class MyAgent(Agent):

    @chat_model_connection
    @staticmethod
    def ollama_connection() -> ResourceDescriptor:
        return ResourceDescriptor(
            clazz=ResourceName.ChatModel.OLLAMA_CONNECTION,
            base_url="http://localhost:11434",
            request_timeout=30.0
        )

    @chat_model_setup
    @staticmethod
    def ollama_chat_model() -> ResourceDescriptor:
        return ResourceDescriptor(
            clazz=ResourceName.ChatModel.OLLAMA_SETUP,
            connection="ollama_connection",
            model="qwen3:8b",
            temperature=0.7
        )

    @action(InputEvent)
    @staticmethod
    def process_input(event: InputEvent, ctx: RunnerContext) -> None:
        # Create a chat request with user message
        user_message = ChatMessage(
            role=MessageRole.USER,
            content=f"input: {event.input}"
        )
        ctx.send_event(
            ChatRequestEvent(model="ollama_chat_model", messages=[user_message])
        )

    @action(ChatResponseEvent)
    @staticmethod
    def process_response(event: ChatResponseEvent, ctx: RunnerContext) -> None:
        response_content = event.response.content
        # Handle the LLM's response
        # Process the response as needed for your use case

public class MyAgent extends Agent {
    @ChatModelConnection
    public static ResourceDescriptor ollamaConnection() {
        return ResourceDescriptor.Builder.newBuilder(ResourceName.ChatModel.OLLAMA_CONNECTION)
                .addInitialArgument("endpoint", "http://localhost:11434")
                .build();
    }

    @ChatModelSetup
    public static ResourceDescriptor ollamaChatModel() {
        return ResourceDescriptor.Builder.newBuilder(ResourceName.ChatModel.OLLAMA_SETUP)
                .addInitialArgument("connection", "ollamaConnection")
                .addInitialArgument("model", "qwen3:8b")
                .build();
    }

    @Action(listenEvents = {InputEvent.class})
    public static void processInput(InputEvent event, RunnerContext ctx) throws Exception {
        ChatMessage userMessage =
                new ChatMessage(MessageRole.USER, String.format("input: {%s}", event.getInput()));
        ctx.sendEvent(new ChatRequestEvent("ollamaChatModel", List.of(userMessage)));
    }

    @Action(listenEvents = {ChatResponseEvent.class})
    public static void processResponse(ChatResponseEvent event, RunnerContext ctx)
            throws Exception {
        String response = event.getResponse().getContent();
        // Handle the LLM's response
        // Process the response as needed for your use case
    }
}

class MyAgent(Agent):

    @chat_model_connection
    @staticmethod
    def anthropic_connection() -> ResourceDescriptor:
        return ResourceDescriptor(
            clazz=ResourceName.ChatModel.ANTHROPIC_CONNECTION,
            api_key="<your-api-key>",
            max_retries=3,
            timeout=60.0
        )

    @chat_model_setup
    @staticmethod
    def anthropic_chat_model() -> ResourceDescriptor:
        return ResourceDescriptor(
            clazz=ResourceName.ChatModel.ANTHROPIC_SETUP,
            connection="anthropic_connection",
            model="claude-sonnet-4-20250514",
            max_tokens=2048,
            temperature=0.7
        )

    ...

public class MyAgent extends Agent {
    @ChatModelConnection
    public static ResourceDescriptor anthropicConnection() {
        return ResourceDescriptor.Builder.newBuilder(ResourceName.ChatModel.ANTHROPIC_CONNECTION)
                .addInitialArgument("api_key", "<your-api-key>")
                .addInitialArgument("timeout", 120)
                .addInitialArgument("max_retries", 3)
                .build();
    }

    @ChatModelSetup
    public static ResourceDescriptor anthropicChatModel() {
        return ResourceDescriptor.Builder.newBuilder(ResourceName.ChatModel.ANTHROPIC_SETUP)
                .addInitialArgument("connection", "anthropicConnection")
                .addInitialArgument("model", "claude-sonnet-4-20250514")
                .addInitialArgument("temperature", 0.7d)
                .addInitialArgument("max_tokens", 2048)
                .build();
    }

