Feature: LangChain chat provider coverage boost
  As a maintainer focused on test coverage completeness
  I want Behave scenarios that exercise remaining uncovered paths
  So that langchain_chat_provider.py reaches full line coverage

  Background:
    Given a fresh LangChain chat provider with mocked dependencies

  @coverage @langchain @token_cost
  Scenario: Token cost resolution returns None when float conversion fails
    When the usage tracker reports a non-numeric cost value
    Then resolving token cost should return None

  @coverage @langchain @extract_event
  Scenario: Extract event returns unknown sentinel for empty event dict
    When an empty event dictionary is passed to extract_event
    Then the extracted node name should be "__unknown__" with an empty payload

  @coverage @langchain @token_estimate
  Scenario: Token estimation returns zero when the estimator callable raises
    When the LLM token estimator raises an exception during estimation
    Then the estimated token count should be zero

  @coverage @langchain @token_estimate
  Scenario: Token estimation returns zero when the result is not int-convertible
    When the LLM token estimator returns a value that cannot be cast to int
    Then the estimated token count should be zero

  @coverage @langchain @error_extraction
  Scenario: Error extraction returns None for an empty list
    When the error value is an empty list
    Then the extracted error message should be None

  @coverage @langchain @error_extraction
  Scenario: Error extraction returns None for an empty tuple
    When the error value is an empty tuple
    Then the extracted error message should be None

  @coverage @langchain @error_extraction
  Scenario: Error extraction returns None for an empty set
    When the error value is an empty set
    Then the extracted error message should be None

  @coverage @langchain @error_extraction
  Scenario: Error extraction returns None for a list of only None values
    When the error value is a list containing only None entries
    Then the extracted error message should be None

  @coverage @langchain @callback_discovery
  Scenario: OpenAI callback is discovered from langchain.callbacks module
    When the langchain callbacks module exposes get_openai_callback
    Then the callback discovery logic should bind the callable

  @coverage @langchain @callback_discovery
  Scenario: OpenAI callback discovery skips when attribute is not callable
    When the langchain callbacks module exposes a non-callable get_openai_callback
    Then the callback discovery logic should not bind any callable

  @coverage @langchain @token_cost
  Scenario: Token cost resolution returns None when cost attribute is missing
    When the usage tracker has no total_cost attribute
    Then resolving token cost should return None

  @coverage @langchain @token_estimate
  Scenario: Token estimation aggregates prompt and context content
    When the LLM token estimator counts tokens for plan and multiple contexts
    Then the estimated token count should reflect the combined prompt text

  @coverage @langchain @generate @exception
  Scenario: Generate changes returns error response when workflow raises
    When generate_changes is called and the workflow raises an exception
    Then the response should contain the exception message with zero changes
    And progress should be reported as complete despite the failure