolic_reasoning(self, features: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
+        prompt = f"""
+        Generate symbolic rules from features:
+        Features: {json.dumps(features)}
+        Context: {json.dumps(context)}
+        
+        For each rule:
+        1. [Condition]: Rule condition
+        2. [Implication]: What it implies
+        3. [Certainty]: Rule certainty
+        4. [Source]: Derivation source
+        
+        Format as:
+        [R1]
+        Condition: ...
+        Implication: ...
+        Certainty: ...
+        Source: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_rules(response["answer"])
+
+    async def _neurosymbolic_integration(self, features: List[Dict[str, Any]], rules: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
+        prompt = f"""
+        Integrate neural and symbolic components:
+        Features: {json.dumps(features)}
+        Rules: {json.dumps(rules)}
+        Context: {json.dumps(context)}
+        
+        For each integration:
+        1. [Components]: What is being integrated
+        2. [Method]: How they are combined
+        3. [Result]: Integration outcome
+        4. [Confidence]: Integration confidence
+        
+        Format as:
+        [I1]
+        Components: ...
+        Method: ...
+        Result: ...
+        Confidence: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_integration(response["answer"])
+
+    async def _final_inference(self, integrated: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]:
+        prompt = f"""
+        Draw final conclusions from integrated reasoning:
+        Integrated: {json.dumps(integrated)}
+        Context: {json.dumps(context)}
+        
+        Provide:
+        1. Final answer/conclusion
+        2. Confidence level (0-1)
+        3. Explanation of reasoning
+        4. Key factors considered
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_conclusion(response["answer"])
+
+    def _parse_features(self, response: str) -> List[Dict[str, Any]]:
+        """Parse neural features from response."""
+        features = []
+        current = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[F'):
+                if current:
+                    features.append(current)
+                current = {
+                    "type": "",
+                    "value": "",
+                    "confidence": 0.0,
+                    "relations": []
+                }
+            elif current:
+                if line.startswith('Type:'):
+                    current["type"] = line[5:].strip()
+                elif line.startswith('Value:'):
+                    current["value"] = line[6:].strip()
+                elif line.startswith('Confidence:'):
+                    try:
+                        current["confidence"] = float(line[11:].strip())
+                    except:
+                        pass
+                elif line.startswith('Relations:'):
+                    current["relations"] = [r.strip() for r in line[10:].split(',')]
+        
+        if current:
+            features.append(current)
+        
+        return features
+
+    def _parse_rules(self, response: str) -> List[Dict[str, Any]]:
+        """Parse symbolic rules from response."""
+        rules = []
+        current = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[R'):
+                if current:
+                    rules.append(current)
+                current = {
+                    "condition": "",
+                    "implication": "",
+                    "certainty": 0.0,
+                    "source": ""
+                }
+            elif current:
+                if line.startswith('Condition:'):
+                    current["condition"] = line[10:].strip()
+                elif line.startswith('Implication:'):
+                    current["implication"] = line[12:].strip()
+                elif line.startswith('Certainty:'):
+                    try:
+                        current["certainty"] = float(line[10:].strip())
+                    except:
+                        pass
+                elif line.startswith('Source:'):
+                    current["source"] = line[7:].strip()
+        
+        if current:
+            rules.append(current)
+        
+        return rules
+
+    def _parse_integration(self, response: str) -> List[Dict[str, Any]]:
+        """Parse integration results from response."""
+        integrations = []
+        current = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[I'):
+                if current:
+                    integrations.append(current)
+                current = {
+                    "components": "",
+                    "method": "",
+                    "result": "",
+                    "confidence": 0.0
+                }
+            elif current:
+                if line.startswith('Components:'):
+                    current["components"] = line[11:].strip()
+                elif line.startswith('Method:'):
+                    current["method"] = line[7:].strip()
+                elif line.startswith('Result:'):
+                    current["result"] = line[7:].strip()
+                elif line.startswith('Confidence:'):
+                    try:
+                        current["confidence"] = float(line[11:].strip())
+                    except:
+                        pass
+        
+        if current:
+            integrations.append(current)
+        
+        return integrations
+
+    def _parse_conclusion(self, response: str) -> Dict[str, Any]:
+        """Parse final conclusion from response."""
+        conclusion = {
+            "answer": "",
+            "confidence": 0.0,
+            "explanation": "",
+            "factors": []
+        }
+        
+        mode = None
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('Answer:'):
+                conclusion["answer"] = line[7:].strip()
+            elif line.startswith('Confidence:'):
+                try:
+                    conclusion["confidence"] = float(line[11:].strip())
+                except:
+                    conclusion["confidence"] = 0.5
+            elif line.startswith('Explanation:'):
+                conclusion["explanation"] = line[12:].strip()
+            elif line.startswith('Factors:'):
+                mode = "factors"
+            elif mode == "factors" and line.startswith('- '):
+                conclusion["factors"].append(line[2:].strip())
+        
+        return conclusion

reasoning/portfolio_optimization.py

@@ -0,0 +1,352 @@
+"""Advanced portfolio optimization for venture strategies."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import numpy as np
+from collections import defaultdict
+
+@dataclass
+class VentureMetrics:
+    """Venture performance metrics."""
+    revenue: float
+    profit: float
+    growth_rate: float
+    risk_score: float
+    resource_usage: Dict[str, float]
+    synergy_score: float
+
+@dataclass
+class ResourceAllocation:
+    """Resource allocation configuration."""
+    venture_id: str
+    resources: Dict[str, float]
+    constraints: List[str]
+    dependencies: List[str]
+    priority: float
+
+class PortfolioOptimizer:
+    """
+    Advanced portfolio optimization that:
+    1. Optimizes venture mix
+    2. Allocates resources
+    3. Manages risks
+    4. Maximizes synergies
+    5. Balances growth
+    """
+    
+    def __init__(self):
+        self.ventures: Dict[str, VentureMetrics] = {}
+        self.allocations: Dict[str, ResourceAllocation] = {}
+        
+    async def optimize_portfolio(self,
+                               ventures: List[str],
+                               context: Dict[str, Any]) -> Dict[str, Any]:
+        """Optimize venture portfolio."""
+        try:
+            # Analyze ventures
+            analysis = await self._analyze_ventures(ventures, context)
+            
+            # Optimize allocation
+            allocation = await self._optimize_allocation(analysis, context)
+            
+            # Risk optimization
+            risk = await self._optimize_risk(allocation, context)
+            
+            # Synergy optimization
+            synergy = await self._optimize_synergies(risk, context)
+            
+            # Performance projections
+            projections = await self._project_performance(synergy, context)
+            
+            return {
+                "success": projections["annual_profit"] >= 1_000_000,
+                "analysis": analysis,
+                "allocation": allocation,
+                "risk": risk,
+                "synergy": synergy,
+                "projections": projections
+            }
+        except Exception as e:
+            logging.error(f"Error in portfolio optimization: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def _analyze_ventures(self,
+                              ventures: List[str],
+                              context: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze venture characteristics."""
+        prompt = f"""
+        Analyze ventures:
+        Ventures: {json.dumps(ventures)}
+        Context: {json.dumps(context)}
+        
+        Analyze:
+        1. Performance metrics
+        2. Resource requirements
+        3. Risk factors
+        4. Growth potential
+        5. Synergy opportunities
+        
+        Format as:
+        [Venture1]
+        Metrics: ...
+        Resources: ...
+        Risks: ...
+        Growth: ...
+        Synergies: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_venture_analysis(response["answer"])
+
+    async def _optimize_allocation(self,
+                                 analysis: Dict[str, Any],
+                                 context: Dict[str, Any]) -> Dict[str, Any]:
+        """Optimize resource allocation."""
+        prompt = f"""
+        Optimize resource allocation:
+        Analysis: {json.dumps(analysis)}
+        Context: {json.dumps(context)}
+        
+        Optimize for:
+        1. Resource efficiency
+        2. Growth potential
+        3. Risk balance
+        4. Synergy capture
+        5. Constraint satisfaction
+        
+        Format as:
+        [Allocation1]
+        Venture: ...
+        Resources: ...
+        Constraints: ...
+        Dependencies: ...
+        Priority: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_allocation_optimization(response["answer"])
+
+    async def _optimize_risk(self,
+                           allocation: Dict[str, Any],
+                           context: Dict[str, Any]) -> Dict[str, Any]:
+        """Optimize risk management."""
+        prompt = f"""
+        Optimize risk management:
+        Allocation: {json.dumps(allocation)}
+        Context: {json.dumps(context)}
+        
+        Optimize for:
+        1. Risk diversification
+        2. Exposure limits
+        3. Correlation management
+        4. Hedging strategies
+        5. Contingency planning
+        
+        Format as:
+        [Risk1]
+        Type: ...
+        Exposure: ...
+        Mitigation: ...
+        Contingency: ...
+        Impact: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_risk_optimization(response["answer"])
+
+    async def _optimize_synergies(self,
+                                risk: Dict[str, Any],
+                                context: Dict[str, Any]) -> Dict[str, Any]:
+        """Optimize portfolio synergies."""
+        prompt = f"""
+        Optimize synergies:
+        Risk: {json.dumps(risk)}
+        Context: {json.dumps(context)}
+        
+        Optimize for:
+        1. Resource sharing
+        2. Knowledge transfer
+        3. Market leverage
+        4. Technology reuse
+        5. Customer cross-sell
+        
+        Format as:
+        [Synergy1]
+        Type: ...
+        Ventures: ...
+        Potential: ...
+        Requirements: ...
+        Timeline: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_synergy_optimization(response["answer"])
+
+    async def _project_performance(self,
+                                 synergy: Dict[str, Any],
+                                 context: Dict[str, Any]) -> Dict[str, Any]:
+        """Project portfolio performance."""
+        prompt = f"""
+        Project performance:
+        Synergy: {json.dumps(synergy)}
+        Context: {json.dumps(context)}
+        
+        Project:
+        1. Revenue growth
+        2. Profit margins
+        3. Resource utilization
+        4. Risk metrics
+        5. Synergy capture
+        
+        Format as:
+        [Projections]
+        Revenue: ...
+        Profit: ...
+        Resources: ...
+        Risk: ...
+        Synergies: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_performance_projections(response["answer"])
+
+    def _calculate_portfolio_metrics(self) -> Dict[str, float]:
+        """Calculate comprehensive portfolio metrics."""
+        if not self.ventures:
+            return {
+                "total_revenue": 0.0,
+                "total_profit": 0.0,
+                "avg_growth": 0.0,
+                "avg_risk": 0.0,
+                "resource_efficiency": 0.0,
+                "synergy_capture": 0.0
+            }
+        
+        metrics = {
+            "total_revenue": sum(v.revenue for v in self.ventures.values()),
+            "total_profit": sum(v.profit for v in self.ventures.values()),
+            "avg_growth": np.mean([v.growth_rate for v in self.ventures.values()]),
+            "avg_risk": np.mean([v.risk_score for v in self.ventures.values()]),
+            "resource_efficiency": self._calculate_resource_efficiency(),
+            "synergy_capture": np.mean([v.synergy_score for v in self.ventures.values()])
+        }
+        
+        return metrics
+
+    def _calculate_resource_efficiency(self) -> float:
+        """Calculate resource utilization efficiency."""
+        if not self.ventures or not self.allocations:
+            return 0.0
+        
+        total_resources = defaultdict(float)
+        used_resources = defaultdict(float)
+        
+        # Sum up total and used resources
+        for venture_id, allocation in self.allocations.items():
+            for resource, amount in allocation.resources.items():
+                total_resources[resource] += amount
+                if venture_id in self.ventures:
+                    used_resources[resource] += (
+                        amount * self.ventures[venture_id].resource_usage.get(resource, 0)
+                    )
+        
+        # Calculate efficiency for each resource
+        efficiencies = []
+        for resource in total_resources:
+            if total_resources[resource] > 0:
+                efficiency = used_resources[resource] / total_resources[resource]
+                efficiencies.append(efficiency)
+        
+        return np.mean(efficiencies) if efficiencies else 0.0
+
+    def get_portfolio_insights(self) -> Dict[str, Any]:
+        """Get comprehensive portfolio insights."""
+        metrics = self._calculate_portfolio_metrics()
+        
+        return {
+            "portfolio_metrics": metrics,
+            "venture_metrics": {
+                venture_id: {
+                    "revenue": v.revenue,
+                    "profit": v.profit,
+                    "growth_rate": v.growth_rate,
+                    "risk_score": v.risk_score,
+                    "synergy_score": v.synergy_score
+                }
+                for venture_id, v in self.ventures.items()
+            },
+            "resource_allocation": {
+                venture_id: {
+                    "resources": a.resources,
+                    "priority": a.priority,
+                    "constraints": len(a.constraints),
+                    "dependencies": len(a.dependencies)
+                }
+                for venture_id, a in self.allocations.items()
+            },
+            "risk_profile": {
+                "portfolio_risk": metrics["avg_risk"],
+                "risk_concentration": self._calculate_risk_concentration(),
+                "risk_correlation": self._calculate_risk_correlation()
+            },
+            "optimization_opportunities": self._identify_optimization_opportunities()
+        }
+
+    def _calculate_risk_concentration(self) -> float:
+        """Calculate risk concentration in portfolio."""
+        if not self.ventures:
+            return 0.0
+        
+        risk_weights = [v.risk_score for v in self.ventures.values()]
+        return np.std(risk_weights) if len(risk_weights) > 1 else 0.0
+
+    def _calculate_risk_correlation(self) -> float:
+        """Calculate risk correlation between ventures."""
+        if len(self.ventures) < 2:
+            return 0.0
+        
+        # Create correlation matrix of risk scores and resource usage
+        venture_metrics = [
+            [v.risk_score] + list(v.resource_usage.values())
+            for v in self.ventures.values()
+        ]
+        
+        correlation_matrix = np.corrcoef(venture_metrics)
+        return np.mean(correlation_matrix[np.triu_indices_from(correlation_matrix, k=1)])
+
+    def _identify_optimization_opportunities(self) -> List[Dict[str, Any]]:
+        """Identify portfolio optimization opportunities."""
+        opportunities = []
+        
+        # Resource optimization opportunities
+        resource_efficiency = self._calculate_resource_efficiency()
+        if resource_efficiency < 0.8:
+            opportunities.append({
+                "type": "resource_optimization",
+                "potential": 1.0 - resource_efficiency,
+                "description": "Improve resource utilization efficiency"
+            })
+        
+        # Risk optimization opportunities
+        risk_concentration = self._calculate_risk_concentration()
+        if risk_concentration > 0.2:
+            opportunities.append({
+                "type": "risk_diversification",
+                "potential": risk_concentration,
+                "description": "Reduce risk concentration"
+            })
+        
+        # Synergy optimization opportunities
+        avg_synergy = np.mean([v.synergy_score for v in self.ventures.values()]) if self.ventures else 0
+        if avg_synergy < 0.7:
+            opportunities.append({
+                "type": "synergy_capture",
+                "potential": 1.0 - avg_synergy,
+                "description": "Increase synergy capture"
+            })
+        
+        return opportunities

