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An Agent Strategy Plugin helps an LLM carry out tasks like reasoning or decision-making, including choosing and calling tools, as well as handling results. This allows the system to address problems more autonomously.

Below, you’ll see how to develop a plugin that supports Function Calling to automatically fetch the current time.

​
Prerequisites

  • Dify plugin scaffolding tool
  • Python environment (version ≥ 3.12)

For details on preparing the plugin development tool, see Initializing the Development Tool.

Tip: Run dify version in your terminal to confirm that the scaffolding tool is installed.

​
1. Initializing the Plugin Template

Run the following command to create a development template for your Agent plugin:

dify plugin init

Follow the on-screen prompts and refer to the sample comments for guidance.

➜ ./dify-plugin-darwin-arm64 plugin init                                                                                                                                 ─╯
Edit profile of the plugin
Plugin name (press Enter to next step): # Enter the plugin name
Author (press Enter to next step): # Enter the plugin author
Description (press Enter to next step): # Enter the plugin description
---
Select the language you want to use for plugin development, and press Enter to continue,
BTW, you need Python 3.12+ to develop the Plugin if you choose Python.
-> python # Select Python environment
  go (not supported yet)
---
Based on the ability you want to extend, we have divided the Plugin into four types: Tool, Model, Extension, and Agent Strategy.

- Tool: It's a tool provider, but not only limited to tools, you can implement an endpoint there, for example, you need both Sending Message and Receiving Message if you are
- Model: Just a model provider, extending others is not allowed.
- Extension: Other times, you may only need a simple http service to extend the functionalities, Extension is the right choice for you.
- Agent Strategy: Implement your own logics here, just by focusing on Agent itself

What's more, we have provided the template for you, you can choose one of them below:
  tool
-> agent-strategy # Select Agent strategy template
  llm
  text-embedding
---
Configure the permissions of the plugin, use up and down to navigate, tab to select, after selection, press enter to finish
Backwards Invocation:
Tools:
    Enabled: [✔]  You can invoke tools inside Dify if it's enabled # Enabled by default
Models:
    Enabled: [✔]  You can invoke models inside Dify if it's enabled # Enabled by default
    LLM: [✔]  You can invoke LLM models inside Dify if it's enabled # Enabled by default
  → Text Embedding: [✘]  You can invoke text embedding models inside Dify if it's enabled
    Rerank: [✘]  You can invoke rerank models inside Dify if it's enabled
    TTS: [✘]  You can invoke TTS models inside Dify if it's enabled
    Speech2Text: [✘]  You can invoke speech2text models inside Dify if it's enabled
    Moderation: [✘]  You can invoke moderation models inside Dify if it's enabled
Apps:
    Enabled: [✘]  Ability to invoke apps like BasicChat/ChatFlow/Agent/Workflow etc.
Resources:
Storage:
    Enabled: [✘]  Persistence storage for the plugin
    Size: N/A  The maximum size of the storage
Endpoints:
    Enabled: [✘]  Ability to register endpoints

After initialization, you’ll get a folder containing all the resources needed for plugin development. Familiarizing yourself with the overall structure of an Agent Strategy Plugin will streamline the development process:

├── GUIDE.md               # User guide and documentation
├── PRIVACY.md             # Privacy policy and data handling guidelines
├── README.md              # Project overview and setup instructions
├── _assets/               # Static assets directory
│   └── icon.svg           # Agent strategy provider icon/logo
├── main.py                # Main application entry point
├── manifest.yaml          # Basic plugin configuration
├── provider/              # Provider configurations directory
│   └── basic_agent.yaml   # Your agent provider settings
├── requirements.txt       # Python dependencies list
└── strategies/            # Strategy implementation directory
    ├── basic_agent.py     # Basic agent strategy implementation
    └── basic_agent.yaml   # Basic agent strategy configuration

All key functionality for this plugin is in the strategies/ directory.

