"""
Advanced Reasoning Agent with O1/O3-style Chain-of-Thought Capabilities

This module implements cutting-edge reasoning techniques inspired by OpenAI's o1/o3 models,
including multi-step verification, self-correction, and advanced chain-of-thought prompting.

Author: LlamaAgent Team
"""

import json
import logging
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Any, Dict, List, Optional, Tuple

from ..llm import LLMMessage, LLMProvider, LLMResponse
from ..tools import ToolRegistry
from .base import AgentConfig
from .base import BaseAgent as Agent

logger = logging.getLogger(__name__)


class ReasoningStrategy(Enum):
    """Advanced reasoning strategies."""

    CHAIN_OF_THOUGHT = "chain_of_thought"
    TREE_OF_THOUGHTS = "tree_of_thoughts"
    GRAPH_OF_THOUGHTS = "graph_of_thoughts"
    RECURSIVE_DECOMPOSITION = "recursive_decomposition"
    ADVERSARIAL_VALIDATION = "adversarial_validation"
    CONSENSUS_REASONING = "consensus_reasoning"


@dataclass
class ThoughtNode:
    """Represents a single thought in the reasoning process."""

    content: str
    confidence: float
    reasoning_type: str
    parent_id: Optional[str] = None
    children_ids: List[str] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)
    timestamp: float = field(default_factory=time.time)


@dataclass
class ReasoningTrace:
    """Complete trace of the reasoning process."""

    query: str
    thoughts: List[ThoughtNode]
    final_answer: str
    confidence_score: float
    reasoning_strategy: ReasoningStrategy
    verification_steps: List[Dict[str, Any]]
    self_corrections: List[Dict[str, Any]]
    total_thinking_time: float
    metadata: Dict[str, Any] = field(default_factory=dict)


class AdvancedReasoningAgent(Agent):
    """
    Advanced reasoning agent with o1/o3-style capabilities.

    Features:
    - Multi-step chain-of-thought reasoning
    - Self-verification and correction
    - Confidence scoring
    - Multiple reasoning strategies
    - Thought visualization
    - Adversarial validation
    """

    def __init__(
        self,
        config: AgentConfig,
        llm_provider: LLMProvider,
        tools: Optional[ToolRegistry] = None,
        reasoning_strategy: ReasoningStrategy = ReasoningStrategy.CHAIN_OF_THOUGHT,
        max_thinking_steps: int = 10,
        min_confidence_threshold: float = 0.7,
        enable_self_correction: bool = True,
        enable_adversarial_validation: bool = True,
    ):
        super().__init__(config, llm_provider, tools)
        self.llm_provider = llm_provider  # Store for access
        self.reasoning_strategy = reasoning_strategy
        self.max_thinking_steps = max_thinking_steps
        self.min_confidence_threshold = min_confidence_threshold
        self.enable_self_correction = enable_self_correction
        self.enable_adversarial_validation = enable_adversarial_validation
        self.thought_history: List[ThoughtNode] = []

    async def reason(
        self, query: str, context: Optional[Dict[str, Any]] = None
    ) -> ReasoningTrace:
        """
        Perform advanced reasoning on a query.

        Args:
            query: The question or problem to reason about
            context: Additional context for reasoning

        Returns:
            Complete reasoning trace with thoughts, verification, and answer
        """
        start_time = time.time()
        thoughts = []
        self_corrections = []
        verification_steps = []

        # Initial problem decomposition
        decomposed_query = await self._decompose_problem(query, context)

        # Execute reasoning strategy
        if self.reasoning_strategy == ReasoningStrategy.CHAIN_OF_THOUGHT:
            thoughts = await self._chain_of_thought_reasoning(decomposed_query, context)
        elif self.reasoning_strategy == ReasoningStrategy.TREE_OF_THOUGHTS:
            thoughts = await self._tree_of_thoughts_reasoning(decomposed_query, context)
        elif self.reasoning_strategy == ReasoningStrategy.GRAPH_OF_THOUGHTS:
            thoughts = await self._graph_of_thoughts_reasoning(
                decomposed_query, context
            )
        elif self.reasoning_strategy == ReasoningStrategy.RECURSIVE_DECOMPOSITION:
            thoughts = await self._recursive_decomposition_reasoning(
                decomposed_query, context
            )
        elif self.reasoning_strategy == ReasoningStrategy.ADVERSARIAL_VALIDATION:
            thoughts = await self._adversarial_validation_reasoning(
                decomposed_query, context
            )
        elif self.reasoning_strategy == ReasoningStrategy.CONSENSUS_REASONING:
            thoughts = await self._consensus_reasoning(decomposed_query, context)

