Create app.py

app.py

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+# Multi-Agent AI Collaboration System
+# Author: Spencer Purdy
+# Description: Enterprise-grade multi-agent system with specialized AI agents collaborating
+# to solve complex problems through task decomposition and parallel processing.
+
+# Installation (uncomment for Google Colab)
+# !pip install gradio langchain langchain-openai openai networkx matplotlib asyncio aiohttp pandas numpy plotly python-dotenv pydantic
+
+import os
+import json
+import time
+import asyncio
+import hashlib
+import logging
+from datetime import datetime
+from typing import Dict, List, Tuple, Optional, Any, Union, Set
+from dataclasses import dataclass, field
+from enum import Enum
+import warnings
+warnings.filterwarnings('ignore')
+
+# Core libraries
+import gradio as gr
+import pandas as pd
+import numpy as np
+import networkx as nx
+import matplotlib.pyplot as plt
+from matplotlib.patches import FancyBboxPatch
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+
+# LangChain and AI libraries
+from langchain.schema import BaseMessage, HumanMessage, AIMessage, SystemMessage
+from langchain_openai import ChatOpenAI
+from langchain.callbacks.base import BaseCallbackHandler
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.memory import ConversationBufferMemory
+from pydantic import BaseModel, Field
+
+# Async libraries
+import aiohttp
+from concurrent.futures import ThreadPoolExecutor
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+class Config:
+    """Configuration settings for the multi-agent system."""
+    
+    # Model settings
+    DEFAULT_MODEL = "gpt-4"
+    TEMPERATURE = 0.7
+    MAX_TOKENS = 1500
+    
+    # Agent settings
+    MAX_ITERATIONS = 10
+    COLLABORATION_TIMEOUT = 300  # seconds
+    
+    # Visualization settings
+    GRAPH_UPDATE_INTERVAL = 0.5  # seconds
+    NODE_COLORS = {
+        'Researcher': '#3498db',
+        'Analyst': '#e74c3c',
+        'Critic': '#f39c12',
+        'Synthesizer': '#2ecc71',
+        'Coordinator': '#9b59b6'
+    }
+    
+    # Report settings
+    CONFIDENCE_THRESHOLD = 0.7
+    MAX_REPORT_SECTIONS = 10
+
+class AgentRole(Enum):
+    """Enumeration of agent roles in the system."""
+    RESEARCHER = "Researcher"
+    ANALYST = "Analyst"
+    CRITIC = "Critic"
+    SYNTHESIZER = "Synthesizer"
+    COORDINATOR = "Coordinator"
+
+class TaskStatus(Enum):
+    """Task execution status."""
+    PENDING = "pending"
+    IN_PROGRESS = "in_progress"
+    COMPLETED = "completed"
+    FAILED = "failed"
+
+@dataclass
+class Task:
+    """Represents a task to be executed by agents."""
+    id: str
+    description: str
+    assigned_to: Optional[str] = None
+    status: TaskStatus = TaskStatus.PENDING
+    dependencies: List[str] = field(default_factory=list)
+    result: Optional[Any] = None
+    confidence: float = 0.0
+    created_at: datetime = field(default_factory=datetime.now)
+    completed_at: Optional[datetime] = None
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+@dataclass
+class AgentMessage:
+    """Message passed between agents."""
+    sender: str
+    recipient: str
+    content: str
+    message_type: str = "task"
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    timestamp: datetime = field(default_factory=datetime.now)
+
+class AgentMemory:
+    """Manages agent conversation history and context."""
+    
+    def __init__(self, max_messages: int = 50):
+        self.messages: List[AgentMessage] = []
+        self.max_messages = max_messages
+        self.context: Dict[str, Any] = {}
+        
+    def add_message(self, message: AgentMessage):
+        """Add a message to memory."""
+        self.messages.append(message)
+        if len(self.messages) > self.max_messages:
+            self.messages.pop(0)
+    
+    def get_recent_messages(self, n: int = 10) -> List[AgentMessage]:
+        """Get n most recent messages."""
+        return self.messages[-n:]
+    
+    def get_messages_by_sender(self, sender: str) -> List[AgentMessage]:
+        """Get all messages from a specific sender."""
+        return [msg for msg in self.messages if msg.sender == sender]
+    
+    def update_context(self, key: str, value: Any):
+        """Update context information."""
+        self.context[key] = value
+    
+    def get_context(self, key: str) -> Any:
+        """Get context information."""
+        return self.context.get(key)
+
+class BaseAgent:
+    """Base class for all AI agents in the system."""
+    
+    def __init__(self, name: str, role: AgentRole, llm: ChatOpenAI):
+        self.name = name
+        self.role = role
+        self.llm = llm
+        self.memory = AgentMemory()
+        self.active = True
+        self.current_task: Optional[Task] = None
+        self.completed_tasks: List[Task] = []
+        
+    async def process_task(self, task: Task) -> Task:
+        """Process a task and return the result."""
+        self.current_task = task
+        task.status = TaskStatus.IN_PROGRESS
+        task.assigned_to = self.name
+        
+        try:
+            # Execute task based on agent role
+            result = await self._execute_task(task)
+            
+            task.result = result
+            task.status = TaskStatus.COMPLETED
+            task.completed_at = datetime.now()
+            task.confidence = self._calculate_confidence(result)
+            
+            self.completed_tasks.append(task)
+            
+        except Exception as e:
+            logger.error(f"Agent {self.name} failed to process task {task.id}: {str(e)}")
+            task.status = TaskStatus.FAILED
+            task.result = f"Error: {str(e)}"
+            task.confidence = 0.0
+        
+        finally:
+            self.current_task = None
+            
+        return task
+    
+    async def _execute_task(self, task: Task) -> Any:
+        """Execute the task - to be implemented by subclasses."""
+        raise NotImplementedError("Subclasses must implement _execute_task")
+    
+    def _calculate_confidence(self, result: Any) -> float:
+        """Calculate confidence score for the result."""
+        # Basic confidence calculation - can be overridden by subclasses
+        if result and isinstance(result, str) and len(result) > 50:
+            return min(0.9, 0.5 + len(result) / 1000)
+        return 0.5
+    
+    async def collaborate(self, other_agent: 'BaseAgent', message: AgentMessage):
+        """Handle collaboration with another agent."""
+        self.memory.add_message(message)
+        
+        # Process collaboration request
+        response = await self._process_collaboration(message)
+        
+        # Send response back
+        response_message = AgentMessage(
+            sender=self.name,
+            recipient=other_agent.name,
+            content=response,
+            message_type="response"
+        )
+        
+        other_agent.memory.add_message(response_message)
+        
+        return response_message
+    
+    async def _process_collaboration(self, message: AgentMessage) -> str:
+        """Process collaboration message - to be implemented by subclasses."""
+        return f"Acknowledged message from {message.sender}"
+
+class ResearcherAgent(BaseAgent):
+    """Agent specialized in researching and gathering information."""
+    
+    def __init__(self, name: str, llm: ChatOpenAI):
+        super().__init__(name, AgentRole.RESEARCHER, llm)
+        self.research_sources: List[str] = []
+        
+    async def _execute_task(self, task: Task) -> Any:
+        """Execute research task."""
+        prompt = ChatPromptTemplate.from_messages([
+            SystemMessage(content="""You are a Research Agent specializing in gathering comprehensive information.