​
2. Developing the Plugin

Agent Strategy Plugin development revolves around two files:

  • Plugin Declaration: strategies/basic_agent.yaml
  • Plugin Implementation: strategies/basic_agent.py

​
2.1 Defining Parameters

To build an Agent plugin, start by specifying the necessary parameters in strategies/basic_agent.yaml. These parameters define the plugin’s core features, such as calling an LLM or using tools.

We recommend including the following four parameters first:

  1. model: The large language model to call (e.g., GPT-4, GPT-4o-mini).
  2. tools: A list of tools that enhance your plugin’s functionality.
  3. query: The user input or prompt content sent to the model.
  4. maximum_iterations: The maximum iteration count to prevent excessive computation.

Example Code:

identity:
  name: basic_agent # the name of the agent_strategy
  author: novice # the author of the agent_strategy
  label:
    en_US: BasicAgent # the engilish label of the agent_strategy
description:
  en_US: BasicAgent # the english description of the agent_strategy
parameters:
  - name: model # the name of the model parameter
    type: model-selector # model-type
    scope: tool-call&llm # the scope of the parameter
    required: true
    label:
      en_US: Model
      zh_Hans: 模型
      pt_BR: Model
  - name: tools # the name of the tools parameter
    type: array[tools] # the type of tool parameter
    required: true
    label:
      en_US: Tools list
      zh_Hans: 工具列表
      pt_BR: Tools list
  - name: query # the name of the query parameter
    type: string # the type of query parameter
    required: true
    label:
      en_US: Query
      zh_Hans: 查询
      pt_BR: Query
  - name: maximum_iterations
    type: number
    required: false
    default: 5
    label:
      en_US: Maxium Iterations
      zh_Hans: 最大迭代次数
      pt_BR: Maxium Iterations
    max: 50 # if you set the max and min value, the display of the parameter will be a slider
    min: 1
extra:
  python:
    source: strategies/basic_agent.py

Once you’ve configured these parameters, the plugin will automatically generate a user-friendly interface so you can easily manage them:

Agent Strategy Plugin UI

​
2.2 Retrieving Parameters and Execution

After users fill out these basic fields, your plugin needs to process the submitted parameters. In strategies/basic_agent.py, define a parameter class for the Agent, then retrieve and apply these parameters in your logic.

Verify incoming parameters:

from dify_plugin.entities.agent import AgentInvokeMessage
from dify_plugin.interfaces.agent import AgentModelConfig, AgentStrategy, ToolEntity
from pydantic import BaseModel

class BasicParams(BaseModel):
    maximum_iterations: int
    model: AgentModelConfig
    tools: list[ToolEntity]
    query: str

After getting the parameters, the specific business logic is executed:

class BasicAgentAgentStrategy(AgentStrategy):
    def _invoke(self, parameters: dict[str, Any]) -> Generator[AgentInvokeMessage]:
        params = BasicParams(**parameters)

​
2.3 Invoking the Model

In an Agent Strategy Plugin, invoking the model is central to the workflow. You can invoke an LLM efficiently using session.model.llm.invoke() from the SDK, handling text generation, dialogue, and so forth.

If you want the LLM handle tools, ensure it outputs structured parameters to match a tool’s interface. In other words, the LLM must produce input arguments that the tool can accept based on the user’s instructions.

Construct the following parameters:

  • model
  • prompt_messages
  • tools
  • stop
  • stream

Example code for method definition:

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:...

To view the complete functionality implementation, please refer to the Example Code for model invocation.

This code achieves the following functionality: after a user inputs a command, the Agent strategy plugin automatically calls the LLM, constructs the necessary parameters for tool invocation based on the generated results, and enables the model to flexibly dispatch integrated tools to efficiently complete complex tasks.

Request parameters for generating tools

​
2.4 Adding Memory to the Model

Adding Memory to your Agent plugin allows the model to remember previous conversations, making interactions more natural and effective. With memory enabled, the model can maintain context and provide more relevant responses.

