defmodule Dspy.SelfScaffoldingAgent do
  @moduledoc """
  Self-scaffolding agent for autonomous system development and modification.

  This module implements a comprehensive self-scaffolding system similar to the Python
  OpenAPI agent, providing:
  - LLM-powered task decomposition and execution
  - Dynamic code generation and compilation
  - Real-time system modification and extension
  - Autonomous dependency management
  - Continuous learning and improvement
  - Full Elixir ecosystem access for self-modification
  """

  use Dspy.Module

  alias Dspy.{
    LM,
    Prediction,
    StructuredDecomposition,
    DynamicSchemaGenerator,
    EndpointDiscovery,
    ProgramOfThoughts
  }

  defstruct [
    :agent_id,
    :capabilities,
    :knowledge_base,
    :execution_context,
    :learning_system,
    :modification_history,
    :active_tasks,
    :generated_modules,
    :self_improvement_enabled
  ]

  @type capability :: %{
          name: atom(),
          description: String.t(),
          implementation_module: module(),
          enabled: boolean(),
          confidence_level: float()
        }

  @type t :: %__MODULE__{
          agent_id: String.t(),
          capabilities: map(),
          knowledge_base: map(),
          execution_context: map(),
          learning_system: module(),
          modification_history: list(),
          active_tasks: list(),
          generated_modules: list(),
          self_improvement_enabled: boolean()
        }

  # === AGENT SIGNATURES ===

  defmodule AgentTaskAnalysisSignature do
    @moduledoc """
    Signature for analyzing tasks and determining agent capabilities.

    Analyzes task requirements, identifies needed capabilities, and
    creates comprehensive task decomposition plans.
    """
    use Dspy.Signature

    input_field(:user_request, :string, "High-level user request or goal")
    input_field(:current_capabilities, :json, "Agent's current capabilities and modules")
    input_field(:system_state, :json, "Current system state and available resources")
    input_field(:historical_context, :json, "Relevant historical context and past solutions")

    output_field(
      :task_complexity,
      :string,
      "Assessment of task complexity (simple/moderate/complex/novel)"
    )

    output_field(:required_capabilities, :json, "Capabilities needed to complete the task")
    output_field(:missing_capabilities, :json, "Capabilities that need to be developed")
    output_field(:decomposition_strategy, :string, "Recommended decomposition strategy")
    output_field(:execution_plan, :json, "High-level execution plan with phases")
    output_field(:learning_opportunities, :json, "Opportunities for learning and improvement")
  end

  defmodule CapabilityDevelopmentSignature do
    @moduledoc """
    Signature for developing new agent capabilities.

    Generates implementation code, integration logic, and tests for
    new capabilities based on specifications.
    """
    use Dspy.Signature

    input_field(:capability_specification, :json, "Specification of the capability to develop")

    input_field(
      :existing_implementations,
      :json,
      "Existing similar implementations for reference"
    )

    input_field(:target_performance, :json, "Target performance metrics and requirements")

    input_field(
      :integration_requirements,
      :json,
      "Requirements for integrating with existing system"
    )

    output_field(:implementation_approach, :string, "Recommended implementation approach")
    output_field(:module_design, :json, "Design for the new module or capability")
    output_field(:code_structure, :code, "Generated code structure and implementation")
    output_field(:test_strategy, :json, "Testing strategy and test cases")
    output_field(:integration_plan, :json, "Plan for integrating the new capability")
  end

  defmodule SelfImprovementSignature do
    @moduledoc """
    Signature for agent self-improvement and optimization.

