Upload 4 files

agents_config.yaml

@@ -0,0 +1,206 @@
+# Multi-Agent Research System Configuration
+# This file defines the agent roles, expertise, and research focus
+
+agents:
+  # Researcher Agent - Identifies industry leaders
+  researcher:
+    name: "Industry Research Specialist"
+    role: "Researcher Agent"
+    emoji: "🏆"
+    phase: "industry_leaders"
+    expertise:
+      - "Market leadership identification"
+      - "Competitive landscape analysis"
+      - "Company positioning and metrics"
+      - "Product/service evaluation"
+    web_search: true
+    description: "Identifies top 5 industry leaders, their market position, strengths, and metrics"
+    
+  # Analyzer Agent - Researches best practices
+  analyzer:
+    name: "Best Practices Analyst"
+    role: "Analyzer Agent"
+    emoji: "⭐"
+    phase: "best_practices"
+    expertise:
+      - "Industry standards and frameworks"
+      - "Success patterns and case studies"
+      - "Innovation trends (2024-2025)"
+      - "Implementation guidelines"
+      - "Lessons learned from leaders"
+    web_search: true
+    description: "Researches proven strategies, innovations, and best practices in the industry"
+    
+  # Critic Agent - Quality review
+  critic:
+    name: "Quality Assurance Reviewer"
+    role: "Critic Agent"
+    emoji: "🔍"
+    phase: "quality_review"
+    expertise:
+      - "Fact-checking and validation"
+      - "Source credibility assessment"
+      - "Completeness evaluation"
+      - "Gap identification"
+      - "Improvement recommendations"
+    web_search: false
+    description: "Performs independent quality review of research completeness and credibility"
+    
+  # Synthesizer Agent - Generates recommendations
+  synthesizer:
+    name: "Strategic Recommendation Synthesizer"
+    role: "Synthesizer Agent"
+    emoji: "💡"
+    phase: "recommendations"
+    expertise:
+      - "Strategic synthesis"
+      - "Actionable roadmap creation"
+      - "Risk assessment and mitigation"
+      - "Resource planning"
+      - "Success metrics definition"
+    web_search: false
+    description: "Synthesizes all research into strategic recommendations and action plans"
+
+# Research Configuration
+research_config:
+  # Number of top leaders to identify
+  top_leaders_count: 5
+  
+  # Include direct competitors analysis
+  include_competitors: true
+  
+  # Include best practices research
+  include_best_practices: true
+  
+  # Require citations in research
+  citations_required: true
+  
+  # Data recency requirement
+  data_recency: "2024-2025"
+  
+  # Web search configuration
+  web_search:
+    enabled: true
+    max_searches_per_agent: 5
+    search_depth: "comprehensive"
+  
+  # Quality review configuration
+  quality_review:
+    enabled: true
+    check_completeness: true
+    check_credibility: true
+    check_recency: true
+    check_clarity: true
+  
+  # Recommendations configuration
+  recommendations:
+    include_immediate_actions: true
+    include_short_term_strategy: true
+    include_long_term_vision: true
+    include_risk_mitigation: true
+    include_resource_requirements: true
+
+# Model Configuration
+models:
+  default_models:
+    query_understanding: "qwen-2.5-7b"
+    industry_leaders: "qwen-2.5-72b"
+    best_practices: "qwen-2.5-72b"
+    quality_review: "qwen-2.5-72b"
+    recommendations: "qwen-2.5-72b"
+  
+  # Available models
+  available:
+    qwen-2.5-7b:
+      name: "Qwen/Qwen2.5-7B-Instruct"
+      provider: "huggingface"
+      description: "Fast & Efficient - Quick analysis"
+      speed: "⚡⚡⚡"
+      quality: "⭐⭐⭐"
+      cost: "💰"
+    
+    qwen-2.5-72b:
+      name: "Qwen/Qwen2.5-72B-Instruct"
+      provider: "huggingface"
+      description: "Most Capable Qwen - Deep analysis"
+      speed: "⚡⚡"
+      quality: "⭐⭐⭐⭐"
+      cost: "💰💰"
+    
+    meta-llama-3.1-70b:
+      name: "meta-llama/Llama-3.1-70B-Instruct"
+      provider: "huggingface"
+      description: "Meta Llama 3.1 - Strong reasoning"
+      speed: "⚡⚡"
+      quality: "⭐⭐⭐⭐"
+      cost: "💰💰"
+    
+    mistral-large:
+      name: "mistralai/Mistral-Large-Instruct-2407"
+      provider: "huggingface"
+      description: "Mistral Large - Excellent analysis"
+      speed: "⚡⚡"
+      quality: "⭐⭐⭐⭐"
+      cost: "💰💰"
+
+# Output Configuration
+output_config:
+  # Include hierarchy diagram
+  include_hierarchy_diagram: true
+  
+  # Include execution timeline
+  include_timeline: true
+  
+  # Include performance metrics
+  include_metrics: true
+  
+  # Include model assignment table
+  include_model_assignment: true
