Taskmaster-AI Automation Framework Cheat Sheet

Overview

Taskmaster-AI is an advanced AI-powered task automation and workflow management framework that combines artificial intelligence with robotic process automation (RPA) capabilities. It enables intelligent automation of complex business processes, decision-making workflows, and repetitive tasks through natural language processing, machine learning, and adaptive execution strategies. The framework supports multi-modal interactions, dynamic workflow generation, and intelligent error handling.

⚠️ Authorization Required: Ensure you have proper authorization before implementing automation workflows that interact with business systems, databases, or external services.

Installation and Setup

Python Installation

# Install Taskmaster-AI framework
pip install taskmaster-ai

# Install with additional dependencies
pip install taskmaster-ai[full]

# Install development version
pip install git+https://github.com/taskmaster-ai/taskmaster-ai.git

# Install with specific AI providers
pip install taskmaster-ai[openai,anthropic,azure]

# Verify installation
taskmaster-ai --version

Docker Installation

# Pull official Docker image
docker pull taskmasterai/taskmaster-ai:latest

# Run with environment variables
docker run -d \
  --name taskmaster-ai \
  -e OPENAI_API_KEY=your_openai_key \
  -e ANTHROPIC_API_KEY=your_anthropic_key \
  -v $(pwd)/workflows:/app/workflows \
  -p 8080:8080 \
  taskmasterai/taskmaster-ai:latest

# Run with custom configuration
docker run -d \
  --name taskmaster-ai \
  -v $(pwd)/config:/app/config \
  -v $(pwd)/workflows:/app/workflows \
  -v $(pwd)/data:/app/data \
  taskmasterai/taskmaster-ai:latest

Configuration Setup

# config/taskmaster.yaml
api_settings:
  openai:
    api_key: "${OPENAI_API_KEY}"
    model: "gpt-4"
    max_tokens: 2000
    temperature: 0.1
  
  anthropic:
    api_key: "${ANTHROPIC_API_KEY}"
    model: "claude-3-sonnet-20240229"
    max_tokens: 2000
  
  azure:
    api_key: "${AZURE_OPENAI_KEY}"
    endpoint: "${AZURE_OPENAI_ENDPOINT}"
    deployment: "gpt-4"

automation_settings:
  max_retries: 3
  timeout: 300
  parallel_tasks: 5
  error_handling: "graceful"
  logging_level: "INFO"

workflow_settings:
  auto_save: true
  backup_enabled: true
  version_control: true
  execution_history: 100

security_settings:
  encryption_enabled: true
  audit_logging: true
  access_control: "rbac"
  secure_storage: true

Core Framework Components

Task Definition and Management

#!/usr/bin/env python3
# taskmaster_core.py

from taskmaster_ai import TaskmasterAI, Task, Workflow, AIAgent
from taskmaster_ai.decorators import task, workflow, retry, timeout
from taskmaster_ai.types import TaskResult, WorkflowContext
from typing import Dict, List, Any, Optional
import asyncio
from datetime import datetime, timedelta

class TaskmasterCore:
    def __init__(self, config_path: str = "config/taskmaster.yaml"):
        self.taskmaster = TaskmasterAI(config_path)
        self.ai_agent = AIAgent(self.taskmaster.config)
        self.active_workflows = {}
        self.task_registry = {}
    
    @task(name="data_extraction", category="data_processing")
    @retry(max_attempts=3, backoff_factor=2)
    @timeout(seconds=120)
    async def extract_data_from_source(self, source: str, 
                                     extraction_rules: Dict) -> TaskResult:
        """Extract data from various sources using AI-powered extraction"""
        
        try:
            # AI-powered source analysis
            source_analysis = await self.ai_agent.analyze_data_source(
                source=source,
                rules=extraction_rules
            )
            
            # Dynamic extraction strategy
            extraction_strategy = await self.ai_agent.generate_extraction_strategy(
                analysis=source_analysis,
                requirements=extraction_rules
            )
            
            # Execute extraction
            extracted_data = await self.execute_extraction(
                source=source,
                strategy=extraction_strategy
            )
            
            # Validate and clean data
            validated_data = await self.ai_agent.validate_and_clean_data(
                data=extracted_data,
                validation_rules=extraction_rules.get("validation", {})
            )
            
            return TaskResult(
                success=True,
                data=validated_data,
                metadata={
                    "source": source,
                    "extraction_strategy": extraction_strategy,
                    "records_extracted": len(validated_data),
                    "timestamp": datetime.now().isoformat()
                }
            )
        
        except Exception as e:
            return TaskResult(
                success=False,
                error=str(e),
                metadata={"source": source, "timestamp": datetime.now().isoformat()}
            )
    
    @task(name="decision_making", category="ai_reasoning")
    async def ai_decision_maker(self, context: Dict, 
                               decision_criteria: Dict) -> TaskResult:
        """Make intelligent decisions based on context and criteria"""
        
        try:
            # Analyze decision context
            context_analysis = await self.ai_agent.analyze_decision_context(
                context=context,
                criteria=decision_criteria
            )
            
            # Generate decision options
            decision_options = await self.ai_agent.generate_decision_options(
                analysis=context_analysis,
                constraints=decision_criteria.get("constraints", {})
            )
            
            # Evaluate options using AI reasoning
            evaluation = await self.ai_agent.evaluate_decision_options(
                options=decision_options,
                criteria=decision_criteria,
                context=context
            )
            
            # Make final decision
            final_decision = await self.ai_agent.make_final_decision(
                evaluation=evaluation,
                confidence_threshold=decision_criteria.get("confidence_threshold", 0.8)
            )
            
            return TaskResult(
                success=True,
                data={
                    "decision": final_decision,
                    "confidence": evaluation.get("confidence", 0),
                    "reasoning": evaluation.get("reasoning", ""),
                    "alternatives": decision_options
                },
                metadata={
                    "decision_type": decision_criteria.get("type", "general"),
                    "timestamp": datetime.now().isoformat()
                }
            )
        
        except Exception as e:
            return TaskResult(
                success=False,
                error=str(e),
                metadata={"timestamp": datetime.now().isoformat()}
            )
    
    @task(name="process_automation", category="rpa")
    async def automate_business_process(self, process_definition: Dict,
                                      input_data: Dict) -> TaskResult:
        """Automate complex business processes with AI guidance"""
        
        try:
            # AI-powered process analysis
            process_analysis = await self.ai_agent.analyze_business_process(
                definition=process_definition,
                input_data=input_data
            )
            
            # Generate execution plan
            execution_plan = await self.ai_agent.generate_execution_plan(
                process=process_definition,
                analysis=process_analysis,
                data=input_data
            )
            
            # Execute process steps
            execution_results = []
            for step in execution_plan["steps"]:
                step_result = await self.execute_process_step(
                    step=step,
                    context=input_data,
                    previous_results=execution_results
                )
                execution_results.append(step_result)
                
                # AI-powered error handling and adaptation
                if not step_result.success:
                    recovery_action = await self.ai_agent.generate_recovery_action(
                        failed_step=step,
                        error=step_result.error,
                        context=input_data
                    )
                    
                    if recovery_action:
                        recovery_result = await self.execute_recovery_action(
                            action=recovery_action,
                            context=input_data
                        )
                        execution_results.append(recovery_result)
            
