Feature: LLM Agent Temperature Override, Cleanup, and Context History
  As a developer
  I want temperature overrides from context, http client cleanup, and conversation history fallback to work correctly
  So that LLM agents adapt to runtime parameters and release resources without leaking connections

  # ---- Temperature Override (lines 251-260) ----

  Scenario: Temperature override from context is applied when different
    Given I setup a test LLM agent for temperature override tests (llm_gaps)
    When I process a message with _temperature_override set to 0.3 (llm_gaps)
    Then the mock chat model temperature should be set to 0.3 (llm_gaps)

  Scenario: Temperature override is skipped when value matches current temperature
    Given I setup a test LLM agent for temperature override tests (llm_gaps)
    When I process a message with _temperature_override equal to current temp (llm_gaps)
    Then the mock chat model temperature should remain at its original value (llm_gaps)

  Scenario: Temperature override works with other context data
    Given I setup a test LLM agent for temperature override tests (llm_gaps)
    When I process a message with _temperature_override 0.9 and extra context keys (llm_gaps)
    Then the mock chat model temperature should be set to 0.9 (llm_gaps)
    And the process message result should be a non-empty string (llm_gaps)

  # ---- Missing API Key (lines 209-217) ----

  Scenario: Missing API key raises ConfigurationError without env vars
    Given I clear all relevant API key environment variables for testing (llm_gaps)
    And I have an API-key-missing config for provider "openai" (coverage gap) (llm_gaps)
    When I try to construct an LLMAgent with that config and cleared env (llm_gaps)
    Then the raised exception should be a ConfigurationError (llm_gaps)
    And the error message should mention "Missing API key" or "Failed to initialize" (llm_gaps)

  Scenario: Missing API key for Google provider raises error
    Given I clear all relevant API key environment variables for testing (llm_gaps)
    And I have an API-key-missing Google config with model "gemini-pro" (coverage gap) (llm_gaps)
    When I try to construct an LLMAgent with that config and cleared env (llm_gaps)
    Then the raised exception should be a ConfigurationError (llm_gaps)

  # ---- Conversation History from Context (line 304) ----

  Scenario: Conversation history from context is used over memory
    Given I setup an LLM agent with memory enabled for history tests (llm_gaps)
    And I populate the agent memory with previous conversation history (llm_gaps)
    When I process a message with conversation_history provided in the call context (llm_gaps)
    Then the chat model invocation should include context history messages (llm_gaps)
    And the number of messages sent to chat model should exceed 2 (llm_gaps)

  Scenario: No conversation history in context falls back to memory
    Given I setup an LLM agent with memory enabled for history tests (llm_gaps)
    And I populate the agent memory with previous conversation history (llm_gaps)
    When I process a message without conversation_history in the call context (llm_gaps)
    Then the agent memory should have been queried for history (llm_gaps)

  Scenario: Empty conversation history list in context
    Given I setup an LLM agent with memory enabled for history tests (llm_gaps)
    And I populate the agent memory with previous conversation history (llm_gaps)
    When I process a message with conversation_history set to empty list (llm_gaps)
    Then only the system message and current user message should be sent to chat model (llm_gaps)

  Scenario: Conversation history from context with mixed roles
    Given I setup an LLM agent with memory enabled for history tests (llm_gaps)
    When I process a message with multi-role conversation_history in context (llm_gaps)
    Then the messages sent to chat model should include user and assistant messages in correct order (llm_gaps)

  # ---- cleanup() Method ----
  # cleanup() only releases the agent's own _chat_model reference; it does NOT
  # close any provider SDK clients (those are managed by shared lru_cache
  # instances — see issue #57 for details).

  Scenario: Cleanup sets _chat_model to None and does not close root_async_client
    Given I setup an LLM agent with a mock root_async_client (llm_gaps)
    When I await the cleanup method (llm_gaps)
    Then the mock root_async_client close should NOT have been called (llm_gaps)
    And the chat model should be None after cleanup (llm_gaps)

  Scenario: Cleanup sets _chat_model to None and does not close root_client
    Given I setup an LLM agent with both mock root_async_client and mock root_client (llm_gaps)
    When I await the cleanup method (llm_gaps)
    Then neither mock client close should have been called (llm_gaps)
    And the chat model should be None after cleanup (llm_gaps)

  Scenario: Cleanup is a no-op when chat_model has no http clients
    Given I setup an LLM agent with a chat model that lacks root_async_client and root_client (llm_gaps)
    When I await the cleanup method (llm_gaps)
    Then the cleanup should complete without raising errors (llm_gaps)

  Scenario: Non-numeric _temperature_override raises ConfigurationError in process_message
    Given I setup a test LLM agent for temperature override tests (llm_gaps)
    When I call process_message with _temperature_override "bad_string" (llm_gaps)
    Then a ConfigurationError mentioning must be a number is raised (llm_gaps)