#!/usr/bin/env python3
"""
Kaizen Framework Example Implementation Testing

This script attempts to implement all documented workflow examples using the current
Kaizen codebase to identify gaps, errors, and feature opportunities.

Each implementation attempt is logged with detailed tracking to compare actual
vs expected behavior and identify Kaizen codebase issues.
"""

import logging
import time
import traceback
from datetime import datetime
from typing import Dict

# Setup comprehensive logging
logging.basicConfig(
    level=logging.DEBUG,
    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
    handlers=[
        logging.FileHandler("kaizen_implementation_test.log"),
        logging.StreamHandler(),
    ],
)

logger = logging.getLogger(__name__)


class KaizenImplementationTester:
    """Test implementation of all documented Kaizen examples."""

    def __init__(self):
        self.gaps_found = []
        self.errors_encountered = []
        self.feature_opportunities = []
        self.successful_patterns = []

    def log_gap(
        self,
        example_name: str,
        gap_type: str,
        description: str,
        expected: str,
        actual: str,
    ):
        """Log a gap found in Kaizen codebase."""
        gap = {
            "timestamp": datetime.now().isoformat(),
            "example": example_name,
            "gap_type": gap_type,
            "description": description,
            "expected": expected,
            "actual": actual,
        }
        self.gaps_found.append(gap)
        logger.error(f"GAP FOUND in {example_name}: {description}")

    def log_error(self, example_name: str, error_type: str, error: Exception):
        """Log an error encountered in Kaizen codebase."""
        error_info = {
            "timestamp": datetime.now().isoformat(),
            "example": example_name,
            "error_type": error_type,
            "error": str(error),
            "traceback": traceback.format_exc(),
        }
        self.errors_encountered.append(error_info)
        logger.error(f"ERROR in {example_name}: {error}")

    def log_feature_opportunity(
        self, example_name: str, opportunity: str, complexity: str
    ):
        """Log a feature opportunity for seamless development."""
        feature = {
            "timestamp": datetime.now().isoformat(),
            "example": example_name,
            "opportunity": opportunity,
            "complexity": complexity,
        }
        self.feature_opportunities.append(feature)
        logger.info(f"FEATURE OPPORTUNITY in {example_name}: {opportunity}")

    def test_example_1_simple_qa_agent(self):
        """Test Example 1: Simple Q&A Agent with Signature-Based Programming"""
        example_name = "Simple Q&A Agent"
        logger.info(f"=== Testing {example_name} ===")

        # Expected behavior: Create agent with signature, execute Q&A workflow
        expected_steps = [
            "1. Import Kaizen framework",
            "2. Create framework with signature-based programming enabled",
            "3. Define Q&A signature (question -> answer)",
            "4. Create agent with signature",
            "5. Execute workflow with question",
            "6. Get structured answer response",
        ]

        try:
            # Step 1: Import Kaizen framework
            logger.info("Step 1: Importing Kaizen framework...")
            start_time = time.time()

            try:
                from kaizen import Framework, Kaizen

                import_time = (time.time() - start_time) * 1000
                logger.info(f"✓ Import successful in {import_time:.2f}ms")

                if import_time > 100:
                    self.log_feature_opportunity(
                        example_name,
                        "Import time optimization - Framework import took >100ms",
                        "Medium",
                    )

            except ImportError as e:
                self.log_error(example_name, "CRITICAL_IMPORT_ERROR", e)
                return False

            # Step 2: Create framework with signature programming
            logger.info("Step 2: Creating framework with signature programming...")
            try:
                kaizen = Kaizen(
                    config={
                        "signature_programming_enabled": True,
                        "memory_enabled": False,
                        "optimization_enabled": False,
                    }
                )
                logger.info("✓ Framework created successfully")

            except Exception as e:
                self.log_error(example_name, "FRAMEWORK_CREATION_ERROR", e)
                return False

            # Step 3: Define Q&A signature
            logger.info("Step 3: Defining Q&A signature...")
            try:
                # Expected: Simple signature definition
                # question -> answer
                signature_spec = "question -> answer"

                # Test if Kaizen supports this syntax
                if hasattr(kaizen, "create_signature"):
                    signature = kaizen.create_signature(
                        signature_spec,
                        description="Answer questions with factual information",
                    )
                    logger.info("✓ Signature created successfully")
                else:
                    self.log_gap(
                        example_name,
                        "MISSING_FEATURE",
                        "No create_signature method in Framework",
                        "kaizen.create_signature(spec, description)",
                        "Method does not exist",
                    )

            except Exception as e:
                self.log_error(example_name, "SIGNATURE_CREATION_ERROR", e)
                return False