+            Your role is to:
+            1. Break down complex topics into research questions
+            2. Identify key information sources and data points
+            3. Provide detailed, factual information with citations where possible
+            4. Flag areas requiring further investigation"""),
+            HumanMessage(content=f"Research the following: {task.description}")
+        ])
+        
+        response = await self.llm.ainvoke(prompt.format_messages())
+        
+        # Extract research findings
+        research_result = {
+            "findings": response.content,
+            "sources": self._extract_sources(response.content),
+            "key_points": self._extract_key_points(response.content),
+            "areas_for_investigation": self._identify_gaps(response.content)
+        }
+        
+        return research_result
+    
+    def _extract_sources(self, content: str) -> List[str]:
+        """Extract potential sources from research content."""
+        # Simplified source extraction
+        sources = []
+        lines = content.split('\n')
+        for line in lines:
+            if any(keyword in line.lower() for keyword in ['source:', 'reference:', 'based on:', 'according to']):
+                sources.append(line.strip())
+        return sources[:5]  # Limit to top 5 sources
+    
+    def _extract_key_points(self, content: str) -> List[str]:
+        """Extract key points from research."""
+        key_points = []
+        lines = content.split('\n')
+        for line in lines:
+            if line.strip() and (line.strip()[0].isdigit() or line.strip().startswith('-')):
+                key_points.append(line.strip())
+        return key_points[:10]  # Limit to top 10 points
+    
+    def _identify_gaps(self, content: str) -> List[str]:
+        """Identify areas needing more research."""
+        gaps = []
+        keywords = ['unclear', 'requires further', 'need more', 'investigate', 'unknown']
+        lines = content.split('\n')
+        for line in lines:
+            if any(keyword in line.lower() for keyword in keywords):
+                gaps.append(line.strip())
+        return gaps[:5]
+
+class AnalystAgent(BaseAgent):
+    """Agent specialized in analyzing data and identifying patterns."""
+    
+    def __init__(self, name: str, llm: ChatOpenAI):
+        super().__init__(name, AgentRole.ANALYST, llm)
+        self.analysis_methods: List[str] = ["statistical", "comparative", "trend", "causal"]
+        
+    async def _execute_task(self, task: Task) -> Any:
+        """Execute analysis task."""
+        prompt = ChatPromptTemplate.from_messages([
+            SystemMessage(content="""You are an Analyst Agent specializing in data analysis and pattern recognition.
+            Your role is to:
+            1. Analyze information systematically and objectively
+            2. Identify patterns, trends, and correlations
+            3. Provide quantitative insights where possible
+            4. Draw logical conclusions based on evidence"""),
+            HumanMessage(content=f"Analyze the following: {task.description}")
+        ])
+        
+        response = await self.llm.ainvoke(prompt.format_messages())
+        
+        # Structure analysis results
+        analysis_result = {
+            "analysis": response.content,
+            "patterns": self._identify_patterns(response.content),
+            "insights": self._extract_insights(response.content),
+            "recommendations": self._generate_recommendations(response.content),
+            "confidence_metrics": self._calculate_analysis_confidence(response.content)
+        }
+        
+        return analysis_result
+    
+    def _identify_patterns(self, content: str) -> List[Dict[str, str]]:
+        """Identify patterns in the analysis."""
+        patterns = []
+        pattern_keywords = ['pattern', 'trend', 'correlation', 'relationship', 'consistent']
+        
+        lines = content.split('\n')
+        for line in lines:
+            if any(keyword in line.lower() for keyword in pattern_keywords):
+                patterns.append({
+                    "description": line.strip(),
+                    "type": self._classify_pattern(line)
+                })
+        
+        return patterns[:5]
+    
+    def _classify_pattern(self, description: str) -> str:
+        """Classify the type of pattern."""
+        description_lower = description.lower()
+        if 'trend' in description_lower:
+            return 'trend'
+        elif 'correlation' in description_lower:
+            return 'correlation'
+        elif 'cycle' in description_lower or 'periodic' in description_lower:
+            return 'cyclical'
+        else:
+            return 'general'
+    
+    def _extract_insights(self, content: str) -> List[str]:
+        """Extract key insights from analysis."""
+        insights = []
+        insight_keywords = ['shows', 'indicates', 'suggests', 'reveals', 'demonstrates']
+        
+        sentences = content.split('.')
+        for sentence in sentences:
+            if any(keyword in sentence.lower() for keyword in insight_keywords):
+                insights.append(sentence.strip() + '.')
+        
+        return insights[:7]
+    
+    def _generate_recommendations(self, content: str) -> List[str]:
+        """Generate recommendations based on analysis."""
+        recommendations = []
+        rec_keywords = ['recommend', 'suggest', 'should', 'consider', 'advise']
+        
+        sentences = content.split('.')
+        for sentence in sentences:
+            if any(keyword in sentence.lower() for keyword in rec_keywords):
+                recommendations.append(sentence.strip() + '.')
+        
+        return recommendations[:5]
+    
+    def _calculate_analysis_confidence(self, content: str) -> Dict[str, float]:
+        """Calculate confidence metrics for the analysis."""
+        word_count = len(content.split())
+        evidence_count = content.lower().count('evidence') + content.lower().count('data') + content.lower().count('shows')
+        uncertainty_count = content.lower().count('may') + content.lower().count('might') + content.lower().count('possibly')
+        
+        confidence = min(0.95, 0.5 + (evidence_count * 0.1) - (uncertainty_count * 0.05) + (word_count / 1000))
+        
+        return {
+            "overall_confidence": confidence,
+            "evidence_strength": min(1.0, evidence_count / 10),
+            "certainty_level": max(0.0, 1.0 - (uncertainty_count / 10))
+        }
+
+class CriticAgent(BaseAgent):
+    """Agent specialized in critical evaluation and quality assurance."""
+    
+    def __init__(self, name: str, llm: ChatOpenAI):
+        super().__init__(name, AgentRole.CRITIC, llm)
+        self.evaluation_criteria = [
+            "accuracy", "completeness", "logic", "evidence", 
+            "clarity", "relevance", "consistency"
+        ]
+        
+    async def _execute_task(self, task: Task) -> Any:
+        """Execute critical evaluation task."""
+        prompt = ChatPromptTemplate.from_messages([
+            SystemMessage(content="""You are a Critic Agent specializing in rigorous evaluation and quality assurance.
+            Your role is to:
+            1. Critically evaluate arguments and conclusions
+            2. Identify weaknesses, gaps, and potential biases
+            3. Verify logical consistency and evidence quality
+            4. Suggest improvements and alternative perspectives
+            5. Ensure high standards of analysis"""),
+            HumanMessage(content=f"Critically evaluate the following: {task.description}")
+        ])
+        
+        response = await self.llm.ainvoke(prompt.format_messages())
+        
+        # Structure critique results
+        critique_result = {
+            "evaluation": response.content,
+            "strengths": self._identify_strengths(response.content),
+            "weaknesses": self._identify_weaknesses(response.content),
+            "gaps": self._identify_gaps(response.content),
+            "improvements": self._suggest_improvements(response.content),
+            "quality_score": self._calculate_quality_score(response.content)
+        }
+        
+        return critique_result
+    
+    def _identify_strengths(self, content: str) -> List[str]:
+        """Identify strengths in the evaluated content."""
+        strengths = []
+        strength_keywords = ['strong', 'excellent', 'well', 'good', 'effective', 'solid']
+        
+        sentences = content.split('.')
+        for sentence in sentences:
+            if any(keyword in sentence.lower() for keyword in strength_keywords):
+                strengths.append(sentence.strip() + '.')