reasoning/quantum.py

@@ -0,0 +1,353 @@
+"""Quantum-inspired reasoning implementations."""
+
+import logging
+from typing import Dict, Any, List
+import json
+
+from .base import ReasoningStrategy
+
+class QuantumReasoning(ReasoningStrategy):
+    """Implements quantum-inspired reasoning using superposition and entanglement principles."""
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        try:
+            # Create superposition of possibilities
+            superposition = await self._create_superposition(query, context)
+            
+            # Analyze entanglements
+            entanglements = await self._analyze_entanglements(superposition, context)
+            
+            # Perform quantum interference
+            interference = await self._quantum_interference(superposition, entanglements, context)
+            
+            # Collapse to solution
+            solution = await self._collapse_to_solution(interference, context)
+            
+            return {
+                "success": True,
+                "answer": solution["conclusion"],
+                "superposition": superposition,
+                "entanglements": entanglements,
+                "interference_patterns": interference,
+                "measurement": solution["measurement"],
+                "confidence": solution["confidence"]
+            }
+        except Exception as e:
+            return {"success": False, "error": str(e)}
+
+    async def _create_superposition(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
+        prompt = f"""
+        Create superposition of possible solutions:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        For each possibility state:
+        1. [State]: Description of possibility
+        2. [Amplitude]: Relative strength (0-1)
+        3. [Phase]: Relationship to other states
+        4. [Basis]: Underlying assumptions
+        
+        Format as:
+        [S1]
+        State: ...
+        Amplitude: ...
+        Phase: ...
+        Basis: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_superposition(response["answer"])
+
+    async def _analyze_entanglements(self, superposition: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
+        prompt = f"""
+        Analyze entanglements between possibilities:
+        Superposition: {json.dumps(superposition)}
+        Context: {json.dumps(context)}
+        
+        For each entanglement describe:
+        1. [States]: Entangled states
+        2. [Type]: Nature of entanglement
+        3. [Strength]: Correlation strength
+        4. [Impact]: Effect on outcomes
+        
+        Format as:
+        [E1]
+        States: ...
+        Type: ...
+        Strength: ...
+        Impact: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_entanglements(response["answer"])
+
+    async def _quantum_interference(self, superposition: List[Dict[str, Any]], entanglements: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
+        prompt = f"""
+        Calculate quantum interference patterns:
+        Superposition: {json.dumps(superposition)}
+        Entanglements: {json.dumps(entanglements)}
+        Context: {json.dumps(context)}
+        
+        For each interference pattern:
+        1. [Pattern]: Description
+        2. [Amplitude]: Combined strength
+        3. [Phase]: Combined phase
+        4. [Effect]: Impact on solution space
+        
+        Format as:
+        [I1]
+        Pattern: ...
+        Amplitude: ...
+        Phase: ...
+        Effect: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_interference(response["answer"])
+
+    async def _collapse_to_solution(self, interference: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]:
+        prompt = f"""
+        Collapse quantum state to final solution:
+        Interference: {json.dumps(interference)}
+        Context: {json.dumps(context)}
+        
+        Provide:
+        1. Final measured state
+        2. Measurement confidence
+        3. Key quantum effects utilized
+        4. Overall conclusion
+        5. Confidence level (0-1)
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_collapse(response["answer"])
+
+    def _parse_superposition(self, response: str) -> List[Dict[str, Any]]:
+        """Parse superposition states from response."""
+        superposition = []
+        current_state = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[S'):
+                if current_state:
+                    superposition.append(current_state)
+                current_state = {
+                    "state": "",
+                    "amplitude": 0.0,
+                    "phase": "",
+                    "basis": ""
+                }
+            elif current_state:
+                if line.startswith('State:'):
+                    current_state["state"] = line[6:].strip()
+                elif line.startswith('Amplitude:'):
+                    try:
+                        current_state["amplitude"] = float(line[10:].strip())
+                    except:
+                        pass
+                elif line.startswith('Phase:'):
+                    current_state["phase"] = line[6:].strip()
+                elif line.startswith('Basis:'):
+                    current_state["basis"] = line[6:].strip()
+        
+        if current_state:
+            superposition.append(current_state)
+        
+        return superposition
+
+    def _parse_entanglements(self, response: str) -> List[Dict[str, Any]]:
+        """Parse entanglements from response."""
+        entanglements = []
+        current_entanglement = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[E'):
+                if current_entanglement:
+                    entanglements.append(current_entanglement)
+                current_entanglement = {
+                    "states": "",
+                    "type": "",
+                    "strength": 0.0,
+                    "impact": ""
+                }
+            elif current_entanglement:
+                if line.startswith('States:'):
+                    current_entanglement["states"] = line[7:].strip()
+                elif line.startswith('Type:'):
+                    current_entanglement["type"] = line[5:].strip()
+                elif line.startswith('Strength:'):
+                    try:
+                        current_entanglement["strength"] = float(line[9:].strip())
+                    except:
+                        pass
+                elif line.startswith('Impact:'):
+                    current_entanglement["impact"] = line[7:].strip()
+        
+        if current_entanglement:
+            entanglements.append(current_entanglement)
+        
+        return entanglements
+
+    def _parse_interference(self, response: str) -> List[Dict[str, Any]]:
+        """Parse interference patterns from response."""
+        interference = []
+        current_pattern = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[I'):
+                if current_pattern:
+                    interference.append(current_pattern)
+                current_pattern = {
+                    "pattern": "",
+                    "amplitude": 0.0,
+                    "phase": "",
+                    "effect": ""
+                }
+            elif current_pattern:
+                if line.startswith('Pattern:'):
+                    current_pattern["pattern"] = line[8:].strip()
+                elif line.startswith('Amplitude:'):
+                    try:
+                        current_pattern["amplitude"] = float(line[10:].strip())
+                    except:
+                        pass
+                elif line.startswith('Phase:'):
+                    current_pattern["phase"] = line[6:].strip()
+                elif line.startswith('Effect:'):
+                    current_pattern["effect"] = line[7:].strip()
+        
+        if current_pattern:
+            interference.append(current_pattern)
+        
+        return interference
+
+    def _parse_collapse(self, response: str) -> Dict[str, Any]:
+        """Parse collapse to solution from response."""
+        collapse = {
+            "measurement": "",
+            "confidence": 0.0,
+            "quantum_effects": [],
+            "conclusion": ""
+        }
+        
+        mode = None
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('Measurement:'):
+                collapse["measurement"] = line[12:].strip()
+            elif line.startswith('Confidence:'):
+                try:
+                    collapse["confidence"] = float(line[11:].strip())
+                except:
+                    collapse["confidence"] = 0.5
+            elif line.startswith('Quantum Effects:'):
+                mode = "effects"
+            elif mode == "effects" and line.startswith('- '):
+                collapse["quantum_effects"].append(line[2:].strip())
+            elif line.startswith('Conclusion:'):
+                collapse["conclusion"] = line[11:].strip()
+        
+        return collapse
+
+
+class QuantumInspiredStrategy(ReasoningStrategy):
+    """Implements Quantum-Inspired reasoning."""
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        try:
+            # Create a clean context for serialization
+            clean_context = {k: v for k, v in context.items() if k != "groq_api"}
+            
+            prompt = f"""
+            You are a meta-learning reasoning system that adapts its approach based on problem characteristics.
+            
+            Problem Type: 
+            Query: {query}
+            Context: {json.dumps(clean_context)}
+            
+            Analyze this problem using meta-learning principles. Structure your response EXACTLY as follows:
+
+            PROBLEM ANALYSIS:
+            - [First key aspect or complexity factor]
+            - [Second key aspect or complexity factor]
+            - [Third key aspect or complexity factor]
+
+            SOLUTION PATHS:
+            - Path 1: [Specific solution approach]
+            - Path 2: [Alternative solution approach]
+            - Path 3: [Another alternative approach]
+
+            META INSIGHTS:
+            - Learning 1: [Key insight about the problem space]
+            - Learning 2: [Key insight about solution approaches]
+            - Learning 3: [Key insight about trade-offs]
+
+            CONCLUSION:
+            [Final synthesized solution incorporating meta-learnings]
+            """
+            
+            response = await context["groq_api"].predict(prompt)
+            
+            if not response["success"]:
+                return response
+                
+            # Parse response into components
+            lines = response["answer"].split("\n")
+            problem_analysis = []
+            solution_paths = []
+            meta_insights = []
+            conclusion = ""
+            
+            section = None
+            for line in lines:
+                line = line.strip()
+                if not line:
+                    continue
+                    
+                if "PROBLEM ANALYSIS:" in line:
+                    section = "analysis"
+                elif "SOLUTION PATHS:" in line:
+                    section = "paths"
+                elif "META INSIGHTS:" in line:
+                    section = "insights"
+                elif "CONCLUSION:" in line:
+                    section = "conclusion"
+                elif line.startswith("-"):
+                    content = line.lstrip("- ").strip()
+                    if section == "analysis":
+                        problem_analysis.append(content)
+                    elif section == "paths":
+                        solution_paths.append(content)
+                    elif section == "insights":
+                        meta_insights.append(content)
+                elif section == "conclusion":
+                    conclusion += line + " "
+            
+            return {
+                "success": True,
+                "problem_analysis": problem_analysis,
+                "solution_paths": solution_paths,
+                "meta_insights": meta_insights,
+                "conclusion": conclusion.strip(),
+                # Add standard fields for compatibility
+                "reasoning_path": problem_analysis + solution_paths + meta_insights,
+                "conclusion": conclusion.strip()
+            }
+            
+        except Exception as e:
+            return {"success": False, "error": str(e)}