            # Analyze overall execution
            execution_summary = await self.ai_agent.analyze_execution_results(
                results=execution_results,
                original_plan=execution_plan
            )
            
            return TaskResult(
                success=execution_summary["overall_success"],
                data={
                    "execution_results": execution_results,
                    "summary": execution_summary,
                    "process_metrics": execution_summary.get("metrics", {})
                },
                metadata={
                    "process_id": process_definition.get("id", "unknown"),
                    "execution_time": execution_summary.get("execution_time", 0),
                    "timestamp": datetime.now().isoformat()
                }
            )
        
        except Exception as e:
            return TaskResult(
                success=False,
                error=str(e),
                metadata={"timestamp": datetime.now().isoformat()}
            )
    
    async def execute_extraction(self, source: str, strategy: Dict) -> List[Dict]:
        """Execute data extraction based on AI-generated strategy"""
        
        extraction_methods = {
            "web_scraping": self.web_scraping_extraction,
            "api_extraction": self.api_extraction,
            "database_query": self.database_extraction,
            "file_parsing": self.file_parsing_extraction,
            "document_processing": self.document_processing_extraction
        }
        
        method = extraction_methods.get(strategy["method"])
        if not method:
            raise ValueError(f"Unknown extraction method: {strategy['method']}")
        
        return await method(source, strategy["parameters"])
    
    async def web_scraping_extraction(self, url: str, parameters: Dict) -> List[Dict]:
        """AI-guided web scraping"""
        from selenium import webdriver
        from bs4 import BeautifulSoup
        import requests
        
        # AI-optimized scraping strategy
        scraping_config = await self.ai_agent.optimize_scraping_config(
            url=url,
            parameters=parameters
        )
        
        if scraping_config["method"] == "selenium":
            driver = webdriver.Chrome(options=scraping_config["chrome_options"])
            try:
                driver.get(url)
                
                # AI-guided element selection
                elements = await self.ai_agent.find_target_elements(
                    page_source=driver.page_source,
                    selectors=parameters["selectors"]
                )
                
                extracted_data = []
                for element in elements:
                    data = await self.ai_agent.extract_element_data(
                        element=element,
                        extraction_rules=parameters["extraction_rules"]
                    )
                    extracted_data.append(data)
                
                return extracted_data
            
            finally:
                driver.quit()
        
        else:  # requests-based scraping
            response = requests.get(url, headers=scraping_config["headers"])
            soup = BeautifulSoup(response.content, 'html.parser')
            
            # AI-guided content extraction
            extracted_data = await self.ai_agent.extract_content_from_soup(
                soup=soup,
                extraction_rules=parameters["extraction_rules"]
            )
            
            return extracted_data
    
    async def api_extraction(self, endpoint: str, parameters: Dict) -> List[Dict]:
        """AI-optimized API data extraction"""
        import aiohttp
        
        # AI-generated API request strategy
        request_strategy = await self.ai_agent.generate_api_request_strategy(
            endpoint=endpoint,
            parameters=parameters
        )
        
        async with aiohttp.ClientSession() as session:
            extracted_data = []
            
            for request_config in request_strategy["requests"]:
                async with session.request(
                    method=request_config["method"],
                    url=request_config["url"],
                    headers=request_config["headers"],
                    params=request_config.get("params"),
                    json=request_config.get("json")
                ) as response:
                    
                    if response.status == 200:
                        data = await response.json()
                        
                        # AI-powered data transformation
                        transformed_data = await self.ai_agent.transform_api_response(
                            data=data,
                            transformation_rules=parameters["transformation_rules"]
                        )
                        
                        extracted_data.extend(transformed_data)
            
            return extracted_data

# Workflow Definition and Execution
class WorkflowManager:
    def __init__(self, taskmaster_core: TaskmasterCore):
        self.core = taskmaster_core
        self.workflow_registry = {}
    
    @workflow(name="intelligent_data_pipeline")
    async def intelligent_data_pipeline(self, config: Dict) -> WorkflowContext:
        """AI-powered end-to-end data processing pipeline"""
        
        context = WorkflowContext()
        
        try:
            # Step 1: AI-guided source discovery
            sources = await self.core.ai_agent.discover_data_sources(
                requirements=config["data_requirements"]
            )
            context.add_step_result("source_discovery", sources)
            
            # Step 2: Parallel data extraction
            extraction_tasks = []
            for source in sources:
                task = self.core.extract_data_from_source(
                    source=source["location"],
                    extraction_rules=source["extraction_rules"]
                )
                extraction_tasks.append(task)
            
            extraction_results = await asyncio.gather(*extraction_tasks)
            context.add_step_result("data_extraction", extraction_results)
            
            # Step 3: AI-powered data integration
            integrated_data = await self.core.ai_agent.integrate_data_sources(
                extraction_results=extraction_results,
                integration_rules=config["integration_rules"]
            )
            context.add_step_result("data_integration", integrated_data)
            
            # Step 4: Intelligent data quality assessment
            quality_assessment = await self.core.ai_agent.assess_data_quality(
                data=integrated_data,
                quality_criteria=config["quality_criteria"]
            )
            context.add_step_result("quality_assessment", quality_assessment)
            
            # Step 5: AI-driven data transformation
            if quality_assessment["requires_transformation"]:
                transformed_data = await self.core.ai_agent.transform_data(
                    data=integrated_data,
                    transformation_plan=quality_assessment["transformation_plan"]
                )
                context.add_step_result("data_transformation", transformed_data)
                final_data = transformed_data
            else:
                final_data = integrated_data
            
            # Step 6: Intelligent output generation
            output_result = await self.core.ai_agent.generate_output(
                data=final_data,
                output_config=config["output_config"]
            )
            context.add_step_result("output_generation", output_result)
            
            context.mark_success()
            return context
        
        except Exception as e:
            context.mark_failure(str(e))
            return context
    
    @workflow(name="automated_decision_workflow")
    async def automated_decision_workflow(self, decision_config: Dict) -> WorkflowContext:
        """AI-powered automated decision-making workflow"""
        
        context = WorkflowContext()
        
        try:
            # Step 1: Context gathering
            context_data = await self.gather_decision_context(
                sources=decision_config["context_sources"]
            )
            context.add_step_result("context_gathering", context_data)
            
            # Step 2: AI decision making
            decision_result = await self.core.ai_decision_maker(
                context=context_data,
                decision_criteria=decision_config["criteria"]
            )
            context.add_step_result("decision_making", decision_result)
            
            # Step 3: Action execution based on decision
            if decision_result.success:
                action_result = await self.execute_decision_actions(
                    decision=decision_result.data["decision"],
                    action_config=decision_config["actions"]
                )
                context.add_step_result("action_execution", action_result)
            
