Feature: Actor-first port of v2 agent and LangGraph suites
  As a developer migrating v2 agent flows
  I want actor-only agent and LangGraph coverage
  So that v3 matches the v2 behaviors without provider/model flags

# TODO: Uncomment when step definitions are implemented
#   Background:
#     Given the agent system is initialized with actor-first configuration
#
#   # From langgraph_state_management.feature
#   Scenario: LangGraph with actor-based state management
#     Given I have a stateful LangGraph configuration:
#       """
#       {
#         "name": "stateful_graph",
#         "actor": "openai/gpt-4",
#         "checkpointing": true,
#         "enable_time_travel": true,
#         "nodes": {
#           "accumulate": {
#             "type": "function",
#             "function": "add_to_state"
#           }
#         },
#         "edges": [
#           {"source": "start", "target": "accumulate"},
#           {"source": "accumulate", "target": "end"}
#         ]
#       }
#       """
#     When I create the stateful graph
#     Then the graph should be created with actor "openai/gpt-4"
#     And the graph state should be persisted
#     And time travel should be enabled
#
#   # From agent_modules_coverage.feature
#   Scenario: Create agent with actor configuration
#     Given I have an agent configuration:
#       """
#       {
#         "name": "test_agent",
#         "type": "base",
#         "actor": "anthropic/claude-3"
#       }
#       """
#     When I create an agent from the configuration
#     Then the agent should be created successfully
#     And the agent should use actor "anthropic/claude-3"
#     And the agent module should be accessible
#
#   # From chain_agent_comprehensive.feature
#   Scenario: Chain agent with actor and steps
#     Given I have a chain agent configuration:
#       """
#       {
#         "name": "test_chain",
#         "type": "chain",
#         "actor": "openai/gpt-3.5-turbo",
#         "steps": ["validate", "transform", "output"],
#         "prompt": "Process this: {message}"
#       }
#       """
#     When I create a chain agent from the configuration
#     Then the chain agent should be created successfully
#     And the chain agent should have 3 steps
#     And the chain agent should use actor "openai/gpt-3.5-turbo"
#
#   Scenario: Process message with actor-based chain agent
#     Given I have a chain agent with actor "local/custom-processor"
#     And the chain has steps ["parse", "analyze", "format"]
#     When I process the message "Test input"
#     Then the chain should use the specified actor
#     And the result should show processing through all steps
#
#   # From composite_agent_coverage.feature pattern
#   Scenario: Composite agent with multiple actors
#     Given I have a composite agent configuration:
#       """
#       {
#         "name": "multi_actor_composite",
#         "type": "composite",
#         "agents": [
#           {
#             "name": "analyzer",
#             "actor": "openai/gpt-4",
#             "role": "analyze"
#           },
#           {
#             "name": "generator",
#             "actor": "anthropic/claude-3",
#             "role": "generate"
#           },
#           {
#             "name": "validator",
#             "actor": "local/validator",
#             "role": "validate"
#           }
#         ]
#       }
#       """
#     When I create a composite agent from the configuration
#     Then the composite agent should have 3 sub-agents
#     And each sub-agent should use its configured actor
#     And parallel processing should work with all actors
#
#   # From reactive_agents.feature pattern
#   Scenario: Reactive agent with actor-based streaming
#     Given I have a reactive agent configuration:
#       """
#       {
#         "name": "stream_agent",
#         "type": "reactive",
#         "actor": "openai/gpt-4",
#         "stream_config": {
#           "stream_type": "transform",
#           "operators": ["map", "filter", "reduce"]
#         }
#       }
#       """
#     When I create a reactive agent from the configuration
#     Then the agent should support reactive streaming
#     And the stream should use actor "openai/gpt-4"
#     And operators should transform data through the actor
#
#   # From tool_agent.feature pattern
#   Scenario: Tool agent with actor and tool configuration
#     Given I have a tool agent configuration:
#       """
#       {
#         "name": "tool_agent",
#         "type": "tool",
#         "actor": "openai/gpt-4",
#         "tools": [
#           {
#             "name": "calculator",
#             "type": "function",
#             "function": "calculate"
#           },
#           {
#             "name": "search",
#             "type": "api",
#             "endpoint": "search_api"
#           }
#         ]
#       }
#       """
#     When I create a tool agent from the configuration
#     Then the tool agent should have 2 tools available
#     And the tool agent should use actor "openai/gpt-4"
#     And tool calls should be routed through the actor
#
#   # LangGraph visualization with actors
#   Scenario: Visualize actor-based LangGraph
#     Given I have a complex LangGraph with multiple actors:
#       """
#       {
#         "name": "visualization_graph",
#         "nodes": {
#           "input": {
#             "type": "input",
#             "actor": "openai/gpt-3.5-turbo"
#           },
#           "process": {
#             "type": "llm",
#             "actor": "anthropic/claude-3"
#           },
#           "validate": {
#             "type": "tool",
#             "actor": "local/validator"
#           },
#           "output": {
#             "type": "output",
#             "actor": "openai/gpt-4"
#           }
#         },
#         "edges": [
#           {"source": "input", "target": "process"},
#           {"source": "process", "target": "validate"},
#           {"source": "validate", "target": "output"}
#         ]
#       }
#       """
#     When I visualize the graph
#     Then the visualization should show all nodes with their actors
#     And the edge flow should be clearly represented
#     And actor transitions should be highlighted
#
#   # Agent factory with default actor
#   Scenario: Agent factory creates agents with default actor
#     Given I have an agent factory with default actor "openai/gpt-4"
#     When I create multiple agents using the factory:
#       | agent_name | agent_type |
#       | parser     | chain      |
#       | analyzer   | tool       |
#       | generator  | composite  |
#     Then all agents should use the default actor "openai/gpt-4"
#     And each agent should have the correct type
#
#   # LangGraph with conditional routing based on actor capabilities
#   Scenario: Conditional routing based on actor capabilities
#     Given I have a LangGraph with conditional routing:
#       """
#       {
#         "name": "capability_router",
#         "nodes": {
#           "router": {
#             "type": "conditional",
#             "conditions": [
#               {
#                 "if": "needs_vision",
#                 "then": "vision_processor",
#                 "actor": "openai/gpt-4-vision"
#               },
#               {
#                 "if": "needs_code",
#                 "then": "code_generator",
#                 "actor": "anthropic/claude-3-opus"
#               },
#               {
#                 "else": "general_processor",
#                 "actor": "openai/gpt-3.5-turbo"
#               }
#             ]
#           }
#         }
#       }
#       """
#     When I route a request that needs vision
#     Then the request should be routed to "openai/gpt-4-vision"
#     And the routing decision should be based on actor capabilities