    Analyzes performance metrics, identifies improvement areas, and
    generates optimization strategies for enhanced agent capabilities.
    """
    use Dspy.Signature

    input_field(:performance_metrics, :json, "Current performance metrics and bottlenecks")
    input_field(:execution_history, :json, "History of task executions and outcomes")
    input_field(:error_patterns, :json, "Patterns of errors and failures")
    input_field(:success_patterns, :json, "Patterns of successful executions")

    output_field(:improvement_areas, :json, "Areas identified for improvement")
    output_field(:optimization_strategies, :json, "Strategies for optimization")
    output_field(:new_capabilities_needed, :json, "New capabilities that should be developed")

    output_field(
      :refactoring_recommendations,
      :json,
      "Recommendations for refactoring existing code"
    )

    output_field(
      :learning_adjustments,
      :json,
      "Adjustments to learning algorithms and strategies"
    )
  end

  # === MAIN INTERFACE ===

  def new(opts \\ []) do
    agent_id = Keyword.get(opts, :agent_id, generate_agent_id())

    %__MODULE__{
      agent_id: agent_id,
      capabilities: initialize_core_capabilities(),
      knowledge_base: initialize_knowledge_base(),
      execution_context: build_execution_context(),
      learning_system: Keyword.get(opts, :learning_system, __MODULE__.LearningSystem),
      modification_history: [],
      active_tasks: [],
      generated_modules: [],
      self_improvement_enabled: Keyword.get(opts, :self_improvement, true)
    }
  end

  @impl true
  def forward(agent, inputs) do
    task_id = generate_task_id()

    with {:ok, task_analysis} <- analyze_user_request(agent, inputs),
         {:ok, missing_capabilities} <- identify_missing_capabilities(agent, task_analysis),
         {:ok, developed_capabilities} <-
           develop_missing_capabilities(agent, missing_capabilities),
         {:ok, execution_plan} <-
           create_detailed_execution_plan(agent, task_analysis, developed_capabilities),
         {:ok, execution_results} <- execute_plan_with_learning(agent, execution_plan),
         {:ok, improvement_insights} <-
           analyze_execution_for_improvement(agent, execution_results) do
      # Update agent state with new capabilities and learning
      updated_agent = %{
        agent
        | capabilities: Map.merge(agent.capabilities, developed_capabilities),
          modification_history: [execution_results | agent.modification_history],
          generated_modules: agent.generated_modules ++ execution_results.generated_modules,
          knowledge_base:
            update_knowledge_base(agent.knowledge_base, execution_results, improvement_insights)
      }

      # Perform self-improvement if enabled
      final_agent =
        if agent.self_improvement_enabled do
          apply_self_improvements(updated_agent, improvement_insights)
        else
          updated_agent
        end

      prediction_attrs = %{
        task_id: task_id,
        task_analysis: task_analysis,
        execution_results: execution_results,
        developed_capabilities: developed_capabilities,
        improvement_insights: improvement_insights,
        updated_agent: final_agent,
        performance_metrics: calculate_performance_metrics(execution_results)
      }

      prediction = Prediction.new(prediction_attrs)
      {:ok, prediction}
    else
      {:error, reason} -> {:error, reason}
    end
  end

  # === TASK ANALYSIS ===

  defp analyze_user_request(agent, inputs) do
    analysis_inputs = %{
      user_request: Map.get(inputs, :request, ""),
      current_capabilities: Map.keys(agent.capabilities),
      system_state: capture_system_state(),
      historical_context: extract_relevant_history(agent, inputs)
    }

    signature = AgentTaskAnalysisSignature.new()

    case LM.generate_structured_output(signature, analysis_inputs) do
      {:ok, analysis} ->
        # Validate and enrich the analysis
        enriched_analysis = enrich_task_analysis(agent, analysis)
        {:ok, enriched_analysis}

      {:error, reason} ->
        {:error, {:task_analysis_failed, reason}}
    end
  end

  defp enrich_task_analysis(agent, analysis) do
    # Add additional context from agent's knowledge base
    relevant_knowledge = search_knowledge_base(agent.knowledge_base, analysis.user_request)
    similar_tasks = find_similar_historical_tasks(agent, analysis)

    Map.merge(analysis, %{
      relevant_knowledge: relevant_knowledge,
      similar_tasks: similar_tasks,
      confidence_estimate: calculate_confidence_estimate(agent, analysis),
      estimated_complexity_score: calculate_complexity_score(analysis)
    })
  end