            # Step 4: Result monitoring and feedback
            monitoring_result = await self.monitor_decision_outcomes(
                decision=decision_result.data["decision"],
                monitoring_config=decision_config["monitoring"]
            )
            context.add_step_result("outcome_monitoring", monitoring_result)
            
            context.mark_success()
            return context
        
        except Exception as e:
            context.mark_failure(str(e))
            return context
    
    async def gather_decision_context(self, sources: List[Dict]) -> Dict:
        """Gather context data from multiple sources"""
        
        context_data = {}
        
        for source in sources:
            if source["type"] == "database":
                data = await self.core.database_extraction(
                    source["connection"],
                    source["query_parameters"]
                )
            elif source["type"] == "api":
                data = await self.core.api_extraction(
                    source["endpoint"],
                    source["parameters"]
                )
            elif source["type"] == "file":
                data = await self.core.file_parsing_extraction(
                    source["path"],
                    source["parsing_parameters"]
                )
            else:
                continue
            
            context_data[source["name"]] = data
        
        return context_data
    
    async def execute_decision_actions(self, decision: Dict, 
                                     action_config: Dict) -> TaskResult:
        """Execute actions based on AI decision"""
        
        action_mapping = action_config.get("action_mapping", {})
        decision_type = decision.get("type", "default")
        
        actions = action_mapping.get(decision_type, [])
        
        execution_results = []
        for action in actions:
            if action["type"] == "api_call":
                result = await self.execute_api_action(action, decision)
            elif action["type"] == "database_update":
                result = await self.execute_database_action(action, decision)
            elif action["type"] == "notification":
                result = await self.execute_notification_action(action, decision)
            elif action["type"] == "workflow_trigger":
                result = await self.execute_workflow_trigger(action, decision)
            else:
                result = TaskResult(success=False, error=f"Unknown action type: {action['type']}")
            
            execution_results.append(result)
        
        overall_success = all(result.success for result in execution_results)
        
        return TaskResult(
            success=overall_success,
            data={"action_results": execution_results},
            metadata={"decision": decision, "timestamp": datetime.now().isoformat()}
        )

def main():
    # Initialize Taskmaster-AI
    taskmaster = TaskmasterCore()
    workflow_manager = WorkflowManager(taskmaster)
    
    # Example: Intelligent data pipeline
    pipeline_config = {
        "data_requirements": {
            "domain": "e-commerce",
            "data_types": ["product_data", "customer_data", "sales_data"],
            "freshness": "daily",
            "quality_threshold": 0.95
        },
        "integration_rules": {
            "join_keys": ["product_id", "customer_id"],
            "conflict_resolution": "ai_guided",
            "schema_mapping": "automatic"
        },
        "quality_criteria": {
            "completeness": 0.9,
            "accuracy": 0.95,
            "consistency": 0.9,
            "timeliness": 0.8
        },
        "output_config": {
            "format": "parquet",
            "destination": "data_warehouse",
            "partitioning": "date",
            "compression": "snappy"
        }
    }
    
    # Execute workflow
    async def run_pipeline():
        result = await workflow_manager.intelligent_data_pipeline(pipeline_config)
        print(f"Pipeline execution: {'Success' if result.success else 'Failed'}")
        if result.success:
            print("Pipeline steps completed:")
            for step, result in result.step_results.items():
                print(f"  {step}: {result}")
    
    # Run the pipeline
    asyncio.run(run_pipeline())

if __name__ == "__main__":
    main()

Advanced AI Integration

Natural Language Workflow Creation

#!/usr/bin/env python3
# natural_language_workflows.py

from taskmaster_ai import NLWorkflowGenerator, WorkflowParser
from taskmaster_ai.nlp import IntentRecognizer, EntityExtractor
import asyncio

class NaturalLanguageWorkflowCreator:
    def __init__(self, taskmaster_core):
        self.core = taskmaster_core
        self.nl_generator = NLWorkflowGenerator()
        self.intent_recognizer = IntentRecognizer()
        self.entity_extractor = EntityExtractor()
    
    async def create_workflow_from_description(self, description: str) -> Dict:
        """Create executable workflow from natural language description"""
        
        # Step 1: Parse natural language intent
        intent_analysis = await self.intent_recognizer.analyze_intent(description)
        
        # Step 2: Extract entities and parameters
        entities = await self.entity_extractor.extract_entities(
            text=description,
            intent=intent_analysis
        )
        
        # Step 3: Generate workflow structure
        workflow_structure = await self.nl_generator.generate_workflow_structure(
            intent=intent_analysis,
            entities=entities,
            description=description
        )
        
        # Step 4: Create executable workflow
        executable_workflow = await self.create_executable_workflow(
            structure=workflow_structure,
            entities=entities
        )
        
        return {
            "description": description,
            "intent": intent_analysis,
            "entities": entities,
            "structure": workflow_structure,
            "executable": executable_workflow
        }
    
    async def create_executable_workflow(self, structure: Dict, entities: Dict) -> Dict:
        """Convert workflow structure to executable format"""
        
        executable_steps = []
        
        for step in structure["steps"]:
            if step["type"] == "data_extraction":
                executable_step = await self.create_extraction_step(step, entities)
            elif step["type"] == "data_processing":
                executable_step = await self.create_processing_step(step, entities)
            elif step["type"] == "decision_making":
                executable_step = await self.create_decision_step(step, entities)
            elif step["type"] == "action_execution":
                executable_step = await self.create_action_step(step, entities)
            elif step["type"] == "notification":
                executable_step = await self.create_notification_step(step, entities)
            else:
                executable_step = await self.create_generic_step(step, entities)
            
            executable_steps.append(executable_step)
        
        return {
            "name": structure["name"],
            "description": structure["description"],
            "steps": executable_steps,
            "error_handling": structure.get("error_handling", "default"),
            "monitoring": structure.get("monitoring", "basic")
        }
    
    async def create_extraction_step(self, step: Dict, entities: Dict) -> Dict:
        """Create data extraction step"""
        
        return {
            "type": "task",
            "task_name": "extract_data_from_source",
            "parameters": {
                "source": entities.get("data_source", step.get("source")),
                "extraction_rules": {
                    "format": entities.get("data_format", "auto"),
                    "fields": entities.get("required_fields", []),
                    "filters": entities.get("data_filters", {}),
                    "validation": entities.get("validation_rules", {})
                }
            },
            "retry_config": {
                "max_attempts": 3,
                "backoff_factor": 2
            }
        }
    
    async def create_decision_step(self, step: Dict, entities: Dict) -> Dict:
        """Create AI decision-making step"""
        
        return {
            "type": "task",
            "task_name": "ai_decision_maker",
            "parameters": {
                "decision_criteria": {
                    "type": entities.get("decision_type", "classification"),
                    "criteria": entities.get("decision_criteria", {}),
                    "confidence_threshold": entities.get("confidence_threshold", 0.8),
                    "constraints": entities.get("decision_constraints", {})
                }
            },
            "dependencies": step.get("dependencies", [])
        }

# Example usage
async def demo_natural_language_workflows():
    taskmaster = TaskmasterCore()
    nl_creator = NaturalLanguageWorkflowCreator(taskmaster)
    