+        
+        return strengths[:5]
+    
+    def _identify_weaknesses(self, content: str) -> List[str]:
+        """Identify weaknesses in the evaluated content."""
+        weaknesses = []
+        weakness_keywords = ['weak', 'lack', 'insufficient', 'poor', 'inadequate', 'missing']
+        
+        sentences = content.split('.')
+        for sentence in sentences:
+            if any(keyword in sentence.lower() for keyword in weakness_keywords):
+                weaknesses.append(sentence.strip() + '.')
+        
+        return weaknesses[:5]
+    
+    def _identify_gaps(self, content: str) -> List[str]:
+        """Identify gaps in the analysis."""
+        gaps = []
+        gap_keywords = ['gap', 'missing', 'overlook', 'fail to', 'does not address', 'ignores']
+        
+        sentences = content.split('.')
+        for sentence in sentences:
+            if any(keyword in sentence.lower() for keyword in gap_keywords):
+                gaps.append(sentence.strip() + '.')
+        
+        return gaps[:5]
+    
+    def _suggest_improvements(self, content: str) -> List[str]:
+        """Suggest improvements based on critique."""
+        improvements = []
+        improvement_keywords = ['could', 'should', 'improve', 'enhance', 'strengthen', 'add']
+        
+        sentences = content.split('.')
+        for sentence in sentences:
+            if any(keyword in sentence.lower() for keyword in improvement_keywords):
+                improvements.append(sentence.strip() + '.')
+        
+        return improvements[:5]
+    
+    def _calculate_quality_score(self, content: str) -> Dict[str, float]:
+        """Calculate quality scores for different criteria."""
+        scores = {}
+        
+        for criterion in self.evaluation_criteria:
+            # Simplified scoring based on keyword presence
+            positive_count = content.lower().count(criterion) + content.lower().count('good') + content.lower().count('strong')
+            negative_count = content.lower().count('poor') + content.lower().count('weak') + content.lower().count('lacking')
+            
+            score = min(1.0, max(0.0, 0.5 + (positive_count * 0.1) - (negative_count * 0.15)))
+            scores[criterion] = score
+        
+        scores['overall'] = sum(scores.values()) / len(scores)
+        
+        return scores
+
+class SynthesizerAgent(BaseAgent):
+    """Agent specialized in synthesizing information and creating coherent narratives."""
+    
+    def __init__(self, name: str, llm: ChatOpenAI):
+        super().__init__(name, AgentRole.SYNTHESIZER, llm)
+        self.synthesis_strategies = ["integrate", "summarize", "reconcile", "consolidate"]
+        
+    async def _execute_task(self, task: Task) -> Any:
+        """Execute synthesis task."""
+        prompt = ChatPromptTemplate.from_messages([
+            SystemMessage(content="""You are a Synthesizer Agent specializing in integrating diverse information.
+            Your role is to:
+            1. Combine multiple perspectives into coherent narratives
+            2. Resolve contradictions and find common ground
+            3. Create comprehensive summaries that capture key insights
+            4. Generate actionable conclusions and recommendations
+            5. Ensure clarity and accessibility of complex information"""),
+            HumanMessage(content=f"Synthesize the following information: {task.description}")
+        ])
+        
+        response = await self.llm.ainvoke(prompt.format_messages())
+        
+        # Structure synthesis results
+        synthesis_result = {
+            "synthesis": response.content,
+            "key_themes": self._extract_themes(response.content),
+            "consensus_points": self._identify_consensus(response.content),
+            "contradictions": self._identify_contradictions(response.content),
+            "final_recommendations": self._generate_final_recommendations(response.content),
+            "executive_summary": self._create_executive_summary(response.content)
+        }
+        
+        return synthesis_result
+    
+    def _extract_themes(self, content: str) -> List[Dict[str, str]]:
+        """Extract major themes from synthesis."""
+        themes = []
+        theme_keywords = ['theme', 'pattern', 'trend', 'common', 'recurring', 'central']
+        
+        paragraphs = content.split('\n\n')
+        for i, paragraph in enumerate(paragraphs[:5]):  # Limit to first 5 paragraphs
+            if any(keyword in paragraph.lower() for keyword in theme_keywords):
+                themes.append({
+                    "theme": f"Theme {i+1}",
+                    "description": paragraph.strip()[:200] + "..." if len(paragraph) > 200 else paragraph.strip()
+                })
+        
+        return themes
+    
+    def _identify_consensus(self, content: str) -> List[str]:
+        """Identify points of consensus."""
+        consensus = []
+        consensus_keywords = ['agree', 'consensus', 'common', 'shared', 'unanimous', 'consistent']
+        
+        sentences = content.split('.')
+        for sentence in sentences:
+            if any(keyword in sentence.lower() for keyword in consensus_keywords):
+                consensus.append(sentence.strip() + '.')
+        
+        return consensus[:5]
+    
+    def _identify_contradictions(self, content: str) -> List[str]:
+        """Identify contradictions or conflicts."""
+        contradictions = []
+        conflict_keywords = ['however', 'contrary', 'conflict', 'disagree', 'opposing', 'contradicts']
+        
+        sentences = content.split('.')
+        for sentence in sentences:
+            if any(keyword in sentence.lower() for keyword in conflict_keywords):
+                contradictions.append(sentence.strip() + '.')
+        
+        return contradictions[:3]
+    
+    def _generate_final_recommendations(self, content: str) -> List[str]:
+        """Generate final synthesized recommendations."""
+        recommendations = []
+        
+        # Look for recommendation sections
+        lines = content.split('\n')
+        in_recommendations = False
+        
+        for line in lines:
+            if 'recommend' in line.lower() or 'conclusion' in line.lower():
+                in_recommendations = True
+            elif in_recommendations and line.strip():
+                recommendations.append(line.strip())
+                if len(recommendations) >= 5:
+                    break
+        
+        return recommendations
+    
+    def _create_executive_summary(self, content: str) -> str:
+        """Create an executive summary of the synthesis."""
+        # Take first paragraph or create summary
+        paragraphs = content.split('\n\n')
+        if paragraphs:
+            summary = paragraphs[0][:300]
+            if len(paragraphs[0]) > 300:
+                summary += "..."
+            return summary
+        return "Summary generation in progress..."
+
+class CoordinatorAgent(BaseAgent):
+    """Agent responsible for coordinating other agents and managing workflow."""
+    
+    def __init__(self, name: str, llm: ChatOpenAI):
+        super().__init__(name, AgentRole.COORDINATOR, llm)
+        self.agents: Dict[str, BaseAgent] = {}
+        self.task_queue: List[Task] = []
+        self.completed_tasks: List[Task] = []
+        self.workflow_graph = nx.DiGraph()
+        self.execution_history: List[Dict[str, Any]] = []
+        
+    def register_agent(self, agent: BaseAgent):
+        """Register an agent with the coordinator."""
+        self.agents[agent.name] = agent
+        self.workflow_graph.add_node(agent.name, role=agent.role.value)
+        
+    async def decompose_problem(self, problem: str) -> List[Task]:
+        """Decompose a complex problem into subtasks."""
+        prompt = ChatPromptTemplate.from_messages([
+            SystemMessage(content="""You are a Coordinator Agent responsible for breaking down complex problems.