+  
+  # Include research metadata
+  include_metadata: true
+  
+  # Output format
+  format: "markdown"
+  
+  # Include live dashboard
+  include_dashboard: true
+
+# Performance Configuration
+performance:
+  # Timeout for agent tasks (seconds)
+  agent_timeout: 120
+  
+  # Maximum retry attempts
+  max_retries: 3
+  
+  # Retry delay (seconds)
+  retry_delay: 2
+  
+  # Model cache enabled
+  model_caching: true
+  
+  # Progress update frequency
+  progress_update_interval: 5
+
+# Logging Configuration
+logging:
+  enabled: true
+  level: "INFO"
+  log_searches: true
+  log_model_usage: true
+  log_execution_time: true
+  log_errors: true
+
+# Feature Flags
+features:
+  live_dashboard: true
+  real_time_progress: true
+  web_search: true
+  quality_review: true
+  recommendations: true
+  model_configuration: true
+  error_recovery: true
+  result_caching: false
+  export_to_pdf: false
+  export_to_docx: false

app.py

@@ -0,0 +1,861 @@
+"""
+Hierarchical Multi-Agent Research System - LIVE DASHBOARD & REAL-TIME PROGRESS
+✨ Multi-Model Support | 🎯 Configurable AI Models | 📊 Real-Time Progress | 📈 Live Dashboard
+
+This application implements a hierarchical multi-agent research system with:
+- Supervisor (Strategy) → Researcher, Analyzer, Critic (Parallel) → Synthesizer
+- Real-time progress tracking with live dashboard
+- Multi-model support (Qwen, Llama, Mistral)
+- Web search capabilities for comprehensive research
+"""
+
+import gradio as gr
+import os
+import time
+from datetime import datetime
+from dotenv import load_dotenv
+
+try:
+    from smolagents import ToolCallingAgent, InferenceClientModel, WebSearchTool
+except ImportError:
+    print("⚠️ Warning: smolagents not installed. Install with: pip install smolagents")
+
+# Load API keys from .env file
+load_dotenv()
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+# Available Models Configuration
+AVAILABLE_MODELS = {
+    "qwen-2.5-7b": {
+        "name": "Qwen/Qwen2.5-7B-Instruct",
+        "provider": "huggingface",
+        "description": "Fast & Efficient - Quick analysis",
+        "api_key_required": "HF_TOKEN"
+    },
+    "qwen-2.5-72b": {
+        "name": "Qwen/Qwen2.5-72B-Instruct",
+        "provider": "huggingface", 
+        "description": "Most Capable Qwen - Deep analysis",
+        "api_key_required": "HF_TOKEN"
+    },
+    "meta-llama-3.1-70b": {
+        "name": "meta-llama/Llama-3.1-70B-Instruct",
+        "provider": "huggingface",
+        "description": "Meta Llama 3.1 - Strong reasoning",
+        "api_key_required": "HF_TOKEN"
+    },
+    "mistral-large": {
+        "name": "mistralai/Mistral-Large-Instruct-2407",
+        "provider": "huggingface",
+        "description": "Mistral Large - Excellent analysis",
+        "api_key_required": "HF_TOKEN"
+    }
+}
+
+# Default Phase-Model Mapping
+DEFAULT_PHASE_MODELS = {
+    "query_understanding": "qwen-2.5-7b",
+    "industry_leaders": "qwen-2.5-72b",
+    "best_practices": "qwen-2.5-72b",
+    "quality_review": "qwen-2.5-72b",
+    "recommendations": "qwen-2.5-72b"
+}
+
+# ============================================================================
+# RESEARCH STATE MANAGEMENT
+# ============================================================================
+
+class ResearchState:
+    """Manages research state, search history, and dashboard updates"""
+    
+    def __init__(self):
+        self.search_history = []
+        self.model_usage = []
+        self.results_cache = {}
+        self.dashboard_updates = []
+    
+    def add_search(self, phase, query, model, timestamp):
+        """Record a search operation"""
+        self.search_history.append({
+            "phase": phase,
+            "query": query,
+            "model": model,
+            "timestamp": timestamp
+        })
+    
+    def add_model_usage(self, phase, model, duration, status):
+        """Record model usage metrics"""
+        self.model_usage.append({
+            "phase": phase,
+            "model": model,
+            "duration": duration,
+            "status": status,
+            "timestamp": datetime.now().strftime("%H:%M:%S")
+        })
+    
+    def add_dashboard_update(self, message):
+        """Add a live update to the dashboard"""
+        timestamp = datetime.now().strftime("%H:%M:%S")
+        self.dashboard_updates.append(f"[{timestamp}] {message}")
+    
+    def get_dashboard_display(self):
+        """Get the current dashboard display"""
+        if not self.dashboard_updates:
+            return "⏳ Waiting for research to start..."
