CoreRasurae
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Add 13 BDD scenarios covering previously uncovered code paths: - SAFE_BUILTINS validation (sandbox.py: 0% → 100%) - ConfigurationError re-raise path (config.py: 99.3% → 100%) - CLI hello/main functions (cli.py: 72.7% → 90.9%) - GraphState message truncation (state.py: 98.7% → 100%) - ProgressBarManager update/context rendering (progress.py: 0% → 87.7%) - MessageRouter regex/exact/contains routing (message_router.py: 0% → 59.4%) - RoutingAdapter parse_routing_command (routing_adapter.py) - DynamicRouterNode pattern-based routing (dynamic_router.py) - EnhancedTemplateRegistry unknown template type (enhanced_registry.py: 99.2%) - CompositeAgent null-graph error path (composite.py: 97.8% → 98.6%) Cover routing_adapter.py (17% → 100%): all GOTO/ROUTE patterns, create_routing_node, create_conditional_router, dynamic config conversion. Cover dynamic_router.py (21% → 87%): execute with empty/dict/string messages, extract_message with colon parsing, config creation, graph extension with edge generation. Cover message_router.py (59% → 78%): regex, exact, contains, prefix, suffix match types, invalid regex handling, non-string message, set_state. Exercise Node._prepare_conversation_history with invalid configs, _runtime error paths, _execute_message_router with rules, _execute_agent with current_message and metadata propagation, _execute_function with dynamic_router, and _execute_conditional with content_contains/content_not_contains/content_starts_with and custom condition types. Improves nodes.py from 71.3% to 72.5%. Exercise PureGraphConfig, PureLangGraph init with dict/config, RxPyLangGraphBridge registration/connection/lookup, ReactiveStreamRouter operator creation and condition functions, ReactiveConfigParser config/route/graph parsing, ToolAgent tool execution with JSON, space-separated, single, file_read, progress_bar invocations, and ReactiveCleverAgentsApp init/dispose/visualization.
207 lines
8.4 KiB
Gherkin
207 lines
8.4 KiB
Gherkin
Feature: Agent Lifecycle and Message Processing
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As a developer
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I want agents to handle initialization, message processing, memory, streaming, and disposal correctly
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So that agent lifecycle and RxPy integration behave reliably in production
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Background:
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Given I have a clean test environment for agents base
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Scenario: Test Agent class initialization
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Given I have agent configuration with name and config
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And I have a template renderer
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When I create an Agent instance
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Then the agent should be initialized correctly
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And input and output streams should be created
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And the processing pipeline should be set up
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Scenario: Test Agent message processing with context
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Given I have an initialized Agent instance
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When I send a message with context to the agent
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Then the message should be processed with context
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And the output stream should emit the result
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Scenario: Test Agent message processing without context
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Given I have an initialized Agent instance
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When I send a message without context to the agent
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Then the message should be processed with empty context
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And the output stream should emit the result
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Scenario: Test Agent processing error handling
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Given I have an Agent instance that raises errors
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When I send a message that causes processing error
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Then an ExecutionError should be raised
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And the error message should contain agent name
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Scenario: Test Agent get_capabilities method
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Given I have an initialized Agent instance
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When I call get_capabilities
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Then a list of capabilities should be returned
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Scenario: Test Agent get_metadata method
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Given I have an initialized Agent instance with model and provider
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When I call get_metadata
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Then metadata should include name type and capabilities
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And metadata should include model and provider if configured
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Scenario: Test Agent legacy process method
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Given I have an initialized Agent instance
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When I call the legacy process method
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Then it should delegate to process_message
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Scenario: Test Agent subscribe_to_output method
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Given I have an initialized Agent instance
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And I have an observer
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When I subscribe the observer to output
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Then the observer should receive output messages
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Scenario: Test Agent create_observable method
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Given I have an initialized Agent instance
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When I create an observable from the agent
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Then an observable should be returned
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Scenario: Test Agent dispose method
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Given I have an initialized Agent instance
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When I dispose the agent
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Then streams should be disposed properly
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Scenario: Test AgentWithMemory initialization
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Given I have agent configuration for memory agent
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And I have a template renderer
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When I create an AgentWithMemory instance
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Then the agent should have empty memory
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And a memory lock should be created
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Scenario: Test AgentWithMemory save_memory
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Given I have an AgentWithMemory instance with data
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When I save the memory
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Then a deep copy of memory should be returned
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Scenario: Test AgentWithMemory load_memory success
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Given I have an AgentWithMemory instance
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When I load valid memory data
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Then the memory should be updated
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Scenario: Test AgentWithMemory load_memory with invalid data
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Given I have an AgentWithMemory instance
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When I load non-dict memory data
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Then an AgentCreationError should be raised
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Scenario: Test AgentWithMemory update_memory
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Given I have an AgentWithMemory instance
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When I update memory with key and value
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Then the memory should be updated asynchronously
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And memory lock should be used
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Scenario: Test AgentWithMemory get_memory with existing key