+            Decompose the problem into specific subtasks that can be assigned to specialized agents:
+            - Researcher: For gathering information and facts
+            - Analyst: For analyzing data and identifying patterns
+            - Critic: For evaluating quality and identifying issues
+            - Synthesizer: For combining insights and creating summaries
+            
+            Create clear, actionable subtasks with dependencies."""),
+            HumanMessage(content=f"Decompose this problem into subtasks: {problem}")
+        ])
+        
+        response = await self.llm.ainvoke(prompt.format_messages())
+        
+        # Parse response into tasks
+        tasks = self._parse_tasks(response.content, problem)
+        
+        return tasks
+    
+    def _parse_tasks(self, content: str, original_problem: str) -> List[Task]:
+        """Parse LLM response into Task objects."""
+        tasks = []
+        lines = content.split('\n')
+        
+        task_id = 1
+        current_role = None
+        
+        for line in lines:
+            line = line.strip()
+            if not line:
+                continue
+                
+            # Check if line indicates a role
+            for role in AgentRole:
+                if role.value in line:
+                    current_role = role.value
+                    break
+            
+            # If line starts with number or dash, it's likely a task
+            if (line[0].isdigit() or line.startswith('-')) and current_role:
+                # Extract task description
+                task_desc = line.lstrip('0123456789.-').strip()
+                
+                task = Task(
+                    id=f"task_{task_id}",
+                    description=task_desc,
+                    metadata={
+                        "original_problem": original_problem,
+                        "suggested_role": current_role
+                    }
+                )
+                
+                tasks.append(task)
+                task_id += 1
+        
+        # If no tasks were parsed, create default tasks
+        if not tasks:
+            tasks = [
+                Task(id="task_1", description=f"Research background information on: {original_problem}",
+                     metadata={"suggested_role": "Researcher"}),
+                Task(id="task_2", description=f"Analyze key aspects of: {original_problem}",
+                     metadata={"suggested_role": "Analyst"}),
+                Task(id="task_3", description="Critically evaluate the research and analysis",
+                     metadata={"suggested_role": "Critic"}),
+                Task(id="task_4", description="Synthesize all findings into actionable insights",
+                     metadata={"suggested_role": "Synthesizer"})
+            ]
+        
+        return tasks
+    
+    async def execute_workflow(self, tasks: List[Task], parallel: bool = True) -> Dict[str, Any]:
+        """Execute the workflow with given tasks."""
+        start_time = datetime.now()
+        
+        # Build task dependency graph
+        self._build_dependency_graph(tasks)
+        
+        # Execute tasks
+        if parallel:
+            results = await self._execute_parallel(tasks)
+        else:
+            results = await self._execute_sequential(tasks)
+        
+        # Compile final results
+        end_time = datetime.now()
+        execution_time = (end_time - start_time).total_seconds()
+        
+        workflow_result = {
+            "tasks": tasks,
+            "execution_time": execution_time,
+            "success_rate": self._calculate_success_rate(tasks),
+            "agent_contributions": self._compile_agent_contributions(tasks),
+            "workflow_graph": self.workflow_graph,
+            "timestamp": datetime.now()
+        }
+        
+        self.execution_history.append(workflow_result)
+        
+        return workflow_result
+    
+    def _build_dependency_graph(self, tasks: List[Task]):
+        """Build a dependency graph for tasks."""
+        # For simplicity, create a linear dependency chain based on suggested roles
+        role_order = ["Researcher", "Analyst", "Critic", "Synthesizer"]
+        
+        # Group tasks by role
+        tasks_by_role = {}
+        for task in tasks:
+            role = task.metadata.get("suggested_role", "Researcher")
+            if role not in tasks_by_role:
+                tasks_by_role[role] = []
+            tasks_by_role[role].append(task)
+        
+        # Create dependencies
+        for i in range(len(role_order) - 1):
+            current_role = role_order[i]
+            next_role = role_order[i + 1]
+            
+            if current_role in tasks_by_role and next_role in tasks_by_role:
+                for current_task in tasks_by_role[current_role]:
+                    for next_task in tasks_by_role[next_role]:
+                        next_task.dependencies.append(current_task.id)
+    
+    async def _execute_parallel(self, tasks: List[Task]) -> List[Task]:
+        """Execute tasks in parallel where possible."""
+        completed = set()
+        pending = tasks.copy()
+        
+        while pending:
+            # Find tasks that can be executed (no pending dependencies)
+            ready_tasks = [
+                task for task in pending
+                if all(dep in completed for dep in task.dependencies)
+            ]
+            
+            if not ready_tasks:
+                # Deadlock prevention - execute first pending task
+                ready_tasks = [pending[0]]
+            
+            # Execute ready tasks in parallel
+            task_futures = []
+            for task in ready_tasks:
+                agent_name = self._select_agent_for_task(task)
+                if agent_name and agent_name in self.agents:
+                    agent = self.agents[agent_name]
+                    task_futures.append(agent.process_task(task))
+                    
+                    # Update workflow graph
+                    self.workflow_graph.add_edge(
+                        self.name, agent_name,
+                        task_id=task.id,
+                        timestamp=datetime.now()
+                    )
+            
+            # Wait for tasks to complete
+            if task_futures:
+                completed_tasks = await asyncio.gather(*task_futures)
+                
+                for task in completed_tasks:
+                    completed.add(task.id)
+                    pending.remove(task)
+                    self.completed_tasks.append(task)
+        
+        return tasks
+    
+    async def _execute_sequential(self, tasks: List[Task]) -> List[Task]:
+        """Execute tasks sequentially."""
+        for task in tasks:
+            agent_name = self._select_agent_for_task(task)
+            if agent_name and agent_name in self.agents:
+                agent = self.agents[agent_name]
+                await agent.process_task(task)
+                
+                # Update workflow graph
+                self.workflow_graph.add_edge(
+                    self.name, agent_name,
+                    task_id=task.id,
+                    timestamp=datetime.now()
+                )
+                
+                self.completed_tasks.append(task)
+        
+        return tasks
+    
+    def _select_agent_for_task(self, task: Task) -> Optional[str]:
+        """Select the best agent for a given task."""
+        suggested_role = task.metadata.get("suggested_role")
+        
+        # Find agent with matching role
+        for agent_name, agent in self.agents.items():
+            if agent.role.value == suggested_role:
+                return agent_name
+        
+        # Fallback to first available agent
+        return list(self.agents.keys())[0] if self.agents else None
+    
+    def _calculate_success_rate(self, tasks: List[Task]) -> float:
+        """Calculate the success rate of task execution."""
+        if not tasks:
+            return 0.0
+        
+        successful = sum(1 for task in tasks if task.status == TaskStatus.COMPLETED)
+        return successful / len(tasks)
+    
+    def _compile_agent_contributions(self, tasks: List[Task]) -> Dict[str, Any]:
+        """Compile contributions from each agent."""
+        contributions = {}
+        
+        for agent_name, agent in self.agents.items():
+            agent_tasks = [task for task in tasks if task.assigned_to == agent_name]
+            
+            contributions[agent_name] = {
+                "role": agent.role.value,
+                "tasks_completed": len(agent_tasks),
+                "average_confidence": np.mean([task.confidence for task in agent_tasks]) if agent_tasks else 0.0,
+                "total_execution_time": sum(
+                    (task.completed_at - task.created_at).total_seconds()
+                    for task in agent_tasks
+                    if task.completed_at
+                )
+            }
+        
+        return contributions
+
+class WorkflowVisualizer:
+    """Handles visualization of agent interactions and workflow."""
+    
+    def __init__(self):
+        self.color_map = Config.NODE_COLORS
+        self.layout_cache = {}
+        
+    def create_workflow_graph(self, workflow_graph: nx.DiGraph, 
+                            active_agents: List[str] = None) -> go.Figure:
+        """Create an interactive workflow visualization."""