            # Step 4: Create agent with signature
            logger.info("Step 4: Creating agent with signature...")
            try:
                agent = kaizen.create_agent(
                    name="qa_agent",
                    config={
                        "model": "gpt-4",
                        "temperature": 0.7,
                        "signature": signature_spec,
                    },
                )
                logger.info("✓ Agent created successfully")

            except Exception as e:
                self.log_error(example_name, "AGENT_CREATION_ERROR", e)
                return False

            # Step 5: Execute workflow with question
            logger.info("Step 5: Executing workflow...")
            try:
                from kailash.runtime.local import LocalRuntime
                from kailash.workflow.builder import WorkflowBuilder

                # Create workflow using agent
                workflow = WorkflowBuilder()

                # Test if agent can be added to workflow
                if hasattr(agent, "to_workflow_node"):
                    node_config = agent.to_workflow_node()
                    workflow.add_node(
                        node_config["type"], "qa_agent", node_config["config"]
                    )
                else:
                    self.log_gap(
                        example_name,
                        "MISSING_FEATURE",
                        "Agent cannot be converted to workflow node",
                        "agent.to_workflow_node() method",
                        "Method does not exist",
                    )
                    # Fallback to direct node creation
                    workflow.add_node(
                        "LLMAgent",
                        "qa_agent",
                        {
                            "model": "gpt-4",
                            "temperature": 0.7,
                            "messages": [
                                {
                                    "role": "user",
                                    "content": "What is the capital of France?",
                                }
                            ],
                        },
                    )

                # Execute workflow
                runtime = LocalRuntime()
                test_question = "What is the capital of France?"

                results, run_id = runtime.execute(
                    workflow.build(), {"question": test_question}
                )

                logger.info(f"✓ Workflow executed successfully, run_id: {run_id}")
                logger.info(f"Results: {results}")

            except Exception as e:
                self.log_error(example_name, "WORKFLOW_EXECUTION_ERROR", e)
                return False

            # Step 6: Validate structured response
            logger.info("Step 6: Validating response structure...")

            # Expected: Structured response with answer field
            if "answer" in str(results):
                logger.info("✓ Example completed successfully")
                self.successful_patterns.append(example_name)
                return True
            else:
                self.log_gap(
                    example_name,
                    "RESPONSE_STRUCTURE",
                    "Response not structured according to signature",
                    "{'answer': 'Paris'}",
                    str(results),
                )
                return False

        except Exception as e:
            self.log_error(example_name, "UNEXPECTED_ERROR", e)
            return False

    def test_example_2_react_agent_with_mcp(self):
        """Test Example 2: ReAct Agent with MCP Tool Usage"""
        example_name = "ReAct Agent with MCP"
        logger.info(f"=== Testing {example_name} ===")

        # Expected behavior: Agent reasons, acts using MCP tools, observes, repeats
        expected_mcp_flow = [
            "1. Agent receives complex query requiring tool usage",
            "2. Agent reasons about what tools are needed",
            "3. Agent discovers available MCP tools automatically",
            "4. Agent uses tools through MCP protocol",
            "5. Agent observes tool results",
            "6. Agent continues reasoning or provides final answer",
        ]

        try:
            # Step 1: Import and setup
            from kaizen import Kaizen

            logger.info("Step 1: Setting up ReAct agent with MCP integration...")

            # Expected: Simple MCP configuration
            # Current reality: Complex server configuration required
            mcp_config = {
                "mcp_enabled": True,
                "mcp_servers": [
                    {
                        "name": "search-server",
                        "transport": "stdio",
                        "command": "python",
                        "args": ["-m", "mcp_search_server"],
                        "env": {"DEBUG": "1"},
                        "timeout": 30,
                    }
                ],
                "auto_discover_tools": True,
                "mcp_client_config": {
                    "retry_strategy": "exponential",
                    "max_retries": 3,
                    "timeout": 15,
                },
            }

            # Test current MCP UX
            kaizen = Kaizen(config={"mcp_integration": mcp_config})

            # Test agent creation with MCP
            agent = kaizen.create_agent(
                name="react_agent",
                config={
                    "model": "gpt-4",
                    "temperature": 0.7,
                    "agent_type": "react",  # Does this exist?
                    "mcp_tools": ["search", "calculate", "analyze"],
                },
            )

            logger.info("Step 2: Testing ReAct reasoning loop...")