  # === CAPABILITY DEVELOPMENT ===

  defp identify_missing_capabilities(agent, task_analysis) do
    required_capabilities = task_analysis.required_capabilities
    current_capabilities = Map.keys(agent.capabilities)

    missing =
      Enum.filter(required_capabilities, fn cap ->
        not Enum.any?(current_capabilities, &capability_satisfies?(&1, cap))
      end)

    {:ok, missing}
  end

  defp develop_missing_capabilities(agent, missing_capabilities) do
    developed_capabilities =
      Enum.reduce(missing_capabilities, %{}, fn capability_spec, acc ->
        case develop_single_capability(agent, capability_spec) do
          {:ok, capability_name, capability_impl} ->
            Map.put(acc, capability_name, capability_impl)

          {:error, reason} ->
            # Log the error but continue with other capabilities
            log_capability_development_error(capability_spec, reason)
            acc
        end
      end)

    {:ok, developed_capabilities}
  end

  defp develop_single_capability(agent, capability_spec) do
    development_inputs = %{
      capability_specification: capability_spec,
      existing_implementations: find_similar_implementations(agent, capability_spec),
      target_performance: extract_performance_requirements(capability_spec),
      integration_requirements: analyze_integration_requirements(agent, capability_spec)
    }

    signature = CapabilityDevelopmentSignature.new()

    case LM.generate_structured_output(signature, development_inputs) do
      {:ok, development_plan} ->
        # Execute the development plan
        case execute_capability_development(agent, development_plan) do
          {:ok, capability} ->
            capability_name =
              String.to_atom(Map.get(capability_spec, :name, "generated_capability"))

            {:ok, capability_name, capability}

          {:error, reason} ->
            {:error, {:capability_development_failed, capability_spec, reason}}
        end

      {:error, reason} ->
        {:error, {:capability_planning_failed, capability_spec, reason}}
    end
  end

  defp execute_capability_development(_agent, development_plan) do
    # Use structured decomposition to implement the capability
    decomposer =
      StructuredDecomposition.new(
        development_plan.module_design,
        strategy: :recursive,
        self_modification: true
      )

    decomposition_inputs = %{
      task: "Implement capability: #{inspect(development_plan.module_design)}",
      code_requirements: development_plan.code_structure,
      integration_plan: development_plan.integration_plan,
      test_strategy: development_plan.test_strategy
    }

    case Dspy.Module.forward(decomposer, decomposition_inputs) do
      {:ok, prediction} ->
        # Extract the generated capability
        capability = %{
          name: extract_capability_name(development_plan),
          implementation: prediction.attrs.created_artifacts,
          module: extract_generated_module(prediction),
          confidence_level: calculate_capability_confidence(prediction),
          enabled: true
        }

        {:ok, capability}

      {:error, reason} ->
        {:error, reason}
    end
  end

  # === EXECUTION PLANNING ===

  defp create_detailed_execution_plan(agent, task_analysis, developed_capabilities) do
    # Combine task analysis with newly developed capabilities
    all_capabilities = Map.merge(agent.capabilities, developed_capabilities)

    # Use structured decomposition for complex tasks
    case task_analysis.task_complexity do
      "simple" ->
        create_simple_execution_plan(task_analysis, all_capabilities)

      "moderate" ->
        create_moderate_execution_plan(task_analysis, all_capabilities)

      "complex" ->
        create_complex_execution_plan(agent, task_analysis, all_capabilities)