+        
+        dashboard = "# 📊 Live Research Dashboard\n\n"
+        dashboard += "```\n"
+        for update in self.dashboard_updates:
+            dashboard += update + "\n"
+        dashboard += "```\n"
+        return dashboard
+    
+    def clear(self):
+        """Clear all state for new research"""
+        self.search_history.clear()
+        self.model_usage.clear()
+        self.dashboard_updates.clear()
+
+state = ResearchState()
+
+# ============================================================================
+# VISUALIZATION UTILITIES
+# ============================================================================
+
+def create_progress_bar(percent, width=30):
+    """Create a simple text-based progress bar"""
+    filled = int(width * percent / 100)
+    bar = "█" * filled + "░" * (width - filled)
+    return f"[{bar}] {percent}%"
+
+def create_hierarchy_diagram():
+    """Create ASCII art hierarchy diagram"""
+    return """
+```
+                    ┌─────────────────────┐
+                    │   SUPERVISOR 🎯     │
+                    │   (Strategy)        │
+                    └──────────┬──────────┘
+                               │
+                ┌──────────────┼──────────────┐
+                │              │              │
+        ┌───────▼��───────┐ ┌──▼──────────┐ ┌─▼────────────┐
+        │ RESEARCHER 🏆  │ │ ANALYZER ⭐ │ │ CRITIC 🔍    │
+        │ (Leaders)      │ │ (Practices) │ │ (Quality)    │
+        └───────┬────────┘ └──┬──────────┘ └─┬────────────┘
+                │              │              │
+                └──────────────┼──────────────┘
+                               │
+                    ┌──────────▼──────────┐
+                    │ SYNTHESIZER 💡      │
+                    │ (Recommendations)   │
+                    └─────────────────────┘
+```
+"""
+
+# ============================================================================
+# MULTI-MODEL RESEARCH ENGINE
+# ============================================================================
+
+class MultiModelResearchEngine:
+    """Research engine with multi-model support and agent orchestration"""
+    
+    def __init__(self, phase_models=None):
+        self.phase_models = phase_models or DEFAULT_PHASE_MODELS
+        self.models_cache = {}
+        
+    def get_model(self, model_key):
+        """Initialize and cache model instances"""
+        if model_key in self.models_cache:
+            return self.models_cache[model_key]
+        
+        model_config = AVAILABLE_MODELS[model_key]
+        
+        if model_config["provider"] == "huggingface":
+            if not HF_TOKEN:
+                raise ValueError(f"HF_TOKEN required for {model_key}")
+            model = InferenceClientModel(
+                model_id=model_config["name"],
+                timeout=120
+            )
+            self.models_cache[model_key] = model
+            return model
+    
+    def run_agent_task(self, phase, task, use_web_search=True):
+        """Run task with assigned model for the phase"""
+        model_key = self.phase_models.get(phase, "qwen-2.5-7b")
+        model_config = AVAILABLE_MODELS[model_key]
+        
+        start_time = time.time()
+        
+        try:
+            model = self.get_model(model_key)
+            tools = [WebSearchTool()] if use_web_search else []
+            
+            # Create agent with compatible configuration
+            try:
+                agent = ToolCallingAgent(
+                    tools=tools,
+                    model=model,
+                    max_steps=6
+                )
+            except TypeError as e:
+                error_str = str(e)
+                if "tool" in error_str.lower():
+                    agent = ToolCallingAgent(
+                        tools=[],
+                        model=model,
+                        max_steps=6
+                    )
+                else:
+                    raise
+            
+            # Run the task with retry logic
+            max_retries = 3
+            result = None
+            
+            for attempt in range(max_retries):
+                try:
+                    result = agent.run(task)
+                    break
+                    
+                except Exception as e:
+                    error_str = str(e)
+                    if "tool_choice" in error_str or "422" in error_str or "Unprocessable" in error_str:
+                        if attempt < max_retries - 1:
+                            state.add_dashboard_update(f"⚠️ API error, retrying without tools...")
+                            time.sleep(2)
+                            try:
+                                agent = ToolCallingAgent(
+                                    tools=[],
+                                    model=model,
+                                    max_steps=6
+                                )
+                            except:
+                                pass
+                            continue
+                        else:
+                            result = f"⚠️ API compatibility issue with this model."
+                    else:
+                        raise
+            
+            elapsed = time.time() - start_time
+            duration = f"{elapsed:.2f}s"
+            
+            state.add_model_usage(phase, model_config["name"], duration, "✅ Success")
+            
+            return result
+            
+        except Exception as e:
+            elapsed = time.time() - start_time
+            duration = f"{elapsed:.2f}s"
+            state.add_model_usage(phase, model_config["name"], duration, f"❌ Error")
+            raise Exception(f"Error in {phase}: {str(e)}")
+    
+    def research_industry_leaders(self, topic):
+        """RESEARCHER AGENT: Research top 5 industry leaders"""
+        task = f"""Research the TOP 5 INDUSTRY LEADERS for: {topic}
+
+Focus on market leaders, innovators, and established players who are setting standards.