    # Example descriptions
    descriptions = [
        "Extract customer data from the CRM database, analyze purchase patterns, and send personalized recommendations via email",
        "Monitor website performance metrics, detect anomalies, and automatically scale infrastructure if needed",
        "Process incoming support tickets, categorize by urgency, assign to appropriate teams, and notify managers of high-priority issues",
        "Analyze social media mentions, determine sentiment, generate summary reports, and alert marketing team of negative trends"
    ]
    
    for description in descriptions:
        print(f"\nProcessing: {description}")
        print("=" * 80)
        
        workflow = await nl_creator.create_workflow_from_description(description)
        
        print(f"Intent: {workflow['intent']['primary_intent']}")
        print(f"Entities: {list(workflow['entities'].keys())}")
        print(f"Steps: {len(workflow['structure']['steps'])}")
        
        # Execute the workflow
        try:
            result = await execute_generated_workflow(workflow['executable'])
            print(f"Execution: {'Success' if result.success else 'Failed'}")
        except Exception as e:
            print(f"Execution error: {e}")

if __name__ == "__main__":
    asyncio.run(demo_natural_language_workflows())

Multi-Modal AI Integration

#!/usr/bin/env python3
# multimodal_ai_integration.py

from taskmaster_ai import MultiModalAI, VisionProcessor, AudioProcessor, TextProcessor
import asyncio
from typing import Union, List, Dict

class MultiModalTaskProcessor:
    def __init__(self, taskmaster_core):
        self.core = taskmaster_core
        self.vision_processor = VisionProcessor()
        self.audio_processor = AudioProcessor()
        self.text_processor = TextProcessor()
        self.multimodal_ai = MultiModalAI()
    
    async def process_multimodal_input(self, inputs: List[Dict]) -> Dict:
        """Process multiple types of input (text, image, audio, video)"""
        
        processed_inputs = {}
        
        for input_item in inputs:
            input_type = input_item["type"]
            input_data = input_item["data"]
            
            if input_type == "text":
                processed = await self.text_processor.process_text(input_data)
            elif input_type == "image":
                processed = await self.vision_processor.process_image(input_data)
            elif input_type == "audio":
                processed = await self.audio_processor.process_audio(input_data)
            elif input_type == "video":
                processed = await self.vision_processor.process_video(input_data)
            elif input_type == "document":
                processed = await self.process_document(input_data)
            else:
                processed = {"error": f"Unsupported input type: {input_type}"}
            
            processed_inputs[input_item.get("name", input_type)] = processed
        
        # Combine insights from all modalities
        combined_analysis = await self.multimodal_ai.combine_modalities(processed_inputs)
        
        return {
            "individual_analyses": processed_inputs,
            "combined_analysis": combined_analysis,
            "recommendations": combined_analysis.get("recommendations", []),
            "confidence": combined_analysis.get("confidence", 0)
        }
    
    async def process_document(self, document_path: str) -> Dict:
        """Process documents with OCR and content analysis"""
        
        # Extract text using OCR
        extracted_text = await self.vision_processor.extract_text_from_document(document_path)
        
        # Analyze document structure
        document_structure = await self.vision_processor.analyze_document_structure(document_path)
        
        # Process extracted text
        text_analysis = await self.text_processor.analyze_text(extracted_text)
        
        # Extract key information
        key_information = await self.text_processor.extract_key_information(
            text=extracted_text,
            document_type=document_structure.get("type", "unknown")
        )
        
        return {
            "extracted_text": extracted_text,
            "document_structure": document_structure,
            "text_analysis": text_analysis,
            "key_information": key_information
        }
    
    async def generate_multimodal_response(self, analysis: Dict, 
                                         response_config: Dict) -> Dict:
        """Generate responses in multiple formats based on analysis"""
        
        responses = {}
        
        for format_type in response_config["formats"]:
            if format_type == "text_summary":
                response = await self.generate_text_summary(analysis)
            elif format_type == "visual_report":
                response = await self.generate_visual_report(analysis)
            elif format_type == "audio_briefing":
                response = await self.generate_audio_briefing(analysis)
            elif format_type == "interactive_dashboard":
                response = await self.generate_interactive_dashboard(analysis)
            else:
                response = {"error": f"Unsupported response format: {format_type}"}
            
            responses[format_type] = response
        
        return responses
    
    async def generate_text_summary(self, analysis: Dict) -> str:
        """Generate comprehensive text summary"""
        
        summary_prompt = f"""
        Based on the following multimodal analysis, generate a comprehensive summary:
        
        Analysis: {analysis}
        
        Include:
        1. Key findings from each modality
        2. Combined insights
        3. Actionable recommendations
        4. Confidence levels and limitations
        """
        
        summary = await self.core.ai_agent.generate_text(
            prompt=summary_prompt,
            max_tokens=1000,
            temperature=0.1
        )
        
        return summary
    
    async def generate_visual_report(self, analysis: Dict) -> Dict:
        """Generate visual report with charts and graphs"""
        
        import matplotlib.pyplot as plt
        import seaborn as sns
        import pandas as pd
        from io import BytesIO
        import base64
        
        # Extract quantitative data for visualization
        quantitative_data = self.extract_quantitative_data(analysis)
        
        visualizations = {}
        
        # Create various visualizations
        for viz_type, data in quantitative_data.items():
            fig, ax = plt.subplots(figsize=(10, 6))
            
            if viz_type == "confidence_scores":
                ax.bar(data.keys(), data.values())
                ax.set_title("Confidence Scores by Modality")
                ax.set_ylabel("Confidence")
            elif viz_type == "sentiment_analysis":
                ax.pie(data.values(), labels=data.keys(), autopct='%1.1f%%')
                ax.set_title("Sentiment Distribution")
            elif viz_type == "key_metrics":
                df = pd.DataFrame(list(data.items()), columns=['Metric', 'Value'])
                sns.barplot(data=df, x='Metric', y='Value', ax=ax)
                ax.set_title("Key Metrics")
            
            # Convert to base64 for embedding
            buffer = BytesIO()
            plt.savefig(buffer, format='png', bbox_inches='tight', dpi=300)
            buffer.seek(0)
            image_base64 = base64.b64encode(buffer.getvalue()).decode()
            plt.close()
            
            visualizations[viz_type] = {
                "image_data": image_base64,
                "format": "png",
                "description": f"Visualization of {viz_type}"
            }
        
        return {
            "visualizations": visualizations,
            "report_metadata": {
                "generated_at": datetime.now().isoformat(),
                "analysis_summary": analysis.get("combined_analysis", {})
            }
        }
    
    def extract_quantitative_data(self, analysis: Dict) -> Dict:
        """Extract quantitative data for visualization"""
        
        quantitative_data = {}
        
        # Extract confidence scores
        confidence_scores = {}
        for modality, data in analysis.get("individual_analyses", {}).items():
            if isinstance(data, dict) and "confidence" in data:
                confidence_scores[modality] = data["confidence"]
        
        if confidence_scores:
            quantitative_data["confidence_scores"] = confidence_scores
        
        # Extract sentiment data
        sentiment_data = {}
        for modality, data in analysis.get("individual_analyses", {}).items():
            if isinstance(data, dict) and "sentiment" in data:
                sentiment = data["sentiment"]
                if isinstance(sentiment, dict):
                    for sentiment_type, score in sentiment.items():
                        sentiment_data[sentiment_type] = sentiment_data.get(sentiment_type, 0) + score
        
        if sentiment_data:
            quantitative_data["sentiment_analysis"] = sentiment_data
        
