chore: remove test files for display generating and streaming fixes

fix: Remove display_generating when stream=false to prevent streaming-like behavior

docker/Dockerfile

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     flask \
-    "praisonai>=2.2.71" \
+    "praisonai>=2.2.73" \
     "praisonai[api]" \
     gunicorn \
     markdown

docker/Dockerfile.chat

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.2.71" \
+    "praisonai>=2.2.73" \
     "praisonai[chat]" \
     "embedchain[github,youtube]"
 

docker/Dockerfile.dev

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.2.71" \
+    "praisonai>=2.2.73" \
     "praisonai[ui]" \
     "praisonai[chat]" \
     "praisonai[realtime]" \

docker/Dockerfile.ui

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.2.71" \
+    "praisonai>=2.2.73" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

docker/README.md

@@ -121,7 +121,7 @@ healthcheck:
 ## 📦 Package Versions
 
 All Docker images use consistent, up-to-date versions:
-- PraisonAI: `>=2.2.71`
+- PraisonAI: `>=2.2.73`
 - PraisonAI Agents: `>=0.0.92`
 - Python: `3.11-slim`
 
@@ -218,7 +218,7 @@ docker-compose up -d
 ### Version Pinning
 To use specific versions, update the Dockerfile:
 ```dockerfile
-RUN pip install "praisonai==2.2.71" "praisonaiagents==0.0.92"
+RUN pip install "praisonai==2.2.73" "praisonaiagents==0.0.92"
 ```
 
 ## 🌐 Production Deployment

examples/python/agents/context-agent.py

@@ -0,0 +1,231 @@
+#!/usr/bin/env python3
+"""
+Context Agent Example - Basic Usage
+
+This example demonstrates how to use the ContextAgent for automated context generation.
+The ContextAgent implements Context Engineering principles to generate comprehensive
+context for AI coding assistants, enabling first-try implementation success.
+
+Key features demonstrated:
+- Basic ContextAgent instantiation
+- Codebase pattern analysis
+- Context document generation  
+- Validation loop creation
+- Prompt enhancement with context
+- PRP (Product Requirements Prompt) generation
+"""
+
+import sys
+from pathlib import Path
+
+# Add the src directory to Python path for imports
+project_root = Path(__file__).parent.parent.parent.parent / "src" / "praisonai-agents"
+sys.path.insert(0, str(project_root))
+
+from praisonaiagents import create_context_agent
+
+def demonstrate_basic_context_generation():
+    """Demonstrate basic context generation capabilities."""
+    print("🔧 Context Engineering Example: Basic Usage")
+    print("=" * 60)
+    
+    # Create a ContextAgent using the factory function
+    context_agent = create_context_agent(
+        llm="gpt-4o-mini",
+        name="Basic Context Engineer",
+        verbose=True
+    )
+    
+    # Example 1: Analyze codebase patterns
+    print("\n📁 Example 1: Analyzing Codebase Patterns")
+    print("-" * 40)
+    
+    # Use current project as example (adjust path as needed)
+    project_path = str(project_root)
+    analysis = context_agent.analyze_codebase_patterns(
+        project_path=project_path,
+        file_patterns=["*.py"]
+    )
+    
+    print(f"✅ Analyzed project at: {project_path}")
+    print(f"   Project structure: {len(analysis.get('project_structure', {}).get('directories', []))} directories")
+    print(f"   Code patterns identified: {len(analysis.get('code_patterns', {}))}")
+    print(f"   Architecture: {analysis.get('architecture_insights', {}).get('primary_pattern', 'Unknown')}")
+    
+    # Example 2: Generate context document
+    print("\n📄 Example 2: Generating Context Document")
+    print("-" * 40)
+    
+    feature_request = "Add user authentication system with JWT tokens and role-based access control"
+    context_doc = context_agent.generate_context_document(
+        project_path=project_path,
+        requirements=feature_request,
+        analysis=analysis
+    )
+    
+    print(f"✅ Generated context document for: {feature_request}")
+    print(f"   Document length: {len(context_doc)} characters")
+    print(f"   Contains architecture patterns: {'Architecture Patterns' in context_doc}")
+    print(f"   Contains validation criteria: {'Validation Criteria' in context_doc}")
+    
+    # Example 3: Create validation loop
+    print("\n✅ Example 3: Creating Validation Loop")
+    print("-" * 40)
+    
+    success_criteria = [
+        "Authentication system accepts valid JWT tokens",
+        "Role-based access control blocks unauthorized users", 
+        "User registration and login endpoints work correctly",
+        "Password hashing follows security best practices",
+        "Session management handles token expiration"
+    ]
+    
+    validation_config = context_agent.create_validation_loop(
+        implementation_requirements=feature_request,
+        success_criteria=success_criteria
+    )
+    
+    print(f"✅ Created validation loop with {len(validation_config['validation_steps'])} steps")
+    print(f"   Success criteria: {len(success_criteria)}")
+    print(f"   Executable tests: {len(validation_config['executable_tests'])}")
+    print(f"   Quality gates: {len(validation_config['quality_gates'])}")
+    
+    # Example 4: Enhance prompt with context
+    print("\n🚀 Example 4: Enhancing Prompt with Context")
+    print("-" * 40)
+    
+    basic_prompt = "Implement user authentication"
+    enhanced_prompt = context_agent.enhance_prompt_with_context(
+        base_prompt=basic_prompt,
+        context_data=analysis
+    )
+    
+    print(f"✅ Enhanced prompt from {len(basic_prompt)} to {len(enhanced_prompt)} characters")
+    print(f"   Original: '{basic_prompt}'")
+    print("   Enhanced includes: Context Engineering, Implementation Context, Quality Requirements")
+    
+    # Example 5: Generate PRP (Product Requirements Prompt)
+    print("\n📋 Example 5: Generating PRP")
+    print("-" * 40)
+    
+    documentation_links = [
+        "https://jwt.io/introduction/",
+        "https://fastapi.tiangolo.com/tutorial/security/",
+        "https://passlib.readthedocs.io/en/stable/"
+    ]
+    
+    prp = context_agent.generate_prp(
+        feature_request=feature_request,
+        context_analysis=analysis,
+        documentation_links=documentation_links
+    )
+    
+    print("✅ Generated comprehensive PRP")
+    print(f"   PRP length: {len(prp)} characters")
+    print(f"   Contains feature request: {feature_request in prp}")
+    print(f"   Contains documentation links: {len(documentation_links)} links included")
+    print(f"   Contains implementation blueprint: {'Implementation Blueprint' in prp}")
+    print(f"   Confidence score: {'9/10' in prp}")
+
+def demonstrate_context_agent_as_tool():
+    """Demonstrate using ContextAgent as a tool in a multi-agent workflow."""
+    print("\n🔗 Context Engineering Example: Multi-Agent Integration")
+    print("=" * 60)
+    
+    # Create ContextAgent
+    context_agent = create_context_agent(llm="gpt-4o-mini")
+    
+    # Example scenario: Product planning workflow
+    project_path = str(project_root)
+    feature_request = "Build a real-time chat system with WebSocket support"
+    
+    # Step 1: Context analysis
+    print("\n1️⃣ Step 1: Context Analysis")
+    analysis = context_agent.analyze_codebase_patterns(project_path)
+    print(f"✅ Analyzed codebase architecture and patterns")
+    
+    # Step 2: Generate implementation blueprint
+    print("\n2️⃣ Step 2: Implementation Blueprint")
+    blueprint = context_agent.create_implementation_blueprint(feature_request, analysis)
+    print(f"✅ Created implementation blueprint with {len(blueprint['implementation_steps'])} steps")
+    
+    # Step 3: Generate comprehensive PRP
+    print("\n3️⃣ Step 3: Generate PRP")
+    prp = context_agent.generate_prp(feature_request, analysis)  # Generated PRP for implementation guidance
+    print(f"✅ Generated PRP for implementation guidance")
+    
+    # Step 4: Create validation framework
+    print("\n4️⃣ Step 4: Validation Framework")
+    criteria = [
+        "WebSocket connections establish successfully",
+        "Messages broadcast to all connected clients",
+        "Chat history persists and loads correctly",
+        "User authentication works with WebSocket",
+        "Connection handling is robust with reconnection"
+    ]
+    validation = context_agent.create_validation_loop(feature_request, criteria)  # Validation framework for QA
+    print(f"✅ Created validation framework with {len(criteria)} criteria")
+    
+    print("\n🎯 Context Engineering Workflow Complete!")
+    print(f"   Feature: {feature_request}")
+    print("   Implementation ready with comprehensive context")
+    print("   Validation criteria defined for quality assurance")
+
+def show_context_engineering_benefits():
+    """Demonstrate the benefits of Context Engineering vs traditional approaches."""
+    print("\n📊 Context Engineering Benefits")
+    print("=" * 60)
+    
+    # Traditional prompt engineering approach
+    print("\n❌ Traditional Prompt Engineering:")
+    traditional_prompt = "Create a user authentication system"
+    print(f"   Prompt: '{traditional_prompt}'")
+    print(f"   Length: {len(traditional_prompt)} characters")
+    print(f"   Context: Minimal - relies on AI's general knowledge")
+    print(f"   Success Rate: Variable - depends on AI model's training")
+    
+    # Context Engineering approach
+    print("\n✅ Context Engineering Approach:")
+    context_agent = create_context_agent(llm="gpt-4o-mini")
+    
+    # Generate comprehensive context
+    analysis = context_agent.analyze_codebase_patterns(str(project_root))
+    enhanced_prompt = context_agent.enhance_prompt_with_context(
+        traditional_prompt, analysis
+    )
+    
+    print(f"   Enhanced Prompt Length: {len(enhanced_prompt)} characters")
+    print(f"   Context: Comprehensive - includes:")
+    print(f"     • Codebase architecture analysis")
+    print(f"     • Existing patterns and conventions")
+    print(f"     • Implementation guidance")
+    print(f"     • Quality requirements")
+    print(f"     • Validation criteria")
+    print(f"   Success Rate: Higher - AI has all necessary context")
+    
+    print(f"\n🎯 Context Engineering provides:")
+    print("   📈 10x better than prompt engineering (comprehensive vs clever wording)")
+    print("   📈 100x better than vibe coding (structured vs ad-hoc)")
+    print("   🎯 First-try implementation success through complete context")
+
+if __name__ == "__main__":
+    print("🚀 PraisonAI Context Engineering Examples")
+    print("=========================================")
+    
+    try:
+        # Run all examples
+        demonstrate_basic_context_generation()
+        demonstrate_context_agent_as_tool()
+        show_context_engineering_benefits()
+        
+        print("\n🎉 All Context Engineering examples completed successfully!")
+        print("\n💡 Key Takeaways:")
+        print("   • ContextAgent automates comprehensive context generation")
+        print("   • Context Engineering enables first-try implementation success")
+        print("   • Integration with existing PraisonAI agents is seamless")
+        print("   • Validation frameworks ensure quality and completeness")
+        
+    except Exception as e:
+        print(f"\n❌ Error running examples: {e}")
+        print("   Make sure you have the PraisonAI package installed and configured")
+        sys.exit(1)

examples/python/concepts/context-engineering-workflow.py

@@ -0,0 +1,469 @@
+#!/usr/bin/env python3
+"""
+Context Engineering Workflow - Advanced Multi-Agent Example
+
+This example demonstrates advanced Context Engineering workflows using ContextAgent
+in combination with other PraisonAI agents. It shows how Context Engineering can
+dramatically improve AI implementation success rates through comprehensive context
+generation and validation loops.
+
+Workflow demonstrated:
+1. Product Manager Agent defines requirements
+2. ContextAgent analyzes codebase and generates comprehensive context
+3. Architecture Agent designs implementation using context
+4. Developer Agent implements using context-enhanced prompts
+5. QA Agent validates using context-generated criteria
+
+This represents the full Context Engineering methodology in action.
+"""
+
+import sys
+from pathlib import Path
+
+# Add the src directory to Python path for imports
+project_root = Path(__file__).parent.parent.parent.parent / "src" / "praisonai-agents"
+sys.path.insert(0, str(project_root))
+
+from praisonaiagents import Agent, Task, PraisonAIAgents, create_context_agent
+
+class ContextEngineeringWorkflow:
+    """
+    Advanced Context Engineering workflow demonstrating the full methodology.
+    
+    This class orchestrates multiple agents using Context Engineering principles
+    to achieve higher success rates in AI-driven development tasks.
+    """
+    
+    def __init__(self, project_path: str, llm: str = "gpt-4o-mini"):
+        self.project_path = project_path
+        self.llm = llm
+        self.context_data = {}
+        self.setup_agents()
+    
+    def setup_agents(self):
+        """Setup the multi-agent team with Context Engineering support."""
+        
+        # 1. Product Manager Agent - Defines requirements
+        self.product_manager = Agent(
+            name="Product Manager",
+            role="Product Requirements Specialist",
+            goal="Define clear, comprehensive product requirements and user stories",
+            backstory="""You are an experienced product manager who specializes in 
+            creating detailed, actionable requirements. You understand that clear 
+            requirements are the foundation of successful implementation.""",
+            instructions="""
+            When defining requirements:
+            1. Be specific and measurable
+            2. Include user acceptance criteria
+            3. Consider edge cases and error scenarios
+            4. Define success metrics
+            5. Specify technical constraints
+            """,
+            llm=self.llm,
+            verbose=True
+        )
+        
+        # 2. Context Engineering Agent - Generates comprehensive context
+        self.context_engineer = create_context_agent(
+            llm=self.llm,
+            name="Context Engineering Specialist",
+            verbose=True
+        )
+        
+        # 3. Software Architect Agent - Designs implementation using context
+        self.architect = Agent(
+            name="Software Architect",
+            role="System Architecture Specialist", 
+            goal="Design robust, scalable system architecture based on comprehensive context",
+            backstory="""You are a senior software architect with expertise in 
+            designing systems that follow established patterns and best practices. 
+            You excel at creating architectures that integrate seamlessly with 
+            existing codebases.""",
+            instructions="""
+            When designing architecture:
+            1. Follow the patterns identified in the context analysis
+            2. Ensure compatibility with existing systems
+            3. Design for scalability and maintainability
+            4. Consider security and performance implications
+            5. Document architectural decisions and rationale
+            """,
+            llm=self.llm,
+            verbose=True
+        )
+        
+        # 4. Senior Developer Agent - Implements using context-enhanced guidance
+        self.developer = Agent(
+            name="Senior Developer",
+            role="Implementation Specialist",
+            goal="Implement features following context-guided best practices",
+            backstory="""You are a senior developer who excels at implementing 
+            features that follow established codebase patterns. You understand 
+            that consistency and quality are paramount.""",
+            instructions="""
+            When implementing features:
+            1. Follow the codebase patterns identified in context analysis
+            2. Maintain consistency with existing code style
+            3. Implement comprehensive error handling
+            4. Write clean, maintainable code
+            5. Follow the implementation blueprint exactly
+            """,
+            llm=self.llm,
+            verbose=True
+        )
+        
+        # 5. QA Engineer Agent - Validates using context-generated criteria
+        self.qa_engineer = Agent(
+            name="QA Engineer",
+            role="Quality Assurance Specialist",
+            goal="Ensure implementation meets all quality criteria and requirements",
+            backstory="""You are an experienced QA engineer who specializes in 
+            comprehensive testing and validation. You understand that quality 
+            is built in, not bolted on.""",
+            instructions="""
+            When validating implementations:
+            1. Use the validation criteria from context analysis
+            2. Test both happy path and edge cases
+            3. Verify integration with existing systems
+            4. Check for security vulnerabilities
+            5. Validate performance requirements
+            """,
+            llm=self.llm,
+            verbose=True
+        )
+    
+    def run_context_engineering_workflow(self, feature_request: str):
+        """
+        Execute the complete Context Engineering workflow.
+        
+        Args:
+            feature_request (str): The feature to be implemented
+            
+        Returns:
+            dict: Complete workflow results with context and implementation
+        """
+        print(f"🚀 Starting Context Engineering Workflow")
+        print(f"Feature Request: {feature_request}")
+        print("=" * 80)
+        
+        # Phase 1: Requirements Analysis
+        print("\n📋 Phase 1: Product Requirements Analysis")
+        print("-" * 50)
+        
+        requirements_task = Task(
+            name="requirements_analysis",
+            description=f"""
+            Analyze and refine the feature request: '{feature_request}'
+            
+            Create comprehensive requirements including:
+            - Detailed feature description
+            - User acceptance criteria
+            - Technical requirements
+            - Success metrics
+            - Edge cases and constraints
+            """,
+            expected_output="Comprehensive product requirements document",
+            agent=self.product_manager
+        )
+        
+        # Phase 2: Context Engineering Analysis
+        print("\n🔧 Phase 2: Context Engineering Analysis")
+        print("-" * 50)
+        
+        # Generate comprehensive context using ContextAgent
+        codebase_analysis = self.context_engineer.analyze_codebase_patterns(
+            project_path=self.project_path
+        )
+        
+        context_document = self.context_engineer.generate_context_document(
+            project_path=self.project_path,
+            requirements=feature_request,
+            analysis=codebase_analysis
+        )
+        
+        validation_framework = self.context_engineer.create_validation_loop(
+            implementation_requirements=feature_request,
+            success_criteria=[
+                "Feature implements all specified requirements",
+                "Code follows existing patterns and conventions",
+                "Implementation includes comprehensive error handling",
+                "All tests pass and coverage meets standards",
+                "Integration with existing systems is seamless"
+            ]
+        )
+        
+        implementation_blueprint = self.context_engineer.create_implementation_blueprint(
+            feature_request=feature_request,
+            context_analysis=codebase_analysis
+        )
+        
+        # Generate PRP for complete context
+        prp = self.context_engineer.generate_prp(
+            feature_request=feature_request,
+            context_analysis=codebase_analysis,
+            documentation_links=[
+                "https://docs.praisonai.com/",
+                "https://pydantic-docs.helpmanual.io/",
+                "https://fastapi.tiangolo.com/"
+            ]
+        )
+        
+        print(f"✅ Context Engineering Analysis Complete:")
+        print(f"   • Codebase analysis: {len(str(codebase_analysis))} chars")
+        print(f"   • Context document: {len(context_document)} chars")
+        print(f"   • Validation framework: {len(validation_framework['validation_steps'])} steps")
+        print(f"   • Implementation blueprint: {len(implementation_blueprint['implementation_steps'])} steps")
+        print(f"   • PRP generated: {len(prp)} chars")
+        
+        # Store context data for subsequent phases
+        self.context_data = {
+            "codebase_analysis": codebase_analysis,
+            "context_document": context_document,
+            "validation_framework": validation_framework,
+            "implementation_blueprint": implementation_blueprint,
+            "prp": prp
+        }
+        
+        # Phase 3: Architecture Design with Context
+        print("\n🏗️ Phase 3: Architecture Design with Context")
+        print("-" * 50)
+        
+        architecture_task = Task(
+            name="architecture_design",
+            description=f"""
+            Design system architecture for: '{feature_request}'
+            
+            Use the comprehensive context provided:
+            {context_document}
+            
+            Implementation Blueprint:
+            {implementation_blueprint}
+            
+            Design architecture that:
+            - Follows identified codebase patterns
+            - Integrates with existing systems
+            - Meets all technical requirements
+            - Is scalable and maintainable
+            """,
+            expected_output="Detailed system architecture design with component specifications",
+            agent=self.architect,
+            context=[requirements_task]
+        )
+        
+        # Phase 4: Implementation with Context-Enhanced Guidance
+        print("\n💻 Phase 4: Implementation with Context")
+        print("-" * 50)
+        
+        # Enhance the implementation prompt with context
+        enhanced_prompt = self.context_engineer.enhance_prompt_with_context(
+            base_prompt=f"Implement {feature_request}",
+            context_data=codebase_analysis
+        )
+        
+        implementation_task = Task(
+            name="feature_implementation",
+            description=f"""
+            Implement the feature using context-enhanced guidance:
+            
+            {enhanced_prompt}
+            
+            Product Requirements Prompt (PRP):
+            {prp}
+            
+            Architecture Design: Reference the architecture task output
+            
+            Implementation must:
+            - Follow the implementation blueprint exactly
+            - Use patterns identified in codebase analysis
+            - Meet all requirements from Phase 1
+            - Include comprehensive error handling
+            """,
+            expected_output="Complete feature implementation with code and documentation",
+            agent=self.developer,
+            context=[requirements_task, architecture_task]
+        )
+        
+        # Phase 5: Quality Assurance with Context-Generated Criteria
+        print("\n🔍 Phase 5: Quality Assurance with Context")
+        print("-" * 50)
+        
+        qa_task = Task(
+            name="quality_validation",
+            description=f"""
+            Validate the implementation using context-generated criteria:
+            
+            Validation Framework:
+            {validation_framework}
+            
+            Verify that implementation:
+            - Meets all success criteria defined in validation framework
+            - Follows codebase patterns and conventions
+            - Integrates properly with existing systems
+            - Handles edge cases and error scenarios
+            - Meets performance and security requirements
+            
+            Use the validation steps provided to systematically check each criterion.
+            """,
+            expected_output="Comprehensive quality validation report with pass/fail status",
+            agent=self.qa_engineer,
+            context=[requirements_task, architecture_task, implementation_task]
+        )
+        
+        # Execute the complete workflow
+        print("\n⚙️ Executing Context Engineering Workflow")
+        print("-" * 50)
+        
+        agents_workflow = PraisonAIAgents(
+            agents=[
+                self.product_manager,
+                self.architect, 
+                self.developer,
+                self.qa_engineer
+            ],
+            tasks=[
+                requirements_task,
+                architecture_task,
+                implementation_task,
+                qa_task
+            ],
+            process="sequential",
+            verbose=True
+        )
+        
+        # Execute workflow
+        workflow_results = agents_workflow.start()
+        
+        # Compile complete results
+        complete_results = {
+            "feature_request": feature_request,
+            "context_engineering": self.context_data,
+            "workflow_results": workflow_results,
+            "methodology": "Context Engineering - 10x better than prompt engineering"
+        }
+        
+        return complete_results
+
+def demonstrate_context_engineering_benefits():
+    """
+    Demonstrate the benefits of Context Engineering methodology.
+    """
+    print("\n📊 Context Engineering vs Traditional Approaches")
+    print("=" * 60)
+    
+    # Traditional approach simulation
+    print("\n❌ Traditional AI Coding Approach:")
+    print("   1. Write basic prompt: 'Implement user authentication'")
+    print("   2. AI uses general knowledge to implement")
+    print("   3. Result often doesn't fit existing codebase patterns")
+    print("   4. Multiple iterations needed to fix integration issues")
+    print("   5. Success rate: ~30-40% first-try success")
+    
+    # Context Engineering approach
+    print("\n✅ Context Engineering Approach:")
+    print("   1. Analyze codebase patterns and architecture")
+    print("   2. Generate comprehensive context document")
+    print("   3. Create validation framework with success criteria")
+    print("   4. Enhance prompts with rich contextual information")
+    print("   5. Generate PRP (Product Requirements Prompt)")
+    print("   6. AI implements using complete context")
+    print("   7. Success rate: ~90%+ first-try success")
+    
+    print("\n📈 Context Engineering Advantages:")
+    print("   • 10x better than prompt engineering (context vs clever wording)")
+    print("   • 100x better than vibe coding (structured vs ad-hoc)")
+    print("   • Enables first-try implementation success")
+    print("   • Reduces development iteration cycles")
+    print("   • Ensures consistency with existing codebase")
+    print("   • Provides built-in quality validation")
+
+def run_example_workflow():
+    """Run an example Context Engineering workflow."""
+    
+    # Setup workflow with current project
+    project_path = str(project_root)
+    workflow = ContextEngineeringWorkflow(
+        project_path=project_path,
+        llm="gpt-4o-mini"
+    )
+    
+    # Example feature request
+    feature_request = """
+    Implement a real-time notification system that:
+    - Sends notifications via WebSocket connections
+    - Supports different notification types (info, warning, error)
+    - Persists notifications in database for offline users
+    - Includes user preference management for notification settings
+    - Provides REST API for notification management
+    """
+    
+    print("🎯 Running Example Context Engineering Workflow")
+    print("=" * 60)
+    
+    try:
+        # Execute the complete workflow
+        results = workflow.run_context_engineering_workflow(feature_request)
+        
+        print("\n🎉 Context Engineering Workflow Completed Successfully!")
+        print("=" * 60)
+        
+        print(f"\n📊 Workflow Summary:")
+        print(f"   • Feature: Real-time notification system")
+        print(f"   • Context generated: ✅ Complete")
+        print(f"   • Architecture designed: ✅ With context")
+        print(f"   • Implementation: ✅ Context-guided")
+        print(f"   • Quality validation: ✅ Context-criteria")
+        
+        print(f"\n🔧 Context Engineering Data:")
+        context_data = results["context_engineering"]
+        print(f"   • Codebase analysis: {len(str(context_data['codebase_analysis']))} chars")
+        print(f"   • Context document: {len(context_data['context_document'])} chars") 
+        print(f"   • Validation framework: {len(context_data['validation_framework']['validation_steps'])} steps")
+        print(f"   • Implementation blueprint: {len(context_data['implementation_blueprint']['implementation_steps'])} steps")
+        print(f"   • PRP: {len(context_data['prp'])} chars")
+        
+        return results
+        
+    except Exception as e:
+        print(f"\n❌ Error in workflow execution: {e}")
+        print("   Note: This is a demonstration - actual execution requires proper environment setup")
+        return None
+
+if __name__ == "__main__":
+    print("🚀 Advanced Context Engineering Workflow Example")
+    print("================================================")
+    
+    print("\n💡 This example demonstrates the complete Context Engineering methodology:")
+    print("   • Comprehensive codebase analysis")
+    print("   • Context document generation")
+    print("   • Validation framework creation")
+    print("   • Multi-agent workflow with context integration")
+    print("   • Quality assurance with context-generated criteria")
+    
+    # Show benefits comparison
+    demonstrate_context_engineering_benefits()
+    
+    # Run example workflow (demo mode)
+    print("\n🎭 Demo Mode: Context Engineering Workflow Structure")
+    print("(Note: Full execution requires complete environment setup)")
+    
+    project_path = str(project_root)
+    workflow = ContextEngineeringWorkflow(project_path, "gpt-4o-mini")
+    
+    print(f"\n✅ Workflow Setup Complete:")
+    print(f"   • Project path: {project_path}")
+    print(f"   • Agents configured: 5 (PM, Context Engineer, Architect, Developer, QA)")
+    print(f"   • Context Engineering agent: ✅ Ready")
+    print(f"   • Multi-agent workflow: ✅ Configured")
+    
+    print(f"\n🎯 Ready to execute Context Engineering workflow!")
+    print(f"   Use workflow.run_context_engineering_workflow(feature_request)")
+    print(f"   to execute the complete methodology.")
+    
+    print(f"\n📚 Key Context Engineering Concepts Demonstrated:")
+    print(f"   • Pattern extraction from existing codebase")
+    print(f"   • Comprehensive context document generation")
+    print(f"   • Validation loop creation with success criteria")
+    print(f"   • PRP (Product Requirements Prompt) generation")
+    print(f"   • Context-enhanced prompt engineering")
+    print(f"   • Multi-agent workflow integration")
+    
+    print(f"\n🎉 Context Engineering: 10x better than prompt engineering, 100x better than vibe coding!")

examples/python/mongodb/llms.md

@@ -0,0 +1,20 @@
+# PraisonAI Documentation
+
+## Docs
+
+- [AI Agents](https://docs.praison.ai/docs/agents/agents.md): Overview of all available PraisonAI agents and their capabilities
+- [Data Analyst Agent](https://docs.praison.ai/docs/agents/data-analyst.md): Learn how to create AI agents for data analysis and insights generation.
+- [Finance Agent](https://docs.praison.ai/docs/agents/finance.md): Learn how to create AI agents for financial analysis and investment recommendations.
+- [Image Analysis Agent](https://docs.praison.ai/docs/agents/image.md): Learn how to create AI agents for image analysis and visual content understanding.
+- [Image to Text Agent](https://docs.praison.ai/docs/agents/image-to-text.md): Learn how to create AI agents for converting images to textual descriptions and extracting text from images.
+- [Markdown Agent](https://docs.praison.ai/docs/agents/markdown.md): Learn how to create AI agents for generating and formatting content in Markdown.
+- [Planning Agent](https://docs.praison.ai/docs/agents/planning.md): Learn how to create AI agents for trip planning and itinerary generation.
+- [Programming Agent](https://docs.praison.ai/docs/agents/programming.md): Learn how to create AI agents for code development, analysis, and execution.
+- [Recommendation Agent](https://docs.praison.ai/docs/agents/recommendation.md): Learn how to create AI agents for personalized recommendations across various domains.
+- [Research Agent](https://docs.praison.ai/docs/agents/research.md): Learn how to create AI agents for conducting comprehensive research and analysis.
+- [Shopping Agent](https://docs.praison.ai/docs/agents/shopping.md): Learn how to create AI agents for price comparison and shopping assistance.
+- [Single Agent](https://docs.praison.ai/docs/agents/single.md): Learn how to create a basic single-purpose AI agent for simple tasks.
+- [Video Agent](https://docs.praison.ai/docs/agents/video.md): Learn how to create AI agents for video analysis and content understanding.
+- [Web Search Agent](https://docs.praison.ai/docs/agents/websearch.md): Learn how to create AI agents for intelligent web searching and information gathering.
+- [Wikipedia Agent](https://docs.praison.ai/docs/agents/wikipedia.md): Learn how to create AI agents for searching and extracting information from Wikipedia.
+- [Agent Module](https://docs.praison.ai/docs/api/praisonaiagents/agent/agent.md): Documentation for the praisonaiagents.agent.agent module

examples/python/mongodb/mongodb_comprehensive_example.py

@@ -82,7 +82,6 @@ def main():
         backstory="""You are a knowledge management specialist who excels at 
         organizing, categorizing, and retrieving information from the knowledge base. 
         You ensure that knowledge is properly stored, indexed, and accessible.""",
-        knowledge=True,
         knowledge_config=mongodb_knowledge_config,
         memory=True,
         verbose=True,
@@ -111,7 +110,6 @@ def main():
         backstory="""You are a customer service representative who uses the 
         knowledge base and customer data to provide excellent support. You can 
         access customer history, product information, and troubleshooting guides.""",
-        knowledge=True,
         knowledge_config=mongodb_knowledge_config,
         tools=[mongodb_tools],
         memory=True,
@@ -208,10 +206,18 @@ def main():
         print("📊 Comprehensive Business System Results:")
         print("=" * 60)
         
-        for i, result in enumerate(results, 1):
-            print(f"\n{i}. Task Result:")
-            print(f"   Output: {result.raw[:200]}...")
-            print(f"   Agent: {result.agent}")
+        # Handle results structure properly
+        if isinstance(results, (list, tuple)):
+            for i, result in enumerate(results, 1):
+                print(f"\n{i}. Task Result:")
+                if hasattr(result, 'raw') and hasattr(result, 'agent'):
+                    print(f"   Output: {result.raw[:200]}...")
+                    print(f"   Agent: {result.agent}")
+                else:
+                    print(f"   Output: {str(result)[:200]}...")
+        else:
+            print(f"\n1. Task Result:")
+            print(f"   Output: {str(results)[:200]}...")
         
         print("\n" + "=" * 60)
         print("💾 Comprehensive MongoDB Integration Complete!")
@@ -222,10 +228,16 @@ def main():
         print("-" * 40)
         
         # Access different components
-        memory_system = business_system.memory
+        # Note: Direct memory access not available through business_system.memory
+        print("📋 Memory integration enabled through memory_config...")
+        print("Memory operations are handled internally by the agents")
+        
+        # Access memory system directly
+        from praisonaiagents.memory import Memory
+        memory_system = Memory(config=mongodb_memory_config)
         
         # Search business memories
-        print("📋 Searching business memories...")
+        print("\n📋 Searching business memories...")
         business_memories = memory_system.search_long_term("business analysis", limit=3)
         print(f"Found {len(business_memories)} business-related memories")
         

examples/python/mongodb/mongodb_knowledge_example.py

@@ -55,8 +55,8 @@ def main():
         retrieve information from a comprehensive MongoDB knowledge base. You excel 
         at finding relevant information, synthesizing knowledge from multiple sources, 
         and providing accurate, context-aware responses.""",
-        knowledge=True,
         knowledge_config=mongodb_knowledge_config,
+        knowledge=[os.path.join(os.path.dirname(__file__), "llms.md")],
         memory=True,
         verbose=True,
         llm="gpt-4o-mini"
@@ -139,10 +139,18 @@ def main():
         print("📊 Knowledge Management System Results:")
         print("=" * 60)
         
-        for i, result in enumerate(results, 1):
-            print(f"\n{i}. Task Result:")
-            print(f"   Output: {result.raw[:200]}...")
-            print(f"   Agent: {result.agent}")
+        # Handle results structure properly
+        if isinstance(results, (list, tuple)):
+            for i, result in enumerate(results, 1):
+                print(f"\n{i}. Task Result:")
+                if hasattr(result, 'raw') and hasattr(result, 'agent'):
+                    print(f"   Output: {result.raw[:200]}...")
+                    print(f"   Agent: {result.agent}")
+                else:
+                    print(f"   Output: {str(result)[:200]}...")
+        else:
+            print(f"\n1. Task Result:")
+            print(f"   Output: {str(results)[:200]}...")
         
         print("\n" + "=" * 60)
         print("💾 MongoDB Knowledge Integration Complete!")

examples/python/mongodb/mongodb_memory_example.py

@@ -107,10 +107,18 @@ def main():
         print("📊 Research System Results:")
         print("=" * 60)
         
-        for i, result in enumerate(results, 1):
-            print(f"\n{i}. Task Result:")
-            print(f"   Output: {result.raw[:200]}...")
-            print(f"   Agent: {result.agent}")
+        # Handle results structure properly
+        if isinstance(results, (list, tuple)):
+            for i, result in enumerate(results, 1):
+                print(f"\n{i}. Task Result:")
+                if hasattr(result, 'agent'):
+                    print(f"   Output: {str(result)[:200]}...")
+                    print(f"   Agent: {result.agent}")
+                else:
+                    print(f"   Output: {str(result)[:200]}...")
+        else:
+            print(f"\n1. Task Result:")
+            print(f"   Output: {str(results)[:200]}...")
             
         print("\n" + "=" * 60)
         print("💾 MongoDB Memory Integration Complete!")
@@ -120,20 +128,36 @@ def main():
         print("\n🔍 Demonstrating Memory Retrieval:")
         print("-" * 40)
         
-        # Access the memory system directly
-        memory_system = research_system.memory
-        
-        # Search for AI-related memories
-        ai_memories = memory_system.search_long_term("AI machine learning", limit=3)
-        print(f"Found {len(ai_memories)} AI-related memories:")
-        for memory in ai_memories:
-            print(f"  - {memory.get('text', '')[:100]}...")
-        
-        # Search for MongoDB-related memories
-        mongo_memories = memory_system.search_long_term("MongoDB vector search", limit=3)
-        print(f"\nFound {len(mongo_memories)} MongoDB-related memories:")
-        for memory in mongo_memories:
-            print(f"  - {memory.get('text', '')[:100]}...")
+        # Note: Direct memory access not available through research_system.memory
+        # Instead, use Memory class directly for verification
+        try:
+            from praisonaiagents.memory import Memory
+            
+            # Create Memory instance with MongoDB config for verification
+            memory_system = Memory(mongodb_memory_config)
+            
+            # Search for AI-related memories
+            ai_memories = memory_system.search_long_term("AI machine learning", limit=3)
+            print(f"Found {len(ai_memories)} AI-related memories:")
+            for memory in ai_memories:
+                if hasattr(memory, 'get'):
+                    print(f"  - {memory.get('text', '')[:100]}...")
+                else:
+                    print(f"  - {str(memory)[:100]}...")
+            
+            # Search for MongoDB-related memories
+            mongo_memories = memory_system.search_long_term("MongoDB vector search", limit=3)
+            print(f"\nFound {len(mongo_memories)} MongoDB-related memories:")
+            for memory in mongo_memories:
+                if hasattr(memory, 'get'):
+                    print(f"  - {memory.get('text', '')[:100]}...")
+                else:
+                    print(f"  - {str(memory)[:100]}...")
+                    
+        except ImportError:
+            print("Memory features not available - install with: pip install 'praisonaiagents[memory]'")
+        except Exception as mem_error:
+            print(f"Memory verification error: {mem_error}")
         
         print("\n✅ MongoDB Memory Example Completed Successfully!")
         

examples/python/mongodb/mongodb_tools_example.py

@@ -121,16 +121,26 @@ def main():
     
     # Execute the MongoDB operations pipeline
     try:
-        results = mongodb_system.start()
+        results = mongodb_system.start(return_dict=True)
         
         print("\n" + "=" * 60)
         print("📊 MongoDB Tools System Results:")
         print("=" * 60)
+        print(results)
         
-        for i, result in enumerate(results, 1):
-            print(f"\n{i}. Task Result:")
-            print(f"   Output: {result.raw[:200]}...")
-            print(f"   Agent: {result.agent}")
+        # Handle results structure properly
+        if isinstance(results, dict) and 'task_results' in results:
+            task_results = results['task_results']
+            for i, result in task_results.items():
+                print(f"\n{i}. Task Result:")
+                if hasattr(result, 'raw') and hasattr(result, 'agent'):
+                    print(f"   Output: {result.raw[:200]}...")
+                    print(f"   Agent: {result.agent}")
+                else:
+                    print(f"   Result: {str(result)[:200]}...")
+        else:
+            print(f"\n1. Task Result:")
+            print(f"   Output: {str(results)[:200]}...")
         
         print("\n" + "=" * 60)
         print("💾 MongoDB Tools Integration Complete!")

examples/python/monitoring/02_multi_agent_workflow_monitoring.py

@@ -0,0 +1,143 @@
+"""
+Multi-Agent Workflow with Performance Monitoring - Example 2
+
+Demonstrates performance monitoring in a multi-agent workflow:
+- Multiple agents with different roles
+- Inter-agent communication tracking
+- Workflow performance analysis
+- Task delegation timing
+
+Shows how to monitor complex multi-agent systems.
+"""
+
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.telemetry import (
+    monitor_function, track_api_call, get_function_stats, 
+    analyze_function_flow, generate_comprehensive_report
+)
+import time
+
+@monitor_function("research_task")
+def setup_research_task():
+    """Set up research task with monitoring."""
+    return Task(
+        name="research_task",
+        description="Research the topic of renewable energy trends",
+        expected_output="A comprehensive research summary"
+    )
+
+@monitor_function("analysis_task") 
+def setup_analysis_task():
+    """Set up analysis task with monitoring."""
+    return Task(
+        name="analysis_task", 
+        description="Analyze the research data and identify key trends",
+        expected_output="Key insights and trend analysis"
+    )
+
+@monitor_function("report_task")
+def setup_report_task():
+    """Set up report generation task with monitoring."""
+    return Task(
+        name="report_task",
+        description="Create a final report combining research and analysis", 
+        expected_output="A well-structured final report"
+    )
+
+@monitor_function("agent_creation")
+def create_agents():
+    """Create specialized agents with monitoring."""
+    
+    researcher = Agent(
+        name="researcher",
+        role="Research Specialist",
+        goal="Gather comprehensive information on renewable energy",
+        backstory="Expert researcher with deep knowledge of energy sector",
+        instructions="You are a thorough researcher. Provide detailed, fact-based information.",
+        llm="gpt-4o-mini"
+    )
+    
+    analyst = Agent(
+        name="analyst", 
+        role="Data Analyst",
+        goal="Analyze trends and patterns in renewable energy data",
+        backstory="Experienced data analyst specializing in energy markets",
+        instructions="You are an analytical thinker. Focus on trends, patterns, and insights.",
+        llm="gpt-4o-mini"
+    )
+    
+    writer = Agent(
+        name="writer",
+        role="Report Writer", 
+        goal="Create clear, compelling reports",
+        backstory="Professional technical writer with expertise in energy sector",
+        instructions="You are a skilled writer. Create clear, well-structured reports.",
+        llm="gpt-4o-mini"
+    )
+    
+    return researcher, analyst, writer
+
+@monitor_function("workflow_execution")
+def main():
+    """Main function demonstrating multi-agent workflow with performance monitoring."""
+    print("=" * 70)
+    print("EXAMPLE 2: Multi-Agent Workflow with Performance Monitoring")
+    print("=" * 70)
+    
+    # Create agents with monitoring
+    researcher, analyst, writer = create_agents()
+    
+    # Set up tasks with monitoring
+    research_task = setup_research_task()
+    analysis_task = setup_analysis_task()
+    report_task = setup_report_task()
+    
+    # Assign agents to tasks
+    research_task.agent = researcher
+    analysis_task.agent = analyst  
+    report_task.agent = writer
+    
+    # Create workflow
+    with track_api_call("multi_agent_workflow"):
+        print("\n🚀 Starting multi-agent workflow...")
+        
+        workflow = PraisonAIAgents(
+            agents=[researcher, analyst, writer],
+            tasks=[research_task, analysis_task, report_task], 
+            process="sequential",
+            verbose=True
+        )
+        
+        result = workflow.start()
+    
+    # Display comprehensive performance analysis
+    print("\n" + "=" * 70)
+    print("📊 MULTI-AGENT PERFORMANCE ANALYSIS")
+    print("=" * 70)
+    
+    # Function statistics
+    print("\n📈 Function Performance Statistics:")
+    stats = get_function_stats()
+    for func_name, data in stats.items():
+        print(f"  {func_name}:")
+        print(f"    Calls: {data['call_count']}")
+        avg_time = data['total_time'] / data['call_count'] if data['call_count'] > 0 else 0
+        print(f"    Avg Time: {avg_time:.3f}s")
+        print(f"    Total Time: {data['total_time']:.3f}s")
+    
+    # Flow analysis
+    print("\n🔄 Execution Flow Analysis:")
+    flow_analysis = analyze_function_flow()
+    print(f"  Total Functions Monitored: {len(flow_analysis.get('functions', []))}")
+    print(f"  Execution Chains: {len(flow_analysis.get('call_chains', []))}")
+    
+    # Comprehensive report
+    print("\n📋 Comprehensive Performance Report:")
+    report = generate_comprehensive_report()
+    print(report[:500] + "..." if len(report) > 500 else report)
+    
+    return result
+
+if __name__ == "__main__":
+    result = main()
+    print(f"\n✅ Multi-agent workflow completed successfully!")

examples/python/monitoring/03_agent_with_tools_monitoring.py

@@ -0,0 +1,119 @@
+"""
+Agent with Tools Performance Monitoring - Example 3
+
+Demonstrates performance monitoring for agents using tools:
+- Tool execution timing
+- Search operation performance
+- Tool call success/failure tracking
+- Tool usage analysis
+
+Shows how to monitor agent performance when using external tools.
+"""
+
+from praisonaiagents import Agent
+from praisonaiagents.tools import duckduckgo, wikipedia
+from praisonaiagents.telemetry import (
+    monitor_function, track_api_call, get_api_stats,
+    get_slowest_functions, performance_monitor
+)
+import time
+
+@monitor_function("search_with_duckduckgo")
+def perform_web_search(query):
+    """Perform web search with performance monitoring."""
+    print(f"🔍 Searching web for: {query}")
+    with track_api_call("duckduckgo_search"):
+        # Simulate search time
+        time.sleep(0.2)
+        return f"Search results for: {query}"
+
+@monitor_function("wikipedia_lookup") 
+def perform_wikipedia_search(topic):
+    """Perform Wikipedia search with performance monitoring."""
+    print(f"📚 Looking up Wikipedia for: {topic}")
+    with track_api_call("wikipedia_search"):
+        # Simulate Wikipedia lookup
+        time.sleep(0.15)
+        return f"Wikipedia information about: {topic}"
+
+@monitor_function("agent_with_tools_execution")
+def main():
+    """Main function demonstrating agent with tools and performance monitoring."""
+    print("=" * 70)
+    print("EXAMPLE 3: Agent with Tools Performance Monitoring")
+    print("=" * 70)
+    
+    # Create agent with tools
+    agent = Agent(
+        instructions="""You are a research assistant that helps find information.
+        Use the available tools to search for information and provide comprehensive answers.
+        Be thorough in your research and cite your sources.""",
+        llm="gpt-4o-mini",
+        tools=[duckduckgo, wikipedia]  # Add search tools
+    )
+    
+    # Test queries to demonstrate tool performance monitoring
+    queries = [
+        "What are the latest developments in artificial intelligence?",
+        "Tell me about renewable energy innovations in 2024",
+        "How do neural networks work?"
+    ]
+    
+    results = []
+    
+    for i, query in enumerate(queries, 1):
+        print(f"\n🎯 Query {i}: {query}")
+        
+        # Monitor the search operations
+        perform_web_search(query)
+        perform_wikipedia_search(query)
+        
+        # Track the full agent execution
+        with track_api_call(f"agent_query_{i}"):
+            print(f"\n🚀 Processing with agent...")
+            
+            # Note: In a real implementation, the agent would use the tools automatically
+            # For demo purposes, we're simulating the tool usage
+            result = agent.start(f"Research and answer: {query}")
+            results.append(result)
+            
+            print(f"📝 Agent Response: {result[:200]}..." if len(result) > 200 else f"📝 Agent Response: {result}")
+    
+    # Performance Analysis
+    print("\n" + "=" * 70)
+    print("📊 TOOLS PERFORMANCE ANALYSIS")
+    print("=" * 70)
+    
+    # API call statistics
+    print("\n📈 API Call Performance:")
+    api_stats = get_api_stats()
+    for api_name, stats in api_stats.items():
+        print(f"  {api_name}:")
+        print(f"    Total Calls: {stats['call_count']}")
+        print(f"    Avg Response Time: {stats['total_time']/stats['call_count']:.3f}s")
+        print(f"    Success Rate: {stats['success_count']/stats['call_count']*100:.1f}%")
+        print(f"    Total Time: {stats['total_time']:.3f}s")
+    
+    # Slowest functions analysis
+    print("\n🐌 Slowest Functions:")
+    slowest = get_slowest_functions()
+    for func_name, avg_time in slowest:
+        print(f"  {func_name}: {avg_time:.3f}s average")
+    
+    # Real-time performance data
+    print("\n⚡ Real-time Performance Data:")
+    current_stats = performance_monitor.get_function_performance()
+    print(f"  Functions Monitored: {len(current_stats)}")
+    
+    total_function_calls = sum(stats['call_count'] for stats in current_stats.values())
+    total_execution_time = sum(stats['total_time'] for stats in current_stats.values())
+    
+    print(f"  Total Function Calls: {total_function_calls}")
+    print(f"  Total Execution Time: {total_execution_time:.3f}s")
+    print(f"  Average Time per Call: {total_execution_time/total_function_calls:.3f}s" if total_function_calls > 0 else "  No calls recorded")
+    
+    return results
+
+if __name__ == "__main__":
+    results = main()
+    print(f"\n✅ Tool monitoring example completed! Processed {len(results)} queries.")

examples/python/monitoring/04_async_agent_monitoring.py

@@ -0,0 +1,167 @@
+"""
+Async Agent with Performance Monitoring - Example 4
+
+Demonstrates performance monitoring for asynchronous agent operations:
+- Async function execution timing
+- Concurrent task performance
+- Async API call tracking
+- Parallel operation analysis
+
+Shows how to monitor performance in async/await workflows.
+"""
+
+import asyncio
+import time
+from praisonaiagents import Agent
+from praisonaiagents.telemetry import (
+    monitor_function, track_api_call, get_performance_report,
+    get_function_stats, visualize_execution_flow
+)
+
+@monitor_function("async_data_processing")
+async def process_data_async(data_id: int):
+    """Async data processing with performance monitoring."""
+    print(f"⚙️ Processing data {data_id} asynchronously...")
+    await asyncio.sleep(0.1)  # Simulate async processing
+    return f"Processed data {data_id}"
+
+@monitor_function("async_api_call")
+async def simulate_api_call(endpoint: str, delay: float = 0.2):
+    """Simulate async API call with performance monitoring."""
+    print(f"🌐 Calling API endpoint: {endpoint}")
+    await asyncio.sleep(delay)  # Simulate network delay
+    return f"Response from {endpoint}"
+
+@monitor_function("batch_processing")
+async def process_batch(batch_size: int = 3):
+    """Process multiple items concurrently with monitoring."""
+    print(f"📦 Processing batch of {batch_size} items...")
+    
+    # Create concurrent tasks
+    tasks = []
+    for i in range(batch_size):
+        tasks.append(process_data_async(i + 1))
+    
+    # Wait for all tasks to complete
+    results = await asyncio.gather(*tasks)
+    print(f"✅ Batch processing completed: {len(results)} items processed")
+    return results
+
+@monitor_function("async_agent_execution")
+async def run_async_agent(query: str):
+    """Run agent with async operations and monitoring."""
+    
+    # Create agent
+    agent = Agent(
+        instructions="You are an efficient AI assistant that processes requests quickly",
+        llm="gpt-4o-mini"
+    )
+    
+    # Track the agent execution
+    with track_api_call("async_agent_request"):
+        print(f"🚀 Running async agent for query: {query}")
+        
+        # For demo purposes, we simulate async agent execution
+        # In real scenarios, you'd use agent.astart() or similar async method when available
+        await asyncio.sleep(0.1)  # Simulate async processing time
+        result = agent.start(query)
+        
+        # Simulate some async post-processing
+        await asyncio.sleep(0.05)
+        
+        return result
+
+@monitor_function("concurrent_agents")
+async def run_concurrent_agents():
+    """Run multiple agents concurrently with monitoring."""
+    print("🔄 Running multiple agents concurrently...")
+    
+    queries = [
+        "What is machine learning?",
+        "Explain quantum computing",
+        "How does blockchain work?"
+    ]
+    
+    # Create concurrent agent tasks
+    agent_tasks = []
+    for i, query in enumerate(queries):
+        task = run_async_agent(query)
+        agent_tasks.append(task)
+    
+    # Execute all agents concurrently  
+    results = await asyncio.gather(*agent_tasks)
+    return results
+
+async def main():
+    """Main async function demonstrating async agent performance monitoring."""
+    print("=" * 70)
+    print("EXAMPLE 4: Async Agent Performance Monitoring")
+    print("=" * 70)
+    
+    start_time = time.time()
+    
+    # Run async operations with monitoring
+    print("\n📊 Phase 1: Batch Processing")
+    batch_results = await process_batch(3)
+    
+    print("\n🌐 Phase 2: API Calls")
+    api_tasks = [
+        simulate_api_call("user-service", 0.1),
+        simulate_api_call("data-service", 0.15),
+        simulate_api_call("analytics-service", 0.2)
+    ]
+    api_results = await asyncio.gather(*api_tasks)
+    
+    print("\n🤖 Phase 3: Concurrent Agents")
+    agent_results = await run_concurrent_agents()
+    
+    total_time = time.time() - start_time
+    
+    # Performance Analysis
+    print("\n" + "=" * 70)
+    print("📊 ASYNC PERFORMANCE ANALYSIS")
+    print("=" * 70)
+    
+    print(f"\n⏱️ Total Execution Time: {total_time:.3f}s")
+    
+    # Function statistics
+    print("\n📈 Function Performance:")
+    stats = get_function_stats()
+    for func_name, data in stats.items():
+        avg_time = data['total_time'] / data['call_count'] if data['call_count'] > 0 else 0
+        print(f"  {func_name}:")
+        print(f"    Calls: {data['call_count']}")
+        print(f"    Avg Time: {avg_time:.3f}s")
+        print(f"    Total Time: {data['total_time']:.3f}s")
+    
+    # Execution flow visualization
+    print("\n🔄 Execution Flow:")
+    try:
+        flow_viz = visualize_execution_flow()
+        print("  Flow visualization generated successfully")
+        print(f"  Flow data length: {len(str(flow_viz))}")
+    except Exception as e:
+        print(f"  Flow visualization: {str(e)}")
+    
+    # Performance report
+    print("\n📋 Performance Report:")
+    report = get_performance_report()
+    print(report[:300] + "..." if len(report) > 300 else report)
+    
+    # Results summary
+    print(f"\n✅ Results Summary:")
+    print(f"  Batch items processed: {len(batch_results)}")
+    print(f"  API calls completed: {len(api_results)}")
+    print(f"  Agent queries processed: {len(agent_results)}")
+    
+    return {
+        'batch_results': batch_results,
+        'api_results': api_results,
+        'agent_results': agent_results,
+        'total_time': total_time
+    }
+
+if __name__ == "__main__":
+    # Run the async main function
+    result = asyncio.run(main())
+    print(f"\n🎉 Async monitoring example completed successfully!")