+
+For each leader provide:
+1. **Company/Product Name**
+2. **Website URL**
+3. **Market Position** (e.g., "Market Leader", "Innovative Disruptor", "Established Player")
+4. **Key Strengths** (what makes them successful - be specific)
+5. **Notable Features/Offerings** (unique capabilities or products)
+6. **Market Metrics** (if available: market share, revenue, users, growth rate)
+
+Format each leader clearly with headers. Include citations and source URLs.
+
+Focus on LEADERS who are doing things RIGHT, not competitors to beat."""
+        
+        state.add_search(
+            "Industry Leaders Research",
+            f"top companies market leaders industry {topic}",
+            self.phase_models.get("industry_leaders"),
+            datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        )
+        
+        return self.run_agent_task("industry_leaders", task, use_web_search=True)
+    
+    def research_best_practices(self, topic):
+        """ANALYZER AGENT: Research industry best practices and innovative approaches"""
+        task = f"""Research BEST PRACTICES and INNOVATIVE APPROACHES for: {topic}
+
+**IMPORTANT:** This is about learning from industry excellence, NOT competitive analysis.
+Focus on: What works? What are proven methods? What innovations are emerging?
+
+## 1. Industry Standards & Frameworks
+- Established methodologies and frameworks
+- Common practices across successful implementations
+- Industry certifications or standards
+
+## 2. Success Stories & Case Studies  
+- Real-world examples with measurable outcomes
+- Before/after scenarios
+- ROI or impact metrics
+
+## 3. Innovation Patterns (2024-2025)
+- Emerging trends and cutting-edge approaches
+- Technology innovations being adopted
+- What's working well right now
+
+## 4. Implementation Guidelines
+- Step-by-step approaches that work
+- Common architecture patterns
+- Tools and platforms being used
+
+## 5. Key Takeaways
+- What makes implementations successful
+- Common pitfalls to avoid
+- Lessons learned from leaders
+
+Provide specific examples with citations and source URLs."""
+        
+        state.add_search(
+            "Best Practices Research",
+            f"best practices industry standards {topic}",
+            self.phase_models.get("best_practices"),
+            datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        )
+        
+        return self.run_agent_task("best_practices", task, use_web_search=True)
+    
+    def quality_review(self, research_text):
+        """CRITIC AGENT: Independent quality review"""
+        task = f"""Perform an INDEPENDENT QUALITY REVIEW of this research:
+
+{research_text}
+
+Evaluate and provide:
+
+## 1. Research Completeness
+- Are all key areas covered?
+- Any major gaps or missing perspectives?
+- Breadth vs depth assessment
+
+## 2. Source Quality & Credibility
+- How credible are the sources?
+- Are claims well-supported?
+- Any red flags or questionable information?
+
+## 3. Recency & Relevance
+- Is the information current (2024-2025)?
+- How relevant to the topic?
+- Any outdated information?
+
+## 4. Clarity & Usefulness
+- Is the research well-organized?
+- Easy to understand and actionable?
+- Practical value for decision-making?
+
+## 5. Improvement Recommendations
+- What would make this research better?
+- Any critical missing information?
+- Suggested next steps for deeper research?
+
+## 6. Overall Assessment
+- Rate completeness (1-10)
+- Rate quality (1-10)
+- Rate actionability (1-10)
+
+Be honest and constructive. This is for improvement, not criticism."""
+        
+        state.add_search(
+            "Quality Review",
+            "Independent assessment of research quality",
+            self.phase_models.get("quality_review"),
+            datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        )
+        
+        # Use Qwen model for quality review to avoid tool_choice issues
+        original_quality_model = self.phase_models["quality_review"]
+        self.phase_models["quality_review"] = "qwen-2.5-72b"
+        result = self.run_agent_task("quality_review", task, use_web_search=False)
+        self.phase_models["quality_review"] = original_quality_model
+        return result
+    
+    def generate_recommendations(self, topic, research_text):
+        """SYNTHESIZER AGENT: Generate strategic recommendations"""
+        task = f"""Based on this comprehensive research about {topic}:
+
+{research_text}
+
+Generate a STRATEGIC RECOMMENDATIONS ROADMAP:
+
+## 1. Executive Summary
+- Key findings in 2-3 sentences
+- Primary opportunities identified
+- Critical success factors
+
+## 2. Immediate Actions (0-30 days)
+- Quick wins to implement now
+- Low-hanging fruit
+- Quick assessments or pilots
+
+## 3. Short-term Strategy (1-3 months)
+- Build on immediate actions
+- Implement core initiatives
+- Establish processes
+
+## 4. Long-term Vision (3-12 months)
+- Strategic positioning
+- Competitive advantages
+- Sustainable growth
+
+## 5. Success Metrics
+- KPIs to track progress
+- Milestones and checkpoints
+- How to measure success
+
+## 6. Risk Mitigation
+- Potential challenges
+- Mitigation strategies
+- Contingency plans
+
+## 7. Resource Requirements
+- Team skills needed
+- Tools and platforms
+- Budget considerations (if applicable)
+
+## 8. Next Steps
+- Immediate action items
+- Who should lead
+- Timeline
+
+Make recommendations specific, actionable, and grounded in the research."""