+        
+        # Use hierarchical layout
+        pos = self._hierarchical_layout(workflow_graph)
+        
+        # Create edge trace
+        edge_trace = self._create_edge_trace(workflow_graph, pos)
+        
+        # Create node trace
+        node_trace = self._create_node_trace(workflow_graph, pos, active_agents)
+        
+        # Create figure
+        fig = go.Figure(data=[edge_trace, node_trace],
+                       layout=go.Layout(
+                           title='Agent Collaboration Network',
+                           titlefont_size=16,
+                           showlegend=False,
+                           hovermode='closest',
+                           margin=dict(b=20, l=5, r=5, t=40),
+                           xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
+                           yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
+                           plot_bgcolor='white'
+                       ))
+        
+        return fig
+    
+    def _hierarchical_layout(self, G: nx.DiGraph) -> Dict:
+        """Create hierarchical layout for the graph."""
+        if len(G) == 0:
+            return {}
+            
+        # Try to use cached layout if graph structure hasn't changed
+        graph_hash = hash(tuple(G.edges()))
+        if graph_hash in self.layout_cache:
+            return self.layout_cache[graph_hash]
+        
+        # Define role hierarchy
+        hierarchy = {
+            'Coordinator': 0,
+            'Researcher': 1,
+            'Analyst': 2,
+            'Critic': 3,
+            'Synthesizer': 4
+        }
+        
+        # Position nodes based on hierarchy
+        pos = {}
+        role_counts = {}
+        
+        for node in G.nodes():
+            role = G.nodes[node].get('role', 'Unknown')
+            level = hierarchy.get(role, 5)
+            
+            if level not in role_counts:
+                role_counts[level] = 0
+            
+            x = role_counts[level] * 2 - (len([n for n in G.nodes() if hierarchy.get(G.nodes[n].get('role'), 5) == level]) - 1)
+            y = -level * 2
+            
+            pos[node] = (x, y)
+            role_counts[level] += 1
+        
+        # Cache the layout
+        self.layout_cache[graph_hash] = pos
+        
+        return pos
+    
+    def _create_edge_trace(self, G: nx.DiGraph, pos: Dict) -> go.Scatter:
+        """Create edge trace for the graph."""
+        edge_x = []
+        edge_y = []
+        
+        for edge in G.edges():
+            x0, y0 = pos.get(edge[0], (0, 0))
+            x1, y1 = pos.get(edge[1], (0, 0))
+            edge_x.extend([x0, x1, None])
+            edge_y.extend([y0, y1, None])
+        
+        edge_trace = go.Scatter(
+            x=edge_x, y=edge_y,
+            line=dict(width=2, color='#888'),
+            hoverinfo='none',
+            mode='lines'
+        )
+        
+        return edge_trace
+    
+    def _create_node_trace(self, G: nx.DiGraph, pos: Dict, 
+                          active_agents: List[str] = None) -> go.Scatter:
+        """Create node trace for the graph."""
+        node_x = []
+        node_y = []
+        node_colors = []
+        node_sizes = []
+        node_text = []
+        
+        for node in G.nodes():
+            x, y = pos.get(node, (0, 0))
+            node_x.append(x)
+            node_y.append(y)
+            
+            # Get node attributes
+            role = G.nodes[node].get('role', 'Unknown')
+            color = self.color_map.get(role, '#666')
+            
+            # Highlight active agents
+            if active_agents and node in active_agents:
+                size = 30
+                color = self._brighten_color(color)
+            else:
+                size = 20
+            
+            node_colors.append(color)
+            node_sizes.append(size)
+            
+            # Create hover text
+            degree = G.degree(node)
+            hover_text = f"{node}<br>Role: {role}<br>Connections: {degree}"
+            node_text.append(hover_text)
+        
+        node_trace = go.Scatter(
+            x=node_x, y=node_y,
+            mode='markers+text',
+            hoverinfo='text',
+            text=[node for node in G.nodes()],
+            textposition="top center",
+            hovertext=node_text,
+            marker=dict(
+                showscale=False,
+                color=node_colors,
+                size=node_sizes,
+                line=dict(color='white', width=2)
+            )
+        )
+        
+        return node_trace
+    
+    def _brighten_color(self, color: str) -> str:
+        """Make a color brighter for highlighting."""
+        # Simple brightening by mixing with white
+        if color.startswith('#'):
+            r = int(color[1:3], 16)
+            g = int(color[3:5], 16)
+            b = int(color[5:7], 16)
+            
+            # Mix with white (255, 255, 255)
+            r = int(r + (255 - r) * 0.3)
+            g = int(g + (255 - g) * 0.3)
+            b = int(b + (255 - b) * 0.3)
+            
+            return f"#{r:02x}{g:02x}{b:02x}"
+        
+        return color
+    
+    def create_task_timeline(self, tasks: List[Task]) -> go.Figure:
+        """Create a timeline visualization of task execution."""
+        
+        # Prepare data for timeline
+        timeline_data = []
+        
+        for task in tasks:
+            if task.created_at and task.completed_at:
+                timeline_data.append({
+                    'Task': task.id,
+                    'Agent': task.assigned_to or 'Unassigned',
+                    'Start': task.created_at,
+                    'Finish': task.completed_at,
+                    'Status': task.status.value,
+                    'Confidence': task.confidence
+                })
+        
+        if not timeline_data:
+            # Return empty figure if no completed tasks
+            fig = go.Figure()
+            fig.add_annotation(
+                text="No completed tasks to display",
+                xref="paper", yref="paper",
+                x=0.5, y=0.5,
+                showarrow=False
+            )
+            return fig
+        
+        df = pd.DataFrame(timeline_data)
+        
+        # Create Gantt chart
+        fig = px.timeline(
+            df, 
+            x_start="Start", 
+            x_end="Finish", 
+            y="Agent",
+            color="Confidence",
+            hover_data=["Task", "Status", "Confidence"],
+            title="Task Execution Timeline",
+            color_continuous_scale="Viridis"
+        )
+        
+        fig.update_yaxis(categoryorder="total ascending")
+        fig.update_layout(height=400)
+        
+        return fig
+    
+    def create_confidence_heatmap(self, agent_contributions: Dict[str, Any]) -> go.Figure:
+        """Create a heatmap showing agent confidence levels."""
+        
+        agents = list(agent_contributions.keys())
+        metrics = ['tasks_completed', 'average_confidence', 'total_execution_time']
+        
+        # Normalize data for heatmap
+        data = []
+        for metric in metrics:
+            row = []
+            for agent in agents:
+                value = agent_contributions[agent].get(metric, 0)
+                if metric == 'total_execution_time':
+                    value = value / 60  # Convert to minutes
+                row.append(value)
+            data.append(row)
+        
+        # Create heatmap
+        fig = go.Figure(data=go.Heatmap(
+            z=data,
+            x=agents,
+            y=['Tasks Completed', 'Avg Confidence', 'Time (min)'],
+            colorscale='Blues',
+            text=np.round(data, 2),
+            texttemplate='%{text}',
+            textfont={"size": 10}
+        ))
+        
+        fig.update_layout(
+            title="Agent Performance Metrics",
+            xaxis_title="Agents",
+            yaxis_title="Metrics",
+            height=300
+        )
+        
+        return fig
+
+class ReportGenerator:
+    """Generates comprehensive reports from multi-agent collaboration."""