    ...
}

public class MyAgent extends Agent {
    @ChatModelConnection
    public static ResourceDescriptor azureAIConnection() {
        return ResourceDescriptor.Builder.newBuilder(ResourceName.ChatModel.AZURE_CONNECTION)
                .addInitialArgument("endpoint", "https://your-resource.inference.ai.azure.com")
                .addInitialArgument("apiKey", "your-api-key-here")
                .build();
    }

    @ChatModelSetup
    public static ResourceDescriptor azureAIChatModel() {
        return ResourceDescriptor.Builder.newBuilder(ResourceName.ChatModel.AZURE_SETUP)
                .addInitialArgument("connection", "azureAIConnection")
                .addInitialArgument("model", "gpt-4o")
                .build();
    }

    ...
}

class MyAgent(Agent):

    @chat_model_connection
    @staticmethod
    def azure_openai_connection() -> ResourceDescriptor:
        return ResourceDescriptor(
            clazz=ResourceName.ChatModel.AZURE_OPENAI_CONNECTION,
            api_key="<your-api-key>",
            api_version="2024-02-15-preview",
            azure_endpoint="https://your-resource.openai.azure.com"
        )

    @chat_model_setup
    @staticmethod
    def azure_openai_chat_model() -> ResourceDescriptor:
        return ResourceDescriptor(
            clazz=ResourceName.ChatModel.AZURE_OPENAI_SETUP,
            connection="azure_openai_connection",
            model="my-gpt4-deployment",  # Your Azure deployment name
            model_of_azure_deployment="gpt-4",  # Underlying model for metrics
            max_tokens=1000
        )

    ...

class MyAgent(Agent):

    @chat_model_connection
    @staticmethod
    def ollama_connection() -> ResourceDescriptor:
        return ResourceDescriptor(
            clazz=ResourceName.ChatModel.OLLAMA_CONNECTION,
            base_url="http://localhost:11434",
            request_timeout=120.0
        )

    @chat_model_setup
    @staticmethod
    def my_chat_model() -> ResourceDescriptor:
        return ResourceDescriptor(
            clazz=ResourceName.ChatModel.OLLAMA_SETUP,
            connection="ollama_connection",
            model="qwen3:8b",
            temperature=0.7,
            num_ctx=4096,
            keep_alive="10m",
            think=True,
            extract_reasoning=True
        )

    ...

public class MyAgent extends Agent {
    @ChatModelConnection
    public static ResourceDescriptor ollamaConnection() {
        return ResourceDescriptor.Builder.newBuilder(ResourceName.ChatModel.OLLAMA_CONNECTION)
                .addInitialArgument("endpoint", "http://localhost:11434")
                .addInitialArgument("requestTimeout", 120)
                .build();
    }

    @ChatModelSetup
    public static ResourceDescriptor ollamaChatModel() {
        return ResourceDescriptor.Builder.newBuilder(ResourceName.ChatModel.OLLAMA_SETUP)
                .addInitialArgument("connection", "ollamaConnection")
                .addInitialArgument("model", "qwen3:8b")
                .addInitialArgument("extract_reasoning", true)
                .build();
    }

    ...
}

class MyAgent(Agent):

    @chat_model_connection
    @staticmethod
    def openai_connection() -> ResourceDescriptor:
        return ResourceDescriptor(
            clazz=ResourceName.ChatModel.OPENAI_CONNECTION,
            api_key="<your-api-key>",
            api_base_url="https://api.openai.com/v1",
            max_retries=3,
            timeout=60.0
        )

    @chat_model_setup
    @staticmethod
    def openai_chat_model() -> ResourceDescriptor:
        return ResourceDescriptor(
            clazz=ResourceName.ChatModel.OPENAI_SETUP,
            connection="openai_connection",
            model="gpt-4",
            temperature=0.7,
            max_tokens=1000
        )

    ...