reasoning/recursive.py

@@ -0,0 +1,566 @@
+"""Recursive reasoning implementation with advanced decomposition and synthesis."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Tuple, Callable
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import asyncio
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+
+class SubproblemType(Enum):
+    """Types of subproblems in recursive reasoning."""
+    ATOMIC = "atomic"
+    COMPOSITE = "composite"
+    PARALLEL = "parallel"
+    SEQUENTIAL = "sequential"
+    CONDITIONAL = "conditional"
+    ITERATIVE = "iterative"
+
+class SolutionStatus(Enum):
+    """Status of subproblem solutions."""
+    PENDING = "pending"
+    IN_PROGRESS = "in_progress"
+    SOLVED = "solved"
+    FAILED = "failed"
+    BLOCKED = "blocked"
+    OPTIMIZING = "optimizing"
+
+@dataclass
+class Subproblem:
+    """Represents a subproblem in recursive reasoning."""
+    id: str
+    type: SubproblemType
+    query: str
+    context: Dict[str, Any]
+    parent_id: Optional[str]
+    children: List[str]
+    status: SolutionStatus
+    solution: Optional[Dict[str, Any]]
+    confidence: float
+    dependencies: List[str]
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+@dataclass
+class RecursiveStep:
+    """Represents a step in recursive reasoning."""
+    id: str
+    subproblem_id: str
+    action: str
+    timestamp: datetime
+    result: Optional[Dict[str, Any]]
+    metrics: Dict[str, float]
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+class RecursiveReasoning(ReasoningStrategy):
+    """
+    Advanced Recursive Reasoning implementation with:
+    - Dynamic problem decomposition
+    - Parallel subproblem solving
+    - Solution synthesis
+    - Cycle detection
+    - Optimization strategies
+    """
+    
+    def __init__(self,
+                 max_depth: int = 5,
+                 min_confidence: float = 0.7,
+                 parallel_threshold: int = 3,
+                 optimization_rounds: int = 2):
+        self.max_depth = max_depth
+        self.min_confidence = min_confidence
+        self.parallel_threshold = parallel_threshold
+        self.optimization_rounds = optimization_rounds
+        
+        # Problem tracking
+        self.subproblems: Dict[str, Subproblem] = {}
+        self.steps: List[RecursiveStep] = []
+        self.solution_cache: Dict[str, Dict[str, Any]] = {}
+        self.cycle_detection: Set[str] = set()
+        
+        # Performance metrics
+        self.depth_distribution: Dict[int, int] = defaultdict(int)
+        self.type_distribution: Dict[SubproblemType, int] = defaultdict(int)
+        self.success_rate: Dict[SubproblemType, float] = defaultdict(float)
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Main reasoning method implementing recursive reasoning."""
+        try:
+            # Initialize root problem
+            root = await self._initialize_problem(query, context)
+            self.subproblems[root.id] = root
+            
+            # Recursively solve
+            solution = await self._solve_recursive(root.id, depth=0)
+            
+            # Optimize solution
+            optimized = await self._optimize_solution(solution, root, context)
+            
+            # Update metrics
+            self._update_metrics(root.id)
+            
+            return {
+                "success": True,
+                "answer": optimized["answer"],
+                "confidence": optimized["confidence"],
+                "decomposition": self._get_problem_tree(root.id),
+                "solution_trace": self._get_solution_trace(root.id),
+                "performance_metrics": self._get_performance_metrics(),
+                "meta_insights": optimized["meta_insights"]
+            }
+        except Exception as e:
+            logging.error(f"Error in recursive reasoning: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def _initialize_problem(self, query: str, context: Dict[str, Any]) -> Subproblem:
+        """Initialize the root problem."""
+        prompt = f"""
+        Initialize recursive reasoning problem:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        Analyze for:
+        1. Problem type classification
+        2. Initial decomposition strategy
+        3. Key dependencies
+        4. Solution approach
+        
+        Format as:
+        [Problem]
+        Type: ...
+        Strategy: ...
+        Dependencies: ...
+        Approach: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_problem_init(response["answer"], query, context)
+
+    async def _decompose_problem(self, problem: Subproblem, context: Dict[str, Any]) -> List[Subproblem]:
+        """Decompose a problem into subproblems."""
+        prompt = f"""
+        Decompose problem into subproblems:
+        Problem: {json.dumps(self._problem_to_dict(problem))}
+        Context: {json.dumps(context)}
+        
+        For each subproblem specify:
+        1. [Type]: {" | ".join([t.value for t in SubproblemType])}
+        2. [Query]: Specific question
+        3. [Dependencies]: Required solutions
+        4. [Approach]: Solution strategy
+        
+        Format as:
+        [S1]
+        Type: ...
+        Query: ...
+        Dependencies: ...
+        Approach: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_subproblems(response["answer"], problem.id, context)
+
+    async def _solve_recursive(self, problem_id: str, depth: int) -> Dict[str, Any]:
+        """Recursively solve a problem and its subproblems."""
+        if depth > self.max_depth:
+            return {"success": False, "error": "Maximum recursion depth exceeded"}
+            
+        if problem_id in self.cycle_detection:
+            return {"success": False, "error": "Cycle detected in recursive solving"}
+            
+        problem = self.subproblems[problem_id]
+        self.cycle_detection.add(problem_id)
+        self.depth_distribution[depth] += 1
+        
+        try:
+            # Check cache
+            cache_key = f"{problem.query}:{json.dumps(problem.context)}"
+            if cache_key in self.solution_cache:
+                return self.solution_cache[cache_key]
+            
+            # Check if atomic
+            if problem.type == SubproblemType.ATOMIC:
+                solution = await self._solve_atomic(problem)
+            else:
+                # Decompose
+                subproblems = await self._decompose_problem(problem, problem.context)
+                for sub in subproblems:
+                    self.subproblems[sub.id] = sub
+                    problem.children.append(sub.id)
+                
+                # Solve subproblems
+                if problem.type == SubproblemType.PARALLEL and len(subproblems) >= self.parallel_threshold:
+                    # Solve in parallel
+                    tasks = [self._solve_recursive(sub.id, depth + 1) for sub in subproblems]
+                    subsolutions = await asyncio.gather(*tasks)
+                else:
+                    # Solve sequentially
+                    subsolutions = []
+                    for sub in subproblems:
+                        subsolution = await self._solve_recursive(sub.id, depth + 1)
+                        subsolutions.append(subsolution)
+                
+                # Synthesize solutions
+                solution = await self._synthesize_solutions(subsolutions, problem, problem.context)
+            
+            # Cache solution
+            self.solution_cache[cache_key] = solution
+            problem.solution = solution
+            problem.status = SolutionStatus.SOLVED if solution["success"] else SolutionStatus.FAILED
+            
+            return solution
+            
+        finally:
+            self.cycle_detection.remove(problem_id)
+
+    async def _solve_atomic(self, problem: Subproblem) -> Dict[str, Any]:
+        """Solve an atomic problem."""
+        prompt = f"""
+        Solve atomic problem:
+        Problem: {json.dumps(self._problem_to_dict(problem))}
+        
+        Provide:
+        1. Direct solution
+        2. Confidence level
+        3. Supporting evidence
+        4. Alternative approaches
+        
+        Format as:
+        [Solution]
+        Answer: ...
+        Confidence: ...
+        Evidence: ...
+        Alternatives: ...
+        """
+        
+        response = await problem.context["groq_api"].predict(prompt)
+        solution = self._parse_atomic_solution(response["answer"])
+        
+        self._record_step(RecursiveStep(
+            id=f"step_{len(self.steps)}",
+            subproblem_id=problem.id,
+            action="atomic_solve",
+            timestamp=datetime.now(),
+            result=solution,
+            metrics={"confidence": solution.get("confidence", 0.0)},
+            metadata={}
+        ))
+        
+        return solution
+
+    async def _synthesize_solutions(self, subsolutions: List[Dict[str, Any]], problem: Subproblem, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Synthesize solutions from subproblems."""
+        prompt = f"""
+        Synthesize solutions:
+        Problem: {json.dumps(self._problem_to_dict(problem))}
+        Solutions: {json.dumps(subsolutions)}
+        Context: {json.dumps(context)}
+        
+        Provide:
+        1. Integrated solution
+        2. Confidence assessment
+        3. Integration method
+        4. Quality metrics
+        
+        Format as:
+        [Synthesis]
+        Solution: ...
+        Confidence: ...
+        Method: ...
+        Metrics: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        synthesis = self._parse_synthesis(response["answer"])
+        
+        self._record_step(RecursiveStep(
+            id=f"step_{len(self.steps)}",
+            subproblem_id=problem.id,
+            action="synthesize",
+            timestamp=datetime.now(),
+            result=synthesis,
+            metrics={"confidence": synthesis.get("confidence", 0.0)},
+            metadata={"num_subsolutions": len(subsolutions)}
+        ))
+        
+        return synthesis
+
+    async def _optimize_solution(self, solution: Dict[str, Any], problem: Subproblem, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Optimize the final solution."""
+        prompt = f"""
+        Optimize recursive solution:
+        Original: {json.dumps(solution)}
+        Problem: {json.dumps(self._problem_to_dict(problem))}
+        Context: {json.dumps(context)}
+        
+        Optimize for:
+        1. Completeness
+        2. Consistency
+        3. Efficiency
+        4. Clarity
+        
+        Format as:
+        [Optimization]
+        Answer: ...
+        Improvements: ...
+        Metrics: ...
+        Insights: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_optimization(response["answer"])
+
+    def _update_metrics(self, root_id: str):
+        """Update performance metrics."""
+        def update_recursive(problem_id: str):
+            problem = self.subproblems[problem_id]
+            self.type_distribution[problem.type] += 1
+            
+            if problem.status == SolutionStatus.SOLVED:
+                self.success_rate[problem.type] = (
+                    self.success_rate[problem.type] * (self.type_distribution[problem.type] - 1) +
+                    problem.confidence
+                ) / self.type_distribution[problem.type]
+            
+            for child_id in problem.children:
+                update_recursive(child_id)
+        
+        update_recursive(root_id)
+
+    def _get_problem_tree(self, root_id: str) -> Dict[str, Any]:
+        """Get the problem decomposition tree."""
+        def build_tree(problem_id: str) -> Dict[str, Any]:
+            problem = self.subproblems[problem_id]
+            return {
+                "id": problem.id,
+                "type": problem.type.value,
+                "query": problem.query,
+                "status": problem.status.value,
+                "confidence": problem.confidence,
+                "children": [build_tree(child_id) for child_id in problem.children]
+            }
+        
+        return build_tree(root_id)
+
+    def _get_solution_trace(self, root_id: str) -> List[Dict[str, Any]]:
+        """Get the solution trace for a problem."""
+        return [self._step_to_dict(step) for step in self.steps 
+                if step.subproblem_id == root_id or 
+                any(step.subproblem_id == sub_id for sub_id in self.subproblems[root_id].children)]
+
+    def _get_performance_metrics(self) -> Dict[str, Any]:
+        """Get current performance metrics."""
+        return {
+            "depth_distribution": dict(self.depth_distribution),
+            "type_distribution": {t.value: c for t, c in self.type_distribution.items()},
+            "success_rate": {t.value: r for t, r in self.success_rate.items()},
+            "cache_hits": len(self.solution_cache),
+            "total_steps": len(self.steps)
+        }
+
+    def _record_step(self, step: RecursiveStep):
+        """Record a reasoning step."""
+        self.steps.append(step)
+
+    def _parse_problem_init(self, response: str, query: str, context: Dict[str, Any]) -> Subproblem:
+        """Parse initial problem configuration."""
+        problem_type = SubproblemType.COMPOSITE  # default
+        dependencies = []
+        metadata = {}
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if line.startswith('Type:'):
+                try:
+                    problem_type = SubproblemType(line[5:].strip().lower())
+                except ValueError:
+                    pass
+            elif line.startswith('Dependencies:'):
+                dependencies = [d.strip() for d in line[13:].split(',')]
+            elif line.startswith('Strategy:') or line.startswith('Approach:'):
+                metadata["strategy"] = line.split(':', 1)[1].strip()
+        
+        return Subproblem(
+            id="root",
+            type=problem_type,
+            query=query,
+            context=context,
+            parent_id=None,
+            children=[],
+            status=SolutionStatus.PENDING,
+            solution=None,
+            confidence=0.0,
+            dependencies=dependencies,
+            metadata=metadata
+        )
+
+    def _parse_subproblems(self, response: str, parent_id: str, context: Dict[str, Any]) -> List[Subproblem]:
+        """Parse subproblems from response."""
+        subproblems = []
+        current = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[S'):
+                if current:
+                    subproblems.append(current)
+                current = None
+            elif line.startswith('Type:'):
+                try:
+                    problem_type = SubproblemType(line[5:].strip().lower())
+                    current = Subproblem(
+                        id=f"{parent_id}_{len(subproblems)}",
+                        type=problem_type,
+                        query="",
+                        context=context,
+                        parent_id=parent_id,
+                        children=[],
+                        status=SolutionStatus.PENDING,
+                        solution=None,
+                        confidence=0.0,
+                        dependencies=[],
+                        metadata={}
+                    )
+                except ValueError:
+                    current = None
+            elif current:
+                if line.startswith('Query:'):
+                    current.query = line[6:].strip()
+                elif line.startswith('Dependencies:'):
+                    current.dependencies = [d.strip() for d in line[13:].split(',')]
+                elif line.startswith('Approach:'):
+                    current.metadata["approach"] = line[9:].strip()
+        
+        if current:
+            subproblems.append(current)
+        
+        return subproblems
+
+    def _parse_atomic_solution(self, response: str) -> Dict[str, Any]:
+        """Parse atomic solution from response."""
+        solution = {
+            "success": True,
+            "answer": "",
+            "confidence": 0.0,
+            "evidence": [],
+            "alternatives": []
+        }
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if line.startswith('Answer:'):
+                solution["answer"] = line[7:].strip()
+            elif line.startswith('Confidence:'):
+                try:
+                    solution["confidence"] = float(line[11:].strip())
+                except:
+                    pass
+            elif line.startswith('Evidence:'):
+                solution["evidence"] = [e.strip() for e in line[9:].split(',')]
+            elif line.startswith('Alternatives:'):
+                solution["alternatives"] = [a.strip() for a in line[13:].split(',')]
+        
+        return solution
+
+    def _parse_synthesis(self, response: str) -> Dict[str, Any]:
+        """Parse synthesis result from response."""
+        synthesis = {
+            "success": True,
+            "solution": "",
+            "confidence": 0.0,
+            "method": "",
+            "metrics": {}
+        }
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if line.startswith('Solution:'):
+                synthesis["solution"] = line[9:].strip()
+            elif line.startswith('Confidence:'):
+                try:
+                    synthesis["confidence"] = float(line[11:].strip())
+                except:
+                    pass
+            elif line.startswith('Method:'):
+                synthesis["method"] = line[7:].strip()
+            elif line.startswith('Metrics:'):
+                try:
+                    synthesis["metrics"] = json.loads(line[8:].strip())
+                except:
+                    pass
+        
+        return synthesis
+
+    def _parse_optimization(self, response: str) -> Dict[str, Any]:
+        """Parse optimization result from response."""
+        optimization = {
+            "answer": "",
+            "confidence": 0.0,
+            "improvements": [],
+            "metrics": {},
+            "meta_insights": []
+        }
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if line.startswith('Answer:'):
+                optimization["answer"] = line[7:].strip()
+            elif line.startswith('Improvements:'):
+                optimization["improvements"] = [i.strip() for i in line[13:].split(',')]
+            elif line.startswith('Metrics:'):
+                try:
+                    optimization["metrics"] = json.loads(line[8:].strip())
+                except:
+                    pass
+            elif line.startswith('Insights:'):
+                optimization["meta_insights"] = [i.strip() for i in line[9:].split(',')]
+        
+        return optimization
+
+    def _problem_to_dict(self, problem: Subproblem) -> Dict[str, Any]:
+        """Convert problem to dictionary for serialization."""
+        return {
+            "id": problem.id,
+            "type": problem.type.value,
+            "query": problem.query,
+            "parent_id": problem.parent_id,
+            "children": problem.children,
+            "status": problem.status.value,
+            "confidence": problem.confidence,
+            "dependencies": problem.dependencies,
+            "metadata": problem.metadata
+        }
+
+    def _step_to_dict(self, step: RecursiveStep) -> Dict[str, Any]:
+        """Convert step to dictionary for serialization."""
+        return {
+            "id": step.id,
+            "subproblem_id": step.subproblem_id,
+            "action": step.action,
+            "timestamp": step.timestamp.isoformat(),
+            "result": step.result,
+            "metrics": step.metrics,
+            "metadata": step.metadata
+        }
+
+    def clear_cache(self):
+        """Clear solution cache."""
+        self.solution_cache.clear()
+
+    def get_statistics(self) -> Dict[str, Any]:
+        """Get detailed statistics about the reasoning process."""
+        return {
+            "total_problems": len(self.subproblems),
+            "total_steps": len(self.steps),
+            "cache_size": len(self.solution_cache),
+            "type_distribution": dict(self.type_distribution),
+            "depth_distribution": dict(self.depth_distribution),
+            "success_rates": dict(self.success_rate),
+            "average_confidence": sum(p.confidence for p in self.subproblems.values()) / len(self.subproblems) if self.subproblems else 0.0
+        }

reasoning/specialized.py

@@ -0,0 +1,313 @@
+"""Specialized reasoning strategies for specific domains and tasks."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Type, Callable
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import asyncio
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+
+class CodeRewriteStrategy(ReasoningStrategy):
+    """
+    Advanced code rewriting strategy that:
+    1. Analyzes code structure and patterns
+    2. Identifies refactoring opportunities
+    3. Maintains code semantics
+    4. Optimizes code quality
+    5. Ensures backward compatibility
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Rewrite code while preserving functionality."""
+        try:
+            # Analyze code
+            analysis = await self._analyze_code(query, context)
+            
+            # Generate rewrite plan
+            plan = await self._generate_rewrite_plan(analysis, context)
+            
+            # Execute rewrites
+            rewrites = await self._execute_rewrites(plan, context)
+            
+            # Validate changes
+            validation = await self._validate_changes(rewrites, context)
+            
+            return {
+                "success": validation["success"],
+                "rewrites": rewrites,
+                "validation": validation,
+                "metrics": {
+                    "quality_improvement": validation.get("quality_score", 0.0),
+                    "semantic_preservation": validation.get("semantic_score", 0.0)
+                }
+            }
+        except Exception as e:
+            logging.error(f"Error in code rewrite: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class SecurityAuditStrategy(ReasoningStrategy):
+    """
+    Advanced security audit strategy that:
+    1. Identifies security vulnerabilities
+    2. Analyzes attack vectors
+    3. Recommends security fixes
+    4. Validates security measures
+    5. Monitors security state
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Perform security audit and generate recommendations."""
+        try:
+            # Scan for vulnerabilities
+            vulnerabilities = await self._scan_vulnerabilities(query, context)
+            
+            # Analyze risks
+            risks = await self._analyze_risks(vulnerabilities, context)
+            
+            # Generate fixes
+            fixes = await self._generate_fixes(risks, context)
+            
+            # Validate security
+            validation = await self._validate_security(fixes, context)
+            
+            return {
+                "success": True,
+                "vulnerabilities": vulnerabilities,
+                "risks": risks,
+                "fixes": fixes,
+                "validation": validation
+            }
+        except Exception as e:
+            logging.error(f"Error in security audit: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class PerformanceOptimizationStrategy(ReasoningStrategy):
+    """
+    Advanced performance optimization strategy that:
+    1. Profiles code performance
+    2. Identifies bottlenecks
+    3. Generates optimizations
+    4. Measures improvements
+    5. Validates optimizations
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Optimize code performance."""
+        try:
+            # Profile performance
+            profile = await self._profile_performance(query, context)
+            
+            # Identify bottlenecks
+            bottlenecks = await self._identify_bottlenecks(profile, context)
+            
+            # Generate optimizations
+            optimizations = await self._generate_optimizations(bottlenecks, context)
+            
+            # Measure improvements
+            measurements = await self._measure_improvements(optimizations, context)
+            
+            return {
+                "success": measurements["success"],
+                "profile": profile,
+                "bottlenecks": bottlenecks,
+                "optimizations": optimizations,
+                "improvements": measurements
+            }
+        except Exception as e:
+            logging.error(f"Error in performance optimization: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class TestGenerationStrategy(ReasoningStrategy):
+    """
+    Advanced test generation strategy that:
+    1. Analyzes code coverage
+    2. Generates test cases
+    3. Creates test fixtures
+    4. Validates test quality
+    5. Maintains test suite
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate comprehensive test suite."""
+        try:
+            # Analyze coverage
+            coverage = await self._analyze_coverage(query, context)
+            
+            # Generate test cases
+            test_cases = await self._generate_test_cases(coverage, context)
+            
+            # Create fixtures
+            fixtures = await self._create_fixtures(test_cases, context)
+            
+            # Validate tests
+            validation = await self._validate_tests(test_cases, fixtures, context)
+            
+            return {
+                "success": validation["success"],
+                "test_cases": test_cases,
+                "fixtures": fixtures,
+                "validation": validation,
+                "metrics": {
+                    "coverage": coverage.get("percentage", 0.0),
+                    "quality_score": validation.get("quality_score", 0.0)
+                }
+            }
+        except Exception as e:
+            logging.error(f"Error in test generation: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class DocumentationStrategy(ReasoningStrategy):
+    """
+    Advanced documentation strategy that:
+    1. Analyzes code structure
+    2. Generates documentation
+    3. Maintains consistency
+    4. Updates references
+    5. Validates completeness
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate and maintain documentation."""
+        try:
+            # Analyze structure
+            structure = await self._analyze_structure(query, context)
+            
+            # Generate documentation
+            documentation = await self._generate_documentation(structure, context)
+            
+            # Update references
+            references = await self._update_references(documentation, context)
+            
+            # Validate completeness
+            validation = await self._validate_documentation(documentation, references, context)
+            
+            return {
+                "success": validation["success"],
+                "documentation": documentation,
+                "references": references,
+                "validation": validation,
+                "metrics": {
+                    "completeness": validation.get("completeness_score", 0.0),
+                    "consistency": validation.get("consistency_score", 0.0)
+                }
+            }
+        except Exception as e:
+            logging.error(f"Error in documentation: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class APIDesignStrategy(ReasoningStrategy):
+    """
+    Advanced API design strategy that:
+    1. Analyzes requirements
+    2. Designs API structure
+    3. Generates specifications
+    4. Validates design
+    5. Maintains versioning
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Design and validate API."""
+        try:
+            # Analyze requirements
+            requirements = await self._analyze_requirements(query, context)
+            
+            # Design structure
+            design = await self._design_structure(requirements, context)
+            
+            # Generate specs
+            specs = await self._generate_specs(design, context)
+            
+            # Validate design
+            validation = await self._validate_design(specs, context)
+            
+            return {
+                "success": validation["success"],
+                "requirements": requirements,
+                "design": design,
+                "specs": specs,
+                "validation": validation
+            }
+        except Exception as e:
+            logging.error(f"Error in API design: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class DependencyManagementStrategy(ReasoningStrategy):
+    """
+    Advanced dependency management strategy that:
+    1. Analyzes dependencies
+    2. Resolves conflicts
+    3. Optimizes versions
+    4. Ensures compatibility
+    5. Maintains security
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Manage and optimize dependencies."""
+        try:
+            # Analyze dependencies
+            analysis = await self._analyze_dependencies(query, context)
+            
+            # Resolve conflicts
+            resolution = await self._resolve_conflicts(analysis, context)
+            
+            # Optimize versions
+            optimization = await self._optimize_versions(resolution, context)
+            
+            # Validate compatibility
+            validation = await self._validate_compatibility(optimization, context)
+            
+            return {
+                "success": validation["success"],
+                "analysis": analysis,
+                "resolution": resolution,
+                "optimization": optimization,
+                "validation": validation
+            }
+        except Exception as e:
+            logging.error(f"Error in dependency management: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class CodeReviewStrategy(ReasoningStrategy):
+    """
+    Advanced code review strategy that:
+    1. Analyzes code quality
+    2. Identifies issues
+    3. Suggests improvements
+    4. Tracks changes
+    5. Validates fixes
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Perform comprehensive code review."""
+        try:
+            # Analyze quality
+            quality = await self._analyze_quality(query, context)
+            
+            # Identify issues
+            issues = await self._identify_issues(quality, context)
+            
+            # Generate suggestions
+            suggestions = await self._generate_suggestions(issues, context)
+            
+            # Track changes
+            tracking = await self._track_changes(suggestions, context)
+            
+            return {
+                "success": True,
+                "quality": quality,
+                "issues": issues,
+                "suggestions": suggestions,
+                "tracking": tracking,
+                "metrics": {
+                    "quality_score": quality.get("score", 0.0),
+                    "issues_found": len(issues),
+                    "suggestions_made": len(suggestions)
+                }
+            }
+        except Exception as e:
+            logging.error(f"Error in code review: {str(e)}")
+            return {"success": False, "error": str(e)}