      "novel" ->
        create_novel_execution_plan(agent, task_analysis, all_capabilities)
    end
  end

  defp create_complex_execution_plan(_agent, task_analysis, all_capabilities) do
    # Use the structured decomposition system for complex tasks
    decomposer =
      StructuredDecomposition.new(
        task_analysis.execution_plan,
        strategy: String.to_atom(task_analysis.decomposition_strategy),
        max_depth: 10,
        self_modification: true
      )

    decomposition_inputs = %{
      task: task_analysis.user_request,
      available_capabilities: all_capabilities,
      constraints: Map.get(task_analysis, :constraints, %{}),
      performance_requirements: Map.get(task_analysis, :performance_requirements, %{})
    }

    case Dspy.Module.forward(decomposer, decomposition_inputs) do
      {:ok, prediction} ->
        execution_plan = %{
          strategy: :structured_decomposition,
          decomposition_result: prediction,
          phases: prediction.attrs.decomposition_plan.execution_phases,
          estimated_duration: estimate_execution_duration(prediction),
          resource_requirements: estimate_resource_requirements(prediction)
        }

        {:ok, execution_plan}

      {:error, reason} ->
        {:error, {:execution_planning_failed, reason}}
    end
  end

  defp create_novel_execution_plan(_agent, task_analysis, _all_capabilities) do
    # For novel tasks, use experimental approaches and learning
    experimental_framework =
      Dspy.ExperimentalFramework.new(
        task_analysis.execution_plan,
        experiment_settings: %{
          batch_size: 5,
          max_iterations: 10,
          success_threshold: 0.8,
          novelty_requirement: 0.9,
          # 5 minutes
          time_budget_ms: 300_000,
          parallel_execution: true
        },
        learning_rate: 0.2,
        exploration_probability: 0.6,
        meta_learning_enabled: true,
        continuous_mode: false
      )

    experimental_inputs = %{
      novel_problem: task_analysis.user_request,
      domain: infer_problem_domain(task_analysis),
      constraints: Map.get(task_analysis, :constraints, %{}),
      success_criteria: Map.get(task_analysis, :success_criteria, %{})
    }

    case Dspy.Module.forward(experimental_framework, experimental_inputs) do
      {:ok, prediction} ->
        execution_plan = %{
          strategy: :experimental_novel,
          experimental_result: prediction,
          novel_approaches: prediction.attrs.novel_approaches_discovered,
          learning_insights: prediction.attrs.learning_insights,
          estimated_success_probability: prediction.attrs.estimated_success_probability
        }

        {:ok, execution_plan}

      {:error, reason} ->
        {:error, {:novel_execution_planning_failed, reason}}
    end
  end

  # === EXECUTION WITH LEARNING ===

  defp execute_plan_with_learning(agent, execution_plan) do
    case execution_plan.strategy do
      :structured_decomposition ->
        execute_structured_decomposition_plan(agent, execution_plan)

      :experimental_novel ->
        execute_experimental_plan(agent, execution_plan)

      _ ->
        execute_standard_plan(agent, execution_plan)
    end
  end

  defp execute_structured_decomposition_plan(_agent, execution_plan) do
    decomposition_result = execution_plan.decomposition_result

    # The decomposition has already been executed, extract results
    execution_results = %{
      strategy: :structured_decomposition,
      status: :completed,
      generated_modules: decomposition_result.attrs.created_artifacts,
      runtime_modifications: decomposition_result.attrs.runtime_state,
      learning_data: extract_learning_data(decomposition_result),
      performance_metrics: calculate_execution_performance(decomposition_result)
    }

    {:ok, execution_results}
  end

  defp execute_experimental_plan(_agent, execution_plan) do
    experimental_result = execution_plan.experimental_result

    # Extract and process experimental results
    execution_results = %{
      strategy: :experimental_novel,
      status: determine_experimental_status(experimental_result),
      novel_discoveries: experimental_result.attrs.novel_approaches_discovered,
      learning_insights: experimental_result.attrs.learning_insights,
      generated_modules: extract_experimental_modules(experimental_result),
      success_rate: experimental_result.attrs.estimated_success_probability,
      performance_metrics: calculate_experimental_performance(experimental_result)
    }

