# Example configuration demonstrating user-defined message routing
# This shows how to use the generic message_router node type
# where users define their own routing patterns and targets

name: message_router_example

agents:
  # Define your agents here
  task_handler:
    type: llm
    config:
      provider: openai
      model: gpt-4
      system_prompt: "You handle various tasks based on user requests"

  code_assistant:
    type: llm
    config:
      provider: openai
      model: gpt-4
      system_prompt: "You help with coding tasks"

  research_assistant:
    type: llm
    config:
      provider: openai
      model: gpt-4
      system_prompt: "You help with research and information gathering"

  creative_writer:
    type: llm
    config:
      provider: openai
      model: gpt-4
      system_prompt: "You help with creative writing tasks"

routes:
  main:
    type: graph

    nodes:
      # Entry point
      start:
        type: START

      # Message router node with user-defined routing patterns
      message_router:
        type: message_router
        rules:
          # Route based on prefixes
          - match_type: prefix
            pattern: "CODE:"
            target: code_assistant
            extract_message: true
            separator: ":"

          - match_type: prefix
            pattern: "RESEARCH:"
            target: research_assistant
            extract_message: true
            separator: ":"

          - match_type: prefix
            pattern: "WRITE:"
            target: creative_writer
            extract_message: true
            separator: ":"

          # Route based on content keywords
          - match_type: contains
            pattern: "debug"
            target: code_assistant
            extract_message: false

          - match_type: contains
            pattern: "investigate"
            target: research_assistant
            extract_message: false

          # Route based on regex patterns
          - match_type: regex
            pattern: "^\\[URGENT\\](.*)$"
            target: priority_handler
            extract_message: true

          # Route based on exact match
          - match_type: exact
            pattern: "!help"
            target: help_handler
            extract_message: false

          # Default fallback (using suffix match as a trick)
          - match_type: suffix
            pattern: ""  # Matches everything
            target: task_handler
            extract_message: false

      # Agent nodes
      code_assistant:
        type: AGENT
        agent: code_assistant

      research_assistant:
        type: AGENT
        agent: research_assistant

      creative_writer:
        type: AGENT
        agent: creative_writer

      task_handler:
        type: AGENT
        agent: task_handler

      priority_handler:
        type: AGENT
        agent: task_handler  # Using same agent but could be different

      help_handler:
        type: FUNCTION
        function: |
          async def help_handler(state):
              state["messages"].append({
                  "role": "assistant",
                  "content": """Available commands:
                  CODE: <request> - Route to code assistant
                  RESEARCH: <request> - Route to research assistant
                  WRITE: <request> - Route to creative writer
                  [URGENT] <request> - Mark as priority
                  !help - Show this help message

                  Keywords 'debug' or 'investigate' will also trigger routing."""
              })
              return state

      # End point
      end:
        type: END

    edges:
      # Initial routing
      - source: start
        target: message_router

      # From router to each possible target
      # These edges use the next_node state value set by the router
      - source: message_router
        target: code_assistant
        condition:
          type: context_value
          key: next_node
          value: code_assistant

      - source: message_router
        target: research_assistant
        condition:
          type: context_value
          key: next_node
          value: research_assistant

      - source: message_router
        target: creative_writer
        condition:
          type: context_value
          key: next_node
          value: creative_writer

      - source: message_router
        target: task_handler
        condition:
          type: context_value
          key: next_node
          value: task_handler

      - source: message_router
        target: priority_handler
        condition:
          type: context_value
          key: next_node
          value: priority_handler

      - source: message_router
        target: help_handler
        condition:
          type: context_value
          key: next_node
          value: help_handler

      # All agents route back to end
      - source: code_assistant
        target: end

      - source: research_assistant
        target: end

      - source: creative_writer
        target: end

      - source: task_handler
        target: end

      - source: priority_handler
        target: end

      - source: help_handler
        target: end

    # Entry point for the graph
    entry_point: start

# Output configuration
publications:
  - __output__

merges:
  - sources: [__input__]
    target: main