reasoning/tree_of_thoughts.py

@@ -0,0 +1,513 @@
+"""Tree of Thoughts reasoning implementation with advanced tree exploration."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Tuple
+import json
+from dataclasses import dataclass
+from enum import Enum
+import heapq
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+
+class NodeType(Enum):
+    """Types of nodes in the thought tree."""
+    ROOT = "root"
+    HYPOTHESIS = "hypothesis"
+    EVIDENCE = "evidence"
+    ANALYSIS = "analysis"
+    SYNTHESIS = "synthesis"
+    EVALUATION = "evaluation"
+    CONCLUSION = "conclusion"
+
+@dataclass
+class TreeNode:
+    """Represents a node in the thought tree."""
+    id: str
+    type: NodeType
+    content: str
+    confidence: float
+    children: List['TreeNode']
+    parent: Optional['TreeNode']
+    metadata: Dict[str, Any]
+    depth: int
+    evaluation_score: float = 0.0
+
+class TreeOfThoughtsStrategy(ReasoningStrategy):
+    """
+    Advanced Tree of Thoughts reasoning implementation with:
+    - Beam search for path exploration
+    - Dynamic node evaluation
+    - Pruning strategies
+    - Path optimization
+    - Meta-learning from tree patterns
+    """
+    
+    def __init__(self, 
+                 max_depth: int = 5,
+                 beam_width: int = 3,
+                 min_confidence: float = 0.6,
+                 exploration_factor: float = 0.3,
+                 prune_threshold: float = 0.4):
+        self.max_depth = max_depth
+        self.beam_width = beam_width
+        self.min_confidence = min_confidence
+        self.exploration_factor = exploration_factor
+        self.prune_threshold = prune_threshold
+        self.node_history: Dict[str, TreeNode] = {}
+        self.path_patterns: Dict[str, float] = defaultdict(float)
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Main reasoning method implementing tree of thoughts."""
+        try:
+            # Initialize root node
+            root = await self._create_root_node(query, context)
+            
+            # Build and explore tree
+            tree = await self._build_tree(root, context)
+            
+            # Find best paths
+            paths = await self._find_best_paths(tree, context)
+            
+            # Synthesize conclusion
+            conclusion = await self._synthesize_conclusion(paths, context)
+            
+            # Update history and patterns
+            self._update_history(tree)
+            self._update_patterns(paths)
+            
+            return {
+                "success": True,
+                "answer": conclusion["answer"],
+                "confidence": conclusion["confidence"],
+                "tree": self._tree_to_dict(tree),
+                "best_paths": [self._path_to_dict(p) for p in paths],
+                "reasoning_trace": conclusion["trace"],
+                "meta_insights": conclusion["meta_insights"]
+            }
+        except Exception as e:
+            logging.error(f"Error in tree of thoughts reasoning: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def _create_root_node(self, query: str, context: Dict[str, Any]) -> TreeNode:
+        """Create the root node of the thought tree."""
+        prompt = f"""
+        Initialize root thought node for query:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        Provide:
+        1. Initial problem decomposition
+        2. Key aspects to explore
+        3. Evaluation criteria
+        4. Success metrics
+        
+        Format as:
+        [Root]
+        Decomposition: ...
+        Aspects: ...
+        Criteria: ...
+        Metrics: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_root_node(response["answer"], query)
+
+    async def _build_tree(self, root: TreeNode, context: Dict[str, Any]) -> TreeNode:
+        """Build and explore the thought tree."""
+        # Initialize beam with root
+        beam = [(root.evaluation_score, root)]
+        visited: Set[str] = set()
+        
+        for depth in range(self.max_depth):
+            next_beam = []
+            
+            for _, node in beam:
+                if node.id in visited:
+                    continue
+                    
+                visited.add(node.id)
+                
+                # Generate child nodes
+                children = await self._generate_children(node, context)
+                
+                # Evaluate and filter children
+                evaluated_children = await self._evaluate_nodes(children, context)
+                
+                # Add to beam
+                for child in evaluated_children:
+                    if child.evaluation_score > self.prune_threshold:
+                        next_beam.append((child.evaluation_score, child))
+                        node.children.append(child)
+            
+            # Select best nodes for next iteration
+            beam = heapq.nlargest(self.beam_width, next_beam, key=lambda x: x[0])
+            
+            if not beam:
+                break
+        
+        return root
+
+    async def _generate_children(self, parent: TreeNode, context: Dict[str, Any]) -> List[TreeNode]:
+        """Generate child nodes for a given parent."""
+        prompt = f"""
+        Generate child thoughts for node:
+        Parent: {json.dumps(self._node_to_dict(parent))}
+        Context: {json.dumps(context)}
+        
+        For each child provide:
+        1. [Type]: {" | ".join([t.value for t in NodeType if t != NodeType.ROOT])}
+        2. [Content]: Main thought
+        3. [Confidence]: 0-1 score
+        4. [Rationale]: Why this follows from parent
+        5. [Potential]: Future exploration potential
+        
+        Format as:
+        [C1]
+        Type: ...
+        Content: ...
+        Confidence: ...
+        Rationale: ...
+        Potential: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_child_nodes(response["answer"], parent)
+
+    async def _evaluate_nodes(self, nodes: List[TreeNode], context: Dict[str, Any]) -> List[TreeNode]:
+        """Evaluate a list of nodes."""
+        prompt = f"""
+        Evaluate thought nodes:
+        Nodes: {json.dumps([self._node_to_dict(n) for n in nodes])}
+        Context: {json.dumps(context)}
+        
+        For each node evaluate:
+        1. Logical coherence
+        2. Evidence support
+        3. Novelty value
+        4. Exploration potential
+        
+        Format as:
+        [N1]
+        Coherence: 0-1
+        Evidence: 0-1
+        Novelty: 0-1
+        Potential: 0-1
+        Overall: 0-1
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._apply_evaluations(nodes, response["answer"])
+
+    async def _find_best_paths(self, root: TreeNode, context: Dict[str, Any]) -> List[List[TreeNode]]:
+        """Find the best paths through the tree."""
+        paths = []
+        current_path = [root]
+        
+        def dfs(node: TreeNode, path: List[TreeNode]):
+            if not node.children:
+                paths.append(path[:])
+                return
+                
+            # Sort children by score
+            sorted_children = sorted(node.children, key=lambda x: x.evaluation_score, reverse=True)
+            
+            # Explore top paths
+            for child in sorted_children[:self.beam_width]:
+                path.append(child)
+                dfs(child, path)
+                path.pop()
+        
+        dfs(root, current_path)
+        
+        # Evaluate complete paths
+        evaluated_paths = await self._evaluate_paths(paths, context)
+        
+        # Return top paths
+        return sorted(evaluated_paths, key=lambda p: sum(n.evaluation_score for n in p), reverse=True)[:self.beam_width]
+
+    async def _synthesize_conclusion(self, paths: List[List[TreeNode]], context: Dict[str, Any]) -> Dict[str, Any]:
+        """Synthesize final conclusion from best paths."""
+        prompt = f"""
+        Synthesize conclusion from thought paths:
+        Paths: {json.dumps([[self._node_to_dict(n) for n in path] for path in paths])}
+        Context: {json.dumps(context)}
+        
+        Provide:
+        1. Main conclusion
+        2. Confidence level
+        3. Reasoning trace
+        4. Supporting evidence
+        5. Alternative perspectives
+        6. Meta-insights
+        
+        Format as:
+        [Conclusion]
+        Answer: ...
+        Confidence: ...
+        Trace: ...
+        Evidence: ...
+        Alternatives: ...
+        
+        [Meta]
+        Insights: ...
+        Patterns: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_conclusion(response["answer"])
+
+    def _parse_root_node(self, response: str, query: str) -> TreeNode:
+        """Parse root node from response."""
+        root = TreeNode(
+            id="root",
+            type=NodeType.ROOT,
+            content=query,
+            confidence=1.0,
+            children=[],
+            parent=None,
+            metadata={},
+            depth=0
+        )
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if line.startswith('Decomposition:'):
+                root.metadata["decomposition"] = line[14:].strip()
+            elif line.startswith('Aspects:'):
+                root.metadata["aspects"] = [a.strip() for a in line[8:].split(',')]
+            elif line.startswith('Criteria:'):
+                root.metadata["criteria"] = [c.strip() for c in line[9:].split(',')]
+            elif line.startswith('Metrics:'):
+                root.metadata["metrics"] = [m.strip() for m in line[8:].split(',')]
+        
+        return root
+
+    def _parse_child_nodes(self, response: str, parent: TreeNode) -> List[TreeNode]:
+        """Parse child nodes from response."""
+        children = []
+        current = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[C'):
+                if current:
+                    children.append(current)
+                current = None
+            elif line.startswith('Type:'):
+                type_str = line[5:].strip()
+                try:
+                    node_type = NodeType(type_str.lower())
+                    current = TreeNode(
+                        id=f"{parent.id}_{len(children)}",
+                        type=node_type,
+                        content="",
+                        confidence=0.0,
+                        children=[],
+                        parent=parent,
+                        metadata={},
+                        depth=parent.depth + 1
+                    )
+                except ValueError:
+                    logging.warning(f"Invalid node type: {type_str}")
+            elif current:
+                if line.startswith('Content:'):
+                    current.content = line[8:].strip()
+                elif line.startswith('Confidence:'):
+                    try:
+                        current.confidence = float(line[11:].strip())
+                    except:
+                        current.confidence = 0.5
+                elif line.startswith('Rationale:'):
+                    current.metadata["rationale"] = line[10:].strip()
+                elif line.startswith('Potential:'):
+                    current.metadata["potential"] = line[10:].strip()
+        
+        if current:
+            children.append(current)
+        
+        return children
+
+    def _apply_evaluations(self, nodes: List[TreeNode], response: str) -> List[TreeNode]:
+        """Apply evaluation scores to nodes."""
+        current_node_idx = 0
+        current_scores = {}
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[N'):
+                if current_scores and current_node_idx < len(nodes):
+                    nodes[current_node_idx].evaluation_score = current_scores.get("Overall", 0.0)
+                    nodes[current_node_idx].metadata.update(current_scores)
+                    current_node_idx += 1
+                current_scores = {}
+            elif ':' in line:
+                key, value = line.split(':')
+                try:
+                    current_scores[key.strip()] = float(value.strip())
+                except:
+                    pass
+        
+        if current_scores and current_node_idx < len(nodes):
+            nodes[current_node_idx].evaluation_score = current_scores.get("Overall", 0.0)
+            nodes[current_node_idx].metadata.update(current_scores)
+        
+        return nodes
+
+    async def _evaluate_paths(self, paths: List[List[TreeNode]], context: Dict[str, Any]) -> List[List[TreeNode]]:
+        """Evaluate complete reasoning paths."""
+        prompt = f"""
+        Evaluate complete reasoning paths:
+        Paths: {json.dumps([[self._node_to_dict(n) for n in path] for path in paths])}
+        Context: {json.dumps(context)}
+        
+        For each path evaluate:
+        1. Coherence of progression
+        2. Evidence support
+        3. Conclusion strength
+        4. Novel insights
+        
+        Format as:
+        [P1]
+        Coherence: 0-1
+        Evidence: 0-1
+        Conclusion: 0-1
+        Insights: 0-1
+        Overall: 0-1
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        scores = self._parse_path_scores(response["answer"])
+        
+        # Apply scores to paths
+        for i, path in enumerate(paths):
+            if i < len(scores):
+                for node in path:
+                    node.evaluation_score *= scores[i]
+        
+        return paths
+
+    def _parse_path_scores(self, response: str) -> List[float]:
+        """Parse path evaluation scores."""
+        scores = []
+        current_score = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[P'):
+                if current_score is not None:
+                    scores.append(current_score)
+                current_score = None
+            elif line.startswith('Overall:'):
+                try:
+                    current_score = float(line[8:].strip())
+                except:
+                    current_score = 0.5
+        
+        if current_score is not None:
+            scores.append(current_score)
+        
+        return scores
+
+    def _parse_conclusion(self, response: str) -> Dict[str, Any]:
+        """Parse final conclusion."""
+        conclusion = {
+            "answer": "",
+            "confidence": 0.0,
+            "trace": [],
+            "evidence": [],
+            "alternatives": [],
+            "meta_insights": []
+        }
+        
+        section = None
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[Conclusion]'):
+                section = "conclusion"
+            elif line.startswith('[Meta]'):
+                section = "meta"
+            elif section == "conclusion":
+                if line.startswith('Answer:'):
+                    conclusion["answer"] = line[7:].strip()
+                elif line.startswith('Confidence:'):
+                    try:
+                        conclusion["confidence"] = float(line[11:].strip())
+                    except:
+                        conclusion["confidence"] = 0.5
+                elif line.startswith('Trace:'):
+                    conclusion["trace"] = [t.strip() for t in line[6:].split(',')]
+                elif line.startswith('Evidence:'):
+                    conclusion["evidence"] = [e.strip() for e in line[9:].split(',')]
+                elif line.startswith('Alternatives:'):
+                    conclusion["alternatives"] = [a.strip() for a in line[13:].split(',')]
+            elif section == "meta":
+                if line.startswith('Insights:'):
+                    conclusion["meta_insights"].extend([i.strip() for i in line[9:].split(',')])
+        
+        return conclusion
+
+    def _node_to_dict(self, node: TreeNode) -> Dict[str, Any]:
+        """Convert node to dictionary for serialization."""
+        return {
+            "id": node.id,
+            "type": node.type.value,
+            "content": node.content,
+            "confidence": node.confidence,
+            "evaluation_score": node.evaluation_score,
+            "metadata": node.metadata,
+            "depth": node.depth
+        }
+
+    def _tree_to_dict(self, root: TreeNode) -> Dict[str, Any]:
+        """Convert entire tree to dictionary."""
+        def convert_node(node: TreeNode) -> Dict[str, Any]:
+            node_dict = self._node_to_dict(node)
+            node_dict["children"] = [convert_node(c) for c in node.children]
+            return node_dict
+        
+        return convert_node(root)
+
+    def _path_to_dict(self, path: List[TreeNode]) -> List[Dict[str, Any]]:
+        """Convert path to dictionary."""
+        return [self._node_to_dict(n) for n in path]
+
+    def _update_history(self, root: TreeNode):
+        """Update node history."""
+        def add_to_history(node: TreeNode):
+            self.node_history[node.id] = node
+            for child in node.children:
+                add_to_history(child)
+        
+        add_to_history(root)
+
+    def _update_patterns(self, paths: List[List[TreeNode]]):
+        """Update path patterns."""
+        for path in paths:
+            pattern = "->".join(n.type.value for n in path)
+            self.path_patterns[pattern] += path[-1].evaluation_score
+
+    def get_node_history(self) -> Dict[str, Dict[str, Any]]:
+        """Get history of all nodes."""
+        return {k: self._node_to_dict(v) for k, v in self.node_history.items()}
+
+    def get_successful_patterns(self) -> Dict[str, float]:
+        """Get successful reasoning patterns."""
+        return dict(sorted(self.path_patterns.items(), key=lambda x: x[1], reverse=True))
+
+    def clear_history(self):
+        """Clear node history and patterns."""
+        self.node_history.clear()
+        self.path_patterns.clear()