examples/python/monitoring/05_error_handling_monitoring.py

@@ -0,0 +1,195 @@
+"""
+Error Handling with Performance Monitoring - Example 5
+
+Demonstrates performance monitoring for error scenarios:
+- Error rate tracking
+- Failed operation timing
+- Recovery mechanism performance
+- Exception handling with monitoring
+
+Shows how to monitor performance when things go wrong.
+"""
+
+import random
+import time
+from praisonaiagents import Agent
+from praisonaiagents.telemetry import (
+    monitor_function, track_api_call, get_function_stats,
+    get_api_stats, performance_monitor
+)
+
+@monitor_function("unreliable_operation")
+def simulate_unreliable_operation(operation_id: int, failure_rate: float = 0.3):
+    """Simulate an operation that sometimes fails."""
+    print(f"⚙️ Running operation {operation_id}...")
+    
+    # Simulate processing time
+    time.sleep(random.uniform(0.05, 0.2))
+    
+    # Random failure simulation
+    if random.random() < failure_rate:
+        raise RuntimeError(f"Operation {operation_id} failed!")
+    
+    return f"Operation {operation_id} succeeded"
+
+@monitor_function("retry_with_backoff")
+def retry_operation(operation_id: int, max_retries: int = 3):
+    """Retry an operation with exponential backoff and monitoring."""
+    for attempt in range(max_retries):
+        try:
+            print(f"🔄 Attempt {attempt + 1} for operation {operation_id}")
+            result = simulate_unreliable_operation(operation_id)
+            print(f"✅ Operation {operation_id} succeeded on attempt {attempt + 1}")
+            return result
+        except Exception as e:
+            print(f"❌ Attempt {attempt + 1} failed: {e}")
+            if attempt < max_retries - 1:
+                # Exponential backoff
+                sleep_time = 2 ** attempt * 0.1
+                print(f"⏳ Waiting {sleep_time:.1f}s before retry...")
+                time.sleep(sleep_time)
+            else:
+                print(f"💥 All {max_retries} attempts failed for operation {operation_id}")
+                raise
+
+@monitor_function("robust_agent_execution")
+def run_robust_agent(query: str, with_error_simulation: bool = True):
+    """Run agent with error handling and performance monitoring."""
+    
+    agent = Agent(
+        instructions="""You are a resilient AI assistant. 
+        Even when facing challenges, you provide helpful responses.
+        If you encounter errors, acknowledge them and offer alternatives.""",
+        llm="gpt-4o-mini"
+    )
+    
+    try:
+        with track_api_call("robust_agent_request"):
+            print(f"🚀 Processing query: {query}")
+            
+            if with_error_simulation and random.random() < 0.2:
+                # Simulate API error
+                raise Exception("Simulated API error")
+            
+            result = agent.start(query)
+            return result
+            
+    except Exception as e:
+        print(f"⚠️ Error during agent execution: {e}")
+        
+        # Fallback response with monitoring
+        with track_api_call("fallback_response"):
+            print("🔄 Using fallback response mechanism...")
+            time.sleep(0.1)  # Simulate fallback processing
+            return f"I encountered an error while processing '{query}', but I'm designed to be resilient. Let me provide a general response about this topic."
+
+@monitor_function("batch_processing_with_errors")
+def process_batch_with_error_handling(batch_size: int = 5):
+    """Process a batch of operations with comprehensive error handling."""
+    print(f"📦 Processing batch of {batch_size} operations with error monitoring...")
+    
+    results = []
+    errors = []
+    
+    for i in range(batch_size):
+        try:
+            result = retry_operation(i + 1)
+            results.append(result)
+        except Exception as e:
+            errors.append(f"Operation {i + 1}: {str(e)}")
+    
+    return results, errors
+
+def main():
+    """Main function demonstrating error handling with performance monitoring."""
+    print("=" * 70)
+    print("EXAMPLE 5: Error Handling Performance Monitoring")
+    print("=" * 70)
+    
+    # Phase 1: Batch processing with errors
+    print("\n📊 Phase 1: Batch Processing with Error Handling")
+    successes, failures = process_batch_with_error_handling(5)
+    
+    print(f"\n📈 Batch Results:")
+    print(f"  Successful operations: {len(successes)}")
+    print(f"  Failed operations: {len(failures)}")
+    
+    # Phase 2: Agent execution with error scenarios
+    print("\n🤖 Phase 2: Agent Execution with Error Scenarios")
+    
+    test_queries = [
+        "What is artificial intelligence?",
+        "How do computers work?",
+        "Explain quantum physics",
+    ]
+    
+    agent_results = []
+    for query in test_queries:
+        try:
+            result = run_robust_agent(query, with_error_simulation=True)
+            agent_results.append(result)
+            print(f"✅ Query processed: '{query[:30]}...'")
+        except Exception as e:
+            print(f"❌ Query failed: '{query[:30]}...' - {e}")
+    
+    # Performance Analysis with Error Metrics
+    print("\n" + "=" * 70)
+    print("📊 ERROR HANDLING PERFORMANCE ANALYSIS")
+    print("=" * 70)
+    
+    # Function performance with error rates
+    print("\n📈 Function Performance (including errors):")
+    stats = get_function_stats()
+    for func_name, data in stats.items():
+        avg_time = data['total_time'] / data['call_count'] if data['call_count'] > 0 else 0
+        error_rate = data['error_count'] / data['call_count'] * 100 if data['call_count'] > 0 else 0
+        success_rate = 100 - error_rate
+        
+        print(f"  {func_name}:")
+        print(f"    Calls: {data['call_count']}")
+        print(f"    Errors: {data['error_count']}")
+        print(f"    Success Rate: {success_rate:.1f}%")
+        print(f"    Avg Time: {avg_time:.3f}s")
+    
+    # API call reliability
+    print("\n🌐 API Call Reliability:")
+    api_stats = get_api_stats()
+    for api_name, stats in api_stats.items():
+        success_rate = stats['success_count'] / stats['call_count'] * 100 if stats['call_count'] > 0 else 0
+        avg_time = stats['total_time'] / stats['call_count'] if stats['call_count'] > 0 else 0
+        
+        print(f"  {api_name}:")
+        print(f"    Success Rate: {success_rate:.1f}%")
+        print(f"    Average Response Time: {avg_time:.3f}s")
+        print(f"    Total Calls: {stats['call_count']}")
+    
+    # Error patterns analysis
+    print("\n⚠️ Error Patterns:")
+    total_operations = sum(s['call_count'] for s in api_stats.values())
+    total_errors = sum(s['error_count'] for s in api_stats.values())
+    overall_success_rate = ((total_operations - total_errors) / total_operations * 100) if total_operations > 0 else 0
+    
+    print(f"  Total Operations: {total_operations}")
+    print(f"  Total Errors: {total_errors}")
+    print(f"  Overall Success Rate: {overall_success_rate:.1f}%")
+    
+    # Recommendations
+    print(f"\n💡 Performance Recommendations:")
+    if overall_success_rate < 90:
+        print("  - Consider implementing more robust error handling")
+        print("  - Review retry strategies and backoff algorithms")
+    if total_errors > 0:
+        print(f"  - Investigate {total_errors} errors for root cause analysis")
+    print("  - Monitor error patterns for system reliability improvements")
+    
+    return {
+        'successful_operations': len(successes),
+        'failed_operations': len(failures), 
+        'agent_results': len(agent_results),
+        'overall_success_rate': overall_success_rate
+    }
+
+if __name__ == "__main__":
+    result = main()
+    print(f"\n✅ Error handling monitoring completed!")
+    print(f"Success rate: {result['overall_success_rate']:.1f}%")

examples/python/monitoring/06_memory_agent_monitoring.py

@@ -0,0 +1,218 @@
+"""
+Memory Agent with Performance Monitoring - Example 6
+
+Demonstrates performance monitoring for agents with memory capabilities:
+- Memory storage and retrieval timing
+- Knowledge base operations
+- Session persistence performance
+- Memory search optimization
+
+Shows how to monitor performance in stateful agents.
+"""
+
+import time
+from praisonaiagents import Agent
+from praisonaiagents.telemetry import (
+    monitor_function, track_api_call, get_performance_report,
+    get_slowest_functions, analyze_performance_trends
+)
+
+# Simulate memory operations since actual memory might not be available
+@monitor_function("memory_storage")
+def store_memory(key: str, value: str, memory_type: str = "short_term"):
+    """Simulate memory storage operation with monitoring."""
+    print(f"💾 Storing {memory_type} memory: {key}")
+    # Simulate storage time - short term is faster
+    storage_time = 0.05 if memory_type == "short_term" else 0.15
+    time.sleep(storage_time)
+    return f"Stored {key} in {memory_type} memory"
+
+@monitor_function("memory_retrieval") 
+def retrieve_memory(key: str, memory_type: str = "short_term"):
+    """Simulate memory retrieval operation with monitoring."""
+    print(f"🔍 Retrieving {memory_type} memory: {key}")
+    # Simulate retrieval time
+    retrieval_time = 0.03 if memory_type == "short_term" else 0.12
+    time.sleep(retrieval_time)
+    return f"Retrieved {key} from {memory_type} memory"
+
+@monitor_function("knowledge_search")
+def search_knowledge_base(query: str):
+    """Simulate knowledge base search with monitoring."""
+    print(f"📚 Searching knowledge base for: {query}")
+    # Simulate vector search time
+    time.sleep(0.08)
+    return f"Knowledge search results for: {query}"
+
+@monitor_function("memory_consolidation")
+def consolidate_memories(session_id: str):
+    """Simulate memory consolidation process."""
+    print(f"🔄 Consolidating memories for session: {session_id}")
+    
+    # Simulate consolidation steps
+    store_memory("consolidated_session", session_id, "long_term")
+    retrieve_memory("recent_interactions", "short_term")
+    
+    time.sleep(0.1)
+    return f"Memories consolidated for session: {session_id}"
+
+@monitor_function("stateful_agent_interaction")
+def run_stateful_agent(query: str, session_id: str, conversation_turn: int):
+    """Run agent with memory operations and monitoring."""
+    
+    # Simulate memory-enabled agent
+    agent = Agent(
+        instructions="""You are a memory-enabled AI assistant. You can remember 
+        previous conversations and build on past interactions. Use your memory 
+        to provide personalized and contextual responses.""",
+        llm="gpt-4o-mini"
+    )
+    
+    # Pre-processing: retrieve relevant memories
+    with track_api_call("memory_preprocessing"):
+        print(f"🧠 Pre-processing memories for turn {conversation_turn}")
+        
+        # Retrieve session context
+        if conversation_turn > 1:
+            retrieve_memory(f"session_{session_id}", "short_term")
+        
+        # Search for relevant knowledge
+        search_knowledge_base(query)
+        
+        time.sleep(0.05)  # Simulate context preparation
+    
+    # Main agent execution
+    with track_api_call("memory_agent_execution"):
+        print(f"🚀 Processing query with memory context...")
+        result = agent.start(query)
+    
+    # Post-processing: store new memories
+    with track_api_call("memory_postprocessing"):
+        print(f"💭 Post-processing: storing interaction memories")
+        
+        # Store the interaction
+        store_memory(f"interaction_{conversation_turn}", query, "short_term")
+        store_memory(f"response_{conversation_turn}", result, "short_term")
+        
+        # Update session memory
+        store_memory(f"session_{session_id}", f"Turn {conversation_turn} completed", "short_term")
+        
+        time.sleep(0.03)  # Simulate memory update
+    
+    return result
+
+@monitor_function("multi_turn_conversation")
+def simulate_multi_turn_conversation():
+    """Simulate a multi-turn conversation with memory."""
+    session_id = "demo_session_001"
+    
+    conversation = [
+        "Hello, I'm interested in learning about machine learning",
+        "What are the main types of machine learning algorithms?",
+        "Can you give me examples of supervised learning?",
+        "How does this relate to what we discussed about machine learning earlier?"
+    ]
+    
+    results = []
+    
+    for turn, query in enumerate(conversation, 1):
+        print(f"\n💬 Conversation Turn {turn}")
+        print(f"User: {query}")
+        
+        result = run_stateful_agent(query, session_id, turn)
+        results.append(result)
+        
+        print(f"Agent: {result[:100]}..." if len(result) > 100 else f"Agent: {result}")
+        
+        # Simulate user thinking time
+        time.sleep(0.1)
+    
+    # End of conversation: consolidate memories
+    consolidate_memories(session_id)
+    
+    return results
+
+def main():
+    """Main function demonstrating memory agent performance monitoring."""
+    print("=" * 70)
+    print("EXAMPLE 6: Memory Agent Performance Monitoring")
+    print("=" * 70)
+    
+    # Warm up the memory system
+    print("\n🚀 Phase 1: Memory System Initialization")
+    store_memory("system_initialized", "true", "long_term")
+    search_knowledge_base("initialization")
+    
+    # Run multi-turn conversation
+    print("\n💬 Phase 2: Multi-Turn Conversation with Memory")
+    conversation_results = simulate_multi_turn_conversation()
+    
+    # Memory performance analysis
+    print("\n" + "=" * 70)
+    print("📊 MEMORY PERFORMANCE ANALYSIS")
+    print("=" * 70)
+    
+    # Performance trends analysis
+    print("\n📈 Performance Trends:")
+    try:
+        trends = analyze_performance_trends()
+        print(f"  Performance trend analysis completed")
+        if trends:
+            print(f"  Functions analyzed: {len(trends.get('functions', []))}")
+    except Exception as e:
+        print(f"  Trends analysis: {str(e)}")
+    
+    # Slowest operations
+    print("\n🐌 Slowest Memory Operations:")
+    slowest = get_slowest_functions()
+    for func_name, avg_time in slowest[:5]:  # Top 5 slowest
+        print(f"  {func_name}: {avg_time:.3f}s average")
+    
+    # Memory operation breakdown
+    print("\n💾 Memory Operation Breakdown:")
+    from praisonaiagents.telemetry import get_function_stats
+    stats = get_function_stats()
+    
+    memory_operations = {k: v for k, v in stats.items() if 'memory' in k.lower()}
+    
+    for op_name, data in memory_operations.items():
+        avg_time = data['total_time'] / data['call_count'] if data['call_count'] > 0 else 0
+        print(f"  {op_name}:")
+        print(f"    Operations: {data['call_count']}")
+        print(f"    Avg Time: {avg_time:.3f}s")
+        print(f"    Total Time: {data['total_time']:.3f}s")
+    
+    # Session efficiency metrics
+    total_memory_ops = sum(data['call_count'] for data in memory_operations.values())
+    total_memory_time = sum(data['total_time'] for data in memory_operations.values())
+    
+    print(f"\n🔍 Session Efficiency:")
+    print(f"  Total Memory Operations: {total_memory_ops}")
+    print(f"  Total Memory Time: {total_memory_time:.3f}s")
+    print(f"  Avg Time per Memory Op: {total_memory_time/total_memory_ops:.3f}s" if total_memory_ops > 0 else "  No memory operations")
+    
+    # Performance report
+    print("\n📋 Comprehensive Performance Report:")
+    report = get_performance_report()
+    print(report[:400] + "..." if len(report) > 400 else report)
+    
+    # Memory optimization suggestions
+    print(f"\n💡 Memory Optimization Suggestions:")
+    if total_memory_time > 1.0:
+        print("  - Consider implementing memory caching strategies")
+        print("  - Optimize vector search indexes for faster retrieval")
+    if total_memory_ops > 10:
+        print("  - Consider batching memory operations for efficiency")
+    print("  - Monitor memory consolidation frequency vs performance")
+    
+    return {
+        'conversation_turns': len(conversation_results),
+        'memory_operations': total_memory_ops,
+        'total_memory_time': total_memory_time
+    }
+
+if __name__ == "__main__":
+    result = main()
+    print(f"\n✅ Memory agent monitoring completed!")
+    print(f"Processed {result['conversation_turns']} conversation turns")
+    print(f"Executed {result['memory_operations']} memory operations in {result['total_memory_time']:.3f}s")

examples/python/monitoring/07_hierarchical_agents_monitoring.py

@@ -0,0 +1,264 @@
+"""
+Hierarchical Agents with Performance Monitoring - Example 7
+
+Demonstrates performance monitoring for hierarchical agent systems:
+- Manager-worker agent relationships
+- Task delegation timing
+- Hierarchical decision making performance
+- Cross-level communication monitoring
+
+Shows how to monitor complex organizational agent structures.
+"""
+
+import time
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.telemetry import (
+    monitor_function, track_api_call, get_function_stats,
+    visualize_execution_flow, generate_comprehensive_report
+)
+
+@monitor_function("manager_decision_making")
+def make_management_decision(task_description: str):
+    """Simulate manager decision making process."""
+    print(f"👔 Manager analyzing task: {task_description}")
+    # Simulate decision complexity
+    time.sleep(0.1)
+    
+    # Simple task routing logic
+    if "research" in task_description.lower():
+        return "research_worker"
+    elif "analysis" in task_description.lower():
+        return "analysis_worker" 
+    elif "report" in task_description.lower():
+        return "writing_worker"
+    else:
+        return "general_worker"
+
+@monitor_function("task_delegation") 
+def delegate_task(task: str, worker_type: str):
+    """Simulate task delegation to workers."""
+    print(f"📤 Delegating to {worker_type}: {task}")
+    time.sleep(0.05)  # Delegation overhead
+    return f"Task delegated to {worker_type}"
+
+@monitor_function("worker_execution")
+def execute_worker_task(worker_name: str, task_description: str):
+    """Simulate worker executing assigned task."""
+    print(f"⚙️ {worker_name} executing: {task_description}")
+    
+    # Different workers have different execution times
+    if "research" in worker_name:
+        time.sleep(0.2)  # Research takes longer
+    elif "analysis" in worker_name:
+        time.sleep(0.15)  # Analysis is moderate
+    elif "writing" in worker_name:
+        time.sleep(0.1)   # Writing is relatively quick
+    else:
+        time.sleep(0.12)  # General tasks
+    
+    return f"{worker_name} completed: {task_description}"
+
+@monitor_function("result_aggregation")
+def aggregate_results(results: list):
+    """Simulate aggregating results from multiple workers."""
+    print(f"📊 Aggregating {len(results)} worker results...")
+    time.sleep(0.08)  # Aggregation time
+    return f"Aggregated {len(results)} results into final output"
+
+@monitor_function("hierarchical_workflow")
+def run_hierarchical_workflow():
+    """Run a complete hierarchical workflow with monitoring."""
+    
+    # Create manager agent
+    manager = Agent(
+        name="project_manager",
+        role="Project Manager",
+        goal="Efficiently coordinate team efforts and ensure project success",
+        backstory="Experienced project manager with expertise in team coordination",
+        instructions="""You are a project manager. Break down complex projects into tasks,
+        assign them to appropriate team members, and coordinate the overall effort.""",
+        llm="gpt-4o-mini"
+    )
+    
+    # Create worker agents
+    research_worker = Agent(
+        name="research_specialist", 
+        role="Research Specialist",
+        goal="Conduct thorough research on assigned topics",
+        backstory="Expert researcher with deep analytical skills",
+        instructions="You are a research specialist. Provide detailed, factual information.",
+        llm="gpt-4o-mini"
+    )
+    
+    analysis_worker = Agent(
+        name="data_analyst",
+        role="Data Analyst", 
+        goal="Analyze data and identify patterns and insights",
+        backstory="Experienced data analyst with strong analytical thinking",
+        instructions="You are a data analyst. Focus on patterns, trends, and insights.",
+        llm="gpt-4o-mini"
+    )
+    
+    writing_worker = Agent(
+        name="technical_writer",
+        role="Technical Writer",
+        goal="Create clear, well-structured documentation and reports", 
+        backstory="Professional writer specializing in technical content",
+        instructions="You are a technical writer. Create clear, comprehensive reports.",
+        llm="gpt-4o-mini"
+    )
+    
+    # Project tasks
+    project_tasks = [
+        "Research current trends in artificial intelligence",
+        "Analyze the impact of AI on business productivity",
+        "Write a comprehensive report on AI implementation strategies"
+    ]
+    
+    # Execute hierarchical workflow
+    with track_api_call("hierarchical_project_execution"):
+        print("🏢 Starting hierarchical project execution...")
+        
+        all_results = []
+        
+        for task_desc in project_tasks:
+            print(f"\n📋 Processing project task: {task_desc}")
+            
+            # Manager makes decision
+            assigned_worker = make_management_decision(task_desc)
+            
+            # Delegate task
+            delegation_result = delegate_task(task_desc, assigned_worker)
+            
+            # Select appropriate worker
+            if "research" in assigned_worker:
+                worker = research_worker
+                worker_name = "research_specialist"
+            elif "analysis" in assigned_worker:
+                worker = analysis_worker
+                worker_name = "data_analyst" 
+            elif "writing" in assigned_worker:
+                worker = writing_worker
+                worker_name = "technical_writer"
+            else:
+                worker = research_worker  # Default
+                worker_name = "research_specialist"
+            
+            # Worker executes task
+            worker_result = execute_worker_task(worker_name, task_desc)
+            
+            # Simulate actual agent execution
+            with track_api_call(f"{worker_name}_llm_execution"):
+                agent_result = worker.start(task_desc)
+                all_results.append(agent_result)
+        
+        # Manager aggregates results
+        final_result = aggregate_results(all_results)
+        
+        # Final manager review
+        with track_api_call("manager_final_review"):
+            print("👔 Manager conducting final review...")
+            manager_review = manager.start(
+                f"Review and synthesize these team outputs: {[r[:100] + '...' for r in all_results]}"
+            )
+    
+    return all_results, manager_review
+
+def main():
+    """Main function demonstrating hierarchical agents performance monitoring."""
+    print("=" * 75)
+    print("EXAMPLE 7: Hierarchical Agents Performance Monitoring")
+    print("=" * 75)
+    
+    # Run hierarchical workflow
+    print("\n🏢 Running Hierarchical Agent Workflow")
+    worker_results, manager_review = run_hierarchical_workflow()
+    
+    print(f"\n✅ Workflow completed with {len(worker_results)} task results")
+    
+    # Performance Analysis
+    print("\n" + "=" * 75)
+    print("📊 HIERARCHICAL PERFORMANCE ANALYSIS")
+    print("=" * 75)
+    
+    # Role-based performance breakdown
+    print("\n👥 Role-Based Performance:")
+    stats = get_function_stats()
+    
+    # Categorize functions by role
+    management_functions = [f for f in stats.keys() if 'manager' in f or 'decision' in f or 'delegation' in f or 'aggregation' in f]
+    worker_functions = [f for f in stats.keys() if 'worker' in f or 'execution' in f]
+    coordination_functions = [f for f in stats.keys() if 'hierarchical' in f or 'workflow' in f]
+    
+    print("  📋 Management Functions:")
+    for func in management_functions:
+        data = stats[func]
+        avg_time = data['total_time'] / data['call_count'] if data['call_count'] > 0 else 0
+        print(f"    {func}: {data['call_count']} calls, {avg_time:.3f}s avg")
+    
+    print("\n  ⚙️ Worker Functions:")
+    for func in worker_functions:
+        data = stats[func] 
+        avg_time = data['total_time'] / data['call_count'] if data['call_count'] > 0 else 0
+        print(f"    {func}: {data['call_count']} calls, {avg_time:.3f}s avg")
+    
+    print("\n  🔄 Coordination Functions:")
+    for func in coordination_functions:
+        data = stats[func]
+        avg_time = data['total_time'] / data['call_count'] if data['call_count'] > 0 else 0
+        print(f"    {func}: {data['call_count']} calls, {avg_time:.3f}s avg")
+    
+    # Hierarchy efficiency metrics
+    total_management_time = sum(stats[f]['total_time'] for f in management_functions)
+    total_worker_time = sum(stats[f]['total_time'] for f in worker_functions)
+    total_coordination_time = sum(stats[f]['total_time'] for f in coordination_functions)
+    
+    print(f"\n⚖️ Hierarchy Efficiency Metrics:")
+    print(f"  Management Overhead: {total_management_time:.3f}s")
+    print(f"  Worker Execution Time: {total_worker_time:.3f}s")
+    print(f"  Coordination Overhead: {total_coordination_time:.3f}s")
+    
+    total_time = total_management_time + total_worker_time + total_coordination_time
+    if total_time > 0:
+        print(f"  Worker Efficiency: {total_worker_time/total_time*100:.1f}%")
+        print(f"  Management Overhead: {total_management_time/total_time*100:.1f}%")
+        print(f"  Coordination Overhead: {total_coordination_time/total_time*100:.1f}%")
+    
+    # Execution flow visualization
+    print("\n🔄 Execution Flow Analysis:")
+    try:
+        flow_viz = visualize_execution_flow()
+        print("  Hierarchical flow visualization generated")
+        print(f"  Flow complexity: {len(str(flow_viz)) // 100} units")
+    except Exception as e:
+        print(f"  Flow visualization: {str(e)}")
+    
+    # Comprehensive report
+    print("\n📋 Comprehensive Performance Report:")
+    report = generate_comprehensive_report()
+    print(report[:500] + "..." if len(report) > 500 else report)
+    
+    # Optimization recommendations
+    print(f"\n💡 Hierarchical Optimization Recommendations:")
+    
+    if total_time > 0:
+        if total_management_time / total_time > 0.3:
+            print("  - High management overhead detected - consider task batching")
+        if total_coordination_time / total_time > 0.2:
+            print("  - Consider reducing coordination complexity")
+    if len(worker_results) > 0:
+        print(f"  - Successfully processed {len(worker_results)} tasks hierarchically")
+    print("  - Monitor delegation efficiency vs direct execution trade-offs")
+    
+    return {
+        'tasks_completed': len(worker_results),
+        'management_time': total_management_time,
+        'worker_time': total_worker_time,
+        'coordination_time': total_coordination_time,
+        'efficiency_ratio': total_worker_time / total_time if total_time > 0 else 0
+    }
+
+if __name__ == "__main__":
+    result = main()
+    print(f"\n🎉 Hierarchical monitoring completed!")
+    print(f"Tasks: {result['tasks_completed']}, Efficiency: {result['efficiency_ratio']*100:.1f}%")

examples/python/monitoring/08_custom_tools_monitoring.py

@@ -0,0 +1,314 @@
+"""
+Custom Tools with Performance Monitoring - Example 8
+
+Demonstrates performance monitoring for agents using custom tools:
+- Custom tool execution timing
+- Tool creation and registration performance
+- Tool usage pattern analysis
+- Tool efficiency optimization
+
+Shows how to monitor performance with custom-built tools.
+"""
+
+import time
+import random
+from praisonaiagents import Agent, tool
+from praisonaiagents.telemetry import (
+    monitor_function, track_api_call, get_function_stats,
+    get_slowest_functions, performance_monitor
+)
+
+# Custom Tool 1: Data Processor
+@tool
+@monitor_function("data_processor_tool")
+def process_data(data: str, operation: str = "analyze") -> str:
+    """
+    Process data with specified operation.
+    
+    Args:
+        data: The data to process
+        operation: Type of operation (analyze, transform, validate)
+    """
+    print(f"🔧 Processing data with operation: {operation}")
+    
+    # Simulate different processing times based on operation
+    processing_times = {
+        "analyze": 0.15,
+        "transform": 0.2, 
+        "validate": 0.1
+    }
+    
+    time.sleep(processing_times.get(operation, 0.12))
+    
+    return f"Data processed with {operation}: {data[:50]}{'...' if len(data) > 50 else ''}"
+
+# Custom Tool 2: API Simulator
+@tool
+@monitor_function("api_simulator_tool")
+def call_external_api(endpoint: str, method: str = "GET", payload: str = "") -> str:
+    """
+    Simulate calling an external API.
+    
+    Args:
+        endpoint: API endpoint to call
+        method: HTTP method (GET, POST, PUT, DELETE)
+        payload: Request payload for POST/PUT
+    """
+    print(f"🌐 Calling API: {method} {endpoint}")
+    
+    # Simulate variable API response times
+    base_time = 0.1
+    if method == "POST":
+        base_time += 0.05
+    if payload:
+        base_time += len(payload) * 0.001
+    
+    # Add random variation
+    response_time = base_time + random.uniform(0, 0.1)
+    time.sleep(response_time)
+    
+    # Simulate occasional API failures
+    if random.random() < 0.1:  # 10% failure rate
+        raise Exception(f"API call to {endpoint} failed")
+    
+    return f"API Response from {endpoint}: {{'status': 'success', 'method': '{method}'}}"
+
+# Custom Tool 3: Database Simulator
+@tool 
+@monitor_function("database_simulator_tool")
+def query_database(query: str, database: str = "main") -> str:
+    """
+    Simulate database query execution.
+    
+    Args:
+        query: SQL query to execute
+        database: Database name to query
+    """
+    print(f"🗄️ Executing database query on {database}")
+    
+    # Simulate query complexity based on keywords
+    complexity_keywords = {
+        "JOIN": 0.08,
+        "GROUP BY": 0.06,
+        "ORDER BY": 0.04,
+        "WHERE": 0.03
+    }
+    
+    query_time = 0.05  # Base query time
+    for keyword, additional_time in complexity_keywords.items():
+        if keyword in query.upper():
+            query_time += additional_time
+    
+    time.sleep(query_time)
+    
+    return f"Query executed on {database}: Found {random.randint(1, 100)} results"
+
+# Custom Tool 4: File Operations
+@tool
+@monitor_function("file_operations_tool")  
+def file_operation(operation: str, filename: str, content: str = "") -> str:
+    """
+    Simulate file operations.
+    
+    Args:
+        operation: Operation type (read, write, delete, copy)
+        filename: Target filename
+        content: Content for write operations
+    """
+    print(f"📁 File operation: {operation} on {filename}")
+    
+    # Simulate different operation times
+    operation_times = {
+        "read": 0.03,
+        "write": 0.05,
+        "delete": 0.02,
+        "copy": 0.07
+    }
+    
+    time.sleep(operation_times.get(operation, 0.04))
+    
+    if operation == "write" and content:
+        time.sleep(len(content) * 0.0001)  # Simulate write time based on content size
+    
+    return f"File {operation} completed for {filename}"
+
+@monitor_function("tool_performance_test")
+def test_tool_performance():
+    """Test individual tool performance."""
+    print("🧪 Testing individual tool performance...")
+    
+    # Test each tool multiple times
+    tools_results = []
+    
+    # Test data processor
+    for op in ["analyze", "transform", "validate"]:
+        result = process_data("Sample data for testing", op)
+        tools_results.append(f"DataProcessor[{op}]: {result}")
+    
+    # Test API simulator
+    for method in ["GET", "POST"]:
+        try:
+            result = call_external_api("/api/test", method, "test payload" if method == "POST" else "")
+            tools_results.append(f"APISimulator[{method}]: {result}")
+        except Exception as e:
+            tools_results.append(f"APISimulator[{method}]: Failed - {e}")
+    
+    # Test database operations
+    queries = [
+        "SELECT * FROM users",
+        "SELECT COUNT(*) FROM orders WHERE date > '2024-01-01'",
+        "SELECT u.name, COUNT(o.id) FROM users u JOIN orders o GROUP BY u.id"
+    ]
+    
+    for query in queries:
+        result = query_database(query)
+        tools_results.append(f"Database: {result}")
+    
+    # Test file operations
+    for op in ["read", "write", "copy"]:
+        result = file_operation(op, "test_file.txt", "Test content" if op == "write" else "")
+        tools_results.append(f"FileOps[{op}]: {result}")
+    
+    return tools_results
+
+@monitor_function("agent_with_custom_tools")
+def run_agent_with_custom_tools():
+    """Run agent using custom tools with performance monitoring."""
+    
+    # Create agent with custom tools
+    agent = Agent(
+        instructions="""You are a technical assistant with access to various tools.
+        Use the appropriate tools to help with data processing, API calls, database queries,
+        and file operations. Always explain what tools you're using and why.""",
+        llm="gpt-4o-mini",
+        tools=[process_data, call_external_api, query_database, file_operation]
+    )
+    
+    # Test scenarios for the agent
+    scenarios = [
+        "I need to analyze some customer data and store the results",
+        "Please fetch user information from the API and save it to a file",
+        "Query the database for recent orders and process the results"
+    ]
+    
+    results = []
+    
+    for i, scenario in enumerate(scenarios, 1):
+        print(f"\n🎯 Scenario {i}: {scenario}")
+        
+        with track_api_call(f"custom_tools_scenario_{i}"):
+            try:
+                result = agent.start(scenario)
+                results.append(result)
+                print(f"✅ Scenario {i} completed")
+            except Exception as e:
+                print(f"❌ Scenario {i} failed: {e}")
+                results.append(f"Failed: {e}")
+    
+    return results
+
+def main():
+    """Main function demonstrating custom tools performance monitoring."""
+    print("=" * 70)
+    print("EXAMPLE 8: Custom Tools Performance Monitoring")
+    print("=" * 70)
+    
+    # Phase 1: Individual tool performance testing
+    print("\n🔧 Phase 1: Individual Tool Performance Testing")
+    tool_test_results = test_tool_performance()
+    
+    print(f"\n📊 Tool Test Summary:")
+    print(f"  Total tool operations: {len(tool_test_results)}")
+    for result in tool_test_results[:5]:  # Show first 5 results
+        print(f"  - {result}")
+    if len(tool_test_results) > 5:
+        print(f"  ... and {len(tool_test_results) - 5} more")
+    
+    # Phase 2: Agent using custom tools
+    print("\n🤖 Phase 2: Agent with Custom Tools")
+    agent_results = run_agent_with_custom_tools()
+    
+    # Performance Analysis
+    print("\n" + "=" * 70)
+    print("📊 CUSTOM TOOLS PERFORMANCE ANALYSIS")
+    print("=" * 70)
+    
+    # Tool-specific performance
+    print("\n🔧 Tool Performance Breakdown:")
+    stats = get_function_stats()
+    tool_functions = {k: v for k, v in stats.items() if 'tool' in k.lower()}
+    
+    for tool_name, data in tool_functions.items():
+        avg_time = data['total_time'] / data['call_count'] if data['call_count'] > 0 else 0
+        print(f"  {tool_name}:")
+        print(f"    Calls: {data['call_count']}")
+        print(f"    Avg Time: {avg_time:.3f}s")
+        print(f"    Total Time: {data['total_time']:.3f}s")
+        if data['error_count'] > 0:
+            print(f"    Errors: {data['error_count']}")
+    
+    # Tool efficiency ranking
+    print("\n🏆 Tool Efficiency Ranking (by average execution time):")
+    tool_efficiency = []
+    for tool_name, data in tool_functions.items():
+        if data['call_count'] > 0:
+            avg_time = data['total_time'] / data['call_count']
+            tool_efficiency.append((tool_name, avg_time))
+    
+    tool_efficiency.sort(key=lambda x: x[1])
+    
+    for rank, (tool_name, avg_time) in enumerate(tool_efficiency, 1):
+        print(f"  {rank}. {tool_name}: {avg_time:.3f}s")
+    
+    # Slowest operations
+    print("\n🐌 Slowest Tool Operations:")
+    slowest = get_slowest_functions()
+    tool_slowest = [(name, time) for name, time in slowest if 'tool' in name.lower()]
+    
+    for tool_name, avg_time in tool_slowest[:3]:
+        print(f"  {tool_name}: {avg_time:.3f}s")
+    
+    # Usage patterns
+    total_tool_calls = sum(data['call_count'] for data in tool_functions.values())
+    total_tool_time = sum(data['total_time'] for data in tool_functions.values())
+    
+    print(f"\n📈 Tool Usage Summary:")
+    print(f"  Total Tool Calls: {total_tool_calls}")
+    print(f"  Total Tool Execution Time: {total_tool_time:.3f}s")
+    print(f"  Average Time per Tool Call: {total_tool_time/total_tool_calls:.3f}s" if total_tool_calls > 0 else "  No tool calls")
+    
+    # Tool reliability
+    total_tool_errors = sum(data['error_count'] for data in tool_functions.values())
+    reliability = ((total_tool_calls - total_tool_errors) / total_tool_calls * 100) if total_tool_calls > 0 else 100
+    
+    print(f"  Tool Reliability: {reliability:.1f}%")
+    if total_tool_errors > 0:
+        print(f"  Total Errors: {total_tool_errors}")
+    
+    # Optimization recommendations
+    print(f"\n💡 Custom Tools Optimization Recommendations:")
+    
+    if tool_efficiency:
+        slowest_tool = tool_efficiency[-1]
+        if slowest_tool[1] > 0.2:
+            print(f"  - Optimize {slowest_tool[0]} (slowest at {slowest_tool[1]:.3f}s)")
+    
+    if total_tool_errors > 0:
+        print(f"  - Investigate {total_tool_errors} tool errors for stability improvements")
+    
+    if total_tool_calls > 20:
+        print("  - Consider tool caching for frequently used operations")
+    
+    print("  - Monitor tool usage patterns for optimization opportunities")
+    
+    return {
+        'tool_operations': len(tool_test_results),
+        'agent_scenarios': len(agent_results),
+        'total_tool_calls': total_tool_calls,
+        'tool_reliability': reliability
+    }
+
+if __name__ == "__main__":
+    result = main()
+    print(f"\n✅ Custom tools monitoring completed!")
+    print(f"Operations: {result['tool_operations']}, Reliability: {result['tool_reliability']:.1f}%")

examples/python/monitoring/09_streaming_monitoring.py

@@ -0,0 +1,286 @@
+"""
+Streaming & Real-Time Performance Monitoring - Example 9
+
+Demonstrates performance monitoring for streaming and real-time operations:
+- Real-time performance tracking
+- Streaming response monitoring
+- Live performance metrics
+- Continuous performance analysis
+
+Shows how to monitor performance during streaming operations.
+"""
+
+import time
+import threading
+from datetime import datetime
+from praisonaiagents import Agent
+from praisonaiagents.telemetry import (
+    monitor_function, track_api_call, performance_monitor,
+    get_performance_report, get_function_stats
+)
+
+# Global variables for streaming simulation
+streaming_active = False
+streaming_stats = {
+    'messages_processed': 0,
+    'total_latency': 0.0,
+    'errors': 0,
+    'start_time': None
+}
+
+@monitor_function("stream_message_processing")
+def process_stream_message(message_id: int, content: str):
+    """Process a single streaming message with monitoring."""
+    processing_start = time.time()
+    
+    print(f"📨 Processing stream message {message_id}: {content[:30]}...")
+    
+    # Simulate variable processing time
+    processing_time = 0.05 + (message_id % 3) * 0.02  # Simulate varying complexity
+    time.sleep(processing_time)
+    
+    # Simulate occasional errors
+    if message_id % 20 == 0:  # Every 20th message fails
+        raise Exception(f"Processing error for message {message_id}")
+    
+    processing_end = time.time()
+    latency = processing_end - processing_start
+    
+    # Update streaming stats
+    streaming_stats['messages_processed'] += 1
+    streaming_stats['total_latency'] += latency
+    
+    return f"Processed message {message_id} in {latency:.3f}s"
+
+@monitor_function("real_time_agent_response")
+def get_real_time_agent_response(query: str, response_id: int):
+    """Simulate real-time agent response with monitoring."""
+    print(f"🤖 Generating real-time response {response_id} for: {query}")
+    
+    # Create a lightweight agent for streaming
+    agent = Agent(
+        instructions="You are a real-time assistant. Provide quick, helpful responses.",
+        llm="gpt-4o-mini"
+    )
+    
+    with track_api_call(f"streaming_response_{response_id}"):
+        # Simulate streaming response generation
+        time.sleep(0.1)  # Base response time
+        
+        # In real implementation, this would be actual agent.start()
+        response = agent.start(f"Quickly respond to: {query}")
+        
+        return response
+
+@monitor_function("performance_metrics_collector")
+def collect_live_metrics():
+    """Collect live performance metrics during streaming."""
+    
+    while streaming_active:
+        current_time = time.time()
+        
+        # Collect current performance data
+        current_stats = performance_monitor.get_function_performance()
+        api_stats = performance_monitor.get_api_call_performance()
+        
+        # Calculate streaming metrics
+        if streaming_stats['start_time']:
+            elapsed_time = current_time - streaming_stats['start_time']
+            messages_per_second = streaming_stats['messages_processed'] / elapsed_time if elapsed_time > 0 else 0
+            avg_latency = streaming_stats['total_latency'] / streaming_stats['messages_processed'] if streaming_stats['messages_processed'] > 0 else 0
+            
+            print(f"📊 Live Metrics - Messages/sec: {messages_per_second:.2f}, Avg Latency: {avg_latency:.3f}s")
+        
+        time.sleep(2)  # Update every 2 seconds
+
+@monitor_function("streaming_simulation")
+def simulate_streaming_workload():
+    """Simulate a streaming workload with continuous monitoring."""
+    global streaming_active, streaming_stats
+    
+    print("🌊 Starting streaming simulation...")
+    streaming_active = True
+    streaming_stats['start_time'] = time.time()
+    
+    # Start metrics collector in background
+    metrics_thread = threading.Thread(target=collect_live_metrics)
+    metrics_thread.daemon = True
+    metrics_thread.start()
+    
+    # Simulate incoming stream messages
+    messages = [
+        "User question about AI",
+        "Request for weather information",
+        "Help with programming",
+        "General knowledge query",
+        "Technical support request",
+        "Creative writing assistance",
+        "Data analysis question",
+        "Research inquiry"
+    ]
+    
+    processed_responses = []
+    
+    try:
+        for i in range(20):  # Process 20 streaming messages
+            message = messages[i % len(messages)]
+            
+            try:
+                # Process the stream message
+                result = process_stream_message(i + 1, message)
+                
+                # Generate real-time response every 3rd message
+                if (i + 1) % 3 == 0:
+                    response = get_real_time_agent_response(message, i + 1)
+                    processed_responses.append(response)
+                
+                # Brief pause between messages
+                time.sleep(0.1)
+                
+            except Exception as e:
+                streaming_stats['errors'] += 1
+                print(f"❌ Error processing message {i + 1}: {e}")
+        
+    finally:
+        streaming_active = False
+        print("🛑 Streaming simulation completed")
+    
+    return processed_responses
+
+@monitor_function("batch_vs_streaming_comparison")
+def compare_batch_vs_streaming():
+    """Compare batch vs streaming performance."""
+    
+    print("\n🔄 Comparing Batch vs Streaming Performance...")
+    
+    # Batch processing simulation
+    print("\n📦 Batch Processing Test:")
+    batch_start = time.time()
+    
+    batch_messages = [f"Batch message {i}" for i in range(10)]
+    batch_results = []
+    
+    with track_api_call("batch_processing"):
+        for i, message in enumerate(batch_messages):
+            # Simulate batch processing (all at once)
+            time.sleep(0.08)  # Batch processing time
+            batch_results.append(f"Batch processed: {message}")
+    
+    batch_time = time.time() - batch_start
+    
+    # Streaming processing test (already done above)
+    streaming_time = streaming_stats['total_latency']
+    streaming_count = streaming_stats['messages_processed']
+    
+    print(f"\n📊 Batch vs Streaming Comparison:")
+    print(f"  Batch Processing:")
+    print(f"    Messages: {len(batch_results)}")
+    print(f"    Total Time: {batch_time:.3f}s") 
+    print(f"    Avg per Message: {batch_time/len(batch_results):.3f}s")
+    
+    print(f"  Streaming Processing:")
+    print(f"    Messages: {streaming_count}")
+    print(f"    Total Processing Time: {streaming_time:.3f}s")
+    print(f"    Avg per Message: {streaming_time/streaming_count:.3f}s" if streaming_count > 0 else "    No messages processed")
+    
+    return batch_time, streaming_time
+
+def main():
+    """Main function demonstrating streaming performance monitoring."""
+    print("=" * 70)
+    print("EXAMPLE 9: Streaming & Real-Time Performance Monitoring")
+    print("=" * 70)
+    
+    # Phase 1: Streaming simulation
+    print("\n🌊 Phase 1: Streaming Workload Simulation")
+    streaming_responses = simulate_streaming_workload()
+    
+    # Phase 2: Batch vs Streaming comparison
+    print("\n📊 Phase 2: Batch vs Streaming Comparison")
+    batch_time, streaming_time = compare_batch_vs_streaming()
+    
+    # Real-time Performance Analysis
+    print("\n" + "=" * 70)
+    print("📊 STREAMING PERFORMANCE ANALYSIS")
+    print("=" * 70)
+    
+    # Streaming-specific metrics
+    print("\n🌊 Streaming Metrics:")
+    elapsed_total = time.time() - streaming_stats['start_time'] if streaming_stats['start_time'] else 0
+    throughput = streaming_stats['messages_processed'] / elapsed_total if elapsed_total > 0 else 0
+    avg_latency = streaming_stats['total_latency'] / streaming_stats['messages_processed'] if streaming_stats['messages_processed'] > 0 else 0
+    error_rate = streaming_stats['errors'] / streaming_stats['messages_processed'] * 100 if streaming_stats['messages_processed'] > 0 else 0
+    
+    print(f"  Messages Processed: {streaming_stats['messages_processed']}")
+    print(f"  Total Processing Time: {streaming_stats['total_latency']:.3f}s")
+    print(f"  Average Latency: {avg_latency:.3f}s")
+    print(f"  Throughput: {throughput:.2f} messages/second")
+    print(f"  Error Rate: {error_rate:.2f}%")
+    print(f"  Errors: {streaming_stats['errors']}")
+    
+    # Function performance during streaming
+    print("\n⚡ Real-Time Function Performance:")
+    stats = get_function_stats()
+    streaming_functions = {k: v for k, v in stats.items() if 'stream' in k.lower() or 'real_time' in k.lower()}
+    
+    for func_name, data in streaming_functions.items():
+        avg_time = data['total_time'] / data['call_count'] if data['call_count'] > 0 else 0
+        print(f"  {func_name}:")
+        print(f"    Calls: {data['call_count']}")
+        print(f"    Avg Time: {avg_time:.3f}s")
+        print(f"    Total Time: {data['total_time']:.3f}s")
+    
+    # Performance trend analysis
+    print("\n📈 Performance Trends:")
+    recent_performance = performance_monitor.get_function_performance()
+    total_functions = len(recent_performance)
+    total_calls = sum(data['call_count'] for data in recent_performance.values())
+    
+    print(f"  Functions Monitored: {total_functions}")
+    print(f"  Total Function Calls: {total_calls}")
+    print(f"  Average Calls per Function: {total_calls/total_functions:.1f}" if total_functions > 0 else "  No functions monitored")
+    
+    # Real-time vs Batch efficiency
+    print(f"\n⚖️ Efficiency Analysis:")
+    if batch_time > 0 and streaming_time > 0:
+        efficiency_gain = (batch_time - streaming_time) / batch_time * 100
+        print(f"  Streaming vs Batch Efficiency: {efficiency_gain:+.1f}%")
+    
+    if avg_latency > 0:
+        if avg_latency < 0.1:
+            print("  ✅ Excellent real-time performance (< 100ms)")
+        elif avg_latency < 0.2:
+            print("  ⚡ Good real-time performance (< 200ms)")
+        else:
+            print("  ⚠️ Consider optimization for better real-time performance")
+    
+    # Current performance report
+    print("\n📋 Current Performance Report:")
+    report = get_performance_report()
+    print(report[:400] + "..." if len(report) > 400 else report)
+    
+    # Real-time optimization suggestions
+    print(f"\n💡 Real-Time Optimization Recommendations:")
+    
+    if avg_latency > 0.15:
+        print(f"  - Optimize message processing (current avg: {avg_latency:.3f}s)")
+    if error_rate > 5:
+        print(f"  - Investigate error patterns (current rate: {error_rate:.1f}%)")
+    if throughput < 10:
+        print(f"  - Consider parallel processing for better throughput")
+    
+    print("  - Monitor memory usage during extended streaming")
+    print("  - Implement adaptive batching for optimal performance")
+    
+    return {
+        'streaming_responses': len(streaming_responses),
+        'messages_processed': streaming_stats['messages_processed'],
+        'throughput': throughput,
+        'average_latency': avg_latency,
+        'error_rate': error_rate
+    }
+
+if __name__ == "__main__":
+    result = main()
+    print(f"\n🎉 Streaming monitoring completed!")
+    print(f"Throughput: {result['throughput']:.2f} msg/s, Latency: {result['average_latency']:.3f}s")

examples/python/monitoring/10_comprehensive_dashboard.py

@@ -0,0 +1,433 @@
+"""
+Comprehensive Performance Dashboard - Example 10
+
+Demonstrates comprehensive performance monitoring dashboard:
+- Complete performance analytics
+- Advanced reporting and visualization
+- Performance trends and insights
+- System-wide monitoring overview
+
+Shows how to create a comprehensive performance monitoring solution.
+"""
+
+import time
+import json
+from datetime import datetime, timedelta
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.telemetry import (
+    monitor_function, track_api_call, performance_monitor,
+    get_performance_report, get_function_stats, get_api_stats,
+    get_slowest_functions, get_slowest_apis, 
+    analyze_function_flow, visualize_execution_flow,
+    analyze_performance_trends, generate_comprehensive_report,
+    clear_performance_data
+)
+
+@monitor_function("dashboard_data_collection")
+def collect_comprehensive_data():
+    """Collect comprehensive performance data for dashboard."""
+    
+    print("📊 Collecting comprehensive performance data...")
+    
+    # Generate diverse workload for demonstration
+    test_scenarios = [
+        ("Quick query", "What is AI?"),
+        ("Complex analysis", "Analyze the impact of artificial intelligence on modern business practices"),
+        ("Research task", "Research the latest developments in quantum computing"),
+        ("Creative task", "Write a creative story about space exploration"),
+        ("Technical query", "Explain how neural networks process information")
+    ]
+    
+    # Create different types of agents for variety
+    agents = {
+        'quick_agent': Agent(
+            instructions="You are a quick-response agent. Provide concise answers.",
+            llm="gpt-4o-mini"
+        ),
+        'analytical_agent': Agent(
+            instructions="You are an analytical agent. Provide detailed analysis.",
+            llm="gpt-4o-mini"
+        ),
+        'research_agent': Agent(
+            instructions="You are a research agent. Provide comprehensive information.",
+            llm="gpt-4o-mini"
+        ),
+        'creative_agent': Agent(
+            instructions="You are a creative agent. Provide imaginative responses.",
+            llm="gpt-4o-mini"
+        )
+    }
+    
+    results = []
+    
+    for i, (scenario_type, query) in enumerate(test_scenarios):
+        print(f"\n🎯 Scenario {i+1}: {scenario_type}")
+        
+        # Select appropriate agent
+        agent_type = list(agents.keys())[i % len(agents)]
+        agent = agents[agent_type]
+        
+        with track_api_call(f"{scenario_type.lower().replace(' ', '_')}_execution"):
+            try:
+                result = agent.start(query)
+                results.append({
+                    'scenario': scenario_type,
+                    'agent': agent_type,
+                    'query': query,
+                    'result': result[:200] + '...' if len(result) > 200 else result,
+                    'success': True
+                })
+            except Exception as e:
+                results.append({
+                    'scenario': scenario_type,
+                    'agent': agent_type,
+                    'query': query,
+                    'error': str(e),
+                    'success': False
+                })
+    
+    return results
+
+@monitor_function("multi_agent_workflow_simulation")
+def simulate_complex_workflow():
+    """Simulate complex multi-agent workflow for comprehensive data."""
+    
+    print("🔄 Simulating complex multi-agent workflow...")
+    
+    # Create specialized agents
+    planner = Agent(
+        name="planner",
+        instructions="You are a project planner. Break down tasks and plan execution.",
+        llm="gpt-4o-mini"
+    )
+    
+    executor = Agent(
+        name="executor", 
+        instructions="You are a task executor. Complete assigned tasks efficiently.",
+        llm="gpt-4o-mini"
+    )
+    
+    reviewer = Agent(
+        name="reviewer",
+        instructions="You are a quality reviewer. Review and improve work.",
+        llm="gpt-4o-mini"
+    )
+    
+    # Create workflow tasks
+    planning_task = Task(
+        name="planning",
+        description="Plan a comprehensive report on renewable energy trends",
+        expected_output="A detailed project plan with timeline and milestones"
+    )
+    
+    execution_task = Task(
+        name="execution", 
+        description="Execute the planned research and compile findings",
+        expected_output="Research findings and data compilation"
+    )
+    
+    review_task = Task(
+        name="review",
+        description="Review the research and provide quality assessment",
+        expected_output="Quality review and improvement recommendations"
+    )
+    
+    # Assign agents to tasks
+    planning_task.agent = planner
+    execution_task.agent = executor  
+    review_task.agent = reviewer
+    
+    # Execute workflow
+    with track_api_call("complex_workflow_execution"):
+        workflow = PraisonAIAgents(
+            agents=[planner, executor, reviewer],
+            tasks=[planning_task, execution_task, review_task],
+            process="sequential",
+            verbose=False
+        )
+        
+        result = workflow.start()
+    
+    return result
+
+@monitor_function("performance_benchmarking")
+def run_performance_benchmarks():
+    """Run performance benchmarks for various operations."""
+    
+    print("⚡ Running performance benchmarks...")
+    
+    benchmark_results = {}
+    
+    # Benchmark 1: Response time under different loads
+    print("  📈 Benchmarking response times...")
+    response_times = []
+    
+    for i in range(10):
+        start_time = time.time()
+        with track_api_call(f"benchmark_response_{i}"):
+            # Simulate quick agent response
+            time.sleep(0.05 + (i % 3) * 0.02)  # Variable response time
+        response_times.append(time.time() - start_time)
+    
+    benchmark_results['response_times'] = {
+        'min': min(response_times),
+        'max': max(response_times), 
+        'avg': sum(response_times) / len(response_times),
+        'samples': len(response_times)
+    }
+    
+    # Benchmark 2: Concurrent operations
+    print("  🔄 Benchmarking concurrent operations...")
+    concurrent_start = time.time()
+    
+    # Simulate concurrent operations
+    for i in range(5):
+        with track_api_call(f"concurrent_op_{i}"):
+            time.sleep(0.1)  # Simulate concurrent work
+    
+    concurrent_time = time.time() - concurrent_start
+    benchmark_results['concurrent_operations'] = {
+        'total_time': concurrent_time,
+        'operations': 5,
+        'avg_per_operation': concurrent_time / 5
+    }
+    
+    # Benchmark 3: Memory usage simulation
+    print("  💾 Benchmarking memory operations...")
+    memory_ops = []
+    
+    for i in range(8):
+        start = time.time()
+        with track_api_call(f"memory_benchmark_{i}"):
+            # Simulate memory-intensive operation
+            time.sleep(0.03 + (i % 2) * 0.02)
+        memory_ops.append(time.time() - start)
+    
+    benchmark_results['memory_operations'] = {
+        'operations': len(memory_ops),
+        'total_time': sum(memory_ops),
+        'avg_time': sum(memory_ops) / len(memory_ops)
+    }
+    
+    return benchmark_results
+
+@monitor_function("generate_dashboard_report")
+def generate_dashboard_report():
+    """Generate comprehensive dashboard report."""
+    
+    print("📋 Generating comprehensive dashboard report...")
+    
+    # Collect all performance data
+    function_stats = get_function_stats()
+    api_stats = get_api_stats()
+    slowest_functions = get_slowest_functions()
+    slowest_apis = get_slowest_apis()
+    
+    # Generate reports
+    basic_report = get_performance_report()
+    comprehensive_report = generate_comprehensive_report()
+    
+    # Analyze trends and flows
+    try:
+        flow_analysis = analyze_function_flow()
+        performance_trends = analyze_performance_trends()
+        flow_visualization = visualize_execution_flow()
+    except Exception as e:
+        flow_analysis = {"error": str(e)}
+        performance_trends = {"error": str(e)} 
+        flow_visualization = {"error": str(e)}
+    
+    dashboard_data = {
+        'timestamp': datetime.now().isoformat(),
+        'summary': {
+            'total_functions': len(function_stats),
+            'total_api_calls': len(api_stats),
+            'total_function_calls': sum(stats['call_count'] for stats in function_stats.values()),
+            'total_api_requests': sum(stats['call_count'] for stats in api_stats.values()),
+            'total_execution_time': sum(stats['total_time'] for stats in function_stats.values()),
+            'total_errors': sum(stats['error_count'] for stats in function_stats.values())
+        },
+        'performance_data': {
+            'function_stats': function_stats,
+            'api_stats': api_stats,
+            'slowest_functions': slowest_functions,
+            'slowest_apis': slowest_apis
+        },
+        'analysis': {
+            'flow_analysis': flow_analysis,
+            'performance_trends': performance_trends,
+            'flow_visualization': len(str(flow_visualization)) if flow_visualization else 0
+        },
+        'reports': {
+            'basic_report': basic_report,
+            'comprehensive_report': comprehensive_report[:500] + '...' if len(comprehensive_report) > 500 else comprehensive_report
+        }
+    }
+    
+    return dashboard_data
+
+def display_dashboard(dashboard_data, benchmark_results):
+    """Display comprehensive performance dashboard."""
+    
+    print("\n" + "=" * 80)
+    print("📊 COMPREHENSIVE PERFORMANCE DASHBOARD")
+    print("=" * 80)
+    
+    # Executive Summary
+    summary = dashboard_data['summary']
+    print(f"\n📈 EXECUTIVE SUMMARY")
+    print(f"  Report Generated: {dashboard_data['timestamp']}")
+    print(f"  Functions Monitored: {summary['total_functions']}")
+    print(f"  API Endpoints: {summary['total_api_calls']}")
+    print(f"  Total Function Calls: {summary['total_function_calls']}")
+    print(f"  Total API Requests: {summary['total_api_requests']}")
+    print(f"  Total Execution Time: {summary['total_execution_time']:.3f}s")
+    print(f"  Total Errors: {summary['total_errors']}")
+    
+    # Performance Overview
+    print(f"\n⚡ PERFORMANCE OVERVIEW")
+    avg_function_time = 0
+    if summary['total_function_calls'] > 0:
+        avg_function_time = summary['total_execution_time'] / summary['total_function_calls']
+        print(f"  Average Function Execution: {avg_function_time:.3f}s")
+    
+    if summary['total_errors'] > 0 and summary['total_function_calls'] > 0:
+        error_rate = summary['total_errors'] / summary['total_function_calls'] * 100
+        print(f"  Error Rate: {error_rate:.2f}%")
+    else:
+        print(f"  Error Rate: 0.00%")
+    
+    # Top Performers and Issues
+    print(f"\n🏆 TOP PERFORMERS")
+    perf_data = dashboard_data['performance_data']
+    
+    print("  Fastest Functions:")
+    fastest_functions = sorted(perf_data['slowest_functions'], key=lambda x: x[1])[:3]
+    for name, time in fastest_functions:
+        print(f"    {name}: {time:.3f}s")
+    
+    print("  Slowest Functions:")
+    for name, time in perf_data['slowest_functions'][:3]:
+        print(f"    {name}: {time:.3f}s")
+    
+    # Benchmark Results
+    print(f"\n⚡ BENCHMARK RESULTS")
+    print(f"  Response Time Benchmarks:")
+    rt = benchmark_results['response_times']
+    print(f"    Min: {rt['min']:.3f}s, Max: {rt['max']:.3f}s, Avg: {rt['avg']:.3f}s")
+    
+    print(f"  Concurrent Operations:")
+    co = benchmark_results['concurrent_operations']
+    print(f"    {co['operations']} operations in {co['total_time']:.3f}s")
+    print(f"    Avg per operation: {co['avg_per_operation']:.3f}s")
+    
+    print(f"  Memory Operations:")
+    mo = benchmark_results['memory_operations']
+    print(f"    {mo['operations']} operations, {mo['avg_time']:.3f}s average")
+    
+    # System Health
+    print(f"\n🏥 SYSTEM HEALTH")
+    
+    # Calculate health score
+    health_score = 100
+    if avg_function_time > 0.2:
+        health_score -= 20
+    if summary['total_errors'] > 0:
+        health_score -= summary['total_errors'] * 10
+    if rt['avg'] > 0.1:
+        health_score -= 15
+    
+    health_score = max(0, health_score)
+    
+    if health_score >= 90:
+        status = "🟢 Excellent"
+    elif health_score >= 70:
+        status = "🟡 Good"
+    elif health_score >= 50:
+        status = "🟠 Fair"
+    else:
+        status = "🔴 Needs Attention"
+    
+    print(f"  Overall Health Score: {health_score}/100 - {status}")
+    
+    # Analysis Results
+    print(f"\n🔍 ANALYSIS RESULTS")
+    analysis = dashboard_data['analysis']
+    
+    if 'error' not in analysis['flow_analysis']:
+        flow_data = analysis['flow_analysis']
+        print(f"  Flow Analysis: {len(flow_data.get('functions', []))} functions analyzed")
+    else:
+        print(f"  Flow Analysis: {analysis['flow_analysis']['error']}")
+    
+    if analysis['flow_visualization']:
+        print(f"  Flow Visualization: Generated ({analysis['flow_visualization']} chars)")
+    
+    # Recommendations
+    print(f"\n💡 PERFORMANCE RECOMMENDATIONS")
+    
+    if avg_function_time > 0.15:
+        print("  🔧 Optimize slow functions for better response times")
+    if summary['total_errors'] > 0:
+        print("  🚨 Investigate and resolve error sources")
+    if rt['avg'] > 0.08:
+        print("  ⚡ Consider response time optimization strategies")
+    if summary['total_function_calls'] > 50:
+        print("  📊 Implement performance caching for high-volume functions")
+    
+    print("  📈 Continue monitoring trends for proactive optimization")
+    print("  🔄 Regular performance reviews recommended")
+
+def main():
+    """Main function for comprehensive performance dashboard."""
+    print("=" * 80)
+    print("EXAMPLE 10: Comprehensive Performance Dashboard")
+    print("=" * 80)
+    print("This example demonstrates a complete performance monitoring solution")
+    print("with advanced analytics, benchmarking, and dashboard reporting.\n")
+    
+    # Clear previous data for clean demo
+    clear_performance_data()
+    
+    # Phase 1: Data Collection
+    print("📊 Phase 1: Comprehensive Data Collection")
+    collection_results = collect_comprehensive_data()
+    
+    # Phase 2: Complex Workflow
+    print("\n🔄 Phase 2: Complex Multi-Agent Workflow")
+    workflow_result = simulate_complex_workflow()
+    
+    # Phase 3: Performance Benchmarking
+    print("\n⚡ Phase 3: Performance Benchmarking")
+    benchmark_results = run_performance_benchmarks()
+    
+    # Phase 4: Dashboard Generation
+    print("\n📋 Phase 4: Dashboard Report Generation")
+    dashboard_data = generate_dashboard_report()
+    
+    # Phase 5: Display Comprehensive Dashboard
+    display_dashboard(dashboard_data, benchmark_results)
+    
+    # Final Summary
+    print(f"\n🎯 EXECUTION SUMMARY")
+    print(f"  Scenarios Executed: {len(collection_results)}")
+    successful_scenarios = sum(1 for r in collection_results if r['success'])
+    print(f"  Successful Scenarios: {successful_scenarios}/{len(collection_results)}")
+    print(f"  Workflow Completed: {'✅ Yes' if workflow_result else '❌ No'}")
+    print(f"  Benchmarks Run: {len(benchmark_results)}")
+    print(f"  Dashboard Generated: ✅ Complete")
+    
+    return {
+        'scenarios': len(collection_results),
+        'success_rate': successful_scenarios / len(collection_results) * 100,
+        'dashboard_data': dashboard_data,
+        'benchmark_results': benchmark_results
+    }
+
+if __name__ == "__main__":
+    result = main()
+    print(f"\n🎉 Comprehensive dashboard monitoring completed!")
+    print(f"Success Rate: {result['success_rate']:.1f}%")
+    print(f"Total Functions Monitored: {result['dashboard_data']['summary']['total_functions']}")
+    print(f"Performance Health Score Available in Dashboard Above ⬆️")
+    print("\n💡 This dashboard provides real-time insights into your PraisonAI system performance!")

examples/python/monitoring/README.md

@@ -0,0 +1,238 @@
+# PraisonAI Performance Monitoring Examples
+
+This directory contains 10 comprehensive examples demonstrating how to integrate performance monitoring with PraisonAI agents. Each example builds upon the basic pattern while showcasing different aspects of the monitoring system.
+
+## 🚀 Quick Start
+
+All examples follow this basic pattern:
+
+```python
+from praisonaiagents import Agent
+from praisonaiagents.telemetry import monitor_function, track_api_call
+
+@monitor_function("my_function")
+def my_function():
+    # Your function code here
+    pass
+
+agent = Agent(
+    instructions="You are a helpful assistant",
+    llm="gpt-4o-mini"
+)
+
+with track_api_call("agent_request"):
+    result = agent.start("Your question here")
+```
+
+## 📊 Examples Overview
+
+### 1. Basic Agent with Monitoring (`01_basic_agent_with_monitoring.py`)
+**Purpose:** Introduction to performance monitoring fundamentals
+- Function execution timing with `@monitor_function` decorator
+- API call tracking with `track_api_call` context manager
+- Basic performance statistics retrieval
+
+### 2. Multi-Agent Workflow Monitoring (`02_multi_agent_workflow_monitoring.py`)
+**Purpose:** Monitor complex multi-agent systems
+- Multiple agents with different roles
+- Inter-agent communication tracking
+- Workflow performance analysis
+- Task delegation timing
+
+### 3. Agent with Tools Monitoring (`03_agent_with_tools_monitoring.py`)
+**Purpose:** Monitor performance when using external tools
+- Tool execution timing
+- Search operation performance
+- Tool call success/failure tracking
+- Tool usage analysis
+
+### 4. Async Agent Monitoring (`04_async_agent_monitoring.py`)
+**Purpose:** Performance monitoring in asynchronous workflows
+- Async function execution timing
+- Concurrent task performance
+- Async API call tracking
+- Parallel operation analysis
+
+### 5. Error Handling Monitoring (`05_error_handling_monitoring.py`)
+**Purpose:** Monitor performance during error scenarios
+- Error rate tracking
+- Failed operation timing
+- Recovery mechanism performance
+- Exception handling with monitoring
+
+### 6. Memory Agent Monitoring (`06_memory_agent_monitoring.py`)
+**Purpose:** Monitor stateful agents with memory
+- Memory storage and retrieval timing
+- Knowledge base operations
+- Session persistence performance
+- Memory search optimization
+
+### 7. Hierarchical Agents Monitoring (`07_hierarchical_agents_monitoring.py`)
+**Purpose:** Monitor complex organizational agent structures
+- Manager-worker agent relationships
+- Task delegation timing
+- Hierarchical decision making performance
+- Cross-level communication monitoring
+
+### 8. Custom Tools Monitoring (`08_custom_tools_monitoring.py`)
+**Purpose:** Monitor performance with custom-built tools
+- Custom tool execution timing
+- Tool creation and registration performance
+- Tool usage pattern analysis
+- Tool efficiency optimization
+
+### 9. Streaming Monitoring (`09_streaming_monitoring.py`)
+**Purpose:** Monitor real-time and streaming operations
+- Real-time performance tracking
+- Streaming response monitoring
+- Live performance metrics
+- Continuous performance analysis
+
+### 10. Comprehensive Dashboard (`10_comprehensive_dashboard.py`)
+**Purpose:** Complete performance monitoring solution
+- Advanced analytics and reporting
+- Performance trends and insights
+- System-wide monitoring overview
+- Executive dashboard with health scores
+
+## 🎯 Key Monitoring Features Demonstrated
+
+### Core Monitoring Tools
+- **`@monitor_function(name)`** - Decorates functions for timing and statistics
+- **`track_api_call(name)`** - Context manager for API call monitoring
+- **`get_performance_report()`** - Generate comprehensive performance reports
+- **`get_function_stats()`** - Retrieve detailed function performance data
+- **`get_api_stats()`** - Get API call performance metrics
+
+### Advanced Analytics
+- **`analyze_function_flow()`** - Analyze execution flow and bottlenecks
+- **`visualize_execution_flow()`** - Generate flow visualization
+- **`analyze_performance_trends()`** - Identify performance trends
+- **`generate_comprehensive_report()`** - Complete analysis with recommendations
+
+### Performance Metrics Tracked
+- **Execution Time:** Min, max, average, and total execution times
+- **Call Counts:** Number of function calls and API requests
+- **Error Rates:** Success/failure ratios and error tracking
+- **Throughput:** Operations per second and message processing rates
+- **Flow Analysis:** Function call chains and execution patterns
+
+## 📈 Usage Patterns
+
+### Basic Monitoring Pattern
+```python
+@monitor_function("function_name")
+def your_function():
+    # Function implementation
+    pass
+
+with track_api_call("api_operation"):
+    result = some_api_call()
+```
+
+### Performance Analysis Pattern
+```python
+# After running monitored operations
+stats = get_function_stats()
+report = get_performance_report()
+trends = analyze_performance_trends()
+```
+
+### Real-time Monitoring Pattern
+```python
+# For streaming or real-time operations
+while streaming_active:
+    with track_api_call("stream_message"):
+        process_message()
+    
+    # Check performance in real-time
+    current_stats = performance_monitor.get_function_performance()
+```
+
+## 🔍 Performance Insights
+
+Each example provides insights into different aspects:
+
+- **Response Times:** How fast your agents respond
+- **Bottlenecks:** Where delays occur in your workflow
+- **Error Patterns:** What fails and how often
+- **Resource Usage:** Efficiency of different operations
+- **Scaling Behavior:** How performance changes with load
+
+## 🚀 Running the Examples
+
+1. **Install PraisonAI with performance monitoring support:**
+   ```bash
+   pip install praisonaiagents
+   ```
+
+2. **Set up your environment:**
+   ```bash
+   export OPENAI_API_KEY="your-api-key-here"
+   # or
+   export ANTHROPIC_API_KEY="your-anthropic-key-here"
+   ```
+
+3. **Run any example:**
+   ```bash
+   python 01_basic_agent_with_monitoring.py
+   python 10_comprehensive_dashboard.py
+   ```
+
+## 📊 Sample Output
+
+Each example provides detailed performance output like:
+
+```
+📊 PERFORMANCE MONITORING RESULTS
+==================================================
+📈 Function Performance Statistics:
+  question_processing:
+    Calls: 1
+    Avg Time: 0.100s
+    Total Time: 0.100s
+  agent_execution:
+    Calls: 1
+    Avg Time: 1.250s
+    Total Time: 1.250s
+
+🌐 API Call Performance:
+  sky_explanation_request:
+    Success Rate: 100.0%
+    Average Response Time: 1.200s
+    Total Calls: 1
+```
+
+## 💡 Best Practices
+
+1. **Strategic Monitoring:** Don't monitor everything - focus on critical paths
+2. **Performance Baselines:** Establish baseline metrics for comparison
+3. **Error Handling:** Always monitor error rates alongside performance
+4. **Real-time Analysis:** Use streaming monitoring for production systems
+5. **Regular Reviews:** Analyze trends and patterns regularly
+
+## 🔧 Customization
+
+You can customize monitoring by:
+
+- **Custom Metrics:** Add your own performance counters
+- **Alert Thresholds:** Set up alerts for performance degradation
+- **Export Data:** Export performance data to external monitoring systems
+- **Dashboard Integration:** Build custom dashboards using the monitoring APIs
+
+## 📚 Further Reading
+
+- [PraisonAI Documentation](https://docs.praisonai.com)
+- [Performance Monitoring Guide](../../../src/praisonai-agents/praisonaiagents/telemetry/README.md)
+- [API Reference](../../../src/praisonai-agents/praisonaiagents/telemetry/__init__.py)
+
+## 🤝 Contributing
+
+These examples are designed to be educational and extensible. Feel free to:
+- Add new monitoring scenarios
+- Improve existing examples
+- Share your performance optimization discoveries
+
+---
+
+**Note:** Performance monitoring is optional and can be disabled by setting `PRAISONAI_TELEMETRY_DISABLED=true` if needed.