            # Expected: Agent automatically uses ReAct pattern
            # Thought -> Action -> Observation -> Repeat
            complex_query = "Find the current population of Tokyo and calculate what percentage of Japan's total population that represents"

            # This should trigger:
            # Thought: I need to find Tokyo's population and Japan's total population
            # Action: search("Tokyo population 2024")
            # Observation: Tokyo has ~14 million people
            # Thought: Now I need Japan's total population
            # Action: search("Japan total population 2024")
            # Observation: Japan has ~125 million people
            # Thought: Now I can calculate the percentage
            # Action: calculate(14000000 / 125000000 * 100)
            # Observation: 11.2%
            # Final Answer: Tokyo represents approximately 11.2% of Japan's population

            if hasattr(agent, "execute_react"):
                result = agent.execute_react(query=complex_query)
                logger.info(f"✓ ReAct execution completed: {result}")
            else:
                self.log_gap(
                    example_name,
                    "MISSING_FEATURE",
                    "No ReAct execution method in Agent",
                    "agent.execute_react(query) method with reasoning loop",
                    "Method does not exist",
                )

                # Test fallback through workflow
                from kailash.runtime.local import LocalRuntime
                from kailash.workflow.builder import WorkflowBuilder

                workflow = WorkflowBuilder()
                workflow.add_node(
                    "LLMAgent",
                    "react_agent",
                    {
                        "model": "gpt-4",
                        "messages": [{"role": "user", "content": complex_query}],
                        "mcp_servers": mcp_config["mcp_servers"],
                        "auto_discover_tools": True,
                        "auto_execute_tools": True,
                    },
                )

                runtime = LocalRuntime()
                results, run_id = runtime.execute(workflow.build())
                logger.info(f"Fallback workflow result: {results}")

        except Exception as e:
            self.log_error(example_name, "REACT_MCP_ERROR", e)
            return False

    def test_example_3_multi_agent_debate(self):
        """Test Example 3: Multi-Agent Debate for Decision Making"""
        example_name = "Multi-Agent Debate"
        logger.info(f"=== Testing {example_name} ===")

        # Expected behavior: Multiple agents debate to reach consensus
        expected_debate_flow = [
            "1. Create 3 specialized agents (Proponent, Opponent, Moderator)",
            "2. Present complex decision to agents",
            "3. Agents engage in structured debate rounds",
            "4. Moderator synthesizes arguments and makes decision",
            "5. Full audit trail of reasoning captured",
        ]

        try:
            from kaizen import Kaizen

            kaizen = Kaizen(
                config={
                    "multi_agent_enabled": True,
                    "transparency_enabled": True,
                    "audit_trail_enabled": True,
                }
            )

            # Step 1: Create specialized agents
            logger.info("Creating debate agents...")

            # Test multi-agent creation
            agents = {}
            agent_configs = [
                {
                    "name": "proponent",
                    "role": "Argue in favor of the proposal with evidence",
                    "model": "gpt-4",
                    "temperature": 0.8,  # More creative for debate
                },
                {
                    "name": "opponent",
                    "role": "Argue against the proposal with evidence",
                    "model": "gpt-4",
                    "temperature": 0.8,
                },
                {
                    "name": "moderator",
                    "role": "Synthesize arguments and make balanced decision",
                    "model": "gpt-4",
                    "temperature": 0.3,  # More conservative for decisions
                },
            ]

            for config in agent_configs:
                if hasattr(kaizen, "create_specialized_agent"):
                    agent = kaizen.create_specialized_agent(
                        name=config["name"], role=config["role"], config=config
                    )
                    agents[config["name"]] = agent
                else:
                    self.log_gap(
                        example_name,
                        "MISSING_FEATURE",
                        "No specialized agent creation method",
                        "kaizen.create_specialized_agent(name, role, config)",
                        "Method does not exist",
                    )
                    break

            # Step 2: Test multi-agent coordination
            logger.info("Testing multi-agent coordination...")

            decision_topic = "Should our company invest in AI infrastructure or hire more developers?"

            if hasattr(kaizen, "create_debate_workflow"):
                debate_workflow = kaizen.create_debate_workflow(
                    agents=list(agents.values()),
                    topic=decision_topic,
                    rounds=3,
                    decision_criteria="evidence-based consensus",
                )

                result = debate_workflow.execute()
                logger.info(f"✓ Debate completed: {result}")

            else:
                self.log_gap(
                    example_name,
                    "MISSING_FEATURE",
                    "No multi-agent debate workflow creation",
                    "kaizen.create_debate_workflow(agents, topic, rounds)",
                    "Method does not exist",
                )

                # Test manual workflow creation
                self._test_manual_multi_agent_workflow(
                    example_name, agents, decision_topic
                )

        except Exception as e:
            self.log_error(example_name, "MULTI_AGENT_ERROR", e)
            return False

    def _test_manual_multi_agent_workflow(
        self, example_name: str, agents: Dict, topic: str
    ):
        """Test manual multi-agent workflow creation."""
        logger.info("Testing manual multi-agent workflow...")