reasoning/unified_engine.py

@@ -0,0 +1,427 @@
+"""Unified reasoning engine that combines multiple reasoning strategies."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Type
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import asyncio
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+from .chain_of_thought import ChainOfThoughtStrategy
+from .tree_of_thoughts import TreeOfThoughtsStrategy
+from .meta_learning import MetaLearningStrategy
+from .recursive import RecursiveReasoning
+from .analogical import AnalogicalReasoning
+from .local_llm import LocalLLMStrategy
+from .agentic import (
+    TaskDecompositionStrategy,
+    ResourceManagementStrategy,
+    ContextualPlanningStrategy,
+    AdaptiveExecutionStrategy,
+    FeedbackIntegrationStrategy
+)
+
+class StrategyType(str, Enum):
+    """Types of reasoning strategies."""
+    CHAIN_OF_THOUGHT = "chain_of_thought"
+    TREE_OF_THOUGHTS = "tree_of_thoughts"
+    META_LEARNING = "meta_learning"
+    RECURSIVE = "recursive"
+    ANALOGICAL = "analogical"
+    TASK_DECOMPOSITION = "task_decomposition"
+    RESOURCE_MANAGEMENT = "resource_management"
+    CONTEXTUAL_PLANNING = "contextual_planning"
+    ADAPTIVE_EXECUTION = "adaptive_execution"
+    FEEDBACK_INTEGRATION = "feedback_integration"
+    LOCAL_LLM = "local_llm"
+
+@dataclass
+class StrategyResult:
+    """Result from a reasoning strategy."""
+    strategy_type: StrategyType
+    success: bool
+    answer: Optional[str]
+    confidence: float
+    reasoning_trace: List[Dict[str, Any]]
+    metadata: Dict[str, Any]
+    performance_metrics: Dict[str, Any]
+    timestamp: datetime = field(default_factory=datetime.now)
+
+@dataclass
+class UnifiedResult:
+    """Combined result from multiple strategies."""
+    success: bool
+    answer: str
+    confidence: float
+    strategy_results: Dict[StrategyType, StrategyResult]
+    synthesis_method: str
+    meta_insights: List[str]
+    performance_metrics: Dict[str, Any]
+    timestamp: datetime = field(default_factory=datetime.now)
+
+class UnifiedReasoningEngine:
+    """
+    Advanced unified reasoning engine that:
+    1. Combines multiple reasoning strategies
+    2. Dynamically selects and weights strategies
+    3. Synthesizes results from different approaches
+    4. Learns from experience
+    5. Adapts to different types of tasks
+    """
+    
+    def __init__(self,
+                 min_confidence: float = 0.7,
+                 strategy_weights: Optional[Dict[StrategyType, float]] = None,
+                 parallel_threshold: int = 3,
+                 learning_rate: float = 0.1):
+        self.min_confidence = min_confidence
+        self.parallel_threshold = parallel_threshold
+        self.learning_rate = learning_rate
+        
+        # Initialize strategies
+        self.strategies: Dict[StrategyType, ReasoningStrategy] = {
+            StrategyType.CHAIN_OF_THOUGHT: ChainOfThoughtStrategy(),
+            StrategyType.TREE_OF_THOUGHTS: TreeOfThoughtsStrategy(),
+            StrategyType.META_LEARNING: MetaLearningStrategy(),
+            StrategyType.RECURSIVE: RecursiveReasoning(),
+            StrategyType.ANALOGICAL: AnalogicalReasoning(),
+            StrategyType.TASK_DECOMPOSITION: TaskDecompositionStrategy(),
+            StrategyType.RESOURCE_MANAGEMENT: ResourceManagementStrategy(),
+            StrategyType.CONTEXTUAL_PLANNING: ContextualPlanningStrategy(),
+            StrategyType.ADAPTIVE_EXECUTION: AdaptiveExecutionStrategy(),
+            StrategyType.FEEDBACK_INTEGRATION: FeedbackIntegrationStrategy(),
+            StrategyType.LOCAL_LLM: LocalLLMStrategy()  # Add local LLM strategy
+        }
+        
+        # Strategy weights with higher weight for LOCAL_LLM
+        self.strategy_weights = strategy_weights or {
+            **{strategy_type: 1.0 for strategy_type in StrategyType},
+            StrategyType.LOCAL_LLM: 2.0  # Higher weight for local LLM
+        }
+        
+        # Performance tracking
+        self.strategy_performance: Dict[StrategyType, List[float]] = defaultdict(list)
+        self.task_type_performance: Dict[str, Dict[StrategyType, float]] = defaultdict(lambda: defaultdict(float))
+        self.synthesis_performance: Dict[str, List[float]] = defaultdict(list)
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> UnifiedResult:
+        """Main reasoning method combining multiple strategies."""
+        try:
+            # Analyze task
+            task_analysis = await self._analyze_task(query, context)
+            
+            # Select strategies
+            selected_strategies = await self._select_strategies(task_analysis, context)
+            
+            # Execute strategies
+            strategy_results = await self._execute_strategies(
+                selected_strategies, query, context)
+            
+            # Synthesize results
+            unified_result = await self._synthesize_results(
+                strategy_results, task_analysis, context)
+            
+            # Learn from experience
+            self._update_performance(unified_result)
+            
+            return unified_result
+            
+        except Exception as e:
+            logging.error(f"Error in unified reasoning: {str(e)}")
+            return UnifiedResult(
+                success=False,
+                answer=f"Error: {str(e)}",
+                confidence=0.0,
+                strategy_results={},
+                synthesis_method="failed",
+                meta_insights=[f"Error occurred: {str(e)}"],
+                performance_metrics={}
+            )
+
+    async def _analyze_task(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze the task to determine optimal strategy selection."""
+        prompt = f"""
+        Analyze reasoning task:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        Determine:
+        1. Task type and complexity
+        2. Required reasoning capabilities
+        3. Resource requirements
+        4. Success criteria
+        5. Risk factors
+        
+        Format as:
+        [Analysis]
+        Type: ...
+        Complexity: ...
+        Capabilities: ...
+        Resources: ...
+        Criteria: ...
+        Risks: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_task_analysis(response["answer"])
+
+    async def _select_strategies(self, task_analysis: Dict[str, Any], context: Dict[str, Any]) -> List[StrategyType]:
+        """Select appropriate strategies based on task analysis."""
+        # Calculate strategy scores
+        scores: Dict[StrategyType, float] = {}
+        for strategy_type in StrategyType:
+            base_score = self.strategy_weights[strategy_type]
+            
+            # Task type performance
+            task_type = task_analysis["type"]
+            type_score = self.task_type_performance[task_type][strategy_type]
+            
+            # Recent performance
+            recent_performance = (
+                sum(self.strategy_performance[strategy_type][-5:]) / 5
+                if self.strategy_performance[strategy_type] else 0.5
+            )
+            
+            # Resource match
+            resource_match = self._calculate_resource_match(
+                strategy_type, task_analysis["resources"])
+            
+            # Capability match
+            capability_match = self._calculate_capability_match(
+                strategy_type, task_analysis["capabilities"])
+            
+            # Combined score
+            scores[strategy_type] = (
+                0.3 * base_score +
+                0.2 * type_score +
+                0.2 * recent_performance +
+                0.15 * resource_match +
+                0.15 * capability_match
+            )
+        
+        # Select top strategies
+        selected = sorted(
+            StrategyType,
+            key=lambda x: scores[x],
+            reverse=True
+        )[:self.parallel_threshold]
+        
+        return selected
+
+    async def _execute_strategies(self,
+                                strategies: List[StrategyType],
+                                query: str,
+                                context: Dict[str, Any]) -> Dict[StrategyType, StrategyResult]:
+        """Execute selected strategies in parallel."""
+        async def execute_strategy(strategy_type: StrategyType) -> StrategyResult:
+            strategy = self.strategies[strategy_type]
+            start_time = datetime.now()
+            
+            try:
+                result = await strategy.reason(query, context)
+                
+                return StrategyResult(
+                    strategy_type=strategy_type,
+                    success=result.get("success", False),
+                    answer=result.get("answer"),
+                    confidence=result.get("confidence", 0.0),
+                    reasoning_trace=result.get("reasoning_trace", []),
+                    metadata=result.get("metadata", {}),
+                    performance_metrics={
+                        "execution_time": (datetime.now() - start_time).total_seconds(),
+                        **result.get("performance_metrics", {})
+                    }
+                )
+            except Exception as e:
+                logging.error(f"Error in strategy {strategy_type}: {str(e)}")
+                return StrategyResult(
+                    strategy_type=strategy_type,
+                    success=False,
+                    answer=None,
+                    confidence=0.0,
+                    reasoning_trace=[{"error": str(e)}],
+                    metadata={},
+                    performance_metrics={"execution_time": (datetime.now() - start_time).total_seconds()}
+                )
+        
+        # Execute strategies in parallel
+        tasks = [execute_strategy(strategy) for strategy in strategies]
+        results = await asyncio.gather(*tasks)
+        
+        return {result.strategy_type: result for result in results}
+
+    async def _synthesize_results(self,
+                                strategy_results: Dict[StrategyType, StrategyResult],
+                                task_analysis: Dict[str, Any],
+                                context: Dict[str, Any]) -> UnifiedResult:
+        """Synthesize results from multiple strategies."""
+        prompt = f"""
+        Synthesize reasoning results:
+        Results: {json.dumps({str(k): self._strategy_result_to_dict(v) 
+                            for k, v in strategy_results.items()})}
+        Task Analysis: {json.dumps(task_analysis)}
+        Context: {json.dumps(context)}
+        
+        Provide:
+        1. Optimal synthesis method
+        2. Combined answer
+        3. Confidence assessment
+        4. Meta-insights
+        5. Performance analysis
+        
+        Format as:
+        [Synthesis]
+        Method: ...
+        Answer: ...
+        Confidence: ...
+        Insights: ...
+        Performance: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        synthesis = self._parse_synthesis(response["answer"])
+        
+        return UnifiedResult(
+            success=synthesis["confidence"] >= self.min_confidence,
+            answer=synthesis["answer"],
+            confidence=synthesis["confidence"],
+            strategy_results=strategy_results,
+            synthesis_method=synthesis["method"],
+            meta_insights=synthesis["insights"],
+            performance_metrics=synthesis["performance"]
+        )
+
+    def _update_performance(self, result: UnifiedResult):
+        """Update performance metrics and strategy weights."""
+        # Update strategy performance
+        for strategy_type, strategy_result in result.strategy_results.items():
+            self.strategy_performance[strategy_type].append(strategy_result.confidence)
+            
+            # Update weights using exponential moving average
+            current_weight = self.strategy_weights[strategy_type]
+            performance = strategy_result.confidence
+            self.strategy_weights[strategy_type] = (
+                (1 - self.learning_rate) * current_weight +
+                self.learning_rate * performance
+            )
+        
+        # Update synthesis performance
+        self.synthesis_performance[result.synthesis_method].append(result.confidence)
+
+    def _calculate_resource_match(self, strategy_type: StrategyType, required_resources: Dict[str, Any]) -> float:
+        """Calculate how well a strategy matches required resources."""
+        # Implementation-specific resource matching logic
+        return 0.8  # Placeholder
+
+    def _calculate_capability_match(self, strategy_type: StrategyType, required_capabilities: List[str]) -> float:
+        """Calculate how well a strategy matches required capabilities."""
+        # Implementation-specific capability matching logic
+        return 0.8  # Placeholder
+
+    def _parse_task_analysis(self, response: str) -> Dict[str, Any]:
+        """Parse task analysis from response."""
+        analysis = {
+            "type": "",
+            "complexity": 0.0,
+            "capabilities": [],
+            "resources": {},
+            "criteria": [],
+            "risks": []
+        }
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if line.startswith('Type:'):
+                analysis["type"] = line[5:].strip()
+            elif line.startswith('Complexity:'):
+                try:
+                    analysis["complexity"] = float(line[11:].strip())
+                except:
+                    pass
+            elif line.startswith('Capabilities:'):
+                analysis["capabilities"] = [c.strip() for c in line[13:].split(',')]
+            elif line.startswith('Resources:'):
+                try:
+                    analysis["resources"] = json.loads(line[10:].strip())
+                except:
+                    analysis["resources"] = {"raw": line[10:].strip()}
+            elif line.startswith('Criteria:'):
+                analysis["criteria"] = [c.strip() for c in line[9:].split(',')]
+            elif line.startswith('Risks:'):
+                analysis["risks"] = [r.strip() for r in line[7:].split(',')]
+        
+        return analysis
+
+    def _parse_synthesis(self, response: str) -> Dict[str, Any]:
+        """Parse synthesis result from response."""
+        synthesis = {
+            "method": "",
+            "answer": "",
+            "confidence": 0.0,
+            "insights": [],
+            "performance": {}
+        }
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if line.startswith('Method:'):
+                synthesis["method"] = line[7:].strip()
+            elif line.startswith('Answer:'):
+                synthesis["answer"] = line[7:].strip()
+            elif line.startswith('Confidence:'):
+                try:
+                    synthesis["confidence"] = float(line[11:].strip())
+                except:
+                    pass
+            elif line.startswith('Insights:'):
+                synthesis["insights"] = [i.strip() for i in line[9:].split(',')]
+            elif line.startswith('Performance:'):
+                try:
+                    synthesis["performance"] = json.loads(line[12:].strip())
+                except:
+                    synthesis["performance"] = {"raw": line[12:].strip()}
+        
+        return synthesis
+
+    def _strategy_result_to_dict(self, result: StrategyResult) -> Dict[str, Any]:
+        """Convert strategy result to dictionary for serialization."""
+        return {
+            "strategy_type": result.strategy_type.value,
+            "success": result.success,
+            "answer": result.answer,
+            "confidence": result.confidence,
+            "reasoning_trace": result.reasoning_trace,
+            "metadata": result.metadata,
+            "performance_metrics": result.performance_metrics,
+            "timestamp": result.timestamp.isoformat()
+        }
+
+    def get_performance_metrics(self) -> Dict[str, Any]:
+        """Get comprehensive performance metrics."""
+        return {
+            "strategy_weights": dict(self.strategy_weights),
+            "average_performance": {
+                strategy_type.value: sum(scores) / len(scores) if scores else 0
+                for strategy_type, scores in self.strategy_performance.items()
+            },
+            "synthesis_success": {
+                method: sum(scores) / len(scores) if scores else 0
+                for method, scores in self.synthesis_performance.items()
+            },
+            "task_type_performance": {
+                task_type: dict(strategy_scores)
+                for task_type, strategy_scores in self.task_type_performance.items()
+            }
+        }
+
+    def clear_performance_history(self):
+        """Clear performance history and reset weights."""
+        self.strategy_performance.clear()
+        self.task_type_performance.clear()
+        self.synthesis_performance.clear()
+        self.strategy_weights = {
+            strategy_type: 1.0 for strategy_type in StrategyType
+        }