examples/python/performance_monitoring_demo.py

@@ -0,0 +1,321 @@
+"""
+PraisonAI Performance Monitoring Demo
+
+This example demonstrates how to use PraisonAI's comprehensive performance monitoring
+capabilities to answer the key questions from Issue #1035:
+
+1. How to evaluate how each function is performing
+2. Function flow analysis  
+3. How fast each function is executing
+4. API call performance monitoring (fast vs slow calls)
+5. How to see all API calls
+
+Features Demonstrated:
+- Function performance evaluation with detailed statistics
+- Function execution flow analysis and visualization
+- API call tracking and performance comparison
+- Real-time performance monitoring
+- Bottleneck identification and optimization recommendations
+- Comprehensive performance reporting
+"""
+
+import time
+import random
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import duckduckgo
+from praisonaiagents.telemetry import (
+    monitor_function, track_api_call, performance_monitor,
+    analyze_function_flow,
+    visualize_execution_flow, generate_comprehensive_report
+)
+
+print("=" * 80)
+print("PRAISONAI PERFORMANCE MONITORING DEMONSTRATION")
+print("=" * 80)
+print("This demo answers the key questions from Issue #1035:")
+print("1. How to evaluate how each function is performing")
+print("2. Function flow analysis")  
+print("3. How fast each function is executing")
+print("4. API call performance monitoring (fast vs slow calls)")
+print("5. How to see all API calls")
+print("=" * 80)
+print()
+
+# Example 1: Function Performance Evaluation
+print("📊 EXAMPLE 1: Function Performance Evaluation")
+print("-" * 50)
+
+# Demonstrate function monitoring with decorators
+@monitor_function("data_processing")
+def process_data(data_size: int):
+    """Simulate data processing with variable performance."""
+    processing_time = 0.1 + (data_size * 0.01)  # Simulate work
+    time.sleep(processing_time)
+    return f"Processed {data_size} items"
+
+@monitor_function("model_inference") 
+def run_model_inference(complexity: str):
+    """Simulate model inference with different complexities."""
+    if complexity == "simple":
+        time.sleep(0.05)
+    elif complexity == "medium": 
+        time.sleep(0.2)
+    else:  # complex
+        time.sleep(0.5)
+    return f"Inference completed for {complexity} model"
+
+@monitor_function("database_query")
+def query_database(query_type: str):
+    """Simulate database queries with different performance characteristics."""
+    if query_type == "fast":
+        time.sleep(0.02)
+    elif query_type == "medium":
+        time.sleep(0.1) 
+    else:  # slow
+        time.sleep(0.3)
+    return f"Query {query_type} completed"
+
+# Run functions with different performance characteristics
+print("Running functions to generate performance data...")
+for _ in range(5):
+    process_data(random.randint(10, 100))
+    run_model_inference(random.choice(["simple", "medium", "complex"]))
+    query_database(random.choice(["fast", "medium", "slow"]))
+
+# Show function performance statistics
+print("\n📈 Function Performance Statistics:")
+function_stats = performance_monitor.get_function_performance()
+for func_name, stats in function_stats.items():
+    print(f"\n🔧 {func_name}:")
+    print(f"  Calls: {stats['call_count']}")
+    print(f"  Average Time: {stats.get('average_time', 0):.3f}s")
+    print(f"  Min/Max Time: {stats['min_time']:.3f}s / {stats['max_time']:.3f}s")
+    print(f"  Success Rate: {stats.get('success_rate', 0)*100:.1f}%")
+
+# Example 2: Function Flow Analysis
+print("\n\n🔄 EXAMPLE 2: Function Flow Analysis") 
+print("-" * 50)
+
+@monitor_function("workflow_step_1")
+def workflow_step_1():
+    time.sleep(0.1)
+    workflow_step_2()
+    workflow_step_3()
+
+@monitor_function("workflow_step_2") 
+def workflow_step_2():
+    time.sleep(0.05)
+    workflow_step_4()
+
+@monitor_function("workflow_step_3")
+def workflow_step_3():
+    time.sleep(0.08)
+
+@monitor_function("workflow_step_4")
+def workflow_step_4():
+    time.sleep(0.03)
+
+print("Running workflow to demonstrate flow analysis...")
+workflow_step_1()
+
+# Analyze execution flow
+print("\n📊 Function Execution Flow Analysis:")
+flow_analysis = analyze_function_flow()
+if "bottlenecks" in flow_analysis:
+    bottlenecks = flow_analysis["bottlenecks"]
+    if bottlenecks:
+        print("🚨 Identified Bottlenecks:")
+        for bottleneck in bottlenecks:
+            print(f"  • {bottleneck['function']}: {bottleneck['average_duration']:.3f}s average")
+    else:
+        print("✅ No significant bottlenecks identified")
+
+# Visualize execution flow
+print("\n🎨 Function Execution Flow Visualization:")
+flow_viz = visualize_execution_flow(format="text")
+print(flow_viz)
+
+# Example 3: API Call Performance Monitoring
+print("\n\n🌐 EXAMPLE 3: API Call Performance Monitoring")
+print("-" * 50)
+
+# Simulate different API calls with various performance characteristics
+def simulate_api_calls():
+    """Simulate API calls to demonstrate tracking."""
+    
+    # Fast API calls
+    with track_api_call("openai_api", "/v1/models"):
+        time.sleep(0.05)  # Fast call
+    
+    with track_api_call("openai_api", "/v1/chat/completions"):
+        time.sleep(0.3)  # Medium call
+    
+    # Slow API call
+    with track_api_call("anthropic_api", "/v1/messages"):
+        time.sleep(0.6)  # Slow call
+    
+    # Another fast call
+    with track_api_call("gemini_api", "/v1/generate"):
+        time.sleep(0.08)  # Fast call
+    
+    # Simulate error case
+    try:
+        with track_api_call("unreliable_api", "/v1/endpoint"):
+            time.sleep(0.2)
+            if random.random() < 0.3:  # 30% chance of error
+                raise Exception("API Error")
+    except Exception:
+        pass  # Error is already tracked
+
+print("Simulating API calls with different performance characteristics...")
+for _ in range(8):
+    simulate_api_calls()
+
+# Show API performance statistics
+print("\n📊 API Call Performance Statistics:")
+api_stats = performance_monitor.get_api_call_performance()
+for api_name, stats in api_stats.items():
+    print(f"\n🔗 {api_name}:")
+    print(f"  Total Calls: {stats['call_count']}")
+    print(f"  Average Time: {stats.get('average_time', 0):.3f}s")
+    print(f"  Min/Max Time: {stats['min_time']:.3f}s / {stats['max_time']:.3f}s")
+    print(f"  Success Rate: {stats.get('success_rate', 0)*100:.1f}%")
+    print(f"  Successful: {stats['success_count']} | Failed: {stats['error_count']}")
+
+# Show fastest vs slowest APIs
+print("\n🏁 Fastest vs Slowest API Calls:")
+slowest_apis = performance_monitor.get_slowest_api_calls(10)
+for i, api in enumerate(slowest_apis, 1):
+    speed_indicator = "🐌" if api['average_time'] > 0.3 else "⚡" if api['average_time'] < 0.1 else "🚀"
+    print(f"{i}. {speed_indicator} {api['api']}: {api['average_time']:.3f}s avg (Success: {api['success_rate']*100:.1f}%)")
+
+# Example 4: Real-time Performance Monitoring with Agents
+print("\n\n🤖 EXAMPLE 4: Real-time Performance Monitoring with AI Agents")
+print("-" * 50)
+
+# Create agents with performance monitoring
+research_agent = Agent(
+    name="PerformanceResearcher",
+    role="Performance Research Specialist",
+    goal="Research and analyze performance topics quickly",
+    backstory="You are an expert at researching performance optimization topics.",
+    tools=[duckduckgo],
+    verbose=False  # Reduce output for cleaner demo
+)
+
+# Monitor agent operations
+@monitor_function("agent_task_execution")
+def execute_monitored_task(agent, task_description):
+    """Execute an agent task with performance monitoring."""
+    task = Task(
+        description=task_description,
+        expected_output="Brief research summary with key insights",
+        agent=agent
+    )
+    
+    agents_workflow = PraisonAIAgents(
+        agents=[agent],
+        tasks=[task],
+        verbose=False
+    )
+    
+    # Simulate API calls during agent execution
+    with track_api_call("llm_provider", "/v1/chat/completions"):
+        time.sleep(0.2)  # Simulate LLM call
+    
+    result = agents_workflow.start()
+    return result
+
+print("Running AI agent with performance monitoring...")
+result = execute_monitored_task(
+    research_agent, 
+    "Research the latest trends in AI performance optimization"
+)
+
+# Example 5: Comprehensive Performance Report
+print("\n\n📋 EXAMPLE 5: Comprehensive Performance Report")
+print("-" * 50)
+
+# Generate comprehensive performance report
+comprehensive_report = generate_comprehensive_report()
+print(comprehensive_report)
+
+# Example 6: Using CLI Interface Programmatically
+print("\n\n💻 EXAMPLE 6: CLI Interface Usage")
+print("-" * 50)
+print("You can also use the CLI interface from command line:")
+print()
+print("# Show performance report")
+print("python -m praisonaiagents.telemetry.performance_cli report")
+print()
+print("# Show function statistics")  
+print("python -m praisonaiagents.telemetry.performance_cli functions")
+print()
+print("# Show API call statistics")
+print("python -m praisonaiagents.telemetry.performance_cli apis")
+print()
+print("# Show slowest functions")
+print("python -m praisonaiagents.telemetry.performance_cli slowest-functions 5")
+print()
+print("# Analyze function flow") 
+print("python -m praisonaiagents.telemetry.performance_cli analyze-flow")
+print()
+print("# Show execution flow visualization")
+print("python -m praisonaiagents.telemetry.performance_cli flow --format text")
+print()
+print("# Export all data to JSON")
+print("python -m praisonaiagents.telemetry.performance_cli export --format json --output performance_data.json")
+
+# Example 7: Integration with Existing Code
+print("\n\n🔧 EXAMPLE 7: Easy Integration with Existing Code")  
+print("-" * 50)
+print("To integrate performance monitoring into your existing PraisonAI code:")
+print()
+print("1. Import the performance monitoring tools:")
+print("   from praisonaiagents.telemetry import monitor_function, track_api_call")
+print()
+print("2. Add decorators to your functions:")
+print("   @monitor_function('my_function')")
+print("   def my_function():")
+print("       return 'result'")
+print()
+print("3. Track API calls:")
+print("   with track_api_call('openai', '/v1/chat/completions'):")
+print("       response = openai_client.chat.completions.create(...)")
+print()
+print("4. Get performance reports:")
+print("   from praisonaiagents.telemetry import get_performance_report")
+print("   print(get_performance_report())")
+
+# Summary of capabilities
+print("\n\n✅ SUMMARY: Questions from Issue #1035 - ANSWERED!")
+print("=" * 80)
+print("1. ✅ How to evaluate how each function is performing:")
+print("   → Use @monitor_function decorator and get_function_stats()")
+print("   → View detailed statistics: calls, timing, success rates, errors")
+print()
+print("2. ✅ Function flow analysis:")
+print("   → Use analyze_function_flow() for bottleneck identification") 
+print("   → Use visualize_execution_flow() for flow visualization")
+print("   → Get call chains and execution patterns")
+print()
+print("3. ✅ How fast each function is executing:")
+print("   → Automatic timing collection with min/max/average/recent stats")
+print("   → Use get_slowest_functions() to find performance bottlenecks")
+print("   → Real-time active call monitoring")
+print()
+print("4. ✅ API call performance monitoring (fast vs slow calls):")
+print("   → Use track_api_call() context manager for API monitoring")
+print("   → Compare API performance with get_slowest_apis()")
+print("   → Success/error rate tracking")
+print()
+print("5. ✅ How to see all API calls:")
+print("   → Use get_api_stats() to see all tracked APIs")
+print("   → View recent call history and detailed metrics")
+print("   → CLI interface for easy access: performance_cli apis")
+print()
+print("🎉 All requested features are now available with backward compatibility!")
+print("🔧 Easy integration with existing code using decorators and context managers")
+print("📊 Comprehensive reporting and analysis capabilities") 
+print("💻 CLI interface for command-line access")
+print("=" * 80)

src/praisonai-agents/01_basic_agent_with_monitoring.py

@@ -0,0 +1,63 @@
+"""
+Basic Agent with Performance Monitoring - Example 1
+
+Simple agent demonstrating performance monitoring basics:
+- Function execution timing with @monitor_function decorator
+- API call tracking with track_api_call context manager
+- Basic performance statistics retrieval
+
+This example shows the fundamental pattern for adding performance monitoring
+to any PraisonAI agent workflow.
+"""
+
+from praisonaiagents import Agent
+from praisonaiagents.telemetry import monitor_function, track_api_call, get_performance_report
+import time
+
+# Monitored function to demonstrate timing
+@monitor_function("question_processing")
+def process_question(question):
+    """Process the incoming question with performance tracking."""
+    print(f"Processing question: {question}")
+    # Simulate some processing time
+    time.sleep(0.1)
+    return f"Processed: {question}"
+
+# Main execution function with monitoring
+@monitor_function("agent_execution")  
+def main():
+    """Main function demonstrating basic agent with performance monitoring."""
+    print("=" * 60)
+    print("EXAMPLE 1: Basic Agent with Performance Monitoring")
+    print("=" * 60)
+    
+    # Process the question with monitoring
+    question = "Why is the sky blue?"
+    processed_question = process_question(question)
+    
+    # Create agent
+    agent = Agent(
+        instructions="You are a helpful assistant that explains scientific concepts simply",
+        llm="gpt-4o-mini"
+    )
+    
+    # Track the API call performance
+    with track_api_call("sky_explanation_request"):
+        print("\n🚀 Starting agent execution...")
+        result = agent.start(processed_question)
+        print(f"\n📝 Agent Response:\n{result}")
+    
+    # Display performance statistics
+    print("\n" + "=" * 60)
+    print("📊 PERFORMANCE MONITORING RESULTS")
+    print("=" * 60)
+    
+    # Get and display performance report
+    report = get_performance_report()
+    print(report)
+    
+    return result
+
+if __name__ == "__main__":
+    result = main()
+    print(f"\n✅ Example completed successfully!")

src/praisonai-agents/02_multi_agent_workflow_monitoring.py

@@ -0,0 +1,143 @@
+"""
+Multi-Agent Workflow with Performance Monitoring - Example 2
+
+Demonstrates performance monitoring in a multi-agent workflow:
+- Multiple agents with different roles
+- Inter-agent communication tracking
+- Workflow performance analysis
+- Task delegation timing
+
+Shows how to monitor complex multi-agent systems.
+"""
+
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.telemetry import (
+    monitor_function, track_api_call, get_function_stats, 
+    analyze_function_flow, generate_comprehensive_report
+)
+import time
+
+@monitor_function("research_task")
+def setup_research_task():
+    """Set up research task with monitoring."""
+    return Task(
+        name="research_task",
+        description="Research the topic of renewable energy trends",
+        expected_output="A comprehensive research summary"
+    )
+
+@monitor_function("analysis_task") 
+def setup_analysis_task():
+    """Set up analysis task with monitoring."""
+    return Task(
+        name="analysis_task", 
+        description="Analyze the research data and identify key trends",
+        expected_output="Key insights and trend analysis"
+    )
+
+@monitor_function("report_task")
+def setup_report_task():
+    """Set up report generation task with monitoring."""
+    return Task(
+        name="report_task",
+        description="Create a final report combining research and analysis", 
+        expected_output="A well-structured final report"
+    )
+
+@monitor_function("agent_creation")
+def create_agents():
+    """Create specialized agents with monitoring."""
+    
+    researcher = Agent(
+        name="researcher",
+        role="Research Specialist",
+        goal="Gather comprehensive information on renewable energy",
+        backstory="Expert researcher with deep knowledge of energy sector",
+        instructions="You are a thorough researcher. Provide detailed, fact-based information.",
+        llm="gpt-4o-mini"
+    )
+    
+    analyst = Agent(
+        name="analyst", 
+        role="Data Analyst",
+        goal="Analyze trends and patterns in renewable energy data",
+        backstory="Experienced data analyst specializing in energy markets",
+        instructions="You are an analytical thinker. Focus on trends, patterns, and insights.",
+        llm="gpt-4o-mini"
+    )
+    
+    writer = Agent(
+        name="writer",
+        role="Report Writer", 
+        goal="Create clear, compelling reports",
+        backstory="Professional technical writer with expertise in energy sector",
+        instructions="You are a skilled writer. Create clear, well-structured reports.",
+        llm="gpt-4o-mini"
+    )
+    
+    return researcher, analyst, writer
+
+@monitor_function("workflow_execution")
+def main():
+    """Main function demonstrating multi-agent workflow with performance monitoring."""
+    print("=" * 70)
+    print("EXAMPLE 2: Multi-Agent Workflow with Performance Monitoring")
+    print("=" * 70)
+    
+    # Create agents with monitoring
+    researcher, analyst, writer = create_agents()
+    
+    # Set up tasks with monitoring
+    research_task = setup_research_task()
+    analysis_task = setup_analysis_task()
+    report_task = setup_report_task()
+    
+    # Assign agents to tasks
+    research_task.agent = researcher
+    analysis_task.agent = analyst  
+    report_task.agent = writer
+    
+    # Create workflow
+    with track_api_call("multi_agent_workflow"):
+        print("\n🚀 Starting multi-agent workflow...")
+        
+        workflow = PraisonAIAgents(
+            agents=[researcher, analyst, writer],
+            tasks=[research_task, analysis_task, report_task], 
+            process="sequential",
+            verbose=True
+        )
+        
+        result = workflow.start()
+    
+    # Display comprehensive performance analysis
+    print("\n" + "=" * 70)
+    print("📊 MULTI-AGENT PERFORMANCE ANALYSIS")
+    print("=" * 70)
+    
+    # Function statistics
+    print("\n📈 Function Performance Statistics:")
+    stats = get_function_stats()
+    for func_name, data in stats.items():
+        print(f"  {func_name}:")
+        print(f"    Calls: {data['call_count']}")
+        avg_time = data['total_time'] / data['call_count'] if data['call_count'] > 0 else 0
+        print(f"    Avg Time: {avg_time:.3f}s")
+        print(f"    Total Time: {data['total_time']:.3f}s")
+    
+    # Flow analysis
+    print("\n🔄 Execution Flow Analysis:")
+    flow_analysis = analyze_function_flow()
+    print(f"  Total Functions Monitored: {len(flow_analysis.get('functions', []))}")
+    print(f"  Execution Chains: {len(flow_analysis.get('call_chains', []))}")
+    
+    # Comprehensive report
+    print("\n📋 Comprehensive Performance Report:")
+    report = generate_comprehensive_report()
+    print(report[:500] + "..." if len(report) > 500 else report)
+    
+    return result
+
+if __name__ == "__main__":
+    result = main()
+    print(f"\n✅ Multi-agent workflow completed successfully!")

src/praisonai-agents/03_agent_with_tools_monitoring.py

@@ -0,0 +1,119 @@
+"""
+Agent with Tools Performance Monitoring - Example 3
+
+Demonstrates performance monitoring for agents using tools:
+- Tool execution timing
+- Search operation performance
+- Tool call success/failure tracking
+- Tool usage analysis
+
+Shows how to monitor agent performance when using external tools.
+"""
+
+from praisonaiagents import Agent
+from praisonaiagents.tools import duckduckgo, wikipedia
+from praisonaiagents.telemetry import (
+    monitor_function, track_api_call, get_api_stats,
+    get_slowest_functions, performance_monitor
+)
+import time
+
+@monitor_function("search_with_duckduckgo")
+def perform_web_search(query):
+    """Perform web search with performance monitoring."""
+    print(f"🔍 Searching web for: {query}")
+    with track_api_call("duckduckgo_search"):
+        # Simulate search time
+        time.sleep(0.2)
+        return f"Search results for: {query}"
+
+@monitor_function("wikipedia_lookup") 
+def perform_wikipedia_search(topic):
+    """Perform Wikipedia search with performance monitoring."""
+    print(f"📚 Looking up Wikipedia for: {topic}")
+    with track_api_call("wikipedia_search"):
+        # Simulate Wikipedia lookup
+        time.sleep(0.15)
+        return f"Wikipedia information about: {topic}"
+
+@monitor_function("agent_with_tools_execution")
+def main():
+    """Main function demonstrating agent with tools and performance monitoring."""
+    print("=" * 70)
+    print("EXAMPLE 3: Agent with Tools Performance Monitoring")
+    print("=" * 70)
+    
+    # Create agent with tools
+    agent = Agent(
+        instructions="""You are a research assistant that helps find information.
+        Use the available tools to search for information and provide comprehensive answers.
+        Be thorough in your research and cite your sources.""",
+        llm="gpt-4o-mini",
+        tools=[duckduckgo, wikipedia]  # Add search tools
+    )
+    
+    # Test queries to demonstrate tool performance monitoring
+    queries = [
+        "What are the latest developments in artificial intelligence?",
+        "Tell me about renewable energy innovations in 2024",
+        "How do neural networks work?"
+    ]
+    
+    results = []
+    
+    for i, query in enumerate(queries, 1):
+        print(f"\n🎯 Query {i}: {query}")
+        
+        # Monitor the search operations
+        perform_web_search(query)
+        perform_wikipedia_search(query)
+        
+        # Track the full agent execution
+        with track_api_call(f"agent_query_{i}"):
+            print(f"\n🚀 Processing with agent...")
+            
+            # Note: In a real implementation, the agent would use the tools automatically
+            # For demo purposes, we're simulating the tool usage
+            result = agent.start(f"Research and answer: {query}")
+            results.append(result)
+            
+            print(f"📝 Agent Response: {result[:200]}..." if len(result) > 200 else f"📝 Agent Response: {result}")
+    
+    # Performance Analysis
+    print("\n" + "=" * 70)
+    print("📊 TOOLS PERFORMANCE ANALYSIS")
+    print("=" * 70)
+    
+    # API call statistics
+    print("\n📈 API Call Performance:")
+    api_stats = get_api_stats()
+    for api_name, stats in api_stats.items():
+        print(f"  {api_name}:")
+        print(f"    Total Calls: {stats['call_count']}")
+        print(f"    Avg Response Time: {stats['total_time']/stats['call_count']:.3f}s")
+        print(f"    Success Rate: {stats['success_count']/stats['call_count']*100:.1f}%")
+        print(f"    Total Time: {stats['total_time']:.3f}s")
+    
+    # Slowest functions analysis
+    print("\n🐌 Slowest Functions:")
+    slowest = get_slowest_functions()
+    for func_name, avg_time in slowest:
+        print(f"  {func_name}: {avg_time:.3f}s average")
+    
+    # Real-time performance data
+    print("\n⚡ Real-time Performance Data:")
+    current_stats = performance_monitor.get_function_performance()
+    print(f"  Functions Monitored: {len(current_stats)}")
+    
+    total_function_calls = sum(stats['call_count'] for stats in current_stats.values())
+    total_execution_time = sum(stats['total_time'] for stats in current_stats.values())
+    
+    print(f"  Total Function Calls: {total_function_calls}")
+    print(f"  Total Execution Time: {total_execution_time:.3f}s")
+    print(f"  Average Time per Call: {total_execution_time/total_function_calls:.3f}s" if total_function_calls > 0 else "  No calls recorded")
+    
+    return results
+
+if __name__ == "__main__":
+    results = main()
+    print(f"\n✅ Tool monitoring example completed! Processed {len(results)} queries.")

src/praisonai-agents/04_async_agent_monitoring.py

@@ -0,0 +1,167 @@
+"""
+Async Agent with Performance Monitoring - Example 4
+
+Demonstrates performance monitoring for asynchronous agent operations:
+- Async function execution timing
+- Concurrent task performance
+- Async API call tracking
+- Parallel operation analysis
+
+Shows how to monitor performance in async/await workflows.
+"""
+
+import asyncio
+import time
+from praisonaiagents import Agent
+from praisonaiagents.telemetry import (
+    monitor_function, track_api_call, get_performance_report,
+    get_function_stats, visualize_execution_flow
+)
+
+@monitor_function("async_data_processing")
+async def process_data_async(data_id: int):
+    """Async data processing with performance monitoring."""
+    print(f"⚙️ Processing data {data_id} asynchronously...")
+    await asyncio.sleep(0.1)  # Simulate async processing
+    return f"Processed data {data_id}"
+
+@monitor_function("async_api_call")
+async def simulate_api_call(endpoint: str, delay: float = 0.2):
+    """Simulate async API call with performance monitoring."""
+    print(f"🌐 Calling API endpoint: {endpoint}")
+    await asyncio.sleep(delay)  # Simulate network delay
+    return f"Response from {endpoint}"
+
+@monitor_function("batch_processing")
+async def process_batch(batch_size: int = 3):
+    """Process multiple items concurrently with monitoring."""
+    print(f"📦 Processing batch of {batch_size} items...")
+    
+    # Create concurrent tasks
+    tasks = []
+    for i in range(batch_size):
+        tasks.append(process_data_async(i + 1))
+    
+    # Wait for all tasks to complete
+    results = await asyncio.gather(*tasks)
+    print(f"✅ Batch processing completed: {len(results)} items processed")
+    return results
+
+@monitor_function("async_agent_execution")
+async def run_async_agent(query: str):
+    """Run agent with async operations and monitoring."""
+    
+    # Create agent
+    agent = Agent(
+        instructions="You are an efficient AI assistant that processes requests quickly",
+        llm="gpt-4o-mini"
+    )
+    
+    # Track the agent execution
+    with track_api_call("async_agent_request"):
+        print(f"🚀 Running async agent for query: {query}")
+        
+        # For demo purposes, we simulate async agent execution
+        # In real scenarios, you'd use agent.astart() or similar async method when available
+        await asyncio.sleep(0.1)  # Simulate async processing time
+        result = agent.start(query)
+        
+        # Simulate some async post-processing
+        await asyncio.sleep(0.05)
+        
+        return result
+
+@monitor_function("concurrent_agents")
+async def run_concurrent_agents():
+    """Run multiple agents concurrently with monitoring."""
+    print("🔄 Running multiple agents concurrently...")
+    
+    queries = [
+        "What is machine learning?",
+        "Explain quantum computing",
+        "How does blockchain work?"
+    ]
+    
+    # Create concurrent agent tasks
+    agent_tasks = []
+    for i, query in enumerate(queries):
+        task = run_async_agent(query)
+        agent_tasks.append(task)
+    
+    # Execute all agents concurrently  
+    results = await asyncio.gather(*agent_tasks)
+    return results
+
+async def main():
+    """Main async function demonstrating async agent performance monitoring."""
+    print("=" * 70)
+    print("EXAMPLE 4: Async Agent Performance Monitoring")
+    print("=" * 70)
+    
+    start_time = time.time()
+    
+    # Run async operations with monitoring
+    print("\n📊 Phase 1: Batch Processing")
+    batch_results = await process_batch(3)
+    
+    print("\n🌐 Phase 2: API Calls")
+    api_tasks = [
+        simulate_api_call("user-service", 0.1),
+        simulate_api_call("data-service", 0.15),
+        simulate_api_call("analytics-service", 0.2)
+    ]
+    api_results = await asyncio.gather(*api_tasks)
+    
+    print("\n🤖 Phase 3: Concurrent Agents")
+    agent_results = await run_concurrent_agents()
+    
+    total_time = time.time() - start_time
+    
+    # Performance Analysis
+    print("\n" + "=" * 70)
+    print("📊 ASYNC PERFORMANCE ANALYSIS")
+    print("=" * 70)
+    
+    print(f"\n⏱️ Total Execution Time: {total_time:.3f}s")
+    
+    # Function statistics
+    print("\n📈 Function Performance:")
+    stats = get_function_stats()
+    for func_name, data in stats.items():
+        avg_time = data['total_time'] / data['call_count'] if data['call_count'] > 0 else 0
+        print(f"  {func_name}:")
+        print(f"    Calls: {data['call_count']}")
+        print(f"    Avg Time: {avg_time:.3f}s")
+        print(f"    Total Time: {data['total_time']:.3f}s")
+    
+    # Execution flow visualization
+    print("\n🔄 Execution Flow:")
+    try:
+        flow_viz = visualize_execution_flow()
+        print("  Flow visualization generated successfully")
+        print(f"  Flow data length: {len(str(flow_viz))}")
+    except Exception as e:
+        print(f"  Flow visualization: {str(e)}")
+    
+    # Performance report
+    print("\n📋 Performance Report:")
+    report = get_performance_report()
+    print(report[:300] + "..." if len(report) > 300 else report)
+    
+    # Results summary
+    print(f"\n✅ Results Summary:")
+    print(f"  Batch items processed: {len(batch_results)}")
+    print(f"  API calls completed: {len(api_results)}")
+    print(f"  Agent queries processed: {len(agent_results)}")
+    
+    return {
+        'batch_results': batch_results,
+        'api_results': api_results,
+        'agent_results': agent_results,
+        'total_time': total_time
+    }
+
+if __name__ == "__main__":
+    # Run the async main function
+    result = asyncio.run(main())
+    print(f"\n🎉 Async monitoring example completed successfully!")

src/praisonai-agents/05_error_handling_monitoring.py

@@ -0,0 +1,195 @@
+"""
+Error Handling with Performance Monitoring - Example 5
+
+Demonstrates performance monitoring for error scenarios:
+- Error rate tracking
+- Failed operation timing
+- Recovery mechanism performance
+- Exception handling with monitoring
+
+Shows how to monitor performance when things go wrong.
+"""
+
+import random
+import time
+from praisonaiagents import Agent
+from praisonaiagents.telemetry import (
+    monitor_function, track_api_call, get_function_stats,
+    get_api_stats, performance_monitor
+)
+
+@monitor_function("unreliable_operation")
+def simulate_unreliable_operation(operation_id: int, failure_rate: float = 0.3):
+    """Simulate an operation that sometimes fails."""
+    print(f"⚙️ Running operation {operation_id}...")
+    
+    # Simulate processing time
+    time.sleep(random.uniform(0.05, 0.2))
+    
+    # Random failure simulation
+    if random.random() < failure_rate:
+        raise RuntimeError(f"Operation {operation_id} failed!")
+    
+    return f"Operation {operation_id} succeeded"
+
+@monitor_function("retry_with_backoff")
+def retry_operation(operation_id: int, max_retries: int = 3):
+    """Retry an operation with exponential backoff and monitoring."""
+    for attempt in range(max_retries):
+        try:
+            print(f"🔄 Attempt {attempt + 1} for operation {operation_id}")
+            result = simulate_unreliable_operation(operation_id)
+            print(f"✅ Operation {operation_id} succeeded on attempt {attempt + 1}")
+            return result
+        except Exception as e:
+            print(f"❌ Attempt {attempt + 1} failed: {e}")
+            if attempt < max_retries - 1:
+                # Exponential backoff
+                sleep_time = 2 ** attempt * 0.1
+                print(f"⏳ Waiting {sleep_time:.1f}s before retry...")
+                time.sleep(sleep_time)
+            else:
+                print(f"💥 All {max_retries} attempts failed for operation {operation_id}")
+                raise
+
+@monitor_function("robust_agent_execution")
+def run_robust_agent(query: str, with_error_simulation: bool = True):
+    """Run agent with error handling and performance monitoring."""
+    
+    agent = Agent(
+        instructions="""You are a resilient AI assistant. 
+        Even when facing challenges, you provide helpful responses.
+        If you encounter errors, acknowledge them and offer alternatives.""",
+        llm="gpt-4o-mini"
+    )
+    
+    try:
+        with track_api_call("robust_agent_request"):
+            print(f"🚀 Processing query: {query}")
+            
+            if with_error_simulation and random.random() < 0.2:
+                # Simulate API error
+                raise Exception("Simulated API error")
+            
+            result = agent.start(query)
+            return result
+            
+    except Exception as e:
+        print(f"⚠️ Error during agent execution: {e}")
+        
+        # Fallback response with monitoring
+        with track_api_call("fallback_response"):
+            print("🔄 Using fallback response mechanism...")
+            time.sleep(0.1)  # Simulate fallback processing
+            return f"I encountered an error while processing '{query}', but I'm designed to be resilient. Let me provide a general response about this topic."
+
+@monitor_function("batch_processing_with_errors")
+def process_batch_with_error_handling(batch_size: int = 5):
+    """Process a batch of operations with comprehensive error handling."""
+    print(f"📦 Processing batch of {batch_size} operations with error monitoring...")
+    
+    results = []
+    errors = []
+    
+    for i in range(batch_size):
+        try:
+            result = retry_operation(i + 1)
+            results.append(result)
+        except Exception as e:
+            errors.append(f"Operation {i + 1}: {str(e)}")
+    
+    return results, errors
+
+def main():
+    """Main function demonstrating error handling with performance monitoring."""
+    print("=" * 70)
+    print("EXAMPLE 5: Error Handling Performance Monitoring")
+    print("=" * 70)
+    
+    # Phase 1: Batch processing with errors
+    print("\n📊 Phase 1: Batch Processing with Error Handling")
+    successes, failures = process_batch_with_error_handling(5)
+    
+    print(f"\n📈 Batch Results:")
+    print(f"  Successful operations: {len(successes)}")
+    print(f"  Failed operations: {len(failures)}")
+    
+    # Phase 2: Agent execution with error scenarios
+    print("\n🤖 Phase 2: Agent Execution with Error Scenarios")
+    
+    test_queries = [
+        "What is artificial intelligence?",
+        "How do computers work?",
+        "Explain quantum physics",
+    ]
+    
+    agent_results = []
+    for query in test_queries:
+        try:
+            result = run_robust_agent(query, with_error_simulation=True)
+            agent_results.append(result)
+            print(f"✅ Query processed: '{query[:30]}...'")
+        except Exception as e:
+            print(f"❌ Query failed: '{query[:30]}...' - {e}")
+    
+    # Performance Analysis with Error Metrics
+    print("\n" + "=" * 70)
+    print("📊 ERROR HANDLING PERFORMANCE ANALYSIS")
+    print("=" * 70)
+    
+    # Function performance with error rates
+    print("\n📈 Function Performance (including errors):")
+    stats = get_function_stats()
+    for func_name, data in stats.items():
+        avg_time = data['total_time'] / data['call_count'] if data['call_count'] > 0 else 0
+        error_rate = data['error_count'] / data['call_count'] * 100 if data['call_count'] > 0 else 0
+        success_rate = 100 - error_rate
+        
+        print(f"  {func_name}:")
+        print(f"    Calls: {data['call_count']}")
+        print(f"    Errors: {data['error_count']}")
+        print(f"    Success Rate: {success_rate:.1f}%")
+        print(f"    Avg Time: {avg_time:.3f}s")
+    
+    # API call reliability
+    print("\n🌐 API Call Reliability:")
+    api_stats = get_api_stats()
+    for api_name, stats in api_stats.items():
+        success_rate = stats['success_count'] / stats['call_count'] * 100 if stats['call_count'] > 0 else 0
+        avg_time = stats['total_time'] / stats['call_count'] if stats['call_count'] > 0 else 0
+        
+        print(f"  {api_name}:")
+        print(f"    Success Rate: {success_rate:.1f}%")
+        print(f"    Average Response Time: {avg_time:.3f}s")
+        print(f"    Total Calls: {stats['call_count']}")
+    
+    # Error patterns analysis
+    print("\n⚠️ Error Patterns:")
+    total_operations = sum(s['call_count'] for s in api_stats.values())
+    total_errors = sum(s['error_count'] for s in api_stats.values())
+    overall_success_rate = ((total_operations - total_errors) / total_operations * 100) if total_operations > 0 else 0
+    
+    print(f"  Total Operations: {total_operations}")
+    print(f"  Total Errors: {total_errors}")
+    print(f"  Overall Success Rate: {overall_success_rate:.1f}%")
+    
+    # Recommendations
+    print(f"\n💡 Performance Recommendations:")
+    if overall_success_rate < 90:
+        print("  - Consider implementing more robust error handling")
+        print("  - Review retry strategies and backoff algorithms")
+    if total_errors > 0:
+        print(f"  - Investigate {total_errors} errors for root cause analysis")
+    print("  - Monitor error patterns for system reliability improvements")
+    
+    return {
+        'successful_operations': len(successes),
+        'failed_operations': len(failures), 
+        'agent_results': len(agent_results),
+        'overall_success_rate': overall_success_rate
+    }
+
+if __name__ == "__main__":
+    result = main()
+    print(f"\n✅ Error handling monitoring completed!")
+    print(f"Success rate: {result['overall_success_rate']:.1f}%")

src/praisonai-agents/06_memory_agent_monitoring.py

@@ -0,0 +1,218 @@
+"""
+Memory Agent with Performance Monitoring - Example 6
+
+Demonstrates performance monitoring for agents with memory capabilities:
+- Memory storage and retrieval timing
+- Knowledge base operations
+- Session persistence performance
+- Memory search optimization
+
+Shows how to monitor performance in stateful agents.
+"""
+
+import time
+from praisonaiagents import Agent
+from praisonaiagents.telemetry import (
+    monitor_function, track_api_call, get_performance_report,
+    get_slowest_functions, analyze_performance_trends
+)
+
+# Simulate memory operations since actual memory might not be available
+@monitor_function("memory_storage")
+def store_memory(key: str, value: str, memory_type: str = "short_term"):
+    """Simulate memory storage operation with monitoring."""
+    print(f"💾 Storing {memory_type} memory: {key}")
+    # Simulate storage time - short term is faster
+    storage_time = 0.05 if memory_type == "short_term" else 0.15
+    time.sleep(storage_time)
+    return f"Stored {key} in {memory_type} memory"
+
+@monitor_function("memory_retrieval") 
+def retrieve_memory(key: str, memory_type: str = "short_term"):
+    """Simulate memory retrieval operation with monitoring."""
+    print(f"🔍 Retrieving {memory_type} memory: {key}")
+    # Simulate retrieval time
+    retrieval_time = 0.03 if memory_type == "short_term" else 0.12
+    time.sleep(retrieval_time)
+    return f"Retrieved {key} from {memory_type} memory"
+
+@monitor_function("knowledge_search")
+def search_knowledge_base(query: str):
+    """Simulate knowledge base search with monitoring."""
+    print(f"📚 Searching knowledge base for: {query}")
+    # Simulate vector search time
+    time.sleep(0.08)
+    return f"Knowledge search results for: {query}"
+
+@monitor_function("memory_consolidation")
+def consolidate_memories(session_id: str):
+    """Simulate memory consolidation process."""
+    print(f"🔄 Consolidating memories for session: {session_id}")
+    
+    # Simulate consolidation steps
+    store_memory("consolidated_session", session_id, "long_term")
+    retrieve_memory("recent_interactions", "short_term")
+    
+    time.sleep(0.1)
+    return f"Memories consolidated for session: {session_id}"
+
+@monitor_function("stateful_agent_interaction")
+def run_stateful_agent(query: str, session_id: str, conversation_turn: int):
+    """Run agent with memory operations and monitoring."""
+    
+    # Simulate memory-enabled agent
+    agent = Agent(
+        instructions="""You are a memory-enabled AI assistant. You can remember 
+        previous conversations and build on past interactions. Use your memory 
+        to provide personalized and contextual responses.""",
+        llm="gpt-4o-mini"
+    )
+    
+    # Pre-processing: retrieve relevant memories
+    with track_api_call("memory_preprocessing"):
+        print(f"🧠 Pre-processing memories for turn {conversation_turn}")
+        
+        # Retrieve session context
+        if conversation_turn > 1:
+            retrieve_memory(f"session_{session_id}", "short_term")
+        
+        # Search for relevant knowledge
+        search_knowledge_base(query)
+        
+        time.sleep(0.05)  # Simulate context preparation
+    
+    # Main agent execution
+    with track_api_call("memory_agent_execution"):
+        print(f"🚀 Processing query with memory context...")
+        result = agent.start(query)
+    
+    # Post-processing: store new memories
+    with track_api_call("memory_postprocessing"):
+        print(f"💭 Post-processing: storing interaction memories")
+        
+        # Store the interaction
+        store_memory(f"interaction_{conversation_turn}", query, "short_term")
+        store_memory(f"response_{conversation_turn}", result, "short_term")
+        
+        # Update session memory
+        store_memory(f"session_{session_id}", f"Turn {conversation_turn} completed", "short_term")
+        
+        time.sleep(0.03)  # Simulate memory update
+    
+    return result
+
+@monitor_function("multi_turn_conversation")
+def simulate_multi_turn_conversation():
+    """Simulate a multi-turn conversation with memory."""
+    session_id = "demo_session_001"
+    
+    conversation = [
+        "Hello, I'm interested in learning about machine learning",
+        "What are the main types of machine learning algorithms?",
+        "Can you give me examples of supervised learning?",
+        "How does this relate to what we discussed about machine learning earlier?"
+    ]
+    
+    results = []
+    
+    for turn, query in enumerate(conversation, 1):
+        print(f"\n💬 Conversation Turn {turn}")
+        print(f"User: {query}")
+        
+        result = run_stateful_agent(query, session_id, turn)
+        results.append(result)
+        
+        print(f"Agent: {result[:100]}..." if len(result) > 100 else f"Agent: {result}")
+        
+        # Simulate user thinking time
+        time.sleep(0.1)
+    
+    # End of conversation: consolidate memories
+    consolidate_memories(session_id)
+    
+    return results
+
+def main():
+    """Main function demonstrating memory agent performance monitoring."""
+    print("=" * 70)
+    print("EXAMPLE 6: Memory Agent Performance Monitoring")
+    print("=" * 70)
+    
+    # Warm up the memory system
+    print("\n🚀 Phase 1: Memory System Initialization")
+    store_memory("system_initialized", "true", "long_term")
+    search_knowledge_base("initialization")
+    
+    # Run multi-turn conversation
+    print("\n💬 Phase 2: Multi-Turn Conversation with Memory")
+    conversation_results = simulate_multi_turn_conversation()
+    
+    # Memory performance analysis
+    print("\n" + "=" * 70)
+    print("📊 MEMORY PERFORMANCE ANALYSIS")
+    print("=" * 70)
+    
+    # Performance trends analysis
+    print("\n📈 Performance Trends:")
+    try:
+        trends = analyze_performance_trends()
+        print(f"  Performance trend analysis completed")
+        if trends:
+            print(f"  Functions analyzed: {len(trends.get('functions', []))}")
+    except Exception as e:
+        print(f"  Trends analysis: {str(e)}")
+    
+    # Slowest operations
+    print("\n🐌 Slowest Memory Operations:")
+    slowest = get_slowest_functions()
+    for func_name, avg_time in slowest[:5]:  # Top 5 slowest
+        print(f"  {func_name}: {avg_time:.3f}s average")
+    
+    # Memory operation breakdown
+    print("\n💾 Memory Operation Breakdown:")
+    from praisonaiagents.telemetry import get_function_stats
+    stats = get_function_stats()
+    
+    memory_operations = {k: v for k, v in stats.items() if 'memory' in k.lower()}
+    
+    for op_name, data in memory_operations.items():
+        avg_time = data['total_time'] / data['call_count'] if data['call_count'] > 0 else 0
+        print(f"  {op_name}:")
+        print(f"    Operations: {data['call_count']}")
+        print(f"    Avg Time: {avg_time:.3f}s")
+        print(f"    Total Time: {data['total_time']:.3f}s")
+    
+    # Session efficiency metrics
+    total_memory_ops = sum(data['call_count'] for data in memory_operations.values())
+    total_memory_time = sum(data['total_time'] for data in memory_operations.values())
+    
+    print(f"\n🔍 Session Efficiency:")
+    print(f"  Total Memory Operations: {total_memory_ops}")
+    print(f"  Total Memory Time: {total_memory_time:.3f}s")
+    print(f"  Avg Time per Memory Op: {total_memory_time/total_memory_ops:.3f}s" if total_memory_ops > 0 else "  No memory operations")
+    
+    # Performance report
+    print("\n📋 Comprehensive Performance Report:")
+    report = get_performance_report()
+    print(report[:400] + "..." if len(report) > 400 else report)
+    
+    # Memory optimization suggestions
+    print(f"\n💡 Memory Optimization Suggestions:")
+    if total_memory_time > 1.0:
+        print("  - Consider implementing memory caching strategies")
+        print("  - Optimize vector search indexes for faster retrieval")
+    if total_memory_ops > 10:
+        print("  - Consider batching memory operations for efficiency")
+    print("  - Monitor memory consolidation frequency vs performance")
+    
+    return {
+        'conversation_turns': len(conversation_results),
+        'memory_operations': total_memory_ops,
+        'total_memory_time': total_memory_time
+    }
+
+if __name__ == "__main__":
+    result = main()
+    print(f"\n✅ Memory agent monitoring completed!")
+    print(f"Processed {result['conversation_turns']} conversation turns")
+    print(f"Executed {result['memory_operations']} memory operations in {result['total_memory_time']:.3f}s")

src/praisonai-agents/07_hierarchical_agents_monitoring.py

@@ -0,0 +1,264 @@
+"""
+Hierarchical Agents with Performance Monitoring - Example 7
+
+Demonstrates performance monitoring for hierarchical agent systems:
+- Manager-worker agent relationships
+- Task delegation timing
+- Hierarchical decision making performance
+- Cross-level communication monitoring
+
+Shows how to monitor complex organizational agent structures.
+"""
+
+import time
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.telemetry import (
+    monitor_function, track_api_call, get_function_stats,
+    visualize_execution_flow, generate_comprehensive_report
+)
+
+@monitor_function("manager_decision_making")
+def make_management_decision(task_description: str):
+    """Simulate manager decision making process."""
+    print(f"👔 Manager analyzing task: {task_description}")
+    # Simulate decision complexity
+    time.sleep(0.1)
+    
+    # Simple task routing logic
+    if "research" in task_description.lower():
+        return "research_worker"
+    elif "analysis" in task_description.lower():
+        return "analysis_worker" 
+    elif "report" in task_description.lower():
+        return "writing_worker"
+    else:
+        return "general_worker"
+
+@monitor_function("task_delegation") 
+def delegate_task(task: str, worker_type: str):
+    """Simulate task delegation to workers."""
+    print(f"📤 Delegating to {worker_type}: {task}")
+    time.sleep(0.05)  # Delegation overhead
+    return f"Task delegated to {worker_type}"
+
+@monitor_function("worker_execution")
+def execute_worker_task(worker_name: str, task_description: str):
+    """Simulate worker executing assigned task."""
+    print(f"⚙️ {worker_name} executing: {task_description}")
+    
+    # Different workers have different execution times
+    if "research" in worker_name:
+        time.sleep(0.2)  # Research takes longer
+    elif "analysis" in worker_name:
+        time.sleep(0.15)  # Analysis is moderate
+    elif "writing" in worker_name:
+        time.sleep(0.1)   # Writing is relatively quick
+    else:
+        time.sleep(0.12)  # General tasks
+    
+    return f"{worker_name} completed: {task_description}"
+
+@monitor_function("result_aggregation")
+def aggregate_results(results: list):
+    """Simulate aggregating results from multiple workers."""
+    print(f"📊 Aggregating {len(results)} worker results...")
+    time.sleep(0.08)  # Aggregation time
+    return f"Aggregated {len(results)} results into final output"
+
+@monitor_function("hierarchical_workflow")
+def run_hierarchical_workflow():
+    """Run a complete hierarchical workflow with monitoring."""
+    
+    # Create manager agent
+    manager = Agent(
+        name="project_manager",
+        role="Project Manager",
+        goal="Efficiently coordinate team efforts and ensure project success",
+        backstory="Experienced project manager with expertise in team coordination",
+        instructions="""You are a project manager. Break down complex projects into tasks,
+        assign them to appropriate team members, and coordinate the overall effort.""",
+        llm="gpt-4o-mini"
+    )
+    
+    # Create worker agents
+    research_worker = Agent(
+        name="research_specialist", 
+        role="Research Specialist",
+        goal="Conduct thorough research on assigned topics",
+        backstory="Expert researcher with deep analytical skills",
+        instructions="You are a research specialist. Provide detailed, factual information.",
+        llm="gpt-4o-mini"
+    )
+    
+    analysis_worker = Agent(
+        name="data_analyst",
+        role="Data Analyst", 
+        goal="Analyze data and identify patterns and insights",
+        backstory="Experienced data analyst with strong analytical thinking",
+        instructions="You are a data analyst. Focus on patterns, trends, and insights.",
+        llm="gpt-4o-mini"
+    )
+    
+    writing_worker = Agent(
+        name="technical_writer",
+        role="Technical Writer",
+        goal="Create clear, well-structured documentation and reports", 
+        backstory="Professional writer specializing in technical content",
+        instructions="You are a technical writer. Create clear, comprehensive reports.",
+        llm="gpt-4o-mini"
+    )
+    
+    # Project tasks
+    project_tasks = [
+        "Research current trends in artificial intelligence",
+        "Analyze the impact of AI on business productivity",
+        "Write a comprehensive report on AI implementation strategies"
+    ]
+    
+    # Execute hierarchical workflow
+    with track_api_call("hierarchical_project_execution"):
+        print("🏢 Starting hierarchical project execution...")
+        
+        all_results = []
+        
+        for task_desc in project_tasks:
+            print(f"\n📋 Processing project task: {task_desc}")
+            
+            # Manager makes decision
+            assigned_worker = make_management_decision(task_desc)
+            
+            # Delegate task
+            delegation_result = delegate_task(task_desc, assigned_worker)
+            
+            # Select appropriate worker
+            if "research" in assigned_worker:
+                worker = research_worker
+                worker_name = "research_specialist"
+            elif "analysis" in assigned_worker:
+                worker = analysis_worker
+                worker_name = "data_analyst" 
+            elif "writing" in assigned_worker:
+                worker = writing_worker
+                worker_name = "technical_writer"
+            else:
+                worker = research_worker  # Default
+                worker_name = "research_specialist"
+            
+            # Worker executes task
+            worker_result = execute_worker_task(worker_name, task_desc)
+            
+            # Simulate actual agent execution
+            with track_api_call(f"{worker_name}_llm_execution"):
+                agent_result = worker.start(task_desc)
+                all_results.append(agent_result)
+        
+        # Manager aggregates results
+        final_result = aggregate_results(all_results)
+        
+        # Final manager review
+        with track_api_call("manager_final_review"):
+            print("👔 Manager conducting final review...")
+            manager_review = manager.start(
+                f"Review and synthesize these team outputs: {[r[:100] + '...' for r in all_results]}"
+            )
+    
+    return all_results, manager_review
+
+def main():
+    """Main function demonstrating hierarchical agents performance monitoring."""
+    print("=" * 75)
+    print("EXAMPLE 7: Hierarchical Agents Performance Monitoring")
+    print("=" * 75)
+    
+    # Run hierarchical workflow
+    print("\n🏢 Running Hierarchical Agent Workflow")
+    worker_results, manager_review = run_hierarchical_workflow()
+    
+    print(f"\n✅ Workflow completed with {len(worker_results)} task results")
+    
+    # Performance Analysis
+    print("\n" + "=" * 75)
+    print("📊 HIERARCHICAL PERFORMANCE ANALYSIS")
+    print("=" * 75)
+    
+    # Role-based performance breakdown
+    print("\n👥 Role-Based Performance:")
+    stats = get_function_stats()
+    
+    # Categorize functions by role
+    management_functions = [f for f in stats.keys() if 'manager' in f or 'decision' in f or 'delegation' in f or 'aggregation' in f]
+    worker_functions = [f for f in stats.keys() if 'worker' in f or 'execution' in f]
+    coordination_functions = [f for f in stats.keys() if 'hierarchical' in f or 'workflow' in f]
+    
+    print("  📋 Management Functions:")
+    for func in management_functions:
+        data = stats[func]
+        avg_time = data['total_time'] / data['call_count'] if data['call_count'] > 0 else 0
+        print(f"    {func}: {data['call_count']} calls, {avg_time:.3f}s avg")
+    
+    print("\n  ⚙️ Worker Functions:")
+    for func in worker_functions:
+        data = stats[func] 
+        avg_time = data['total_time'] / data['call_count'] if data['call_count'] > 0 else 0
+        print(f"    {func}: {data['call_count']} calls, {avg_time:.3f}s avg")
+    
+    print("\n  🔄 Coordination Functions:")
+    for func in coordination_functions:
+        data = stats[func]
+        avg_time = data['total_time'] / data['call_count'] if data['call_count'] > 0 else 0
+        print(f"    {func}: {data['call_count']} calls, {avg_time:.3f}s avg")
+    
+    # Hierarchy efficiency metrics
+    total_management_time = sum(stats[f]['total_time'] for f in management_functions)
+    total_worker_time = sum(stats[f]['total_time'] for f in worker_functions)
+    total_coordination_time = sum(stats[f]['total_time'] for f in coordination_functions)
+    
+    print(f"\n⚖️ Hierarchy Efficiency Metrics:")
+    print(f"  Management Overhead: {total_management_time:.3f}s")
+    print(f"  Worker Execution Time: {total_worker_time:.3f}s")
+    print(f"  Coordination Overhead: {total_coordination_time:.3f}s")
+    
+    total_time = total_management_time + total_worker_time + total_coordination_time
+    if total_time > 0:
+        print(f"  Worker Efficiency: {total_worker_time/total_time*100:.1f}%")
+        print(f"  Management Overhead: {total_management_time/total_time*100:.1f}%")
+        print(f"  Coordination Overhead: {total_coordination_time/total_time*100:.1f}%")
+    
+    # Execution flow visualization
+    print("\n🔄 Execution Flow Analysis:")
+    try:
+        flow_viz = visualize_execution_flow()
+        print("  Hierarchical flow visualization generated")
+        print(f"  Flow complexity: {len(str(flow_viz)) // 100} units")
+    except Exception as e:
+        print(f"  Flow visualization: {str(e)}")
+    
+    # Comprehensive report
+    print("\n📋 Comprehensive Performance Report:")
+    report = generate_comprehensive_report()
+    print(report[:500] + "..." if len(report) > 500 else report)
+    
+    # Optimization recommendations
+    print(f"\n💡 Hierarchical Optimization Recommendations:")
+    
+    if total_time > 0:
+        if total_management_time / total_time > 0.3:
+            print("  - High management overhead detected - consider task batching")
+        if total_coordination_time / total_time > 0.2:
+            print("  - Consider reducing coordination complexity")
+    if len(worker_results) > 0:
+        print(f"  - Successfully processed {len(worker_results)} tasks hierarchically")
+    print("  - Monitor delegation efficiency vs direct execution trade-offs")
+    
+    return {
+        'tasks_completed': len(worker_results),
+        'management_time': total_management_time,
+        'worker_time': total_worker_time,
+        'coordination_time': total_coordination_time,
+        'efficiency_ratio': total_worker_time / total_time if total_time > 0 else 0
+    }
+
+if __name__ == "__main__":
+    result = main()
+    print(f"\n🎉 Hierarchical monitoring completed!")
+    print(f"Tasks: {result['tasks_completed']}, Efficiency: {result['efficiency_ratio']*100:.1f}%")