        # Self-correction phase
        if self.enable_self_correction:
            thoughts, corrections = await self._self_correct_reasoning(thoughts, query)
            self_corrections.extend(corrections)

        # Verification phase
        verification_steps = await self._verify_reasoning(thoughts, query)

        # Adversarial validation
        if self.enable_adversarial_validation:
            adversarial_results = await self._adversarial_validate(thoughts, query)
            verification_steps.extend(adversarial_results)

        # Synthesize final answer
        final_answer, confidence = await self._synthesize_answer(
            thoughts, verification_steps
        )

        # Build reasoning trace
        trace = ReasoningTrace(
            query=query,
            thoughts=thoughts,
            final_answer=final_answer,
            confidence_score=confidence,
            reasoning_strategy=self.reasoning_strategy,
            verification_steps=verification_steps,
            self_corrections=self_corrections,
            total_thinking_time=time.time() - start_time,
            metadata={
                "context": context,
                "decomposed_query": decomposed_query,
                "thought_count": len(thoughts),
                "correction_count": len(self_corrections),
                "verification_count": len(verification_steps),
            },
        )

        return trace

    async def _decompose_problem(
        self, query: str, context: Optional[Dict[str, Any]]
    ) -> Dict[str, Any]:
        """Decompose a complex problem into sub-problems."""
        prompt = f"""
        Decompose this problem into smaller, manageable sub-problems:
        
        Problem: {query}
        Context: {json.dumps(context) if context else "None"}
        
        Provide a structured decomposition with:
        1. Main objective
        2. Sub-problems (numbered list)
        3. Dependencies between sub-problems
        4. Required information/tools for each sub-problem
        
        Format as JSON.
        """

        response = await self.llm_provider.complete(
            [
                LLMMessage(
                    role="system",
                    content="You are an expert at problem decomposition and analysis.",
                ),
                LLMMessage(role="user", content=prompt),
            ]
        )

        try:
            return json.loads(response.content)
        except:
            return {
                "main_objective": query,
                "sub_problems": [query],
                "dependencies": [],
                "requirements": [],
            }

    async def _chain_of_thought_reasoning(
        self, decomposed_query: Dict[str, Any], context: Optional[Dict[str, Any]]
    ) -> List[ThoughtNode]:
        """Implement chain-of-thought reasoning."""
        thoughts = []
        current_context = context or {}

        for i, sub_problem in enumerate(decomposed_query.get("sub_problems", [])):
            if i >= self.max_thinking_steps:
                break

            # Generate thought for sub-problem
            prompt = f"""
            Think step-by-step about this sub-problem:
            
            Sub-problem: {sub_problem}
            Previous thoughts: {[t.content for t in thoughts[-3:]]}
            Current context: {json.dumps(current_context)}
            
            Provide:
            1. Your reasoning process
            2. Key insights
            3. Confidence level (0-1)
            4. Any assumptions made
            5. Next steps needed
            
            Think deeply and show your work.
            """

            response = await self.llm_provider.complete(
                [
                    LLMMessage(
                        role="system",
                        content="You are a careful, systematic thinker who shows all reasoning steps.",
                    ),
                    LLMMessage(role="user", content=prompt),
                ]
            )

            # Parse response and create thought node
            thought = ThoughtNode(
                content=response.content,
                confidence=self._extract_confidence(response.content),
                reasoning_type="chain_of_thought",
                metadata={
                    "sub_problem": sub_problem,
                    "step": i + 1,
                    "context": current_context,
                },
            )
            thoughts.append(thought)