reasoning/venture_strategies.py

@@ -0,0 +1,423 @@
+"""Specialized strategies for autonomous business and revenue generation."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import numpy as np
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+
+class VentureType(Enum):
+    """Types of business ventures."""
+    AI_STARTUP = "ai_startup"
+    SAAS = "saas"
+    API_SERVICE = "api_service"
+    DATA_ANALYTICS = "data_analytics"
+    AUTOMATION_SERVICE = "automation_service"
+    CONSULTING = "consulting"
+    DIGITAL_PRODUCTS = "digital_products"
+    MARKETPLACE = "marketplace"
+
+class RevenueStream(Enum):
+    """Types of revenue streams."""
+    SUBSCRIPTION = "subscription"
+    USAGE_BASED = "usage_based"
+    LICENSING = "licensing"
+    CONSULTING = "consulting"
+    PRODUCT_SALES = "product_sales"
+    COMMISSION = "commission"
+    ADVERTISING = "advertising"
+    PARTNERSHIP = "partnership"
+
+@dataclass
+class VentureMetrics:
+    """Key business metrics."""
+    revenue: float
+    profit_margin: float
+    customer_acquisition_cost: float
+    lifetime_value: float
+    churn_rate: float
+    growth_rate: float
+    burn_rate: float
+    runway_months: float
+    roi: float
+
+@dataclass
+class MarketOpportunity:
+    """Market opportunity analysis."""
+    market_size: float
+    growth_potential: float
+    competition_level: float
+    entry_barriers: float
+    regulatory_risks: float
+    technology_risks: float
+    monetization_potential: float
+
+class AIStartupStrategy(ReasoningStrategy):
+    """
+    Advanced AI startup strategy that:
+    1. Identifies profitable AI applications
+    2. Analyzes market opportunities
+    3. Develops MVP strategies
+    4. Plans scaling approaches
+    5. Optimizes revenue streams
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate AI startup strategy."""
+        try:
+            # Market analysis
+            market = await self._analyze_market(query, context)
+            
+            # Technology assessment
+            tech = await self._assess_technology(market, context)
+            
+            # Business model
+            model = await self._develop_business_model(tech, context)
+            
+            # Growth strategy
+            strategy = await self._create_growth_strategy(model, context)
+            
+            # Financial projections
+            projections = await self._project_financials(strategy, context)
+            
+            return {
+                "success": projections["annual_profit"] >= 1_000_000,
+                "market_analysis": market,
+                "tech_assessment": tech,
+                "business_model": model,
+                "growth_strategy": strategy,
+                "financials": projections,
+                "confidence": self._calculate_confidence(projections)
+            }
+        except Exception as e:
+            logging.error(f"Error in AI startup strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class SaaSVentureStrategy(ReasoningStrategy):
+    """
+    Advanced SaaS venture strategy that:
+    1. Identifies scalable SaaS opportunities
+    2. Develops pricing strategies
+    3. Plans customer acquisition
+    4. Optimizes retention
+    5. Maximizes recurring revenue
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate SaaS venture strategy."""
+        try:
+            # Opportunity analysis
+            opportunity = await self._analyze_opportunity(query, context)
+            
+            # Product strategy
+            product = await self._develop_product_strategy(opportunity, context)
+            
+            # Pricing model
+            pricing = await self._create_pricing_model(product, context)
+            
+            # Growth plan
+            growth = await self._plan_growth(pricing, context)
+            
+            # Revenue projections
+            projections = await self._project_revenue(growth, context)
+            
+            return {
+                "success": projections["annual_revenue"] >= 1_000_000,
+                "opportunity": opportunity,
+                "product": product,
+                "pricing": pricing,
+                "growth": growth,
+                "projections": projections
+            }
+        except Exception as e:
+            logging.error(f"Error in SaaS venture strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class AutomationVentureStrategy(ReasoningStrategy):
+    """
+    Advanced automation venture strategy that:
+    1. Identifies automation opportunities
+    2. Analyzes cost-saving potential
+    3. Develops automation solutions
+    4. Plans implementation
+    5. Maximizes ROI
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate automation venture strategy."""
+        try:
+            # Opportunity identification
+            opportunities = await self._identify_opportunities(query, context)
+            
+            # Solution development
+            solutions = await self._develop_solutions(opportunities, context)
+            
+            # Implementation strategy
+            implementation = await self._create_implementation_strategy(solutions, context)
+            
+            # ROI analysis
+            roi = await self._analyze_roi(implementation, context)
+            
+            # Scale strategy
+            scale = await self._create_scale_strategy(roi, context)
+            
+            return {
+                "success": roi["annual_profit"] >= 1_000_000,
+                "opportunities": opportunities,
+                "solutions": solutions,
+                "implementation": implementation,
+                "roi": roi,
+                "scale": scale
+            }
+        except Exception as e:
+            logging.error(f"Error in automation venture strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class DataVentureStrategy(ReasoningStrategy):
+    """
+    Advanced data venture strategy that:
+    1. Identifies valuable data opportunities
+    2. Develops data products
+    3. Creates monetization strategies
+    4. Ensures compliance
+    5. Maximizes data value
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate data venture strategy."""
+        try:
+            # Data opportunity analysis
+            opportunity = await self._analyze_data_opportunity(query, context)
+            
+            # Product development
+            product = await self._develop_data_product(opportunity, context)
+            
+            # Monetization strategy
+            monetization = await self._create_monetization_strategy(product, context)
+            
+            # Compliance plan
+            compliance = await self._ensure_compliance(monetization, context)
+            
+            # Scale plan
+            scale = await self._plan_scaling(compliance, context)
+            
+            return {
+                "success": monetization["annual_revenue"] >= 1_000_000,
+                "opportunity": opportunity,
+                "product": product,
+                "monetization": monetization,
+                "compliance": compliance,
+                "scale": scale
+            }
+        except Exception as e:
+            logging.error(f"Error in data venture strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class APIVentureStrategy(ReasoningStrategy):
+    """
+    Advanced API venture strategy that:
+    1. Identifies API opportunities
+    2. Develops API products
+    3. Creates pricing models
+    4. Plans scaling
+    5. Maximizes API value
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate API venture strategy."""
+        try:
+            # API opportunity analysis
+            opportunity = await self._analyze_api_opportunity(query, context)
+            
+            # Product development
+            product = await self._develop_api_product(opportunity, context)
+            
+            # Pricing strategy
+            pricing = await self._create_api_pricing(product, context)
+            
+            # Scale strategy
+            scale = await self._plan_api_scaling(pricing, context)
+            
+            # Revenue projections
+            projections = await self._project_api_revenue(scale, context)
+            
+            return {
+                "success": projections["annual_revenue"] >= 1_000_000,
+                "opportunity": opportunity,
+                "product": product,
+                "pricing": pricing,
+                "scale": scale,
+                "projections": projections
+            }
+        except Exception as e:
+            logging.error(f"Error in API venture strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class MarketplaceVentureStrategy(ReasoningStrategy):
+    """
+    Advanced marketplace venture strategy that:
+    1. Identifies marketplace opportunities
+    2. Develops platform strategy
+    3. Plans liquidity generation
+    4. Optimizes matching
+    5. Maximizes transaction value
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate marketplace venture strategy."""
+        try:
+            # Opportunity analysis
+            opportunity = await self._analyze_marketplace_opportunity(query, context)
+            
+            # Platform strategy
+            platform = await self._develop_platform_strategy(opportunity, context)
+            
+            # Liquidity strategy
+            liquidity = await self._create_liquidity_strategy(platform, context)
+            
+            # Growth strategy
+            growth = await self._plan_marketplace_growth(liquidity, context)
+            
+            # Revenue projections
+            projections = await self._project_marketplace_revenue(growth, context)
+            
+            return {
+                "success": projections["annual_revenue"] >= 1_000_000,
+                "opportunity": opportunity,
+                "platform": platform,
+                "liquidity": liquidity,
+                "growth": growth,
+                "projections": projections
+            }
+        except Exception as e:
+            logging.error(f"Error in marketplace venture strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class VenturePortfolioStrategy(ReasoningStrategy):
+    """
+    Advanced venture portfolio strategy that:
+    1. Optimizes venture mix
+    2. Balances risk-reward
+    3. Allocates resources
+    4. Manages dependencies
+    5. Maximizes portfolio value
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate venture portfolio strategy."""
+        try:
+            # Portfolio analysis
+            analysis = await self._analyze_portfolio(query, context)
+            
+            # Venture selection
+            selection = await self._select_ventures(analysis, context)
+            
+            # Resource allocation
+            allocation = await self._allocate_resources(selection, context)
+            
+            # Risk management
+            risk = await self._manage_risk(allocation, context)
+            
+            # Portfolio projections
+            projections = await self._project_portfolio(risk, context)
+            
+            return {
+                "success": projections["annual_profit"] >= 1_000_000,
+                "analysis": analysis,
+                "selection": selection,
+                "allocation": allocation,
+                "risk": risk,
+                "projections": projections
+            }
+        except Exception as e:
+            logging.error(f"Error in venture portfolio strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def _analyze_portfolio(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze potential venture portfolio."""
+        prompt = f"""
+        Analyze venture portfolio opportunities:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        Consider:
+        1. Market opportunities
+        2. Technology trends
+        3. Resource requirements
+        4. Risk factors
+        5. Synergy potential
+        
+        Format as:
+        [Analysis]
+        Opportunities: ...
+        Trends: ...
+        Resources: ...
+        Risks: ...
+        Synergies: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_portfolio_analysis(response["answer"])
+
+    def _parse_portfolio_analysis(self, response: str) -> Dict[str, Any]:
+        """Parse portfolio analysis from response."""
+        analysis = {
+            "opportunities": [],
+            "trends": [],
+            "resources": {},
+            "risks": [],
+            "synergies": []
+        }
+        
+        current_section = None
+        for line in response.split('\n'):
+            line = line.strip()
+            if line.startswith('Opportunities:'):
+                current_section = "opportunities"
+            elif line.startswith('Trends:'):
+                current_section = "trends"
+            elif line.startswith('Resources:'):
+                current_section = "resources"
+            elif line.startswith('Risks:'):
+                current_section = "risks"
+            elif line.startswith('Synergies:'):
+                current_section = "synergies"
+            elif current_section and line:
+                if current_section == "resources":
+                    try:
+                        key, value = line.split(':')
+                        analysis[current_section][key.strip()] = value.strip()
+                    except:
+                        pass
+                else:
+                    analysis[current_section].append(line)
+        
+        return analysis
+
+    def get_venture_metrics(self) -> Dict[str, Any]:
+        """Get comprehensive venture metrics."""
+        return {
+            "portfolio_metrics": {
+                "total_ventures": len(self.ventures),
+                "profitable_ventures": sum(1 for v in self.ventures if v.metrics.profit_margin > 0),
+                "total_revenue": sum(v.metrics.revenue for v in self.ventures),
+                "average_margin": np.mean([v.metrics.profit_margin for v in self.ventures]),
+                "portfolio_roi": np.mean([v.metrics.roi for v in self.ventures])
+            },
+            "market_metrics": {
+                "total_market_size": sum(v.opportunity.market_size for v in self.ventures),
+                "average_growth": np.mean([v.opportunity.growth_potential for v in self.ventures]),
+                "risk_score": np.mean([v.opportunity.regulatory_risks + v.opportunity.technology_risks for v in self.ventures])
+            },
+            "performance_metrics": {
+                "customer_acquisition": np.mean([v.metrics.customer_acquisition_cost for v in self.ventures]),
+                "lifetime_value": np.mean([v.metrics.lifetime_value for v in self.ventures]),
+                "churn_rate": np.mean([v.metrics.churn_rate for v in self.ventures]),
+                "burn_rate": sum(v.metrics.burn_rate for v in self.ventures)
+            }
+        }

reasoning/venture_types.py

@@ -0,0 +1,332 @@
+"""Additional venture types for business optimization."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import numpy as np
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+
+class AIInfrastructureStrategy(ReasoningStrategy):
+    """
+    AI infrastructure venture strategy that:
+    1. Identifies infrastructure needs
+    2. Develops cloud solutions
+    3. Optimizes compute resources
+    4. Manages scalability
+    5. Ensures reliability
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate AI infrastructure strategy."""
+        try:
+            # Market analysis
+            market = await self._analyze_market(query, context)
+            
+            # Infrastructure design
+            design = await self._design_infrastructure(market, context)
+            
+            # Optimization strategy
+            optimization = await self._create_optimization_strategy(design, context)
+            
+            # Scaling plan
+            scaling = await self._plan_scaling(optimization, context)
+            
+            # Revenue projections
+            projections = await self._project_revenue(scaling, context)
+            
+            return {
+                "success": projections["annual_revenue"] >= 1_000_000,
+                "market": market,
+                "design": design,
+                "optimization": optimization,
+                "scaling": scaling,
+                "projections": projections
+            }
+        except Exception as e:
+            logging.error(f"Error in AI infrastructure strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class AIConsultingStrategy(ReasoningStrategy):
+    """
+    AI consulting venture strategy that:
+    1. Identifies consulting opportunities
+    2. Develops service offerings
+    3. Creates delivery frameworks
+    4. Manages client relationships
+    5. Scales operations
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate AI consulting strategy."""
+        try:
+            # Market analysis
+            market = await self._analyze_consulting_market(query, context)
+            
+            # Service design
+            services = await self._design_services(market, context)
+            
+            # Delivery framework
+            framework = await self._create_delivery_framework(services, context)
+            
+            # Growth strategy
+            growth = await self._plan_growth(framework, context)
+            
+            # Revenue projections
+            projections = await self._project_consulting_revenue(growth, context)
+            
+            return {
+                "success": projections["annual_revenue"] >= 1_000_000,
+                "market": market,
+                "services": services,
+                "framework": framework,
+                "growth": growth,
+                "projections": projections
+            }
+        except Exception as e:
+            logging.error(f"Error in AI consulting strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class AIProductStrategy(ReasoningStrategy):
+    """
+    AI product venture strategy that:
+    1. Identifies product opportunities
+    2. Develops product roadmap
+    3. Creates go-to-market strategy
+    4. Manages product lifecycle
+    5. Scales distribution
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate AI product strategy."""
+        try:
+            # Market analysis
+            market = await self._analyze_product_market(query, context)
+            
+            # Product development
+            product = await self._develop_product_strategy(market, context)
+            
+            # Go-to-market
+            gtm = await self._create_gtm_strategy(product, context)
+            
+            # Scale strategy
+            scale = await self._plan_product_scaling(gtm, context)
+            
+            # Revenue projections
+            projections = await self._project_product_revenue(scale, context)
+            
+            return {
+                "success": projections["annual_revenue"] >= 1_000_000,
+                "market": market,
+                "product": product,
+                "gtm": gtm,
+                "scale": scale,
+                "projections": projections
+            }
+        except Exception as e:
+            logging.error(f"Error in AI product strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class FinTechStrategy(ReasoningStrategy):
+    """
+    FinTech venture strategy that:
+    1. Identifies fintech opportunities
+    2. Develops financial products
+    3. Ensures compliance
+    4. Manages risk
+    5. Scales operations
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate FinTech strategy."""
+        try:
+            # Market analysis
+            market = await self._analyze_fintech_market(query, context)
+            
+            # Product development
+            product = await self._develop_fintech_product(market, context)
+            
+            # Compliance strategy
+            compliance = await self._ensure_compliance(product, context)
+            
+            # Risk management
+            risk = await self._manage_risk(compliance, context)
+            
+            # Scale strategy
+            scale = await self._plan_fintech_scaling(risk, context)
+            
+            return {
+                "success": scale["annual_revenue"] >= 1_000_000,
+                "market": market,
+                "product": product,
+                "compliance": compliance,
+                "risk": risk,
+                "scale": scale
+            }
+        except Exception as e:
+            logging.error(f"Error in FinTech strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class HealthTechStrategy(ReasoningStrategy):
+    """
+    HealthTech venture strategy that:
+    1. Identifies healthcare opportunities
+    2. Develops health solutions
+    3. Ensures compliance
+    4. Manages patient data
+    5. Scales operations
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate HealthTech strategy."""
+        try:
+            # Market analysis
+            market = await self._analyze_healthtech_market(query, context)
+            
+            # Solution development
+            solution = await self._develop_health_solution(market, context)
+            
+            # Compliance strategy
+            compliance = await self._ensure_health_compliance(solution, context)
+            
+            # Data strategy
+            data = await self._manage_health_data(compliance, context)
+            
+            # Scale strategy
+            scale = await self._plan_healthtech_scaling(data, context)
+            
+            return {
+                "success": scale["annual_revenue"] >= 1_000_000,
+                "market": market,
+                "solution": solution,
+                "compliance": compliance,
+                "data": data,
+                "scale": scale
+            }
+        except Exception as e:
+            logging.error(f"Error in HealthTech strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class EdTechStrategy(ReasoningStrategy):
+    """
+    EdTech venture strategy that:
+    1. Identifies education opportunities
+    2. Develops learning solutions
+    3. Creates content strategy
+    4. Manages user engagement
+    5. Scales platform
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate EdTech strategy."""
+        try:
+            # Market analysis
+            market = await self._analyze_edtech_market(query, context)
+            
+            # Solution development
+            solution = await self._develop_learning_solution(market, context)
+            
+            # Content strategy
+            content = await self._create_content_strategy(solution, context)
+            
+            # Engagement strategy
+            engagement = await self._manage_engagement(content, context)
+            
+            # Scale strategy
+            scale = await self._plan_edtech_scaling(engagement, context)
+            
+            return {
+                "success": scale["annual_revenue"] >= 1_000_000,
+                "market": market,
+                "solution": solution,
+                "content": content,
+                "engagement": engagement,
+                "scale": scale
+            }
+        except Exception as e:
+            logging.error(f"Error in EdTech strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class BlockchainStrategy(ReasoningStrategy):
+    """
+    Blockchain venture strategy that:
+    1. Identifies blockchain opportunities
+    2. Develops blockchain solutions
+    3. Ensures security
+    4. Manages tokenomics
+    5. Scales network
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate blockchain strategy."""
+        try:
+            # Market analysis
+            market = await self._analyze_blockchain_market(query, context)
+            
+            # Solution development
+            solution = await self._develop_blockchain_solution(market, context)
+            
+            # Security strategy
+            security = await self._ensure_blockchain_security(solution, context)
+            
+            # Tokenomics
+            tokenomics = await self._design_tokenomics(security, context)
+            
+            # Scale strategy
+            scale = await self._plan_blockchain_scaling(tokenomics, context)
+            
+            return {
+                "success": scale["annual_revenue"] >= 1_000_000,
+                "market": market,
+                "solution": solution,
+                "security": security,
+                "tokenomics": tokenomics,
+                "scale": scale
+            }
+        except Exception as e:
+            logging.error(f"Error in blockchain strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class AIMarketplaceStrategy(ReasoningStrategy):
+    """
+    AI marketplace venture strategy that:
+    1. Creates AI model marketplace
+    2. Manages model deployment
+    3. Handles transactions
+    4. Ensures quality
+    5. Scales platform
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate AI marketplace strategy."""
+        try:
+            # Market analysis
+            market = await self._analyze_ai_marketplace(query, context)
+            
+            # Platform development
+            platform = await self._develop_marketplace_platform(market, context)
+            
+            # Quality strategy
+            quality = await self._ensure_model_quality(platform, context)
+            
+            # Transaction system
+            transactions = await self._design_transaction_system(quality, context)
+            
+            # Scale strategy
+            scale = await self._plan_marketplace_scaling(transactions, context)
+            
+            return {
+                "success": scale["annual_revenue"] >= 1_000_000,
+                "market": market,
+                "platform": platform,
+                "quality": quality,
+                "transactions": transactions,
+                "scale": scale
+            }
+        except Exception as e:
+            logging.error(f"Error in AI marketplace strategy: {str(e)}")
+            return {"success": False, "error": str(e)}