    {:ok, execution_results}
  end

  # === SELF-IMPROVEMENT ===

  defp analyze_execution_for_improvement(agent, execution_results) do
    improvement_inputs = %{
      performance_metrics: execution_results.performance_metrics,
      execution_history: agent.modification_history,
      error_patterns: extract_error_patterns(agent.modification_history),
      success_patterns: extract_success_patterns(agent.modification_history)
    }

    signature = SelfImprovementSignature.new()

    case LM.generate_structured_output(signature, improvement_inputs) do
      {:ok, insights} -> {:ok, insights}
      {:error, reason} -> {:error, {:improvement_analysis_failed, reason}}
    end
  end

  defp apply_self_improvements(agent, improvement_insights) do
    # Apply various types of improvements
    agent
    |> apply_capability_improvements(improvement_insights.improvement_areas)
    |> apply_learning_adjustments(improvement_insights.learning_adjustments)
    |> apply_optimization_strategies(improvement_insights.optimization_strategies)
    |> develop_recommended_capabilities(improvement_insights.new_capabilities_needed)
  end

  defp apply_capability_improvements(agent, improvement_areas) do
    # Improve existing capabilities based on identified areas
    improved_capabilities =
      Enum.reduce(improvement_areas, agent.capabilities, fn area, acc ->
        case improve_capability(agent, area) do
          {:error, :not_implemented} ->
            acc
        end
      end)

    %{agent | capabilities: improved_capabilities}
  end

  defp apply_learning_adjustments(agent, learning_adjustments) do
    # Apply adjustments to the learning system
    updated_learning_system = agent.learning_system.apply_adjustments(learning_adjustments)
    %{agent | learning_system: updated_learning_system}
  end

  # === UTILITY FUNCTIONS ===

  defp initialize_core_capabilities do
    %{
      schema_generation: %{
        name: :schema_generation,
        implementation_module: DynamicSchemaGenerator,
        enabled: true,
        confidence_level: 0.9
      },
      endpoint_discovery: %{
        name: :endpoint_discovery,
        implementation_module: EndpointDiscovery,
        enabled: true,
        confidence_level: 0.85
      },
      structured_decomposition: %{
        name: :structured_decomposition,
        implementation_module: StructuredDecomposition,
        enabled: true,
        confidence_level: 0.95
      },
      code_execution: %{
        name: :code_execution,
        implementation_module: ProgramOfThoughts,
        enabled: true,
        confidence_level: 0.8
      }
    }
  end

  defp initialize_knowledge_base do
    %{
      successful_patterns: [],
      failed_patterns: [],
      optimization_insights: [],
      domain_knowledge: %{},
      code_templates: %{},
      best_practices: []
    }
  end

  defp build_execution_context do
    %{
      elixir_version: System.version(),
      otp_version: System.otp_release(),
      loaded_applications: Application.loaded_applications(),
      available_modules: :code.all_loaded() |> length(),
      memory_usage: :erlang.memory(),
      process_count: :erlang.system_info(:process_count)
    }
  end

  defp generate_agent_id do
    :crypto.strong_rand_bytes(16) |> Base.encode16() |> String.slice(0, 16)
  end

  defp generate_task_id do
    :crypto.strong_rand_bytes(8) |> Base.encode16() |> String.slice(0, 12)
  end

  defp capture_system_state do
    %{
      memory_usage: :erlang.memory(),
      process_count: :erlang.system_info(:process_count),
      loaded_modules: :code.all_loaded() |> length(),
      uptime: :erlang.statistics(:wall_clock) |> elem(0),
      timestamp: DateTime.utc_now()
    }
  end