+    
+    def __init__(self):
+        self.section_generators = {
+            'executive_summary': self._generate_executive_summary,
+            'task_analysis': self._generate_task_analysis,
+            'agent_contributions': self._generate_agent_contributions,
+            'key_findings': self._generate_key_findings,
+            'recommendations': self._generate_recommendations,
+            'confidence_analysis': self._generate_confidence_analysis
+        }
+        
+    def generate_report(self, 
+                       workflow_result: Dict[str, Any],
+                       problem_statement: str,
+                       include_sections: List[str] = None) -> str:
+        """Generate a comprehensive report from workflow results."""
+        
+        if include_sections is None:
+            include_sections = list(self.section_generators.keys())
+        
+        report_sections = []
+        
+        # Header
+        report_sections.append(self._generate_header(problem_statement))
+        
+        # Generate requested sections
+        for section in include_sections:
+            if section in self.section_generators:
+                section_content = self.section_generators[section](workflow_result, problem_statement)
+                report_sections.append(section_content)
+        
+        # Footer
+        report_sections.append(self._generate_footer(workflow_result))
+        
+        return "\n\n".join(report_sections)
+    
+    def _generate_header(self, problem_statement: str) -> str:
+        """Generate report header."""
+        return f"""# Multi-Agent Analysis Report
+
+**Generated:** {datetime.now().strftime('%B %d, %Y at %I:%M %p')}
+
+**Problem Statement:** {problem_statement}
+
+---"""
+    
+    def _generate_executive_summary(self, workflow_result: Dict[str, Any], 
+                                  problem_statement: str) -> str:
+        """Generate executive summary section."""
+        tasks = workflow_result.get('tasks', [])
+        success_rate = workflow_result.get('success_rate', 0)
+        execution_time = workflow_result.get('execution_time', 0)
+        
+        # Find synthesis results
+        synthesis_task = None
+        for task in tasks:
+            if task.assigned_to and 'Synthesizer' in task.assigned_to:
+                synthesis_task = task
+                break
+        
+        summary = f"""## Executive Summary
+
+The multi-agent system successfully analyzed the problem with a **{success_rate:.0%} task completion rate** in **{execution_time:.1f} seconds**.
+
+"""
+        
+        if synthesis_task and synthesis_task.result:
+            if isinstance(synthesis_task.result, dict) and 'executive_summary' in synthesis_task.result:
+                summary += synthesis_task.result['executive_summary']
+            else:
+                summary += "The analysis revealed key insights across multiple dimensions of the problem."
+        
+        return summary
+    
+    def _generate_task_analysis(self, workflow_result: Dict[str, Any], 
+                               problem_statement: str) -> str:
+        """Generate task analysis section."""
+        tasks = workflow_result.get('tasks', [])
+        
+        content = "## Task Analysis\n\n"
+        
+        # Group tasks by status
+        completed_tasks = [t for t in tasks if t.status == TaskStatus.COMPLETED]
+        failed_tasks = [t for t in tasks if t.status == TaskStatus.FAILED]
+        
+        content += f"**Total Tasks:** {len(tasks)}\n"
+        content += f"**Completed:** {len(completed_tasks)}\n"
+        content += f"**Failed:** {len(failed_tasks)}\n\n"
+        
+        # List tasks by agent
+        content += "### Tasks by Agent\n\n"
+        
+        agents_tasks = {}
+        for task in tasks:
+            agent = task.assigned_to or "Unassigned"
+            if agent not in agents_tasks:
+                agents_tasks[agent] = []
+            agents_tasks[agent].append(task)
+        
+        for agent, agent_tasks in agents_tasks.items():
+            content += f"**{agent}:**\n"
+            for task in agent_tasks:
+                status_emoji = "✓" if task.status == TaskStatus.COMPLETED else "✗"
+                confidence = f"({task.confidence:.0%} confidence)" if task.confidence > 0 else ""
+                content += f"- {status_emoji} {task.description} {confidence}\n"
+            content += "\n"
+        
+        return content
+    
+    def _generate_agent_contributions(self, workflow_result: Dict[str, Any], 
+                                    problem_statement: str) -> str:
+        """Generate agent contributions section."""
+        contributions = workflow_result.get('agent_contributions', {})
+        
+        content = "## Agent Contributions\n\n"
+        
+        for agent, stats in contributions.items():
+            role = stats.get('role', 'Unknown')
+            tasks_completed = stats.get('tasks_completed', 0)
+            avg_confidence = stats.get('average_confidence', 0)
+            exec_time = stats.get('total_execution_time', 0)
+            
+            content += f"### {agent} ({role})\n"
+            content += f"- Tasks Completed: {tasks_completed}\n"
+            content += f"- Average Confidence: {avg_confidence:.0%}\n"
+            content += f"- Total Execution Time: {exec_time:.1f}s\n\n"
+        
+        return content
+    
+    def _generate_key_findings(self, workflow_result: Dict[str, Any], 
+                             problem_statement: str) -> str:
+        """Generate key findings section."""
+        tasks = workflow_result.get('tasks', [])
+        
+        content = "## Key Findings\n\n"
+        
+        # Extract findings from different agent types
+        for task in tasks:
+            if task.status == TaskStatus.COMPLETED and task.result:
+                agent_role = task.metadata.get('suggested_role', '')
+                
+                if 'Researcher' in agent_role and isinstance(task.result, dict):
+                    if 'key_points' in task.result:
+                        content += "### Research Findings\n"
+                        for point in task.result['key_points'][:5]:
+                            content += f"- {point}\n"
+                        content += "\n"
+                
+                elif 'Analyst' in agent_role and isinstance(task.result, dict):
+                    if 'insights' in task.result:
+                        content += "### Analytical Insights\n"
+                        for insight in task.result['insights'][:5]:
+                            content += f"- {insight}\n"
+                        content += "\n"
+                
+                elif 'Critic' in agent_role and isinstance(task.result, dict):
+                    if 'strengths' in task.result:
+                        content += "### Identified Strengths\n"
+                        for strength in task.result['strengths'][:3]:
+                            content += f"- {strength}\n"
+                        content += "\n"
+                    
+                    if 'gaps' in task.result:
+                        content += "### Identified Gaps\n"
+                        for gap in task.result['gaps'][:3]:
+                            content += f"- {gap}\n"
+                        content += "\n"
+        
+        return content
+    
+    def _generate_recommendations(self, workflow_result: Dict[str, Any], 
+                                problem_statement: str) -> str:
+        """Generate recommendations section."""
+        tasks = workflow_result.get('tasks', [])
+        
+        content = "## Recommendations\n\n"
+        
+        all_recommendations = []
+        
+        # Collect recommendations from all agents
+        for task in tasks:
+            if task.status == TaskStatus.COMPLETED and task.result:
+                if isinstance(task.result, dict):
+                    if 'recommendations' in task.result:
+                        all_recommendations.extend(task.result['recommendations'])
+                    elif 'final_recommendations' in task.result:
+                        all_recommendations.extend(task.result['final_recommendations'])
+                    elif 'improvements' in task.result:
+                        all_recommendations.extend(task.result['improvements'])
+        
+        # Remove duplicates and limit
+        unique_recommendations = []
+        seen = set()
+        for rec in all_recommendations:
+            rec_lower = rec.lower()
+            if rec_lower not in seen:
+                seen.add(rec_lower)
+                unique_recommendations.append(rec)
+        
+        # Categorize recommendations
+        high_priority = []
+        medium_priority = []
+        low_priority = []
+        
+        for rec in unique_recommendations[:10]:
+            # Simple prioritization based on keywords
+            if any(word in rec.lower() for word in ['critical', 'must', 'essential', 'immediately']):
+                high_priority.append(rec)
+            elif any(word in rec.lower() for word in ['should', 'recommend', 'important']):
+                medium_priority.append(rec)
+            else:
+                low_priority.append(rec)
+        
+        if high_priority:
+            content += "### High Priority\n"
+            for rec in high_priority:
+                content += f"- {rec}\n"
+            content += "\n"
+        
+        if medium_priority:
+            content += "### Medium Priority\n"
+            for rec in medium_priority:
+                content += f"- {rec}\n"
+            content += "\n"
+        
+        if low_priority:
+            content += "### Low Priority\n"
+            for rec in low_priority:
+                content += f"- {rec}\n"
+            content += "\n"
+        
+        return content
+    
+    def _generate_confidence_analysis(self, workflow_result: Dict[str, Any], 
+                                    problem_statement: str) -> str:
+        """Generate confidence analysis section."""