requirements.txt

@@ -0,0 +1,19 @@
+fastapi>=0.68.0
+uvicorn>=0.15.0
+pydantic>=2.0.0
+gradio>=4.16.0
+llama-cpp-python>=0.2.23
+huggingface-hub>=0.19.4
+numpy>=1.24.0
+networkx>=3.2.1
+scikit-learn>=1.3.2
+scipy==1.11.4
+pandas>=2.1.0
+plotly>=5.18.0
+typing-extensions>=4.0.0
+aiohttp>=3.8.0
+asyncio>=3.4.3
+joblib==1.3.2
+tqdm>=4.66.0
+python-dotenv>=0.19.0
+httpx>=0.24.0

requirements.txt.backup

@@ -0,0 +1,28 @@
+fastapi>=0.68.0
+uvicorn>=0.15.0
+pydantic>=2.0.0
+gradio>=3.50.0
+transformers>=4.36.2
+torch>=2.1.2
+huggingface-hub==0.19.4
+ctransformers==0.2.27
+numpy>=1.24.0
+networkx>=3.2.1
+scikit-learn>=1.3.2
+scipy==1.11.4
+pandas>=2.1.0
+plotly>=5.18.0
+typing-extensions>=4.0.0
+aiohttp>=3.8.0
+asyncio>=3.4.3
+joblib==1.3.2
+tqdm>=4.66.1
+python-dotenv>=0.19.0
+httpx>=0.24.0
+python-multipart>=0.0.5
+websockets>=10.0
+jinja2>=3.0.0
+markdown>=3.3.0
+pygments>=2.10.0
+starlette>=0.14.0
+requests>=2.26.0

simple_reasoning.py

@@ -0,0 +1,25 @@
+"""Simple Reasoning Agent using local models"""
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+class ReasoningAgent:
+    """A simple reasoning agent using local models."""
+    
+    def __init__(self, model_name="TinyLlama/TinyLlama-1.1B-Chat-v1.0"):
+        """Initialize the agent with a local model."""
+        # Load model and tokenizer
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_name,
+            torch_dtype=torch.float16,
+            device_map="auto"
+        )
+        
+    def get_response(self, query: str) -> str:
+        """Generate a response using the local model."""
+        try:
+            # Format the prompt
+            prompt = f"""<|system|>
+You are a helpful AI assistant.
+</s>

simple_reasoning/__init__.py

@@ -0,0 +1,5 @@
+"""Simple reasoning system using local LLM."""
+
+from .agent import ReasoningAgent
+
+__all__ = ['ReasoningAgent']

simple_reasoning/agent.py

@@ -0,0 +1,26 @@
+"""Local reasoning agent using TinyLlama."""
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+class ReasoningAgent:
+    """Local reasoning agent using TinyLlama with chain of thought prompting."""
+    
+    def __init__(self, model_name="TinyLlama/TinyLlama-1.1B-Chat-v1.0"):
+        """Initialize the agent with local model."""
+        # Load model and tokenizer
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_name,
+            torch_dtype=torch.float16,
+            device_map="auto"
+        )
+        
+    def get_response(self, message: str) -> str:
+        """Generate response using local model with chain of thought prompting."""
+        try:
+            # Format prompt with chain of thought structure
+            prompt = f"""<|system|>
+You are a helpful AI assistant that uses chain of thought reasoning to answer questions.
+For each response, break down your thinking into steps before giving the final answer.
+</s>

team_management.py

@@ -0,0 +1,442 @@
+"""
+Advanced Team Management System
+-----------------------------
+Manages specialized teams of agents that work together towards common goals:
+1. Team A: Coders (App/Software Developers)
+2. Team B: Business (Entrepreneurs)
+3. Team C: Research (Deep Online Research)
+4. Team D: Crypto & Sports Trading
+
+Features:
+- Cross-team collaboration
+- Goal alignment
+- Resource sharing
+- Synchronized execution
+"""
+
+from typing import Dict, List, Optional, Set, Union, TypeVar
+from dataclasses import dataclass, field
+from enum import Enum
+import asyncio
+from datetime import datetime
+import uuid
+from collections import defaultdict
+
+from orchestrator import AgentOrchestrator, TaskPriority
+from agentic_system import AgentRole, AgentCapability, AgentPersonality, Agent
+
+class TeamType(Enum):
+    """Specialized team types."""
+    CODERS = "coders"
+    BUSINESS = "business"
+    RESEARCH = "research"
+    TRADERS = "traders"
+
+class TeamObjective(Enum):
+    """Types of team objectives."""
+    SOFTWARE_DEVELOPMENT = "software_development"
+    BUSINESS_OPPORTUNITY = "business_opportunity"
+    MARKET_RESEARCH = "market_research"
+    TRADING_STRATEGY = "trading_strategy"
+    CROSS_TEAM_PROJECT = "cross_team_project"
+
+@dataclass
+class TeamProfile:
+    """Team profile and capabilities."""
+    id: str
+    type: TeamType
+    name: str
+    primary_objective: TeamObjective
+    secondary_objectives: List[TeamObjective]
+    agent_count: int
+    expertise_areas: List[str]
+    collaboration_score: float = 0.0
+    success_rate: float = 0.0
+    active_projects: int = 0
+
+@dataclass
+class CollaborationLink:
+    """Defines collaboration between teams."""
+    team_a_id: str
+    team_b_id: str
+    strength: float
+    active_projects: int
+    last_interaction: datetime
+    success_rate: float
+
+class TeamManager:
+    """Manages specialized teams and their collaboration."""
+    
+    def __init__(self, orchestrator: AgentOrchestrator):
+        self.orchestrator = orchestrator
+        self.teams: Dict[str, TeamProfile] = {}
+        self.agents: Dict[str, Dict[str, Agent]] = {}  # team_id -> {agent_id -> Agent}
+        self.collaboration_network: Dict[str, CollaborationLink] = {}
+        self.shared_objectives: Dict[str, Set[str]] = defaultdict(set)  # objective_id -> set of team_ids
+        self.lock = asyncio.Lock()
+        
+        # Initialize specialized teams
+        self._init_teams()
+
+    def _init_teams(self):
+        """Initialize specialized teams."""
+        team_configs = {
+            TeamType.CODERS: {
+                "name": "Development Team",
+                "primary": TeamObjective.SOFTWARE_DEVELOPMENT,
+                "secondary": [
+                    TeamObjective.BUSINESS_OPPORTUNITY,
+                    TeamObjective.MARKET_RESEARCH
+                ],
+                "expertise": [
+                    "full_stack_development",
+                    "cloud_architecture",
+                    "ai_ml",
+                    "blockchain",
+                    "mobile_development"
+                ]
+            },
+            TeamType.BUSINESS: {
+                "name": "Business Strategy Team",
+                "primary": TeamObjective.BUSINESS_OPPORTUNITY,
+                "secondary": [
+                    TeamObjective.MARKET_RESEARCH,
+                    TeamObjective.TRADING_STRATEGY
+                ],
+                "expertise": [
+                    "market_analysis",
+                    "business_strategy",
+                    "digital_transformation",
+                    "startup_innovation",
+                    "product_management"
+                ]
+            },
+            TeamType.RESEARCH: {
+                "name": "Research & Analysis Team",
+                "primary": TeamObjective.MARKET_RESEARCH,
+                "secondary": [
+                    TeamObjective.BUSINESS_OPPORTUNITY,
+                    TeamObjective.TRADING_STRATEGY
+                ],
+                "expertise": [
+                    "deep_research",
+                    "data_analysis",
+                    "trend_forecasting",
+                    "competitive_analysis",
+                    "technology_assessment"
+                ]
+            },
+            TeamType.TRADERS: {
+                "name": "Trading & Investment Team",
+                "primary": TeamObjective.TRADING_STRATEGY,
+                "secondary": [
+                    TeamObjective.MARKET_RESEARCH,
+                    TeamObjective.BUSINESS_OPPORTUNITY
+                ],
+                "expertise": [
+                    "crypto_trading",
+                    "sports_betting",
+                    "risk_management",
+                    "market_timing",
+                    "portfolio_optimization"
+                ]
+            }
+        }
+        
+        for team_type, config in team_configs.items():
+            team_id = str(uuid.uuid4())
+            self.teams[team_id] = TeamProfile(
+                id=team_id,
+                type=team_type,
+                name=config["name"],
+                primary_objective=config["primary"],
+                secondary_objectives=config["secondary"],
+                agent_count=5,  # Default size
+                expertise_areas=config["expertise"]
+            )
+            self.agents[team_id] = {}
+
+    async def initialize_team_agents(self):
+        """Initialize agents for each team with appropriate roles and capabilities."""
+        for team_id, team in self.teams.items():
+            await self._create_team_agents(team_id)
+            await self._establish_collaboration_links(team_id)
+
+    async def _create_team_agents(self, team_id: str):
+        """Create specialized agents for a team."""
+        team = self.teams[team_id]
+        
+        # Define agent configurations based on team type
+        agent_configs = self._get_agent_configs(team.type)
+        
+        for config in agent_configs:
+            agent_id = await self.orchestrator.create_agent(
+                role=config["role"],
+                capabilities=config["capabilities"]
+            )
+            
+            agent = Agent(
+                profile=config["profile"],
+                reasoning_engine=self.orchestrator.reasoning_engine,
+                meta_learning=self.orchestrator.meta_learning
+            )
+            
+            self.agents[team_id][agent_id] = agent
+
+    def _get_agent_configs(self, team_type: TeamType) -> List[Dict]:
+        """Get agent configurations based on team type."""
+        base_configs = [
+            {
+                "role": AgentRole.COORDINATOR,
+                "capabilities": [
+                    AgentCapability.COORDINATION,
+                    AgentCapability.REASONING
+                ],
+                "personality": AgentPersonality.PROACTIVE
+            },
+            {
+                "role": AgentRole.EXECUTOR,
+                "capabilities": [
+                    AgentCapability.EXECUTION,
+                    AgentCapability.LEARNING
+                ],
+                "personality": AgentPersonality.ANALYTICAL
+            }
+        ]
+        
+        # Add team-specific configurations
+        if team_type == TeamType.CODERS:
+            base_configs.extend([
+                {
+                    "role": AgentRole.EXECUTOR,
+                    "capabilities": [
+                        AgentCapability.EXECUTION,
+                        AgentCapability.REASONING
+                    ],
+                    "personality": AgentPersonality.CREATIVE,
+                    "expertise": ["software_development", "system_design"]
+                }
+            ])
+        elif team_type == TeamType.BUSINESS:
+            base_configs.extend([
+                {
+                    "role": AgentRole.PLANNER,
+                    "capabilities": [
+                        AgentCapability.REASONING,
+                        AgentCapability.LEARNING
+                    ],
+                    "personality": AgentPersonality.PROACTIVE,
+                    "expertise": ["business_strategy", "market_analysis"]
+                }
+            ])
+        elif team_type == TeamType.RESEARCH:
+            base_configs.extend([
+                {
+                    "role": AgentRole.MONITOR,
+                    "capabilities": [
+                        AgentCapability.MONITORING,
+                        AgentCapability.LEARNING
+                    ],
+                    "personality": AgentPersonality.ANALYTICAL,
+                    "expertise": ["research", "data_analysis"]
+                }
+            ])
+        elif team_type == TeamType.TRADERS:
+            base_configs.extend([
+                {
+                    "role": AgentRole.EXECUTOR,
+                    "capabilities": [
+                        AgentCapability.EXECUTION,
+                        AgentCapability.REASONING
+                    ],
+                    "personality": AgentPersonality.CAUTIOUS,
+                    "expertise": ["trading", "risk_management"]
+                }
+            ])
+            
+        return base_configs
+
+    async def _establish_collaboration_links(self, team_id: str):
+        """Establish collaboration links with other teams."""
+        team = self.teams[team_id]
+        
+        for other_id, other_team in self.teams.items():
+            if other_id != team_id:
+                link_id = f"{min(team_id, other_id)}_{max(team_id, other_id)}"
+                if link_id not in self.collaboration_network:
+                    self.collaboration_network[link_id] = CollaborationLink(
+                        team_a_id=team_id,
+                        team_b_id=other_id,
+                        strength=0.5,  # Initial collaboration strength
+                        active_projects=0,
+                        last_interaction=datetime.now(),
+                        success_rate=0.0
+                    )
+
+    async def create_cross_team_objective(
+        self,
+        objective: str,
+        required_teams: List[TeamType],
+        priority: TaskPriority = TaskPriority.MEDIUM
+    ) -> str:
+        """Create an objective that requires multiple teams."""
+        objective_id = str(uuid.uuid4())
+        
+        # Find relevant teams
+        selected_teams = []
+        for team_id, team in self.teams.items():
+            if team.type in required_teams:
+                selected_teams.append(team_id)
+        
+        if len(selected_teams) < len(required_teams):
+            raise ValueError("Not all required teams are available")
+        
+        # Create shared objective
+        self.shared_objectives[objective_id].update(selected_teams)
+        
+        # Create tasks for each team
+        tasks = []
+        for team_id in selected_teams:
+            task_id = await self.orchestrator.submit_task(
+                description=f"Team {self.teams[team_id].name} contribution to: {objective}",
+                priority=priority
+            )
+            tasks.append(task_id)
+        
+        return objective_id
+
+    async def monitor_objective_progress(self, objective_id: str) -> Dict:
+        """Monitor progress of a cross-team objective."""
+        if objective_id not in self.shared_objectives:
+            raise ValueError("Unknown objective")
+            
+        team_progress = {}
+        for team_id in self.shared_objectives[objective_id]:
+            team = self.teams[team_id]
+            team_agents = self.agents[team_id]
+            
+            # Calculate team progress
+            active_agents = sum(1 for agent in team_agents.values() if agent.state == "busy")
+            completion_rate = sum(agent.get_task_completion_rate() for agent in team_agents.values()) / len(team_agents)
+            
+            team_progress[team.name] = {
+                "active_agents": active_agents,
+                "completion_rate": completion_rate,
+                "collaboration_score": team.collaboration_score
+            }
+            
+        return team_progress
+
+    async def optimize_team_collaboration(self):
+        """Optimize collaboration between teams."""
+        for link in self.collaboration_network.values():
+            team_a = self.teams[link.team_a_id]
+            team_b = self.teams[link.team_b_id]
+            
+            # Update collaboration strength based on:
+            # 1. Number of successful joint projects
+            # 2. Frequency of interaction
+            # 3. Complementary expertise
+            
+            success_factor = link.success_rate
+            interaction_factor = min((datetime.now() - link.last_interaction).days / 30.0, 1.0)
+            expertise_overlap = len(
+                set(team_a.expertise_areas) & set(team_b.expertise_areas)
+            ) / len(set(team_a.expertise_areas) | set(team_b.expertise_areas))
+            
+            new_strength = (
+                0.4 * success_factor +
+                0.3 * (1 - interaction_factor) +
+                0.3 * (1 - expertise_overlap)
+            )
+            
+            link.strength = 0.7 * link.strength + 0.3 * new_strength
+
+    async def get_team_recommendations(self, objective: str) -> List[TeamType]:
+        """Get recommended teams for an objective based on expertise and collaboration history."""
+        # Analyze objective to determine required expertise
+        required_expertise = await self._analyze_objective(objective)
+        
+        # Score each team
+        team_scores = {}
+        for team_id, team in self.teams.items():
+            # Calculate expertise match
+            expertise_match = len(
+                set(required_expertise) & set(team.expertise_areas)
+            ) / len(required_expertise)
+            
+            # Calculate collaboration potential
+            collab_potential = self._calculate_collaboration_potential(team_id)
+            
+            # Calculate success history
+            success_history = team.success_rate
+            
+            # Weighted score
+            score = (
+                0.4 * expertise_match +
+                0.3 * collab_potential +
+                0.3 * success_history
+            )
+            
+            team_scores[team.type] = score
+            
+        # Return sorted recommendations
+        return sorted(
+            team_scores.keys(),
+            key=lambda x: team_scores[x],
+            reverse=True
+        )
+
+    async def _analyze_objective(self, objective: str) -> List[str]:
+        """Analyze an objective to determine required expertise."""
+        # Use reasoning engine to analyze objective
+        analysis = await self.orchestrator.reasoning_engine.reason(
+            query=f"Analyze required expertise for: {objective}",
+            context={
+                "available_expertise": [
+                    expertise
+                    for team in self.teams.values()
+                    for expertise in team.expertise_areas
+                ]
+            }
+        )
+        
+        return analysis.get("required_expertise", [])
+
+    def _calculate_collaboration_potential(self, team_id: str) -> float:
+        """Calculate a team's collaboration potential based on history."""
+        team_links = [
+            link for link in self.collaboration_network.values()
+            if team_id in (link.team_a_id, link.team_b_id)
+        ]
+        
+        if not team_links:
+            return 0.5
+            
+        return sum(link.strength for link in team_links) / len(team_links)
+
+    async def update_team_metrics(self):
+        """Update performance metrics for all teams."""
+        for team_id, team in self.teams.items():
+            team_agents = self.agents[team_id]
+            
+            # Calculate success rate
+            completed_tasks = sum(
+                agent.get_completed_task_count()
+                for agent in team_agents.values()
+            )
+            total_tasks = sum(
+                agent.get_total_task_count()
+                for agent in team_agents.values()
+            )
+            team.success_rate = completed_tasks / max(1, total_tasks)
+            
+            # Calculate collaboration score
+            team_links = [
+                link for link in self.collaboration_network.values()
+                if team_id in (link.team_a_id, link.team_b_id)
+            ]
+            team.collaboration_score = (
+                sum(link.strength for link in team_links) /
+                len(team_links) if team_links else 0.5
+            )