public class MyAgent extends Agent {
    @ChatModelConnection
    public static ResourceDescriptor openaiConnection() {
        return ResourceDescriptor.Builder.newBuilder(ResourceName.ChatModel.OPENAI_CONNECTION)
                .addInitialArgument("api_key", "<your-api-key>")
                .addInitialArgument("api_base_url", "https://api.openai.com/v1")
                .addInitialArgument("timeout", 60)
                .addInitialArgument("max_retries", 3)
                .build();
    }

    @ChatModelSetup
    public static ResourceDescriptor openaiChatModel() {
        return ResourceDescriptor.Builder.newBuilder(ResourceName.ChatModel.OPENAI_SETUP)
                .addInitialArgument("connection", "openaiConnection")
                .addInitialArgument("model", "gpt-4")
                .addInitialArgument("temperature", 0.7d)
                .addInitialArgument("max_tokens", 1000)
                .build();
    }

    ...
}

class MyAgent(Agent):

    @chat_model_connection
    @staticmethod
    def tongyi_connection() -> ResourceDescriptor:
        return ResourceDescriptor(
            clazz=ResourceName.ChatModel.TONGYI_CONNECTION,
            api_key="your-api-key-here",  # Or set DASHSCOPE_API_KEY env var
            request_timeout=60.0
        )

    @chat_model_setup
    @staticmethod
    def tongyi_chat_model() -> ResourceDescriptor:
        return ResourceDescriptor(
            clazz=ResourceName.ChatModel.TONGYI_SETUP,
            connection="tongyi_connection",
            model="qwen-plus",
            temperature=0.7,
            extract_reasoning=True
        )

    ...

class MyAgent(Agent):

    @chat_model_connection
    @staticmethod
    def java_chat_model_connection() -> ResourceDescriptor:
        # In pure Java, the equivalent ResourceDescriptor would be:
        # ResourceDescriptor.Builder
        #     .newBuilder(ResourceName.ChatModel.OLLAMA_CONNECTION)
        #     .addInitialArgument("endpoint", "http://localhost:11434")
        #     .addInitialArgument("requestTimeout", 120)
        #     .build();
        return ResourceDescriptor(
            clazz=ResourceName.ChatModel.JAVA_WRAPPER_CONNECTION,
            java_clazz=ResourceName.ChatModel.Java.OLLAMA_CONNECTION,
            endpoint="http://localhost:11434",
            requestTimeout=120,
        )

    @chat_model_setup
    @staticmethod
    def java_chat_model() -> ResourceDescriptor:
        # In pure Java, the equivalent ResourceDescriptor would be:
        # ResourceDescriptor.Builder
        #     .newBuilder(ResourceName.ChatModel.OLLAMA_SETUP)
        #     .addInitialArgument("connection", "java_chat_model_connection")
        #     .addInitialArgument("model", "qwen3:8b")
        #     .addInitialArgument("prompt", "my_prompt")
        #     .addInitialArgument("tools", List.of("my_tool1", "my_tool2"))
        #     .addInitialArgument("extractReasoning", true)
        #     .build();
        return ResourceDescriptor(
            clazz=ResourceName.ChatModel.JAVA_WRAPPER_SETUP,
            java_clazz=ResourceName.ChatModel.Java.OLLAMA_SETUP,
            connection="java_chat_model_connection",
            model="qwen3:8b",
            prompt="my_prompt",
            tools=["my_tool1", "my_tool2"],
            extract_reasoning=True,
        )

    @action(InputEvent)
    @staticmethod
    def process_input(event: InputEvent, ctx: RunnerContext) -> None:
        # Create a chat request with user message
        user_message = ChatMessage(
            role=MessageRole.USER,
            content=f"input: {event.input}"
        )
        ctx.send_event(
            ChatRequestEvent(model="java_chat_model", messages=[user_message])
        )

    @action(ChatResponseEvent)
    @staticmethod
    def process_response(event: ChatResponseEvent, ctx: RunnerContext) -> None:
        response_content = event.response.content
        # Handle the LLM's response
        # Process the response as needed for your use case

public class MyAgent extends Agent {

    @ChatModelConnection
    public static ResourceDescriptor pythonChatModelConnection() {
        // In pure Python, the equivalent ResourceDescriptor would be:
        // ResourceDescriptor(
        //     clazz=ResourceName.ChatModel.OLLAMA_CONNECTION,
        //     request_timeout=120.0
        // )
        return ResourceDescriptor.Builder.newBuilder(ResourceName.ChatModel.PYTHON_WRAPPER_CONNECTION)
                .addInitialArgument("pythonClazz", ResourceName.ChatModel.Python.OLLAMA_CONNECTION)
                .addInitialArgument("request_timeout", 120.0)
                .build();
    }