+        tasks = workflow_result.get('tasks', [])
+        contributions = workflow_result.get('agent_contributions', {})
+        
+        content = "## Confidence Analysis\n\n"
+        
+        # Overall confidence
+        overall_confidence = np.mean([t.confidence for t in tasks if t.confidence > 0])
+        content += f"**Overall Confidence Score:** {overall_confidence:.0%}\n\n"
+        
+        # Confidence by agent type
+        content += "### Confidence by Agent Role\n"
+        
+        for agent, stats in contributions.items():
+            avg_conf = stats.get('average_confidence', 0)
+            role = stats.get('role', 'Unknown')
+            content += f"- **{role}**: {avg_conf:.0%}\n"
+        
+        content += "\n### Confidence Distribution\n"
+        
+        # Categorize confidence levels
+        high_conf = [t for t in tasks if t.confidence >= 0.8]
+        medium_conf = [t for t in tasks if 0.5 <= t.confidence < 0.8]
+        low_conf = [t for t in tasks if t.confidence < 0.5]
+        
+        content += f"- High Confidence (≥80%): {len(high_conf)} tasks\n"
+        content += f"- Medium Confidence (50-79%): {len(medium_conf)} tasks\n"
+        content += f"- Low Confidence (<50%): {len(low_conf)} tasks\n"
+        
+        return content
+    
+    def _generate_footer(self, workflow_result: Dict[str, Any]) -> str:
+        """Generate report footer."""
+        execution_time = workflow_result.get('execution_time', 0)
+        timestamp = workflow_result.get('timestamp', datetime.now())
+        
+        return f"""---
+
+**Report Generation Details:**
+- Analysis completed in {execution_time:.1f} seconds
+- Report generated at {timestamp.strftime('%B %d, %Y at %I:%M %p')}
+- Powered by Multi-Agent AI Collaboration System"""
+
+# Gradio Interface Functions
+def create_gradio_interface():
+    """Create the main Gradio interface for the multi-agent system."""
+    
+    # Initialize components
+    coordinator = None
+    visualizer = WorkflowVisualizer()
+    report_generator = ReportGenerator()
+    
+    # State variables
+    current_workflow = None
+    current_problem = ""
+    
+    def initialize_agents(api_key: str, model: str = "gpt-4") -> str:
+        """Initialize the multi-agent system."""
+        nonlocal coordinator
+        
+        if not api_key:
+            return "Please provide an OpenAI API key to initialize the agents."
+        
+        try:
+            # Initialize LLM
+            llm = ChatOpenAI(
+                api_key=api_key,
+                model=model,
+                temperature=Config.TEMPERATURE,
+                max_tokens=Config.MAX_TOKENS
+            )
+            
+            # Create coordinator
+            coordinator = CoordinatorAgent("Coordinator", llm)
+            
+            # Create specialized agents
+            researcher = ResearcherAgent("Researcher-1", llm)
+            analyst = AnalystAgent("Analyst-1", llm)
+            critic = CriticAgent("Critic-1", llm)
+            synthesizer = SynthesizerAgent("Synthesizer-1", llm)
+            
+            # Register agents with coordinator
+            coordinator.register_agent(researcher)
+            coordinator.register_agent(analyst)
+            coordinator.register_agent(critic)
+            coordinator.register_agent(synthesizer)
+            
+            return f"Successfully initialized multi-agent system with {len(coordinator.agents)} agents."
+            
+        except Exception as e:
+            logger.error(f"Error initializing agents: {str(e)}")
+            return f"Error initializing agents: {str(e)}"
+    
+    async def analyze_problem(problem: str, execution_mode: str) -> Tuple[str, Any, Any, Any]:
+        """Analyze a problem using the multi-agent system."""
+        nonlocal current_workflow, current_problem
+        
+        if not coordinator:
+            return "Please initialize the agents first.", None, None, None
+        
+        if not problem:
+            return "Please enter a problem to analyze.", None, None, None
+        
+        current_problem = problem
+        
+        try:
+            # Decompose problem into tasks
+            status = "Decomposing problem into subtasks..."
+            tasks = await coordinator.decompose_problem(problem)
+            
+            if not tasks:
+                return "Failed to decompose problem into tasks.", None, None, None
+            
+            # Execute workflow
+            status = f"Executing {len(tasks)} tasks using {execution_mode} mode..."
+            parallel = execution_mode == "Parallel"
+            
+            current_workflow = await coordinator.execute_workflow(tasks, parallel=parallel)
+            
+            # Create visualizations
+            workflow_graph = visualizer.create_workflow_graph(
+                current_workflow['workflow_graph'],
+                active_agents=list(coordinator.agents.keys())
+            )
+            
+            timeline_chart = visualizer.create_task_timeline(tasks)
+            
+            confidence_heatmap = visualizer.create_confidence_heatmap(
+                current_workflow['agent_contributions']
+            )
+            
+            # Generate status summary
+            success_rate = current_workflow['success_rate']
+            execution_time = current_workflow['execution_time']
+            
+            status = f"""Analysis completed successfully!
+            
+- Tasks executed: {len(tasks)}
+- Success rate: {success_rate:.0%}
+- Execution time: {execution_time:.1f} seconds
+- Agents involved: {len(coordinator.agents)}"""
+            
+            return status, workflow_graph, timeline_chart, confidence_heatmap
+            
+        except Exception as e:
+            logger.error(f"Error analyzing problem: {str(e)}")
+            return f"Error during analysis: {str(e)}", None, None, None
+    
+    def generate_report(selected_sections: List[str]) -> str:
+        """Generate a report from the current workflow results."""
+        
+        if not current_workflow:
+            return "No analysis results available. Please run an analysis first."
+        
+        try:
+            report = report_generator.generate_report(
+                current_workflow,
+                current_problem,
+                include_sections=selected_sections
+            )
+            
+            return report
+            
+        except Exception as e:
+            logger.error(f"Error generating report: {str(e)}")
+            return f"Error generating report: {str(e)}"
+    
+    def get_agent_details(agent_name: str) -> str:
+        """Get detailed information about a specific agent."""
+        
+        if not coordinator or agent_name not in coordinator.agents:
+            return "Agent not found or system not initialized."