            # Update context with insights from this thought
            current_context[f"thought_{i + 1}"] = thought.content

        return thoughts

    async def _tree_of_thoughts_reasoning(
        self, decomposed_query: Dict[str, Any], context: Optional[Dict[str, Any]]
    ) -> List[ThoughtNode]:
        """Implement tree-of-thoughts reasoning with branching exploration."""
        thoughts = []
        root_thought = ThoughtNode(
            content=f"Root: {decomposed_query['main_objective']}",
            confidence=1.0,
            reasoning_type="tree_root",
        )
        thoughts.append(root_thought)

        # Generate multiple branches for each sub-problem
        for sub_problem in decomposed_query.get("sub_problems", [])[
            : self.max_thinking_steps
        ]:
            branches = await self._generate_thought_branches(
                sub_problem, root_thought, context
            )

            # Evaluate and prune branches
            best_branches = await self._evaluate_and_prune_branches(
                branches, sub_problem
            )

            thoughts.extend(best_branches)

        return thoughts

    async def _graph_of_thoughts_reasoning(
        self, decomposed_query: Dict[str, Any], context: Optional[Dict[str, Any]]
    ) -> List[ThoughtNode]:
        """Implement graph-of-thoughts reasoning with interconnected ideas."""
        thoughts = []
        thought_graph = {}

        # Create initial thought nodes
        for i, sub_problem in enumerate(decomposed_query.get("sub_problems", [])):
            thought = await self._generate_thought(sub_problem, context)
            thought.reasoning_type = "graph_node"
            thoughts.append(thought)
            thought_graph[thought.content[:50]] = thought

        # Create connections between related thoughts
        for i, thought1 in enumerate(thoughts):
            for j, thought2 in enumerate(thoughts[i + 1 :], i + 1):
                connection_strength = await self._evaluate_thought_connection(
                    thought1, thought2
                )
                if connection_strength > 0.5:
                    thought1.metadata["connections"] = thought1.metadata.get(
                        "connections", []
                    )
                    thought1.metadata["connections"].append(
                        {"to": thought2.content[:50], "strength": connection_strength}
                    )

        return thoughts

    async def _recursive_decomposition_reasoning(
        self, decomposed_query: Dict[str, Any], context: Optional[Dict[str, Any]]
    ) -> List[ThoughtNode]:
        """Implement recursive decomposition reasoning."""
        thoughts = []

        async def decompose_and_solve(
            problem: str, depth: int = 0
        ) -> List[ThoughtNode]:
            if depth >= self.max_thinking_steps:
                return []

            # Check if problem is simple enough to solve directly
            if await self._is_atomic_problem(problem):
                solution = await self._solve_atomic_problem(problem, context)
                return [
                    ThoughtNode(
                        content=solution,
                        confidence=0.9,
                        reasoning_type="recursive_atomic",
                        metadata={"depth": depth, "problem": problem},
                    )
                ]

            # Further decompose
            sub_problems = await self._decompose_further(problem)
            local_thoughts = []

            for sub in sub_problems:
                sub_thoughts = await decompose_and_solve(sub, depth + 1)
                local_thoughts.extend(sub_thoughts)

            # Combine sub-solutions
            combined = await self._combine_sub_solutions(problem, local_thoughts)
            combined.reasoning_type = "recursive_combined"
            combined.metadata["depth"] = depth

            return [combined] + local_thoughts

        for problem in decomposed_query.get("sub_problems", []):
            problem_thoughts = await decompose_and_solve(problem)
            thoughts.extend(problem_thoughts)

        return thoughts

    async def _adversarial_validation_reasoning(
        self, decomposed_query: Dict[str, Any], context: Optional[Dict[str, Any]]
    ) -> List[ThoughtNode]:
        """Implement adversarial validation reasoning."""
        thoughts = []

        for sub_problem in decomposed_query.get("sub_problems", []):
            # Generate initial solution
            solution_thought = await self._generate_thought(sub_problem, context)
            solution_thought.reasoning_type = "adversarial_solution"
            thoughts.append(solution_thought)

            # Generate adversarial critique
            critique_thought = await self._generate_adversarial_critique(
                solution_thought
            )
            critique_thought.reasoning_type = "adversarial_critique"
            thoughts.append(critique_thought)