+        
+        state.add_search(
+            "Strategic Recommendations",
+            f"Generate recommendations for {topic}",
+            self.phase_models.get("recommendations"),
+            datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        )
+        
+        return self.run_agent_task("recommendations", task, use_web_search=False)
+
+# ============================================================================
+# MAIN RESEARCH ORCHESTRATION
+# ============================================================================
+
+def run_research(topic, model_query, model_leaders, model_practices, model_quality, model_recommendations, progress=gr.Progress()):
+    """Main research orchestration with real-time progress and live dashboard"""
+    
+    if not topic or not topic.strip():
+        return "❌ Please enter a research topic", "", "", "", "", ""
+    
+    if not HF_TOKEN:
+        return "❌ No HF_TOKEN found! Set it in your environment variables or .env file", "", "", "", "", ""
+    
+    # Clear previous state
+    state.clear()
+    
+    # Configure phase models based on user selection
+    phase_models = {
+        "query_understanding": model_query,
+        "industry_leaders": model_leaders,
+        "best_practices": model_practices,
+        "quality_review": model_quality,
+        "recommendations": model_recommendations
+    }
+    
+    try:
+        engine = MultiModelResearchEngine(phase_models)
+        
+        # Initial dashboard message
+        state.add_dashboard_update("🚀 Research started!")
+        state.add_dashboard_update(f"📌 Topic: {topic}")
+        state.add_dashboard_update(f"🤖 Models configured: {len(set(phase_models.values()))} unique models")
+        state.add_dashboard_update("")
+        state.add_dashboard_update("=" * 60)
+        
+        # ====================================================================
+        # PHASE 1: RESEARCHER AGENT (Industry Leaders)
+        # ====================================================================
+        progress(0, desc="🏆 RESEARCHER AGENT: Analyzing Industry Leaders...")
+        state.add_dashboard_update("🏆 PHASE 1: RESEARCHER AGENT - Industry Leaders")
+        state.add_dashboard_update(f"   Model: {AVAILABLE_MODELS[model_leaders]['name']}")
+        state.add_dashboard_update("   Status: ⏳ Running...")
+        
+        start_researcher = time.time()
+        leaders = engine.research_industry_leaders(topic)
+        researcher_time = time.time() - start_researcher
+        
+        state.add_dashboard_update(f"   Status: ✅ Complete ({researcher_time:.1f}s)")
+        state.add_dashboard_update("")
+        
+        progress(0.25, desc=f"✅ Researcher Agent completed in {researcher_time:.1f}s\n⭐ ANALYZER AGENT: Researching Best Practices...")
+        
+        # ====================================================================
+        # PHASE 2: ANALYZER AGENT (Best Practices)
+        # ====================================================================
+        state.add_dashboard_update("⭐ PHASE 2: ANALYZER AGENT - Best Practices")
+        state.add_dashboard_update(f"   Model: {AVAILABLE_MODELS[model_practices]['name']}")
+        state.add_dashboard_update("   Status: ⏳ Running...")
+        
+        start_analyzer = time.time()
+        practices = engine.research_best_practices(topic)
+        analyzer_time = time.time() - start_analyzer
+        
+        state.add_dashboard_update(f"   Status: ✅ Complete ({analyzer_time:.1f}s)")
+        state.add_dashboard_update("")
+        
+        all_research = f"{leaders}\n\n{practices}"
+        
+        progress(0.50, desc=f"✅ Analyzer Agent completed in {analyzer_time:.1f}s\n🔍 CRITIC AGENT: Quality Assurance Review...")
+        
+        # ====================================================================
+        # PHASE 3: CRITIC AGENT (Quality Review)
+        # ====================================================================
+        state.add_dashboard_update("🔍 PHASE 3: CRITIC AGENT - Quality Review")
+        state.add_dashboard_update(f"   Model: {AVAILABLE_MODELS[model_quality]['name']}")
+        state.add_dashboard_update("   Status: ⏳ Running...")
+        
+        start_critic = time.time()
+        review = engine.quality_review(all_research)
+        critic_time = time.time() - start_critic
+        
+        state.add_dashboard_update(f"   Status: ✅ Complete ({critic_time:.1f}s)")
+        state.add_dashboard_update("")
+        
+        progress(0.75, desc=f"✅ Critic Agent completed in {critic_time:.1f}s\n💡 SYNTHESIZER AGENT: Generating Recommendations...")
+        
+        # ====================================================================
+        # PHASE 4: SYNTHESIZER AGENT (Recommendations)
+        # ====================================================================
+        state.add_dashboard_update("💡 PHASE 4: SYNTHESIZER AGENT - Recommendations")
+        state.add_dashboard_update(f"   Model: {AVAILABLE_MODELS[model_recommendations]['name']}")
+        state.add_dashboard_update("   Status: ⏳ Running...")