Steps:

  1. Configure Memory Functionality

Add the history-messages feature to the Agent plugin’s YAML configuration file strategies/agent.yaml:

identity:
  name: basic_agent  # Agent strategy name
  author: novice     # Author
  label:
    en_US: BasicAgent  # English label
description:
  en_US: BasicAgent    # English description
features:
  - history-messages   # Enable history messages feature
...
  1. Enable Memory Settings

After modifying the plugin configuration and restarting, you will see the Memory toggle. Click the toggle button on the right to enable memory.

Once enabled, you can adjust the memory window size using the slider, which determines how many previous conversation turns the model can “remember”.

  1. Debug History Messages

Add the following code to check the history messages:

class BasicAgentAgentStrategy(AgentStrategy):
    def _invoke(self, parameters: dict[str, Any]) -> Generator[AgentInvokeMessage]:
        params = BasicParams(**parameters)
        print(f"history_messages: {params.model.history_prompt_messages}")
        ...

History messages

The console will display an output similar to:

history_messages: []
history_messages: [UserPromptMessage(role=<PromptMessageRole.USER: 'user'>, content='hello, my name is novice', name=None), AssistantPromptMessage(role=<PromptMessageRole.ASSISTANT: 'assistant'>, content='Hello, Novice! How can I assist you today?', name=None, tool_calls=[])]
  1. Integrate History Messages into Model Calls

Update the model call to incorporate conversation history with the current query:

class BasicAgentAgentStrategy(AgentStrategy):
    def _invoke(self, parameters: dict[str, Any]) -> Generator[AgentInvokeMessage]:
        params = BasicParams(**parameters)

        chunks: Generator[LLMResultChunk, None, None] | LLMResult = (
            self.session.model.llm.invoke(
                model_config=LLMModelConfig(**params.model.model_dump(mode="json")),
                # Add history messages
                prompt_messages=params.model.history_prompt_messages
                + [UserPromptMessage(content=params.query)],
                tools=[
                    self._convert_tool_to_prompt_message_tool(tool)
                    for tool in params.tools
                ],
                stop=params.model.completion_params.get("stop", [])
                if params.model.completion_params
                else [],
                stream=True,
            )
        )
        ...
  1. Check the Outcome

After implementing Memory, the model can respond based on conversation history. In the example below, the model successfully remembers the user’s name mentioned in previous conversation.

Outcome

​
2.5 Handling a Tool

After specifying the tool parameters, the Agent Strategy Plugin must actually call these tools. Use session.tool.invoke() to make those requests.

Construct the following parameters:

  • provider
  • tool_name
  • parameters

Example code for method definition:

 def invoke(
        self,
        provider_type: ToolProviderType,
        provider: str,
        tool_name: str,
        parameters: dict[str, Any],
    ) -> Generator[ToolInvokeMessage, None, None]:...

If you’d like the LLM itself to generate the parameters needed for tool calls, you can do so by combining the model’s output with your tool-calling code.

tool_instances = (
    {tool.identity.name: tool for tool in params.tools} if params.tools else {}
)
for tool_call_id, tool_call_name, tool_call_args in tool_calls:
    tool_instance = tool_instances[tool_call_name]
    self.session.tool.invoke(
        provider_type=ToolProviderType.BUILT_IN,
        provider=tool_instance.identity.provider,
        tool_name=tool_instance.identity.name,
        parameters={**tool_instance.runtime_parameters, **tool_call_args},
    )

With this in place, your Agent Strategy Plugin can automatically perform Function Calling—for instance, retrieving the current time.

Tool Invocation

​
2.6 Creating Logs

Often, multiple steps are necessary to complete a complex task in an Agent Strategy Plugin. It’s crucial for developers to track each step’s results, analyze the decision process, and optimize strategy. Using create_log_message and finish_log_message from the SDK, you can log real-time states before and after calls, aiding in quick problem diagnosis.