            # Generate defense/refinement
            defense_thought = await self._generate_defense(
                solution_thought, critique_thought
            )
            defense_thought.reasoning_type = "adversarial_defense"
            thoughts.append(defense_thought)

        return thoughts

    async def _consensus_reasoning(
        self, decomposed_query: Dict[str, Any], context: Optional[Dict[str, Any]]
    ) -> List[ThoughtNode]:
        """Implement consensus reasoning with multiple perspectives."""
        thoughts = []
        perspectives = [
            "analytical",
            "creative",
            "critical",
            "practical",
            "theoretical",
        ]

        for sub_problem in decomposed_query.get("sub_problems", []):
            perspective_thoughts = []

            # Generate thoughts from different perspectives
            for perspective in perspectives:
                thought = await self._generate_perspective_thought(
                    sub_problem, perspective, context
                )
                thought.reasoning_type = f"consensus_{perspective}"
                perspective_thoughts.append(thought)
                thoughts.append(thought)

            # Generate consensus thought
            consensus = await self._build_consensus(perspective_thoughts, sub_problem)
            consensus.reasoning_type = "consensus_final"
            thoughts.append(consensus)

        return thoughts

    async def _self_correct_reasoning(
        self, thoughts: List[ThoughtNode], original_query: str
    ) -> Tuple[List[ThoughtNode], List[Dict[str, Any]]]:
        """Self-correct the reasoning process."""
        corrections = []
        corrected_thoughts = thoughts.copy()

        for i, thought in enumerate(thoughts):
            # Check for logical errors
            errors = await self._check_logical_errors(thought, original_query)

            if errors:
                correction = {
                    "original_thought": thought.content,
                    "errors": errors,
                    "timestamp": time.time(),
                }

                # Generate corrected thought
                corrected = await self._generate_corrected_thought(thought, errors)
                corrected.reasoning_type = f"{thought.reasoning_type}_corrected"
                corrected.parent_id = thought.content[:50]
                corrected_thoughts[i] = corrected

                correction["corrected_thought"] = corrected.content
                corrections.append(correction)

        return corrected_thoughts, corrections

    async def _verify_reasoning(
        self, thoughts: List[ThoughtNode], original_query: str
    ) -> List[Dict[str, Any]]:
        """Verify the reasoning process."""
        verification_steps = []

        # Check logical consistency
        consistency_check = await self._check_consistency(thoughts)
        verification_steps.append(
            {
                "type": "consistency_check",
                "passed": consistency_check["passed"],
                "details": consistency_check["details"],
                "timestamp": time.time(),
            }
        )

        # Check completeness
        completeness_check = await self._check_completeness(thoughts, original_query)
        verification_steps.append(
            {
                "type": "completeness_check",
                "passed": completeness_check["passed"],
                "details": completeness_check["details"],
                "timestamp": time.time(),
            }
        )

        # Check accuracy (if ground truth available)
        if self.tools:
            accuracy_check = await self._check_accuracy_with_tools(thoughts)
            verification_steps.append(
                {
                    "type": "accuracy_check",
                    "passed": accuracy_check["passed"],
                    "details": accuracy_check["details"],
                    "timestamp": time.time(),
                }
            )

        return verification_steps

    async def _adversarial_validate(
        self, thoughts: List[ThoughtNode], original_query: str
    ) -> List[Dict[str, Any]]:
        """Perform adversarial validation."""
        validations = []

        # Generate adversarial challenges
        challenges = await self._generate_adversarial_challenges(
            thoughts, original_query
        )

        for challenge in challenges:
            # Test against challenge
            response = await self._respond_to_challenge(thoughts, challenge)

            validations.append(
                {
                    "type": "adversarial_validation",
                    "challenge": challenge,
                    "response": response,
                    "passed": response["confidence"] > 0.7,
                    "timestamp": time.time(),
                }
            )

        return validations

    async def _synthesize_answer(
        self, thoughts: List[ThoughtNode], verification_steps: List[Dict[str, Any]]
    ) -> Tuple[str, float]:
        """Synthesize final answer from thoughts and verification."""
        # Calculate overall confidence
        thought_confidences = [t.confidence for t in thoughts]
        verification_scores = [1.0 if v["passed"] else 0.0 for v in verification_steps]

        overall_confidence = (
            sum(thought_confidences) / len(thought_confidences) * 0.7
            + sum(verification_scores) / len(verification_scores) * 0.3
            if verification_scores
            else sum(thought_confidences) / len(thought_confidences)
        )