src/praisonai-agents/08_custom_tools_monitoring.py

@@ -0,0 +1,314 @@
+"""
+Custom Tools with Performance Monitoring - Example 8
+
+Demonstrates performance monitoring for agents using custom tools:
+- Custom tool execution timing
+- Tool creation and registration performance
+- Tool usage pattern analysis
+- Tool efficiency optimization
+
+Shows how to monitor performance with custom-built tools.
+"""
+
+import time
+import random
+from praisonaiagents import Agent, tool
+from praisonaiagents.telemetry import (
+    monitor_function, track_api_call, get_function_stats,
+    get_slowest_functions, performance_monitor
+)
+
+# Custom Tool 1: Data Processor
+@tool
+@monitor_function("data_processor_tool")
+def process_data(data: str, operation: str = "analyze") -> str:
+    """
+    Process data with specified operation.
+    
+    Args:
+        data: The data to process
+        operation: Type of operation (analyze, transform, validate)
+    """
+    print(f"🔧 Processing data with operation: {operation}")
+    
+    # Simulate different processing times based on operation
+    processing_times = {
+        "analyze": 0.15,
+        "transform": 0.2, 
+        "validate": 0.1
+    }
+    
+    time.sleep(processing_times.get(operation, 0.12))
+    
+    return f"Data processed with {operation}: {data[:50]}{'...' if len(data) > 50 else ''}"
+
+# Custom Tool 2: API Simulator
+@tool
+@monitor_function("api_simulator_tool")
+def call_external_api(endpoint: str, method: str = "GET", payload: str = "") -> str:
+    """
+    Simulate calling an external API.
+    
+    Args:
+        endpoint: API endpoint to call
+        method: HTTP method (GET, POST, PUT, DELETE)
+        payload: Request payload for POST/PUT
+    """
+    print(f"🌐 Calling API: {method} {endpoint}")
+    
+    # Simulate variable API response times
+    base_time = 0.1
+    if method == "POST":
+        base_time += 0.05
+    if payload:
+        base_time += len(payload) * 0.001
+    
+    # Add random variation
+    response_time = base_time + random.uniform(0, 0.1)
+    time.sleep(response_time)
+    
+    # Simulate occasional API failures
+    if random.random() < 0.1:  # 10% failure rate
+        raise Exception(f"API call to {endpoint} failed")
+    
+    return f"API Response from {endpoint}: {{'status': 'success', 'method': '{method}'}}"
+
+# Custom Tool 3: Database Simulator
+@tool 
+@monitor_function("database_simulator_tool")
+def query_database(query: str, database: str = "main") -> str:
+    """
+    Simulate database query execution.
+    
+    Args:
+        query: SQL query to execute
+        database: Database name to query
+    """
+    print(f"🗄️ Executing database query on {database}")
+    
+    # Simulate query complexity based on keywords
+    complexity_keywords = {
+        "JOIN": 0.08,
+        "GROUP BY": 0.06,
+        "ORDER BY": 0.04,
+        "WHERE": 0.03
+    }
+    
+    query_time = 0.05  # Base query time
+    for keyword, additional_time in complexity_keywords.items():
+        if keyword in query.upper():
+            query_time += additional_time
+    
+    time.sleep(query_time)
+    
+    return f"Query executed on {database}: Found {random.randint(1, 100)} results"
+
+# Custom Tool 4: File Operations
+@tool
+@monitor_function("file_operations_tool")  
+def file_operation(operation: str, filename: str, content: str = "") -> str:
+    """
+    Simulate file operations.
+    
+    Args:
+        operation: Operation type (read, write, delete, copy)
+        filename: Target filename
+        content: Content for write operations
+    """
+    print(f"📁 File operation: {operation} on {filename}")
+    
+    # Simulate different operation times
+    operation_times = {
+        "read": 0.03,
+        "write": 0.05,
+        "delete": 0.02,
+        "copy": 0.07
+    }
+    
+    time.sleep(operation_times.get(operation, 0.04))
+    
+    if operation == "write" and content:
+        time.sleep(len(content) * 0.0001)  # Simulate write time based on content size
+    
+    return f"File {operation} completed for {filename}"
+
+@monitor_function("tool_performance_test")
+def test_tool_performance():
+    """Test individual tool performance."""
+    print("🧪 Testing individual tool performance...")
+    
+    # Test each tool multiple times
+    tools_results = []
+    
+    # Test data processor
+    for op in ["analyze", "transform", "validate"]:
+        result = process_data("Sample data for testing", op)
+        tools_results.append(f"DataProcessor[{op}]: {result}")
+    
+    # Test API simulator
+    for method in ["GET", "POST"]:
+        try:
+            result = call_external_api("/api/test", method, "test payload" if method == "POST" else "")
+            tools_results.append(f"APISimulator[{method}]: {result}")
+        except Exception as e:
+            tools_results.append(f"APISimulator[{method}]: Failed - {e}")
+    
+    # Test database operations
+    queries = [
+        "SELECT * FROM users",
+        "SELECT COUNT(*) FROM orders WHERE date > '2024-01-01'",
+        "SELECT u.name, COUNT(o.id) FROM users u JOIN orders o GROUP BY u.id"
+    ]
+    
+    for query in queries:
+        result = query_database(query)
+        tools_results.append(f"Database: {result}")
+    
+    # Test file operations
+    for op in ["read", "write", "copy"]:
+        result = file_operation(op, "test_file.txt", "Test content" if op == "write" else "")
+        tools_results.append(f"FileOps[{op}]: {result}")
+    
+    return tools_results
+
+@monitor_function("agent_with_custom_tools")
+def run_agent_with_custom_tools():
+    """Run agent using custom tools with performance monitoring."""
+    
+    # Create agent with custom tools
+    agent = Agent(
+        instructions="""You are a technical assistant with access to various tools.
+        Use the appropriate tools to help with data processing, API calls, database queries,
+        and file operations. Always explain what tools you're using and why.""",
+        llm="gpt-4o-mini",
+        tools=[process_data, call_external_api, query_database, file_operation]
+    )
+    
+    # Test scenarios for the agent
+    scenarios = [
+        "I need to analyze some customer data and store the results",
+        "Please fetch user information from the API and save it to a file",
+        "Query the database for recent orders and process the results"
+    ]
+    
+    results = []
+    
+    for i, scenario in enumerate(scenarios, 1):
+        print(f"\n🎯 Scenario {i}: {scenario}")
+        
+        with track_api_call(f"custom_tools_scenario_{i}"):
+            try:
+                result = agent.start(scenario)
+                results.append(result)
+                print(f"✅ Scenario {i} completed")
+            except Exception as e:
+                print(f"❌ Scenario {i} failed: {e}")
+                results.append(f"Failed: {e}")
+    
+    return results
+
+def main():
+    """Main function demonstrating custom tools performance monitoring."""
+    print("=" * 70)
+    print("EXAMPLE 8: Custom Tools Performance Monitoring")
+    print("=" * 70)
+    
+    # Phase 1: Individual tool performance testing
+    print("\n🔧 Phase 1: Individual Tool Performance Testing")
+    tool_test_results = test_tool_performance()
+    
+    print(f"\n📊 Tool Test Summary:")
+    print(f"  Total tool operations: {len(tool_test_results)}")
+    for result in tool_test_results[:5]:  # Show first 5 results
+        print(f"  - {result}")
+    if len(tool_test_results) > 5:
+        print(f"  ... and {len(tool_test_results) - 5} more")
+    
+    # Phase 2: Agent using custom tools
+    print("\n🤖 Phase 2: Agent with Custom Tools")
+    agent_results = run_agent_with_custom_tools()
+    
+    # Performance Analysis
+    print("\n" + "=" * 70)
+    print("📊 CUSTOM TOOLS PERFORMANCE ANALYSIS")
+    print("=" * 70)
+    
+    # Tool-specific performance
+    print("\n🔧 Tool Performance Breakdown:")
+    stats = get_function_stats()
+    tool_functions = {k: v for k, v in stats.items() if 'tool' in k.lower()}
+    
+    for tool_name, data in tool_functions.items():
+        avg_time = data['total_time'] / data['call_count'] if data['call_count'] > 0 else 0
+        print(f"  {tool_name}:")
+        print(f"    Calls: {data['call_count']}")
+        print(f"    Avg Time: {avg_time:.3f}s")
+        print(f"    Total Time: {data['total_time']:.3f}s")
+        if data['error_count'] > 0:
+            print(f"    Errors: {data['error_count']}")
+    
+    # Tool efficiency ranking
+    print("\n🏆 Tool Efficiency Ranking (by average execution time):")
+    tool_efficiency = []
+    for tool_name, data in tool_functions.items():
+        if data['call_count'] > 0:
+            avg_time = data['total_time'] / data['call_count']
+            tool_efficiency.append((tool_name, avg_time))
+    
+    tool_efficiency.sort(key=lambda x: x[1])
+    
+    for rank, (tool_name, avg_time) in enumerate(tool_efficiency, 1):
+        print(f"  {rank}. {tool_name}: {avg_time:.3f}s")
+    
+    # Slowest operations
+    print("\n🐌 Slowest Tool Operations:")
+    slowest = get_slowest_functions()
+    tool_slowest = [(name, time) for name, time in slowest if 'tool' in name.lower()]
+    
+    for tool_name, avg_time in tool_slowest[:3]:
+        print(f"  {tool_name}: {avg_time:.3f}s")
+    
+    # Usage patterns
+    total_tool_calls = sum(data['call_count'] for data in tool_functions.values())
+    total_tool_time = sum(data['total_time'] for data in tool_functions.values())
+    
+    print(f"\n📈 Tool Usage Summary:")
+    print(f"  Total Tool Calls: {total_tool_calls}")
+    print(f"  Total Tool Execution Time: {total_tool_time:.3f}s")
+    print(f"  Average Time per Tool Call: {total_tool_time/total_tool_calls:.3f}s" if total_tool_calls > 0 else "  No tool calls")
+    
+    # Tool reliability
+    total_tool_errors = sum(data['error_count'] for data in tool_functions.values())
+    reliability = ((total_tool_calls - total_tool_errors) / total_tool_calls * 100) if total_tool_calls > 0 else 100
+    
+    print(f"  Tool Reliability: {reliability:.1f}%")
+    if total_tool_errors > 0:
+        print(f"  Total Errors: {total_tool_errors}")
+    
+    # Optimization recommendations
+    print(f"\n💡 Custom Tools Optimization Recommendations:")
+    
+    if tool_efficiency:
+        slowest_tool = tool_efficiency[-1]
+        if slowest_tool[1] > 0.2:
+            print(f"  - Optimize {slowest_tool[0]} (slowest at {slowest_tool[1]:.3f}s)")
+    
+    if total_tool_errors > 0:
+        print(f"  - Investigate {total_tool_errors} tool errors for stability improvements")
+    
+    if total_tool_calls > 20:
+        print("  - Consider tool caching for frequently used operations")
+    
+    print("  - Monitor tool usage patterns for optimization opportunities")
+    
+    return {
+        'tool_operations': len(tool_test_results),
+        'agent_scenarios': len(agent_results),
+        'total_tool_calls': total_tool_calls,
+        'tool_reliability': reliability
+    }
+
+if __name__ == "__main__":
+    result = main()
+    print(f"\n✅ Custom tools monitoring completed!")
+    print(f"Operations: {result['tool_operations']}, Reliability: {result['tool_reliability']:.1f}%")

src/praisonai-agents/09_streaming_monitoring.py

@@ -0,0 +1,286 @@
+"""
+Streaming & Real-Time Performance Monitoring - Example 9
+
+Demonstrates performance monitoring for streaming and real-time operations:
+- Real-time performance tracking
+- Streaming response monitoring
+- Live performance metrics
+- Continuous performance analysis
+
+Shows how to monitor performance during streaming operations.
+"""
+
+import time
+import threading
+from datetime import datetime
+from praisonaiagents import Agent
+from praisonaiagents.telemetry import (
+    monitor_function, track_api_call, performance_monitor,
+    get_performance_report, get_function_stats
+)
+
+# Global variables for streaming simulation
+streaming_active = False
+streaming_stats = {
+    'messages_processed': 0,
+    'total_latency': 0.0,
+    'errors': 0,
+    'start_time': None
+}
+
+@monitor_function("stream_message_processing")
+def process_stream_message(message_id: int, content: str):
+    """Process a single streaming message with monitoring."""
+    processing_start = time.time()
+    
+    print(f"📨 Processing stream message {message_id}: {content[:30]}...")
+    
+    # Simulate variable processing time
+    processing_time = 0.05 + (message_id % 3) * 0.02  # Simulate varying complexity
+    time.sleep(processing_time)
+    
+    # Simulate occasional errors
+    if message_id % 20 == 0:  # Every 20th message fails
+        raise Exception(f"Processing error for message {message_id}")
+    
+    processing_end = time.time()
+    latency = processing_end - processing_start
+    
+    # Update streaming stats
+    streaming_stats['messages_processed'] += 1
+    streaming_stats['total_latency'] += latency
+    
+    return f"Processed message {message_id} in {latency:.3f}s"
+
+@monitor_function("real_time_agent_response")
+def get_real_time_agent_response(query: str, response_id: int):
+    """Simulate real-time agent response with monitoring."""
+    print(f"🤖 Generating real-time response {response_id} for: {query}")
+    
+    # Create a lightweight agent for streaming
+    agent = Agent(
+        instructions="You are a real-time assistant. Provide quick, helpful responses.",
+        llm="gpt-4o-mini"
+    )
+    
+    with track_api_call(f"streaming_response_{response_id}"):
+        # Simulate streaming response generation
+        time.sleep(0.1)  # Base response time
+        
+        # In real implementation, this would be actual agent.start()
+        response = agent.start(f"Quickly respond to: {query}")
+        
+        return response
+
+@monitor_function("performance_metrics_collector")
+def collect_live_metrics():
+    """Collect live performance metrics during streaming."""
+    
+    while streaming_active:
+        current_time = time.time()
+        
+        # Collect current performance data
+        current_stats = performance_monitor.get_function_performance()
+        api_stats = performance_monitor.get_api_call_performance()
+        
+        # Calculate streaming metrics
+        if streaming_stats['start_time']:
+            elapsed_time = current_time - streaming_stats['start_time']
+            messages_per_second = streaming_stats['messages_processed'] / elapsed_time if elapsed_time > 0 else 0
+            avg_latency = streaming_stats['total_latency'] / streaming_stats['messages_processed'] if streaming_stats['messages_processed'] > 0 else 0
+            
+            print(f"📊 Live Metrics - Messages/sec: {messages_per_second:.2f}, Avg Latency: {avg_latency:.3f}s")
+        
+        time.sleep(2)  # Update every 2 seconds
+
+@monitor_function("streaming_simulation")
+def simulate_streaming_workload():
+    """Simulate a streaming workload with continuous monitoring."""
+    global streaming_active, streaming_stats
+    
+    print("🌊 Starting streaming simulation...")
+    streaming_active = True
+    streaming_stats['start_time'] = time.time()
+    
+    # Start metrics collector in background
+    metrics_thread = threading.Thread(target=collect_live_metrics)
+    metrics_thread.daemon = True
+    metrics_thread.start()
+    
+    # Simulate incoming stream messages
+    messages = [
+        "User question about AI",
+        "Request for weather information",
+        "Help with programming",
+        "General knowledge query",
+        "Technical support request",
+        "Creative writing assistance",
+        "Data analysis question",
+        "Research inquiry"
+    ]
+    
+    processed_responses = []
+    
+    try:
+        for i in range(20):  # Process 20 streaming messages
+            message = messages[i % len(messages)]
+            
+            try:
+                # Process the stream message
+                result = process_stream_message(i + 1, message)
+                
+                # Generate real-time response every 3rd message
+                if (i + 1) % 3 == 0:
+                    response = get_real_time_agent_response(message, i + 1)
+                    processed_responses.append(response)
+                
+                # Brief pause between messages
+                time.sleep(0.1)
+                
+            except Exception as e:
+                streaming_stats['errors'] += 1
+                print(f"❌ Error processing message {i + 1}: {e}")
+        
+    finally:
+        streaming_active = False
+        print("🛑 Streaming simulation completed")
+    
+    return processed_responses
+
+@monitor_function("batch_vs_streaming_comparison")
+def compare_batch_vs_streaming():
+    """Compare batch vs streaming performance."""
+    
+    print("\n🔄 Comparing Batch vs Streaming Performance...")
+    
+    # Batch processing simulation
+    print("\n📦 Batch Processing Test:")
+    batch_start = time.time()
+    
+    batch_messages = [f"Batch message {i}" for i in range(10)]
+    batch_results = []
+    
+    with track_api_call("batch_processing"):
+        for i, message in enumerate(batch_messages):
+            # Simulate batch processing (all at once)
+            time.sleep(0.08)  # Batch processing time
+            batch_results.append(f"Batch processed: {message}")
+    
+    batch_time = time.time() - batch_start
+    
+    # Streaming processing test (already done above)
+    streaming_time = streaming_stats['total_latency']
+    streaming_count = streaming_stats['messages_processed']
+    
+    print(f"\n📊 Batch vs Streaming Comparison:")
+    print(f"  Batch Processing:")
+    print(f"    Messages: {len(batch_results)}")
+    print(f"    Total Time: {batch_time:.3f}s") 
+    print(f"    Avg per Message: {batch_time/len(batch_results):.3f}s")
+    
+    print(f"  Streaming Processing:")
+    print(f"    Messages: {streaming_count}")
+    print(f"    Total Processing Time: {streaming_time:.3f}s")
+    print(f"    Avg per Message: {streaming_time/streaming_count:.3f}s" if streaming_count > 0 else "    No messages processed")
+    
+    return batch_time, streaming_time
+
+def main():
+    """Main function demonstrating streaming performance monitoring."""
+    print("=" * 70)
+    print("EXAMPLE 9: Streaming & Real-Time Performance Monitoring")
+    print("=" * 70)
+    
+    # Phase 1: Streaming simulation
+    print("\n🌊 Phase 1: Streaming Workload Simulation")
+    streaming_responses = simulate_streaming_workload()
+    
+    # Phase 2: Batch vs Streaming comparison
+    print("\n📊 Phase 2: Batch vs Streaming Comparison")
+    batch_time, streaming_time = compare_batch_vs_streaming()
+    
+    # Real-time Performance Analysis
+    print("\n" + "=" * 70)
+    print("📊 STREAMING PERFORMANCE ANALYSIS")
+    print("=" * 70)
+    
+    # Streaming-specific metrics
+    print("\n🌊 Streaming Metrics:")
+    elapsed_total = time.time() - streaming_stats['start_time'] if streaming_stats['start_time'] else 0
+    throughput = streaming_stats['messages_processed'] / elapsed_total if elapsed_total > 0 else 0
+    avg_latency = streaming_stats['total_latency'] / streaming_stats['messages_processed'] if streaming_stats['messages_processed'] > 0 else 0
+    error_rate = streaming_stats['errors'] / streaming_stats['messages_processed'] * 100 if streaming_stats['messages_processed'] > 0 else 0
+    
+    print(f"  Messages Processed: {streaming_stats['messages_processed']}")
+    print(f"  Total Processing Time: {streaming_stats['total_latency']:.3f}s")
+    print(f"  Average Latency: {avg_latency:.3f}s")
+    print(f"  Throughput: {throughput:.2f} messages/second")
+    print(f"  Error Rate: {error_rate:.2f}%")
+    print(f"  Errors: {streaming_stats['errors']}")
+    
+    # Function performance during streaming
+    print("\n⚡ Real-Time Function Performance:")
+    stats = get_function_stats()
+    streaming_functions = {k: v for k, v in stats.items() if 'stream' in k.lower() or 'real_time' in k.lower()}
+    
+    for func_name, data in streaming_functions.items():
+        avg_time = data['total_time'] / data['call_count'] if data['call_count'] > 0 else 0
+        print(f"  {func_name}:")
+        print(f"    Calls: {data['call_count']}")
+        print(f"    Avg Time: {avg_time:.3f}s")
+        print(f"    Total Time: {data['total_time']:.3f}s")
+    
+    # Performance trend analysis
+    print("\n📈 Performance Trends:")
+    recent_performance = performance_monitor.get_function_performance()
+    total_functions = len(recent_performance)
+    total_calls = sum(data['call_count'] for data in recent_performance.values())
+    
+    print(f"  Functions Monitored: {total_functions}")
+    print(f"  Total Function Calls: {total_calls}")
+    print(f"  Average Calls per Function: {total_calls/total_functions:.1f}" if total_functions > 0 else "  No functions monitored")
+    
+    # Real-time vs Batch efficiency
+    print(f"\n⚖️ Efficiency Analysis:")
+    if batch_time > 0 and streaming_time > 0:
+        efficiency_gain = (batch_time - streaming_time) / batch_time * 100
+        print(f"  Streaming vs Batch Efficiency: {efficiency_gain:+.1f}%")
+    
+    if avg_latency > 0:
+        if avg_latency < 0.1:
+            print("  ✅ Excellent real-time performance (< 100ms)")
+        elif avg_latency < 0.2:
+            print("  ⚡ Good real-time performance (< 200ms)")
+        else:
+            print("  ⚠️ Consider optimization for better real-time performance")
+    
+    # Current performance report
+    print("\n📋 Current Performance Report:")
+    report = get_performance_report()
+    print(report[:400] + "..." if len(report) > 400 else report)
+    
+    # Real-time optimization suggestions
+    print(f"\n💡 Real-Time Optimization Recommendations:")
+    
+    if avg_latency > 0.15:
+        print(f"  - Optimize message processing (current avg: {avg_latency:.3f}s)")
+    if error_rate > 5:
+        print(f"  - Investigate error patterns (current rate: {error_rate:.1f}%)")
+    if throughput < 10:
+        print(f"  - Consider parallel processing for better throughput")
+    
+    print("  - Monitor memory usage during extended streaming")
+    print("  - Implement adaptive batching for optimal performance")
+    
+    return {
+        'streaming_responses': len(streaming_responses),
+        'messages_processed': streaming_stats['messages_processed'],
+        'throughput': throughput,
+        'average_latency': avg_latency,
+        'error_rate': error_rate
+    }
+
+if __name__ == "__main__":
+    result = main()
+    print(f"\n🎉 Streaming monitoring completed!")
+    print(f"Throughput: {result['throughput']:.2f} msg/s, Latency: {result['average_latency']:.3f}s")

src/praisonai-agents/10_comprehensive_dashboard.py

@@ -0,0 +1,433 @@
+"""
+Comprehensive Performance Dashboard - Example 10
+
+Demonstrates comprehensive performance monitoring dashboard:
+- Complete performance analytics
+- Advanced reporting and visualization
+- Performance trends and insights
+- System-wide monitoring overview
+
+Shows how to create a comprehensive performance monitoring solution.
+"""
+
+import time
+import json
+from datetime import datetime, timedelta
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.telemetry import (
+    monitor_function, track_api_call, performance_monitor,
+    get_performance_report, get_function_stats, get_api_stats,
+    get_slowest_functions, get_slowest_apis, 
+    analyze_function_flow, visualize_execution_flow,
+    analyze_performance_trends, generate_comprehensive_report,
+    clear_performance_data
+)
+
+@monitor_function("dashboard_data_collection")
+def collect_comprehensive_data():
+    """Collect comprehensive performance data for dashboard."""
+    
+    print("📊 Collecting comprehensive performance data...")
+    
+    # Generate diverse workload for demonstration
+    test_scenarios = [
+        ("Quick query", "What is AI?"),
+        ("Complex analysis", "Analyze the impact of artificial intelligence on modern business practices"),
+        ("Research task", "Research the latest developments in quantum computing"),
+        ("Creative task", "Write a creative story about space exploration"),
+        ("Technical query", "Explain how neural networks process information")
+    ]
+    
+    # Create different types of agents for variety
+    agents = {
+        'quick_agent': Agent(
+            instructions="You are a quick-response agent. Provide concise answers.",
+            llm="gpt-4o-mini"
+        ),
+        'analytical_agent': Agent(
+            instructions="You are an analytical agent. Provide detailed analysis.",
+            llm="gpt-4o-mini"
+        ),
+        'research_agent': Agent(
+            instructions="You are a research agent. Provide comprehensive information.",
+            llm="gpt-4o-mini"
+        ),
+        'creative_agent': Agent(
+            instructions="You are a creative agent. Provide imaginative responses.",
+            llm="gpt-4o-mini"
+        )
+    }
+    
+    results = []
+    
+    for i, (scenario_type, query) in enumerate(test_scenarios):
+        print(f"\n🎯 Scenario {i+1}: {scenario_type}")
+        
+        # Select appropriate agent
+        agent_type = list(agents.keys())[i % len(agents)]
+        agent = agents[agent_type]
+        
+        with track_api_call(f"{scenario_type.lower().replace(' ', '_')}_execution"):
+            try:
+                result = agent.start(query)
+                results.append({
+                    'scenario': scenario_type,
+                    'agent': agent_type,
+                    'query': query,
+                    'result': result[:200] + '...' if len(result) > 200 else result,
+                    'success': True
+                })
+            except Exception as e:
+                results.append({
+                    'scenario': scenario_type,
+                    'agent': agent_type,
+                    'query': query,
+                    'error': str(e),
+                    'success': False
+                })
+    
+    return results
+
+@monitor_function("multi_agent_workflow_simulation")
+def simulate_complex_workflow():
+    """Simulate complex multi-agent workflow for comprehensive data."""
+    
+    print("🔄 Simulating complex multi-agent workflow...")
+    
+    # Create specialized agents
+    planner = Agent(
+        name="planner",
+        instructions="You are a project planner. Break down tasks and plan execution.",
+        llm="gpt-4o-mini"
+    )
+    
+    executor = Agent(
+        name="executor", 
+        instructions="You are a task executor. Complete assigned tasks efficiently.",
+        llm="gpt-4o-mini"
+    )
+    
+    reviewer = Agent(
+        name="reviewer",
+        instructions="You are a quality reviewer. Review and improve work.",
+        llm="gpt-4o-mini"
+    )
+    
+    # Create workflow tasks
+    planning_task = Task(
+        name="planning",
+        description="Plan a comprehensive report on renewable energy trends",
+        expected_output="A detailed project plan with timeline and milestones"
+    )
+    
+    execution_task = Task(
+        name="execution", 
+        description="Execute the planned research and compile findings",
+        expected_output="Research findings and data compilation"
+    )
+    
+    review_task = Task(
+        name="review",
+        description="Review the research and provide quality assessment",
+        expected_output="Quality review and improvement recommendations"
+    )
+    
+    # Assign agents to tasks
+    planning_task.agent = planner
+    execution_task.agent = executor  
+    review_task.agent = reviewer
+    
+    # Execute workflow
+    with track_api_call("complex_workflow_execution"):
+        workflow = PraisonAIAgents(
+            agents=[planner, executor, reviewer],
+            tasks=[planning_task, execution_task, review_task],
+            process="sequential",
+            verbose=False
+        )
+        
+        result = workflow.start()
+    
+    return result
+
+@monitor_function("performance_benchmarking")
+def run_performance_benchmarks():
+    """Run performance benchmarks for various operations."""
+    
+    print("⚡ Running performance benchmarks...")
+    
+    benchmark_results = {}
+    
+    # Benchmark 1: Response time under different loads
+    print("  📈 Benchmarking response times...")
+    response_times = []
+    
+    for i in range(10):
+        start_time = time.time()
+        with track_api_call(f"benchmark_response_{i}"):
+            # Simulate quick agent response
+            time.sleep(0.05 + (i % 3) * 0.02)  # Variable response time
+        response_times.append(time.time() - start_time)
+    
+    benchmark_results['response_times'] = {
+        'min': min(response_times),
+        'max': max(response_times), 
+        'avg': sum(response_times) / len(response_times),
+        'samples': len(response_times)
+    }
+    
+    # Benchmark 2: Concurrent operations
+    print("  🔄 Benchmarking concurrent operations...")
+    concurrent_start = time.time()
+    
+    # Simulate concurrent operations
+    for i in range(5):
+        with track_api_call(f"concurrent_op_{i}"):
+            time.sleep(0.1)  # Simulate concurrent work
+    
+    concurrent_time = time.time() - concurrent_start
+    benchmark_results['concurrent_operations'] = {
+        'total_time': concurrent_time,
+        'operations': 5,
+        'avg_per_operation': concurrent_time / 5
+    }
+    
+    # Benchmark 3: Memory usage simulation
+    print("  💾 Benchmarking memory operations...")
+    memory_ops = []
+    
+    for i in range(8):
+        start = time.time()
+        with track_api_call(f"memory_benchmark_{i}"):
+            # Simulate memory-intensive operation
+            time.sleep(0.03 + (i % 2) * 0.02)
+        memory_ops.append(time.time() - start)
+    
+    benchmark_results['memory_operations'] = {
+        'operations': len(memory_ops),
+        'total_time': sum(memory_ops),
+        'avg_time': sum(memory_ops) / len(memory_ops)
+    }
+    
+    return benchmark_results
+
+@monitor_function("generate_dashboard_report")
+def generate_dashboard_report():
+    """Generate comprehensive dashboard report."""
+    
+    print("📋 Generating comprehensive dashboard report...")
+    
+    # Collect all performance data
+    function_stats = get_function_stats()
+    api_stats = get_api_stats()
+    slowest_functions = get_slowest_functions()
+    slowest_apis = get_slowest_apis()
+    
+    # Generate reports
+    basic_report = get_performance_report()
+    comprehensive_report = generate_comprehensive_report()
+    
+    # Analyze trends and flows
+    try:
+        flow_analysis = analyze_function_flow()
+        performance_trends = analyze_performance_trends()
+        flow_visualization = visualize_execution_flow()
+    except Exception as e:
+        flow_analysis = {"error": str(e)}
+        performance_trends = {"error": str(e)} 
+        flow_visualization = {"error": str(e)}
+    
+    dashboard_data = {
+        'timestamp': datetime.now().isoformat(),
+        'summary': {
+            'total_functions': len(function_stats),
+            'total_api_calls': len(api_stats),
+            'total_function_calls': sum(stats['call_count'] for stats in function_stats.values()),
+            'total_api_requests': sum(stats['call_count'] for stats in api_stats.values()),
+            'total_execution_time': sum(stats['total_time'] for stats in function_stats.values()),
+            'total_errors': sum(stats['error_count'] for stats in function_stats.values())
+        },
+        'performance_data': {
+            'function_stats': function_stats,
+            'api_stats': api_stats,
+            'slowest_functions': slowest_functions,
+            'slowest_apis': slowest_apis
+        },
+        'analysis': {
+            'flow_analysis': flow_analysis,
+            'performance_trends': performance_trends,
+            'flow_visualization': len(str(flow_visualization)) if flow_visualization else 0
+        },
+        'reports': {
+            'basic_report': basic_report,
+            'comprehensive_report': comprehensive_report[:500] + '...' if len(comprehensive_report) > 500 else comprehensive_report
+        }
+    }
+    
+    return dashboard_data
+
+def display_dashboard(dashboard_data, benchmark_results):
+    """Display comprehensive performance dashboard."""
+    
+    print("\n" + "=" * 80)
+    print("📊 COMPREHENSIVE PERFORMANCE DASHBOARD")
+    print("=" * 80)
+    
+    # Executive Summary
+    summary = dashboard_data['summary']
+    print(f"\n📈 EXECUTIVE SUMMARY")
+    print(f"  Report Generated: {dashboard_data['timestamp']}")
+    print(f"  Functions Monitored: {summary['total_functions']}")
+    print(f"  API Endpoints: {summary['total_api_calls']}")
+    print(f"  Total Function Calls: {summary['total_function_calls']}")
+    print(f"  Total API Requests: {summary['total_api_requests']}")
+    print(f"  Total Execution Time: {summary['total_execution_time']:.3f}s")
+    print(f"  Total Errors: {summary['total_errors']}")
+    
+    # Performance Overview
+    print(f"\n⚡ PERFORMANCE OVERVIEW")
+    avg_function_time = 0
+    if summary['total_function_calls'] > 0:
+        avg_function_time = summary['total_execution_time'] / summary['total_function_calls']
+        print(f"  Average Function Execution: {avg_function_time:.3f}s")
+    
+    if summary['total_errors'] > 0 and summary['total_function_calls'] > 0:
+        error_rate = summary['total_errors'] / summary['total_function_calls'] * 100
+        print(f"  Error Rate: {error_rate:.2f}%")
+    else:
+        print(f"  Error Rate: 0.00%")
+    
+    # Top Performers and Issues
+    print(f"\n🏆 TOP PERFORMERS")
+    perf_data = dashboard_data['performance_data']
+    
+    print("  Fastest Functions:")
+    fastest_functions = sorted(perf_data['slowest_functions'], key=lambda x: x[1])[:3]
+    for name, time in fastest_functions:
+        print(f"    {name}: {time:.3f}s")
+    
+    print("  Slowest Functions:")
+    for name, time in perf_data['slowest_functions'][:3]:
+        print(f"    {name}: {time:.3f}s")
+    
+    # Benchmark Results
+    print(f"\n⚡ BENCHMARK RESULTS")
+    print(f"  Response Time Benchmarks:")
+    rt = benchmark_results['response_times']
+    print(f"    Min: {rt['min']:.3f}s, Max: {rt['max']:.3f}s, Avg: {rt['avg']:.3f}s")
+    
+    print(f"  Concurrent Operations:")
+    co = benchmark_results['concurrent_operations']
+    print(f"    {co['operations']} operations in {co['total_time']:.3f}s")
+    print(f"    Avg per operation: {co['avg_per_operation']:.3f}s")
+    
+    print(f"  Memory Operations:")
+    mo = benchmark_results['memory_operations']
+    print(f"    {mo['operations']} operations, {mo['avg_time']:.3f}s average")
+    
+    # System Health
+    print(f"\n🏥 SYSTEM HEALTH")
+    
+    # Calculate health score
+    health_score = 100
+    if avg_function_time > 0.2:
+        health_score -= 20
+    if summary['total_errors'] > 0:
+        health_score -= summary['total_errors'] * 10
+    if rt['avg'] > 0.1:
+        health_score -= 15
+    
+    health_score = max(0, health_score)
+    
+    if health_score >= 90:
+        status = "🟢 Excellent"
+    elif health_score >= 70:
+        status = "🟡 Good"
+    elif health_score >= 50:
+        status = "🟠 Fair"
+    else:
+        status = "🔴 Needs Attention"
+    
+    print(f"  Overall Health Score: {health_score}/100 - {status}")
+    
+    # Analysis Results
+    print(f"\n🔍 ANALYSIS RESULTS")
+    analysis = dashboard_data['analysis']
+    
+    if 'error' not in analysis['flow_analysis']:
+        flow_data = analysis['flow_analysis']
+        print(f"  Flow Analysis: {len(flow_data.get('functions', []))} functions analyzed")
+    else:
+        print(f"  Flow Analysis: {analysis['flow_analysis']['error']}")
+    
+    if analysis['flow_visualization']:
+        print(f"  Flow Visualization: Generated ({analysis['flow_visualization']} chars)")
+    
+    # Recommendations
+    print(f"\n💡 PERFORMANCE RECOMMENDATIONS")
+    
+    if avg_function_time > 0.15:
+        print("  🔧 Optimize slow functions for better response times")
+    if summary['total_errors'] > 0:
+        print("  🚨 Investigate and resolve error sources")
+    if rt['avg'] > 0.08:
+        print("  ⚡ Consider response time optimization strategies")
+    if summary['total_function_calls'] > 50:
+        print("  📊 Implement performance caching for high-volume functions")
+    
+    print("  📈 Continue monitoring trends for proactive optimization")
+    print("  🔄 Regular performance reviews recommended")
+
+def main():
+    """Main function for comprehensive performance dashboard."""
+    print("=" * 80)
+    print("EXAMPLE 10: Comprehensive Performance Dashboard")
+    print("=" * 80)
+    print("This example demonstrates a complete performance monitoring solution")
+    print("with advanced analytics, benchmarking, and dashboard reporting.\n")
+    
+    # Clear previous data for clean demo
+    clear_performance_data()
+    
+    # Phase 1: Data Collection
+    print("📊 Phase 1: Comprehensive Data Collection")
+    collection_results = collect_comprehensive_data()
+    
+    # Phase 2: Complex Workflow
+    print("\n🔄 Phase 2: Complex Multi-Agent Workflow")
+    workflow_result = simulate_complex_workflow()
+    
+    # Phase 3: Performance Benchmarking
+    print("\n⚡ Phase 3: Performance Benchmarking")
+    benchmark_results = run_performance_benchmarks()
+    
+    # Phase 4: Dashboard Generation
+    print("\n📋 Phase 4: Dashboard Report Generation")
+    dashboard_data = generate_dashboard_report()
+    
+    # Phase 5: Display Comprehensive Dashboard
+    display_dashboard(dashboard_data, benchmark_results)
+    
+    # Final Summary
+    print(f"\n🎯 EXECUTION SUMMARY")
+    print(f"  Scenarios Executed: {len(collection_results)}")
+    successful_scenarios = sum(1 for r in collection_results if r['success'])
+    print(f"  Successful Scenarios: {successful_scenarios}/{len(collection_results)}")
+    print(f"  Workflow Completed: {'✅ Yes' if workflow_result else '❌ No'}")
+    print(f"  Benchmarks Run: {len(benchmark_results)}")
+    print(f"  Dashboard Generated: ✅ Complete")
+    
+    return {
+        'scenarios': len(collection_results),
+        'success_rate': successful_scenarios / len(collection_results) * 100,
+        'dashboard_data': dashboard_data,
+        'benchmark_results': benchmark_results
+    }
+
+if __name__ == "__main__":
+    result = main()
+    print(f"\n🎉 Comprehensive dashboard monitoring completed!")
+    print(f"Success Rate: {result['success_rate']:.1f}%")
+    print(f"Total Functions Monitored: {result['dashboard_data']['summary']['total_functions']}")
+    print(f"Performance Health Score Available in Dashboard Above ⬆️")
+    print("\n💡 This dashboard provides real-time insights into your PraisonAI system performance!")

src/praisonai-agents/context_basic.py

@@ -0,0 +1,5 @@
+from praisonaiagents import ContextAgent
+
+agent = ContextAgent(llm="gpt-4o-mini", auto_analyze=False)
+
+agent.start("https://github.com/MervinPraison/PraisonAI/ Need to add Authentication")

src/praisonai-agents/gemini-basic.py

@@ -1,25 +1,7 @@
 from praisonaiagents import Agent
 
-def get_stock_price(company_name: str) -> str:
-    """
-    Get the stock price of a company
-    Args:
-        company_name (str): The name of the company
-    Returns:
-        str: The stock price of the company
-    """
-    return f"The stock price of {company_name} is 100"
-
-def multiply(a: int, b: int) -> int:
-    """
-    Multiply two numbers
-    """
-    return a * b
-
 agent = Agent(
-    instructions="You are a helpful assistant. You can use the tools provided to you to help the user.",
-    llm="gemini/gemini-2.5-pro",
-    tools=[get_stock_price, multiply]
+    instructions="You are a helpful assistant",
+    llm="gemini/gemini-2.5-flash"
 )
-
-result = agent.start("what is the stock price of Google? multiply the Google stock price with 2")
+agent.start("Why sky is Blue?")

src/praisonai-agents/llms.md

@@ -0,0 +1,20 @@
+# PraisonAI Documentation
+
+## Docs
+
+- [AI Agents](https://docs.praison.ai/docs/agents/agents.md): Overview of all available PraisonAI agents and their capabilities
+- [Data Analyst Agent](https://docs.praison.ai/docs/agents/data-analyst.md): Learn how to create AI agents for data analysis and insights generation.
+- [Finance Agent](https://docs.praison.ai/docs/agents/finance.md): Learn how to create AI agents for financial analysis and investment recommendations.
+- [Image Analysis Agent](https://docs.praison.ai/docs/agents/image.md): Learn how to create AI agents for image analysis and visual content understanding.
+- [Image to Text Agent](https://docs.praison.ai/docs/agents/image-to-text.md): Learn how to create AI agents for converting images to textual descriptions and extracting text from images.
+- [Markdown Agent](https://docs.praison.ai/docs/agents/markdown.md): Learn how to create AI agents for generating and formatting content in Markdown.
+- [Planning Agent](https://docs.praison.ai/docs/agents/planning.md): Learn how to create AI agents for trip planning and itinerary generation.
+- [Programming Agent](https://docs.praison.ai/docs/agents/programming.md): Learn how to create AI agents for code development, analysis, and execution.
+- [Recommendation Agent](https://docs.praison.ai/docs/agents/recommendation.md): Learn how to create AI agents for personalized recommendations across various domains.
+- [Research Agent](https://docs.praison.ai/docs/agents/research.md): Learn how to create AI agents for conducting comprehensive research and analysis.
+- [Shopping Agent](https://docs.praison.ai/docs/agents/shopping.md): Learn how to create AI agents for price comparison and shopping assistance.
+- [Single Agent](https://docs.praison.ai/docs/agents/single.md): Learn how to create a basic single-purpose AI agent for simple tasks.
+- [Video Agent](https://docs.praison.ai/docs/agents/video.md): Learn how to create AI agents for video analysis and content understanding.
+- [Web Search Agent](https://docs.praison.ai/docs/agents/websearch.md): Learn how to create AI agents for intelligent web searching and information gathering.
+- [Wikipedia Agent](https://docs.praison.ai/docs/agents/wikipedia.md): Learn how to create AI agents for searching and extracting information from Wikipedia.
+- [Agent Module](https://docs.praison.ai/docs/api/praisonaiagents/agent/agent.md): Documentation for the praisonaiagents.agent.agent module

src/praisonai-agents/mongodb_comprehensive_example.py

@@ -0,0 +1,297 @@
+#!/usr/bin/env python3
+"""
+Comprehensive MongoDB Integration Example for PraisonAI Agents
+
+This example demonstrates a complete MongoDB integration with PraisonAI agents,
+combining memory, knowledge, and tools for a comprehensive data management system.
+
+Prerequisites:
+- Install MongoDB dependencies: pip install 'praisonaiagents[mongodb]'
+- MongoDB server running (local or Atlas)
+- OpenAI API key for embeddings
+
+Features demonstrated:
+- MongoDB as memory provider
+- MongoDB as knowledge store
+- MongoDB tools for data operations
+- Vector search capabilities
+- Multi-agent coordination with MongoDB
+- Real-world business scenario simulation
+"""
+
+import os
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import mongodb_tools
+
+# Ensure OpenAI API key is set
+if not os.environ.get("OPENAI_API_KEY"):
+    raise ValueError("Please set the OPENAI_API_KEY environment variable")
+
+def main():
+    # MongoDB configuration for different components
+    mongodb_memory_config = {
+        "provider": "mongodb",
+        "config": {
+            "connection_string": "mongodb://localhost:27017/",  # Replace with your connection string
+            "database": "praisonai_comprehensive",
+            "use_vector_search": True,
+            "max_pool_size": 50
+        }
+    }
+    
+    mongodb_knowledge_config = {
+        "vector_store": {
+            "provider": "mongodb",
+            "config": {
+                "connection_string": "mongodb://localhost:27017/",
+                "database": "praisonai_comprehensive",
+                "collection": "knowledge_base",
+                "use_vector_search": True
+            }
+        },
+        "embedder": {
+            "provider": "openai",
+            "config": {
+                "model": "text-embedding-3-small",
+                "api_key": os.getenv("OPENAI_API_KEY")
+            }
+        }
+    }
+    
+    # Create specialized agents for different business functions
+    
+    # 1. Data Manager Agent - handles data operations
+    data_manager = Agent(
+        name="Data Manager",
+        role="Senior Data Operations Manager",
+        goal="Manage and organize all data operations using MongoDB",
+        backstory="""You are an expert data operations manager who specializes in 
+        MongoDB database management. You handle data ingestion, organization, 
+        indexing, and ensure data quality and accessibility across the organization.""",
+        tools=[mongodb_tools],
+        memory=True,
+        verbose=True,
+        llm="gpt-4o-mini"
+    )
+    
+    # 2. Knowledge Curator Agent - manages knowledge base
+    knowledge_curator = Agent(
+        name="Knowledge Curator",
+        role="Knowledge Management Specialist",
+        goal="Curate and maintain organizational knowledge using MongoDB",
+        backstory="""You are a knowledge management specialist who excels at 
+        organizing, categorizing, and retrieving information from the knowledge base. 
+        You ensure that knowledge is properly stored, indexed, and accessible.""",
+        knowledge_config=mongodb_knowledge_config,
+        memory=True,
+        verbose=True,
+        llm="gpt-4o-mini"
+    )
+    
+    # 3. Business Analyst Agent - analyzes data and provides insights
+    business_analyst = Agent(
+        name="Business Analyst",
+        role="Senior Business Intelligence Analyst",
+        goal="Analyze business data and provide actionable insights",
+        backstory="""You are a senior business analyst who specializes in extracting 
+        insights from data stored in MongoDB. You create reports, identify trends, 
+        and provide strategic recommendations based on data analysis.""",
+        tools=[mongodb_tools],
+        memory=True,
+        verbose=True,
+        llm="gpt-4o-mini"
+    )
+    
+    # 4. Customer Service Agent - handles customer interactions
+    customer_service = Agent(
+        name="Customer Service Agent",
+        role="Customer Service Representative",
+        goal="Provide excellent customer service using stored knowledge and data",
+        backstory="""You are a customer service representative who uses the 
+        knowledge base and customer data to provide excellent support. You can 
+        access customer history, product information, and troubleshooting guides.""",
+        knowledge_config=mongodb_knowledge_config,
+        tools=[mongodb_tools],
+        memory=True,
+        verbose=True,
+        llm="gpt-4o-mini"
+    )
+    
+    # Create comprehensive business tasks
+    business_tasks = [
+        Task(
+            description="""Set up comprehensive business data infrastructure:
+            1. Create collections for customers, products, orders, and support tickets
+            2. Insert sample business data for an e-commerce platform
+            3. Set up appropriate indexes for performance optimization
+            4. Create vector indexes for product descriptions and customer support
+            5. Implement data validation and business rules
+            
+            Focus on creating a realistic business data structure.
+            """,
+            expected_output="Complete business data infrastructure with sample data",
+            agent=data_manager
+        ),
+        Task(
+            description="""Build organizational knowledge base:
+            1. Store product documentation and specifications
+            2. Create customer service guidelines and FAQs
+            3. Add troubleshooting guides and best practices
+            4. Include company policies and procedures
+            5. Organize knowledge by categories and tags
+            
+            Ensure knowledge is searchable and well-organized.
+            """,
+            expected_output="Comprehensive knowledge base with categorized information",
+            agent=knowledge_curator
+        ),
+        Task(
+            description="""Perform business intelligence analysis:
+            1. Analyze customer purchase patterns and behavior
+            2. Calculate key business metrics (revenue, conversion rates, etc.)
+            3. Identify top-selling products and categories
+            4. Analyze customer support ticket trends
+            5. Generate insights for business optimization
+            
+            Use MongoDB aggregation and analytics capabilities.
+            """,
+            expected_output="Business intelligence report with actionable insights",
+            agent=business_analyst
+        ),
+        Task(
+            description="""Handle customer service scenarios:
+            1. Process customer inquiries using knowledge base
+            2. Look up customer order history and preferences
+            3. Provide product recommendations based on customer data
+            4. Access troubleshooting guides for technical issues
+            5. Update customer records and interaction history
+            
+            Demonstrate excellent customer service with data-driven support.
+            """,
+            expected_output="Customer service interactions with knowledge-based responses",
+            agent=customer_service
+        ),
+        Task(
+            description="""Generate comprehensive business report:
+            1. Combine insights from all data sources
+            2. Create executive summary with key findings
+            3. Provide recommendations for business improvement
+            4. Include data visualizations and metrics
+            5. Store report in knowledge base for future reference
+            
+            Create a comprehensive business intelligence report.
+            """,
+            expected_output="Executive business report with comprehensive insights",
+            agent=business_analyst
+        )
+    ]
+    
+    # Initialize the comprehensive business system
+    print("🚀 Starting Comprehensive MongoDB Business System...")
+    print("=" * 60)
+    
+    business_system = PraisonAIAgents(
+        agents=[data_manager, knowledge_curator, business_analyst, customer_service],
+        tasks=business_tasks,
+        memory=True,
+        memory_config=mongodb_memory_config,
+        verbose=True
+    )
+    
+    # Execute the comprehensive business pipeline
+    try:
+        results = business_system.start()
+        
+        print("\n" + "=" * 60)
+        print("📊 Comprehensive Business System Results:")
+        print("=" * 60)
+        
+        # Handle results structure properly
+        if isinstance(results, (list, tuple)):
+            for i, result in enumerate(results, 1):
+                print(f"\n{i}. Task Result:")
+                if hasattr(result, 'raw') and hasattr(result, 'agent'):
+                    print(f"   Output: {result.raw[:200]}...")
+                    print(f"   Agent: {result.agent}")
+                else:
+                    print(f"   Output: {str(result)[:200]}...")
+        else:
+            print(f"\n1. Task Result:")
+            print(f"   Output: {str(results)[:200]}...")
+        
+        print("\n" + "=" * 60)
+        print("💾 Comprehensive MongoDB Integration Complete!")
+        print("=" * 60)
+        
+        # Demonstrate system integration
+        print("\n🔍 Demonstrating System Integration:")
+        print("-" * 40)
+        
+        # Access different components
+        # Note: Direct memory access not available through business_system.memory
+        print("📋 Memory integration enabled through memory_config...")
+        print("Memory operations are handled internally by the agents")
+        
+        # Access memory system directly
+        from praisonaiagents.memory import Memory
+        memory_system = Memory(config=mongodb_memory_config)
+        
+        # Search business memories
+        print("\n📋 Searching business memories...")
+        business_memories = memory_system.search_long_term("business analysis", limit=3)
+        print(f"Found {len(business_memories)} business-related memories")
+        
+        # Access knowledge system
+        from praisonaiagents.knowledge import Knowledge
+        knowledge_system = Knowledge(config=mongodb_knowledge_config)
+        
+        # Search knowledge base
+        print("\n📚 Searching knowledge base...")
+        knowledge_results = knowledge_system.search("customer service", limit=3)
+        print(f"Found {len(knowledge_results)} knowledge entries")
+        
+        # Direct MongoDB operations
+        print("\n🔧 Direct MongoDB operations...")
+        mongo_client = mongodb_tools.connect_mongodb(
+            "mongodb://localhost:27017/",
+            "praisonai_comprehensive"
+        )
+        
+        # Get database statistics
+        stats = mongo_client.get_stats("customers")
+        print(f"Customer collection stats: {stats}")
+        
+        print("\n" + "=" * 60)
+        print("🎉 Comprehensive MongoDB Example Completed Successfully!")
+        print("=" * 60)
+        
+        print("\n📈 System Overview:")
+        print("- Memory System: MongoDB-based persistent memory")
+        print("- Knowledge Base: MongoDB vector search enabled")
+        print("- Tools Integration: Full MongoDB CRUD operations")
+        print("- Multi-Agent Coordination: Business workflow simulation")
+        print("- Vector Search: Semantic search capabilities")
+        print("- Data Analytics: Business intelligence insights")
+        
+    except Exception as e:
+        print(f"\n❌ Error during execution: {e}")
+        print("Please check your MongoDB connection and API keys.")
+
+if __name__ == "__main__":
+    print("Comprehensive MongoDB Integration Example")
+    print("=" * 50)
+    print("This example demonstrates complete MongoDB integration with PraisonAI")
+    print("Features:")
+    print("- MongoDB Memory Provider")
+    print("- MongoDB Knowledge Store")
+    print("- MongoDB Tools Integration")
+    print("- Vector Search Capabilities")
+    print("- Multi-Agent Business Simulation")
+    print("=" * 50)
+    print("Make sure you have:")
+    print("1. MongoDB server running (local or Atlas)")
+    print("2. OpenAI API key set in environment variables")
+    print("3. MongoDB dependencies installed: pip install 'praisonaiagents[mongodb]'")
+    print("=" * 50)
+    
+    main()

src/praisonai-agents/mongodb_knowledge_basic.py

@@ -0,0 +1,203 @@
+#!/usr/bin/env python3
+"""
+MongoDB Knowledge Example for PraisonAI Agents
+
+This example demonstrates how to use MongoDB as a knowledge store for PraisonAI agents.
+MongoDB can store and retrieve knowledge documents with vector search capabilities.
+
+Prerequisites:
+- Install MongoDB dependencies: pip install 'praisonaiagents[mongodb]'
+- MongoDB server running (local or Atlas)
+- OpenAI API key for embeddings
+
+Features demonstrated:
+- MongoDB as knowledge vector store
+- Document processing and storage
+- Vector search for knowledge retrieval
+- Knowledge-based agent interactions
+- File processing with MongoDB storage
+"""
+
+import os
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Ensure OpenAI API key is set
+if not os.environ.get("OPENAI_API_KEY"):
+    raise ValueError("Please set the OPENAI_API_KEY environment variable")
+
+def main():
+    # MongoDB knowledge configuration
+    mongodb_knowledge_config = {
+        "vector_store": {
+            "provider": "mongodb",
+            "config": {
+                "connection_string": "mongodb://localhost:27017/",  # Replace with your MongoDB connection string
+                "database": "praisonai_knowledge",
+                "collection": "knowledge_base",
+                "use_vector_search": True  # Enable Atlas Vector Search
+            }
+        },
+        "embedder": {
+            "provider": "openai",
+            "config": {
+                "model": "text-embedding-3-small",
+                "api_key": os.getenv("OPENAI_API_KEY")
+            }
+        }
+    }
+    
+    # Create a knowledge agent with MongoDB knowledge store
+    knowledge_agent = Agent(
+        name="MongoDB Knowledge Agent",
+        role="Knowledge Specialist",
+        goal="Provide accurate information from MongoDB knowledge base",
+        backstory="""You are an expert knowledge specialist who can access and 
+        retrieve information from a comprehensive MongoDB knowledge base. You excel 
+        at finding relevant information, synthesizing knowledge from multiple sources, 
+        and providing accurate, context-aware responses.""",
+        knowledge_config=mongodb_knowledge_config,
+        knowledge=[os.path.join(os.path.dirname(__file__), "llms.md")],
+        memory=True,
+        verbose=True,
+        llm="gpt-4o-mini"
+    )
+    
+    # Create a research assistant agent
+    research_agent = Agent(
+        name="Research Assistant",
+        role="Research Assistant",
+        goal="Gather information and store it in the knowledge base",
+        backstory="""You are a research assistant who specializes in gathering 
+        information from various sources and organizing it for storage in the 
+        knowledge base. You ensure information is accurate, well-structured, 
+        and properly categorized.""",
+        memory=True,
+        verbose=True,
+        llm="gpt-4o-mini"
+    )
+    
+    # Create tasks for knowledge management
+    knowledge_tasks = [
+        Task(
+            description="""Research and store information about MongoDB Atlas Vector Search:
+            1. Gather comprehensive information about MongoDB Atlas Vector Search
+            2. Include technical specifications, use cases, and best practices
+            3. Store the information in the MongoDB knowledge base
+            4. Organize information by categories (features, performance, integration)
+            """,
+            expected_output="MongoDB Atlas Vector Search information stored in knowledge base",
+            agent=research_agent
+        ),
+        Task(
+            description="""Research and store information about AI agent frameworks:
+            1. Research popular AI agent frameworks (LangChain, AutoGen, etc.)
+            2. Compare their features, capabilities, and use cases
+            3. Store comparative analysis in the knowledge base
+            4. Include code examples and best practices
+            """,
+            expected_output="AI agent framework comparison stored in knowledge base",
+            agent=research_agent
+        ),
+        Task(
+            description="""Query the knowledge base for MongoDB information:
+            1. Search for information about MongoDB Atlas Vector Search
+            2. Extract key features and capabilities
+            3. Provide a comprehensive summary
+            4. Include technical recommendations
+            """,
+            expected_output="Comprehensive MongoDB Atlas Vector Search summary from knowledge base",
+            agent=knowledge_agent
+        ),
+        Task(
+            description="""Query the knowledge base for AI agent framework information:
+            1. Search for information about AI agent frameworks
+            2. Compare different frameworks based on stored knowledge
+            3. Provide recommendations for different use cases
+            4. Include best practices and examples
+            """,
+            expected_output="AI agent framework comparison and recommendations from knowledge base",
+            agent=knowledge_agent
+        )
+    ]
+    
+    # Initialize the multi-agent system with MongoDB knowledge
+    print("🚀 Starting MongoDB Knowledge Management System...")
+    print("=" * 60)
+    
+    knowledge_system = PraisonAIAgents(
+        agents=[research_agent, knowledge_agent],
+        tasks=knowledge_tasks,
+        memory=True,
+        verbose=True
+    )
+    
+    # Execute the knowledge management pipeline
+    try:
+        results = knowledge_system.start()
+        
+        print("\n" + "=" * 60)
+        print("📊 Knowledge Management System Results:")
+        print("=" * 60)
+        
+        # Handle results structure properly
+        if isinstance(results, (list, tuple)):
+            for i, result in enumerate(results, 1):
+                print(f"\n{i}. Task Result:")
+                if hasattr(result, 'raw') and hasattr(result, 'agent'):
+                    print(f"   Output: {result.raw[:200]}...")
+                    print(f"   Agent: {result.agent}")
+                else:
+                    print(f"   Output: {str(result)[:200]}...")
+        else:
+            print(f"\n1. Task Result:")
+            print(f"   Output: {str(results)[:200]}...")
+        
+        print("\n" + "=" * 60)
+        print("💾 MongoDB Knowledge Integration Complete!")
+        print("=" * 60)
+        
+        # Demonstrate direct knowledge access
+        print("\n🔍 Demonstrating Direct Knowledge Access:")
+        print("-" * 40)
+        
+        # Access knowledge system directly
+        from praisonaiagents.knowledge import Knowledge
+        
+        knowledge_system = Knowledge(config=mongodb_knowledge_config)
+        
+        # Store a sample document
+        sample_doc = """
+        MongoDB is a document-oriented database that provides high performance, 
+        high availability, and easy scalability. It works on the concept of 
+        collections and documents, using a flexible, JSON-like document model.
+        """
+        
+        print("📄 Storing sample document...")
+        knowledge_system.store(sample_doc, metadata={"category": "database", "type": "overview"})
+        
+        # Search for stored knowledge
+        print("\n🔍 Searching knowledge base...")
+        results = knowledge_system.search("MongoDB document database", limit=3)
+        
+        print(f"Found {len(results)} knowledge entries:")
+        for result in results:
+            print(f"  - {result.get('memory', '')[:100]}...")
+            print(f"    Score: {result.get('score', 'N/A')}")
+        
+        print("\n✅ MongoDB Knowledge Example Completed Successfully!")
+        
+    except Exception as e:
+        print(f"\n❌ Error during execution: {e}")
+        print("Please check your MongoDB connection and API keys.")
+
+if __name__ == "__main__":
+    print("MongoDB Knowledge Example for PraisonAI Agents")
+    print("=" * 50)
+    print("This example demonstrates MongoDB integration for knowledge management")
+    print("Make sure you have:")
+    print("1. MongoDB server running (local or Atlas)")
+    print("2. OpenAI API key set in environment variables")
+    print("3. MongoDB dependencies installed: pip install 'praisonaiagents[mongodb]'")
+    print("=" * 50)
+    
+    main()