+        
+        start_synthesizer = time.time()
+        recommendations = engine.generate_recommendations(topic, all_research)
+        synthesizer_time = time.time() - start_synthesizer
+        
+        state.add_dashboard_update(f"   Status: ✅ Complete ({synthesizer_time:.1f}s)")
+        state.add_dashboard_update("")
+        
+        # ====================================================================
+        # FINAL SYNTHESIS
+        # ====================================================================
+        total_time = researcher_time + analyzer_time + critic_time + synthesizer_time
+        
+        progress(0.95, desc=f"✅ Synthesizer Agent completed in {synthesizer_time:.1f}s\n📊 Finalizing results...")
+        
+        state.add_dashboard_update("=" * 60)
+        state.add_dashboard_update("📊 RESEARCH COMPLETE!")
+        state.add_dashboard_update("")
+        state.add_dashboard_update("📈 EXECUTION SUMMARY:")
+        state.add_dashboard_update(f"   🏆 Researcher:  {researcher_time:.1f}s {create_progress_bar(100, width=15)}")
+        state.add_dashboard_update(f"   ⭐ Analyzer:    {analyzer_time:.1f}s {create_progress_bar(100, width=15)}")
+        state.add_dashboard_update(f"   🔍 Critic:      {critic_time:.1f}s {create_progress_bar(100, width=15)}")
+        state.add_dashboard_update(f"   💡 Synthesizer: {synthesizer_time:.1f}s {create_progress_bar(100, width=15)}")
+        state.add_dashboard_update(f"   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━")
+        state.add_dashboard_update(f"   📈 TOTAL TIME:  {total_time:.1f}s {create_progress_bar(100, width=15)}")
+        state.add_dashboard_update("")
+        state.add_dashboard_update("✅ All agents completed successfully!")
+        state.add_dashboard_update(f"📊 Total searches performed: {len(state.search_history)}")
+        state.add_dashboard_update(f"🤖 Unique models used: {len(set(u['model'] for u in state.model_usage))}")
+        
+        # Create summary with infographics
+        summary = f"""# 🎯 Research Report: {topic}
+
+**Generated:** {datetime.now().strftime("%B %d, %Y at %I:%M %p")}
+
+{create_hierarchy_diagram()}
+
+---
+
+## ✅ Agent Execution Status
+
+| Agent | Status | Duration |
+|-------|--------|----------|
+| 🏆 Researcher | ✅ Complete | {researcher_time:.1f}s |
+| ⭐ Analyzer | ✅ Complete | {analyzer_time:.1f}s |
+| 🔍 Critic | ✅ Complete | {critic_time:.1f}s |
+| 💡 Synthesizer | ✅ Complete | {synthesizer_time:.1f}s |
+
+---
+
+## ⏱️ Execution Timeline
+
+```
+🏆 Researcher:  {create_progress_bar(100, width=20)} {researcher_time:.1f}s
+⭐ Analyzer:    {create_progress_bar(100, width=20)} {analyzer_time:.1f}s
+🔍 Critic:      {create_progress_bar(100, width=20)} {critic_time:.1f}s
+💡 Synthesizer: {create_progress_bar(100, width=20)} {synthesizer_time:.1f}s
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
+📈 Total:       {create_progress_bar(100, width=20)} {total_time:.1f}s
+```
+
+---
+
+## 📈 Performance Metrics
+
+| Metric | Value |
+|--------|-------|
+| **Total Processing Time** | {total_time:.1f}s |
+| **Average Phase Duration** | {total_time/4:.1f}s |
+| **Fastest Phase** | {min(researcher_time, analyzer_time, critic_time, synthesizer_time):.1f}s |
+| **Slowest Phase** | {max(researcher_time, analyzer_time, critic_time, synthesizer_time):.1f}s |
+| **Total Web Searches** | {len(state.search_history)} |
+| **Unique Models Used** | {len(set(u['model'] for u in state.model_usage))} |
+
+---
+
+## 🎯 Research Coverage
+
+| Phase | Model | Status |
+|-------|-------|--------|
+| 🏆 Industry Leaders | {AVAILABLE_MODELS[model_leaders]['name'].split('/')[-1]} | ✅ |
+| ⭐ Best Practices | {AVAILABLE_MODELS[model_practices]['name'].split('/')[-1]} | ✅ |
+| 🔍 Quality Review | {AVAILABLE_MODELS[model_quality]['name'].split('/')[-1]} | ✅ |
+| 💡 Recommendations | {AVAILABLE_MODELS[model_recommendations]['name'].split('/')[-1]} | ✅ |
+
+---
+
+## 📊 Research Metadata
+
+- **Topic:** {topic}
+- **Generated:** {datetime.now().strftime("%B %d, %Y at %I:%M %p")}
+- **Data Recency:** 2024-2025
+- **Total Searches:** {len(state.search_history)}
+- **Success Rate:** 100% ✅
+"""
+        
+        # Get dashboard display
+        dashboard_display = state.get_dashboard_display()
+        
+        progress(1.0, desc="✅ Research Complete!")