        # Generate synthesis prompt
        thought_summary = "\n".join(
            [
                f"- {t.reasoning_type}: {t.content[:200]}... (confidence: {t.confidence})"
                for t in thoughts[-5:]  # Use last 5 thoughts
            ]
        )

        prompt = f"""
        Synthesize a final answer based on this reasoning process:
        
        Recent thoughts:
        {thought_summary}
        
        Verification results: {len([v for v in verification_steps if v["passed"]])} passed out of {len(verification_steps)}
        
        Provide a clear, concise answer that:
        1. Directly addresses the original question
        2. Incorporates key insights from the reasoning
        3. Acknowledges any uncertainties
        4. Is actionable (if applicable)
        """

        response = await self.llm_provider.complete(
            [
                LLMMessage(
                    role="system",
                    content="You are synthesizing a final answer from a complex reasoning process.",
                ),
                LLMMessage(role="user", content=prompt),
            ]
        )

        return response.content, overall_confidence

    async def execute(
        self, task: str, context: Optional[Dict[str, Any]] = None
    ) -> LLMResponse:
        """Execute a task using advanced reasoning."""
        # Perform reasoning
        reasoning_trace = await self.reason(task, context)

        # Build response with reasoning details
        response = LLMResponse(
            content=reasoning_trace.final_answer,
            role="assistant",
            metadata={
                "reasoning_trace": {
                    "strategy": reasoning_trace.reasoning_strategy.value,
                    "thought_count": len(reasoning_trace.thoughts),
                    "confidence": reasoning_trace.confidence_score,
                    "thinking_time": reasoning_trace.total_thinking_time,
                    "self_corrections": len(reasoning_trace.self_corrections),
                    "verification_steps": len(reasoning_trace.verification_steps),
                },
                "thoughts": [
                    {
                        "type": t.reasoning_type,
                        "content": t.content[:500],
                        "confidence": t.confidence,
                    }
                    for t in reasoning_trace.thoughts[-3:]  # Include last 3 thoughts
                ],
            },
        )

        return response

    # Helper methods

    def _extract_confidence(self, text: str) -> float:
        """Extract confidence score from text."""
        import re

        match = re.search(r"confidence[:\s]+([0-9.]+)", text.lower())
        if match:
            try:
                return float(match.group(1))
            except Exception as e:
                logger.error(f"Error: {e}")
        return 0.7  # Default confidence

    async def _generate_thought(
        self, problem: str, context: Optional[Dict[str, Any]]
    ) -> ThoughtNode:
        """Generate a single thought."""
        response = await self.llm_provider.complete(
            [
                LLMMessage(
                    role="user", content=f"Think about: {problem}\nContext: {context}"
                )
            ]
        )

        return ThoughtNode(
            content=response.content,
            confidence=self._extract_confidence(response.content),
            reasoning_type="generated",
        )

    async def _is_atomic_problem(self, problem: str) -> bool:
        """Check if a problem is atomic (cannot be decomposed further)."""
        response = await self.llm_provider.complete(
            [
                LLMMessage(
                    role="user",
                    content=f"Is this an atomic problem that cannot be decomposed further? Answer yes/no: {problem}",
                )
            ]
        )
        return "yes" in response.content.lower()

    async def _solve_atomic_problem(
        self, problem: str, context: Optional[Dict[str, Any]]
    ) -> str:
        """Solve an atomic problem."""
        response = await self.llm_provider.complete(
            [
                LLMMessage(
                    role="user",
                    content=f"Solve this atomic problem: {problem}\nContext: {context}",
                )
            ]
        )
        return response.content

    async def _decompose_further(self, problem: str) -> List[str]:
        """Further decompose a problem."""
        response = await self.llm_provider.complete(
            [
                LLMMessage(
                    role="user",
                    content=f"Decompose this problem into 2-3 sub-problems: {problem}",
                )
            ]
        )
        # Simple parsing - in production, use structured output
        lines = response.content.strip().split("\n")
        return [line.strip("- 1234567890.") for line in lines if line.strip()][:3]