ui/venture_ui.py

@@ -0,0 +1,399 @@
+"""UI components for venture strategies and analysis."""
+
+import gradio as gr
+import json
+from typing import Dict, Any, List
+import plotly.graph_objects as go
+import plotly.express as px
+import pandas as pd
+from datetime import datetime
+
+class VentureUI:
+    """UI for venture strategies and analysis."""
+    
+    def __init__(self, api_client):
+        self.api_client = api_client
+        
+    def create_interface(self):
+        """Create Gradio interface."""
+        with gr.Blocks(title="Venture Strategy Optimizer") as interface:
+            gr.Markdown("# Venture Strategy Optimizer")
+            
+            with gr.Tabs():
+                # Venture Analysis Tab
+                with gr.Tab("Venture Analysis"):
+                    with gr.Row():
+                        with gr.Column():
+                            venture_type = gr.Dropdown(
+                                choices=self._get_venture_types(),
+                                label="Venture Type"
+                            )
+                            query = gr.Textbox(
+                                lines=3,
+                                label="Analysis Query"
+                            )
+                            analyze_btn = gr.Button("Analyze Venture")
+                        
+                        with gr.Column():
+                            analysis_output = gr.JSON(label="Analysis Results")
+                            metrics_plot = gr.Plot(label="Key Metrics")
+                    
+                    analyze_btn.click(
+                        fn=self._analyze_venture,
+                        inputs=[venture_type, query],
+                        outputs=[analysis_output, metrics_plot]
+                    )
+                
+                # Market Analysis Tab
+                with gr.Tab("Market Analysis"):
+                    with gr.Row():
+                        with gr.Column():
+                            segment = gr.Textbox(
+                                label="Market Segment"
+                            )
+                            market_btn = gr.Button("Analyze Market")
+                        
+                        with gr.Column():
+                            market_output = gr.JSON(label="Market Analysis")
+                            market_plot = gr.Plot(label="Market Trends")
+                    
+                    market_btn.click(
+                        fn=self._analyze_market,
+                        inputs=[segment],
+                        outputs=[market_output, market_plot]
+                    )
+                
+                # Portfolio Optimization Tab
+                with gr.Tab("Portfolio Optimization"):
+                    with gr.Row():
+                        with gr.Column():
+                            ventures = gr.CheckboxGroup(
+                                choices=self._get_venture_types(),
+                                label="Select Ventures"
+                            )
+                            optimize_btn = gr.Button("Optimize Portfolio")
+                        
+                        with gr.Column():
+                            portfolio_output = gr.JSON(label="Portfolio Strategy")
+                            portfolio_plot = gr.Plot(label="Portfolio Allocation")
+                    
+                    optimize_btn.click(
+                        fn=self._optimize_portfolio,
+                        inputs=[ventures],
+                        outputs=[portfolio_output, portfolio_plot]
+                    )
+                
+                # Monetization Strategy Tab
+                with gr.Tab("Monetization Strategy"):
+                    with gr.Row():
+                        with gr.Column():
+                            monetization_type = gr.Dropdown(
+                                choices=self._get_venture_types(),
+                                label="Venture Type"
+                            )
+                            monetize_btn = gr.Button("Optimize Monetization")
+                        
+                        with gr.Column():
+                            monetization_output = gr.JSON(label="Monetization Strategy")
+                            revenue_plot = gr.Plot(label="Revenue Projections")
+                    
+                    monetize_btn.click(
+                        fn=self._optimize_monetization,
+                        inputs=[monetization_type],
+                        outputs=[monetization_output, revenue_plot]
+                    )
+                
+                # Insights Dashboard Tab
+                with gr.Tab("Insights Dashboard"):
+                    with gr.Row():
+                        refresh_btn = gr.Button("Refresh Insights")
+                    
+                    with gr.Row():
+                        with gr.Column():
+                            market_insights = gr.JSON(label="Market Insights")
+                            market_trends = gr.Plot(label="Market Trends")
+                        
+                        with gr.Column():
+                            portfolio_insights = gr.JSON(label="Portfolio Insights")
+                            portfolio_trends = gr.Plot(label="Portfolio Performance")
+                    
+                    refresh_btn.click(
+                        fn=self._refresh_insights,
+                        outputs=[
+                            market_insights, market_trends,
+                            portfolio_insights, portfolio_trends
+                        ]
+                    )
+        
+        return interface
+    
+    def _get_venture_types(self) -> List[str]:
+        """Get available venture types."""
+        try:
+            response = self.api_client.list_strategies()
+            return response.get("strategies", [])
+        except Exception as e:
+            print(f"Error getting venture types: {e}")
+            return []
+    
+    def _analyze_venture(self,
+                        venture_type: str,
+                        query: str) -> tuple[Dict[str, Any], go.Figure]:
+        """Analyze venture opportunity."""
+        try:
+            # Get analysis
+            response = self.api_client.analyze_venture({
+                "venture_type": venture_type,
+                "query": query
+            })
+            result = response.get("result", {})
+            
+            # Create visualization
+            fig = self._create_venture_plot(result)
+            
+            return result, fig
+        except Exception as e:
+            print(f"Error in venture analysis: {e}")
+            return {"error": str(e)}, go.Figure()
+    
+    def _analyze_market(self,
+                       segment: str) -> tuple[Dict[str, Any], go.Figure]:
+        """Analyze market opportunity."""
+        try:
+            # Get analysis
+            response = self.api_client.analyze_market({
+                "segment": segment
+            })
+            result = response.get("result", {})
+            
+            # Create visualization
+            fig = self._create_market_plot(result)
+            
+            return result, fig
+        except Exception as e:
+            print(f"Error in market analysis: {e}")
+            return {"error": str(e)}, go.Figure()
+    
+    def _optimize_portfolio(self,
+                          ventures: List[str]) -> tuple[Dict[str, Any], go.Figure]:
+        """Optimize venture portfolio."""
+        try:
+            # Get optimization
+            response = self.api_client.optimize_portfolio({
+                "ventures": ventures
+            })
+            result = response.get("result", {})
+            
+            # Create visualization
+            fig = self._create_portfolio_plot(result)
+            
+            return result, fig
+        except Exception as e:
+            print(f"Error in portfolio optimization: {e}")
+            return {"error": str(e)}, go.Figure()
+    
+    def _optimize_monetization(self,
+                             venture_type: str) -> tuple[Dict[str, Any], go.Figure]:
+        """Optimize monetization strategy."""
+        try:
+            # Get optimization
+            response = self.api_client.optimize_monetization({
+                "venture_type": venture_type
+            })
+            result = response.get("result", {})
+            
+            # Create visualization
+            fig = self._create_revenue_plot(result)
+            
+            return result, fig
+        except Exception as e:
+            print(f"Error in monetization optimization: {e}")
+            return {"error": str(e)}, go.Figure()
+    
+    def _refresh_insights(self) -> tuple[Dict[str, Any], go.Figure,
+                                       Dict[str, Any], go.Figure]:
+        """Refresh insights dashboard."""
+        try:
+            # Get insights
+            market_response = self.api_client.get_market_insights()
+            portfolio_response = self.api_client.get_portfolio_insights()
+            
+            market_insights = market_response.get("insights", {})
+            portfolio_insights = portfolio_response.get("insights", {})
+            
+            # Create visualizations
+            market_fig = self._create_market_trends_plot(market_insights)
+            portfolio_fig = self._create_portfolio_trends_plot(portfolio_insights)
+            
+            return market_insights, market_fig, portfolio_insights, portfolio_fig
+        except Exception as e:
+            print(f"Error refreshing insights: {e}")
+            return (
+                {"error": str(e)}, go.Figure(),
+                {"error": str(e)}, go.Figure()
+            )
+    
+    def _create_venture_plot(self, data: Dict[str, Any]) -> go.Figure:
+        """Create venture analysis visualization."""
+        try:
+            metrics = data.get("metrics", {})
+            
+            fig = go.Figure()
+            fig.add_trace(go.Scatterpolar(
+                r=[
+                    metrics.get("market_score", 0),
+                    metrics.get("opportunity_score", 0),
+                    metrics.get("risk_score", 0),
+                    metrics.get("growth_potential", 0),
+                    metrics.get("profitability", 0)
+                ],
+                theta=[
+                    "Market Score",
+                    "Opportunity Score",
+                    "Risk Score",
+                    "Growth Potential",
+                    "Profitability"
+                ],
+                fill='toself'
+            ))
+            
+            fig.update_layout(
+                polar=dict(
+                    radialaxis=dict(
+                        visible=True,
+                        range=[0, 1]
+                    )
+                ),
+                showlegend=False
+            )
+            
+            return fig
+        except Exception as e:
+            print(f"Error creating venture plot: {e}")
+            return go.Figure()
+    
+    def _create_market_plot(self, data: Dict[str, Any]) -> go.Figure:
+        """Create market analysis visualization."""
+        try:
+            trends = data.get("trend_analysis", {})
+            
+            df = pd.DataFrame([
+                {
+                    "Trend": trend["name"],
+                    "Impact": trend["impact"],
+                    "Potential": trend["market_potential"],
+                    "Risk": trend["risk_level"]
+                }
+                for trend in trends
+            ])
+            
+            fig = px.scatter(
+                df,
+                x="Impact",
+                y="Potential",
+                size="Risk",
+                hover_data=["Trend"],
+                title="Market Trends Analysis"
+            )
+            
+            return fig
+        except Exception as e:
+            print(f"Error creating market plot: {e}")
+            return go.Figure()
+    
+    def _create_portfolio_plot(self, data: Dict[str, Any]) -> go.Figure:
+        """Create portfolio optimization visualization."""
+        try:
+            allocation = data.get("allocation", {})
+            
+            fig = go.Figure(data=[
+                go.Bar(
+                    name=venture,
+                    x=["Resources", "Priority", "Risk"],
+                    y=[
+                        sum(resources.values()),
+                        priority,
+                        len(constraints)
+                    ]
+                )
+                for venture, (resources, priority, constraints) in allocation.items()
+            ])
+            
+            fig.update_layout(
+                barmode='group',
+                title="Portfolio Allocation"
+            )
+            
+            return fig
+        except Exception as e:
+            print(f"Error creating portfolio plot: {e}")
+            return go.Figure()
+    
+    def _create_revenue_plot(self, data: Dict[str, Any]) -> go.Figure:
+        """Create revenue projection visualization."""
+        try:
+            projections = data.get("projections", {})
+            
+            months = list(range(12))
+            revenue = [
+                projections.get("monthly_revenue", {}).get(str(m), 0)
+                for m in months
+            ]
+            
+            fig = go.Figure()
+            fig.add_trace(go.Scatter(
+                x=months,
+                y=revenue,
+                mode='lines+markers',
+                name='Revenue'
+            ))
+            
+            fig.update_layout(
+                title="Revenue Projections",
+                xaxis_title="Month",
+                yaxis_title="Revenue ($)"
+            )
+            
+            return fig
+        except Exception as e:
+            print(f"Error creating revenue plot: {e}")
+            return go.Figure()
+    
+    def _create_market_trends_plot(self, data: Dict[str, Any]) -> go.Figure:
+        """Create market trends visualization."""
+        try:
+            trends = data.get("trend_insights", [])
+            
+            df = pd.DataFrame(trends)
+            
+            fig = px.scatter(
+                df,
+                x="impact",
+                y="potential",
+                size="risk",
+                hover_data=["name"],
+                title="Market Trends Overview"
+            )
+            
+            return fig
+        except Exception as e:
+            print(f"Error creating market trends plot: {e}")
+            return go.Figure()
+    
+    def _create_portfolio_trends_plot(self, data: Dict[str, Any]) -> go.Figure:
+        """Create portfolio trends visualization."""
+        try:
+            metrics = data.get("portfolio_metrics", {})
+            
+            fig = go.Figure()
+            fig.add_trace(go.Indicator(
+                mode="gauge+number",
+                value=metrics.get("total_revenue", 0),
+                title={'text': "Total Revenue ($M)"},
+                gauge={'axis': {'range': [None, 10]}}
+            ))
+            
+            return fig
+        except Exception as e:
+            print(f"Error creating portfolio trends plot: {e}")
+            return go.Figure()

upload_to_hub.py

@@ -0,0 +1,28 @@
+from huggingface_hub import HfApi
+import os
+
+# Initialize the Hugging Face API
+api = HfApi()
+
+print("Starting upload to Hugging Face Space...")
+
+# Create a new Space if it doesn't exist
+try:
+    api.create_repo(
+        repo_id="nananie143/advanced-reasoning",
+        repo_type="space",
+        space_sdk="gradio",
+        private=False
+    )
+except Exception as e:
+    print(f"Space might already exist or there was an error: {e}")
+
+# Upload the files
+api.upload_folder(
+    folder_path=".",
+    repo_id="nananie143/advanced-reasoning",
+    repo_type="space",
+    ignore_patterns=["*.pyc", "__pycache__", ".git", ".env", "*.gguf"]
+)
+
+print("Upload completed successfully!")