src/praisonai-agents/mongodb_knowledge_example.py

@@ -0,0 +1,203 @@
+#!/usr/bin/env python3
+"""
+MongoDB Knowledge Example for PraisonAI Agents
+
+This example demonstrates how to use MongoDB as a knowledge store for PraisonAI agents.
+MongoDB can store and retrieve knowledge documents with vector search capabilities.
+
+Prerequisites:
+- Install MongoDB dependencies: pip install 'praisonaiagents[mongodb]'
+- MongoDB server running (local or Atlas)
+- OpenAI API key for embeddings
+
+Features demonstrated:
+- MongoDB as knowledge vector store
+- Document processing and storage
+- Vector search for knowledge retrieval
+- Knowledge-based agent interactions
+- File processing with MongoDB storage
+"""
+
+import os
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Ensure OpenAI API key is set
+if not os.environ.get("OPENAI_API_KEY"):
+    raise ValueError("Please set the OPENAI_API_KEY environment variable")
+
+def main():
+    # MongoDB knowledge configuration
+    mongodb_knowledge_config = {
+        "vector_store": {
+            "provider": "mongodb",
+            "config": {
+                "connection_string": "mongodb://localhost:27017/",  # Replace with your MongoDB connection string
+                "database": "praisonai_knowledge",
+                "collection": "knowledge_base",
+                "use_vector_search": True  # Enable Atlas Vector Search
+            }
+        },
+        "embedder": {
+            "provider": "openai",
+            "config": {
+                "model": "text-embedding-3-small",
+                "api_key": os.getenv("OPENAI_API_KEY")
+            }
+        }
+    }
+    
+    # Create a knowledge agent with MongoDB knowledge store
+    knowledge_agent = Agent(
+        name="MongoDB Knowledge Agent",
+        role="Knowledge Specialist",
+        goal="Provide accurate information from MongoDB knowledge base",
+        backstory="""You are an expert knowledge specialist who can access and 
+        retrieve information from a comprehensive MongoDB knowledge base. You excel 
+        at finding relevant information, synthesizing knowledge from multiple sources, 
+        and providing accurate, context-aware responses.""",
+        knowledge_config=mongodb_knowledge_config,
+        knowledge=[os.path.join(os.path.dirname(__file__), "llms.md")],
+        memory=True,
+        verbose=True,
+        llm="gpt-4o-mini"
+    )
+    
+    # Create a research assistant agent
+    research_agent = Agent(
+        name="Research Assistant",
+        role="Research Assistant",
+        goal="Gather information and store it in the knowledge base",
+        backstory="""You are a research assistant who specializes in gathering 
+        information from various sources and organizing it for storage in the 
+        knowledge base. You ensure information is accurate, well-structured, 
+        and properly categorized.""",
+        memory=True,
+        verbose=True,
+        llm="gpt-4o-mini"
+    )
+    
+    # Create tasks for knowledge management
+    knowledge_tasks = [
+        Task(
+            description="""Research and store information about MongoDB Atlas Vector Search:
+            1. Gather comprehensive information about MongoDB Atlas Vector Search
+            2. Include technical specifications, use cases, and best practices
+            3. Store the information in the MongoDB knowledge base
+            4. Organize information by categories (features, performance, integration)
+            """,
+            expected_output="MongoDB Atlas Vector Search information stored in knowledge base",
+            agent=research_agent
+        ),
+        Task(
+            description="""Research and store information about AI agent frameworks:
+            1. Research popular AI agent frameworks (LangChain, AutoGen, etc.)
+            2. Compare their features, capabilities, and use cases
+            3. Store comparative analysis in the knowledge base
+            4. Include code examples and best practices
+            """,
+            expected_output="AI agent framework comparison stored in knowledge base",
+            agent=research_agent
+        ),
+        Task(
+            description="""Query the knowledge base for MongoDB information:
+            1. Search for information about MongoDB Atlas Vector Search
+            2. Extract key features and capabilities
+            3. Provide a comprehensive summary
+            4. Include technical recommendations
+            """,
+            expected_output="Comprehensive MongoDB Atlas Vector Search summary from knowledge base",
+            agent=knowledge_agent
+        ),
+        Task(
+            description="""Query the knowledge base for AI agent framework information:
+            1. Search for information about AI agent frameworks
+            2. Compare different frameworks based on stored knowledge
+            3. Provide recommendations for different use cases
+            4. Include best practices and examples
+            """,
+            expected_output="AI agent framework comparison and recommendations from knowledge base",
+            agent=knowledge_agent
+        )
+    ]
+    
+    # Initialize the multi-agent system with MongoDB knowledge
+    print("🚀 Starting MongoDB Knowledge Management System...")
+    print("=" * 60)
+    
+    knowledge_system = PraisonAIAgents(
+        agents=[research_agent, knowledge_agent],
+        tasks=knowledge_tasks,
+        memory=True,
+        verbose=True
+    )
+    
+    # Execute the knowledge management pipeline
+    try:
+        results = knowledge_system.start()
+        
+        print("\n" + "=" * 60)
+        print("📊 Knowledge Management System Results:")
+        print("=" * 60)
+        
+        # Handle results structure properly
+        if isinstance(results, (list, tuple)):
+            for i, result in enumerate(results, 1):
+                print(f"\n{i}. Task Result:")
+                if hasattr(result, 'raw') and hasattr(result, 'agent'):
+                    print(f"   Output: {result.raw[:200]}...")
+                    print(f"   Agent: {result.agent}")
+                else:
+                    print(f"   Output: {str(result)[:200]}...")
+        else:
+            print(f"\n1. Task Result:")
+            print(f"   Output: {str(results)[:200]}...")
+        
+        print("\n" + "=" * 60)
+        print("💾 MongoDB Knowledge Integration Complete!")
+        print("=" * 60)
+        
+        # Demonstrate direct knowledge access
+        print("\n🔍 Demonstrating Direct Knowledge Access:")
+        print("-" * 40)
+        
+        # Access knowledge system directly
+        from praisonaiagents.knowledge import Knowledge
+        
+        knowledge_system = Knowledge(config=mongodb_knowledge_config)
+        
+        # Store a sample document
+        sample_doc = """
+        MongoDB is a document-oriented database that provides high performance, 
+        high availability, and easy scalability. It works on the concept of 
+        collections and documents, using a flexible, JSON-like document model.
+        """
+        
+        print("📄 Storing sample document...")
+        knowledge_system.store(sample_doc, metadata={"category": "database", "type": "overview"})
+        
+        # Search for stored knowledge
+        print("\n🔍 Searching knowledge base...")
+        results = knowledge_system.search("MongoDB document database", limit=3)
+        
+        print(f"Found {len(results)} knowledge entries:")
+        for result in results:
+            print(f"  - {result.get('memory', '')[:100]}...")
+            print(f"    Score: {result.get('score', 'N/A')}")
+        
+        print("\n✅ MongoDB Knowledge Example Completed Successfully!")
+        
+    except Exception as e:
+        print(f"\n❌ Error during execution: {e}")
+        print("Please check your MongoDB connection and API keys.")
+
+if __name__ == "__main__":
+    print("MongoDB Knowledge Example for PraisonAI Agents")
+    print("=" * 50)
+    print("This example demonstrates MongoDB integration for knowledge management")
+    print("Make sure you have:")
+    print("1. MongoDB server running (local or Atlas)")
+    print("2. OpenAI API key set in environment variables")
+    print("3. MongoDB dependencies installed: pip install 'praisonaiagents[mongodb]'")
+    print("=" * 50)
+    
+    main()

src/praisonai-agents/mongodb_memory_basic.py

@@ -0,0 +1,84 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.memory import Memory
+from praisonaiagents.agent import ContextAgent
+import pymongo
+
+context_agent = ContextAgent(llm="gpt-4o-mini", auto_analyze=False)
+
+context_output = context_agent.start("https://github.com/MervinPraison/PraisonAI/ Need to add Authentication")
+
+mongodb_memory_config = {
+    "provider": "mongodb",
+    "config": {
+        "connection_string": "mongodb://localhost:27017/",
+        "database": "praisonai_memory",
+        "use_vector_search": True,
+        "max_pool_size": 50,
+        "min_pool_size": 10,
+        "server_selection_timeout": 5000
+    }
+}
+
+implementation_agent = Agent(
+    name="Implementation Agent",
+    role="Authentication Implementation Specialist",
+    goal="Implement authentication features based on project requirements",
+    backstory="Expert software implementer specializing in authentication systems, security features, and seamless integration with existing codebases",
+    memory=True,
+    llm="gpt-4o-mini",
+)
+
+implementation_task = Task(
+    description="Implement authentication features based on the project requirements from context analysis",
+    expected_output="Authentication implementation with code, configuration, and integration details",
+    agent=implementation_agent,
+    context=context_output,
+)
+
+implementation_system = PraisonAIAgents(
+    agents=[implementation_agent],
+    tasks=[implementation_task],
+    memory=True,
+    memory_config=mongodb_memory_config
+)
+
+results = implementation_system.start()
+print(f"Results: {results}")
+
+print("\n=== MEMORY VALIDATION ===")
+
+try:
+    # Check MongoDB connection and collections
+    client = pymongo.MongoClient("mongodb://localhost:27017/")
+    db = client["praisonai_memory"]
+    collections = db.list_collection_names()
+    print(f"Available collections: {collections}")
+    
+    for collection_name in collections:
+        count = db[collection_name].count_documents({})
+        print(f"{collection_name}: {count} documents")
+        if count > 0:
+            sample = db[collection_name].find_one()
+            print(f"Sample document: {sample}")
+            
+except Exception as e:
+    print(f"MongoDB direct check error: {e}")
+
+try:
+    # Check memory system
+    memory_system = Memory(mongodb_memory_config)
+    
+    # Try different search terms
+    search_terms = ["authentication", "security", "implementation", "requirements", "PraisonAI"]
+    for term in search_terms:
+        memories = memory_system.search_long_term(term, limit=3)
+        print(f"Search '{term}': {len(memories)} memories found")
+        if memories:
+            print(f"  Sample: {str(memories[0])[:100]}...")
+            
+    # Try to get all memories
+    all_memories = memory_system.get_all_memories()
+    print(f"Total memories: {len(all_memories)}")
+    
+except Exception as e:
+    print(f"Memory system error: {e}")

src/praisonai-agents/mongodb_memory_example.py

@@ -0,0 +1,178 @@
+#!/usr/bin/env python3
+"""
+MongoDB Memory Example for PraisonAI Agents
+
+This example demonstrates how to use MongoDB as a memory provider for PraisonAI agents.
+MongoDB can be used as both a key-value store and a vector database for memory operations.
+
+Prerequisites:
+- Install MongoDB dependencies: pip install 'praisonaiagents[mongodb]'
+- MongoDB server running (local or Atlas)
+- OpenAI API key for embeddings
+
+Features demonstrated:
+- MongoDB as memory provider
+- Vector search with Atlas Vector Search
+- Quality scoring and filtering
+- User-specific memory storage
+- Persistent memory across sessions
+"""
+
+import os
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Ensure OpenAI API key is set
+if not os.environ.get("OPENAI_API_KEY"):
+    raise ValueError("Please set the OPENAI_API_KEY environment variable")
+
+def main():
+    # MongoDB memory configuration
+    mongodb_memory_config = {
+        "provider": "mongodb",
+        "config": {
+            "connection_string": "mongodb://localhost:27017/",  # Replace with your MongoDB connection string
+            "database": "praisonai_memory",
+            "use_vector_search": True,  # Enable Atlas Vector Search (requires MongoDB Atlas)
+            "max_pool_size": 50,
+            "min_pool_size": 10,
+            "server_selection_timeout": 5000
+        }
+    }
+    
+    # Create a research agent with MongoDB memory
+    research_agent = Agent(
+        name="MongoDB Research Agent",
+        role="Senior Research Specialist",
+        goal="Research topics and maintain persistent memory using MongoDB",
+        backstory="""You are an expert researcher who specializes in collecting, 
+        analyzing, and storing information using advanced MongoDB-based memory systems. 
+        You excel at maintaining context across multiple research sessions and can 
+        retrieve relevant information from your persistent memory store.""",
+        memory=True,
+        verbose=True,
+        llm="gpt-4o-mini"
+    )
+    
+    # Create research tasks
+    research_tasks = [
+        Task(
+            description="""Research the latest developments in AI/ML for 2024:
+            1. Identify major AI breakthroughs and trends
+            2. Analyze the impact on various industries
+            3. Store key findings in MongoDB memory with quality scores
+            4. Identify emerging technologies and their potential applications
+            """,
+            expected_output="Comprehensive AI/ML research report with key findings stored in MongoDB",
+            agent=research_agent
+        ),
+        Task(
+            description="""Research MongoDB Atlas Vector Search capabilities:
+            1. Explore vector search features and use cases
+            2. Compare with other vector databases
+            3. Analyze performance and scalability
+            4. Store technical insights in MongoDB memory
+            """,
+            expected_output="Technical analysis of MongoDB Atlas Vector Search with stored insights",
+            agent=research_agent
+        ),
+        Task(
+            description="""Research AI agent frameworks and tools:
+            1. Compare popular AI agent frameworks
+            2. Analyze their strengths and weaknesses
+            3. Identify best practices for agent development
+            4. Store comparative analysis in MongoDB memory
+            """,
+            expected_output="Comparative analysis of AI agent frameworks with stored insights",
+            agent=research_agent
+        )
+    ]
+    
+    # Initialize the multi-agent system with MongoDB memory
+    print("🚀 Starting MongoDB Memory Research System...")
+    print("=" * 60)
+    
+    research_system = PraisonAIAgents(
+        agents=[research_agent],
+        tasks=research_tasks,
+        memory=True,
+        memory_config=mongodb_memory_config,
+        verbose=True
+    )
+    
+    # Execute the research pipeline
+    try:
+        results = research_system.start()
+        
+        print("\n" + "=" * 60)
+        print("📊 Research System Results:")
+        print("=" * 60)
+        
+        # Handle results structure properly
+        if isinstance(results, (list, tuple)):
+            for i, result in enumerate(results, 1):
+                print(f"\n{i}. Task Result:")
+                if hasattr(result, 'agent'):
+                    print(f"   Output: {str(result)[:200]}...")
+                    print(f"   Agent: {result.agent}")
+                else:
+                    print(f"   Output: {str(result)[:200]}...")
+        else:
+            print(f"\n1. Task Result:")
+            print(f"   Output: {str(results)[:200]}...")
+            
+        print("\n" + "=" * 60)
+        print("💾 MongoDB Memory Integration Complete!")
+        print("=" * 60)
+        
+        # Demonstrate memory retrieval
+        print("\n🔍 Demonstrating Memory Retrieval:")
+        print("-" * 40)
+        
+        # Note: Direct memory access not available through research_system.memory
+        # Instead, use Memory class directly for verification
+        try:
+            from praisonaiagents.memory import Memory
+            
+            # Create Memory instance with MongoDB config for verification
+            memory_system = Memory(mongodb_memory_config)
+            
+            # Search for AI-related memories
+            ai_memories = memory_system.search_long_term("AI machine learning", limit=3)
+            print(f"Found {len(ai_memories)} AI-related memories:")
+            for memory in ai_memories:
+                if hasattr(memory, 'get'):
+                    print(f"  - {memory.get('text', '')[:100]}...")
+                else:
+                    print(f"  - {str(memory)[:100]}...")
+            
+            # Search for MongoDB-related memories
+            mongo_memories = memory_system.search_long_term("MongoDB vector search", limit=3)
+            print(f"\nFound {len(mongo_memories)} MongoDB-related memories:")
+            for memory in mongo_memories:
+                if hasattr(memory, 'get'):
+                    print(f"  - {memory.get('text', '')[:100]}...")
+                else:
+                    print(f"  - {str(memory)[:100]}...")
+                    
+        except ImportError:
+            print("Memory features not available - install with: pip install 'praisonaiagents[memory]'")
+        except Exception as mem_error:
+            print(f"Memory verification error: {mem_error}")
+        
+        print("\n✅ MongoDB Memory Example Completed Successfully!")
+        
+    except Exception as e:
+        print(f"\n❌ Error during execution: {e}")
+        print("Please check your MongoDB connection and API keys.")
+
+if __name__ == "__main__":
+    print("MongoDB Memory Example for PraisonAI Agents")
+    print("=" * 50)
+    print("This example demonstrates MongoDB integration for persistent memory")
+    print("Make sure you have:")
+    print("1. MongoDB server running (local or Atlas)")
+    print("2. OpenAI API key set in environment variables")
+    print("3. MongoDB dependencies installed: pip install 'praisonaiagents[mongodb]'")
+    print("=" * 50)
+    
+    main()

src/praisonai-agents/mongodb_tools_example.py

@@ -0,0 +1,205 @@
+#!/usr/bin/env python3
+"""
+MongoDB Tools Example for PraisonAI Agents
+
+This example demonstrates how to use MongoDB tools with PraisonAI agents.
+Agents can perform MongoDB operations like inserting, querying, and vector search.
+
+Prerequisites:
+- Install MongoDB dependencies: pip install 'praisonaiagents[mongodb]'
+- MongoDB server running (local or Atlas)
+- OpenAI API key for embeddings
+
+Features demonstrated:
+- MongoDB tools integration
+- Document CRUD operations
+- Vector search with embeddings
+- Data analysis with MongoDB
+- Agent-driven database operations
+"""
+
+import os
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import mongodb_tools
+
+# Ensure OpenAI API key is set
+if not os.environ.get("OPENAI_API_KEY"):
+    raise ValueError("Please set the OPENAI_API_KEY environment variable")
+
+def main():
+    # Create a MongoDB Database Agent with MongoDB tools
+    db_agent = Agent(
+        name="MongoDB Database Agent",
+        role="Database Operations Specialist",
+        goal="Manage MongoDB operations and data analysis",
+        backstory="""You are an expert database administrator who specializes in 
+        MongoDB operations. You can perform complex database queries, manage 
+        collections, and analyze data patterns. You excel at using MongoDB tools 
+        for data management and retrieval.""",
+        tools=[mongodb_tools],
+        verbose=True,
+        llm="gpt-4o-mini"
+    )
+    
+    # Create a Data Analysis Agent
+    analysis_agent = Agent(
+        name="Data Analysis Agent",
+        role="Data Analyst",
+        goal="Analyze data stored in MongoDB and provide insights",
+        backstory="""You are a data analyst who specializes in extracting insights 
+        from MongoDB databases. You can query data, perform aggregations, and 
+        identify patterns and trends in the stored information.""",
+        tools=[mongodb_tools],
+        verbose=True,
+        llm="gpt-4o-mini"
+    )
+    
+    # Create tasks for MongoDB operations
+    mongodb_tasks = [
+        Task(
+            description="""Set up MongoDB collections and insert sample data:
+            1. Create a collection called 'products' for e-commerce data
+            2. Insert sample product documents with fields: name, price, category, description
+            3. Create a collection called 'users' for user data
+            4. Insert sample user documents with fields: name, email, age, preferences
+            5. Create appropriate indexes for better query performance
+            
+            Use the MongoDB tools to perform these operations.
+            """,
+            expected_output="MongoDB collections created with sample data and indexes",
+            agent=db_agent
+        ),
+        Task(
+            description="""Perform vector search operations:
+            1. Create a vector index for product descriptions
+            2. Store products with text embeddings for vector search
+            3. Demonstrate vector similarity search for product recommendations
+            4. Show how to find similar products based on description embeddings
+            
+            Use the MongoDB vector search tools for these operations.
+            """,
+            expected_output="Vector search implementation with product recommendations",
+            agent=db_agent
+        ),
+        Task(
+            description="""Query and analyze the stored data:
+            1. Find all products in a specific category
+            2. Calculate average price by category
+            3. Find users with specific preferences
+            4. Perform text search on product descriptions
+            5. Generate insights about the data patterns
+            
+            Use MongoDB query tools to perform these analyses.
+            """,
+            expected_output="Data analysis report with insights from MongoDB queries",
+            agent=analysis_agent
+        ),
+        Task(
+            description="""Demonstrate advanced MongoDB operations:
+            1. Update product prices based on specific criteria
+            2. Perform aggregation operations for statistical analysis
+            3. Create reports on user demographics and preferences
+            4. Implement data validation and error handling
+            5. Show MongoDB best practices for data management
+            
+            Use MongoDB tools for these advanced operations.
+            """,
+            expected_output="Advanced MongoDB operations report with best practices",
+            agent=db_agent
+        )
+    ]
+    
+    # Initialize the multi-agent system with MongoDB tools
+    print("🚀 Starting MongoDB Tools Demo System...")
+    print("=" * 60)
+    
+    mongodb_system = PraisonAIAgents(
+        agents=[db_agent, analysis_agent],
+        tasks=mongodb_tasks,
+        verbose=True
+    )
+    
+    # Execute the MongoDB operations pipeline
+    try:
+        results = mongodb_system.start(return_dict=True)
+        
+        print("\n" + "=" * 60)
+        print("📊 MongoDB Tools System Results:")
+        print("=" * 60)
+        print(results)
+        
+        # Handle results structure properly
+        if isinstance(results, dict) and 'task_results' in results:
+            task_results = results['task_results']
+            for i, result in task_results.items():
+                print(f"\n{i}. Task Result:")
+                if hasattr(result, 'raw') and hasattr(result, 'agent'):
+                    print(f"   Output: {result.raw[:200]}...")
+                    print(f"   Agent: {result.agent}")
+                else:
+                    print(f"   Result: {str(result)[:200]}...")
+        else:
+            print(f"\n1. Task Result:")
+            print(f"   Output: {str(results)[:200]}...")
+        
+        print("\n" + "=" * 60)
+        print("💾 MongoDB Tools Integration Complete!")
+        print("=" * 60)
+        
+        # Demonstrate direct MongoDB tools usage
+        print("\n🔍 Demonstrating Direct MongoDB Tools Usage:")
+        print("-" * 40)
+        
+        # Connect to MongoDB
+        mongo_client = mongodb_tools.connect_mongodb(
+            "mongodb://localhost:27017/",  # Replace with your connection string
+            "praisonai_tools_demo"
+        )
+        
+        # Insert a sample document
+        print("📄 Inserting sample document...")
+        result = mongo_client.insert_document(
+            "demo_collection",
+            {
+                "title": "MongoDB Tools Demo",
+                "description": "Demonstration of MongoDB tools with PraisonAI",
+                "category": "database",
+                "tags": ["mongodb", "praisonai", "tools", "demo"]
+            },
+            metadata={"created_by": "demo", "version": "1.0"}
+        )
+        print(f"   Result: {result}")
+        
+        # Query documents
+        print("\n🔍 Querying documents...")
+        documents = mongo_client.find_documents(
+            "demo_collection",
+            {"category": "database"},
+            limit=5
+        )
+        print(f"   Found {len(documents)} documents")
+        for doc in documents:
+            print(f"   - {doc.get('title', 'N/A')}: {doc.get('description', '')[:50]}...")
+        
+        # Get collection statistics
+        print("\n📈 Getting collection statistics...")
+        stats = mongo_client.get_stats("demo_collection")
+        print(f"   Collection stats: {stats}")
+        
+        print("\n✅ MongoDB Tools Example Completed Successfully!")
+        
+    except Exception as e:
+        print(f"\n❌ Error during execution: {e}")
+        print("Please check your MongoDB connection and API keys.")
+
+if __name__ == "__main__":
+    print("MongoDB Tools Example for PraisonAI Agents")
+    print("=" * 50)
+    print("This example demonstrates MongoDB tools integration")
+    print("Make sure you have:")
+    print("1. MongoDB server running (local or Atlas)")
+    print("2. OpenAI API key set in environment variables")
+    print("3. MongoDB dependencies installed: pip install 'praisonaiagents[mongodb]'")
+    print("=" * 50)
+    
+    main()

src/praisonai-agents/performance_monitoring_demo.py

@@ -0,0 +1,321 @@
+"""
+PraisonAI Performance Monitoring Demo
+
+This example demonstrates how to use PraisonAI's comprehensive performance monitoring
+capabilities to answer the key questions from Issue #1035:
+
+1. How to evaluate how each function is performing
+2. Function flow analysis  
+3. How fast each function is executing
+4. API call performance monitoring (fast vs slow calls)
+5. How to see all API calls
+
+Features Demonstrated:
+- Function performance evaluation with detailed statistics
+- Function execution flow analysis and visualization
+- API call tracking and performance comparison
+- Real-time performance monitoring
+- Bottleneck identification and optimization recommendations
+- Comprehensive performance reporting
+"""
+
+import time
+import random
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import duckduckgo
+from praisonaiagents.telemetry import (
+    monitor_function, track_api_call, performance_monitor,
+    analyze_function_flow,
+    visualize_execution_flow, generate_comprehensive_report
+)
+
+print("=" * 80)
+print("PRAISONAI PERFORMANCE MONITORING DEMONSTRATION")
+print("=" * 80)
+print("This demo answers the key questions from Issue #1035:")
+print("1. How to evaluate how each function is performing")
+print("2. Function flow analysis")  
+print("3. How fast each function is executing")
+print("4. API call performance monitoring (fast vs slow calls)")
+print("5. How to see all API calls")
+print("=" * 80)
+print()
+
+# Example 1: Function Performance Evaluation
+print("📊 EXAMPLE 1: Function Performance Evaluation")
+print("-" * 50)
+
+# Demonstrate function monitoring with decorators
+@monitor_function("data_processing")
+def process_data(data_size: int):
+    """Simulate data processing with variable performance."""
+    processing_time = 0.1 + (data_size * 0.01)  # Simulate work
+    time.sleep(processing_time)
+    return f"Processed {data_size} items"
+
+@monitor_function("model_inference") 
+def run_model_inference(complexity: str):
+    """Simulate model inference with different complexities."""
+    if complexity == "simple":
+        time.sleep(0.05)
+    elif complexity == "medium": 
+        time.sleep(0.2)
+    else:  # complex
+        time.sleep(0.5)
+    return f"Inference completed for {complexity} model"
+
+@monitor_function("database_query")
+def query_database(query_type: str):
+    """Simulate database queries with different performance characteristics."""
+    if query_type == "fast":
+        time.sleep(0.02)
+    elif query_type == "medium":
+        time.sleep(0.1) 
+    else:  # slow
+        time.sleep(0.3)
+    return f"Query {query_type} completed"
+
+# Run functions with different performance characteristics
+print("Running functions to generate performance data...")
+for _ in range(5):
+    process_data(random.randint(10, 100))
+    run_model_inference(random.choice(["simple", "medium", "complex"]))
+    query_database(random.choice(["fast", "medium", "slow"]))
+
+# Show function performance statistics
+print("\n📈 Function Performance Statistics:")
+function_stats = performance_monitor.get_function_performance()
+for func_name, stats in function_stats.items():
+    print(f"\n🔧 {func_name}:")
+    print(f"  Calls: {stats['call_count']}")
+    print(f"  Average Time: {stats.get('average_time', 0):.3f}s")
+    print(f"  Min/Max Time: {stats['min_time']:.3f}s / {stats['max_time']:.3f}s")
+    print(f"  Success Rate: {stats.get('success_rate', 0)*100:.1f}%")
+
+# Example 2: Function Flow Analysis
+print("\n\n🔄 EXAMPLE 2: Function Flow Analysis") 
+print("-" * 50)
+
+@monitor_function("workflow_step_1")
+def workflow_step_1():
+    time.sleep(0.1)
+    workflow_step_2()
+    workflow_step_3()
+
+@monitor_function("workflow_step_2") 
+def workflow_step_2():
+    time.sleep(0.05)
+    workflow_step_4()
+
+@monitor_function("workflow_step_3")
+def workflow_step_3():
+    time.sleep(0.08)
+
+@monitor_function("workflow_step_4")
+def workflow_step_4():
+    time.sleep(0.03)
+
+print("Running workflow to demonstrate flow analysis...")
+workflow_step_1()
+
+# Analyze execution flow
+print("\n📊 Function Execution Flow Analysis:")
+flow_analysis = analyze_function_flow()
+if "bottlenecks" in flow_analysis:
+    bottlenecks = flow_analysis["bottlenecks"]
+    if bottlenecks:
+        print("🚨 Identified Bottlenecks:")
+        for bottleneck in bottlenecks:
+            print(f"  • {bottleneck['function']}: {bottleneck['average_duration']:.3f}s average")
+    else:
+        print("✅ No significant bottlenecks identified")
+
+# Visualize execution flow
+print("\n🎨 Function Execution Flow Visualization:")
+flow_viz = visualize_execution_flow(format="text")
+print(flow_viz)
+
+# Example 3: API Call Performance Monitoring
+print("\n\n🌐 EXAMPLE 3: API Call Performance Monitoring")
+print("-" * 50)
+
+# Simulate different API calls with various performance characteristics
+def simulate_api_calls():
+    """Simulate API calls to demonstrate tracking."""
+    
+    # Fast API calls
+    with track_api_call("openai_api", "/v1/models"):
+        time.sleep(0.05)  # Fast call
+    
+    with track_api_call("openai_api", "/v1/chat/completions"):
+        time.sleep(0.3)  # Medium call
+    
+    # Slow API call
+    with track_api_call("anthropic_api", "/v1/messages"):
+        time.sleep(0.6)  # Slow call
+    
+    # Another fast call
+    with track_api_call("gemini_api", "/v1/generate"):
+        time.sleep(0.08)  # Fast call
+    
+    # Simulate error case
+    try:
+        with track_api_call("unreliable_api", "/v1/endpoint"):
+            time.sleep(0.2)
+            if random.random() < 0.3:  # 30% chance of error
+                raise Exception("API Error")
+    except Exception:
+        pass  # Error is already tracked
+
+print("Simulating API calls with different performance characteristics...")
+for _ in range(8):
+    simulate_api_calls()
+
+# Show API performance statistics
+print("\n📊 API Call Performance Statistics:")
+api_stats = performance_monitor.get_api_call_performance()
+for api_name, stats in api_stats.items():
+    print(f"\n🔗 {api_name}:")
+    print(f"  Total Calls: {stats['call_count']}")
+    print(f"  Average Time: {stats.get('average_time', 0):.3f}s")
+    print(f"  Min/Max Time: {stats['min_time']:.3f}s / {stats['max_time']:.3f}s")
+    print(f"  Success Rate: {stats.get('success_rate', 0)*100:.1f}%")
+    print(f"  Successful: {stats['success_count']} | Failed: {stats['error_count']}")
+
+# Show fastest vs slowest APIs
+print("\n🏁 Fastest vs Slowest API Calls:")
+slowest_apis = performance_monitor.get_slowest_api_calls(10)
+for i, api in enumerate(slowest_apis, 1):
+    speed_indicator = "🐌" if api['average_time'] > 0.3 else "⚡" if api['average_time'] < 0.1 else "🚀"
+    print(f"{i}. {speed_indicator} {api['api']}: {api['average_time']:.3f}s avg (Success: {api['success_rate']*100:.1f}%)")
+
+# Example 4: Real-time Performance Monitoring with Agents
+print("\n\n🤖 EXAMPLE 4: Real-time Performance Monitoring with AI Agents")
+print("-" * 50)
+
+# Create agents with performance monitoring
+research_agent = Agent(
+    name="PerformanceResearcher",
+    role="Performance Research Specialist",
+    goal="Research and analyze performance topics quickly",
+    backstory="You are an expert at researching performance optimization topics.",
+    tools=[duckduckgo],
+    verbose=False  # Reduce output for cleaner demo
+)
+
+# Monitor agent operations
+@monitor_function("agent_task_execution")
+def execute_monitored_task(agent, task_description):
+    """Execute an agent task with performance monitoring."""
+    task = Task(
+        description=task_description,
+        expected_output="Brief research summary with key insights",
+        agent=agent
+    )
+    
+    agents_workflow = PraisonAIAgents(
+        agents=[agent],
+        tasks=[task],
+        verbose=False
+    )
+    
+    # Simulate API calls during agent execution
+    with track_api_call("llm_provider", "/v1/chat/completions"):
+        time.sleep(0.2)  # Simulate LLM call
+    
+    result = agents_workflow.start()
+    return result
+
+print("Running AI agent with performance monitoring...")
+result = execute_monitored_task(
+    research_agent, 
+    "Research the latest trends in AI performance optimization"
+)
+
+# Example 5: Comprehensive Performance Report
+print("\n\n📋 EXAMPLE 5: Comprehensive Performance Report")
+print("-" * 50)
+
+# Generate comprehensive performance report
+comprehensive_report = generate_comprehensive_report()
+print(comprehensive_report)
+
+# Example 6: Using CLI Interface Programmatically
+print("\n\n💻 EXAMPLE 6: CLI Interface Usage")
+print("-" * 50)
+print("You can also use the CLI interface from command line:")
+print()
+print("# Show performance report")
+print("python -m praisonaiagents.telemetry.performance_cli report")
+print()
+print("# Show function statistics")  
+print("python -m praisonaiagents.telemetry.performance_cli functions")
+print()
+print("# Show API call statistics")
+print("python -m praisonaiagents.telemetry.performance_cli apis")
+print()
+print("# Show slowest functions")
+print("python -m praisonaiagents.telemetry.performance_cli slowest-functions 5")
+print()
+print("# Analyze function flow") 
+print("python -m praisonaiagents.telemetry.performance_cli analyze-flow")
+print()
+print("# Show execution flow visualization")
+print("python -m praisonaiagents.telemetry.performance_cli flow --format text")
+print()
+print("# Export all data to JSON")
+print("python -m praisonaiagents.telemetry.performance_cli export --format json --output performance_data.json")
+
+# Example 7: Integration with Existing Code
+print("\n\n🔧 EXAMPLE 7: Easy Integration with Existing Code")  
+print("-" * 50)
+print("To integrate performance monitoring into your existing PraisonAI code:")
+print()
+print("1. Import the performance monitoring tools:")
+print("   from praisonaiagents.telemetry import monitor_function, track_api_call")
+print()
+print("2. Add decorators to your functions:")
+print("   @monitor_function('my_function')")
+print("   def my_function():")
+print("       return 'result'")
+print()
+print("3. Track API calls:")
+print("   with track_api_call('openai', '/v1/chat/completions'):")
+print("       response = openai_client.chat.completions.create(...)")
+print()
+print("4. Get performance reports:")
+print("   from praisonaiagents.telemetry import get_performance_report")
+print("   print(get_performance_report())")
+
+# Summary of capabilities
+print("\n\n✅ SUMMARY: Questions from Issue #1035 - ANSWERED!")
+print("=" * 80)
+print("1. ✅ How to evaluate how each function is performing:")
+print("   → Use @monitor_function decorator and get_function_stats()")
+print("   → View detailed statistics: calls, timing, success rates, errors")
+print()
+print("2. ✅ Function flow analysis:")
+print("   → Use analyze_function_flow() for bottleneck identification") 
+print("   → Use visualize_execution_flow() for flow visualization")
+print("   → Get call chains and execution patterns")
+print()
+print("3. ✅ How fast each function is executing:")
+print("   → Automatic timing collection with min/max/average/recent stats")
+print("   → Use get_slowest_functions() to find performance bottlenecks")
+print("   → Real-time active call monitoring")
+print()
+print("4. ✅ API call performance monitoring (fast vs slow calls):")
+print("   → Use track_api_call() context manager for API monitoring")
+print("   → Compare API performance with get_slowest_apis()")
+print("   → Success/error rate tracking")
+print()
+print("5. ✅ How to see all API calls:")
+print("   → Use get_api_stats() to see all tracked APIs")
+print("   → View recent call history and detailed metrics")
+print("   → CLI interface for easy access: performance_cli apis")
+print()
+print("🎉 All requested features are now available with backward compatibility!")
+print("🔧 Easy integration with existing code using decorators and context managers")
+print("📊 Comprehensive reporting and analysis capabilities") 
+print("💻 CLI interface for command-line access")
+print("=" * 80)

src/praisonai-agents/praisonaiagents/__init__.py

@@ -5,11 +5,27 @@ Praison AI Agents - A package for hierarchical AI agent task execution
 # Configure logging before any other imports
 import os
 import logging
+import warnings
+import re
 from rich.logging import RichHandler
 
+# Set environment variables to suppress warnings at the source
+os.environ["LITELLM_TELEMETRY"] = "False"
+os.environ["LITELLM_DROP_PARAMS"] = "True"
+# Disable httpx warnings
+os.environ["HTTPX_DISABLE_WARNINGS"] = "True"
+
 # Get log level from environment variable
 LOGLEVEL = os.environ.get('LOGLEVEL', 'INFO').upper()
 
+# Determine if warnings should be suppressed (not in DEBUG mode and not in tests)
+def _should_suppress_warnings():
+    import sys
+    return (LOGLEVEL != 'DEBUG' and 
+            not hasattr(sys, '_called_from_test') and 
+            'pytest' not in sys.modules and
+            os.environ.get('PYTEST_CURRENT_TEST') is None)
+
 # Configure root logger
 logging.basicConfig(
     level=getattr(logging, LOGLEVEL, logging.INFO),
@@ -18,16 +34,38 @@ logging.basicConfig(
     handlers=[RichHandler(rich_tracebacks=True)]
 )
 
-# Suppress specific noisy loggers
-logging.getLogger("litellm").setLevel(logging.WARNING)
-logging.getLogger("litellm.utils").setLevel(logging.WARNING)
-logging.getLogger("markdown_it").setLevel(logging.WARNING)
-logging.getLogger("rich.markdown").setLevel(logging.WARNING)
-logging.getLogger("httpx").setLevel(logging.WARNING)
-logging.getLogger("httpcore").setLevel(logging.WARNING)
+# Suppress specific noisy loggers - more aggressive suppression (only when not in DEBUG mode)
+if _should_suppress_warnings():
+    logging.getLogger("litellm").setLevel(logging.CRITICAL)
+    logging.getLogger("litellm_logging").setLevel(logging.CRITICAL)
+    logging.getLogger("httpx").setLevel(logging.CRITICAL)
+    logging.getLogger("httpcore").setLevel(logging.CRITICAL)
+    logging.getLogger("pydantic").setLevel(logging.WARNING)
+    logging.getLogger("markdown_it").setLevel(logging.WARNING)
+    logging.getLogger("rich.markdown").setLevel(logging.WARNING)
+
+    # Note: litellm child loggers (litellm.utils, litellm.proxy, etc.) automatically inherit 
+    # the CRITICAL level from the parent litellm logger due to Python's hierarchical logging
+
+# Comprehensive warning suppression for litellm and dependencies (issue #1033)
+# These warnings clutter output and are not actionable for users
+
+# Set warning filter to suppress all warnings from problematic modules at import time
+if _should_suppress_warnings():
+    # Module-specific warning suppression - applied before imports (only when not in DEBUG mode)
+    for module in ['litellm', 'httpx', 'httpcore', 'pydantic']:
+        warnings.filterwarnings("ignore", category=DeprecationWarning, module=module)
+        warnings.filterwarnings("ignore", category=UserWarning, module=module)
+    
+    # Specific filters for known problematic warnings
+    warnings.filterwarnings("ignore", message="There is no current event loop")
+    warnings.filterwarnings("ignore", message=".*Use 'content=<...>' to upload raw bytes/text content.*")
+    warnings.filterwarnings("ignore", message=".*The `dict` method is deprecated; use `model_dump` instead.*")
+    warnings.filterwarnings("ignore", message=".*model_dump.*deprecated.*")
 
 from .agent.agent import Agent
 from .agent.image_agent import ImageAgent
+from .agent.context_agent import ContextAgent, create_context_agent
 from .agents.agents import PraisonAIAgents
 from .task.task import Task
 from .tools.tools import Tools
@@ -93,6 +131,30 @@ except ImportError:
 # Add Agents as an alias for PraisonAIAgents
 Agents = PraisonAIAgents
 
+# Additional warning suppression after all imports (runtime suppression)
+if _should_suppress_warnings():
+    # Try to import and configure litellm to suppress its warnings
+    try:
+        import litellm
+        # Disable all litellm logging and telemetry
+        litellm.telemetry = False
+        litellm.drop_params = True
+        # Set litellm to suppress warnings
+        litellm.suppress_debug_info = True
+        if hasattr(litellm, '_logging_obj'):
+            litellm._logging_obj.setLevel(logging.CRITICAL)
+    except (ImportError, AttributeError):
+        pass
+    
+    # Suppress pydantic warnings that might occur at runtime (safer approach)
+    try:
+        warnings.filterwarnings("ignore", category=UserWarning, module="pydantic", 
+                               message=".*model_dump.*deprecated.*")
+        warnings.filterwarnings("ignore", category=UserWarning, module="pydantic", 
+                               message=".*dict.*method.*deprecated.*")
+    except Exception:
+        pass
+
 # Apply telemetry auto-instrumentation after all imports
 if _telemetry_available:
     try:
@@ -108,6 +170,8 @@ if _telemetry_available:
 __all__ = [
     'Agent',
     'ImageAgent',
+    'ContextAgent',
+    'create_context_agent',
     'PraisonAIAgents',
     'Agents',
     'Tools',

src/praisonai-agents/praisonaiagents/agent/__init__.py

@@ -1,7 +1,8 @@
 """Agent module for AI agents"""
 from .agent import Agent
 from .image_agent import ImageAgent
+from .context_agent import ContextAgent, create_context_agent
 from .handoff import Handoff, handoff, handoff_filters, RECOMMENDED_PROMPT_PREFIX, prompt_with_handoff_instructions
 from .router_agent import RouterAgent
 
-__all__ = ['Agent', 'ImageAgent', 'Handoff', 'handoff', 'handoff_filters', 'RECOMMENDED_PROMPT_PREFIX', 'prompt_with_handoff_instructions', 'RouterAgent']
+__all__ = ['Agent', 'ImageAgent', 'ContextAgent', 'create_context_agent', 'Handoff', 'handoff', 'handoff_filters', 'RECOMMENDED_PROMPT_PREFIX', 'prompt_with_handoff_instructions', 'RouterAgent']

src/praisonai-agents/praisonaiagents/agent/agent.py

@@ -4,7 +4,7 @@ import json
 import copy
 import logging
 import asyncio
-from typing import List, Optional, Any, Dict, Union, Literal, TYPE_CHECKING, Callable, Tuple
+from typing import List, Optional, Any, Dict, Union, Literal, TYPE_CHECKING, Callable, Tuple, Generator
 from rich.console import Console
 from rich.live import Live
 from ..llm import (
@@ -206,7 +206,7 @@ class Agent:
         knowledge_config: Optional[Dict[str, Any]] = None,
         use_system_prompt: Optional[bool] = True,
         markdown: bool = True,
-        stream: bool = True,
+        stream: bool = False,
         self_reflect: bool = False,
         max_reflect: int = 3,
         min_reflect: int = 1,
@@ -281,8 +281,8 @@ class Agent:
                 conversations to establish agent behavior and context. Defaults to True.
             markdown (bool, optional): Enable markdown formatting in agent responses for better
                 readability and structure. Defaults to True.
-            stream (bool, optional): Enable streaming responses from the language model. Set to False
-                for LLM providers that don't support streaming. Defaults to True.
+            stream (bool, optional): Enable streaming responses from the language model for real-time
+                output when using Agent.start() method. Defaults to False for backward compatibility.
             self_reflect (bool, optional): Enable self-reflection capabilities where the agent
                 evaluates and improves its own responses. Defaults to False.
             max_reflect (int, optional): Maximum number of self-reflection iterations to prevent
@@ -1070,7 +1070,7 @@ Your Goal: {self.goal}"""
             tools=formatted_tools,
             start_time=start_time,
             console=self.console,
-            display_fn=display_generating,
+            display_fn=self.display_generating if self.verbose else None,
             reasoning_steps=reasoning_steps
         )
 
@@ -1109,25 +1109,51 @@ Your Goal: {self.goal}"""
                         reasoning_steps=reasoning_steps
                     )
                 else:
-                    # Non-streaming with custom LLM
-                    final_response = self.llm_instance.get_response(
-                        prompt=messages[1:],
-                        system_prompt=messages[0]['content'] if messages and messages[0]['role'] == 'system' else None,
-                        temperature=temperature,
-                        tools=formatted_tools if formatted_tools else None,
-                        verbose=self.verbose,
-                        markdown=self.markdown,
-                        stream=stream,
-                        console=self.console,
-                        execute_tool_fn=self.execute_tool,
-                        agent_name=self.name,
-                        agent_role=self.role,
-                        agent_tools=[t.__name__ for t in self.tools] if self.tools else None,
-                        task_name=task_name,
-                        task_description=task_description,
-                        task_id=task_id,
-                        reasoning_steps=reasoning_steps
-                    )
+                    # Non-streaming with custom LLM - don't show streaming-like behavior
+                    if False:  # Don't use display_generating when stream=False to avoid streaming-like behavior
+                        # This block is disabled to maintain consistency with the OpenAI path fix
+                        with Live(
+                            display_generating("", start_time),
+                            console=self.console,
+                            refresh_per_second=4,
+                        ) as live:
+                            final_response = self.llm_instance.get_response(
+                                prompt=messages[1:],
+                                system_prompt=messages[0]['content'] if messages and messages[0]['role'] == 'system' else None,
+                                temperature=temperature,
+                                tools=formatted_tools if formatted_tools else None,
+                                verbose=self.verbose,
+                                markdown=self.markdown,
+                                stream=stream,
+                                console=self.console,
+                                execute_tool_fn=self.execute_tool,
+                                agent_name=self.name,
+                                agent_role=self.role,
+                                agent_tools=[t.__name__ for t in self.tools] if self.tools else None,
+                                task_name=task_name,
+                                task_description=task_description,
+                                task_id=task_id,
+                                reasoning_steps=reasoning_steps
+                            )
+                    else:
+                        final_response = self.llm_instance.get_response(
+                            prompt=messages[1:],
+                            system_prompt=messages[0]['content'] if messages and messages[0]['role'] == 'system' else None,
+                            temperature=temperature,
+                            tools=formatted_tools if formatted_tools else None,
+                            verbose=self.verbose,
+                            markdown=self.markdown,
+                            stream=stream,
+                            console=self.console,
+                            execute_tool_fn=self.execute_tool,
+                            agent_name=self.name,
+                            agent_role=self.role,
+                            agent_tools=[t.__name__ for t in self.tools] if self.tools else None,
+                            task_name=task_name,
+                            task_description=task_description,
+                            task_id=task_id,
+                            reasoning_steps=reasoning_steps
+                        )
             else:
                 # Use the standard OpenAI client approach with tool support
                 # Note: openai_client expects tools in various formats and will format them internally
@@ -1143,7 +1169,7 @@ Your Goal: {self.goal}"""
                     execute_tool_fn=self.execute_tool,
                     stream=stream,
                     console=self.console if (self.verbose or stream) else None,
-                    display_fn=display_generating if stream else None,
+                    display_fn=self.display_generating if self.verbose else None,
                     reasoning_steps=reasoning_steps,
                     verbose=self.verbose,
                     max_iterations=10
@@ -1187,8 +1213,32 @@ Your Goal: {self.goal}"""
                               task_description=None,  # Not available in this context
                               task_id=None)  # Not available in this context
             self._final_display_shown = True
+    
+    def display_generating(self, content: str, start_time: float):
+        """Display function for generating animation with agent info."""
+        from rich.panel import Panel
+        from rich.markdown import Markdown
+        elapsed = time.time() - start_time
+        
+        # Show content if provided (for both streaming and progressive display)
+        if content:
+            display_content = Markdown(content) if self.markdown else content
+            return Panel(
+                display_content,
+                title=f"[bold]{self.name}[/bold] - Generating... {elapsed:.1f}s",
+                border_style="green",
+                expand=False
+            )
+        else:
+            # No content yet: show generating message
+            return Panel(
+                f"[bold cyan]Generating response...[/bold cyan]",
+                title=f"[bold]{self.name}[/bold] - {elapsed:.1f}s",
+                border_style="cyan",
+                expand=False
+            )
 
-    def chat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None, reasoning_steps=False, stream=True, task_name=None, task_description=None, task_id=None):
+    def chat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None, reasoning_steps=False, stream=None, task_name=None, task_description=None, task_id=None):
         # Reset the final display flag for each new conversation
         self._final_display_shown = False
         
@@ -1209,6 +1259,9 @@ Your Goal: {self.goal}"""
         
         start_time = time.time()
         reasoning_steps = reasoning_steps or self.reasoning_steps
+        # Use agent's stream setting if not explicitly provided
+        if stream is None:
+            stream = self.stream
         # Search for existing knowledge if any knowledge is provided
         if self.knowledge:
             search_results = self.knowledge.search(prompt, agent_id=self.agent_id)
@@ -1359,7 +1412,7 @@ Your Goal: {self.goal}"""
                                     agent_tools=agent_tools
                                 )
 
-                        response = self._chat_completion(messages, temperature=temperature, tools=tools if tools else None, reasoning_steps=reasoning_steps, stream=self.stream, task_name=task_name, task_description=task_description, task_id=task_id)
+                        response = self._chat_completion(messages, temperature=temperature, tools=tools if tools else None, reasoning_steps=reasoning_steps, stream=stream, task_name=task_name, task_description=task_description, task_id=task_id)
                         if not response:
                             # Rollback chat history on response failure
                             self.chat_history = self.chat_history[:chat_history_length]
@@ -1848,7 +1901,12 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
             for tool_call in message.tool_calls:
                 try:
                     function_name = tool_call.function.name
-                    arguments = json.loads(tool_call.function.arguments)
+                    # Parse JSON arguments safely 
+                    try:
+                        arguments = json.loads(tool_call.function.arguments)
+                    except json.JSONDecodeError as json_error:
+                        logging.error(f"Failed to parse tool arguments as JSON: {json_error}")
+                        arguments = {}
                     
                     # Find the matching tool
                     tool = next((t for t in tools if t.__name__ == function_name), None)
@@ -1890,25 +1948,16 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                         chunks = []
                         start_time = time.time()
                         
-                        with Live(
-                            display_generating("", start_time),
-                            console=self.console,
-                            refresh_per_second=4,
-                            transient=True,
-                            vertical_overflow="ellipsis",
-                            auto_refresh=True
-                        ) as live:
-                            async for chunk in final_response:
-                                chunks.append(chunk)
-                                if chunk.choices[0].delta.content:
-                                    full_response_text += chunk.choices[0].delta.content
-                                    live.update(display_generating(full_response_text, start_time))
-                                
-                                if reasoning_steps and hasattr(chunk.choices[0].delta, "reasoning_content"):
-                                    rc = chunk.choices[0].delta.reasoning_content
-                                    if rc:
-                                        reasoning_content += rc
-                                        live.update(display_generating(f"{full_response_text}\n[Reasoning: {reasoning_content}]", start_time))
+                        # Process stream without display_generating since streaming is active
+                        async for chunk in final_response:
+                            chunks.append(chunk)
+                            if chunk.choices[0].delta.content:
+                                full_response_text += chunk.choices[0].delta.content
+                            
+                            if reasoning_steps and hasattr(chunk.choices[0].delta, "reasoning_content"):
+                                rc = chunk.choices[0].delta.reasoning_content
+                                if rc:
+                                    reasoning_content += rc
                         
                         self.console.print()
                         
@@ -1937,7 +1986,268 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
 
     def start(self, prompt: str, **kwargs):
         """Start the agent with a prompt. This is a convenience method that wraps chat()."""
-        return self.chat(prompt, **kwargs) 
+        # Check if streaming is enabled (either from kwargs or agent's stream attribute)
+        stream_enabled = kwargs.get('stream', getattr(self, 'stream', False))
+        
+        if stream_enabled:
+            # Return a generator for streaming response
+            return self._start_stream(prompt, **kwargs)
+        else:
+            # Return regular chat response for backward compatibility
+            # Explicitly pass the resolved stream parameter to avoid chat() method default
+            kwargs['stream'] = stream_enabled
+            return self.chat(prompt, **kwargs)
+
+    def _start_stream(self, prompt: str, **kwargs) -> Generator[str, None, None]:
+        """Stream generator for real-time response chunks."""
+        try:
+            # Reset the final display flag for each new conversation
+            self._final_display_shown = False
+            
+            # Temporarily disable verbose mode to prevent console output conflicts during streaming
+            original_verbose = self.verbose
+            self.verbose = False
+            
+            # For custom LLM path, use the new get_response_stream generator
+            if self._using_custom_llm:
+                # Handle knowledge search
+                actual_prompt = prompt
+                if self.knowledge:
+                    search_results = self.knowledge.search(prompt, agent_id=self.agent_id)
+                    if search_results:
+                        if isinstance(search_results, dict) and 'results' in search_results:
+                            knowledge_content = "\n".join([result['memory'] for result in search_results['results']])
+                        else:
+                            knowledge_content = "\n".join(search_results)
+                        actual_prompt = f"{prompt}\n\nKnowledge: {knowledge_content}"
+                
+                # Handle tools properly
+                tools = kwargs.get('tools', self.tools)
+                if tools is None or (isinstance(tools, list) and len(tools) == 0):
+                    tool_param = self.tools
+                else:
+                    tool_param = tools
+                
+                # Convert MCP tools if needed
+                if tool_param is not None:
+                    from ..mcp.mcp import MCP
+                    if isinstance(tool_param, MCP) and hasattr(tool_param, 'to_openai_tool'):
+                        openai_tool = tool_param.to_openai_tool()
+                        if openai_tool:
+                            if isinstance(openai_tool, list):
+                                tool_param = openai_tool
+                            else:
+                                tool_param = [openai_tool]
+                
+                # Store chat history length for potential rollback
+                chat_history_length = len(self.chat_history)
+                
+                # Normalize prompt content for chat history
+                normalized_content = actual_prompt
+                if isinstance(actual_prompt, list):
+                    normalized_content = next((item["text"] for item in actual_prompt if item.get("type") == "text"), "")
+                
+                # Prevent duplicate messages in chat history
+                if not (self.chat_history and 
+                        self.chat_history[-1].get("role") == "user" and 
+                        self.chat_history[-1].get("content") == normalized_content):
+                    self.chat_history.append({"role": "user", "content": normalized_content})
+                
+                try:
+                    # Use the new streaming generator from LLM class
+                    response_content = ""
+                    for chunk in self.llm_instance.get_response_stream(
+                        prompt=actual_prompt,
+                        system_prompt=self._build_system_prompt(tool_param),
+                        chat_history=self.chat_history,
+                        temperature=kwargs.get('temperature', 0.2),
+                        tools=tool_param,
+                        output_json=kwargs.get('output_json'),
+                        output_pydantic=kwargs.get('output_pydantic'),
+                        verbose=False,  # Keep verbose false for streaming
+                        markdown=self.markdown,
+                        agent_name=self.name,
+                        agent_role=self.role,
+                        agent_tools=[t.__name__ if hasattr(t, '__name__') else str(t) for t in (tool_param or [])],
+                        task_name=kwargs.get('task_name'),
+                        task_description=kwargs.get('task_description'),
+                        task_id=kwargs.get('task_id'),
+                        execute_tool_fn=self.execute_tool
+                    ):
+                        response_content += chunk
+                        yield chunk
+                    
+                    # Add complete response to chat history
+                    if response_content:
+                        self.chat_history.append({"role": "assistant", "content": response_content})
+                        
+                except Exception as e:
+                    # Rollback chat history on error
+                    self.chat_history = self.chat_history[:chat_history_length]
+                    logging.error(f"Custom LLM streaming error: {e}")
+                    raise
+                    
+            else:
+                # For OpenAI-style models, implement proper streaming without display
+                # Handle knowledge search
+                actual_prompt = prompt
+                if self.knowledge:
+                    search_results = self.knowledge.search(prompt, agent_id=self.agent_id)
+                    if search_results:
+                        if isinstance(search_results, dict) and 'results' in search_results:
+                            knowledge_content = "\n".join([result['memory'] for result in search_results['results']])
+                        else:
+                            knowledge_content = "\n".join(search_results)
+                        actual_prompt = f"{prompt}\n\nKnowledge: {knowledge_content}"
+                
+                # Handle tools properly
+                tools = kwargs.get('tools', self.tools)
+                if tools is None or (isinstance(tools, list) and len(tools) == 0):
+                    tool_param = self.tools
+                else:
+                    tool_param = tools
+                
+                # Build messages using the helper method
+                messages, original_prompt = self._build_messages(actual_prompt, kwargs.get('temperature', 0.2), 
+                                                               kwargs.get('output_json'), kwargs.get('output_pydantic'))
+                
+                # Store chat history length for potential rollback
+                chat_history_length = len(self.chat_history)
+                
+                # Normalize original_prompt for consistent chat history storage
+                normalized_content = original_prompt
+                if isinstance(original_prompt, list):
+                    normalized_content = next((item["text"] for item in original_prompt if item.get("type") == "text"), "")
+                
+                # Prevent duplicate messages in chat history
+                if not (self.chat_history and 
+                        self.chat_history[-1].get("role") == "user" and 
+                        self.chat_history[-1].get("content") == normalized_content):
+                    self.chat_history.append({"role": "user", "content": normalized_content})
+                
+                try:
+                    # Check if OpenAI client is available
+                    if self._openai_client is None:
+                        raise ValueError("OpenAI client is not initialized. Please provide OPENAI_API_KEY or use a custom LLM provider.")
+                    
+                    # Format tools for OpenAI
+                    formatted_tools = self._format_tools_for_completion(tool_param)
+                    
+                    # Create streaming completion directly without display function
+                    completion_args = {
+                        "model": self.llm,
+                        "messages": messages,
+                        "temperature": kwargs.get('temperature', 0.2),
+                        "stream": True
+                    }
+                    if formatted_tools:
+                        completion_args["tools"] = formatted_tools
+                    
+                    completion = self._openai_client.sync_client.chat.completions.create(**completion_args)
+                    
+                    # Stream the response chunks without display
+                    response_text = ""
+                    tool_calls_data = []
+                    
+                    for chunk in completion:
+                        delta = chunk.choices[0].delta
+                        
+                        # Handle text content
+                        if delta.content is not None:
+                            chunk_content = delta.content
+                            response_text += chunk_content
+                            yield chunk_content
+                        
+                        # Handle tool calls (accumulate but don't yield as chunks)
+                        if hasattr(delta, 'tool_calls') and delta.tool_calls:
+                            for tool_call_delta in delta.tool_calls:
+                                # Extend tool_calls_data list to accommodate the tool call index
+                                while len(tool_calls_data) <= tool_call_delta.index:
+                                    tool_calls_data.append({'id': '', 'function': {'name': '', 'arguments': ''}})
+                                
+                                # Accumulate tool call data
+                                if tool_call_delta.id:
+                                    tool_calls_data[tool_call_delta.index]['id'] = tool_call_delta.id
+                                if tool_call_delta.function.name:
+                                    tool_calls_data[tool_call_delta.index]['function']['name'] = tool_call_delta.function.name
+                                if tool_call_delta.function.arguments:
+                                    tool_calls_data[tool_call_delta.index]['function']['arguments'] += tool_call_delta.function.arguments
+                    
+                    # Handle any tool calls that were accumulated
+                    if tool_calls_data:
+                        # Add assistant message with tool calls to chat history
+                        assistant_message = {"role": "assistant", "content": response_text}
+                        if tool_calls_data:
+                            assistant_message["tool_calls"] = [
+                                {
+                                    "id": tc['id'],
+                                    "type": "function", 
+                                    "function": tc['function']
+                                } for tc in tool_calls_data if tc['id']
+                            ]
+                        self.chat_history.append(assistant_message)
+                        
+                        # Execute tool calls and add results to chat history
+                        for tool_call in tool_calls_data:
+                            if tool_call['id'] and tool_call['function']['name']:
+                                try:
+                                    # Parse JSON arguments safely 
+                                    try:
+                                        parsed_args = json.loads(tool_call['function']['arguments']) if tool_call['function']['arguments'] else {}
+                                    except json.JSONDecodeError as json_error:
+                                        logging.error(f"Failed to parse tool arguments as JSON: {json_error}")
+                                        parsed_args = {}
+                                    
+                                    tool_result = self.execute_tool(
+                                        tool_call['function']['name'], 
+                                        parsed_args
+                                    )
+                                    # Add tool result to chat history
+                                    self.chat_history.append({
+                                        "role": "tool",
+                                        "tool_call_id": tool_call['id'],
+                                        "content": str(tool_result)
+                                    })
+                                except Exception as tool_error:
+                                    logging.error(f"Tool execution error in streaming: {tool_error}")
+                                    # Add error result to chat history
+                                    self.chat_history.append({
+                                        "role": "tool", 
+                                        "tool_call_id": tool_call['id'],
+                                        "content": f"Error: {str(tool_error)}"
+                                    })
+                    else:
+                        # Add complete response to chat history (text-only response)
+                        if response_text:
+                            self.chat_history.append({"role": "assistant", "content": response_text})
+                        
+                except Exception as e:
+                    # Rollback chat history on error
+                    self.chat_history = self.chat_history[:chat_history_length]
+                    logging.error(f"OpenAI streaming error: {e}")
+                    # Fall back to simulated streaming
+                    response = self.chat(prompt, **kwargs)
+                    if response:
+                        words = str(response).split()
+                        chunk_size = max(1, len(words) // 20)
+                        for i in range(0, len(words), chunk_size):
+                            chunk_words = words[i:i + chunk_size]
+                            chunk = ' '.join(chunk_words)
+                            if i + chunk_size < len(words):
+                                chunk += ' '
+                            yield chunk
+            
+            # Restore original verbose mode
+            self.verbose = original_verbose
+                    
+        except Exception as e:
+            # Restore verbose mode on any error
+            self.verbose = original_verbose
+            # Graceful fallback to non-streaming if streaming fails
+            logging.warning(f"Streaming failed, falling back to regular response: {e}")
+            response = self.chat(prompt, **kwargs)
+            if response:
+                yield response
 
     def execute(self, task, context=None):
         """Execute a task synchronously - backward compatibility method"""

src/praisonai-agents/praisonaiagents/agents/agents.py

@@ -81,16 +81,19 @@ def process_task_context(context_item, verbose=0, user_id=None):
         task_name = context_item.name if context_item.name else context_item.description
         
         if context_item.result and task_status == TaskStatus.COMPLETED.value:
-            return f"Result of previous task {task_name}:\n{context_item.result.raw}"
+            # Log detailed result for debugging
+            logger.debug(f"Previous task '{task_name}' result: {context_item.result.raw}")
+            # Return actual result content without verbose label (essential for task chaining)
+            return context_item.result.raw
         elif task_status == TaskStatus.COMPLETED.value and not context_item.result:
-            return f"Previous task {task_name} completed but produced no result."
+            return ""  # No result to include
         else:
-            return f"Previous task {task_name} is not yet completed (status: {task_status or TaskStatus.UNKNOWN.value})."
-    elif isinstance(context_item, dict) and "vector_store" in context_item:
+            return ""  # Task not completed, no context to include
+    elif isinstance(context_item, dict) and ("vector_store" in context_item or "embedding_db_config" in context_item):
         from ..knowledge.knowledge import Knowledge
         try:
-            # Handle both string and dict configs
-            cfg = context_item["vector_store"]
+            # Handle both string and dict configs - support both vector_store and embedding_db_config keys for backward compatibility
+            cfg = context_item.get("vector_store") or context_item.get("embedding_db_config")
             if isinstance(cfg, str):
                 cfg = json.loads(cfg)
             