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Given I have an AgentWithMemory instance with data
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When I get memory for existing key
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Then the correct value should be returned
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Scenario: Test AgentWithMemory get_memory with missing key
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Given I have an AgentWithMemory instance
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When I get memory for missing key with default
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Then the default value should be returned
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Scenario: Test AgentWithMemory process wrapper with lock
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Given I have an AgentWithMemory instance
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When I process a message through the agent
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Then memory lock should be acquired during processing
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Scenario: Test StreamableAgent as_operator method
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Given I have a StreamableAgent instance
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When I create an operator from the agent
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Then an RxPy operator should be returned
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And the operator should process messages through agent
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Scenario: Test StreamableAgent map_operator method
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Given I have a StreamableAgent instance
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When I create a map operator from the agent
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Then a map operator should be returned
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And the operator should process values asynchronously
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Scenario: Test StreamableAgent filter_operator method
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Given I have a StreamableAgent instance
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And I have a filter condition function
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When I create a filter operator from the agent
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Then a filter operator should be returned
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And the operator should filter based on agent output
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Scenario: Test Agent tuple message data handling
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Given I have an initialized Agent instance
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When I send a tuple message data with context
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Then the message and context should be extracted correctly
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Scenario: Test Agent non-tuple message data handling
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Given I have an initialized Agent instance
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When I send non-tuple message data
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Then the message should be processed with empty context
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Scenario: Test StreamableAgent operator subscription flow
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Given I have a StreamableAgent instance
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And I have a source observable
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When I apply the agent as operator to source
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Then the agent should subscribe to source
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And output should be forwarded to observer
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Scenario: Test StreamableAgent operator error propagation
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Given I have a StreamableAgent instance
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And I have a source observable that errors
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When I apply the agent as operator to source
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Then errors should be propagated to observer
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Scenario: Test StreamableAgent operator completion
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Given I have a StreamableAgent instance
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And I have a source observable that completes
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When I apply the agent as operator to source
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Then completion should be propagated to observer
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Scenario: Test StreamableAgent map_operator result handling
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Given I have a StreamableAgent instance
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When I use map_operator with observable
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Then results should be awaited from future
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And only first result should be used
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Scenario: Test StreamableAgent filter_operator result handling
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Given I have a clean test environment for agents base
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Given I have a StreamableAgent instance
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And I have a condition that returns boolean
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When I use filter_operator with observable
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Then condition should be applied to agent output
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And boolean result should determine filtering
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Scenario: Test Agent send_message method directly with RxPy pipeline
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Given I have a clean test environment for agents base
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Given I have an initialized Agent instance
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When I use send_message method with the RxPy pipeline
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Then the message should be sent to input stream
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And RxPy pipeline should process the message
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Scenario: Test Agent actual RxPy pipeline integration
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Given I have a clean test environment for agents base
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Given I have an initialized Agent instance with working pipeline
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When I send a message using the actual RxPy pipeline
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Then the pipeline should process the message asynchronously
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And the result should be emitted to output stream
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Scenario: Test Agent dispose method with actual streams
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Given I have a clean test environment for agents base
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Given I have an initialized Agent instance
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When I dispose the agent with real streams
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Then stream dispose methods should be called properly
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And resources should be cleaned up
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Scenario: Test Agent process_wrapper exception handling
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Given I have a clean test environment for agents base
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Given I have an Agent instance that throws processing exceptions
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When I test the process_wrapper exception handling
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Then ExecutionError should be raised with agent name
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And original exception should be wrapped
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Scenario: Test StreamableAgent operator creation and usage
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Given I have a clean test environment for agents base
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Given I have a StreamableAgent instance
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When I create and test all operator methods
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Then as_operator should return functional operator
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And map_operator should return functional map operator
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And filter_operator should return functional filter operator
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