An Agent strategy is an extensible template that defines standard input content and output formats. By developing the functional code for specific Agent strategy interfaces, you can implement various Agent strategies such as CoT (Chain of Thought) / ToT (Tree of Thoughts) / GoT (Graph of Thoughts) / BoT (Skeleton of Thought), enabling complex strategies like Semantic Kernel.

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Add Fields in Manifest

To add an Agent strategy in a plugin, you need to add the plugins.agent_strategies field in the manifest.yaml file and also define the Agent provider. Here is an example: 
version: 0.0.2
type: plugin
author: "langgenius"
name: "agent"
plugins:
  agent_strategies:
    - "provider/agent.yaml"
Some irrelevant fields in the manifest file have been omitted here. For the detailed format of the Manifest, please refer to the Define Plugin Information via Manifest File document.

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Define Agent Provider

Next, you need to create a new agent.yaml file and fill in the basic Agent provider information. 
identity:
  author: langgenius
  name: agent
  label:
    en_US: Agent
    zh_Hans: Agent
    pt_BR: Agent
  description:
    en_US: Agent
    zh_Hans: Agent
    pt_BR: Agent
  icon: icon.svg
strategies:
  - strategies/function_calling.yaml
It mainly contains basic descriptive content and specifies which strategies the current provider includes. In the example code above, only the most basic function_calling.yaml strategy file is specified.

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Define and Implement Agent Strategy

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Definition

Next, you need to define the code that implements the Agent strategy. Create a new function_calling.yaml file: 
identity:
  name: function_calling
  author: Dify
  label:
    en_US: FunctionCalling
    zh_Hans: FunctionCalling
    pt_BR: FunctionCalling
description:
  en_US: Function Calling is a basic strategy for agent, model will use the tools provided to perform the task.
  zh_Hans: Function Calling 是一个基本的 Agent 策略，模型将使用提供的工具来执行任务。
  pt_BR: Function Calling is a basic strategy for agent, model will use the tools provided to perform the task.
parameters:
  - name: model
    type: model-selector
    scope: tool-call&llm
    required: true
    label:
      en_US: Model
      zh_Hans: 模型
      pt_BR: Model
  - name: tools
    type: array[tools]
    required: true
    label:
      en_US: Tools list
      zh_Hans: 工具列表
      pt_BR: Tools list
  - name: query
    type: string
    required: true
    label:
      en_US: Query
      zh_Hans: 用户提问
      pt_BR: Query
  - name: max_iterations
    type: number
    required: false
    default: 5
    label:
      en_US: Max Iterations
      zh_Hans: 最大迭代次数
      pt_BR: Max Iterations
    max: 50
    min: 1
extra:
  python:
    source: strategies/function_calling.py
The code format is similar to the Tool standard format, defining four parameters: model, tools, query, and max_iterations, to implement the most basic Agent strategy. This code allows users to select a model and the tools to use, configure the maximum number of iterations, and finally input a query to start executing the Agent.

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Write Functional Implementation Code

Get Parameters Based on the four parameters defined above, the model type parameter is model-selector, and the tool type parameter is a special array[tools]. The forms obtained in the parameters can be converted using the built-in AgentModelConfig and list[ToolEntity] in the SDK. 
from dify_plugin.interfaces.agent import AgentModelConfig, AgentStrategy, ToolEntity

class FunctionCallingParams(BaseModel):
    query: str
    model: AgentModelConfig
    tools: list[ToolEntity] | None
    maximum_iterations: int = 3
    
 class FunctionCallingAgentStrategy(AgentStrategy):
    def _invoke(self, parameters: dict[str, Any]) -> Generator[AgentInvokeMessage]:
        """
        Run FunctionCall agent application
        """
        fc_params = FunctionCallingParams(**parameters)
Invoke Model Invoking the specified model is an essential capability in Agent plugins. Use the session.model.invoke() function in the SDK to invoke the model. You can get the required input parameters from the model. Example method signature for invoking the model: 
def invoke(
        self,
        model_config: LLMModelConfig,
        prompt_messages: list[PromptMessage],
        tools: list[PromptMessageTool] | None = None,
        stop: list[str] | None = None,
        stream: bool = True,
    ) -> Generator[LLMResultChunk, None, None] | LLMResult:
You need to pass the model information model_config, prompt information prompt_messages, and tool information tools. The prompt_messages parameter can be invoked using the example code below; the tool_messages require some conversion. Please refer to the example code for using invoke model: 
from collections.abc import Generator
from typing import Any