      @ChatModelSetup
    public static ResourceDescriptor pythonChatModel() {
        // In pure Python, the equivalent ResourceDescriptor would be:
        // ResourceDescriptor(
        //     clazz=ResourceName.ChatModel.OLLAMA_SETUP,
        //     connection="pythonChatModelConnection",
        //     model="qwen3:8b",
        //     tools=["tool1", "tool2"],
        //     extract_reasoning=True
        // )
        return ResourceDescriptor.Builder.newBuilder(ResourceName.ChatModel.PYTHON_WRAPPER_SETUP)
                .addInitialArgument("pythonClazz", ResourceName.ChatModel.Python.OLLAMA_SETUP)
                .addInitialArgument("connection", "pythonChatModelConnection")
                .addInitialArgument("model", "qwen3:8b")
                .addInitialArgument("tools", List.of("tool1", "tool2"))
                .addInitialArgument("extract_reasoning", true)
                .build();
    }

    @Action(listenEvents = {InputEvent.class})
    public static void processInput(InputEvent event, RunnerContext ctx) throws Exception {
        ChatMessage userMessage =
                new ChatMessage(MessageRole.USER, String.format("input: {%s}", event.getInput()));
        ctx.sendEvent(new ChatRequestEvent("pythonChatModel", List.of(userMessage)));
    }

    @Action(listenEvents = {ChatResponseEvent.class})
    public static void processResponse(ChatResponseEvent event, RunnerContext ctx)
            throws Exception {
        String response = event.getResponse().getContent();
        // Handle the LLM's response
        // Process the response as needed for your use case
    }
}

class MyChatModelConnection(BaseChatModelConnection):

    def chat(
        self,
        messages: Sequence[ChatMessage],
        tools: List[Tool] | None = None,
        **kwargs: Any,
    ) -> ChatMessage:
        # Core method: send messages to LLM and return response
        # - messages: Input message sequence
        # - tools: Optional list of tools available to the model
        # - kwargs: Additional parameters from model_kwargs
        # - Returns: ChatMessage with the model's response
        pass

public class MyChatModelConnection extends BaseChatModelConnection {

    /**
     * Creates a new chat model connection.
     *
     * @param descriptor a resource descriptor contains the initial parameters
     * @param getResource a function to resolve resources (e.g., tools) by name and type
     */
    public MyChatModelConnection(
            ResourceDescriptor descriptor, BiFunction<String, ResourceType, Resource> getResource) {
        super(descriptor, getResource);
        // get custom arguments from descriptor
        String endpoint = descriptor.getArgument("endpoint");
        ...
    }

    @Override
    public ChatMessage chat(
            List<ChatMessage> messages, List<Tool> tools, Map<String, Object> arguments) {
        // Core method: send messages to LLM and return response
        // - messages: Input message sequence
        // - tools: Optional list of tools available to the model
        // - arguments: Additional parameters from ChatModelSetup
        // - Returns: ChatMessage with the model's response
    }
}

class MyChatModelSetup(BaseChatModelSetup):
    # Add your custom configuration fields here

    @property
    def model_kwargs(self) -> Dict[str, Any]:
        # Return model-specific configuration passed to chat()
        # This dictionary is passed as **kwargs to the chat() method
        return {"model": self.model, "temperature": 0.7, ...}

public class MyChatModelSetup extends BaseChatModelSetup {
    // Add your custom configuration fields here

    @Override
    public Map<String, Object> getParameters() {
        Map<String, Object> params = new HashMap<>();
        params.put("model", model);
        ...
        // Return model-specific configuration passed to chat()
        // This dictionary is passed as arguments to the chat() method
        return params;
    }
}