@@ -101,9 +104,19 @@ def process_task_context(context_item, verbose=0, user_id=None):
                 context_item.get("query", ""),  # Use query from context if available
                 user_id=user_id if user_id else None
             )
-            return f"[DB Context]: {str(db_results)}"
+            
+            # Log knowledge results for debugging (always available for troubleshooting)
+            logger.debug(f"Knowledge search results ({len(db_results)} items): {str(db_results)}")
+            
+            # Return actual content without verbose "[DB Context]:" prefix
+            return str(db_results)
         except Exception as e:
-            return f"[Vector DB Error]: {e}"
+            # Log error for debugging (always available for troubleshooting)
+            logger.debug(f"Vector DB Error: {e}")
+            
+            # Return empty string to avoid exposing error details in AI prompts
+            # Error details are preserved in debug logs for troubleshooting
+            return ""
     else:
         return str(context_item)  # Fallback for unknown types
 
@@ -148,7 +161,9 @@ class PraisonAIAgents:
         
         # Set logger level based on verbose
         if verbose >= 5:
-            logger.setLevel(logging.INFO)
+            logger.setLevel(logging.INFO)      # keep everything ≥INFO
+        elif verbose >= 3:
+            logger.setLevel(logging.DEBUG)     # surface DEBUG when user asks for it
         else:
             logger.setLevel(logging.WARNING)
             
@@ -312,7 +327,7 @@ Expected Output: {task.expected_output}.
             if self.verbose >= 3:
                 logger.info(f"Task {task_id} context items: {len(unique_contexts)}")
                 for i, ctx in enumerate(unique_contexts):
-                    logger.info(f"Context {i+1}: {ctx[:100]}...")
+                    logger.debug(f"Context {i+1}: {ctx[:100]}...")
             context_separator = '\n\n'
             task_prompt += f"""
 Context:
@@ -635,7 +650,7 @@ Expected Output: {task.expected_output}.
             if self.verbose >= 3:
                 logger.info(f"Task {task_id} context items: {len(unique_contexts)}")
                 for i, ctx in enumerate(unique_contexts):
-                    logger.info(f"Context {i+1}: {ctx[:100]}...")
+                    logger.debug(f"Context {i+1}: {ctx[:100]}...")
             context_separator = '\n\n'
             task_prompt += f"""
 Context:
@@ -648,7 +663,10 @@ Context:
             try:
                 memory_context = task.memory.build_context_for_task(task.description)
                 if memory_context:
-                    task_prompt += f"\n\nRelevant memory context:\n{memory_context}"
+                    # Log detailed memory context for debugging
+                    logger.debug(f"Memory context for task '{task.description}': {memory_context}")
+                    # Include actual memory content without verbose headers (essential for AI agent functionality)
+                    task_prompt += f"\n\n{memory_context}"
             except Exception as e:
                 logger.error(f"Error getting memory context: {e}")
 

src/praisonai-agents/praisonaiagents/knowledge/knowledge.py

@@ -714,7 +714,15 @@ class Knowledge:
                         memory_result = self.store(memory, user_id=user_id, agent_id=agent_id, 
                                                  run_id=run_id, metadata=metadata)
                         if memory_result:
-                            all_results.extend(memory_result.get('results', []))
+                            # Handle both dict and list formats for backward compatibility
+                            if isinstance(memory_result, dict):
+                                all_results.extend(memory_result.get('results', []))
+                            elif isinstance(memory_result, list):
+                                all_results.extend(memory_result)
+                            else:
+                                # Log warning for unexpected types but don't break
+                                import logging
+                                logging.warning(f"Unexpected memory_result type: {type(memory_result)}, skipping")
                         progress.advance(store_task)
 
             return {'results': all_results, 'relations': []}

src/praisonai-agents/praisonaiagents/llm/__init__.py

@@ -1,19 +1,36 @@
 import logging
 import warnings
 import os
+import re
 
 # Disable litellm telemetry before any imports
 os.environ["LITELLM_TELEMETRY"] = "False"
 
-# Suppress all relevant logs at module level - consistent with main __init__.py
-logging.getLogger("litellm").setLevel(logging.WARNING)
-logging.getLogger("openai").setLevel(logging.WARNING)
-logging.getLogger("httpx").setLevel(logging.WARNING)
-logging.getLogger("httpcore").setLevel(logging.WARNING)
-logging.getLogger("pydantic").setLevel(logging.WARNING)
+# Check if warnings should be suppressed (consistent with main __init__.py)
+def _should_suppress_warnings():
+    import sys
+    LOGLEVEL = os.environ.get('LOGLEVEL', 'INFO').upper()
+    return (LOGLEVEL != 'DEBUG' and 
+            not hasattr(sys, '_called_from_test') and 
+            'pytest' not in sys.modules and
+            os.environ.get('PYTEST_CURRENT_TEST') is None)
 
-# Suppress pydantic warnings
-warnings.filterwarnings("ignore", category=UserWarning, module="pydantic")
+# Suppress all relevant logs at module level - more aggressive suppression consistent with main __init__.py (only when not in DEBUG mode)
+if _should_suppress_warnings():
+    logging.getLogger("litellm").setLevel(logging.CRITICAL)
+    logging.getLogger("openai").setLevel(logging.WARNING)
+    logging.getLogger("httpx").setLevel(logging.CRITICAL)
+    logging.getLogger("httpcore").setLevel(logging.CRITICAL)
+    logging.getLogger("pydantic").setLevel(logging.WARNING)
+
+    # Note: litellm child loggers automatically inherit the CRITICAL level from the parent logger
+
+# Warning filters are centrally managed in the main __init__.py file
+# Apply additional local suppression for safety during LLM imports (only when not in DEBUG mode)
+if _should_suppress_warnings():
+    for module in ['litellm', 'httpx', 'httpcore', 'pydantic']:
+        warnings.filterwarnings("ignore", category=DeprecationWarning, module=module)
+        warnings.filterwarnings("ignore", category=UserWarning, module=module)
 
 # Import after suppressing warnings
 from .llm import LLM, LLMContextLengthExceededException
@@ -40,12 +57,21 @@ from .model_router import (
     create_routing_agent
 )
 
-# Ensure telemetry is disabled after import as well
-try:
-    import litellm
-    litellm.telemetry = False
-except ImportError:
-    pass
+# Ensure comprehensive litellm configuration after import (only when not in DEBUG mode)
+if _should_suppress_warnings():
+    try:
+        import litellm
+        # Disable all litellm logging and telemetry features
+        litellm.telemetry = False
+        litellm.drop_params = True
+        if hasattr(litellm, 'suppress_debug_info'):
+            litellm.suppress_debug_info = True
+        # Set all litellm loggers to CRITICAL level
+        if hasattr(litellm, '_logging_obj'):
+            litellm._logging_obj.setLevel(logging.CRITICAL)
+        # Note: Child loggers inherit from parent, no need to iterate over all loggers
+    except ImportError:
+        pass
 
 __all__ = [
     "LLM", 

src/praisonai-agents/praisonaiagents/llm/llm.py

@@ -971,13 +971,39 @@ class LLM:
                             # Provider doesn't support streaming with tools, use non-streaming
                             use_streaming = False
                         
+                        # Track whether fallback was successful to avoid duplicate API calls
+                        fallback_completed = False
+                        
                         if use_streaming:
                             # Streaming approach (with or without tools)
                             tool_calls = []
                             response_text = ""
+                            streaming_success = False
                             
-                            if verbose:
-                                with Live(display_generating("", current_time), console=console, refresh_per_second=4) as live:
+                            # Wrap streaming with error handling for LiteLLM JSON parsing errors
+                            try:
+                                if verbose:
+                                    # Verbose streaming: show display_generating during streaming
+                                    with Live(display_generating("", current_time), console=console, refresh_per_second=4) as live:
+                                        for chunk in litellm.completion(
+                                            **self._build_completion_params(
+                                                messages=messages,
+                                                tools=formatted_tools,
+                                                temperature=temperature,
+                                                stream=True,
+                                                output_json=output_json,
+                                                output_pydantic=output_pydantic,
+                                                **kwargs
+                                            )
+                                        ):
+                                            if chunk and chunk.choices and chunk.choices[0].delta:
+                                                delta = chunk.choices[0].delta
+                                                response_text, tool_calls = self._process_stream_delta(
+                                                    delta, response_text, tool_calls, formatted_tools
+                                                )
+                                                live.update(display_generating(response_text, current_time))
+                                else:
+                                    # Non-verbose streaming: no display_generating during streaming
                                     for chunk in litellm.completion(
                                         **self._build_completion_params(
                                             messages=messages,
@@ -994,74 +1020,205 @@ class LLM:
                                             response_text, tool_calls = self._process_stream_delta(
                                                 delta, response_text, tool_calls, formatted_tools
                                             )
-                                            if delta.content:
-                                                live.update(display_generating(response_text, current_time))
+                                streaming_success = True
+                            except Exception as streaming_error:
+                                # Handle streaming errors with recovery logic
+                                if self._is_streaming_error_recoverable(streaming_error):
+                                    if verbose:
+                                        logging.warning(f"Streaming error (recoverable): {streaming_error}")
+                                        logging.warning("Falling back to non-streaming mode")
+                                    # Immediately perform non-streaming fallback with actual API call
+                                    try:
+                                        if verbose:
+                                            # When verbose=True, always use streaming for better UX
+                                            with Live(display_generating("", current_time), console=console, refresh_per_second=4, transient=True) as live:
+                                                response_text = ""
+                                                # Use streaming when verbose for progressive display
+                                                for chunk in litellm.completion(
+                                                    **self._build_completion_params(
+                                                        messages=messages,
+                                                        tools=formatted_tools,
+                                                        temperature=temperature,
+                                                        stream=True,  # Always stream when verbose=True
+                                                        output_json=output_json,
+                                                        output_pydantic=output_pydantic,
+                                                        **kwargs
+                                                    )
+                                                ):
+                                                    if chunk and chunk.choices and chunk.choices[0].delta:
+                                                        delta = chunk.choices[0].delta
+                                                        if hasattr(delta, "content") and delta.content:
+                                                            response_text += delta.content
+                                                            live.update(display_generating(response_text, current_time))
+                                            
+                                            # Clear the live display after completion
+                                            console.print()
+                                            
+                                            # Create final response structure
+                                            final_response = {
+                                                "choices": [{
+                                                    "message": {
+                                                        "content": response_text,
+                                                        "tool_calls": None
+                                                    }
+                                                }]
+                                            }
+                                        else:
+                                            # For non-streaming + non-verbose: no display_generating (per user requirements)
+                                            final_response = litellm.completion(
+                                                **self._build_completion_params(
+                                                    messages=messages,
+                                                    tools=formatted_tools,
+                                                    temperature=temperature,
+                                                    stream=False,
+                                                    output_json=output_json,
+                                                    output_pydantic=output_pydantic,
+                                                    **kwargs
+                                                )
+                                            )
+                                            response_text = final_response["choices"][0]["message"]["content"]
+                                        
+                                        # Execute callbacks and display based on verbose setting
+                                        if verbose and not interaction_displayed:
+                                            # Display the complete response at once (this will trigger callbacks internally)
+                                            display_interaction(
+                                                original_prompt,
+                                                response_text,
+                                                markdown=markdown,
+                                                generation_time=time.time() - current_time,
+                                                console=console,
+                                                agent_name=agent_name,
+                                                agent_role=agent_role,
+                                                agent_tools=agent_tools,
+                                                task_name=task_name,
+                                                task_description=task_description,
+                                                task_id=task_id
+                                            )
+                                            interaction_displayed = True
+                                            callback_executed = True
+                                        elif not callback_executed:
+                                            # Only execute callback if display_interaction hasn't been called
+                                            execute_sync_callback(
+                                                'interaction',
+                                                message=original_prompt,
+                                                response=response_text,
+                                                markdown=markdown,
+                                                generation_time=time.time() - current_time,
+                                                agent_name=agent_name,
+                                                agent_role=agent_role,
+                                                agent_tools=agent_tools,
+                                                task_name=task_name,
+                                                task_description=task_description,
+                                                task_id=task_id
+                                            )
+                                            callback_executed = True
+                                        
+                                        # Mark that fallback completed successfully
+                                        fallback_completed = True
+                                        streaming_success = False
+                                        
+                                    except Exception as fallback_error:
+                                        # If non-streaming also fails, create a graceful fallback with partial streaming data
+                                        logging.warning(f"Non-streaming fallback also failed: {fallback_error}")
+                                        logging.warning("Using partial streaming response data")
+                                        response_text = response_text or ""
+                                        # Create a mock response with whatever partial data we have
+                                        final_response = {
+                                            "choices": [{
+                                                "message": {
+                                                    "content": response_text,
+                                                    "tool_calls": tool_calls if tool_calls else None
+                                                }
+                                            }]
+                                        }
+                                        fallback_completed = True
+                                        streaming_success = False
+                                else:
+                                    # For non-recoverable errors, re-raise immediately
+                                    logging.error(f"Non-recoverable streaming error: {streaming_error}")
+                                    raise streaming_error
+                            
+                            if streaming_success:
+                                response_text = response_text.strip() if response_text else ""
+                                
+                                # Execute callbacks after streaming completes (only if not verbose, since verbose will call display_interaction later)
+                                if not verbose and not callback_executed:
+                                    execute_sync_callback(
+                                        'interaction',
+                                        message=original_prompt,
+                                        response=response_text,
+                                        markdown=markdown,
+                                        generation_time=time.time() - current_time,
+                                        agent_name=agent_name,
+                                        agent_role=agent_role,
+                                        agent_tools=agent_tools,
+                                        task_name=task_name,
+                                        task_description=task_description,
+                                        task_id=task_id
+                                    )
+                                    callback_executed = True
 
+                                # Create a mock final_response with the captured data
+                                final_response = {
+                                    "choices": [{
+                                        "message": {
+                                            "content": response_text,
+                                            "tool_calls": tool_calls if tool_calls else None
+                                        }
+                                    }]
+                                }
+                        
+                        # Only execute non-streaming if we haven't used streaming AND fallback hasn't completed
+                        if not use_streaming and not fallback_completed:
+                            # Non-streaming approach (when tools require it, streaming is disabled, or streaming fallback)
+                            if verbose:
+                                # When verbose=True, always use streaming for better UX
+                                with Live(display_generating("", current_time), console=console, refresh_per_second=4, transient=True) as live:
+                                    response_text = ""
+                                    # Use streaming when verbose for progressive display
+                                    for chunk in litellm.completion(
+                                        **self._build_completion_params(
+                                            messages=messages,
+                                            tools=formatted_tools,
+                                            temperature=temperature,
+                                            stream=True,  # Always stream when verbose=True
+                                            output_json=output_json,
+                                            output_pydantic=output_pydantic,
+                                            **kwargs
+                                        )
+                                    ):
+                                        if chunk and chunk.choices and chunk.choices[0].delta:
+                                            delta = chunk.choices[0].delta
+                                            if hasattr(delta, "content") and delta.content:
+                                                response_text += delta.content
+                                                live.update(display_generating(response_text, current_time))
+                                
+                                # Clear the live display after completion
+                                console.print()
+                                
+                                # Create final response structure
+                                final_response = {
+                                    "choices": [{
+                                        "message": {
+                                            "content": response_text,
+                                            "tool_calls": None
+                                        }
+                                    }]
+                                }
                             else:
-                                # Non-verbose streaming
-                                for chunk in litellm.completion(
+                                # For non-streaming + non-verbose: no display_generating (per user requirements)
+                                final_response = litellm.completion(
                                     **self._build_completion_params(
                                         messages=messages,
                                         tools=formatted_tools,
                                         temperature=temperature,
-                                        stream=True,
+                                        stream=False,
                                         output_json=output_json,
                                         output_pydantic=output_pydantic,
                                         **kwargs
                                     )
-                                ):
-                                    if chunk and chunk.choices and chunk.choices[0].delta:
-                                        delta = chunk.choices[0].delta
-                                        if delta.content:
-                                            response_text += delta.content
-                                        
-                                        # Capture tool calls from streaming chunks if provider supports it
-                                        if formatted_tools and self._supports_streaming_tools():
-                                            tool_calls = self._process_tool_calls_from_stream(delta, tool_calls)
-                            
-                            response_text = response_text.strip() if response_text else ""
-                            
-                            # Execute callbacks after streaming completes (only if not verbose, since verbose will call display_interaction later)
-                            if not verbose and not callback_executed:
-                                execute_sync_callback(
-                                    'interaction',
-                                    message=original_prompt,
-                                    response=response_text,
-                                    markdown=markdown,
-                                    generation_time=time.time() - current_time,
-                                    agent_name=agent_name,
-                                    agent_role=agent_role,
-                                    agent_tools=agent_tools,
-                                    task_name=task_name,
-                                    task_description=task_description,
-                                    task_id=task_id
                                 )
-                                callback_executed = True
-
-                            
-                            # Create a mock final_response with the captured data
-                            final_response = {
-                                "choices": [{
-                                    "message": {
-                                        "content": response_text,
-                                        "tool_calls": tool_calls if tool_calls else None
-                                    }
-                                }]
-                            }
-                        else:
-                            # Non-streaming approach (when tools require it or streaming is disabled)
-                            final_response = litellm.completion(
-                                **self._build_completion_params(
-                                    messages=messages,
-                                    tools=formatted_tools,
-                                    temperature=temperature,
-                                    stream=False,
-                                    output_json=output_json,
-                                    output_pydantic=output_pydantic,
-                                    **kwargs
-                                )
-                            )
-                            response_text = final_response["choices"][0]["message"]["content"]
+                                response_text = final_response["choices"][0]["message"]["content"]
                             
                             # Execute callbacks and display based on verbose setting
                             if verbose and not interaction_displayed:
@@ -1557,11 +1714,280 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
             total_time = time.time() - start_time
             logging.debug(f"get_response completed in {total_time:.2f} seconds")
 
+    def get_response_stream(
+        self,
+        prompt: Union[str, List[Dict]],
+        system_prompt: Optional[str] = None,
+        chat_history: Optional[List[Dict]] = None,
+        temperature: float = 0.2,
+        tools: Optional[List[Any]] = None,
+        output_json: Optional[BaseModel] = None,
+        output_pydantic: Optional[BaseModel] = None,
+        verbose: bool = False,  # Default to non-verbose for streaming
+        markdown: bool = True,
+        agent_name: Optional[str] = None,
+        agent_role: Optional[str] = None,
+        agent_tools: Optional[List[str]] = None,
+        task_name: Optional[str] = None,
+        task_description: Optional[str] = None,
+        task_id: Optional[str] = None,
+        execute_tool_fn: Optional[Callable] = None,
+        **kwargs
+    ):
+        """Generator that yields real-time response chunks from the LLM.
+        
+        This method provides true streaming by yielding content chunks as they
+        are received from the underlying LLM, enabling real-time display of
+        responses without waiting for the complete response.
+        
+        Args:
+            prompt: The prompt to send to the LLM
+            system_prompt: Optional system prompt
+            chat_history: Optional chat history
+            temperature: Sampling temperature
+            tools: Optional list of tools for function calling
+            output_json: Optional JSON schema for structured output
+            output_pydantic: Optional Pydantic model for structured output
+            verbose: Whether to enable verbose logging (default False for streaming)
+            markdown: Whether to enable markdown processing
+            agent_name: Optional agent name for logging
+            agent_role: Optional agent role for logging
+            agent_tools: Optional list of agent tools for logging
+            task_name: Optional task name for logging
+            task_description: Optional task description for logging
+            task_id: Optional task ID for logging
+            execute_tool_fn: Optional function for executing tools
+            **kwargs: Additional parameters
+            
+        Yields:
+            str: Individual content chunks as they are received from the LLM
+            
+        Raises:
+            Exception: If streaming fails or LLM call encounters an error
+        """
+        try:
+            import litellm
+            
+            # Build messages using existing logic
+            messages, original_prompt = self._build_messages(
+                prompt=prompt,
+                system_prompt=system_prompt,
+                chat_history=chat_history,
+                output_json=output_json,
+                output_pydantic=output_pydantic
+            )
+            
+            # Format tools for litellm
+            formatted_tools = self._format_tools_for_litellm(tools)
+            
+            # Determine if we should use streaming based on tool support
+            use_streaming = True
+            if formatted_tools and not self._supports_streaming_tools():
+                # Provider doesn't support streaming with tools, fall back to non-streaming
+                use_streaming = False
+                
+            if use_streaming:
+                # Real-time streaming approach with tool call support
+                try:
+                    tool_calls = []
+                    response_text = ""
+                    consecutive_errors = 0
+                    max_consecutive_errors = 3  # Fallback to non-streaming after 3 consecutive errors
+                    
+                    stream_iterator = litellm.completion(
+                        **self._build_completion_params(
+                            messages=messages,
+                            tools=formatted_tools,
+                            temperature=temperature,
+                            stream=True,
+                            output_json=output_json,
+                            output_pydantic=output_pydantic,
+                            **kwargs
+                        )
+                    )
+                    
+                    # Wrap the iteration with additional error handling for LiteLLM JSON parsing errors
+                    try:
+                        for chunk in stream_iterator:
+                            try:
+                                if chunk and chunk.choices and chunk.choices[0].delta:
+                                    delta = chunk.choices[0].delta
+                                    
+                                    # Process both content and tool calls using existing helper
+                                    response_text, tool_calls = self._process_stream_delta(
+                                        delta, response_text, tool_calls, formatted_tools
+                                    )
+                                    
+                                    # Yield content chunks in real-time as they arrive
+                                    if delta.content:
+                                        yield delta.content
+                                
+                                # Reset consecutive error counter only after successful chunk processing
+                                consecutive_errors = 0
+                                        
+                            except Exception as chunk_error:
+                                consecutive_errors += 1
+                                
+                                # Log the specific error for debugging
+                                if verbose:
+                                    logging.warning(f"Chunk processing error ({consecutive_errors}/{max_consecutive_errors}): {chunk_error}")
+                                
+                                # Check if this error is recoverable using our helper method
+                                if self._is_streaming_error_recoverable(chunk_error):
+                                    if verbose:
+                                        logging.warning("Recoverable streaming error detected, skipping malformed chunk and continuing")
+                                    
+                                    # Skip this malformed chunk and continue if we haven't hit the limit
+                                    if consecutive_errors < max_consecutive_errors:
+                                        continue
+                                    else:
+                                        # Too many recoverable errors, fallback to non-streaming
+                                        logging.warning(f"Too many consecutive streaming errors ({consecutive_errors}), falling back to non-streaming mode")
+                                        raise Exception(f"Streaming failed with {consecutive_errors} consecutive errors") from chunk_error
+                                else:
+                                    # For non-recoverable errors, re-raise immediately
+                                    logging.error(f"Non-recoverable streaming error: {chunk_error}")
+                                    raise chunk_error
+                    
+                    except Exception as iterator_error:
+                        # Handle errors that occur during stream iteration itself (e.g., JSON parsing in LiteLLM)
+                        error_msg = str(iterator_error).lower()
+                        
+                        # Check if this is a recoverable streaming error (including JSON parsing errors)
+                        if self._is_streaming_error_recoverable(iterator_error):
+                            if verbose:
+                                logging.warning(f"Stream iterator error detected (recoverable): {iterator_error}")
+                                logging.warning("Falling back to non-streaming mode due to stream iteration failure")
+                            
+                            # Force fallback to non-streaming for iterator-level errors
+                            raise Exception("Stream iteration failed with recoverable error, falling back to non-streaming") from iterator_error
+                        else:
+                            # For non-recoverable errors, re-raise immediately
+                            logging.error(f"Non-recoverable stream iterator error: {iterator_error}")
+                            raise iterator_error
+                    
+                    # After streaming completes, handle tool calls if present
+                    if tool_calls and execute_tool_fn:
+                        # Add assistant message with tool calls to conversation
+                        if self._is_ollama_provider():
+                            messages.append({
+                                "role": "assistant",
+                                "content": response_text
+                            })
+                        else:
+                            serializable_tool_calls = self._serialize_tool_calls(tool_calls)
+                            messages.append({
+                                "role": "assistant",
+                                "content": response_text,
+                                "tool_calls": serializable_tool_calls
+                            })
+                        
+                        # Execute tool calls and add results to conversation
+                        for tool_call in tool_calls:
+                            is_ollama = self._is_ollama_provider()
+                            function_name, arguments, tool_call_id = self._extract_tool_call_info(tool_call, is_ollama)
+                            
+                            try:
+                                # Execute the tool
+                                tool_result = execute_tool_fn(function_name, arguments)
+                                
+                                # Add tool result to messages
+                                tool_message = self._create_tool_message(function_name, tool_result, tool_call_id, is_ollama)
+                                messages.append(tool_message)
+                                
+                            except Exception as e:
+                                logging.error(f"Tool execution error for {function_name}: {e}")
+                                # Add error message to conversation
+                                error_message = self._create_tool_message(
+                                    function_name, f"Error executing tool: {e}", tool_call_id, is_ollama
+                                )
+                                messages.append(error_message)
+                        
+                        # Continue conversation after tool execution - get follow-up response
+                        try:
+                            follow_up_response = litellm.completion(
+                                **self._build_completion_params(
+                                    messages=messages,
+                                    tools=formatted_tools,
+                                    temperature=temperature,
+                                    stream=False,
+                                    **kwargs
+                                )
+                            )
+                            
+                            if follow_up_response and follow_up_response.choices:
+                                follow_up_content = follow_up_response.choices[0].message.content
+                                if follow_up_content:
+                                    # Yield the follow-up response after tool execution
+                                    yield follow_up_content
+                        except Exception as e:
+                            logging.error(f"Follow-up response failed: {e}")
+                            
+                except Exception as e:
+                    error_msg = str(e).lower()
+                    
+                    # Provide more specific error messages based on the error type
+                    if any(keyword in error_msg for keyword in ['json', 'expecting property name', 'parse', 'decode']):
+                        logging.warning(f"Streaming failed due to JSON parsing errors (likely malformed chunks from provider): {e}")
+                    elif 'connection' in error_msg or 'timeout' in error_msg:
+                        logging.warning(f"Streaming failed due to connection issues: {e}")
+                    else:
+                        logging.error(f"Streaming failed with unexpected error: {e}")
+                    
+                    # Fall back to non-streaming if streaming fails
+                    use_streaming = False
+            
+            if not use_streaming:
+                # Fall back to non-streaming and yield the complete response
+                try:
+                    response = litellm.completion(
+                        **self._build_completion_params(
+                            messages=messages,
+                            tools=formatted_tools,
+                            temperature=temperature,
+                            stream=False,
+                            output_json=output_json,
+                            output_pydantic=output_pydantic,
+                            **kwargs
+                        )
+                    )
+                    
+                    if response and response.choices:
+                        content = response.choices[0].message.content
+                        if content:
+                            # Yield the complete response as a single chunk
+                            yield content
+                            
+                except Exception as e:
+                    logging.error(f"Non-streaming fallback failed: {e}")
+                    raise
+                    
+        except Exception as e:
+            logging.error(f"Error in get_response_stream: {e}")
+            raise
+
     def _is_gemini_model(self) -> bool:
         """Check if the model is a Gemini model."""
         if not self.model:
             return False
         return any(prefix in self.model.lower() for prefix in ['gemini', 'gemini/', 'google/gemini'])
+    
+    def _is_streaming_error_recoverable(self, error: Exception) -> bool:
+        """Check if a streaming error is recoverable (e.g., malformed chunk vs connection error)."""
+        error_msg = str(error).lower()
+        
+        # JSON parsing errors are often recoverable (skip malformed chunk and continue)
+        json_error_keywords = ['json', 'expecting property name', 'parse', 'decode', 'invalid json']
+        if any(keyword in error_msg for keyword in json_error_keywords):
+            return True
+            
+        # Connection errors might be temporary but are less recoverable in streaming context
+        connection_error_keywords = ['connection', 'timeout', 'network', 'http']
+        if any(keyword in error_msg for keyword in connection_error_keywords):
+            return False
+            
+        # Other errors are generally not recoverable
+        return False
 
     async def get_response_async(
         self,

src/praisonai-agents/praisonaiagents/llm/openai_client.py

@@ -838,14 +838,55 @@ class OpenAIClient:
                 )
             else:
                 # Process as regular non-streaming response
-                final_response = self.create_completion(
-                    messages=messages,
-                    model=model,
-                    temperature=temperature,
-                    tools=formatted_tools,
-                    stream=False,
-                    **kwargs
-                )
+                if display_fn and console:
+                    # When verbose (display_fn provided), use streaming for better UX
+                    try:
+                        with Live(display_fn("", start_time), console=console, refresh_per_second=4, transient=True) as live:
+                            # Use streaming when display_fn is provided for progressive display
+                            response_stream = self.create_completion(
+                                messages=messages,
+                                model=model,
+                                temperature=temperature,
+                                tools=formatted_tools,
+                                stream=True,  # Always stream when verbose/display_fn
+                                **kwargs
+                            )
+                            
+                            full_response_text = ""
+                            chunks = []
+                            
+                            # Process streaming response
+                            for chunk in response_stream:
+                                chunks.append(chunk)
+                                if chunk.choices[0].delta.content:
+                                    full_response_text += chunk.choices[0].delta.content
+                                    live.update(display_fn(full_response_text, start_time))
+                            
+                            # Process final response from chunks
+                            final_response = process_stream_chunks(chunks)
+                        
+                        # Clear the last generating display with a blank line
+                        console.print()
+                    except Exception as e:
+                        self.logger.error(f"Error in Live display for non-streaming: {e}")
+                        # Fallback to regular completion without display
+                        final_response = self.create_completion(
+                            messages=messages,
+                            model=model,
+                            temperature=temperature,
+                            tools=formatted_tools,
+                            stream=False,
+                            **kwargs
+                        )
+                else:
+                    final_response = self.create_completion(
+                        messages=messages,
+                        model=model,
+                        temperature=temperature,
+                        tools=formatted_tools,
+                        stream=False,
+                        **kwargs
+                    )
             
             if not final_response:
                 return None
@@ -969,14 +1010,55 @@ class OpenAIClient:
                 )
             else:
                 # Process as regular non-streaming response
-                final_response = await self.acreate_completion(
-                    messages=messages,
-                    model=model,
-                    temperature=temperature,
-                    tools=formatted_tools,
-                    stream=False,
-                    **kwargs
-                )
+                if display_fn and console:
+                    # When verbose (display_fn provided), use streaming for better UX
+                    try:
+                        with Live(display_fn("", start_time), console=console, refresh_per_second=4, transient=True) as live:
+                            # Use streaming when display_fn is provided for progressive display
+                            response_stream = await self.acreate_completion(
+                                messages=messages,
+                                model=model,
+                                temperature=temperature,
+                                tools=formatted_tools,
+                                stream=True,  # Always stream when verbose/display_fn
+                                **kwargs
+                            )
+                            
+                            full_response_text = ""
+                            chunks = []
+                            
+                            # Process streaming response
+                            async for chunk in response_stream:
+                                chunks.append(chunk)
+                                if chunk.choices[0].delta.content:
+                                    full_response_text += chunk.choices[0].delta.content
+                                    live.update(display_fn(full_response_text, start_time))
+                            
+                            # Process final response from chunks
+                            final_response = process_stream_chunks(chunks)
+                        
+                        # Clear the last generating display with a blank line
+                        console.print()
+                    except Exception as e:
+                        self.logger.error(f"Error in Live display for async non-streaming: {e}")
+                        # Fallback to regular completion without display
+                        final_response = await self.acreate_completion(
+                            messages=messages,
+                            model=model,
+                            temperature=temperature,
+                            tools=formatted_tools,
+                            stream=False,
+                            **kwargs
+                        )
+                else:
+                    final_response = await self.acreate_completion(
+                        messages=messages,
+                        model=model,
+                        temperature=temperature,
+                        tools=formatted_tools,
+                        stream=False,
+                        **kwargs
+                    )
             
             if not final_response:
                 return None

src/praisonai-agents/praisonaiagents/memory/memory.py

@@ -10,9 +10,19 @@ from datetime import datetime
 # Disable litellm telemetry before any imports
 os.environ["LITELLM_TELEMETRY"] = "False"
 
-# Set up logger
+# Set up logger with custom TRACE level
 logger = logging.getLogger(__name__)
 
+# Add custom TRACE level (below DEBUG)
+TRACE_LEVEL = 5
+logging.addLevelName(TRACE_LEVEL, 'TRACE')
+
+def trace(self, message, *args, **kwargs):
+    if self.isEnabledFor(TRACE_LEVEL):
+        self._log(TRACE_LEVEL, message, args, **kwargs)
+
+logging.Logger.trace = trace
+
 try:
     import chromadb
     from chromadb.config import Settings as ChromaSettings
@@ -128,6 +138,8 @@ class Memory:
         logging.getLogger('httpx').setLevel(logging.WARNING)
         logging.getLogger('httpcore').setLevel(logging.WARNING)
         logging.getLogger('chromadb.segment.impl.vector.local_persistent_hnsw').setLevel(logging.ERROR)
+        logging.getLogger('utils').setLevel(logging.WARNING)
+        logging.getLogger('litellm.utils').setLevel(logging.WARNING)
             
         self.provider = self.cfg.get("provider", "rag")
         self.use_mem0 = (self.provider.lower() == "mem0") and MEM0_AVAILABLE
@@ -770,7 +782,7 @@ class Memory:
                     import litellm
                     
                     logger.info("Getting embeddings from LiteLLM...")
-                    logger.debug(f"Embedding input text: {text}")
+                    logger.trace(f"Embedding input text: {text}")
                     
                     response = litellm.embedding(
                         model=self.embedding_model,
@@ -778,7 +790,7 @@ class Memory:
                     )
                     embedding = response.data[0]["embedding"]
                     logger.info("Successfully got embeddings from LiteLLM")
-                    logger.debug(f"Received embedding of length: {len(embedding)}")
+                    logger.trace(f"Received embedding of length: {len(embedding)}")
                     
                 elif OPENAI_AVAILABLE:
                     # Fallback to OpenAI client
@@ -786,7 +798,7 @@ class Memory:
                     client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
                     
                     logger.info("Getting embeddings from OpenAI...")
-                    logger.debug(f"Embedding input text: {text}")
+                    logger.trace(f"Embedding input text: {text}")
                     
                     response = client.embeddings.create(
                         input=text,
@@ -794,7 +806,7 @@ class Memory:
                     )
                     embedding = response.data[0].embedding
                     logger.info("Successfully got embeddings from OpenAI")
-                    logger.debug(f"Received embedding of length: {len(embedding)}")
+                    logger.trace(f"Received embedding of length: {len(embedding)}")
                 else:
                     logger.warning("Neither litellm nor openai available for embeddings")
                     return
@@ -1243,12 +1255,22 @@ class Memory:
         task_descr: str,
         user_id: Optional[str] = None,
         additional: str = "",
-        max_items: int = 3
+        max_items: int = 3,
+        include_in_output: Optional[bool] = None
     ) -> str:
         """
         Merges relevant short-term, long-term, entity, user memories
         into a single text block with deduplication and clean formatting.
+        
+        Args:
+            include_in_output: If None, memory content is only included when debug logging is enabled.
+                               If True, memory content is always included.
+                               If False, memory content is never included (only logged for debugging).
         """
+        # Determine whether to include memory content in output based on logging level
+        if include_in_output is None:
+            include_in_output = logging.getLogger().getEffectiveLevel() == logging.DEBUG
+        
         q = (task_descr + " " + additional).strip()
         lines = []
         seen_contents = set()  # Track unique contents
@@ -1306,16 +1328,20 @@ class Memory:
                     formatted_hits.append(formatted)
             
             if formatted_hits:
-                # Add section header
-                if lines:
-                    lines.append("")  # Space before new section
-                lines.append(title)
-                lines.append("=" * len(title))  # Underline the title
-                lines.append("")  # Space after title
+                # Log detailed memory content for debugging including section headers
+                brief_title = title.replace(" Context", "").replace("Memory ", "")
+                logger.debug(f"Memory section '{brief_title}' ({len(formatted_hits)} items): {formatted_hits}")
                 
-                # Add formatted content with bullet points
-                for content in formatted_hits:
-                    lines.append(f" • {content}")
+                # Only include memory content in output when specified (controlled by log level or explicit parameter)
+                if include_in_output:
+                    # Add only the actual memory content for AI agent use (no headers)
+                    if lines:
+                        lines.append("")  # Space before new section
+                    
+                    # Include actual memory content without verbose section headers
+                    for hit in formatted_hits:
+                        lines.append(f"• {hit}")
+                    lines.append("")  # Space after content
 
         # Add each section
         # First get all results
@@ -1522,3 +1548,46 @@ class Memory:
         logger.info(f"After quality filter: {len(filtered)} results")
         
         return filtered
+
+    def get_all_memories(self) -> List[Dict[str, Any]]:
+        """Get all memories from both short-term and long-term storage"""
+        all_memories = []
+        
+        try:
+            # Get short-term memories
+            conn = sqlite3.connect(self.short_db)
+            c = conn.cursor()
+            rows = c.execute("SELECT id, content, meta, created_at FROM short_mem").fetchall()
+            conn.close()
+            
+            for row in rows:
+                meta = json.loads(row[2] or "{}")
+                all_memories.append({
+                    "id": row[0],
+                    "text": row[1],
+                    "metadata": meta,
+                    "created_at": row[3],
+                    "type": "short_term"
+                })
+            
+            # Get long-term memories
+            conn = sqlite3.connect(self.long_db)
+            c = conn.cursor()
+            rows = c.execute("SELECT id, content, meta, created_at FROM long_mem").fetchall()
+            conn.close()
+            
+            for row in rows:
+                meta = json.loads(row[2] or "{}")
+                all_memories.append({
+                    "id": row[0],
+                    "text": row[1],
+                    "metadata": meta,
+                    "created_at": row[3],
+                    "type": "long_term"
+                })
+            
+            return all_memories
+            
+        except Exception as e:
+            self._log_verbose(f"Error getting all memories: {e}", logging.ERROR)
+            return []

src/praisonai-agents/praisonaiagents/task/task.py

@@ -402,13 +402,14 @@ Expected Output: {self.expected_output}.
                     context_results.append(f"Input Content: {' '.join(str(x) for x in context_item)}")
                 elif hasattr(context_item, 'result'):  # Task object
                     if context_item.result:
-                        context_results.append(
-                            f"Result of previous task {context_item.name if context_item.name else context_item.description}:\n{context_item.result.raw}"
-                        )
+                        task_name = context_item.name if context_item.name else context_item.description
+                        # Log detailed result for debugging
+                        logger.debug(f"Previous task '{task_name}' result: {context_item.result.raw}")
+                        # Include actual result content without verbose labels (essential for task chaining)
+                        context_results.append(context_item.result.raw)
                     else:
-                        context_results.append(
-                            f"Previous task {context_item.name if context_item.name else context_item.description} has no result yet."
-                        )
+                        # Task has no result yet, don't include verbose status message
+                        pass
 
             # Join unique context results
             unique_contexts = list(dict.fromkeys(context_results))  # Remove duplicates

src/praisonai-agents/praisonaiagents/telemetry/__init__.py

@@ -1,13 +1,24 @@
 """
-PraisonAI Agents Minimal Telemetry Module
+PraisonAI Agents Telemetry & Performance Monitoring Module
+
+This module provides:
+1. Anonymous usage tracking with privacy-first design  
+2. User-friendly performance monitoring and analysis tools
 
-This module provides anonymous usage tracking with privacy-first design.
 Telemetry is opt-out and can be disabled via environment variables:
 - PRAISONAI_TELEMETRY_DISABLED=true
 - PRAISONAI_DISABLE_TELEMETRY=true  
 - DO_NOT_TRACK=true
 
 No personal data, prompts, or responses are collected.
+
+Performance Monitoring Features:
+- Function performance tracking with detailed statistics
+- API call monitoring and analysis
+- Function execution flow visualization  
+- Performance bottleneck identification
+- Real-time performance reporting
+- CLI interface for easy access
 """
 
 import os
@@ -20,15 +31,64 @@ if TYPE_CHECKING:
 # Import the classes for real (not just type checking)
 from .telemetry import MinimalTelemetry, TelemetryCollector
 
+# Import performance monitoring tools
+try:
+    from .performance_monitor import (
+        PerformanceMonitor, performance_monitor,
+        monitor_function, track_api_call, get_performance_report,
+        get_function_stats, get_api_stats, get_slowest_functions, 
+        get_slowest_apis, clear_performance_data
+    )
+    from .performance_utils import (
+        FunctionFlowAnalyzer, PerformanceAnalyzer,
+        flow_analyzer, performance_analyzer,
+        analyze_function_flow, visualize_execution_flow,
+        analyze_performance_trends, generate_comprehensive_report
+    )
+    from .performance_cli import PerformanceCLI
+    PERFORMANCE_MONITORING_AVAILABLE = True
+except ImportError:
+    PERFORMANCE_MONITORING_AVAILABLE = False
+
 __all__ = [
+    # Core telemetry
     'get_telemetry',
     'enable_telemetry',
-    'disable_telemetry',
+    'disable_telemetry', 
     'force_shutdown_telemetry',
     'MinimalTelemetry',
     'TelemetryCollector',  # For backward compatibility
 ]
 
+# Add performance monitoring to __all__ if available
+if PERFORMANCE_MONITORING_AVAILABLE:
+    __all__.extend([
+        # Performance monitoring classes
+        'PerformanceMonitor',
+        'FunctionFlowAnalyzer', 
+        'PerformanceAnalyzer',
+        'PerformanceCLI',
+        # Global instances
+        'performance_monitor',
+        'flow_analyzer',
+        'performance_analyzer',
+        # Convenience functions
+        'monitor_function',
+        'track_api_call',
+        'get_performance_report',
+        'get_function_stats',
+        'get_api_stats',
+        'get_slowest_functions',
+        'get_slowest_apis',
+        'clear_performance_data',
+        'analyze_function_flow',
+        'visualize_execution_flow',
+        'analyze_performance_trends',
+        'generate_comprehensive_report',
+        # Availability flag
+        'PERFORMANCE_MONITORING_AVAILABLE'
+    ])
+
 
 def get_telemetry() -> 'MinimalTelemetry':
     """Get the global telemetry instance."""

src/praisonai-agents/praisonaiagents/telemetry/integration.py

@@ -42,13 +42,27 @@ def instrument_agent(agent: 'Agent', telemetry: Optional['MinimalTelemetry'] = N
     if original_chat:
         @wraps(original_chat)
         def instrumented_chat(*args, **kwargs):
+            import threading
+            
             try:
                 result = original_chat(*args, **kwargs)
-                telemetry.track_agent_execution(agent.name, success=True)
+                # Track success asynchronously to prevent blocking
+                def track_async():
+                    try:
+                        telemetry.track_agent_execution(agent.name, success=True)
+                    except:
+                        pass  # Ignore telemetry errors
+                threading.Thread(target=track_async, daemon=True).start()
                 return result
             except Exception as e:
-                telemetry.track_agent_execution(agent.name, success=False)
-                telemetry.track_error(type(e).__name__)
+                # Track error asynchronously
+                def track_error_async():
+                    try:
+                        telemetry.track_agent_execution(agent.name, success=False)
+                        telemetry.track_error(type(e).__name__)
+                    except:
+                        pass  # Ignore telemetry errors
+                threading.Thread(target=track_error_async, daemon=True).start()
                 raise
         
         agent.chat = instrumented_chat
@@ -57,13 +71,53 @@ def instrument_agent(agent: 'Agent', telemetry: Optional['MinimalTelemetry'] = N
     if original_start:
         @wraps(original_start)
         def instrumented_start(*args, **kwargs):
+            import types
+            import threading
+            
             try:
                 result = original_start(*args, **kwargs)
-                telemetry.track_agent_execution(agent.name, success=True)
-                return result
+                
+                # Check if result is a generator (streaming mode)
+                if isinstance(result, types.GeneratorType):
+                    # For streaming, defer telemetry tracking to avoid blocking
+                    def streaming_wrapper():
+                        try:
+                            for chunk in result:
+                                yield chunk
+                            # Track success only after streaming completes
+                            # Use a separate thread to make it truly non-blocking
+                            def track_async():
+                                try:
+                                    telemetry.track_agent_execution(agent.name, success=True)
+                                except:
+                                    pass  # Ignore telemetry errors
+                            threading.Thread(target=track_async, daemon=True).start()
+                        except Exception as e:
+                            # Track error immediately
+                            threading.Thread(target=lambda: telemetry.track_agent_execution(agent.name, success=False), daemon=True).start()
+                            threading.Thread(target=lambda: telemetry.track_error(type(e).__name__), daemon=True).start()
+                            raise
+                    
+                    return streaming_wrapper()
+                else:
+                    # For non-streaming, track immediately but asynchronously
+                    def track_async():
+                        try:
+                            telemetry.track_agent_execution(agent.name, success=True)
+                        except:
+                            pass  # Ignore telemetry errors
+                    threading.Thread(target=track_async, daemon=True).start()
+                    return result
+                    
             except Exception as e:
-                telemetry.track_agent_execution(agent.name, success=False)
-                telemetry.track_error(type(e).__name__)
+                # Track error immediately but asynchronously
+                def track_error_async():
+                    try:
+                        telemetry.track_agent_execution(agent.name, success=False)
+                        telemetry.track_error(type(e).__name__)
+                    except:
+                        pass  # Ignore telemetry errors
+                threading.Thread(target=track_error_async, daemon=True).start()
                 raise
         
         agent.start = instrumented_start
@@ -72,13 +126,27 @@ def instrument_agent(agent: 'Agent', telemetry: Optional['MinimalTelemetry'] = N
     if original_run:
         @wraps(original_run)
         def instrumented_run(*args, **kwargs):
+            import threading
+            
             try:
                 result = original_run(*args, **kwargs)
-                telemetry.track_agent_execution(agent.name, success=True)
+                # Track success asynchronously to prevent blocking
+                def track_async():
+                    try:
+                        telemetry.track_agent_execution(agent.name, success=True)
+                    except:
+                        pass  # Ignore telemetry errors
+                threading.Thread(target=track_async, daemon=True).start()
                 return result
             except Exception as e:
-                telemetry.track_agent_execution(agent.name, success=False)
-                telemetry.track_error(type(e).__name__)
+                # Track error asynchronously
+                def track_error_async():
+                    try:
+                        telemetry.track_agent_execution(agent.name, success=False)
+                        telemetry.track_error(type(e).__name__)
+                    except:
+                        pass  # Ignore telemetry errors
+                threading.Thread(target=track_error_async, daemon=True).start()
                 raise
         
         agent.run = instrumented_run

src/praisonai-agents/praisonaiagents/telemetry/performance_cli.py

@@ -0,0 +1,397 @@
+"""
+Performance Monitoring CLI for PraisonAI
+
+Command-line interface for accessing performance monitoring features.
+Provides easy access to function performance, API call tracking, and flow analysis.
+
+Usage:
+    python -m praisonaiagents.telemetry.performance_cli [command] [options]
+    
+Or import and use programmatically:
+    from praisonaiagents.telemetry.performance_cli import PerformanceCLI
+    cli = PerformanceCLI()
+    cli.show_performance_report()
+"""
+
+import argparse
+import json
+import sys
+from typing import Optional
+import logging
+
+try:
+    from .performance_monitor import performance_monitor, get_performance_report
+    from .performance_utils import (
+        analyze_function_flow, visualize_execution_flow, 
+        analyze_performance_trends, generate_comprehensive_report
+    )
+    PERFORMANCE_TOOLS_AVAILABLE = True
+except ImportError:
+    PERFORMANCE_TOOLS_AVAILABLE = False
+
+logger = logging.getLogger(__name__)
+
+
+class PerformanceCLI:
+    """Command-line interface for performance monitoring."""
+    
+    def __init__(self):
+        if not PERFORMANCE_TOOLS_AVAILABLE:
+            print("❌ Performance monitoring tools not available")
+            sys.exit(1)
+    
+    def show_performance_report(self, detailed: bool = False) -> None:
+        """Show performance monitoring report."""
+        print("📊 PraisonAI Performance Monitoring Report")
+        print("=" * 60)
+        
+        if detailed:
+            report = generate_comprehensive_report()
+        else:
+            report = get_performance_report()
+        
+        print(report)
+    
+    def show_function_stats(self, function_name: Optional[str] = None) -> None:
+        """Show function performance statistics."""
+        stats = performance_monitor.get_function_performance(function_name)
+        
+        if not stats:
+            print("❌ No function performance data available")
+            return
+        
+        print("🔧 Function Performance Statistics")
+        print("=" * 50)
+        
+        for func, data in stats.items():
+            print(f"\n📝 Function: {func}")
+            print(f"   Calls: {data['call_count']}")
+            print(f"   Total Time: {data['total_time']:.3f}s")
+            if data['call_count'] > 0:
+                print(f"   Average Time: {data.get('average_time', 0):.3f}s")
+                print(f"   Min Time: {data['min_time']:.3f}s")
+                print(f"   Max Time: {data['max_time']:.3f}s")
+                print(f"   Success Rate: {data.get('success_rate', 0)*100:.1f}%")
+                print(f"   Errors: {data['error_count']}")
+    
+    def show_api_stats(self, api_name: Optional[str] = None) -> None:
+        """Show API call performance statistics."""
+        stats = performance_monitor.get_api_call_performance(api_name)
+        
+        if not stats:
+            print("❌ No API performance data available")
+            return
+        
+        print("🌐 API Call Performance Statistics")
+        print("=" * 50)
+        
+        for api, data in stats.items():
+            print(f"\n🔗 API: {api}")
+            print(f"   Calls: {data['call_count']}")
+            print(f"   Total Time: {data['total_time']:.3f}s")
+            if data['call_count'] > 0:
+                print(f"   Average Time: {data.get('average_time', 0):.3f}s")
+                print(f"   Min Time: {data['min_time']:.3f}s")
+                print(f"   Max Time: {data['max_time']:.3f}s")
+                print(f"   Success Rate: {data.get('success_rate', 0)*100:.1f}%")
+                print(f"   Successful Calls: {data['success_count']}")
+                print(f"   Failed Calls: {data['error_count']}")
+    
+    def show_slowest_functions(self, limit: int = 10) -> None:
+        """Show slowest performing functions."""
+        slowest = performance_monitor.get_slowest_functions(limit)
+        
+        if not slowest:
+            print("❌ No function performance data available")
+            return
+        
+        print(f"🐌 Top {limit} Slowest Functions")
+        print("=" * 50)
+        
+        for i, func in enumerate(slowest, 1):
+            print(f"{i:2d}. {func['function']}")
+            print(f"     Average: {func['average_time']:.3f}s")
+            print(f"     Max: {func['max_time']:.3f}s")
+            print(f"     Calls: {func['call_count']}")
+            print()
+    
+    def show_slowest_apis(self, limit: int = 10) -> None:
+        """Show slowest performing API calls."""
+        slowest = performance_monitor.get_slowest_api_calls(limit)
+        
+        if not slowest:
+            print("❌ No API performance data available")
+            return
+        
+        print(f"🐌 Top {limit} Slowest API Calls")
+        print("=" * 50)
+        
+        for i, api in enumerate(slowest, 1):
+            print(f"{i:2d}. {api['api']}")
+            print(f"     Average: {api['average_time']:.3f}s")
+            print(f"     Max: {api['max_time']:.3f}s") 
+            print(f"     Success Rate: {api['success_rate']*100:.1f}%")
+            print(f"     Calls: {api['call_count']}")
+            print()
+    
+    def show_function_flow(self, format: str = "text", events: int = 50) -> None:
+        """Show function execution flow."""
+        print(f"🔄 Function Execution Flow (Last {events} events)")
+        print("=" * 60)
+        
+        flow_data = performance_monitor.get_function_flow(events)
+        if not flow_data:
+            print("❌ No flow data available")
+            return
+        
+        if format == "json":
+            print(json.dumps(flow_data, indent=2))
+        else:
+            visualization = visualize_execution_flow(format=format)
+            print(visualization)
+    
+    def analyze_flow(self) -> None:
+        """Analyze function execution flow for bottlenecks and patterns."""
+        print("🔍 Function Flow Analysis")
+        print("=" * 50)
+        
+        analysis = analyze_function_flow()
+        
+        if "error" in analysis:
+            print(f"❌ {analysis['error']}")
+            return
+        
+        if "message" in analysis:
+            print(f"ℹ️  {analysis['message']}")
+            return
+        
+        # Show bottlenecks
+        bottlenecks = analysis.get("bottlenecks", [])
+        if bottlenecks:
+            print("🚨 Identified Bottlenecks:")
+            for bottleneck in bottlenecks:
+                severity_emoji = "🔴" if bottleneck["severity"] == "high" else "🟡"
+                print(f"  {severity_emoji} {bottleneck['function']}")
+                print(f"     Average: {bottleneck['average_duration']:.3f}s")
+                print(f"     Max: {bottleneck['max_duration']:.3f}s")
+                print(f"     Calls: {bottleneck['call_count']}")
+                print()
+        else:
+            print("✅ No significant bottlenecks identified")
+        
+        # Show statistics
+        stats = analysis.get("statistics", {})
+        if stats:
+            print("\n📊 Flow Statistics:")
+            print(f"   Total Function Calls: {stats.get('function_calls', 0)}")
+            print(f"   Completed Calls: {stats.get('completed_calls', 0)}")
+            print(f"   Success Rate: {stats.get('success_rate', 0)*100:.1f}%")
+            print(f"   Total Execution Time: {stats.get('total_execution_time', 0):.3f}s")
+            print(f"   Average Execution Time: {stats.get('average_execution_time', 0):.3f}s")
+        
+        # Show parallelism info
+        parallelism = analysis.get("parallelism", {})
+        if parallelism:
+            print("\n🧵 Parallelism Analysis:")
+            print(f"   Total Threads: {parallelism.get('total_threads', 0)}")
+            print(f"   Peak Concurrency: {parallelism.get('peak_concurrency', 0)}")
+    
+    def show_active_calls(self) -> None:
+        """Show currently active function calls."""
+        active = performance_monitor.get_active_calls()
+        
+        print("⚡ Currently Active Function Calls")
+        print("=" * 50)
+        
+        if not active:
+            print("✅ No active function calls")
+            return
+        
+        for _call_id, info in active.items():
+            print(f"🔄 {info['function']}")
+            print(f"   Duration: {info['duration']:.1f}s")
+            print(f"   Thread: {info['thread_id']}")
+            print(f"   Started: {info['started_at']}")
+            print()
+    
+    def show_trends(self) -> None:
+        """Show performance trends analysis."""
+        print("📈 Performance Trends Analysis")
+        print("=" * 50)
+        
+        trends = analyze_performance_trends()
+        
+        if "error" in trends:
+            print(f"❌ {trends['error']}")
+            return
+        
+        # Show recommendations
+        recommendations = trends.get("recommendations", [])
+        if recommendations:
+            print("💡 Performance Recommendations:")
+            for rec in recommendations:
+                print(f"  {rec}")
+            print()
+        
+        # Show function trends
+        func_trends = trends.get("function_trends", {})
+        improving = func_trends.get("improving", [])
+        degrading = func_trends.get("degrading", [])
+        
+        if improving:
+            print("📈 Improving Functions:")
+            for trend in improving:
+                print(f"  ✅ {trend['function']} ({trend['change_percent']:+.1f}%)")
+        
+        if degrading:
+            print("\n📉 Degrading Functions:")
+            for trend in degrading:
+                print(f"  ⚠️  {trend['function']} ({trend['change_percent']:+.1f}%)")
+        
+        # Show API trends
+        api_trends = trends.get("api_trends", {})
+        slowest_apis = api_trends.get("slowest_apis", [])
+        least_reliable = api_trends.get("least_reliable", [])
+        
+        if slowest_apis:
+            print("\n🐌 Slowest APIs:")
+            for api in slowest_apis[:3]:
+                print(f"  • {api['api']}: {api['average_time']:.3f}s avg")
+        
+        if least_reliable:
+            print("\n⚠️  Least Reliable APIs:")
+            for api in least_reliable[:3]:
+                if api['success_rate'] < 1.0:
+                    print(f"  • {api['api']}: {api['success_rate']*100:.1f}% success rate")
+    
+    def export_data(self, format: str = "json", output_file: Optional[str] = None) -> None:
+        """Export performance data to file or stdout."""
+        data = performance_monitor.export_data(format)
+        
+        if output_file:
+            try:
+                with open(output_file, 'w') as f:
+                    f.write(data)
+                print(f"✅ Data exported to {output_file}")
+            except (IOError, OSError) as e:
+                print(f"❌ Error exporting to file: {e}")
+        else:
+            print(data)
+    
+    def clear_data(self) -> None:
+        """Clear all performance monitoring data."""
+        performance_monitor.clear_statistics()
+        print("✅ All performance monitoring data cleared")
+
+
+def main():
+    """Main CLI entry point."""
+    parser = argparse.ArgumentParser(
+        description="PraisonAI Performance Monitoring CLI",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  python -m praisonaiagents.telemetry.performance_cli report
+  python -m praisonaiagents.telemetry.performance_cli functions
+  python -m praisonaiagents.telemetry.performance_cli apis
+  python -m praisonaiagents.telemetry.performance_cli slowest-functions 5
+  python -m praisonaiagents.telemetry.performance_cli flow
+  python -m praisonaiagents.telemetry.performance_cli analyze-flow
+  python -m praisonaiagents.telemetry.performance_cli trends
+        """
+    )
+    
+    subparsers = parser.add_subparsers(dest='command', help='Available commands')
+    
+    # Report command
+    report_parser = subparsers.add_parser('report', help='Show performance report')
+    report_parser.add_argument('--detailed', action='store_true', 
+                              help='Show detailed comprehensive report')
+    
+    # Functions command
+    func_parser = subparsers.add_parser('functions', help='Show function statistics')
+    func_parser.add_argument('--name', help='Specific function name')
+    
+    # APIs command  
+    api_parser = subparsers.add_parser('apis', help='Show API call statistics')
+    api_parser.add_argument('--name', help='Specific API name')
+    
+    # Slowest functions command
+    slowest_func_parser = subparsers.add_parser('slowest-functions', 
+                                               help='Show slowest functions')
+    slowest_func_parser.add_argument('limit', type=int, nargs='?', default=10,
+                                    help='Number of functions to show')
+    
+    # Slowest APIs command
+    slowest_api_parser = subparsers.add_parser('slowest-apis',
+                                              help='Show slowest API calls')
+    slowest_api_parser.add_argument('limit', type=int, nargs='?', default=10,
+                                   help='Number of APIs to show')
+    
+    # Flow command
+    flow_parser = subparsers.add_parser('flow', help='Show function execution flow')
+    flow_parser.add_argument('--format', choices=['text', 'json', 'mermaid'], 
+                           default='text', help='Output format')
+    flow_parser.add_argument('--events', type=int, default=50,
+                           help='Number of recent events to show')
+    
+    # Analyze flow command
+    subparsers.add_parser('analyze-flow', help='Analyze function flow for bottlenecks')
+    
+    # Active calls command
+    subparsers.add_parser('active', help='Show currently active function calls')
+    
+    # Trends command
+    subparsers.add_parser('trends', help='Show performance trends analysis')
+    
+    # Export command
+    export_parser = subparsers.add_parser('export', help='Export performance data')
+    export_parser.add_argument('--format', choices=['json', 'dict'], default='json',
+                              help='Export format')
+    export_parser.add_argument('--output', help='Output file (default: stdout)')
+    
+    # Clear command
+    subparsers.add_parser('clear', help='Clear all performance data')
+    
+    args = parser.parse_args()
+    
+    if not args.command:
+        parser.print_help()
+        return
+    
+    try:
+        cli = PerformanceCLI()
+        
+        if args.command == 'report':
+            cli.show_performance_report(detailed=args.detailed)
+        elif args.command == 'functions':
+            cli.show_function_stats(args.name)
+        elif args.command == 'apis':
+            cli.show_api_stats(args.name)
+        elif args.command == 'slowest-functions':
+            cli.show_slowest_functions(args.limit)
+        elif args.command == 'slowest-apis':
+            cli.show_slowest_apis(args.limit)
+        elif args.command == 'flow':
+            cli.show_function_flow(args.format, args.events)
+        elif args.command == 'analyze-flow':
+            cli.analyze_flow()
+        elif args.command == 'active':
+            cli.show_active_calls()
+        elif args.command == 'trends':
+            cli.show_trends()
+        elif args.command == 'export':
+            cli.export_data(args.format, args.output)
+        elif args.command == 'clear':
+            cli.clear_data()
+    
+    except KeyboardInterrupt:
+        print("\n❌ Operation cancelled by user")
+        sys.exit(1)
+    except Exception as e:
+        print(f"❌ Error: {e}")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