from pydantic import BaseModel

from dify_plugin.entities.agent import AgentInvokeMessage
from dify_plugin.entities.model.llm import LLMModelConfig
from dify_plugin.entities.model.message import (
    PromptMessageTool,
    SystemPromptMessage,
    UserPromptMessage,
)
from dify_plugin.entities.tool import ToolParameter
from dify_plugin.interfaces.agent import AgentModelConfig, AgentStrategy, ToolEntity

class FunctionCallingParams(BaseModel):
    query: str
    instruction: str | None
    model: AgentModelConfig
    tools: list[ToolEntity] | None
    maximum_iterations: int = 3

class FunctionCallingAgentStrategy(AgentStrategy):
    def _invoke(self, parameters: dict[str, Any]) -> Generator[AgentInvokeMessage]:
        """
        Run FunctionCall agent application
        """
        # init params
        fc_params = FunctionCallingParams(**parameters)
        query = fc_params.query
        model = fc_params.model
        stop = fc_params.model.completion_params.get("stop", []) if fc_params.model.completion_params else []
        prompt_messages = [
            SystemPromptMessage(content="your system prompt message"),
            UserPromptMessage(content=query),
        ]
        tools = fc_params.tools
        prompt_messages_tools = self._init_prompt_tools(tools)

        # invoke llm
        chunks = self.session.model.llm.invoke(
            model_config=LLMModelConfig(**model.model_dump(mode="json")),
            prompt_messages=prompt_messages,
            stream=True,
            stop=stop,
            tools=prompt_messages_tools,
        )

    def _init_prompt_tools(self, tools: list[ToolEntity] | None) -> list[PromptMessageTool]:
        """
        Init tools
        """

        prompt_messages_tools = []
        for tool in tools or []:
            try:
                prompt_tool = self._convert_tool_to_prompt_message_tool(tool)
            except Exception:
                # api tool may be deleted
                continue

            # save prompt tool
            prompt_messages_tools.append(prompt_tool)

        return prompt_messages_tools

    def _convert_tool_to_prompt_message_tool(self, tool: ToolEntity) -> PromptMessageTool:
        """
        convert tool to prompt message tool
        """
        message_tool = PromptMessageTool(
            name=tool.identity.name,
            description=tool.description.llm if tool.description else "",
            parameters={
                "type": "object",
                "properties": {},
                "required": [],
            },
        )

        parameters = tool.parameters
        for parameter in parameters:
            if parameter.form != ToolParameter.ToolParameterForm.LLM:
                continue

            parameter_type = parameter.type
            if parameter.type in {
                ToolParameter.ToolParameterType.FILE,
                ToolParameter.ToolParameterType.FILES,
            }:
                continue
            enum = []
            if parameter.type == ToolParameter.ToolParameterType.SELECT:
                enum = [option.value for option in parameter.options] if parameter.options else []

            message_tool.parameters["properties"][parameter.name] = {
                "type": parameter_type,
                "description": parameter.llm_description or "",
            }

            if len(enum) > 0:
                message_tool.parameters["properties"][parameter.name]["enum"] = enum

            if parameter.required:
                message_tool.parameters["required"].append(parameter.name)

        return message_tool
Invoke Tool Invoking tools is also an essential capability in Agent plugins. You can use self.session.tool.invoke() to call them. Example method signature for invoking a tool: 
def invoke(
        self,
        provider_type: ToolProviderType,
        provider: str,
        tool_name: str,
        parameters: dict[str, Any],
    ) -> Generator[ToolInvokeMessage, None, None]
The required parameters are provider_type, provider, tool_name, and parameters. In Function Calling, tool_name and parameters are often generated by the LLM. Example code for using invoke tool: 
from dify_plugin.entities.tool import ToolProviderType

class FunctionCallingAgentStrategy(AgentStrategy):
    def _invoke(self, parameters: dict[str, Any]) -> Generator[AgentInvokeMessage]:
        """
        Run FunctionCall agent application
        """
        fc_params = FunctionCallingParams(**parameters)
        