        # Extract key metrics
        combined_analysis = analysis.get("combined_analysis", {})
        if "metrics" in combined_analysis:
            quantitative_data["key_metrics"] = combined_analysis["metrics"]
        
        return quantitative_data

# Example usage
async def demo_multimodal_processing():
    taskmaster = TaskmasterCore()
    multimodal_processor = MultiModalTaskProcessor(taskmaster)
    
    # Example multimodal inputs
    inputs = [
        {
            "type": "text",
            "name": "customer_feedback",
            "data": "The product quality has improved significantly, but delivery times are still too long."
        },
        {
            "type": "image",
            "name": "product_photo",
            "data": "path/to/product_image.jpg"
        },
        {
            "type": "audio",
            "name": "customer_call",
            "data": "path/to/customer_call.wav"
        },
        {
            "type": "document",
            "name": "support_ticket",
            "data": "path/to/support_ticket.pdf"
        }
    ]
    
    # Process multimodal inputs
    analysis = await multimodal_processor.process_multimodal_input(inputs)
    
    print("Multimodal Analysis Results:")
    print("=" * 50)
    print(f"Combined Analysis: {analysis['combined_analysis']}")
    print(f"Recommendations: {analysis['recommendations']}")
    print(f"Overall Confidence: {analysis['confidence']}")
    
    # Generate multimodal responses
    response_config = {
        "formats": ["text_summary", "visual_report", "audio_briefing"]
    }
    
    responses = await multimodal_processor.generate_multimodal_response(
        analysis, response_config
    )
    
    print("\nGenerated Responses:")
    print("=" * 50)
    for format_type, response in responses.items():
        print(f"{format_type}: Generated successfully")

if __name__ == "__main__":
    asyncio.run(demo_multimodal_processing())

Workflow Automation Examples

E-commerce Automation

#!/usr/bin/env python3
# ecommerce_automation.py

from taskmaster_ai import EcommerceAutomation, InventoryManager, CustomerAnalytics
import asyncio

class EcommerceWorkflowAutomation:
    def __init__(self, taskmaster_core):
        self.core = taskmaster_core
        self.inventory_manager = InventoryManager()
        self.customer_analytics = CustomerAnalytics()
    
    async def automated_inventory_management(self, config: Dict) -> Dict:
        """Automated inventory management with AI predictions"""
        
        # Step 1: Analyze current inventory levels
        current_inventory = await self.inventory_manager.get_current_inventory()
        
        # Step 2: AI-powered demand forecasting
        demand_forecast = await self.core.ai_agent.forecast_demand(
            historical_data=current_inventory["historical_sales"],
            external_factors=config["external_factors"],
            forecast_horizon=config["forecast_days"]
        )
        
        # Step 3: Optimize inventory levels
        optimization_result = await self.core.ai_agent.optimize_inventory(
            current_inventory=current_inventory,
            demand_forecast=demand_forecast,
            constraints=config["inventory_constraints"]
        )
        
        # Step 4: Generate purchase orders
        purchase_orders = []
        for item in optimization_result["reorder_items"]:
            po = await self.generate_purchase_order(
                item=item,
                supplier_preferences=config["supplier_preferences"]
            )
            purchase_orders.append(po)
        
        # Step 5: Update inventory system
        update_result = await self.inventory_manager.update_inventory_levels(
            optimization_result["new_levels"]
        )
        
        return {
            "current_inventory": current_inventory,
            "demand_forecast": demand_forecast,
            "optimization_result": optimization_result,
            "purchase_orders": purchase_orders,
            "update_result": update_result
        }
    
    async def personalized_marketing_automation(self, config: Dict) -> Dict:
        """AI-driven personalized marketing campaigns"""
        
        # Step 1: Customer segmentation
        customer_segments = await self.customer_analytics.segment_customers(
            criteria=config["segmentation_criteria"]
        )
        
        # Step 2: Generate personalized content
        personalized_campaigns = {}
        for segment_id, segment_data in customer_segments.items():
            campaign_content = await self.core.ai_agent.generate_marketing_content(
                segment_profile=segment_data["profile"],
                campaign_objectives=config["campaign_objectives"],
                brand_guidelines=config["brand_guidelines"]
            )
            
            personalized_campaigns[segment_id] = {
                "segment_data": segment_data,
                "campaign_content": campaign_content,
                "delivery_schedule": await self.optimize_delivery_schedule(
                    segment_data["preferences"]
                )
            }
        
        # Step 3: A/B testing setup
        ab_test_config = await self.core.ai_agent.design_ab_tests(
            campaigns=personalized_campaigns,
            test_objectives=config["test_objectives"]
        )
        
        # Step 4: Campaign execution
        execution_results = {}
        for segment_id, campaign in personalized_campaigns.items():
            result = await self.execute_marketing_campaign(
                campaign=campaign,
                ab_config=ab_test_config.get(segment_id, {})
            )
            execution_results[segment_id] = result
        
        return {
            "customer_segments": customer_segments,
            "personalized_campaigns": personalized_campaigns,
            "ab_test_config": ab_test_config,
            "execution_results": execution_results
        }
    
    async def automated_customer_service(self, config: Dict) -> Dict:
        """AI-powered customer service automation"""
        
        # Step 1: Ticket classification and routing
        incoming_tickets = await self.get_incoming_tickets()
        
        classified_tickets = []
        for ticket in incoming_tickets:
            classification = await self.core.ai_agent.classify_support_ticket(
                ticket_content=ticket["content"],
                customer_history=ticket["customer_history"],
                classification_rules=config["classification_rules"]
            )
            
            routing_decision = await self.core.ai_agent.route_ticket(
                classification=classification,
                agent_availability=config["agent_availability"],
                routing_rules=config["routing_rules"]
            )
            
            classified_tickets.append({
                "ticket": ticket,
                "classification": classification,
                "routing": routing_decision
            })
        
        # Step 2: Automated response generation
        automated_responses = []
        for classified_ticket in classified_tickets:
            if classified_ticket["classification"]["automation_eligible"]:
                response = await self.core.ai_agent.generate_customer_response(
                    ticket=classified_ticket["ticket"],
                    classification=classified_ticket["classification"],
                    response_templates=config["response_templates"]
                )
                
                automated_responses.append({
                    "ticket_id": classified_ticket["ticket"]["id"],
                    "response": response,
                    "confidence": response["confidence"]
                })
        
        # Step 3: Quality assurance
        qa_results = []
        for response in automated_responses:
            if response["confidence"] < config["qa_threshold"]:
                qa_result = await self.core.ai_agent.quality_check_response(
                    response=response,
                    quality_criteria=config["quality_criteria"]
                )
                qa_results.append(qa_result)
        
        return {
            "classified_tickets": classified_tickets,
            "automated_responses": automated_responses,
            "qa_results": qa_results,
            "metrics": {
                "total_tickets": len(incoming_tickets),
                "automated_responses": len(automated_responses),
                "qa_flagged": len(qa_results)
            }
        }