        try:
            from kailash.runtime.local import LocalRuntime
            from kailash.workflow.builder import WorkflowBuilder

            workflow = WorkflowBuilder()

            # Test A2A (Agent-to-Agent) communication
            # Expected: Agents can communicate and coordinate

            # Add each agent as a node
            for i, (name, agent) in enumerate(agents.items()):
                if hasattr(agent, "to_workflow_node"):
                    node_config = agent.to_workflow_node()
                    workflow.add_node(
                        node_config["type"], f"agent_{i}", node_config["config"]
                    )
                else:
                    # Fallback to direct LLMAgent
                    workflow.add_node(
                        "LLMAgent",
                        f"agent_{i}",
                        {
                            "model": "gpt-4",
                            "temperature": 0.7,
                            "messages": [
                                {"role": "user", "content": f"As {name}: {topic}"}
                            ],
                        },
                    )

            # Test agent coordination
            if len(agents) > 1:
                # Expected: Connect agents for communication
                try:
                    workflow.add_connection("agent_0", "output", "agent_1", "input")
                    workflow.add_connection("agent_1", "output", "agent_2", "input")
                    logger.info("✓ Agent connections created")
                except Exception as e:
                    self.log_gap(
                        example_name,
                        "COORDINATION_GAP",
                        "Cannot connect agents for communication",
                        "Seamless agent-to-agent data flow",
                        f"Connection failed: {e}",
                    )

            # Execute coordinated workflow
            runtime = LocalRuntime()
            results, run_id = runtime.execute(workflow.build())
            logger.info(f"Manual multi-agent workflow result: {results}")

        except Exception as e:
            self.log_error(example_name, "MANUAL_WORKFLOW_ERROR", e)

    def test_example_4_mcp_first_class_citizen(self):
        """Test Example 4: MCP as First-Class Citizen"""
        example_name = "MCP First-Class Citizen"
        logger.info(f"=== Testing {example_name} ===")

        # Expected: Every agent can become MCP server/client with minimal config
        expected_mcp_features = [
            "Agent can expose capabilities as MCP server",
            "Agent can consume external MCP services as client",
            "Auto-discovery of internal and external MCP servers",
            "Seamless tool routing across multiple servers",
            "Enterprise auth and monitoring integration",
        ]

        try:
            from kaizen import Kaizen

            # Test 1: Agent as MCP Server
            logger.info("Testing agent as MCP server...")

            kaizen = Kaizen()
            agent = kaizen.create_agent(
                "research_agent",
                {
                    "model": "gpt-4",
                    "capabilities": ["research", "analyze", "summarize"],
                },
            )

            # Expected: Simple way to expose agent as MCP server
            if hasattr(agent, "expose_as_mcp_server"):
                server_config = agent.expose_as_mcp_server(
                    port=8080, auth="api_key", tools=["research", "analyze"]
                )
                logger.info(f"✓ Agent exposed as MCP server: {server_config}")
            else:
                self.log_gap(
                    example_name,
                    "MISSING_FEATURE",
                    "Cannot expose agent as MCP server",
                    "agent.expose_as_mcp_server(port, auth, tools)",
                    "Method does not exist",
                )

            # Test 2: Agent as MCP Client
            logger.info("Testing agent as MCP client...")

            # Expected: Simple way to connect to external MCP servers
            if hasattr(agent, "connect_to_mcp_servers"):
                connected_tools = agent.connect_to_mcp_servers(
                    [
                        "search-service",  # Auto-discover by name
                        "http://external-api:8080",  # Direct URL
                        {"name": "custom-server", "auth": "jwt"},  # Full config
                    ]
                )
                logger.info(f"✓ Connected to MCP servers: {connected_tools}")
            else:
                self.log_gap(
                    example_name,
                    "MISSING_FEATURE",
                    "Cannot connect agent to MCP servers easily",
                    "agent.connect_to_mcp_servers([servers])",
                    "Method does not exist",
                )

            # Test 3: Auto-discovery and tool routing
            logger.info("Testing MCP auto-discovery...")

            if hasattr(kaizen, "discover_mcp_tools"):
                available_tools = kaizen.discover_mcp_tools(
                    capabilities=["search", "calculate", "analyze"],
                    location="auto",  # internal + external
                )
                logger.info(f"✓ Auto-discovery found tools: {available_tools}")
            else:
                self.log_gap(
                    example_name,
                    "MISSING_FEATURE",
                    "No automatic MCP tool discovery",
                    "kaizen.discover_mcp_tools(capabilities, location)",
                    "Method does not exist",
                )

        except Exception as e:
            self.log_error(example_name, "MCP_INTEGRATION_ERROR", e)
            return False

    def test_example_5_transparency_monitoring(self):
        """Test Example 5: Distributed Transparency and Monitoring"""
        example_name = "Transparency Monitoring"
        logger.info(f"=== Testing {example_name} ===")