        # tool_call_name and tool_call_args parameter is obtained from the output of LLM
        tool_instances = {tool.identity.name: tool for tool in fc_params.tools} if fc_params.tools else {}
        tool_instance = tool_instances[tool_call_name]
        tool_invoke_responses = self.session.tool.invoke(
            provider_type=ToolProviderType.BUILT_IN,
            provider=tool_instance.identity.provider,
            tool_name=tool_instance.identity.name,
            # add the default value
            parameters={**tool_instance.runtime_parameters, **tool_call_args},
        )
The output of the self.session.tool.invoke() function is a Generator, which means it also needs to be parsed streamingly. Please refer to the following function for the parsing method: 
import json
from collections.abc import Generator
from typing import cast

from dify_plugin.entities.agent import AgentInvokeMessage
from dify_plugin.entities.tool import ToolInvokeMessage

def parse_invoke_response(tool_invoke_responses: Generator[AgentInvokeMessage]) -> str:
    result = ""
    for response in tool_invoke_responses:
        if response.type == ToolInvokeMessage.MessageType.TEXT:
            result += cast(ToolInvokeMessage.TextMessage, response.message).text
        elif response.type == ToolInvokeMessage.MessageType.LINK:
            result += (
                f"result link: {cast(ToolInvokeMessage.TextMessage, response.message).text}."
                + " please tell user to check it."
            )
        elif response.type in {
            ToolInvokeMessage.MessageType.IMAGE_LINK,
            ToolInvokeMessage.MessageType.IMAGE,
        }:
            result += (
                "image has been created and sent to user already, "
                + "you do not need to create it, just tell the user to check it now."
            )
        elif response.type == ToolInvokeMessage.MessageType.JSON:
            text = json.dumps(cast(ToolInvokeMessage.JsonMessage, response.message).json_object, ensure_ascii=False)
            result += f"tool response: {text}."
        else:
            result += f"tool response: {response.message!r}."
    return result

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Log

If you want to see the Agent’s thinking process, besides viewing the normally returned messages, you can use a dedicated interface to display the entire Agent’s thinking process in a tree structure. Create Log
  • This interface creates and returns an AgentLogMessage, which represents a node in the log tree.
  • If parent is passed, it indicates that the node has a parent node.
  • The status defaults to “Success”. However, if you want to better display the task execution process, you can first set the status to “start” to show a “running” log, and then update the log’s status to “Success” after the task is completed. This allows users to clearly see the entire process from start to finish.
  • label will be used to display the log title to the user.
    def create_log_message(
        self,
        label: str,
        data: Mapping[str, Any],
        status: AgentInvokeMessage.LogMessage.LogStatus = AgentInvokeMessage.LogMessage.LogStatus.SUCCESS,
        parent: AgentInvokeMessage | None = None,
    ) -> AgentInvokeMessage
Finish Log If you chose the start status as the initial state in the previous step, you can use the finish log interface to change the status. 
    def finish_log_message(
        self,
        log: AgentInvokeMessage,
        status: AgentInvokeMessage.LogMessage.LogStatus = AgentInvokeMessage.LogMessage.LogStatus.SUCCESS,
        error: Optional[str] = None,
    ) -> AgentInvokeMessage
Example This example shows a simple two-step execution process: first, output a log with the status “Thinking”, then complete the actual task processing. 
class FunctionCallingAgentStrategy(AgentStrategy):
    def _invoke(self, parameters: dict[str, Any]) -> Generator[AgentInvokeMessage]:
        thinking_log = self.create_log_message(
            data={
                "Query": parameters.get("query"),
            },
            label="Thinking",
            status=AgentInvokeMessage.LogMessage.LogStatus.START,
        )

        yield thinking_log

        llm_response = self.session.model.llm.invoke(
            model_config=LLMModelConfig(
                provider="openai",
                model="gpt-4o-mini",
                mode="chat",
                completion_params={},
            ),
            prompt_messages=[
                SystemPromptMessage(content="you are a helpful assistant"),
                UserPromptMessage(content=parameters.get("query")),
            ],
            stream=False,
            tools=[],
        )

        thinking_log = self.finish_log_message(
            log=thinking_log,
        )

        yield thinking_log

        yield self.create_text_message(text=llm_response.message.content)
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