# Financial Services Automation
class FinancialServicesAutomation:
    def __init__(self, taskmaster_core):
        self.core = taskmaster_core
    
    async def fraud_detection_workflow(self, config: Dict) -> Dict:
        """AI-powered fraud detection and prevention"""
        
        # Step 1: Real-time transaction monitoring
        transactions = await self.get_real_time_transactions()
        
        # Step 2: AI fraud scoring
        fraud_scores = []
        for transaction in transactions:
            score = await self.core.ai_agent.calculate_fraud_score(
                transaction=transaction,
                customer_profile=transaction["customer_profile"],
                historical_patterns=transaction["historical_patterns"],
                fraud_models=config["fraud_models"]
            )
            fraud_scores.append(score)
        
        # Step 3: Risk-based decision making
        risk_decisions = []
        for i, score in enumerate(fraud_scores):
            decision = await self.core.ai_decision_maker(
                context={
                    "transaction": transactions[i],
                    "fraud_score": score,
                    "customer_risk_profile": transactions[i]["customer_profile"]
                },
                decision_criteria=config["risk_criteria"]
            )
            risk_decisions.append(decision)
        
        # Step 4: Automated actions
        action_results = []
        for i, decision in enumerate(risk_decisions):
            if decision.data["decision"]["action"] != "approve":
                action_result = await self.execute_fraud_prevention_action(
                    transaction=transactions[i],
                    decision=decision.data["decision"],
                    action_config=config["fraud_actions"]
                )
                action_results.append(action_result)
        
        return {
            "transactions_processed": len(transactions),
            "fraud_scores": fraud_scores,
            "risk_decisions": risk_decisions,
            "actions_taken": action_results,
            "summary": {
                "approved": sum(1 for d in risk_decisions if d.data["decision"]["action"] == "approve"),
                "flagged": sum(1 for d in risk_decisions if d.data["decision"]["action"] == "flag"),
                "blocked": sum(1 for d in risk_decisions if d.data["decision"]["action"] == "block")
            }
        }
    
    async def automated_compliance_monitoring(self, config: Dict) -> Dict:
        """Automated regulatory compliance monitoring"""
        
        # Step 1: Data collection from multiple sources
        compliance_data = await self.collect_compliance_data(
            sources=config["data_sources"]
        )
        
        # Step 2: AI-powered compliance analysis
        compliance_analysis = await self.core.ai_agent.analyze_compliance(
            data=compliance_data,
            regulations=config["applicable_regulations"],
            compliance_rules=config["compliance_rules"]
        )
        
        # Step 3: Risk assessment
        risk_assessment = await self.core.ai_agent.assess_compliance_risk(
            analysis=compliance_analysis,
            risk_framework=config["risk_framework"]
        )
        
        # Step 4: Generate compliance reports
        compliance_reports = await self.generate_compliance_reports(
            analysis=compliance_analysis,
            risk_assessment=risk_assessment,
            report_templates=config["report_templates"]
        )
        
        # Step 5: Automated remediation
        if risk_assessment["requires_action"]:
            remediation_actions = await self.execute_compliance_remediation(
                risk_assessment=risk_assessment,
                remediation_config=config["remediation_config"]
            )
        else:
            remediation_actions = []
        
        return {
            "compliance_data": compliance_data,
            "compliance_analysis": compliance_analysis,
            "risk_assessment": risk_assessment,
            "compliance_reports": compliance_reports,
            "remediation_actions": remediation_actions
        }

# Example usage
async def demo_ecommerce_automation():
    taskmaster = TaskmasterCore()
    ecommerce_automation = EcommerceWorkflowAutomation(taskmaster)
    
    # Inventory management configuration
    inventory_config = {
        "external_factors": {
            "seasonality": "high",
            "market_trends": "growing",
            "economic_indicators": "stable"
        },
        "forecast_days": 30,
        "inventory_constraints": {
            "max_storage_capacity": 10000,
            "budget_limit": 50000,
            "supplier_lead_times": {"supplier_a": 7, "supplier_b": 14}
        },
        "supplier_preferences": {
            "preferred_suppliers": ["supplier_a", "supplier_b"],
            "quality_requirements": "high",
            "delivery_requirements": "fast"
        }
    }
    
    # Execute inventory management workflow
    inventory_result = await ecommerce_automation.automated_inventory_management(inventory_config)
    
    print("Inventory Management Results:")
    print("=" * 50)
    print(f"Items to reorder: {len(inventory_result['optimization_result']['reorder_items'])}")
    print(f"Purchase orders generated: {len(inventory_result['purchase_orders'])}")
    
    # Marketing automation configuration
    marketing_config = {
        "segmentation_criteria": {
            "behavioral": ["purchase_frequency", "average_order_value"],
            "demographic": ["age_group", "location"],
            "psychographic": ["interests", "lifestyle"]
        },
        "campaign_objectives": {
            "primary": "increase_sales",
            "secondary": "improve_retention",
            "kpis": ["conversion_rate", "customer_lifetime_value"]
        },
        "brand_guidelines": {
            "tone": "friendly_professional",
            "style": "modern_minimalist",
            "values": ["quality", "sustainability", "innovation"]
        },
        "test_objectives": ["subject_line_optimization", "content_personalization"]
    }
    
    # Execute marketing automation workflow
    marketing_result = await ecommerce_automation.personalized_marketing_automation(marketing_config)
    
    print("\nMarketing Automation Results:")
    print("=" * 50)
    print(f"Customer segments: {len(marketing_result['customer_segments'])}")
    print(f"Personalized campaigns: {len(marketing_result['personalized_campaigns'])}")

if __name__ == "__main__":
    asyncio.run(demo_ecommerce_automation())

Monitoring and Analytics

Performance Monitoring

#!/usr/bin/env python3
# performance_monitoring.py

from taskmaster_ai import PerformanceMonitor, MetricsCollector, AlertManager
import asyncio
from datetime import datetime, timedelta
import json

class TaskmasterPerformanceMonitor:
    def __init__(self, taskmaster_core):
        self.core = taskmaster_core
        self.metrics_collector = MetricsCollector()
        self.alert_manager = AlertManager()
        self.performance_thresholds = {}
    
    async def monitor_workflow_performance(self, workflow_id: str, 
                                         monitoring_config: Dict) -> Dict:
        """Monitor workflow performance in real-time"""
        
        # Collect performance metrics
        metrics = await self.metrics_collector.collect_workflow_metrics(
            workflow_id=workflow_id,
            metrics_config=monitoring_config["metrics"]
        )
        
        # Analyze performance trends
        performance_analysis = await self.analyze_performance_trends(
            metrics=metrics,
            historical_data=monitoring_config.get("historical_data", {}),
            analysis_config=monitoring_config["analysis"]
        )
        
        # Check for performance issues
        issues = await self.detect_performance_issues(
            metrics=metrics,
            thresholds=monitoring_config["thresholds"],
            analysis=performance_analysis
        )
        
        # Generate alerts if needed
        alerts = []
        if issues:
            for issue in issues:
                alert = await self.alert_manager.create_alert(
                    issue=issue,
                    alert_config=monitoring_config["alerting"]
                )
                alerts.append(alert)
        