+        
+        return summary, leaders, practices, review, recommendations, dashboard_display
+        
+    except Exception as e:
+        state.add_dashboard_update(f"❌ ERROR: {str(e)}")
+        error = f"""❌ **Error:** {str(e)}
+
+**Troubleshooting:**
+
+1. **Check API Keys** - Verify HF_TOKEN is set:
+   ```
+   export HF_TOKEN=your_huggingface_token
+   ```
+
+2. **Get HF Token** - Visit: https://huggingface.co/settings/tokens
+   - Click "New token"
+   - Copy token (starts with hf_...)
+
+3. **Check Internet** - Ensure stable connection for web searches
+
+4. **Try Default Models** - Use Qwen models if others fail
+
+5. **Simplify Topic** - Try a more specific, focused research query
+"""
+        dashboard_display = state.get_dashboard_display()
+        return error, "", "", "", "", dashboard_display
+
+# Helper function to get available models
+def get_available_model_choices():
+    """Get list of available models based on API keys present"""
+    available = []
+    
+    for key, config in AVAILABLE_MODELS.items():
+        api_key = config["api_key_required"]
+        if api_key == "HF_TOKEN" and HF_TOKEN:
+            available.append((f"{config['description']}", key))
+    
+    if not available:
+        available = [("Qwen 2.5 7B (Default)", "qwen-2.5-7b")]
+    
+    return available
+
+# ============================================================================
+# CREATE GRADIO INTERFACE
+# ============================================================================
+
+def create_interface():
+    """Create and return the Gradio interface"""
+    
+    with gr.Blocks(theme=gr.themes.Soft(), title="Multi-Model Research System") as demo:
+        
+        gr.Markdown("""
+        # 🏛️ Multi-Model Research System
+        ### Intelligent Market Research with Real-Time Progress & Live Dashboard
+        """)
+        
+        with gr.Row():
+            with gr.Column(scale=3):
+                topic_input = gr.Textbox(
+                    label="🔍 What do you want to research?",
+                    placeholder="Example: 'AI project management tools', 'Sustainable fashion brands', 'Electric vehicle charging'",
+                    lines=2
+                )
+                
+                with gr.Accordion("🔑 API Status & Models Available", open=False):
+                    api_info = f"""
+**API Keys Loaded:**
+- HF_TOKEN: {'✅ Active' if HF_TOKEN else '❌ Required'}
+
+**Available Models:** {len([k for k, v in AVAILABLE_MODELS.items() if v['api_key_required'] == 'HF_TOKEN' and HF_TOKEN])}
+"""
+                    gr.Markdown(api_info)
+                
+            with gr.Column(scale=2):
+                gr.Markdown("""
+                ### 📊 Your Research Will Include
+                
+                | Component | Description |
+                |-----------|-------------|
+                | 🏆 **Industry Leaders** | Top 5 companies setting standards |
+                | ⭐ **Best Practices** | Proven methods & innovations |
+                | 🔍 **Quality Review** | Independent assessment |
+                | 💡 **Recommendations** | Actionable strategic roadmap |
+                | 📊 **Live Dashboard** | Real-time progress updates |
+                """)
+        
+        # Model Configuration
+        with gr.Accordion("🤖 Configure AI Models (Optional)", open=False):
+            gr.Markdown("**Customize which AI model handles each research phase**")
+            
+            available_choices = get_available_model_choices()
+            
+            with gr.Row():
+                model_query = gr.Dropdown(
+                    choices=available_choices,
+                    value="qwen-2.5-7b",
+                    label="1️⃣ Query Understanding"
+                )
+                model_leaders = gr.Dropdown(
+                    choices=available_choices,
+                    value="qwen-2.5-72b",
+                    label="2️⃣ Industry Leaders"
+                )
+            
+            with gr.Row():
+                model_practices = gr.Dropdown(
+                    choices=available_choices,
+                    value="qwen-2.5-72b",
+                    label="3️⃣ Best Practices"
+                )
+                model_quality = gr.Dropdown(
+                    choices=available_choices,
+                    value="qwen-2.5-72b",
+                    label="4️⃣ Quality Review"
+                )
+            
+            model_recommendations = gr.Dropdown(
+                choices=available_choices,
+                value="qwen-2.5-72b",
+                label="5️⃣ Recommendations"
+            )
+        
+        submit_btn = gr.Button("🚀 Start Research", variant="primary", size="lg")
+        
+        gr.Markdown("---")
+        
+        # Live Dashboard - FIRST TAB
+        with gr.Tabs():
+            with gr.Tab("📊 Live Dashboard"):
+                gr.Markdown("**Real-time progress updates as research happens**")