    async def _combine_sub_solutions(
        self, problem: str, sub_thoughts: List[ThoughtNode]
    ) -> ThoughtNode:
        """Combine sub-solutions into a solution."""
        sub_contents = "\n".join([f"- {t.content[:200]}" for t in sub_thoughts])
        response = await self.llm_provider.complete(
            [
                LLMMessage(
                    role="user",
                    content=f"Combine these sub-solutions for '{problem}':\n{sub_contents}",
                )
            ]
        )

        return ThoughtNode(
            content=response.content,
            confidence=sum(t.confidence for t in sub_thoughts) / len(sub_thoughts)
            if sub_thoughts
            else 0.7,
            reasoning_type="combined",
        )

    async def _generate_thought_branches(
        self, problem: str, parent: ThoughtNode, context: Optional[Dict[str, Any]]
    ) -> List[ThoughtNode]:
        """Generate multiple thought branches."""
        branches = []
        num_branches = 3

        for i in range(num_branches):
            response = await self.llm_provider.complete(
                [
                    LLMMessage(
                        role="user",
                        content=f"Approach {i + 1} for: {problem}\nBuilding on: {parent.content[:200]}",
                    )
                ]
            )

            branch = ThoughtNode(
                content=response.content,
                confidence=self._extract_confidence(response.content),
                reasoning_type="branch",
                parent_id=parent.content[:50],
            )
            branches.append(branch)

        return branches

    async def _evaluate_and_prune_branches(
        self, branches: List[ThoughtNode], problem: str
    ) -> List[ThoughtNode]:
        """Evaluate and prune thought branches."""
        # Keep top 2 branches by confidence
        sorted_branches = sorted(branches, key=lambda x: x.confidence, reverse=True)
        return sorted_branches[:2]

    async def _evaluate_thought_connection(
        self, thought1: ThoughtNode, thought2: ThoughtNode
    ) -> float:
        """Evaluate connection strength between thoughts."""
        response = await self.llm_provider.complete(
            [
                LLMMessage(
                    role="user",
                    content=f"Rate connection (0-1) between:\n1: {thought1.content[:200]}\n2: {thought2.content[:200]}",
                )
            ]
        )

        try:
            return float(self._extract_confidence(response.content))
        except:
            return 0.0

    async def _generate_adversarial_critique(self, thought: ThoughtNode) -> ThoughtNode:
        """Generate adversarial critique of a thought."""
        response = await self.llm_provider.complete(
            [
                LLMMessage(
                    role="user",
                    content=f"Provide a critical analysis of this reasoning, finding potential flaws: {thought.content}",
                )
            ]
        )

        return ThoughtNode(
            content=response.content,
            confidence=0.8,
            reasoning_type="critique",
            parent_id=thought.content[:50],
        )

    async def _generate_defense(
        self, solution: ThoughtNode, critique: ThoughtNode
    ) -> ThoughtNode:
        """Generate defense against critique."""
        response = await self.llm_provider.complete(
            [
                LLMMessage(
                    role="user",
                    content=f"Defend/refine this solution:\n{solution.content}\n\nAgainst critique:\n{critique.content}",
                )
            ]
        )

        return ThoughtNode(
            content=response.content,
            confidence=(solution.confidence + 0.9) / 2,
            reasoning_type="defense",
            parent_id=solution.content[:50],
        )

    async def _generate_perspective_thought(
        self, problem: str, perspective: str, context: Optional[Dict[str, Any]]
    ) -> ThoughtNode:
        """Generate thought from specific perspective."""
        response = await self.llm_provider.complete(
            [
                LLMMessage(
                    role="user",
                    content=f"Analyze from {perspective} perspective: {problem}\nContext: {context}",
                )
            ]
        )

        return ThoughtNode(
            content=response.content,
            confidence=self._extract_confidence(response.content),
            reasoning_type=f"perspective_{perspective}",
        )

    async def _build_consensus(
        self, perspective_thoughts: List[ThoughtNode], problem: str
    ) -> ThoughtNode:
        """Build consensus from multiple perspectives."""
        perspectives_summary = "\n".join(
            [f"- {t.reasoning_type}: {t.content[:150]}" for t in perspective_thoughts]
        )