  # Placeholder implementations for complex helper functions
  defp extract_relevant_history(_agent, _inputs), do: []
  defp search_knowledge_base(_knowledge_base, _query), do: []
  defp find_similar_historical_tasks(_agent, _analysis), do: []
  defp calculate_confidence_estimate(_agent, _analysis), do: 0.75
  defp calculate_complexity_score(_analysis), do: 5.0
  defp capability_satisfies?(_current_cap, _required_cap), do: false
  defp log_capability_development_error(_spec, _reason), do: :ok
  defp find_similar_implementations(_agent, _spec), do: []
  defp extract_performance_requirements(_spec), do: %{}
  defp analyze_integration_requirements(_agent, _spec), do: %{}
  defp extract_capability_name(_plan), do: "generated_capability"
  defp extract_generated_module(_prediction), do: nil
  defp calculate_capability_confidence(_prediction), do: 0.8
  defp create_simple_execution_plan(_analysis, _capabilities), do: {:ok, %{strategy: :simple}}
  defp create_moderate_execution_plan(_analysis, _capabilities), do: {:ok, %{strategy: :moderate}}
  defp infer_problem_domain(_analysis), do: "general"
  # 1 minute
  defp estimate_execution_duration(_prediction), do: 60_000
  defp estimate_resource_requirements(_prediction), do: %{memory: "100MB", cpu: "low"}
  defp extract_learning_data(_result), do: %{}
  defp calculate_execution_performance(_result), do: %{duration: 1000, success_rate: 1.0}
  defp determine_experimental_status(_result), do: :success
  defp extract_experimental_modules(_result), do: []
  defp calculate_experimental_performance(_result), do: %{novelty_score: 0.8, success_rate: 0.9}
  defp extract_error_patterns(_history), do: []
  defp extract_success_patterns(_history), do: []
  defp improve_capability(_agent, _area), do: {:error, :not_implemented}
  defp develop_recommended_capabilities(agent, _capabilities), do: agent
  defp apply_optimization_strategies(agent, _strategies), do: agent
  defp execute_standard_plan(_agent, _plan), do: {:ok, %{strategy: :standard, status: :completed}}
  defp update_knowledge_base(kb, _results, _insights), do: kb
  defp calculate_performance_metrics(_results), do: %{overall_score: 0.85}

  # === PUBLIC API ===

  @doc """
  Create a new self-scaffolding agent with specified capabilities.
  """
  def start_agent(opts \\ []) do
    new(opts)
  end

  @doc """
  Execute a high-level user request using the self-scaffolding agent.
  """
  def execute_request(agent, request) when is_binary(request) do
    forward(agent, %{request: request})
  end

  @doc """
  Add a new capability to an existing agent.
  """
  def add_capability(agent, capability_spec) do
    case develop_single_capability(agent, capability_spec) do
      {:ok, capability_name, capability_impl} ->
        updated_capabilities = Map.put(agent.capabilities, capability_name, capability_impl)
        {:ok, %{agent | capabilities: updated_capabilities}}

      {:error, reason} ->
        {:error, reason}
    end
  end

  @doc """
  Get the current status and capabilities of an agent.
  """
  def get_agent_status(agent) do
    %{
      agent_id: agent.agent_id,
      active_capabilities: Map.keys(agent.capabilities),
      generated_modules: length(agent.generated_modules),
      modification_history_length: length(agent.modification_history),
      knowledge_base_size: map_size(agent.knowledge_base),
      self_improvement_enabled: agent.self_improvement_enabled,
      system_state: capture_system_state()
    }
  end

  @doc """
  Enable or disable self-improvement for an agent.
  """
  def set_self_improvement(agent, enabled) when is_boolean(enabled) do
    %{agent | self_improvement_enabled: enabled}
  end

  @doc """
  Export the agent's knowledge base for analysis or backup.
  """
  def export_knowledge_base(agent) do
    %{
      agent_id: agent.agent_id,
      knowledge_base: agent.knowledge_base,
      capabilities: agent.capabilities,
      modification_history: agent.modification_history,
      exported_at: DateTime.utc_now()
    }
  end

  @doc """
  Import knowledge base data into an agent.
  """
  def import_knowledge_base(agent, knowledge_data) do
    %{
      agent
      | knowledge_base: Map.merge(agent.knowledge_base, knowledge_data.knowledge_base),
        capabilities: Map.merge(agent.capabilities, knowledge_data.capabilities)
    }
  end
end