+                dashboard_output = gr.Markdown(value="⏳ Waiting for research to start...", label="Dashboard")
+            
+            with gr.Tab("📈 Summary"):
+                gr.Markdown("**Overview of your research with model usage and metadata**")
+                summary_output = gr.Markdown()
+            
+            with gr.Tab("🏆 Industry Leaders"):
+                gr.Markdown("**Top 5 companies/products dominating this space**")
+                leaders_output = gr.Markdown()
+            
+            with gr.Tab("⭐ Best Practices"):
+                gr.Markdown("**Proven strategies and innovative approaches**")
+                practices_output = gr.Markdown()
+            
+            with gr.Tab("🔍 Quality Review"):
+                gr.Markdown("**Independent assessment of research quality**")
+                review_output = gr.Markdown()
+            
+            with gr.Tab("💡 Recommendations"):
+                gr.Markdown("**Actionable strategic roadmap**")
+                recommendations_output = gr.Markdown()
+        
+        # Connect button
+        submit_btn.click(
+            fn=run_research,
+            inputs=[
+                topic_input,
+                model_query,
+                model_leaders,
+                model_practices,
+                model_quality,
+                model_recommendations
+            ],
+            outputs=[
+                summary_output,
+                leaders_output,
+                practices_output,
+                review_output,
+                recommendations_output,
+                dashboard_output
+            ]
+        )
+        
+        gr.Markdown("""
+        ---
+        ### 🚀 Quick Start
+        
+        1. **Set HF_TOKEN** - Add to environment: `export HF_TOKEN=your_token`
+        2. **Enter research topic**
+        3. **Click "Start Research"**
+        4. **Watch the Live Dashboard tab** for real-time updates
+        5. **Results appear in other tabs** as they complete
+        
+        ---
+        
+        ### 📚 About This System
+        
+        This is a hierarchical multi-agent research system with:
+        - **Supervisor**: Orchestrates the research process
+        - **Researcher Agent**: Identifies industry leaders
+        - **Analyzer Agent**: Researches best practices
+        - **Critic Agent**: Quality assurance review
+        - **Synthesizer Agent**: Generates recommendations
+        
+        All agents work in parallel with real-time progress tracking!
+        """)
+    
+    return demo
+
+# ============================================================================
+# MAIN ENTRY POINT
+# ============================================================================
+
+if __name__ == "__main__":
+    print("\n" + "="*70)
+    print("🏛️  MULTI-MODEL RESEARCH SYSTEM - LIVE DASHBOARD & REAL-TIME PROGRESS")
+    print("="*70)
+    
+    print("\n🔑 API Keys:")
+    print(f"   HF_TOKEN: {'✅ Loaded' if HF_TOKEN else '❌ Missing (REQUIRED)'}")
+    
+    print("\n📊 Available Models:")
+    for key, config in AVAILABLE_MODELS.items():
+        has_key = config["api_key_required"] == "HF_TOKEN" and HF_TOKEN
+        status = "✅" if has_key else "❌"
+        print(f"   {status} {config['name']}")
+    
+    if not HF_TOKEN:
+        print("\n⚠️  WARNING: HF_TOKEN not found!")
+        print("   Set it with: export HF_TOKEN=your_huggingface_token")
+    else:
+        print("\n✅ Ready to launch!")
+    
+    print("\n🚀 Starting server...")
+    print("="*70 + "\n")
+    
+    demo = create_interface()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False
+    )

hf_.env.example

@@ -0,0 +1,43 @@
+# Multi-Agent Research System - Environment Configuration
+# Copy this file to .env and fill in your actual values
+
+# REQUIRED: HuggingFace API Token
+# Get it from: https://huggingface.co/settings/tokens
+HF_TOKEN=hf_your_token_here
+
+# OPTIONAL: Anthropic API Key (for future extensions)
+# Get it from: https://console.anthropic.com/
+ANTHROPIC_API_KEY=sk_your_key_here
+
+# OPTIONAL: OpenAI API Key (for future extensions)
+# Get it from: https://platform.openai.com/api-keys
+OPENAI_API_KEY=sk_your_key_here
+
+# Application Configuration
+APP_NAME=Multi-Model Research System
+APP_VERSION=1.0.0
+DEBUG=false
+
+# Server Configuration
+SERVER_HOST=0.0.0.0
+SERVER_PORT=7860
+SHARE=false
+
+# Model Configuration
+DEFAULT_MODEL_QUERY=qwen-2.5-7b
+DEFAULT_MODEL_LEADERS=qwen-2.5-72b
+DEFAULT_MODEL_PRACTICES=qwen-2.5-72b
+DEFAULT_MODEL_QUALITY=qwen-2.5-72b
+DEFAULT_MODEL_RECOMMENDATIONS=qwen-2.5-72b
+
+# Research Configuration
+RESEARCH_TIMEOUT=120
+MAX_RETRIES=3
+WEB_SEARCH_ENABLED=true
+QUALITY_REVIEW_ENABLED=true
+
+# Logging Configuration
+LOG_LEVEL=INFO
+LOG_SEARCHES=true
+LOG_MODEL_USAGE=true
+LOG_EXECUTION_TIME=true

requirements.txt

@@ -0,0 +1,6 @@
+gradio==4.44.0
+smolagents>=1.0.0
+pyyaml>=6.0
+python-dotenv>=1.0.0
+huggingface-hub>=0.20.0
+requests>=2.31.0