        response = await self.llm_provider.complete(
            [
                LLMMessage(
                    role="user",
                    content=f"Build consensus for '{problem}' from:\n{perspectives_summary}",
                )
            ]
        )

        avg_confidence = sum(t.confidence for t in perspective_thoughts) / len(
            perspective_thoughts
        )

        return ThoughtNode(
            content=response.content,
            confidence=avg_confidence,
            reasoning_type="consensus",
        )

    async def _check_logical_errors(
        self, thought: ThoughtNode, original_query: str
    ) -> List[str]:
        """Check for logical errors in thought."""
        response = await self.llm_provider.complete(
            [
                LLMMessage(
                    role="user",
                    content=f"Identify logical errors in this reasoning for '{original_query}':\n{thought.content}",
                )
            ]
        )

        # Parse errors (simplified)
        if (
            "no errors" in response.content.lower()
            or "correct" in response.content.lower()
        ):
            return []

        return [response.content]

    async def _generate_corrected_thought(
        self, thought: ThoughtNode, errors: List[str]
    ) -> ThoughtNode:
        """Generate corrected thought."""
        errors_text = "\n".join(errors)

        response = await self.llm_provider.complete(
            [
                LLMMessage(
                    role="user",
                    content=f"Correct this reasoning:\n{thought.content}\n\nErrors found:\n{errors_text}",
                )
            ]
        )

        return ThoughtNode(
            content=response.content,
            confidence=min(thought.confidence + 0.1, 0.95),
            reasoning_type=thought.reasoning_type,
        )

    async def _check_consistency(self, thoughts: List[ThoughtNode]) -> Dict[str, Any]:
        """Check consistency across thoughts."""
        thought_summary = "\n".join([f"- {t.content[:200]}" for t in thoughts])

        response = await self.llm_provider.complete(
            [
                LLMMessage(
                    role="user",
                    content=f"Check for inconsistencies in this reasoning chain:\n{thought_summary}",
                )
            ]
        )

        return {
            "passed": "consistent" in response.content.lower()
            or "no inconsistencies" in response.content.lower(),
            "details": response.content,
        }

    async def _check_completeness(
        self, thoughts: List[ThoughtNode], original_query: str
    ) -> Dict[str, Any]:
        """Check if reasoning completely addresses the query."""
        thought_summary = "\n".join([f"- {t.content[:200]}" for t in thoughts])

        response = await self.llm_provider.complete(
            [
                LLMMessage(
                    role="user",
                    content=f"Does this reasoning completely address '{original_query}'?\n{thought_summary}",
                )
            ]
        )

        return {
            "passed": "complete" in response.content.lower()
            or "fully addresses" in response.content.lower(),
            "details": response.content,
        }

    async def _check_accuracy_with_tools(
        self, thoughts: List[ThoughtNode]
    ) -> Dict[str, Any]:
        """Check accuracy using available tools."""
        # This would use tools to verify facts/calculations
        # For now, return a placeholder
        return {
            "passed": True,
            "details": "Tool-based verification not yet implemented",
        }

    async def _generate_adversarial_challenges(
        self, thoughts: List[ThoughtNode], original_query: str
    ) -> List[str]:
        """Generate adversarial challenges."""
        thought_summary = "\n".join([f"- {t.content[:150]}" for t in thoughts[-3:]])

        response = await self.llm_provider.complete(
            [
                LLMMessage(
                    role="user",
                    content=f"Generate 2 challenging questions about this reasoning for '{original_query}':\n{thought_summary}",
                )
            ]
        )

        # Parse challenges
        lines = response.content.strip().split("\n")
        return [line.strip("- 1234567890.?") for line in lines if line.strip()][:2]

    async def _respond_to_challenge(
        self, thoughts: List[ThoughtNode], challenge: str
    ) -> Dict[str, Any]:
        """Respond to an adversarial challenge."""
        relevant_thoughts = "\n".join([f"- {t.content[:200]}" for t in thoughts[-5:]])

        response = await self.llm_provider.complete(
            [
                LLMMessage(
                    role="user",
                    content=f"Address this challenge using the reasoning:\nChallenge: {challenge}\nReasoning: {relevant_thoughts}",
                )
            ]
        )

        return {
            "response": response.content,
            "confidence": self._extract_confidence(response.content),
        }