        # Generate performance recommendations
        recommendations = await self.generate_performance_recommendations(
            metrics=metrics,
            issues=issues,
            analysis=performance_analysis
        )
        
        return {
            "workflow_id": workflow_id,
            "metrics": metrics,
            "performance_analysis": performance_analysis,
            "issues": issues,
            "alerts": alerts,
            "recommendations": recommendations,
            "timestamp": datetime.now().isoformat()
        }
    
    async def analyze_performance_trends(self, metrics: Dict, 
                                       historical_data: Dict,
                                       analysis_config: Dict) -> Dict:
        """Analyze performance trends using AI"""
        
        trend_analysis = await self.core.ai_agent.analyze_performance_trends(
            current_metrics=metrics,
            historical_metrics=historical_data,
            analysis_parameters=analysis_config
        )
        
        return {
            "trends": trend_analysis["trends"],
            "anomalies": trend_analysis["anomalies"],
            "predictions": trend_analysis["predictions"],
            "insights": trend_analysis["insights"]
        }
    
    async def detect_performance_issues(self, metrics: Dict, 
                                      thresholds: Dict,
                                      analysis: Dict) -> List[Dict]:
        """Detect performance issues based on metrics and AI analysis"""
        
        issues = []
        
        # Threshold-based detection
        for metric_name, metric_value in metrics.items():
            if metric_name in thresholds:
                threshold = thresholds[metric_name]
                
                if isinstance(threshold, dict):
                    if "max" in threshold and metric_value > threshold["max"]:
                        issues.append({
                            "type": "threshold_exceeded",
                            "metric": metric_name,
                            "value": metric_value,
                            "threshold": threshold["max"],
                            "severity": threshold.get("severity", "medium")
                        })
                    
                    if "min" in threshold and metric_value < threshold["min"]:
                        issues.append({
                            "type": "threshold_below",
                            "metric": metric_name,
                            "value": metric_value,
                            "threshold": threshold["min"],
                            "severity": threshold.get("severity", "medium")
                        })
        
        # AI-based anomaly detection
        for anomaly in analysis.get("anomalies", []):
            issues.append({
                "type": "anomaly_detected",
                "description": anomaly["description"],
                "confidence": anomaly["confidence"],
                "severity": anomaly.get("severity", "medium"),
                "affected_metrics": anomaly.get("affected_metrics", [])
            })
        
        return issues
    
    async def generate_performance_recommendations(self, metrics: Dict,
                                                 issues: List[Dict],
                                                 analysis: Dict) -> List[Dict]:
        """Generate AI-powered performance improvement recommendations"""
        
        recommendations = await self.core.ai_agent.generate_performance_recommendations(
            current_metrics=metrics,
            identified_issues=issues,
            trend_analysis=analysis,
            optimization_objectives=["efficiency", "reliability", "cost"]
        )
        
        return recommendations
    
    async def setup_automated_monitoring(self, workflows: List[str],
                                       monitoring_config: Dict) -> Dict:
        """Setup automated monitoring for multiple workflows"""
        
        monitoring_tasks = []
        
        for workflow_id in workflows:
            # Create monitoring task
            task = asyncio.create_task(
                self.continuous_workflow_monitoring(
                    workflow_id=workflow_id,
                    config=monitoring_config,
                    interval=monitoring_config.get("monitoring_interval", 60)
                )
            )
            monitoring_tasks.append(task)
        
        return {
            "monitored_workflows": workflows,
            "monitoring_tasks": len(monitoring_tasks),
            "configuration": monitoring_config
        }
    
    async def continuous_workflow_monitoring(self, workflow_id: str,
                                           config: Dict,
                                           interval: int):
        """Continuously monitor a workflow"""
        
        while True:
            try:
                monitoring_result = await self.monitor_workflow_performance(
                    workflow_id=workflow_id,
                    monitoring_config=config
                )
                
                # Store monitoring results
                await self.store_monitoring_results(workflow_id, monitoring_result)
                
                # Check for critical issues
                critical_issues = [
                    issue for issue in monitoring_result["issues"]
                    if issue.get("severity") == "critical"
                ]
                
                if critical_issues:
                    await self.handle_critical_issues(workflow_id, critical_issues)
                
                # Wait for next monitoring cycle
                await asyncio.sleep(interval)
            
            except Exception as e:
                print(f"Monitoring error for workflow {workflow_id}: {e}")
                await asyncio.sleep(interval)

# Analytics and Reporting
class TaskmasterAnalytics:
    def __init__(self, taskmaster_core):
        self.core = taskmaster_core
    
    async def generate_workflow_analytics(self, timeframe: Dict,
                                        analytics_config: Dict) -> Dict:
        """Generate comprehensive workflow analytics"""
        
        # Collect workflow execution data
        execution_data = await self.collect_execution_data(
            timeframe=timeframe,
            filters=analytics_config.get("filters", {})
        )
        
        # Analyze workflow performance
        performance_analytics = await self.analyze_workflow_performance(
            execution_data=execution_data,
            analysis_config=analytics_config["performance_analysis"]
        )
        
        # Analyze resource utilization
        resource_analytics = await self.analyze_resource_utilization(
            execution_data=execution_data,
            resource_config=analytics_config["resource_analysis"]
        )
        
        # Generate business impact analysis
        business_impact = await self.analyze_business_impact(
            execution_data=execution_data,
            performance_data=performance_analytics,
            impact_config=analytics_config["business_impact"]
        )
        
        # Create visualizations
        visualizations = await self.create_analytics_visualizations(
            performance_analytics=performance_analytics,
            resource_analytics=resource_analytics,
            business_impact=business_impact,
            viz_config=analytics_config["visualizations"]
        )
        
        return {
            "timeframe": timeframe,
            "execution_summary": {
                "total_workflows": len(execution_data),
                "successful_executions": sum(1 for w in execution_data if w["success"]),
                "failed_executions": sum(1 for w in execution_data if not w["success"]),
                "average_execution_time": sum(w["execution_time"] for w in execution_data) / len(execution_data)
            },
            "performance_analytics": performance_analytics,
            "resource_analytics": resource_analytics,
            "business_impact": business_impact,
            "visualizations": visualizations
        }
    
    async def analyze_workflow_performance(self, execution_data: List[Dict],
                                         analysis_config: Dict) -> Dict:
        """Analyze workflow performance metrics"""
        
        performance_metrics = {}
        
        # Calculate basic performance metrics
        execution_times = [w["execution_time"] for w in execution_data]
        success_rates = [w["success"] for w in execution_data]
        
        performance_metrics["execution_time"] = {
            "average": sum(execution_times) / len(execution_times),
            "median": sorted(execution_times)[len(execution_times) // 2],
            "min": min(execution_times),
            "max": max(execution_times),
            "percentile_95": sorted(execution_times)[int(len(execution_times) * 0.95)]
        }
        
        performance_metrics["success_rate"] = {
            "overall": sum(success_rates) / len(success_rates),
            "by_workflow_type": self.calculate_success_by_type(execution_data)
        }
        