+        
+        agent = coordinator.agents[agent_name]
+        
+        details = f"""## Agent: {agent.name}
+
+**Role:** {agent.role.value}
+
+**Status:** {'Active' if agent.active else 'Inactive'}
+
+**Completed Tasks:** {len(agent.completed_tasks)}
+
+**Current Task:** {agent.current_task.description if agent.current_task else 'None'}
+
+### Recent Tasks:
+"""
+        
+        for task in agent.completed_tasks[-5:]:
+            details += f"\n- **{task.id}**: {task.description}"
+            details += f"\n  - Status: {task.status.value}"
+            details += f"\n  - Confidence: {task.confidence:.0%}"
+            details += f"\n  - Execution Time: {(task.completed_at - task.created_at).total_seconds():.1f}s\n"
+        
+        return details
+    
+    # Create custom CSS for professional styling
+    custom_css = """
+    .gradio-container {
+        font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', sans-serif;
+    }
+    
+    .gr-button-primary {
+        background-color: #2563eb !important;
+        border-color: #2563eb !important;
+    }
+    
+    .gr-button-primary:hover {
+        background-color: #1d4ed8 !important;
+        border-color: #1d4ed8 !important;
+    }
+    
+    .container {
+        max-width: 1200px;
+        margin: 0 auto;
+    }
+    
+    h1 {
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+        -webkit-background-clip: text;
+        -webkit-text-fill-color: transparent;
+        text-align: center;
+        font-size: 2.5rem;
+        font-weight: 800;
+        margin-bottom: 1rem;
+    }
+    
+    h2 {
+        color: #1f2937;
+        font-size: 1.5rem;
+        font-weight: 600;
+        margin-top: 2rem;
+        margin-bottom: 1rem;
+    }
+    
+    .status-box {
+        background-color: #f3f4f6;
+        border-radius: 8px;
+        padding: 1rem;
+        margin: 1rem 0;
+        border-left: 4px solid #3b82f6;
+    }
+    """
+    
+    # Create Gradio interface
+    with gr.Blocks(title="Multi-Agent AI Collaboration System", 
+                   theme=gr.themes.Base(), 
+                   css=custom_css) as interface:
+        
+        gr.Markdown("""
+        # Multi-Agent AI Collaboration System
+        
+        Advanced AI system with specialized agents working together to solve complex problems through intelligent task decomposition and parallel processing.
+        """)
+        
+        # System Configuration
+        with gr.Row():
+            with gr.Column(scale=3):
+                api_key_input = gr.Textbox(
+                    label="OpenAI API Key",
+                    placeholder="sk-...",
+                    type="password"
+                )
+            with gr.Column(scale=1):
+                model_select = gr.Dropdown(
+                    choices=["gpt-4", "gpt-3.5-turbo"],
+                    value="gpt-4",
+                    label="Model"
+                )
+            with gr.Column(scale=1):
+                init_button = gr.Button("Initialize Agents", variant="primary")
+        
+        init_status = gr.Textbox(label="Initialization Status", interactive=False)
+        
+        # Main tabs
+        with gr.Tabs():
+            # Problem Analysis Tab
+            with gr.TabItem("Problem Analysis"):
+                gr.Markdown("### Enter a complex problem for multi-agent analysis")
+                
+                problem_input = gr.Textbox(
+                    label="Problem Statement",
+                    placeholder="Example: Analyze the potential impact of AI on healthcare delivery in the next 5 years",
+                    lines=3
+                )
+                
+                with gr.Row():
+                    execution_mode = gr.Radio(
+                        choices=["Sequential", "Parallel"],
+                        value="Parallel",
+                        label="Execution Mode"
+                    )
+                    analyze_button = gr.Button("Analyze Problem", variant="primary")
+                
+                analysis_status = gr.Textbox(
+                    label="Analysis Status",
+                    interactive=False,
+                    lines=5
+                )
+                
+                # Visualization outputs
+                with gr.Row():
+                    workflow_graph = gr.Plot(label="Agent Collaboration Network")
+                
+                with gr.Row():
+                    timeline_chart = gr.Plot(label="Task Execution Timeline")
+                    confidence_heatmap = gr.Plot(label="Agent Performance Metrics")
+            
+            # Agent Details Tab
+            with gr.TabItem("Agent Details"):
+                gr.Markdown("### View detailed information about each agent")
+                
+                agent_selector = gr.Dropdown(
+                    choices=["Researcher-1", "Analyst-1", "Critic-1", "Synthesizer-1"],
+                    label="Select Agent"
+                )
+                
+                agent_details_button = gr.Button("Get Agent Details")
+                
+                agent_details_output = gr.Markdown()
+            
+            # Report Generation Tab
+            with gr.TabItem("Report Generation"):
+                gr.Markdown("### Generate comprehensive analysis report")
+                
+                section_selector = gr.CheckboxGroup(
+                    choices=[
+                        "executive_summary",
+                        "task_analysis",
+                        "agent_contributions",
+                        "key_findings",
+                        "recommendations",
+                        "confidence_analysis"
+                    ],
+                    value=[
+                        "executive_summary",
+                        "key_findings",
+                        "recommendations"
+                    ],
+                    label="Select Report Sections"
+                )
+                
+                generate_report_button = gr.Button("Generate Report", variant="primary")
+                
+                report_output = gr.Markdown()
+            
+            # Example Problems Tab
+            with gr.TabItem("Example Problems"):
+                gr.Markdown("""
+                ### Example Problems for Analysis
+                
+                Click on any example to load it into the analysis tab:
+                
+                1. **Business Strategy**: "Develop a comprehensive strategy for a traditional retail company to transition to e-commerce while maintaining customer loyalty"
+                
+                2. **Technology Assessment**: "Evaluate the potential risks and benefits of implementing blockchain technology in supply chain management"
+                
+                3. **Market Analysis**: "Analyze the competitive landscape for electric vehicles and identify key success factors for new entrants"
+                
+                4. **Policy Evaluation**: "Assess the implications of remote work policies on organizational culture and productivity"
+                
+                5. **Innovation Planning**: "Design an innovation framework for a healthcare organization to integrate AI-powered diagnostic tools"
+                """)
+                
+                example_buttons = []
+                example_problems = [
+                    "Develop a comprehensive strategy for a traditional retail company to transition to e-commerce while maintaining customer loyalty",
+                    "Evaluate the potential risks and benefits of implementing blockchain technology in supply chain management",
+                    "Analyze the competitive landscape for electric vehicles and identify key success factors for new entrants",
+                    "Assess the implications of remote work policies on organizational culture and productivity",
+                    "Design an innovation framework for a healthcare organization to integrate AI-powered diagnostic tools"
+                ]
+                
+                for i, problem in enumerate(example_problems):
+                    btn = gr.Button(f"Load Example {i+1}", size="sm")
+                    example_buttons.append(btn)
+        
+        # Event handlers
+        init_button.click(
+            fn=initialize_agents,
+            inputs=[api_key_input, model_select],
+            outputs=init_status
+        )
+        
+        analyze_button.click(
+            fn=lambda p, m: asyncio.run(analyze_problem(p, m)),
+            inputs=[problem_input, execution_mode],
+            outputs=[analysis_status, workflow_graph, timeline_chart, confidence_heatmap]
+        )
+        
+        agent_details_button.click(
+            fn=get_agent_details,
+            inputs=agent_selector,
+            outputs=agent_details_output
+        )
+        
+        generate_report_button.click(
+            fn=generate_report,
+            inputs=section_selector,
+            outputs=report_output
+        )
+        
+        # Example button handlers
+        for i, btn in enumerate(example_buttons):
+            btn.click(
+                fn=lambda idx=i: example_problems[idx],
+                outputs=problem_input
+            )
+    
+    return interface
+
+# Main execution
+if __name__ == "__main__":
+    interface = create_gradio_interface()
+    interface.launch(share=True)