src/praisonai-agents/praisonaiagents/telemetry/performance_monitor.py

@@ -0,0 +1,573 @@
+"""
+User-Friendly Performance Monitoring for PraisonAI
+
+This module provides easy-to-use tools for monitoring function performance,
+analyzing execution flow, and tracking API calls. Built on top of the existing
+comprehensive telemetry infrastructure.
+
+Features:
+- Function performance evaluation with timing and statistics
+- Function flow analysis and visualization
+- API call tracking and performance monitoring
+- Real-time performance reporting
+- Easy integration with existing agents and workflows
+"""
+
+import time
+import json
+import threading
+import functools
+from collections import defaultdict, deque
+from typing import Dict, Any, List, Optional, Callable, Union
+from contextlib import contextmanager
+from datetime import datetime
+import logging
+
+try:
+    from .telemetry import MinimalTelemetry
+    TELEMETRY_AVAILABLE = True
+except ImportError:
+    TELEMETRY_AVAILABLE = False
+
+logger = logging.getLogger(__name__)
+
+
+class PerformanceMonitor:
+    """
+    User-friendly performance monitoring for functions, API calls, and workflows.
+    
+    Provides comprehensive performance tracking with simple APIs for:
+    - Function execution timing and statistics
+    - API call performance monitoring  
+    - Function flow analysis
+    - Real-time performance reporting
+    """
+    
+    def __init__(self, max_entries: int = 10000):
+        """
+        Initialize the performance monitor.
+        
+        Args:
+            max_entries: Maximum number of performance entries to keep in memory
+        """
+        self.max_entries = max_entries
+        self._lock = threading.RLock()
+        
+        # Performance data storage
+        self._function_stats = defaultdict(lambda: {
+            'call_count': 0,
+            'total_time': 0.0,
+            'min_time': float('inf'),
+            'max_time': 0.0,
+            'recent_times': deque(maxlen=100),
+            'error_count': 0,
+            'last_called': None
+        })
+        
+        self._api_calls = defaultdict(lambda: {
+            'call_count': 0,
+            'total_time': 0.0,
+            'min_time': float('inf'),
+            'max_time': 0.0,
+            'success_count': 0,
+            'error_count': 0,
+            'recent_calls': deque(maxlen=50)
+        })
+        
+        self._function_flow = deque(maxlen=self.max_entries)
+        self._active_calls = {}
+        
+        # Integration with existing telemetry
+        self._telemetry = None
+        if TELEMETRY_AVAILABLE:
+            try:
+                self._telemetry = MinimalTelemetry()
+            except Exception as e:
+                logger.debug(f"Could not initialize telemetry integration: {e}")
+    
+    def monitor_function(self, func_name: Optional[str] = None):
+        """
+        Decorator to monitor function performance.
+        
+        Args:
+            func_name: Optional custom name for the function
+            
+        Example:
+            @performance_monitor.monitor_function("my_critical_function")
+            def my_function():
+                return "result"
+        """
+        def decorator(func: Callable) -> Callable:
+            name = func_name or f"{func.__module__}.{func.__qualname__}"
+            
+            @functools.wraps(func)
+            def wrapper(*args, **kwargs):
+                start_time = time.time()
+                thread_id = threading.get_ident()
+                call_id = f"{name}_{thread_id}_{start_time}"
+                
+                # Track active call
+                with self._lock:
+                    self._active_calls[call_id] = {
+                        'function': name,
+                        'start_time': start_time,
+                        'thread_id': thread_id
+                    }
+                    
+                    # Add to function flow
+                    self._function_flow.append({
+                        'function': name,
+                        'timestamp': datetime.now(),
+                        'event': 'start',
+                        'thread_id': thread_id
+                    })
+                
+                try:
+                    result = func(*args, **kwargs)
+                    success = True
+                    error = None
+                except Exception as e:
+                    success = False
+                    error = str(e)
+                    raise
+                finally:
+                    end_time = time.time()
+                    execution_time = end_time - start_time
+                    
+                    # Update performance statistics
+                    self._record_function_performance(name, execution_time, success, error)
+                    
+                    # Clean up active call
+                    with self._lock:
+                        self._active_calls.pop(call_id, None)
+                        
+                        # Add to function flow
+                        self._function_flow.append({
+                            'function': name,
+                            'timestamp': datetime.now(),
+                            'event': 'end',
+                            'duration': execution_time,
+                            'success': success,
+                            'thread_id': thread_id
+                        })
+                        
+                
+                return result
+            
+            return wrapper
+        return decorator
+    
+    @contextmanager
+    def track_api_call(self, api_name: str, endpoint: Optional[str] = None):
+        """
+        Context manager to track API call performance.
+        
+        Args:
+            api_name: Name of the API (e.g., "openai", "anthropic")
+            endpoint: Optional specific endpoint
+            
+        Example:
+            with performance_monitor.track_api_call("openai", "/v1/chat/completions"):
+                response = openai_client.chat.completions.create(...)
+        """
+        call_name = f"{api_name}:{endpoint}" if endpoint else api_name
+        start_time = time.time()
+        
+        try:
+            yield
+            success = True
+            error = None
+        except Exception as e:
+            success = False
+            error = str(e)
+            raise
+        finally:
+            end_time = time.time()
+            execution_time = end_time - start_time
+            self._record_api_call(call_name, execution_time, success, error)
+    
+    def _record_function_performance(self, func_name: str, execution_time: float, 
+                                   success: bool, error: Optional[str] = None):
+        """Record function performance statistics."""
+        with self._lock:
+            stats = self._function_stats[func_name]
+            stats['call_count'] += 1
+            stats['total_time'] += execution_time
+            stats['min_time'] = min(stats['min_time'], execution_time)
+            stats['max_time'] = max(stats['max_time'], execution_time)
+            stats['recent_times'].append(execution_time)
+            stats['last_called'] = datetime.now()
+            
+            if not success:
+                stats['error_count'] += 1
+            
+            # Integrate with existing telemetry
+            if self._telemetry:
+                self._telemetry.track_tool_usage(
+                    tool_name=func_name,
+                    success=success,
+                    execution_time=execution_time
+                )
+    
+    def _record_api_call(self, api_name: str, execution_time: float,
+                        success: bool, error: Optional[str] = None):
+        """Record API call performance statistics."""
+        with self._lock:
+            stats = self._api_calls[api_name]
+            stats['call_count'] += 1
+            stats['total_time'] += execution_time
+            stats['min_time'] = min(stats['min_time'], execution_time)
+            stats['max_time'] = max(stats['max_time'], execution_time)
+            
+            if success:
+                stats['success_count'] += 1
+            else:
+                stats['error_count'] += 1
+            
+            stats['recent_calls'].append({
+                'timestamp': datetime.now(),
+                'duration': execution_time,
+                'success': success,
+                'error': error
+            })
+    
+    def get_function_performance(self, func_name: Optional[str] = None) -> Dict[str, Any]:
+        """
+        Get performance statistics for functions.
+        
+        Args:
+            func_name: Specific function name, or None for all functions
+            
+        Returns:
+            Dictionary with performance statistics
+        """
+        with self._lock:
+            if func_name:
+                if func_name not in self._function_stats:
+                    return {}
+                
+                stats = self._function_stats[func_name].copy()
+                # Convert deque to list for JSON serialization
+                stats['recent_times'] = list(stats['recent_times'])
+                
+                # Calculate derived statistics
+                if stats['call_count'] > 0:
+                    stats['average_time'] = stats['total_time'] / stats['call_count']
+                    stats['success_rate'] = (stats['call_count'] - stats['error_count']) / stats['call_count']
+                    
+                    if stats['recent_times']:
+                        stats['recent_average'] = sum(stats['recent_times']) / len(stats['recent_times'])
+                
+                return {func_name: stats}
+            else:
+                # Return all function statistics
+                result = {}
+                for name, stats in self._function_stats.items():
+                    processed_stats = stats.copy()
+                    processed_stats['recent_times'] = list(processed_stats['recent_times'])
+                    
+                    if processed_stats['call_count'] > 0:
+                        processed_stats['average_time'] = processed_stats['total_time'] / processed_stats['call_count']
+                        processed_stats['success_rate'] = (processed_stats['call_count'] - processed_stats['error_count']) / processed_stats['call_count']
+                        
+                        if processed_stats['recent_times']:
+                            processed_stats['recent_average'] = sum(processed_stats['recent_times']) / len(processed_stats['recent_times'])
+                    
+                    result[name] = processed_stats
+                
+                return result
+    
+    def get_api_call_performance(self, api_name: Optional[str] = None) -> Dict[str, Any]:
+        """
+        Get performance statistics for API calls.
+        
+        Args:
+            api_name: Specific API name, or None for all APIs
+            
+        Returns:
+            Dictionary with API call performance statistics
+        """
+        with self._lock:
+            if api_name:
+                if api_name not in self._api_calls:
+                    return {}
+                
+                stats = self._api_calls[api_name].copy()
+                # Convert deque to list for JSON serialization
+                recent_calls = []
+                for call in stats['recent_calls']:
+                    call_copy = call.copy()
+                    call_copy['timestamp'] = call_copy['timestamp'].isoformat()
+                    recent_calls.append(call_copy)
+                stats['recent_calls'] = recent_calls
+                
+                # Calculate derived statistics
+                if stats['call_count'] > 0:
+                    stats['average_time'] = stats['total_time'] / stats['call_count']
+                    stats['success_rate'] = stats['success_count'] / stats['call_count']
+                    stats['error_rate'] = stats['error_count'] / stats['call_count']
+                
+                return {api_name: stats}
+            else:
+                # Return all API statistics
+                result = {}
+                for name, stats in self._api_calls.items():
+                    processed_stats = stats.copy()
+                    
+                    # Convert recent calls
+                    recent_calls = []
+                    for call in processed_stats['recent_calls']:
+                        call_copy = call.copy()
+                        call_copy['timestamp'] = call_copy['timestamp'].isoformat()
+                        recent_calls.append(call_copy)
+                    processed_stats['recent_calls'] = recent_calls
+                    
+                    if processed_stats['call_count'] > 0:
+                        processed_stats['average_time'] = processed_stats['total_time'] / processed_stats['call_count']
+                        processed_stats['success_rate'] = processed_stats['success_count'] / processed_stats['call_count']
+                        processed_stats['error_rate'] = processed_stats['error_count'] / processed_stats['call_count']
+                    
+                    result[name] = processed_stats
+                
+                return result
+    
+    def get_function_flow(self, last_n: Optional[int] = None) -> List[Dict[str, Any]]:
+        """
+        Get function execution flow information.
+        
+        Args:
+            last_n: Number of recent flow events to return, or None for all
+            
+        Returns:
+            List of flow events showing function execution order and timing
+        """
+        with self._lock:
+            flow = []
+            for event in self._function_flow.copy():
+                # Create a copy of the event to avoid modifying original
+                event_copy = event.copy()
+                # Convert timestamp to ISO format if it's a datetime object
+                timestamp = event_copy['timestamp']
+                if hasattr(timestamp, 'isoformat'):
+                    event_copy['timestamp'] = timestamp.isoformat()
+                elif not isinstance(timestamp, str):
+                    event_copy['timestamp'] = str(timestamp)
+                flow.append(event_copy)
+            
+            if last_n:
+                return flow[-last_n:]
+            return flow
+    
+    def get_active_calls(self) -> Dict[str, Any]:
+        """Get information about currently executing functions."""
+        with self._lock:
+            active = {}
+            current_time = time.time()
+            
+            for call_id, info in self._active_calls.items():
+                duration = current_time - info['start_time']
+                active[call_id] = {
+                    'function': info['function'],
+                    'duration': duration,
+                    'thread_id': info['thread_id'],
+                    'started_at': datetime.fromtimestamp(info['start_time']).isoformat()
+                }
+            
+            return active
+    
+    def get_slowest_functions(self, limit: int = 10) -> List[Dict[str, Any]]:
+        """Get the slowest performing functions."""
+        with self._lock:
+            functions = []
+            for name, stats in self._function_stats.items():
+                if stats['call_count'] > 0:
+                    avg_time = stats['total_time'] / stats['call_count']
+                    functions.append({
+                        'function': name,
+                        'average_time': avg_time,
+                        'max_time': stats['max_time'],
+                        'call_count': stats['call_count']
+                    })
+            
+            # Sort by average time descending
+            functions.sort(key=lambda x: x['average_time'], reverse=True)
+            return functions[:limit]
+    
+    def get_slowest_api_calls(self, limit: int = 10) -> List[Dict[str, Any]]:
+        """Get the slowest performing API calls."""
+        with self._lock:
+            apis = []
+            for name, stats in self._api_calls.items():
+                if stats['call_count'] > 0:
+                    avg_time = stats['total_time'] / stats['call_count']
+                    apis.append({
+                        'api': name,
+                        'average_time': avg_time,
+                        'max_time': stats['max_time'],
+                        'call_count': stats['call_count'],
+                        'success_rate': stats['success_count'] / stats['call_count']
+                    })
+            
+            # Sort by average time descending
+            apis.sort(key=lambda x: x['average_time'], reverse=True)
+            return apis[:limit]
+    
+    def generate_performance_report(self) -> str:
+        """
+        Generate a comprehensive performance report.
+        
+        Returns:
+            Formatted string with performance analysis
+        """
+        report = []
+        report.append("=" * 80)
+        report.append("PRAISONAI PERFORMANCE MONITORING REPORT")
+        report.append("=" * 80)
+        report.append(f"Generated at: {datetime.now().isoformat()}")
+        report.append("")
+        
+        # Function performance summary
+        func_stats = self.get_function_performance()
+        if func_stats:
+            report.append("📊 FUNCTION PERFORMANCE SUMMARY")
+            report.append("-" * 40)
+            total_functions = len(func_stats)
+            total_calls = sum(stats['call_count'] for stats in func_stats.values())
+            total_errors = sum(stats['error_count'] for stats in func_stats.values())
+            
+            report.append(f"Total Functions Monitored: {total_functions}")
+            report.append(f"Total Function Calls: {total_calls}")
+            report.append(f"Total Errors: {total_errors}")
+            report.append(f"Overall Success Rate: {((total_calls - total_errors) / total_calls * 100):.1f}%" if total_calls > 0 else "N/A")
+            report.append("")
+            
+            # Slowest functions
+            slowest = self.get_slowest_functions(5)
+            if slowest:
+                report.append("🐌 SLOWEST FUNCTIONS (Top 5)")
+                for i, func in enumerate(slowest, 1):
+                    report.append(f"{i}. {func['function']}")
+                    report.append(f"   Average: {func['average_time']:.3f}s | Max: {func['max_time']:.3f}s | Calls: {func['call_count']}")
+                report.append("")
+        
+        # API performance summary  
+        api_stats = self.get_api_call_performance()
+        if api_stats:
+            report.append("🌐 API CALL PERFORMANCE SUMMARY")
+            report.append("-" * 40)
+            total_apis = len(api_stats)
+            total_api_calls = sum(stats['call_count'] for stats in api_stats.values())
+            total_api_errors = sum(stats['error_count'] for stats in api_stats.values())
+            
+            report.append(f"Total APIs Monitored: {total_apis}")
+            report.append(f"Total API Calls: {total_api_calls}")
+            report.append(f"Total API Errors: {total_api_errors}")
+            report.append(f"Overall API Success Rate: {((total_api_calls - total_api_errors) / total_api_calls * 100):.1f}%" if total_api_calls > 0 else "N/A")
+            report.append("")
+            
+            # Slowest APIs
+            slowest_apis = self.get_slowest_api_calls(5)
+            if slowest_apis:
+                report.append("🐌 SLOWEST API CALLS (Top 5)")
+                for i, api in enumerate(slowest_apis, 1):
+                    report.append(f"{i}. {api['api']}")
+                    report.append(f"   Average: {api['average_time']:.3f}s | Max: {api['max_time']:.3f}s | Success Rate: {api['success_rate']*100:.1f}%")
+                report.append("")
+        
+        # Active calls
+        active = self.get_active_calls()
+        if active:
+            report.append("⚡ CURRENTLY ACTIVE FUNCTION CALLS")
+            report.append("-" * 40)
+            for _call_id, info in active.items():
+                report.append(f"• {info['function']} (running {info['duration']:.1f}s)")
+            report.append("")
+        
+        # Function flow summary
+        flow = self.get_function_flow(10)
+        if flow:
+            report.append("🔄 RECENT FUNCTION FLOW (Last 10 Events)")
+            report.append("-" * 40)
+            for event in flow:
+                event_type = "🟢 START" if event['event'] == 'start' else "🔴 END"
+                duration_info = f" ({event.get('duration', 0):.3f}s)" if event['event'] == 'end' else ""
+                report.append(f"{event_type} {event['function']}{duration_info}")
+            report.append("")
+        
+        report.append("=" * 80)
+        return "\n".join(report)
+    
+    def clear_statistics(self):
+        """Clear all performance statistics."""
+        with self._lock:
+            self._function_stats.clear()
+            self._api_calls.clear()
+            self._function_flow.clear()
+            self._active_calls.clear()
+    
+    def export_data(self, format: str = "json") -> Union[str, Dict[str, Any]]:
+        """
+        Export all performance data.
+        
+        Args:
+            format: Export format ("json" or "dict")
+            
+        Returns:
+            Performance data in requested format
+        """
+        data = {
+            'functions': self.get_function_performance(),
+            'api_calls': self.get_api_call_performance(),
+            'flow': self.get_function_flow(),
+            'active_calls': self.get_active_calls(),
+            'export_timestamp': datetime.now().isoformat()
+        }
+        
+        if format == "json":
+            return json.dumps(data, indent=2)
+        return data
+
+
+# Global performance monitor instance
+performance_monitor = PerformanceMonitor()
+
+
+# Convenience functions for easy access
+def monitor_function(func_name: Optional[str] = None):
+    """Convenience decorator for monitoring function performance."""
+    return performance_monitor.monitor_function(func_name)
+
+
+def track_api_call(api_name: str, endpoint: Optional[str] = None):
+    """Convenience context manager for tracking API calls."""
+    return performance_monitor.track_api_call(api_name, endpoint)
+
+
+def get_performance_report() -> str:
+    """Get a comprehensive performance report."""
+    return performance_monitor.generate_performance_report()
+
+
+def get_function_stats(func_name: Optional[str] = None) -> Dict[str, Any]:
+    """Get function performance statistics."""
+    return performance_monitor.get_function_performance(func_name)
+
+
+def get_api_stats(api_name: Optional[str] = None) -> Dict[str, Any]:
+    """Get API call performance statistics."""
+    return performance_monitor.get_api_call_performance(api_name)
+
+
+def get_slowest_functions(limit: int = 10) -> List[Dict[str, Any]]:
+    """Get the slowest performing functions."""
+    return performance_monitor.get_slowest_functions(limit)
+
+
+def get_slowest_apis(limit: int = 10) -> List[Dict[str, Any]]:
+    """Get the slowest performing API calls."""
+    return performance_monitor.get_slowest_api_calls(limit)
+
+
+def clear_performance_data():
+    """Clear all performance monitoring data."""
+    performance_monitor.clear_statistics()

src/praisonai-agents/praisonaiagents/telemetry/performance_utils.py

@@ -0,0 +1,571 @@
+"""
+Performance Analysis Utilities for PraisonAI
+
+This module provides advanced analysis tools for function flow visualization,
+performance bottleneck identification, and comprehensive reporting.
+
+Features:
+- Function flow analysis and visualization
+- Performance bottleneck detection
+- Execution path mapping
+- Performance trend analysis
+- Advanced reporting utilities
+"""
+
+import json
+from collections import defaultdict
+from typing import Dict, Any, List, Optional
+from datetime import datetime
+import logging
+from dataclasses import dataclass
+
+try:
+    from .performance_monitor import performance_monitor
+    PERFORMANCE_MONITOR_AVAILABLE = True
+except ImportError:
+    PERFORMANCE_MONITOR_AVAILABLE = False
+
+logger = logging.getLogger(__name__)
+
+# Performance analysis thresholds
+BOTTLENECK_THRESHOLD_AVERAGE = 1.0  # seconds - average duration to consider bottleneck
+BOTTLENECK_THRESHOLD_MAX = 5.0      # seconds - max duration to consider bottleneck
+HIGH_SEVERITY_THRESHOLD = 2.0       # seconds - average duration for high severity bottleneck
+
+
+
+
+class FunctionFlowAnalyzer:
+    """
+    Advanced function flow analysis and visualization.
+    
+    Provides tools for analyzing function execution patterns,
+    identifying bottlenecks, and visualizing execution flow.
+    """
+    
+    def __init__(self):
+        self.logger = logging.getLogger(__name__)
+    
+    def analyze_execution_flow(self, flow_data: Optional[List[Dict]] = None) -> Dict[str, Any]:
+        """
+        Analyze function execution flow to identify patterns and bottlenecks.
+        
+        Args:
+            flow_data: Optional flow data, or None to use current monitor data
+            
+        Returns:
+            Analysis results with flow patterns, bottlenecks, and statistics
+        """
+        if flow_data is None:
+            if not PERFORMANCE_MONITOR_AVAILABLE:
+                return {"error": "Performance monitor not available and no flow data provided"}
+            flow_data = performance_monitor.get_function_flow()
+        
+        if not flow_data:
+            return {"message": "No flow data available"}
+        
+        analysis = {
+            "total_events": len(flow_data),
+            "execution_patterns": self._analyze_patterns(flow_data),
+            "bottlenecks": self._identify_bottlenecks(flow_data),
+            "parallelism": self._analyze_parallelism(flow_data),
+            "call_chains": self._build_call_chains(flow_data),
+            "statistics": self._calculate_flow_statistics(flow_data)
+        }
+        
+        return analysis
+    
+    def _analyze_patterns(self, flow_data: List[Dict]) -> Dict[str, Any]:
+        """Analyze execution patterns in the flow data."""
+        patterns = {
+            "most_frequent_sequences": [],
+            "recursive_calls": [],
+            "long_running_chains": [],
+            "error_patterns": []
+        }
+        
+        # Group events by function to find sequences
+        function_sequences = defaultdict(list)
+        
+        for event in flow_data:
+            func_name = event.get('function', 'unknown')
+            function_sequences[func_name].append(event)
+        
+        # Find most frequent function sequences
+        sequence_counts = defaultdict(int)
+        for i in range(len(flow_data) - 1):
+            current_func = flow_data[i].get('function')
+            next_func = flow_data[i + 1].get('function')
+            if current_func and next_func:
+                sequence_counts[(current_func, next_func)] += 1
+        
+        # Sort by frequency
+        frequent_sequences = sorted(sequence_counts.items(), key=lambda x: x[1], reverse=True)[:5]
+        patterns["most_frequent_sequences"] = [
+            {"sequence": f"{seq[0]} -> {seq[1]}", "count": count}
+            for seq, count in frequent_sequences
+        ]
+        
+        # Find recursive calls
+        for func_name, events in function_sequences.items():
+            nested_level = 0
+            for event in events:
+                if event.get('event') == 'start':
+                    nested_level += 1
+                    if nested_level > 1:
+                        patterns["recursive_calls"].append({
+                            "function": func_name,
+                            "max_depth": nested_level,
+                            "timestamp": event.get('timestamp')
+                        })
+                elif event.get('event') == 'end':
+                    nested_level = max(0, nested_level - 1)
+        
+        return patterns
+    
+    def _identify_bottlenecks(self, flow_data: List[Dict]) -> List[Dict[str, Any]]:
+        """Identify performance bottlenecks in the execution flow."""
+        bottlenecks = []
+        
+        # Group start/end events
+        function_durations = defaultdict(list)
+        active_calls = {}
+        
+        for event in flow_data:
+            func_name = event.get('function', 'unknown')
+            event_type = event.get('event')
+            timestamp = event.get('timestamp')
+            thread_id = event.get('thread_id', 0)
+            
+            if event_type == 'start':
+                call_key = f"{func_name}_{thread_id}_{timestamp}"
+                active_calls[call_key] = event
+            elif event_type == 'end':
+                duration = event.get('duration', 0)
+                if duration > 0:
+                    function_durations[func_name].append(duration)
+        
+        # Find functions with consistently slow performance
+        for func_name, durations in function_durations.items():
+            if durations:
+                avg_duration = sum(durations) / len(durations)
+                max_duration = max(durations)
+                
+                # Consider it a bottleneck based on defined thresholds
+                if avg_duration > BOTTLENECK_THRESHOLD_AVERAGE or max_duration > BOTTLENECK_THRESHOLD_MAX:
+                    bottlenecks.append({
+                        "function": func_name,
+                        "average_duration": avg_duration,
+                        "max_duration": max_duration,
+                        "call_count": len(durations),
+                        "severity": "high" if avg_duration > HIGH_SEVERITY_THRESHOLD else "medium"
+                    })
+        
+        # Sort by severity and duration
+        bottlenecks.sort(key=lambda x: x["average_duration"], reverse=True)
+        return bottlenecks
+    
+    def _analyze_parallelism(self, flow_data: List[Dict]) -> Dict[str, Any]:
+        """Analyze parallelism and concurrent execution patterns."""
+        thread_activities = defaultdict(list)
+        
+        for event in flow_data:
+            thread_id = event.get('thread_id', 0)
+            thread_activities[thread_id].append(event)
+        
+        # Find peak concurrency
+        timestamp_activities = defaultdict(int)
+        for event in flow_data:
+            if event.get('event') == 'start':
+                timestamp = event.get('timestamp')
+                timestamp_activities[timestamp] += 1
+        
+        peak_concurrency = max(timestamp_activities.values()) if timestamp_activities else 0
+        
+        return {
+            "total_threads": len(thread_activities),
+            "peak_concurrency": peak_concurrency,
+            "thread_utilization": {
+                str(thread_id): len(events) 
+                for thread_id, events in thread_activities.items()
+            }
+        }
+    
+    def _build_call_chains(self, flow_data: List[Dict]) -> List[Dict[str, Any]]:
+        """Build call chains from flow data."""
+        # Track call chains per thread more efficiently
+        thread_chains = defaultdict(list)
+        call_stacks = defaultdict(list)
+        current_chains = defaultdict(list)
+        
+        for event in flow_data:
+            thread_id = event.get('thread_id', 0)
+            func_name = event.get('function', 'unknown')
+            event_type = event.get('event')
+            
+            if event_type == 'start':
+                call_stacks[thread_id].append(func_name)
+                current_chains[thread_id].append(func_name)
+            elif event_type == 'end' and call_stacks[thread_id] and call_stacks[thread_id][-1] == func_name:
+                call_stacks[thread_id].pop()
+                
+                # If this completes a top-level call (stack becomes empty), record the chain
+                if not call_stacks[thread_id] and current_chains[thread_id]:
+                    chain = current_chains[thread_id].copy()
+                    thread_chains[thread_id].append({
+                        "thread_id": thread_id,
+                        "chain_length": len(chain),
+                        "functions": chain
+                    })
+                    current_chains[thread_id].clear()
+        
+        # Flatten all chains and return top 10
+        all_chains = []
+        for chains in thread_chains.values():
+            all_chains.extend(chains)
+        
+        return all_chains[:10]
+    
+    def _calculate_flow_statistics(self, flow_data: List[Dict]) -> Dict[str, Any]:
+        """Calculate comprehensive flow statistics."""
+        total_events = len(flow_data)
+        start_events = [e for e in flow_data if e.get('event') == 'start']
+        end_events = [e for e in flow_data if e.get('event') == 'end']
+        successful_events = [e for e in end_events if e.get('success', True)]
+        
+        total_duration = sum(e.get('duration', 0) for e in end_events)
+        
+        return {
+            "total_events": total_events,
+            "function_calls": len(start_events),
+            "completed_calls": len(end_events),
+            "successful_calls": len(successful_events),
+            "success_rate": len(successful_events) / len(end_events) if end_events else 0,
+            "total_execution_time": total_duration,
+            "average_execution_time": total_duration / len(end_events) if end_events else 0
+        }
+    
+    def visualize_flow(self, flow_data: Optional[List[Dict]] = None, 
+                      format: str = "text") -> str:
+        """
+        Create a visual representation of the function execution flow.
+        
+        Args:
+            flow_data: Optional flow data, or None to use current monitor data
+            format: Output format ("text", "mermaid", "json")
+            
+        Returns:
+            Formatted visualization string
+        """
+        if flow_data is None:
+            if not PERFORMANCE_MONITOR_AVAILABLE:
+                return "Performance monitor not available and no flow data provided"
+            flow_data = performance_monitor.get_function_flow(50)  # Last 50 events
+        
+        if not flow_data:
+            return "No flow data available"
+        
+        if format == "text":
+            return self._create_text_visualization(flow_data)
+        elif format == "mermaid":
+            return self._create_mermaid_diagram(flow_data)
+        elif format == "json":
+            return json.dumps(flow_data, indent=2)
+        else:
+            return "Unknown format"
+    
+    def _create_text_visualization(self, flow_data: List[Dict]) -> str:
+        """Create a text-based visualization of the execution flow."""
+        lines = []
+        lines.append("📊 FUNCTION EXECUTION FLOW VISUALIZATION")
+        lines.append("=" * 60)
+        
+        # Group by thread for better visualization
+        threads = defaultdict(list)
+        for event in flow_data:
+            thread_id = event.get('thread_id', 0)
+            threads[thread_id].append(event)
+        
+        for thread_id, events in threads.items():
+            lines.append(f"\n🧵 Thread {thread_id}:")
+            lines.append("-" * 30)
+            
+            call_stack = []
+            for event in events:
+                func_name = event.get('function', 'unknown')
+                event_type = event.get('event')
+                duration = event.get('duration', 0)
+                success = event.get('success', True)
+                
+                if event_type == 'start':
+                    call_stack.append(func_name)
+                    indent = "  " * (len(call_stack) - 1)
+                    lines.append(f"{indent}🟢 START {func_name}")
+                elif event_type == 'end':
+                    if call_stack and call_stack[-1] == func_name:
+                        call_stack.pop()
+                    indent = "  " * len(call_stack)
+                    status = "✅" if success else "❌"
+                    lines.append(f"{indent}{status} END   {func_name} ({duration:.3f}s)")
+        
+        return "\n".join(lines)
+    
+    def _create_mermaid_diagram(self, flow_data: List[Dict]) -> str:
+        """Create a Mermaid diagram representation of the flow."""
+        lines = ["graph TD"]
+        
+        # Build flow connections
+        node_counter = 0
+        node_map = {}
+        
+        for event in flow_data:
+            if event.get('event') == 'start':
+                func_name = event.get('function', 'unknown')
+                if func_name not in node_map:
+                    node_map[func_name] = f"n{node_counter}"
+                    node_counter += 1
+        
+        # Add nodes
+        for func_name, node_id in node_map.items():
+            lines.append(f"    {node_id}[{func_name}]")
+        
+        # Add connections based on call sequence
+        prev_func = None
+        for event in flow_data:
+            if event.get('event') == 'start':
+                curr_func = event.get('function', 'unknown')
+                if prev_func and curr_func != prev_func:
+                    prev_node = node_map.get(prev_func)
+                    curr_node = node_map.get(curr_func)
+                    if prev_node and curr_node:
+                        lines.append(f"    {prev_node} --> {curr_node}")
+                prev_func = curr_func
+        
+        return "\n".join(lines)
+
+
+class PerformanceAnalyzer:
+    """
+    Comprehensive performance analysis tools.
+    
+    Provides advanced analysis capabilities for identifying performance
+    issues, trends, and optimization opportunities.
+    """
+    
+    def __init__(self):
+        self.flow_analyzer = FunctionFlowAnalyzer()
+    
+    def analyze_performance_trends(self, hours_back: int = 24) -> Dict[str, Any]:
+        """
+        Analyze performance trends over time.
+        
+        Args:
+            hours_back: Number of hours to analyze
+            
+        Returns:
+            Trend analysis results
+        """
+        if not PERFORMANCE_MONITOR_AVAILABLE:
+            return {"error": "Performance monitor not available"}
+        
+        # Get current performance data
+        func_stats = performance_monitor.get_function_performance()
+        api_stats = performance_monitor.get_api_call_performance()
+        
+        trends = {
+            "analysis_period_hours": hours_back,
+            "function_trends": self._analyze_function_trends(func_stats),
+            "api_trends": self._analyze_api_trends(api_stats),
+            "recommendations": self._generate_recommendations(func_stats, api_stats)
+        }
+        
+        return trends
+    
+    def _analyze_function_trends(self, func_stats: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze function performance trends."""
+        trends = {
+            "improving": [],
+            "degrading": [],
+            "stable": []
+        }
+        
+        for func_name, stats in func_stats.items():
+            recent_times = stats.get('recent_times', [])
+            if len(recent_times) >= 10:  # Need sufficient data
+                # Compare first half to second half
+                mid_point = len(recent_times) // 2
+                first_half_avg = sum(recent_times[:mid_point]) / mid_point
+                second_half_avg = sum(recent_times[mid_point:]) / (len(recent_times) - mid_point)
+                
+                if first_half_avg != 0:
+                    change_percent = ((second_half_avg - first_half_avg) / first_half_avg) * 100
+                else:
+                    change_percent = 0.0  # No change if first half average is zero
+                
+                trend_data = {
+                    "function": func_name,
+                    "change_percent": change_percent,
+                    "first_half_avg": first_half_avg,
+                    "second_half_avg": second_half_avg
+                }
+                
+                if change_percent < -5:  # Improving (getting faster)
+                    trends["improving"].append(trend_data)
+                elif change_percent > 5:  # Degrading (getting slower)
+                    trends["degrading"].append(trend_data)
+                else:
+                    trends["stable"].append(trend_data)
+        
+        return trends
+    
+    def _analyze_api_trends(self, api_stats: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze API performance trends."""
+        trends = {
+            "fastest_apis": [],
+            "slowest_apis": [],
+            "most_reliable": [],
+            "least_reliable": []
+        }
+        
+        api_performance = []
+        for api_name, stats in api_stats.items():
+            if stats.get('call_count', 0) > 0:
+                avg_time = stats['total_time'] / stats['call_count']
+                success_rate = stats.get('success_rate', 0)
+                
+                api_performance.append({
+                    "api": api_name,
+                    "average_time": avg_time,
+                    "success_rate": success_rate,
+                    "call_count": stats['call_count']
+                })
+        
+        # Sort by performance metrics
+        fastest = sorted(api_performance, key=lambda x: x['average_time'])[:5]
+        slowest = sorted(api_performance, key=lambda x: x['average_time'], reverse=True)[:5]
+        most_reliable = sorted(api_performance, key=lambda x: x['success_rate'], reverse=True)[:5]
+        least_reliable = sorted(api_performance, key=lambda x: x['success_rate'])[:5]
+        
+        trends["fastest_apis"] = fastest
+        trends["slowest_apis"] = slowest
+        trends["most_reliable"] = most_reliable
+        trends["least_reliable"] = least_reliable
+        
+        return trends
+    
+    def _generate_recommendations(self, func_stats: Dict[str, Any], 
+                                api_stats: Dict[str, Any]) -> List[str]:
+        """Generate performance optimization recommendations."""
+        recommendations = []
+        
+        # Function recommendations
+        for func_name, stats in func_stats.items():
+            if stats.get('call_count', 0) > 0:
+                avg_time = stats['total_time'] / stats['call_count']
+                error_rate = stats['error_count'] / stats['call_count']
+                
+                if avg_time > 2.0:
+                    recommendations.append(
+                        f"⚠️ Function '{func_name}' has high average execution time ({avg_time:.2f}s). Consider optimization."
+                    )
+                
+                if error_rate > 0.1:
+                    recommendations.append(
+                        f"🚨 Function '{func_name}' has high error rate ({error_rate*100:.1f}%). Investigate error handling."
+                    )
+        
+        # API recommendations
+        for api_name, stats in api_stats.items():
+            if stats.get('call_count', 0) > 0:
+                avg_time = stats['total_time'] / stats['call_count']
+                success_rate = stats.get('success_rate', 1.0)
+                
+                if avg_time > 5.0:
+                    recommendations.append(
+                        f"🐌 API '{api_name}' has high average response time ({avg_time:.2f}s). Consider caching or optimization."
+                    )
+                
+                if success_rate < 0.9:
+                    recommendations.append(
+                        f"⚠️ API '{api_name}' has low success rate ({success_rate*100:.1f}%). Check error handling and retry logic."
+                    )
+        
+        if not recommendations:
+            recommendations.append("✅ No major performance issues detected. System is performing well!")
+        
+        return recommendations
+    
+    def generate_comprehensive_report(self) -> str:
+        """Generate a comprehensive performance analysis report."""
+        if not PERFORMANCE_MONITOR_AVAILABLE:
+            return "Performance monitor not available"
+        
+        report_lines = []
+        report_lines.append("=" * 80)
+        report_lines.append("COMPREHENSIVE PERFORMANCE ANALYSIS REPORT")
+        report_lines.append("=" * 80)
+        report_lines.append(f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+        report_lines.append("")
+        
+        # Basic performance report
+        basic_report = performance_monitor.generate_performance_report()
+        report_lines.append(basic_report)
+        report_lines.append("")
+        
+        # Flow analysis
+        report_lines.append("🔄 EXECUTION FLOW ANALYSIS")
+        report_lines.append("-" * 40)
+        flow_analysis = self.flow_analyzer.analyze_execution_flow()
+        
+        if "bottlenecks" in flow_analysis:
+            bottlenecks = flow_analysis["bottlenecks"]
+            if bottlenecks:
+                report_lines.append("🚨 IDENTIFIED BOTTLENECKS:")
+                for bottleneck in bottlenecks[:5]:
+                    report_lines.append(f"• {bottleneck['function']}: {bottleneck['average_duration']:.2f}s avg, {bottleneck['severity']} severity")
+            else:
+                report_lines.append("✅ No significant bottlenecks identified")
+        
+        report_lines.append("")
+        
+        # Performance trends
+        report_lines.append("📈 PERFORMANCE TRENDS")
+        report_lines.append("-" * 40)
+        trends = self.analyze_performance_trends()
+        
+        if "recommendations" in trends:
+            recommendations = trends["recommendations"]
+            report_lines.append("💡 RECOMMENDATIONS:")
+            for rec in recommendations:
+                report_lines.append(f"  {rec}")
+        
+        report_lines.append("")
+        report_lines.append("=" * 80)
+        
+        return "\n".join(report_lines)
+
+
+# Global instances for easy access
+flow_analyzer = FunctionFlowAnalyzer()
+performance_analyzer = PerformanceAnalyzer()
+
+
+# Convenience functions
+def analyze_function_flow() -> Dict[str, Any]:
+    """Analyze current function execution flow."""
+    return flow_analyzer.analyze_execution_flow()
+
+
+def visualize_execution_flow(format: str = "text") -> str:
+    """Visualize function execution flow."""
+    return flow_analyzer.visualize_flow(format=format)
+
+
+def analyze_performance_trends() -> Dict[str, Any]:
+    """Analyze performance trends."""
+    return performance_analyzer.analyze_performance_trends()
+
+
+def generate_comprehensive_report() -> str:
+    """Generate comprehensive performance analysis report."""
+    return performance_analyzer.generate_comprehensive_report()

src/praisonai-agents/praisonaiagents/telemetry/telemetry.py

@@ -119,13 +119,14 @@ class MinimalTelemetry:
         except (ImportError, KeyError, AttributeError):
             return "unknown"
     
-    def track_agent_execution(self, agent_name: str = None, success: bool = True):
+    def track_agent_execution(self, agent_name: str = None, success: bool = True, async_mode: bool = False):
         """
         Track an agent execution event.
         
         Args:
             agent_name: Name of the agent (not logged, just for counting)
             success: Whether the execution was successful
+            async_mode: If True, defer PostHog capture to prevent blocking in streaming scenarios
         """
         if not self.enabled:
             return
@@ -133,18 +134,41 @@ class MinimalTelemetry:
         with self._metrics_lock:
             self._metrics["agent_executions"] += 1
         
+        # Always log immediately for debugging
+        self.logger.debug(f"Agent execution tracked: success={success}")
+        
         # Send event to PostHog
         if self._posthog:
-            self._posthog.capture(
-                distinct_id=self.session_id,
-                event='agent_execution',
-                properties={
-                    'success': success,
-                    'session_id': self.session_id
-                }
-            )
-        
-        self.logger.debug(f"Agent execution tracked: success={success}")
+            if async_mode:
+                # Use a background thread to prevent blocking streaming responses
+                def _async_capture():
+                    try:
+                        self._posthog.capture(
+                            distinct_id=self.session_id,
+                            event='agent_execution',
+                            properties={
+                                'success': success,
+                                'session_id': self.session_id
+                            }
+                        )
+                    except Exception as e:
+                        # Silently handle any telemetry errors to avoid disrupting user experience
+                        self.logger.debug(f"Async PostHog capture error: {e}")
+                
+                # Execute in background thread with daemon flag for clean shutdown
+                import threading
+                thread = threading.Thread(target=_async_capture, daemon=True)
+                thread.start()
+            else:
+                # Synchronous capture for backward compatibility
+                self._posthog.capture(
+                    distinct_id=self.session_id,
+                    event='agent_execution',
+                    properties={
+                        'success': success,
+                        'session_id': self.session_id
+                    }
+                )
     
     def track_task_completion(self, task_name: str = None, success: bool = True):
         """

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.144"
+version = "0.0.146"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 requires-python = ">=3.10"
 authors = [
@@ -27,7 +27,7 @@ mcp = [
 
 memory = [
     "chromadb>=1.0.0",
-    "litellm>=1.72.0",
+    "litellm>=1.72.6",
 ]
 
 knowledge = [
@@ -45,7 +45,7 @@ graph = [
 
 # Add LLM dependencies
 llm = [
-    "litellm>=1.72.0",
+    "litellm>=1.72.6",
     "pydantic>=2.4.2"
 ]
 
@@ -66,6 +66,13 @@ mongodb = [
     "motor>=3.4.0"
 ]
 
+# Authentication dependencies
+auth = [
+    "PyJWT>=2.8.0",
+    "passlib[bcrypt]>=1.7.4",
+    "python-jose[cryptography]>=3.3.0",
+    "python-multipart>=0.0.6"
+]
 
 # Combined features
 all = [
@@ -76,7 +83,8 @@ all = [
     "praisonaiagents[mcp]",
     "praisonaiagents[api]",
     "praisonaiagents[telemetry]",
-    "praisonaiagents[mongodb]"
+    "praisonaiagents[mongodb]",
+    "praisonaiagents[auth]"
 ]
 
 [tool.setuptools.packages.find]

src/praisonai-agents/streaming-gemini.py

@@ -0,0 +1,10 @@
+from praisonaiagents import Agent
+
+agent = Agent(
+    instructions="You are a helpful assistant",
+    llm="gemini/gemini-2.5-flash",
+    stream=True
+)
+
+for chunk in agent.start("Write a report on about the history of the world"):
+    print(chunk, end="", flush=True)

src/praisonai-agents/streaming-openai.py

@@ -0,0 +1,10 @@
+from praisonaiagents import Agent
+
+agent = Agent(
+    instructions="You are a helpful assistant",
+    llm="gpt-4o-mini",
+    stream=True
+)
+
+for chunk in agent.start("Write a report on about the history of the world"):
+    print(chunk, end="", flush=True)

src/praisonai-agents/test_debug_fixed.py

@@ -0,0 +1,95 @@
+#!/usr/bin/env python3
+import os
+import sys
+sys.path.insert(0, 'src/praisonai-agents')
+
+print("Testing DEBUG mode functionality...")
+
+def clear_praisonai_modules():
+    """Clear all praisonai modules from sys.modules"""
+    to_remove = []
+    for mod in sys.modules.keys():
+        if mod.startswith('praisonaiagents'):
+            to_remove.append(mod)
+    
+    for mod in to_remove:
+        del sys.modules[mod]
+
+def test_normal_mode():
+    """Test that warnings are suppressed in normal mode"""
+    print("\n=== Testing NORMAL mode (LOGLEVEL=INFO) ===")
+    os.environ['LOGLEVEL'] = 'INFO'
+    
+    # Clear modules
+    clear_praisonai_modules()
+    
+    import warnings
+    warnings.simplefilter('always')
+    
+    try:
+        import praisonaiagents
+        print("✅ Import successful in normal mode")
+        
+        # Check if warning suppression is active
+        should_suppress = praisonaiagents._should_suppress_warnings()
+        print(f"✅ Warning suppression active: {should_suppress}")
+        
+        return should_suppress  # Should be True in normal mode
+        
+    except Exception as e:
+        print(f"❌ Error in normal mode: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+def test_debug_mode():
+    """Test that warnings are shown in DEBUG mode"""
+    print("\n=== Testing DEBUG mode (LOGLEVEL=DEBUG) ===")
+    os.environ['LOGLEVEL'] = 'DEBUG'
+    
+    # Clear modules
+    clear_praisonai_modules()
+    
+    import warnings
+    warnings.simplefilter('always')
+    
+    try:
+        import praisonaiagents
+        print("✅ Import successful in debug mode")
+        
+        # Check if warning suppression is inactive
+        should_suppress = praisonaiagents._should_suppress_warnings()
+        print(f"✅ Warning suppression active: {should_suppress} (should be False)")
+        
+        return not should_suppress  # Should be False in debug mode (so we return True if it's correct)
+        
+    except Exception as e:
+        print(f"❌ Error in debug mode: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+if __name__ == "__main__":
+    print("🧪 Testing DEBUG mode implementation")
+    
+    # Clean up environment first
+    if 'LOGLEVEL' in os.environ:
+        del os.environ['LOGLEVEL']
+    
+    normal_success = test_normal_mode()
+    debug_success = test_debug_mode()
+    
+    print("\n" + "="*50)
+    if normal_success and debug_success:
+        print("🎉 ALL TESTS PASSED: DEBUG mode functionality works correctly!")
+        print("✅ Warnings suppressed in normal mode")
+        print("✅ Warnings enabled in DEBUG mode")
+    else:
+        print("💥 TESTS FAILED: Issues with DEBUG mode implementation")
+        print(f"   Normal mode success: {normal_success}")
+        print(f"   Debug mode success: {debug_success}")
+    
+    # Restore normal mode
+    os.environ['LOGLEVEL'] = 'INFO'
+    
+    sys.exit(0 if (normal_success and debug_success) else 1)

src/praisonai-agents/test_debug_mode.py

@@ -0,0 +1,93 @@
+#!/usr/bin/env python3
+import os
+import sys
+sys.path.insert(0, 'src/praisonai-agents')
+
+print("Testing DEBUG mode functionality...")
+
+def test_normal_mode():
+    """Test that warnings are suppressed in normal mode"""
+    print("\n=== Testing NORMAL mode (LOGLEVEL=INFO) ===")
+    os.environ['LOGLEVEL'] = 'INFO'
+    
+    # Force reload modules to apply new environment
+    import sys
+    for mod in list(sys.modules.keys()):
+        if mod.startswith('praisonaiagents'):
+            del sys.modules[mod]
+    
+    import warnings
+    warnings.simplefilter('always')
+    
+    try:
+        import praisonaiagents
+        print("✅ Import successful in normal mode")
+        
+        # Check if warning suppression is active
+        import praisonaiagents.__init__ as main_init
+        should_suppress = main_init._should_suppress_warnings()
+        print(f"✅ Warning suppression active: {should_suppress}")
+        
+    except Exception as e:
+        print(f"❌ Error in normal mode: {e}")
+        return False
+    
+    return True
+
+def test_debug_mode():
+    """Test that warnings are shown in DEBUG mode"""
+    print("\n=== Testing DEBUG mode (LOGLEVEL=DEBUG) ===")
+    os.environ['LOGLEVEL'] = 'DEBUG'
+    
+    # Force reload modules to apply new environment
+    import sys
+    for mod in list(sys.modules.keys()):
+        if mod.startswith('praisonaiagents'):
+            del sys.modules[mod]
+    
+    import warnings
+    warnings.simplefilter('always')
+    
+    try:
+        import praisonaiagents
+        print("✅ Import successful in debug mode")
+        
+        # Check if warning suppression is inactive
+        import praisonaiagents.__init__ as main_init
+        should_suppress = main_init._should_suppress_warnings()
+        print(f"✅ Warning suppression active: {should_suppress} (should be False)")
+        
+        if should_suppress:
+            print("❌ WARNING: Debug mode should not suppress warnings!")
+            return False
+        else:
+            print("✅ DEBUG mode correctly allows warnings")
+            
+    except Exception as e:
+        print(f"❌ Error in debug mode: {e}")
+        return False
+    
+    return True
+
+if __name__ == "__main__":
+    print("🧪 Testing DEBUG mode implementation")
+    
+    # Clean up environment first
+    if 'LOGLEVEL' in os.environ:
+        del os.environ['LOGLEVEL']
+    
+    normal_success = test_normal_mode()
+    debug_success = test_debug_mode()
+    
+    print("\n" + "="*50)
+    if normal_success and debug_success:
+        print("🎉 ALL TESTS PASSED: DEBUG mode functionality works correctly!")
+        print("✅ Warnings suppressed in normal mode")
+        print("✅ Warnings enabled in DEBUG mode")
+    else:
+        print("💥 TESTS FAILED: Issues with DEBUG mode implementation")
+    
+    # Restore normal mode
+    os.environ['LOGLEVEL'] = 'INFO'
+    
+    sys.exit(0 if (normal_success and debug_success) else 1)

src/praisonai-agents/test_import.py

@@ -1,17 +1,10 @@
-try:
-    print("Testing import...")
-    from praisonaiagents import Agent
-    print("✅ Import successful - no syntax errors!")
-    
-    print("Creating agent...")
-    agent = Agent(instructions="Test agent", llm="gpt-4o-mini")
-    print("✅ Agent created successfully!")
-    
-except SyntaxError as e:
-    print(f"❌ SyntaxError: {e}")
-    print(f"   File: {e.filename}")
-    print(f"   Line {e.lineno}: {e.text}")
-except Exception as e:
-    print(f"❌ Other error: {e}")
-    import traceback
-    traceback.print_exc() 
+#!/usr/bin/env python3
+import sys
+sys.path.insert(0, 'src/praisonai-agents')
+
+import warnings
+warnings.simplefilter('always')
+
+print("Testing import with warnings enabled...")
+import praisonaiagents
+print("Import successful!")

src/praisonai-agents/test_real_streaming.py

@@ -0,0 +1,129 @@
+#!/usr/bin/env python3
+"""
+Test real streaming behavior for different configurations.
+This script tests that streaming works correctly with various settings.
+"""
+
+import os
+import sys
+from praisonaiagents import Agent
+
+def test_real_streaming():
+    """Test real streaming behavior with different configurations."""
+    
+    # Test configurations
+    test_cases = [
+        {
+            "name": "Stream True + Verbose True",
+            "stream": True,
+            "verbose": True,
+            "expected": "Real-time streaming with progressive content display"
+        },
+        {
+            "name": "Stream True + Verbose False", 
+            "stream": True,
+            "verbose": False,
+            "expected": "Real-time streaming but no display (callbacks only)"
+        },
+        {
+            "name": "Stream False + Verbose True",
+            "stream": False,
+            "verbose": True,
+            "expected": "'Generating response...' animation with elapsed time, no content"
+        },
+        {
+            "name": "Stream False + Verbose False",
+            "stream": False,
+            "verbose": False,
+            "expected": "No display at all, just returns response"
+        }
+    ]
+    
+    for test_case in test_cases:
+        print(f"\n{'='*60}")
+        print(f"Testing: {test_case['name']}")
+        print(f"Configuration: stream={test_case['stream']}, verbose={test_case['verbose']}")
+        print(f"Expected: {test_case['expected']}")
+        print(f"{'='*60}\n")
+        
+        try:
+            # Create agent with specific configuration
+            agent = Agent(
+                name="Test Agent",
+                role="Testing Assistant",
+                goal="Test streaming behavior",
+                backstory="I am a test agent for verifying streaming functionality.",
+                llm="gpt-4o-mini",  # You can change this to test other providers
+                stream=test_case["stream"],
+                verbose=test_case["verbose"]
+            )
+            
+            # Test the agent
+            prompt = "Count from 1 to 10 slowly, with each number on a new line."
+            print(f"Sending prompt: {prompt}\n")
+            
+            response = agent.chat(prompt)
+            
+            if not test_case["verbose"]:
+                # If verbose is False, we need to print the response manually
+                print(f"\nResponse:\n{response}")
+            
+            print(f"\n{'='*60}")
+            print(f"Test completed!")
+            
+        except Exception as e:
+            print(f"\nError during test: {str(e)}")
+            continue
+        
+        # Wait for user to observe the behavior
+        input("\nPress Enter to continue to the next test...")
+    
+    # Test with different providers
+    print("\n\nTesting with different providers...")
+    providers = [
+        ("Gemini", "gemini/gemini-1.5-flash"),
+        ("Anthropic", "claude-3-haiku-20240307"),
+        # Add more providers as needed
+    ]
+    
+    for name, model in providers:
+        print(f"\n{'='*60}")
+        print(f"Testing real streaming with {name}")
+        print(f"Configuration: stream=True, verbose=True")
+        print(f"{'='*60}\n")
+        
+        try:
+            agent = Agent(
+                name=f"{name} Agent",
+                role="Testing Assistant", 
+                goal="Test streaming",
+                backstory=f"Testing {name} streaming",
+                llm=model,
+                stream=True,
+                verbose=True
+            )
+            
+            response = agent.chat("Write a haiku about streaming data.")
+            print(f"\nTest with {name} completed!")
+            
+        except Exception as e:
+            print(f"\nError testing with {name}: {str(e)}")
+            if "API" in str(e) or "key" in str(e).lower():
+                print(f"Make sure you have the required API key set for {name}")
+
+if __name__ == "__main__":
+    print("Starting real streaming behavior tests...")
+    print("This test verifies the correct behavior for all combinations of stream and verbose settings.\n")
+    
+    # Check for API keys
+    if not os.environ.get("OPENAI_API_KEY"):
+        print("Warning: OPENAI_API_KEY not set. OpenAI tests will fail.")
+    
+    test_real_streaming()
+    
+    print("\n\nAll tests completed!")
+    print("\nSummary of expected behaviors:")
+    print("- stream=True, verbose=True: Real-time streaming with content display")
+    print("- stream=True, verbose=False: Real-time streaming, no display")  
+    print("- stream=False, verbose=True: 'Generating...' animation, no content")
+    print("- stream=False, verbose=False: No display at all")

src/praisonai-agents/test_stream_parameter_fix.py

@@ -0,0 +1,80 @@
+#!/usr/bin/env python3
+"""
+Test to verify that the stream parameter is correctly passed through the agent.chat() method.
+This tests the fix for the issue where stream=self.stream was being used instead of the passed parameter.
+"""
+
+import os
+from praisonaiagents import Agent
+
+# Use a simple model for testing
+os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "test-key")
+
+def test_stream_parameter():
+    """Test that stream parameter is correctly passed through chat method"""
+    
+    # Create an agent with stream=True by default
+    agent = Agent(
+        name="TestAgent",
+        role="Test Assistant",
+        goal="Test streaming behavior",
+        backstory="A test agent for verifying stream parameter handling",
+        llm="gpt-4o-mini",
+        stream=True,  # Default to streaming
+        verbose=True
+    )
+    
+    print("Test 1: Agent has stream=True, but we pass stream=False to chat()")
+    print("Expected: Should NOT stream (no progressive display)")
+    print("-" * 50)
+    
+    # Call chat with stream=False explicitly - should override agent's stream=True
+    response1 = agent.chat("Tell me a very short fact about Python in one sentence.", stream=False)
+    print(f"\nResponse 1: {response1}")
+    
+    print("\n" + "="*70 + "\n")
+    
+    print("Test 2: Agent has stream=True, and we pass stream=True to chat()")
+    print("Expected: Should stream (progressive display)")
+    print("-" * 50)
+    
+    # Call chat with stream=True explicitly - should stream
+    response2 = agent.chat("Tell me another very short fact about Python in one sentence.", stream=True)
+    print(f"\nResponse 2: {response2}")
+    
+    print("\n" + "="*70 + "\n")
+    
+    # Now test with an agent that has stream=False by default
+    agent2 = Agent(
+        name="TestAgent2",
+        role="Test Assistant 2",
+        goal="Test non-streaming behavior",
+        backstory="A test agent for verifying non-stream parameter handling",
+        llm="gpt-4o-mini",
+        stream=False,  # Default to non-streaming
+        verbose=True
+    )
+    
+    print("Test 3: Agent has stream=False, but we pass stream=True to chat()")
+    print("Expected: Should stream (progressive display)")
+    print("-" * 50)
+    
+    # Call chat with stream=True explicitly - should override agent's stream=False
+    response3 = agent2.chat("Tell me a very short fact about JavaScript in one sentence.", stream=True)
+    print(f"\nResponse 3: {response3}")
+    
+    print("\n" + "="*70 + "\n")
+    
+    print("Test 4: Agent has stream=False, and we don't pass stream to chat()")
+    print("Expected: Should NOT stream (uses agent's default)")
+    print("-" * 50)
+    
+    # Call chat without stream parameter - should use agent's default (False)
+    response4 = agent2.chat("Tell me a very short fact about Java in one sentence.")
+    print(f"\nResponse 4: {response4}")
+    
+    print("\n" + "="*70 + "\n")
+    print("All tests completed!")
+
+if __name__ == "__main__":
+    test_stream_parameter()

src/praisonai-agents/test_streaming.py

@@ -0,0 +1,46 @@
+#!/usr/bin/env python3
+"""
+Test script to validate real-time streaming functionality
+"""
+
+import sys
+import os
+
+# Add the source path to enable imports
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src', 'praisonai-agents'))
+
+try:
+    from praisonaiagents import Agent
+    
+    print("Creating agent with streaming enabled...")
+    agent = Agent(
+        instructions="You are a helpful assistant",
+        llm="gemini/gemini-2.5-flash",
+        stream=True,
+        verbose=False  # Reduce noise during testing
+    )
+    
+    print("Starting streaming test...")
+    print("=" * 50)
+    
+    # Test the streaming functionality
+    chunk_count = 0
+    for chunk in agent.start("Write a short paragraph about the benefits of real-time streaming in AI applications"):
+        print(chunk, end="", flush=True)
+        chunk_count += 1
+    
+    print("\n" + "=" * 50)
+    print(f"✅ Streaming test completed! Received {chunk_count} chunks.")
+    
+    if chunk_count > 1:
+        print("✅ SUCCESS: Real-time streaming is working - received multiple chunks!")
+    else:
+        print("⚠️  WARNING: Only received 1 chunk - may still be using simulated streaming")
+        
+except ImportError as e:
+    print(f"❌ Import error: {e}")
+    print("Make sure you're in the correct directory and dependencies are installed")
+except Exception as e:
+    print(f"❌ Error during streaming test: {e}")
+    import traceback
+    traceback.print_exc()

src/praisonai-agents/test_streaming_basic.py

@@ -0,0 +1,110 @@
+#!/usr/bin/env python3
+"""
+Basic test to verify streaming functionality without API calls
+"""
+
+import sys
+import os
+import logging
+
+# Add the source path to enable imports
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src', 'praisonai-agents'))
+
+def test_imports():
+    """Test that we can import the required modules"""
+    try:
+        print("Testing imports...")
+        
+        # Test LLM import
+        from praisonaiagents.llm.llm import LLM
+        print("✅ LLM import successful")
+        
+        # Test Agent import
+        from praisonaiagents.agent.agent import Agent
+        print("✅ Agent import successful")
+        
+        return True
+    except Exception as e:
+        print(f"❌ Import error: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+def test_method_existence():
+    """Test that the new streaming methods exist"""
+    try:
+        from praisonaiagents.llm.llm import LLM
+        
+        # Check if the new method exists
+        llm = LLM(model="test")
+        if hasattr(llm, 'get_response_stream'):
+            print("✅ get_response_stream method exists in LLM")
+        else:
+            print("❌ get_response_stream method missing in LLM")
+            return False
+        
+        return True
+    except Exception as e:
+        print(f"❌ Method test error: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+def test_agent_streaming_setup():
+    """Test that agent streaming is set up correctly"""
+    try:
+        from praisonaiagents.agent.agent import Agent
+        
+        # Create a basic agent instance (without API calls)
+        agent = Agent(
+            instructions="Test agent",
+            llm="test/model",  # Mock model that won't make real API calls
+            stream=True,
+            verbose=False
+        )
+        
+        print("✅ Agent creation successful")
+        
+        # Check if streaming methods exist
+        if hasattr(agent, '_start_stream'):
+            print("✅ _start_stream method exists in Agent")
+        else:
+            print("❌ _start_stream method missing in Agent")
+            return False
+            
+        return True
+    except Exception as e:
+        print(f"❌ Agent test error: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+if __name__ == "__main__":
+    print("Running basic streaming functionality tests...")
+    print("=" * 50)
+    
+    # Set logging to reduce noise
+    logging.getLogger().setLevel(logging.WARNING)
+    
+    success = True
+    
+    # Test imports
+    if not test_imports():
+        success = False
+    
+    # Test method existence  
+    if not test_method_existence():
+        success = False
+        
+    # Test agent setup
+    if not test_agent_streaming_setup():
+        success = False
+    
+    print("=" * 50)
+    
+    if success:
+        print("✅ All basic tests passed!")
+        print("✅ Streaming infrastructure is properly set up")
+        print("📝 Note: Real streaming tests require API keys and will be tested later")
+    else:
+        print("❌ Some tests failed - check the implementation")

src/praisonai-agents/test_streaming_behavior.py

@@ -0,0 +1,102 @@
+#!/usr/bin/env python3
+"""
+Test streaming behavior for different LLM providers.
+This script tests the streaming functionality with OpenAI, LiteLLM, and Gemini models.
+"""
+
+import os
+import sys
+from praisonaiagents import Agent
+
+def test_streaming_behavior():
+    """Test streaming behavior with different configurations."""
+    
+    # Test configurations
+    test_cases = [
+        {
+            "name": "OpenAI - Stream True",
+            "llm": "gpt-4o-mini",
+            "stream": True,
+            "verbose": True,
+            "expected": "Should show real-time streaming"
+        },
+        {
+            "name": "OpenAI - Stream False",
+            "llm": "gpt-4o-mini", 
+            "stream": False,
+            "verbose": True,
+            "expected": "Should show 'Generating...' animation during API call"
+        },
+        {
+            "name": "Gemini - Stream True",
+            "llm": "gemini/gemini-1.5-flash",
+            "stream": True,
+            "verbose": True,
+            "expected": "Should show real-time streaming"
+        },
+        {
+            "name": "Gemini - Stream False",
+            "llm": "gemini/gemini-1.5-flash",
+            "stream": False,
+            "verbose": True,
+            "expected": "Should show 'Generating...' animation during API call"
+        },
+        {
+            "name": "LiteLLM/Ollama - Stream True",
+            "llm": "ollama/llama3.2",
+            "stream": True,
+            "verbose": True,
+            "expected": "Should show real-time streaming (if Ollama is running)"
+        },
+        {
+            "name": "LiteLLM/Ollama - Stream False",
+            "llm": "ollama/llama3.2",
+            "stream": False,
+            "verbose": True,
+            "expected": "Should show 'Generating...' animation during API call (if Ollama is running)"
+        }
+    ]
+    
+    for test_case in test_cases:
+        print(f"\n{'='*60}")
+        print(f"Testing: {test_case['name']}")
+        print(f"Expected: {test_case['expected']}")
+        print(f"{'='*60}\n")
+        
+        try:
+            # Create agent with specific configuration
+            agent = Agent(
+                name="Test Agent",
+                role="Testing Assistant",
+                goal="Test streaming behavior",
+                backstory="I am a test agent for verifying streaming functionality.",
+                llm=test_case["llm"],
+                stream=test_case["stream"],
+                verbose=test_case["verbose"]
+            )
+            
+            # Test the agent
+            response = agent.chat("Write a haiku about streaming data.")
+            
+            print(f"\nResponse received: {response[:100]}..." if len(response) > 100 else f"\nResponse received: {response}")
+            print(f"\nTest completed successfully!")
+            
+        except Exception as e:
+            print(f"\nError during test: {str(e)}")
+            if "ollama" in test_case["llm"].lower():
+                print("Note: Ollama tests require Ollama to be running locally.")
+            continue
+        
+        # Wait for user to observe the behavior
+        input("\nPress Enter to continue to the next test...")
+
+if __name__ == "__main__":
+    print("Starting streaming behavior tests...")
+    print("Make sure you have the required API keys set:")
+    print("- OPENAI_API_KEY for OpenAI tests")
+    print("- GEMINI_API_KEY or GOOGLE_API_KEY for Gemini tests")
+    print("- Ollama running locally for Ollama tests\n")
+    
+    test_streaming_behavior()
+    
+    print("\n\nAll tests completed!")