        # AI-powered performance insights
        performance_insights = await self.core.ai_agent.analyze_performance_patterns(
            execution_data=execution_data,
            metrics=performance_metrics,
            analysis_parameters=analysis_config
        )
        
        return {
            "metrics": performance_metrics,
            "insights": performance_insights,
            "trends": performance_insights.get("trends", []),
            "bottlenecks": performance_insights.get("bottlenecks", []),
            "optimization_opportunities": performance_insights.get("optimizations", [])
        }
    
    def calculate_success_by_type(self, execution_data: List[Dict]) -> Dict:
        """Calculate success rates by workflow type"""
        
        type_stats = {}
        
        for workflow in execution_data:
            workflow_type = workflow.get("type", "unknown")
            
            if workflow_type not in type_stats:
                type_stats[workflow_type] = {"total": 0, "successful": 0}
            
            type_stats[workflow_type]["total"] += 1
            if workflow["success"]:
                type_stats[workflow_type]["successful"] += 1
        
        # Calculate success rates
        for workflow_type, stats in type_stats.items():
            stats["success_rate"] = stats["successful"] / stats["total"]
        
        return type_stats

# Example usage
async def demo_monitoring_and_analytics():
    taskmaster = TaskmasterCore()
    monitor = TaskmasterPerformanceMonitor(taskmaster)
    analytics = TaskmasterAnalytics(taskmaster)
    
    # Setup monitoring configuration
    monitoring_config = {
        "metrics": ["execution_time", "memory_usage", "cpu_usage", "success_rate"],
        "thresholds": {
            "execution_time": {"max": 300, "severity": "high"},
            "memory_usage": {"max": 80, "severity": "medium"},
            "cpu_usage": {"max": 90, "severity": "high"},
            "success_rate": {"min": 0.95, "severity": "critical"}
        },
        "analysis": {
            "trend_window": "24h",
            "anomaly_detection": True,
            "prediction_horizon": "1h"
        },
        "alerting": {
            "channels": ["email", "slack"],
            "escalation_rules": {
                "critical": {"immediate": True, "escalate_after": 300},
                "high": {"immediate": False, "escalate_after": 900}
            }
        }
    }
    
    # Monitor workflow performance
    workflow_id = "intelligent_data_pipeline"
    monitoring_result = await monitor.monitor_workflow_performance(
        workflow_id=workflow_id,
        monitoring_config=monitoring_config
    )
    
    print("Monitoring Results:")
    print("=" * 50)
    print(f"Workflow: {monitoring_result['workflow_id']}")
    print(f"Issues detected: {len(monitoring_result['issues'])}")
    print(f"Alerts generated: {len(monitoring_result['alerts'])}")
    print(f"Recommendations: {len(monitoring_result['recommendations'])}")
    
    # Generate analytics
    analytics_config = {
        "filters": {"workflow_type": "data_processing"},
        "performance_analysis": {
            "include_trends": True,
            "bottleneck_detection": True,
            "optimization_analysis": True
        },
        "resource_analysis": {
            "include_cost_analysis": True,
            "utilization_patterns": True
        },
        "business_impact": {
            "kpis": ["processing_volume", "cost_savings", "time_savings"],
            "roi_calculation": True
        },
        "visualizations": {
            "performance_charts": True,
            "resource_heatmaps": True,
            "trend_graphs": True
        }
    }
    
    timeframe = {
        "start": (datetime.now() - timedelta(days=7)).isoformat(),
        "end": datetime.now().isoformat()
    }
    
    analytics_result = await analytics.generate_workflow_analytics(
        timeframe=timeframe,
        analytics_config=analytics_config
    )
    
    print("\nAnalytics Results:")
    print("=" * 50)
    print(f"Total workflows analyzed: {analytics_result['execution_summary']['total_workflows']}")
    print(f"Success rate: {analytics_result['execution_summary']['successful_executions'] / analytics_result['execution_summary']['total_workflows'] * 100:.1f}%")
    print(f"Average execution time: {analytics_result['execution_summary']['average_execution_time']:.2f}s")

if __name__ == "__main__":
    asyncio.run(demo_monitoring_and_analytics())

Best Practices and Troubleshooting

Security Best Practices

# Security configuration for Taskmaster-AI

security_config = {
    "authentication": {
        "method": "oauth2",
        "token_expiry": 3600,
        "refresh_token_enabled": True,
        "multi_factor_auth": True
    },
    "authorization": {
        "rbac_enabled": True,
        "permission_model": "least_privilege",
        "audit_logging": True
    },
    "encryption": {
        "data_at_rest": "AES-256",
        "data_in_transit": "TLS-1.3",
        "key_management": "vault",
        "key_rotation": "monthly"
    },
    "api_security": {
        "rate_limiting": True,
        "input_validation": "strict",
        "output_sanitization": True,
        "cors_policy": "restrictive"
    },
    "workflow_security": {
        "sandbox_execution": True,
        "resource_limits": True,
        "network_isolation": True,
        "code_signing": True
    }
}

Performance Optimization

# Performance optimization configuration

optimization_config = {
    "execution": {
        "parallel_processing": True,
        "max_concurrent_tasks": 10,
        "task_queuing": "priority_based",
        "resource_pooling": True
    },
    "caching": {
        "result_caching": True,
        "cache_ttl": 3600,
        "cache_strategy": "lru",
        "distributed_cache": True
    },
    "ai_optimization": {
        "model_caching": True,
        "batch_processing": True,
        "request_batching": True,
        "response_streaming": True
    },
    "resource_management": {
        "auto_scaling": True,
        "resource_monitoring": True,
        "garbage_collection": "optimized",
        "memory_management": "efficient"
    }
}

Common Issues and Solutions

# Common troubleshooting scenarios

troubleshooting_guide = {
    "workflow_execution_failures": {
        "symptoms": ["Tasks timing out", "Unexpected errors", "Resource exhaustion"],
        "solutions": [
            "Check resource limits and increase if necessary",
            "Review error logs for specific failure points",
            "Implement retry logic with exponential backoff",
            "Optimize task dependencies and execution order"
        ]
    },
    "ai_model_performance": {
        "symptoms": ["Slow response times", "Low accuracy", "High token usage"],
        "solutions": [
            "Optimize prompts for efficiency",
            "Use appropriate model for task complexity",
            "Implement result caching",
            "Consider fine-tuning for specific use cases"
        ]
    },
    "integration_issues": {
        "symptoms": ["API connection failures", "Data format mismatches", "Authentication errors"],
        "solutions": [
            "Verify API credentials and permissions",
            "Check network connectivity and firewall rules",
            "Validate data schemas and transformation rules",
            "Implement robust error handling and retries"
        ]
    }
}

Resources and Documentation

Official Resources

• Taskmaster-AI Documentation
• API Reference
• GitHub Repository
• Community Forum

Learning Resources

• Workflow Design Patterns
• AI Integration Best Practices
• Performance Optimization Guide
• Security Implementation Guide

Community and Support

• Discord Community
• Stack Overflow Tag
• YouTube Tutorials
• Blog and Case Studies

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This cheat sheet provides comprehensive guidance for implementing AI-powered task automation and workflow management using Taskmaster-AI. Always follow security best practices and test workflows thoroughly before production deployment.