        # Expected: Low-overhead distributed monitoring across agents and workflows
        expected_transparency_features = [
            "Agent-level decision tracking (<1% overhead)",
            "Workflow-level coordination monitoring (<1% overhead)",
            "Real-time introspection and debugging",
            "Automatic audit trail generation",
            "Performance profiling and optimization insights",
        ]

        try:
            from kaizen import Kaizen

            # Test transparency configuration
            kaizen = Kaizen(
                config={
                    "transparency_enabled": True,
                    "monitoring_level": "detailed",
                    "audit_trail_enabled": True,
                    "performance_profiling": True,
                }
            )

            # Test agent with transparency
            agent = kaizen.create_agent(
                "monitored_agent",
                {
                    "model": "gpt-4",
                    "transparency_config": {
                        "track_decisions": True,
                        "track_tool_usage": True,
                        "track_reasoning": True,
                        "export_traces": True,
                    },
                },
            )

            if hasattr(agent, "get_transparency_interface"):
                transparency = agent.get_transparency_interface()
                logger.info("✓ Transparency interface available")

                # Test real-time monitoring
                if hasattr(transparency, "start_monitoring"):
                    transparency.start_monitoring()
                    logger.info("✓ Real-time monitoring started")
                else:
                    self.log_gap(
                        example_name,
                        "MISSING_FEATURE",
                        "No real-time monitoring capability",
                        "transparency.start_monitoring() method",
                        "Method does not exist",
                    )
            else:
                self.log_gap(
                    example_name,
                    "MISSING_FEATURE",
                    "No transparency interface in agents",
                    "agent.get_transparency_interface()",
                    "Method does not exist",
                )

            # Test workflow-level monitoring
            if hasattr(kaizen, "get_workflow_monitor"):
                workflow_monitor = kaizen.get_workflow_monitor()

                if hasattr(workflow_monitor, "track_execution"):
                    workflow_monitor.track_execution(
                        agent_interactions=True,
                        performance_metrics=True,
                        decision_points=True,
                    )
                    logger.info("✓ Workflow monitoring configured")
                else:
                    self.log_gap(
                        example_name,
                        "MISSING_FEATURE",
                        "No workflow execution tracking",
                        "workflow_monitor.track_execution()",
                        "Method does not exist",
                    )
            else:
                self.log_gap(
                    example_name,
                    "MISSING_FEATURE",
                    "No workflow monitoring system",
                    "kaizen.get_workflow_monitor()",
                    "Method does not exist",
                )

        except Exception as e:
            self.log_error(example_name, "TRANSPARENCY_ERROR", e)
            return False

    def run_all_tests(self):
        """Run all example implementation tests."""
        logger.info("🚀 Starting comprehensive Kaizen implementation testing...")

        test_methods = [
            self.test_example_1_simple_qa_agent,
            self.test_example_2_react_agent_with_mcp,
            self.test_example_3_multi_agent_debate,
            self.test_example_4_mcp_first_class_citizen,
            self.test_example_5_transparency_monitoring,
        ]

        for test_method in test_methods:
            try:
                logger.info(f"\n{'='*60}")
                test_method()
                logger.info(f"{'='*60}\n")
            except Exception as e:
                logger.error(f"Test method {test_method.__name__} failed: {e}")

        # Generate comprehensive gap report
        self.generate_gap_report()

    def generate_gap_report(self):
        """Generate comprehensive gap analysis report."""
        logger.info("\n🔍 GENERATING COMPREHENSIVE GAP ANALYSIS...")

        logger.info("📊 SUMMARY:")
        logger.info(f"  - Gaps Found: {len(self.gaps_found)}")
        logger.info(f"  - Errors Encountered: {len(self.errors_encountered)}")
        logger.info(f"  - Feature Opportunities: {len(self.feature_opportunities)}")
        logger.info(f"  - Successful Patterns: {len(self.successful_patterns)}")

        # Detailed gap analysis will be written to markdown file
        return {
            "gaps": self.gaps_found,
            "errors": self.errors_encountered,
            "opportunities": self.feature_opportunities,
            "successes": self.successful_patterns,
        }


if __name__ == "__main__":
    tester = KaizenImplementationTester()
    tester.run_all_tests()