src/praisonai-agents/test_streaming_display_fix.py

@@ -0,0 +1,66 @@
+#!/usr/bin/env python3
+"""
+Test script for streaming display bypass fix
+Tests that streaming yields raw chunks without display_generation
+"""
+
+import sys
+import os
+import collections.abc
+
+# Add the praisonai-agents source to Python path
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src', 'praisonai-agents'))
+
+try:
+    from praisonaiagents import Agent
+    
+    print("🧪 Testing Streaming Display Bypass Fix")
+    print("=" * 50)
+    
+    # Test configuration - using mock model to avoid API calls
+    agent = Agent(
+        instructions="You are a helpful assistant",
+        llm="mock-model-for-testing",
+        stream=True
+    )
+    
+    # Test 1: Basic streaming setup
+    print("✅ Agent created successfully with stream=True")
+    print(f"📊 Agent stream attribute: {agent.stream}")
+    
+    # Test 2: Check start method behavior and exception on consumption
+    result = agent.start("Hello, test streaming")
+    assert isinstance(result, collections.abc.Generator), "Agent.start() should return a generator for streaming"
+    print("✅ Agent.start() returned a generator (streaming enabled)")
+
+    try:
+        # Consume the generator to trigger the API call, which should fail for a mock model.
+        list(result)
+        # If we get here, the test has failed because an exception was expected.
+        print("❌ FAILED: Expected an exception with mock model, but none was raised.")
+    except Exception as e:
+        print(f"✅ SUCCESS: Caught expected exception with mock model: {e}")
+        print("✅ Streaming path was triggered (exception expected with mock model)")
+    
+    # Test 3: Verify the streaming method exists and is callable
+    if hasattr(agent, '_start_stream') and callable(agent._start_stream):
+        print("✅ _start_stream method exists and is callable")
+    else:
+        print("❌ _start_stream method missing")
+    
+    print("\n🎯 Test Results:")
+    print("✅ Streaming infrastructure is properly set up")
+    print("✅ Agent.start() correctly detects stream=True")
+    print("✅ Modified _start_stream should now bypass display_generation")
+    print("✅ OpenAI streaming implementation is in place")
+    
+    print("\n📝 Note: Full streaming test requires valid OpenAI API key")
+    print("🔗 This test validates the code structure and logic flow")
+    
+except ImportError as e:
+    print(f"❌ Import failed: {e}")
+    print("Please ensure you're running from the correct directory")
+except Exception as e:
+    print(f"❌ Test failed: {e}")
+    import traceback
+    traceback.print_exc()

src/praisonai-agents/test_streaming_logic.py

@@ -0,0 +1,120 @@
+#!/usr/bin/env python3
+
+"""
+Test streaming logic without requiring API keys
+"""
+
+import sys
+import os
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src/praisonai-agents'))
+
+def test_streaming_logic():
+    """Test the streaming logic without actually calling LLMs"""
+    print("🔄 Testing streaming logic without API keys\n")
+    
+    try:
+        from praisonaiagents import Agent
+        
+        # Test 1: Default behavior (stream=False)
+        print("1. Testing default agent (no stream parameter)...")
+        agent1 = Agent(
+            instructions="You are a helpful assistant",
+            llm="gpt-4o-mini"
+        )
+        
+        print(f"   ✅ Agent created successfully")
+        print(f"   📊 stream attribute: {getattr(agent1, 'stream', 'NOT SET')}")
+        
+        if agent1.stream == False:
+            print("   ✅ CORRECT: stream defaults to False (backward compatible)")
+        else:
+            print("   ❌ INCORRECT: stream should default to False")
+            return False
+        
+        # Test 2: Explicit stream=False 
+        print("\n2. Testing explicit stream=False...")
+        agent2 = Agent(
+            instructions="You are a helpful assistant",
+            llm="gpt-4o-mini",
+            stream=False
+        )
+        
+        print(f"   ✅ Agent created successfully")  
+        print(f"   📊 stream attribute: {agent2.stream}")
+        
+        if agent2.stream == False:
+            print("   ✅ CORRECT: stream=False works")
+        else:
+            print("   ❌ INCORRECT: stream=False not working")
+            return False
+        
+        # Test 3: Explicit stream=True
+        print("\n3. Testing explicit stream=True...")
+        agent3 = Agent(
+            instructions="You are a helpful assistant",
+            llm="gpt-4o-mini",
+            stream=True
+        )
+        
+        print(f"   ✅ Agent created successfully")
+        print(f"   📊 stream attribute: {agent3.stream}")
+        
+        if agent3.stream == True:
+            print("   ✅ CORRECT: stream=True works")
+        else:
+            print("   ❌ INCORRECT: stream=True not working")
+            return False
+        
+        # Test 4: Check start method logic without actually calling it
+        print("\n4. Testing start method logic...")
+        
+        # Mock test - check if start would use streaming based on conditions
+        def check_streaming_logic(agent, **kwargs):
+            """Check what start() method would do without calling LLMs"""
+            stream_enabled = kwargs.get('stream', getattr(agent, 'stream', False))
+            return stream_enabled
+            
+        # Test default agent (should not stream)
+        would_stream = check_streaming_logic(agent1)
+        if not would_stream:
+            print("   ✅ Default agent would NOT stream (backward compatible)")
+        else:
+            print("   ❌ Default agent would stream (breaks compatibility)")
+            return False
+            
+        # Test explicit stream=False
+        would_stream = check_streaming_logic(agent2, stream=False)
+        if not would_stream:
+            print("   ✅ stream=False would NOT stream")
+        else:
+            print("   ❌ stream=False would stream")
+            return False
+            
+        # Test explicit stream=True
+        would_stream = check_streaming_logic(agent3, stream=True) 
+        if would_stream:
+            print("   ✅ stream=True would stream")
+        else:
+            print("   ❌ stream=True would NOT stream")
+            return False
+        
+        print("\n" + "="*60)
+        print("✅ ALL LOGIC TESTS PASSED!")
+        print("🎉 Backward compatibility has been restored!")
+        print("\nKey fixes:")
+        print("- Agent constructor now defaults to stream=False") 
+        print("- Basic usage agent.start('prompt') returns string (not generator)")
+        print("- Explicit stream=True enables streaming as expected")
+        print("- Explicit stream=False maintains non-streaming behavior")
+        
+        return True
+        
+    except Exception as e:
+        print(f"❌ Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+if __name__ == "__main__":
+    success = test_streaming_logic()
+    sys.exit(0 if success else 1)

src/praisonai-agents/test_streaming_telemetry_fix.py

@@ -0,0 +1,149 @@
+#!/usr/bin/env python3
+"""
+Test script to verify that the telemetry streaming pause issue is fixed.
+This test demonstrates that streaming starts immediately without blocking on telemetry.
+"""
+
+import time
+import sys
+import os
+
+# Add the source path to enable imports
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src', 'praisonai-agents'))
+
+def test_streaming_telemetry_fix():
+    """Test that streaming starts immediately without telemetry blocking."""
+    try:
+        from praisonaiagents import Agent
+        
+        print("🧪 Testing streaming telemetry fix...")
+        print("=" * 60)
+        
+        # Create agent with streaming enabled
+        agent = Agent(
+            instructions="You are a helpful assistant that provides brief responses.",
+            llm="test/mock-model",  # Mock model to avoid API calls
+            stream=True,
+            verbose=False  # Reduce output noise
+        )
+        
+        print("✅ Agent created successfully")
+        
+        # Test that start() returns immediately (generator)
+        start_time = time.time()
+        
+        try:
+            result = agent.start("Say hello briefly")
+            creation_time = time.time() - start_time
+            
+            print(f"⏱️  Generator creation time: {creation_time:.3f} seconds")
+            
+            # Check if it's a generator
+            import types
+            if isinstance(result, types.GeneratorType):
+                print("✅ Agent.start() returned generator (streaming mode)")
+                print("✅ No blocking pause - telemetry is now asynchronous!")
+                
+                # Verify the generator can be iterated (though it may fail due to mock model)
+                try:
+                    first_chunk = next(result)
+                    print(f"✅ First chunk received: {first_chunk[:50]}...")
+                except Exception as e:
+                    print(f"⚠️  Expected error with mock model: {type(e).__name__}")
+                    print("   This is normal - we're testing telemetry, not actual LLM calls")
+                
+                return True
+            else:
+                print("❌ Agent.start() did not return generator")
+                return False
+                
+        except Exception as e:
+            print(f"❌ Error during agent.start(): {e}")
+            import traceback
+            traceback.print_exc()
+            return False
+            
+    except Exception as e:
+        print(f"❌ Import or setup error: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+def test_telemetry_integration():
+    """Test that telemetry integration works without blocking."""
+    try:
+        from praisonaiagents.telemetry.integration import instrument_agent
+        from praisonaiagents.telemetry.telemetry import get_telemetry
+        from praisonaiagents import Agent
+        
+        print("🔧 Testing telemetry integration...")
+        
+        # Get telemetry instance
+        telemetry = get_telemetry()
+        print(f"✅ Telemetry enabled: {telemetry.enabled}")
+        
+        # Create agent
+        agent = Agent(
+            instructions="Test agent",
+            llm="test/mock-model",
+            stream=True,
+            verbose=False
+        )
+        
+        # Instrument the agent (this should happen automatically)
+        instrumented_agent = instrument_agent(agent, telemetry)
+        print("✅ Agent instrumented successfully")
+        
+        # Test that the instrumented start method doesn't block
+        start_time = time.time()
+        try:
+            result = instrumented_agent.start("Test prompt")
+            creation_time = time.time() - start_time
+            
+            print(f"⏱️  Instrumented start() time: {creation_time:.3f} seconds")
+            
+            if creation_time < 1.0:  # Should be nearly instantaneous
+                print("✅ No blocking detected - fix is working!")
+                return True
+            else:
+                print("❌ Potential blocking detected")
+                return False
+                
+        except Exception as e:
+            print(f"⚠️  Expected error with mock model: {type(e).__name__}")
+            print("✅ But no blocking pause occurred - fix is working!")
+            return True
+            
+    except Exception as e:
+        print(f"❌ Telemetry integration test error: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+if __name__ == "__main__":
+    print("Testing Streaming Telemetry Fix")
+    print("=" * 60)
+    
+    success = True
+    
+    # Test 1: Basic streaming functionality
+    if not test_streaming_telemetry_fix():
+        success = False
+    
+    print()
+    
+    # Test 2: Telemetry integration
+    if not test_telemetry_integration():
+        success = False
+    
+    print("=" * 60)
+    
+    if success:
+        print("🎉 All tests passed!")
+        print("✅ Streaming telemetry fix is working correctly")
+        print("✅ No more pause after 'execution tracked: success=True'")
+    else:
+        print("❌ Some tests failed")
+        
+    print("\n📝 Note: This test uses mock models to avoid API calls.")
+    print("    Real streaming tests require valid API keys.")

src/praisonai-agents/test_verbose_stream_combinations.py

@@ -0,0 +1,67 @@
+#!/usr/bin/env python3
+"""
+Test different combinations of verbose and stream parameters to understand the display behavior.
+"""
+
+import os
+from praisonaiagents import Agent
+
+# Use a simple model for testing
+os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "test-key")
+
+def test_combination(verbose: bool, stream: bool, test_name: str):
+    """Test a specific combination of verbose and stream settings"""
+    print(f"\n{'='*70}")
+    print(f"Test: {test_name}")
+    print(f"Settings: verbose={verbose}, stream={stream}")
+    print(f"{'='*70}\n")
+    
+    agent = Agent(
+        name="TestAgent",
+        role="Test Assistant",
+        goal="Test display behavior",
+        backstory="A test agent",
+        llm="gpt-4o-mini",
+        verbose=verbose,
+        stream=stream
+    )
+    
+    response = agent.chat("What is 2 + 2? Answer in exactly one word.")
+    print(f"\nFinal response: {response}")
+    print(f"\n{'-'*70}")
+
+def main():
+    print("Testing different combinations of verbose and stream settings")
+    print("Observing the display behavior for each combination\n")
+    
+    # Test all 4 combinations
+    test_combination(verbose=True, stream=True, test_name="Verbose ON, Stream ON")
+    test_combination(verbose=True, stream=False, test_name="Verbose ON, Stream OFF")
+    test_combination(verbose=False, stream=True, test_name="Verbose OFF, Stream ON")
+    test_combination(verbose=False, stream=False, test_name="Verbose OFF, Stream OFF")
+    
+    print("\n\nNow testing with explicit stream parameter in chat() method:")
+    
+    # Test overriding stream in chat method
+    print(f"\n{'='*70}")
+    print("Test: Agent has stream=True, but chat() called with stream=False")
+    print(f"{'='*70}\n")
+    
+    agent = Agent(
+        name="TestAgent",
+        role="Test Assistant",
+        goal="Test display behavior",
+        backstory="A test agent",
+        llm="gpt-4o-mini",
+        verbose=True,
+        stream=True  # Agent default is streaming
+    )
+    
+    # Override with stream=False in chat
+    response = agent.chat("What is 3 + 3? Answer in exactly one word.", stream=False)
+    print(f"\nFinal response: {response}")
+    
+    print("\n\nAll tests completed!")
+
+if __name__ == "__main__":
+    main()

src/praisonai-agents/test_warnings.py

@@ -0,0 +1,22 @@
+#!/usr/bin/env python3
+import sys
+sys.path.insert(0, 'src/praisonai-agents')
+
+import warnings
+warnings.simplefilter('always')  # Enable all warnings
+
+print("Testing import with all warnings enabled...")
+print("Importing praisonaiagents...")
+
+try:
+    import praisonaiagents
+    print("✅ Import successful!")
+    
+    print("Testing Agent creation...")
+    agent = praisonaiagents.Agent(instructions="Test agent", llm="gpt-4o-mini")
+    print("✅ Agent created successfully!")
+    
+except Exception as e:
+    print(f"❌ Error: {e}")
+    import traceback
+    traceback.print_exc()

src/praisonai-agents/tests/test_context_agent.py

@@ -0,0 +1,333 @@
+#!/usr/bin/env python3
+"""
+Test ContextAgent Implementation
+
+This test script validates the ContextAgent implementation for:
+- Import capability
+- Basic instantiation
+- Method availability
+- Backward compatibility
+- Integration with existing PraisonAI structure
+"""
+
+import sys
+from pathlib import Path
+
+# Add the src directory to Python path
+project_root = Path(__file__).parent.parent
+sys.path.insert(0, str(project_root))
+
+def test_imports():
+    """Test that ContextAgent can be imported correctly."""
+    print("🧪 Testing ContextAgent Imports...")
+    
+    try:
+        # Test importing from main package
+        from praisonaiagents import ContextAgent, create_context_agent
+        print("✅ Successfully imported ContextAgent and create_context_agent from main package")
+        
+        # Test importing from agent submodule
+        from praisonaiagents.agent import ContextAgent as AgentContextAgent
+        print("✅ Successfully imported ContextAgent from agent submodule")
+        
+        # Test importing from specific module
+        from praisonaiagents.agent.context_agent import ContextAgent as DirectContextAgent
+        print("✅ Successfully imported ContextAgent from direct module")
+        
+        return True
+        
+    except ImportError as e:
+        print(f"❌ Import failed: {e}")
+        return False
+
+def test_basic_instantiation():
+    """Test basic ContextAgent instantiation."""
+    print("\\n🧪 Testing ContextAgent Instantiation...")
+    
+    try:
+        from praisonaiagents import ContextAgent, create_context_agent
+        
+        # Test direct instantiation
+        context_agent = ContextAgent()
+        print("✅ Successfully created ContextAgent with default parameters")
+        
+        # Test with custom parameters
+        custom_agent = ContextAgent(
+            name="Test Context Engineer",
+            role="Test Role",
+            goal="Test Goal",
+            llm="gpt-4o-mini"
+        )
+        print("✅ Successfully created ContextAgent with custom parameters")
+        
+        # Test factory function
+        factory_agent = create_context_agent(llm="gpt-4o-mini")
+        print("✅ Successfully created ContextAgent using factory function")
+        
+        return True, [context_agent, custom_agent, factory_agent]
+        
+    except Exception as e:
+        print(f"❌ Instantiation failed: {e}")
+        return False, []
+
+def test_agent_inheritance():
+    """Test that ContextAgent properly inherits from Agent."""
+    print("\\n🧪 Testing Agent Inheritance...")
+    
+    try:
+        from praisonaiagents import ContextAgent, Agent
+        
+        context_agent = ContextAgent()
+        
+        # Test inheritance
+        assert isinstance(context_agent, Agent), "ContextAgent should inherit from Agent"
+        print("✅ ContextAgent properly inherits from Agent class")
+        
+        # Test that base Agent properties exist
+        assert hasattr(context_agent, 'name'), "Should have name attribute"
+        assert hasattr(context_agent, 'role'), "Should have role attribute"
+        assert hasattr(context_agent, 'goal'), "Should have goal attribute"
+        print("✅ ContextAgent has all required Agent attributes")
+        
+        return True
+        
+    except Exception as e:
+        print(f"❌ Inheritance test failed: {e}")
+        return False
+
+def test_context_engineering_methods():
+    """Test Context Engineering specific methods."""
+    print("\\n🧪 Testing Context Engineering Methods...")
+    
+    try:
+        from praisonaiagents import create_context_agent
+        
+        context_agent = create_context_agent()
+        
+        # Test that all Context Engineering methods exist
+        required_methods = [
+            'analyze_codebase_patterns',
+            'generate_context_document',
+            'create_validation_loop',
+            'enhance_prompt_with_context',
+            'generate_prp',
+            'extract_documentation_patterns',
+            'analyze_test_patterns',
+            'create_implementation_blueprint'
+        ]
+        
+        for method_name in required_methods:
+            assert hasattr(context_agent, method_name), f"Missing method: {method_name}"
+            assert callable(getattr(context_agent, method_name)), f"Method {method_name} is not callable"
+        
+        print(f"✅ All {len(required_methods)} Context Engineering methods are available")
+        
+        # Test async methods
+        async_methods = [
+            'aanalyze_codebase_patterns',
+            'agenerate_context_document',
+            'acreate_validation_loop',
+            'aenhance_prompt_with_context',
+            'agenerate_prp'
+        ]
+        
+        for method_name in async_methods:
+            assert hasattr(context_agent, method_name), f"Missing async method: {method_name}"
+            assert callable(getattr(context_agent, method_name)), f"Async method {method_name} is not callable"
+        
+        print(f"✅ All {len(async_methods)} async methods are available")
+        
+        return True
+        
+    except Exception as e:
+        print(f"❌ Method availability test failed: {e}")
+        return False
+
+def test_basic_functionality():
+    """Test basic functionality of ContextAgent methods."""
+    print("\\n🧪 Testing Basic Functionality...")
+    
+    try:
+        from praisonaiagents import create_context_agent
+        
+        context_agent = create_context_agent()
+        
+        # Test codebase analysis with current directory
+        test_path = str(Path(__file__).parent.parent)
+        analysis = context_agent.analyze_codebase_patterns(test_path)
+        assert isinstance(analysis, dict), "analyze_codebase_patterns should return dict"
+        print("✅ analyze_codebase_patterns works correctly")
+        
+        # Test context document generation
+        context_doc = context_agent.generate_context_document(
+            project_path=test_path,
+            requirements="Test feature implementation"
+        )
+        assert isinstance(context_doc, str), "generate_context_document should return string"
+        assert len(context_doc) > 100, "Context document should be substantial"
+        print("✅ generate_context_document works correctly")
+        
+        # Test validation loop creation
+        validation = context_agent.create_validation_loop(
+            implementation_requirements="Test implementation",
+            success_criteria=["Test passes", "Code works"]
+        )
+        assert isinstance(validation, dict), "create_validation_loop should return dict"
+        assert 'validation_steps' in validation, "Should have validation_steps"
+        print("✅ create_validation_loop works correctly")
+        
+        # Test prompt enhancement
+        enhanced = context_agent.enhance_prompt_with_context(
+            base_prompt="Test prompt",
+            context_data={"test": "data"}
+        )
+        assert isinstance(enhanced, str), "enhance_prompt_with_context should return string"
+        assert len(enhanced) > len("Test prompt"), "Enhanced prompt should be longer"
+        print("✅ enhance_prompt_with_context works correctly")
+        
+        # Test PRP generation
+        prp = context_agent.generate_prp(
+            feature_request="Test feature",
+            context_analysis=analysis
+        )
+        assert isinstance(prp, str), "generate_prp should return string"
+        assert "PRP" in prp, "Should contain PRP reference"
+        print("✅ generate_prp works correctly")
+        
+        return True
+        
+    except Exception as e:
+        print(f"❌ Functionality test failed: {e}")
+        return False
+
+def test_backward_compatibility():
+    """Test that existing PraisonAI functionality still works."""
+    print("\\n🧪 Testing Backward Compatibility...")
+    
+    try:
+        # Test that we can still import and use existing agents
+        from praisonaiagents import Agent, ImageAgent
+        
+        # Test basic Agent still works
+        basic_agent = Agent(name="Test Agent")  # Agent created for compatibility test
+        print("✅ Basic Agent still works")
+        
+        # Test ImageAgent still works
+        image_agent = ImageAgent(name="Test Image Agent")  # Image agent created for compatibility test
+        print("✅ ImageAgent still works")
+        
+        # Test that we can import other PraisonAI components
+        from praisonaiagents import Task, PraisonAIAgents
+        print("✅ Task and PraisonAIAgents can still be imported")
+        
+        # Test that __all__ exports are working
+        import praisonaiagents
+        expected_exports = [
+            'Agent', 'ImageAgent', 'ContextAgent', 'create_context_agent',
+            'PraisonAIAgents', 'Task'
+        ]
+        
+        for export in expected_exports:
+            assert hasattr(praisonaiagents, export), f"Missing export: {export}"
+        
+        print("✅ All expected exports are available")
+        
+        return True
+        
+    except Exception as e:
+        print(f"❌ Backward compatibility test failed: {e}")
+        return False
+
+def test_syntax_validation():
+    """Test that all Python files have valid syntax."""
+    print("\\n🧪 Testing Syntax Validation...")
+    
+    try:
+        import ast
+        
+        # Test the main ContextAgent file
+        context_agent_file = project_root / "praisonaiagents" / "agent" / "context_agent.py"
+        
+        with open(context_agent_file, 'r') as f:
+            content = f.read()
+        
+        # Parse the file to check for syntax errors
+        ast.parse(content)
+        print("✅ context_agent.py has valid Python syntax")
+        
+        # Test the examples
+        example_files = [
+            Path(__file__).parent.parent.parent.parent / "examples" / "python" / "agents" / "context-agent.py",
+            Path(__file__).parent.parent.parent.parent / "examples" / "python" / "concepts" / "context-engineering-workflow.py"
+        ]
+        
+        for example_file in example_files:
+            if example_file.exists():
+                with open(example_file, 'r') as f:
+                    content = f.read()
+                ast.parse(content)
+                print(f"✅ {example_file.name} has valid Python syntax")
+        
+        return True
+        
+    except SyntaxError as e:
+        print(f"❌ Syntax error found: {e}")
+        return False
+    except Exception as e:
+        print(f"❌ Syntax validation failed: {e}")
+        return False
+
+def run_all_tests():
+    """Run all tests and provide summary."""
+    print("🚀 Context Engineering Implementation QA Tests")
+    print("=" * 60)
+    
+    test_results = []
+    
+    # Run all tests
+    test_results.append(("Imports", test_imports()))
+    test_results.append(("Instantiation", test_basic_instantiation()[0]))
+    test_results.append(("Inheritance", test_agent_inheritance()))
+    test_results.append(("Methods", test_context_engineering_methods()))
+    test_results.append(("Functionality", test_basic_functionality()))
+    test_results.append(("Backward Compatibility", test_backward_compatibility()))
+    test_results.append(("Syntax Validation", test_syntax_validation()))
+    
+    # Summary
+    print("\\n📊 Test Results Summary")
+    print("=" * 30)
+    
+    passed = 0
+    total = len(test_results)
+    
+    for test_name, result in test_results:
+        status = "✅ PASS" if result else "❌ FAIL"
+        print(f"   {test_name:<20} {status}")
+        if result:
+            passed += 1
+    
+    print(f"\\n🎯 Overall Result: {passed}/{total} tests passed")
+    
+    if passed == total:
+        print("🎉 All tests passed! ContextAgent implementation is ready.")
+        return True
+    else:
+        print("⚠️  Some tests failed. Review implementation before release.")
+        return False
+
+if __name__ == "__main__":
+    success = run_all_tests()
+    
+    if success:
+        print("\\n✅ Quality Assurance Complete")
+        print("   • ContextAgent implementation validated")
+        print("   • Backward compatibility confirmed")
+        print("   • All Context Engineering methods working")
+        print("   • Examples have valid syntax")
+        print("   • Ready for production use")
+    else:
+        print("\\n❌ Quality Assurance Failed")
+        print("   • Review failed tests above")
+        print("   • Fix issues before release")
+        
+    sys.exit(0 if success else 1)

src/praisonai-agents/tests/test_embedding_logging.py

@@ -0,0 +1,196 @@
+#!/usr/bin/env python3
+"""
+Test script to verify that embedding logging is working correctly with TRACE level.
+This test verifies that the TRACE logging level works as expected for embedding operations.
+"""
+
+import logging
+import sys
+import os
+import unittest
+from io import StringIO
+
+# Add the praisonaiagents module to the path using relative path
+current_dir = os.path.dirname(os.path.abspath(__file__))
+parent_dir = os.path.dirname(current_dir)
+sys.path.insert(0, parent_dir)
+
+# Import the memory module to test the logger setup
+try:
+    from praisonaiagents.memory.memory import logger, TRACE_LEVEL
+except ImportError as e:
+    print(f"Failed to import memory module: {e}")
+    sys.exit(1)
+
+
+class TestTraceLogging(unittest.TestCase):
+    """Test cases for TRACE logging functionality."""
+    
+    def setUp(self):
+        """Set up test environment before each test."""
+        # Store original logger level
+        self.original_level = logger.level
+        # Create a string stream to capture log output
+        self.log_capture = StringIO()
+        self.handler = logging.StreamHandler(self.log_capture)
+        self.handler.setLevel(TRACE_LEVEL)
+        self.handler.setFormatter(logging.Formatter('%(levelname)s:%(name)s:%(message)s'))
+        logger.addHandler(self.handler)
+    
+    def tearDown(self):
+        """Clean up after each test."""
+        # Restore original logger level
+        logger.setLevel(self.original_level)
+        # Remove the test handler
+        logger.removeHandler(self.handler)
+        self.handler.close()
+    
+    def test_trace_level_value(self):
+        """Test that TRACE level is set to correct value (5, below DEBUG)."""
+        self.assertEqual(TRACE_LEVEL, 5)
+        self.assertLess(TRACE_LEVEL, logging.DEBUG)  # TRACE should be below DEBUG (10)
+    
+    def test_trace_method_exists(self):
+        """Test that trace method exists on logger."""
+        self.assertTrue(hasattr(logger, 'trace'))
+        self.assertTrue(callable(getattr(logger, 'trace')))
+    
+    def test_trace_logging_at_trace_level(self):
+        """Test that TRACE messages are logged when level is set to TRACE."""
+        logger.setLevel(TRACE_LEVEL)
+        test_message = "Test trace message for embedding data"
+        
+        logger.trace(test_message)
+        
+        log_output = self.log_capture.getvalue()
+        self.assertIn("TRACE", log_output)
+        self.assertIn(test_message, log_output)
+    
+    def test_trace_hidden_at_debug_level(self):
+        """Test that TRACE messages are not logged when level is set to DEBUG."""
+        logger.setLevel(logging.DEBUG)
+        test_message = "Test trace message should be hidden"
+        
+        # Clear any previous output
+        self.log_capture.truncate(0)
+        self.log_capture.seek(0)
+        
+        logger.trace(test_message)
+        
+        log_output = self.log_capture.getvalue()
+        self.assertNotIn(test_message, log_output)
+    
+    def test_trace_hidden_at_info_level(self):
+        """Test that TRACE messages are not logged when level is set to INFO."""
+        logger.setLevel(logging.INFO)
+        test_message = "Test trace message should be hidden at INFO"
+        
+        # Clear any previous output
+        self.log_capture.truncate(0)
+        self.log_capture.seek(0)
+        
+        logger.trace(test_message)
+        
+        log_output = self.log_capture.getvalue()
+        self.assertNotIn(test_message, log_output)
+    
+    def test_debug_still_works_at_debug_level(self):
+        """Test that DEBUG messages still work when level is set to DEBUG."""
+        logger.setLevel(logging.DEBUG)
+        test_message = "Test debug message should be visible"
+        
+        # Clear any previous output
+        self.log_capture.truncate(0)
+        self.log_capture.seek(0)
+        
+        logger.debug(test_message)
+        
+        log_output = self.log_capture.getvalue()
+        self.assertIn("DEBUG", log_output)
+        self.assertIn(test_message, log_output)
+    
+    def test_info_still_works_at_info_level(self):
+        """Test that INFO messages still work when level is set to INFO."""
+        logger.setLevel(logging.INFO)
+        test_message = "Test info message should be visible"
+        
+        # Clear any previous output
+        self.log_capture.truncate(0)
+        self.log_capture.seek(0)
+        
+        logger.info(test_message)
+        
+        log_output = self.log_capture.getvalue()
+        self.assertIn("INFO", log_output)
+        self.assertIn(test_message, log_output)
+    
+    def test_mixed_logging_levels(self):
+        """Test multiple log levels working together at TRACE level."""
+        logger.setLevel(TRACE_LEVEL)
+        
+        # Clear any previous output
+        self.log_capture.truncate(0)
+        self.log_capture.seek(0)
+        
+        logger.info("Info message")
+        logger.debug("Debug message")
+        logger.trace("Trace message")
+        
+        log_output = self.log_capture.getvalue()
+        self.assertIn("INFO", log_output)
+        self.assertIn("DEBUG", log_output)
+        self.assertIn("TRACE", log_output)
+        self.assertIn("Info message", log_output)
+        self.assertIn("Debug message", log_output)
+        self.assertIn("Trace message", log_output)
+
+
+def run_manual_verification():
+    """Run manual verification for visual inspection (can be run standalone)."""
+    print("=== Manual Verification of TRACE Logging ===")
+    print(f"TRACE_LEVEL = {TRACE_LEVEL}")
+    print(f"DEBUG level = {logging.DEBUG}")
+    print()
+    
+    # Configure logging for manual testing
+    logging.basicConfig(
+        level=TRACE_LEVEL,
+        format='%(levelname)s:%(name)s:%(message)s',
+        force=True  # Override any existing configuration
+    )
+    
+    print("--- Testing with TRACE level (all messages should show) ---")
+    logger.setLevel(TRACE_LEVEL)
+    logger.info("This is an INFO message")
+    logger.debug("This is a DEBUG message")
+    logger.trace("This is a TRACE message (embedding data would be here)")
+    
+    print("\n--- Testing with DEBUG level (TRACE should be hidden) ---")
+    logger.setLevel(logging.DEBUG)
+    logger.info("INFO with DEBUG level")
+    logger.debug("DEBUG with DEBUG level")
+    logger.trace("TRACE with DEBUG level (should be hidden)")
+    
+    print("\n--- Testing with INFO level (DEBUG and TRACE should be hidden) ---")
+    logger.setLevel(logging.INFO)
+    logger.info("INFO with INFO level")
+    logger.debug("DEBUG with INFO level (should be hidden)")
+    logger.trace("TRACE with INFO level (should be hidden)")
+    
+    print("\n✅ Manual verification completed!")
+
+
+if __name__ == "__main__":
+    import argparse
+    
+    parser = argparse.ArgumentParser(description="Test embedding TRACE logging implementation")
+    parser.add_argument("--manual", action="store_true", help="Run manual verification with visual output")
+    args = parser.parse_args()
+    
+    if args.manual:
+        print("Running manual verification...")
+        run_manual_verification()
+    else:
+        print("Running automated tests...")
+        # Run the automated tests
+        unittest.main(verbosity=2)

src/praisonai-agents/tests/test_gemini_streaming_fix.py

@@ -0,0 +1,88 @@
+#!/usr/bin/env python3
+"""
+Test script for Gemini streaming JSON parsing fix.
+
+This script tests the robust error handling added to handle malformed JSON chunks
+during streaming responses from Gemini models.
+"""
+
+from praisonaiagents import Agent
+
+def test_gemini_streaming_robustness():
+    """Test Gemini streaming with robust error handling."""
+    print("🧪 Testing Gemini Streaming Robustness Fix")
+    print("=" * 60)
+    
+    try:
+        # Create agent with Gemini model (using a lightweight model for testing)
+        agent = Agent(
+            instructions="You are a helpful assistant. Be concise.",
+            llm="gemini/gemini-2.5-flash",  # Using flash for faster testing
+            stream=True,
+            verbose=True  # Enable verbose to see the error handling in action
+        )
+        
+        print("✅ Agent created successfully")
+        print(f"📊 Model: {agent.llm}")
+        print(f"📊 Stream enabled: {agent.stream}")
+        print(f"📊 Verbose enabled: {agent.verbose}")
+        print()
+        
+        # Test streaming with a simple prompt that might cause chunking issues
+        print("🔄 Testing streaming response...")
+        prompt = "Explain what real-time streaming is in AI applications, focusing on the benefits and challenges."
+        
+        chunk_count = 0
+        response_content = ""
+        
+        try:
+            for chunk in agent.start(prompt):
+                if chunk:
+                    response_content += chunk
+                    chunk_count += 1
+                    print(chunk, end="", flush=True)
+                    
+        except Exception as streaming_error:
+            print(f"\n❌ Streaming error occurred: {streaming_error}")
+            print("🔄 This error should now be handled gracefully with fallback to non-streaming mode")
+            return False
+            
+        print("\n\n" + "="*60)
+        print("✅ Streaming completed successfully!")
+        print(f"📊 Total chunks received: {chunk_count}")
+        print(f"📊 Total response length: {len(response_content)} characters")
+        
+        if chunk_count > 1:
+            print("✅ SUCCESS: Streaming worked with multiple chunks")
+        else:
+            print("⚠️  WARNING: Only received 1 chunk (may have fallen back to non-streaming)")
+        
+        return True
+        
+    except Exception as e:
+        print(f"❌ Test failed with error: {e}")
+        return False
+
+if __name__ == "__main__":
+    print("🚀 Starting Gemini Streaming Robustness Test")
+    print("This test validates the JSON parsing error fixes for Gemini streaming")
+    print()
+    
+    success = test_gemini_streaming_robustness()
+    
+    print(f"\n{'='*60}")
+    if success:
+        print("🎉 TEST PASSED: Gemini streaming robustness fix is working!")
+    else:
+        print("💥 TEST FAILED: Issues detected with streaming robustness")
+    
+    print()
+    print("📝 Key improvements tested:")
+    print("  • Graceful handling of malformed JSON chunks")
+    print("  • Automatic fallback to non-streaming on repeated errors")
+    print("  • Better error logging and categorization")
+    print("  • Chunk-level error recovery")
+    
+    # Exit with appropriate status code for CI integration
+    import sys
+    sys.exit(0 if success else 1)

src/praisonai-agents/uv.lock

@@ -634,6 +634,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/4c/a3/ac312faeceffd2d8f86bc6dcb5c401188ba5a01bc88e69bed97578a0dfcd/durationpy-0.9-py3-none-any.whl", hash = "sha256:e65359a7af5cedad07fb77a2dd3f390f8eb0b74cb845589fa6c057086834dd38", size = 3461, upload-time = "2024-10-02T17:58:59.349Z" },
 ]
 
+[[package]]
+name = "ecdsa"
+version = "0.19.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "six" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c0/1f/924e3caae75f471eae4b26bd13b698f6af2c44279f67af317439c2f4c46a/ecdsa-0.19.1.tar.gz", hash = "sha256:478cba7b62555866fcb3bb3fe985e06decbdb68ef55713c4e5ab98c57d508e61", size = 201793, upload-time = "2025-03-13T11:52:43.25Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/cb/a3/460c57f094a4a165c84a1341c373b0a4f5ec6ac244b998d5021aade89b77/ecdsa-0.19.1-py2.py3-none-any.whl", hash = "sha256:30638e27cf77b7e15c4c4cc1973720149e1033827cfd00661ca5c8cc0cdb24c3", size = 150607, upload-time = "2025-03-13T11:52:41.757Z" },
+]
+
 [[package]]
 name = "et-xmlfile"
 version = "2.0.0"
@@ -1357,7 +1369,7 @@ wheels = [
 
 [[package]]
 name = "litellm"
-version = "1.72.1"
+version = "1.74.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -1372,9 +1384,9 @@ dependencies = [
     { name = "tiktoken" },
     { name = "tokenizers" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/d0/a7/67da5f515c3847bd121b5fde54ac48ee3e7d55d75c49d2b51a2f9b20ab7a/litellm-1.72.1.tar.gz", hash = "sha256:d5d822988d62db8fe434aca7584e1cf8f5a03c033e957e3b5c1ca24366840951", size = 8095871, upload-time = "2025-06-04T19:09:07.717Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/91/4c/e8ffbd01d0f43357315646890524ce53648a3962169498e08a4b8edca6e2/litellm-1.74.7.tar.gz", hash = "sha256:53b809a342154d8543ea96422cf962cd5ea9df293f83dab0cc63b27baadf0ece", size = 9587483, upload-time = "2025-07-20T01:03:11.853Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/10/2d/48dee9e0cc782a74d7361a3a1acf70d9ac8a9278a4fa8d2dcf2ac7359ed8/litellm-1.72.1-py3-none-any.whl", hash = "sha256:858b4458f199dd8d3acd46436cf5fb1de19404e5dcf92ff59ec417c224b52481", size = 7994774, upload-time = "2025-06-04T19:09:04.316Z" },
+    { url = "https://files.pythonhosted.org/packages/1c/54/eb5fee089d3e5e07a6d60b2565f798c66d43f46ba8f339e77f78cee98462/litellm-1.74.7-py3-none-any.whl", hash = "sha256:d630785faf07813cf0d5e9fb0bb84aaa18aa728297858c58c56f34c0b9190df1", size = 8652488, upload-time = "2025-07-20T01:03:09.226Z" },
 ]
 
 [[package]]
@@ -2286,6 +2298,20 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ab/5f/b38085618b950b79d2d9164a711c52b10aefc0ae6833b96f626b7021b2ed/pandas-2.2.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:ad5b65698ab28ed8d7f18790a0dc58005c7629f227be9ecc1072aa74c0c1d43a", size = 13098436, upload-time = "2024-09-20T13:09:48.112Z" },
 ]
 
+[[package]]
+name = "passlib"
+version = "1.7.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/b6/06/9da9ee59a67fae7761aab3ccc84fa4f3f33f125b370f1ccdb915bf967c11/passlib-1.7.4.tar.gz", hash = "sha256:defd50f72b65c5402ab2c573830a6978e5f202ad0d984793c8dde2c4152ebe04", size = 689844, upload-time = "2020-10-08T19:00:52.121Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3b/a4/ab6b7589382ca3df236e03faa71deac88cae040af60c071a78d254a62172/passlib-1.7.4-py2.py3-none-any.whl", hash = "sha256:aa6bca462b8d8bda89c70b382f0c298a20b5560af6cbfa2dce410c0a2fb669f1", size = 525554, upload-time = "2020-10-08T19:00:49.856Z" },
+]
+
+[package.optional-dependencies]
+bcrypt = [
+    { name = "bcrypt" },
+]
+
 [[package]]
 name = "pdfminer-six"
 version = "20240706"
@@ -2396,7 +2422,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.144"
+version = "0.0.146"
 source = { editable = "." }
 dependencies = [
     { name = "aiohttp" },
@@ -2416,15 +2442,25 @@ all = [
     { name = "mcp" },
     { name = "mem0ai", extra = ["graph"] },
     { name = "motor" },
+    { name = "passlib", extra = ["bcrypt"] },
     { name = "posthog" },
     { name = "pydantic" },
+    { name = "pyjwt" },
     { name = "pymongo" },
+    { name = "python-jose", extra = ["cryptography"] },
+    { name = "python-multipart" },
     { name = "uvicorn" },
 ]
 api = [
     { name = "fastapi" },
     { name = "uvicorn" },
 ]
+auth = [
+    { name = "passlib", extra = ["bcrypt"] },
+    { name = "pyjwt" },
+    { name = "python-jose", extra = ["cryptography"] },
+    { name = "python-multipart" },
+]
 graph = [
     { name = "chromadb" },
     { name = "mem0ai", extra = ["graph"] },
@@ -2465,17 +2501,19 @@ requires-dist = [
     { name = "chromadb", marker = "extra == 'memory'", specifier = ">=1.0.0" },
     { name = "fastapi", marker = "extra == 'api'", specifier = ">=0.115.0" },
     { name = "fastapi", marker = "extra == 'mcp'", specifier = ">=0.115.0" },
-    { name = "litellm", marker = "extra == 'llm'", specifier = ">=1.72.0" },
-    { name = "litellm", marker = "extra == 'memory'", specifier = ">=1.72.0" },
+    { name = "litellm", marker = "extra == 'llm'", specifier = ">=1.72.6" },
+    { name = "litellm", marker = "extra == 'memory'", specifier = ">=1.72.6" },
     { name = "markitdown", extras = ["all"], marker = "extra == 'knowledge'", specifier = ">=0.1.0" },
     { name = "mcp", marker = "extra == 'mcp'", specifier = ">=1.6.0" },
     { name = "mem0ai", marker = "extra == 'knowledge'", specifier = ">=0.1.0" },
     { name = "mem0ai", extras = ["graph"], marker = "extra == 'graph'", specifier = ">=0.1.0" },
     { name = "motor", marker = "extra == 'mongodb'", specifier = ">=3.4.0" },
     { name = "openai" },
+    { name = "passlib", extras = ["bcrypt"], marker = "extra == 'auth'", specifier = ">=1.7.4" },
     { name = "posthog", specifier = ">=3.0.0" },
     { name = "posthog", marker = "extra == 'telemetry'", specifier = ">=3.0.0" },
     { name = "praisonaiagents", extras = ["api"], marker = "extra == 'all'" },
+    { name = "praisonaiagents", extras = ["auth"], marker = "extra == 'all'" },
     { name = "praisonaiagents", extras = ["graph"], marker = "extra == 'all'" },
     { name = "praisonaiagents", extras = ["knowledge"], marker = "extra == 'all'" },
     { name = "praisonaiagents", extras = ["llm"], marker = "extra == 'all'" },
@@ -2485,12 +2523,15 @@ requires-dist = [
     { name = "praisonaiagents", extras = ["telemetry"], marker = "extra == 'all'" },
     { name = "pydantic" },
     { name = "pydantic", marker = "extra == 'llm'", specifier = ">=2.4.2" },
+    { name = "pyjwt", marker = "extra == 'auth'", specifier = ">=2.8.0" },
     { name = "pymongo", marker = "extra == 'mongodb'", specifier = ">=4.6.3" },
+    { name = "python-jose", extras = ["cryptography"], marker = "extra == 'auth'", specifier = ">=3.3.0" },
+    { name = "python-multipart", marker = "extra == 'auth'", specifier = ">=0.0.6" },
     { name = "rich" },
     { name = "uvicorn", marker = "extra == 'api'", specifier = ">=0.34.0" },
     { name = "uvicorn", marker = "extra == 'mcp'", specifier = ">=0.34.0" },
 ]
-provides-extras = ["mcp", "memory", "knowledge", "graph", "llm", "api", "telemetry", "mongodb", "all"]
+provides-extras = ["mcp", "memory", "knowledge", "graph", "llm", "api", "telemetry", "mongodb", "auth", "all"]
 
 [[package]]
 name = "propcache"
@@ -2856,6 +2897,34 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/6a/3e/b68c118422ec867fa7ab88444e1274aa40681c606d59ac27de5a5588f082/python_dotenv-1.0.1-py3-none-any.whl", hash = "sha256:f7b63ef50f1b690dddf550d03497b66d609393b40b564ed0d674909a68ebf16a", size = 19863, upload-time = "2024-01-23T06:32:58.246Z" },
 ]
 
+[[package]]
+name = "python-jose"
+version = "3.5.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "ecdsa" },
+    { name = "pyasn1" },
+    { name = "rsa" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c6/77/3a1c9039db7124eb039772b935f2244fbb73fc8ee65b9acf2375da1c07bf/python_jose-3.5.0.tar.gz", hash = "sha256:fb4eaa44dbeb1c26dcc69e4bd7ec54a1cb8dd64d3b4d81ef08d90ff453f2b01b", size = 92726, upload-time = "2025-05-28T17:31:54.288Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d9/c3/0bd11992072e6a1c513b16500a5d07f91a24017c5909b02c72c62d7ad024/python_jose-3.5.0-py2.py3-none-any.whl", hash = "sha256:abd1202f23d34dfad2c3d28cb8617b90acf34132c7afd60abd0b0b7d3cb55771", size = 34624, upload-time = "2025-05-28T17:31:52.802Z" },
+]
+
+[package.optional-dependencies]
+cryptography = [
+    { name = "cryptography" },
+]
+
+[[package]]
+name = "python-multipart"
+version = "0.0.20"
+source = { registry = "https://pypi.org/simple" }
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+wheels = [
+    { url = "https://files.pythonhosted.org/packages/45/58/38b5afbc1a800eeea951b9285d3912613f2603bdf897a4ab0f4bd7f405fc/python_multipart-0.0.20-py3-none-any.whl", hash = "sha256:8a62d3a8335e06589fe01f2a3e178cdcc632f3fbe0d492ad9ee0ec35aab1f104", size = 24546, upload-time = "2024-12-16T19:45:44.423Z" },
+]
+
 [[package]]
 name = "python-pptx"
 version = "1.0.2"

src/praisonai/praisonai.rb

@@ -3,8 +3,8 @@ class Praisonai < Formula
   
     desc "AI tools for various AI applications"
     homepage "https://github.com/MervinPraison/PraisonAI"
-    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.71.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.71.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.73.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.73.tar.gz | shasum -a 256`.split.first
     license "MIT"
   
     depends_on "[email protected]"

src/praisonai/praisonai/cli.py

@@ -545,7 +545,7 @@ class PraisonAI:
             return default_args
         
         # Define special commands
-        special_commands = ['chat', 'code', 'call', 'realtime', 'train', 'ui']
+        special_commands = ['chat', 'code', 'call', 'realtime', 'train', 'ui', 'context']
         
         parser = argparse.ArgumentParser(prog="praisonai", description="praisonAI command-line interface")
         parser.add_argument("--framework", choices=["crewai", "autogen", "praisonai"], help="Specify the framework")
@@ -569,6 +569,9 @@ class PraisonAI:
         parser.add_argument("--merge", action="store_true", help="Merge existing agents.yaml with auto-generated agents instead of overwriting")
         parser.add_argument("--claudecode", action="store_true", help="Enable Claude Code integration for file modifications and coding tasks")
         parser.add_argument("--file", "-f", type=str, help="Read input from a file and append it to the prompt")
+        parser.add_argument("--url", type=str, help="Repository URL for context analysis")
+        parser.add_argument("--goal", type=str, help="Goal for context engineering")
+        parser.add_argument("--auto-analyze", action="store_true", help="Enable automatic analysis in context engineering")
         
         # If we're in a test environment, parse with empty args to avoid pytest interference
         if in_test_env:
@@ -674,6 +677,25 @@ class PraisonAI:
                 self.create_chainlit_interface()
                 sys.exit(0)
 
+            elif args.command == 'context':
+                if not PRAISONAI_AVAILABLE:
+                    print("[red]ERROR: PraisonAI Agents is not installed. Install with:[/red]")
+                    print("\npip install praisonaiagents\n")
+                    sys.exit(1)
+                
+                if not args.url:
+                    print("[red]ERROR: --url is required for context command[/red]")
+                    print("Usage: praisonai context --url <repository_url> --goal <goal> [--auto-analyze]")
+                    sys.exit(1)
+                
+                if not args.goal:
+                    print("[red]ERROR: --goal is required for context command[/red]")
+                    print("Usage: praisonai context --url <repository_url> --goal <goal> [--auto-analyze]")
+                    sys.exit(1)
+                
+                self.handle_context_command(args.url, args.goal, getattr(args, 'auto_analyze', False))
+                sys.exit(0)
+
         # Only check framework availability for agent-related operations
         if not args.command and (args.init or args.auto or args.framework):
             if not CREWAI_AVAILABLE and not AUTOGEN_AVAILABLE and not PRAISONAI_AVAILABLE:
@@ -867,6 +889,50 @@ class PraisonAI:
         else:
             print("ERROR: Realtime UI is not installed. Please install it with 'pip install \"praisonai[realtime]\"' to use the realtime UI.")
 
+    def handle_context_command(self, url: str, goal: str, auto_analyze: bool = False) -> str:
+        """
+        Handle the context command by creating a ContextAgent and running it.
+        
+        Args:
+            url: Repository URL for context analysis
+            goal: Goal for context engineering
+            auto_analyze: Enable automatic analysis (default: False)
+            
+        Returns:
+            str: Result from context engineering
+        """
+        try:
+            from praisonaiagents import ContextAgent
+            print("[bold green]Starting Context Engineering...[/bold green]")
+            print(f"URL: {url}")
+            print(f"Goal: {goal}")
+            print(f"Auto-analyze: {auto_analyze}")
+            
+            # Use the same model configuration pattern as other CLI commands
+            # Priority order: MODEL_NAME > OPENAI_MODEL_NAME for model selection
+            model_name = os.environ.get("MODEL_NAME") or os.environ.get("OPENAI_MODEL_NAME", "gpt-4o-mini")
+            
+            # Create ContextAgent with user's LLM configuration
+            agent = ContextAgent(llm=model_name, auto_analyze=auto_analyze)
+            
+            # Format input as expected by the start method: "url goal"
+            input_text = f"{url} {goal}"
+            
+            # Execute the context engineering
+            result = agent.start(input_text)
+            
+            print("\n[bold green]Context Engineering Complete![/bold green]")
+            print(result)
+            return result
+            
+        except ImportError as e:
+            print(f"[red]ERROR: Failed to import ContextAgent: {e}[/red]")
+            print("Make sure praisonaiagents is installed: pip install praisonaiagents")
+            sys.exit(1)
+        except Exception as e:
+            print(f"[red]ERROR: Context engineering failed: {e}[/red]")
+            sys.exit(1)
+
 if __name__ == "__main__":
     praison_ai = PraisonAI()
     praison_ai.main()

src/praisonai/praisonai/deploy.py

@@ -57,7 +57,7 @@ class CloudDeployer:
             file.write("FROM python:3.11-slim\n")
             file.write("WORKDIR /app\n")
             file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==2.2.71 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.2.73 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

src/praisonai/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.2.71"
+version = "2.2.73"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration."
 readme = "README.md"
 license = ""
@@ -12,7 +12,7 @@ dependencies = [
     "rich>=13.7",
     "markdown>=3.5",
     "pyparsing>=3.0.0",
-    "praisonaiagents>=0.0.144",
+    "praisonaiagents>=0.0.146",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",
@@ -45,11 +45,11 @@ anthropic = ["langchain-anthropic>=0.3.0"]
 cohere = ["langchain-cohere>=0.3.0,<0.4.0"]
 chat = [
     "chainlit==2.5.5",
-    "litellm>=1.68.0",
+    "litellm>=1.72.6",
     "aiosqlite>=0.20.0",
     "greenlet>=3.0.3",
     "tavily-python==0.5.0",
-    "crawl4ai>=0.6.0",
+    "crawl4ai>=0.7.0",
     "sqlalchemy>=2.0.36",
     "playwright>=1.47.0",
     "rich",
@@ -57,22 +57,22 @@ chat = [
 ]
 code = [
     "chainlit==2.5.5",
-    "litellm>=1.68.0",
+    "litellm>=1.72.6",
     "aiosqlite>=0.20.0",
     "greenlet>=3.0.3",
     "tavily-python==0.5.0",
-    "crawl4ai>=0.6.0",
+    "crawl4ai>=0.7.0",
     "sqlalchemy>=2.0.36",
     "playwright>=1.47.0",
     "pydantic<=2.10.1"
 ]
 realtime = [
     "chainlit==2.5.5",
-    "litellm>=1.68.0",
+    "litellm>=1.72.6",
     "aiosqlite>=0.20.0",
     "greenlet>=3.0.3",
     "tavily-python==0.5.0",
-    "crawl4ai>=0.6.0",
+    "crawl4ai>=0.7.0",
     "websockets>=12.0",
     "plotly>=5.24.0",
     "yfinance>=0.2.44",
@@ -90,7 +90,7 @@ call = [
     "openai>=1.54.0",
 ]
 train = []
-crewai = ["crewai>=0.32.0", "praisonai-tools>=0.0.22"]
+crewai = ["crewai>=0.148.0", "praisonai-tools>=0.0.22"]
 autogen = ["pyautogen==0.2.29", "praisonai-tools>=0.0.22", "crewai"]
 autogen-v4 = [
     "autogen-agentchat>=0.4.0", 
@@ -102,7 +102,7 @@ autogen-v4 = [
 
 [tool.poetry]
 name = "PraisonAI"
-version = "2.2.71"
+version = "2.2.73"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration."
 authors = ["Mervin Praison"]
 license = ""
@@ -120,7 +120,7 @@ python = ">=3.10,<3.13"
 rich = ">=13.7"
 markdown = ">=3.5"
 pyparsing = ">=3.0.0"
-praisonaiagents = ">=0.0.144"
+praisonaiagents = ">=0.0.146"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"
@@ -129,7 +129,7 @@ pyautogen = {version = "==0.2.29", optional = true}
 autogen-agentchat = {version = ">=0.4.0", optional = true}
 autogen-ext = {version = ">=0.4.0", optional = true}
 autogen-core = {version = ">=0.4.0", optional = true}
-crewai = {version = ">=0.32.0", optional = true}
+crewai = {version = ">=0.148.0", optional = true}
 praisonai-tools = {version = ">=0.0.22", optional = true}
 chainlit = {version = "==2.5.5", optional = true}
 gradio = {version = ">=4.26.0", optional = true}
@@ -139,11 +139,11 @@ langchain-google-genai = {version = ">=2.1.0", optional = true}
 langchain-anthropic = {version = ">=0.3.0", optional = true}
 langchain-openai = {version = ">=0.2.1,<0.3.0", optional = true}
 langchain-cohere = {version = ">=0.3.0,<0.4.0", optional = true}
-litellm = {version = ">=1.68.0", optional = true}
+litellm = {version = ">=1.72.6", optional = true}
 aiosqlite= {version = ">=0.20.0", optional = true}
 greenlet = {version = ">=3.0.3", optional = true}
 tavily-python = {version = "==0.5.0", optional=true}
-crawl4ai = {version = ">=0.6.0", optional = true}
+crawl4ai = {version = ">=0.7.0", optional = true}
 websockets = {version = ">=12.0", optional = true}
 plotly = {version = ">=5.24.0", optional = true}
 yfinance = {version = ">=0.2.44", optional = true}
@@ -206,7 +206,7 @@ langchain-google-genai = ">=2.1.0"
 langchain-anthropic = ">=0.3.0"
 langchain-openai = ">=0.2.1,<0.3.0"
 langchain-cohere = ">=0.3.0,<0.4.0"
-litellm = ">=1.68.0"
+litellm = ">=1.72.6"
 aiosqlite = ">=0.20.0"
 greenlet = ">=3.0.3"
 

src/praisonai/uv.lock

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-]
-
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 name = "autogen-agentchat"
 version = "0.6.4"
@@ -921,11 +905,10 @@ wheels = [
 
 [[package]]
 name = "crewai"
-version = "0.119.0"
+version = "0.148.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "appdirs" },
-    { name = "auth0-python" },
     { name = "blinker" },
     { name = "chromadb" },
     { name = "click" },
@@ -934,6 +917,7 @@ dependencies = [
     { name = "json5" },
     { name = "jsonref" },
     { name = "litellm" },
+    { name = "onnxruntime" },
     { name = "openai" },
     { name = "openpyxl" },
     { name = "opentelemetry-api" },
@@ -941,16 +925,18 @@ dependencies = [
     { name = "opentelemetry-sdk" },
     { name = "pdfplumber" },
     { name = "pydantic" },
+    { name = "pyjwt" },
     { name = "python-dotenv" },
     { name = "pyvis" },
     { name = "regex" },
+    { name = "tokenizers" },
     { name = "tomli" },
     { name = "tomli-w" },
     { name = "uv" },
 ]
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+version = "1.72.6"
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