For example:

  • Log a “starting model call” message before calling the model, clarifying the task’s execution progress.
  • Log a “call succeeded” message once the model responds, ensuring the model’s output can be traced end to end.
model_log = self.create_log_message(
            label=f"{params.model.model} Thought",
            data={},
            metadata={"start_at": model_started_at, "provider": params.model.provider},
            status=ToolInvokeMessage.LogMessage.LogStatus.START,
        )
yield model_log
self.session.model.llm.invoke(...)
yield self.finish_log_message(
    log=model_log,
    data={
        "output": response,
        "tool_name": tool_call_names,
        "tool_input": tool_call_inputs,
    },
    metadata={
        "started_at": model_started_at,
        "finished_at": time.perf_counter(),
        "elapsed_time": time.perf_counter() - model_started_at,
        "provider": params.model.provider,
    },
)

When the setup is complete, the workflow log will output the execution results:

Agent Output execution results

If multiple rounds of logs occur, you can structure them hierarchically by setting a parent parameter in your log calls, making them easier to follow.

Reference method:

function_call_round_log = self.create_log_message(
    label="Function Call Round1 ",
    data={},
    metadata={},
)
yield function_call_round_log

model_log = self.create_log_message(
    label=f"{params.model.model} Thought",
    data={},
    metadata={"start_at": model_started_at, "provider": params.model.provider},
    status=ToolInvokeMessage.LogMessage.LogStatus.START,
    # add parent log
    parent=function_call_round_log,
)
yield model_log

​
Sample code for agent-plugin functions

​
Invoke Model

The following code demonstrates how to give the Agent strategy plugin the ability to invoke the model:

import json
from collections.abc import Generator
from typing import Any, cast

from dify_plugin.entities.agent import AgentInvokeMessage
from dify_plugin.entities.model.llm import LLMModelConfig, LLMResult, LLMResultChunk
from dify_plugin.entities.model.message import (
    PromptMessageTool,
    UserPromptMessage,
)
from dify_plugin.entities.tool import ToolInvokeMessage, ToolParameter, ToolProviderType
from dify_plugin.interfaces.agent import AgentModelConfig, AgentStrategy, ToolEntity
from pydantic import BaseModel

class BasicParams(BaseModel):
    maximum_iterations: int
    model: AgentModelConfig
    tools: list[ToolEntity]
    query: str

class BasicAgentAgentStrategy(AgentStrategy):
    def _invoke(self, parameters: dict[str, Any]) -> Generator[AgentInvokeMessage]:
        params = BasicParams(**parameters)
        chunks: Generator[LLMResultChunk, None, None] | LLMResult = (
            self.session.model.llm.invoke(
                model_config=LLMModelConfig(**params.model.model_dump(mode="json")),
                prompt_messages=[UserPromptMessage(content=params.query)],
                tools=[
                    self._convert_tool_to_prompt_message_tool(tool)
                    for tool in params.tools
                ],
                stop=params.model.completion_params.get("stop", [])
                if params.model.completion_params
                else [],
                stream=True,
            )
        )
        response = ""
        tool_calls = []
        tool_instances = (
            {tool.identity.name: tool for tool in params.tools} if params.tools else {}
        )

        for chunk in chunks:
            # check if there is any tool call
            if self.check_tool_calls(chunk):
                tool_calls = self.extract_tool_calls(chunk)
                tool_call_names = ";".join([tool_call[1] for tool_call in tool_calls])
                try:
                    tool_call_inputs = json.dumps(
                        {tool_call[1]: tool_call[2] for tool_call in tool_calls},
                        ensure_ascii=False,
                    )
                except json.JSONDecodeError:
                    # ensure ascii to avoid encoding error
                    tool_call_inputs = json.dumps(
                        {tool_call[1]: tool_call[2] for tool_call in tool_calls}
                    )
                print(tool_call_names, tool_call_inputs)
            if chunk.delta.message and chunk.delta.message.content:
                if isinstance(chunk.delta.message.content, list):
                    for content in chunk.delta.message.content:
                        response += content.data
                        print(content.data, end="", flush=True)
                else:
                    response += str(chunk.delta.message.content)
                    print(str(chunk.delta.message.content), end="", flush=True)

            if chunk.delta.usage:
                # usage of the model
                usage = chunk.delta.usage

        yield self.create_text_message(
            text=f"{response or json.dumps(tool_calls, ensure_ascii=False)}\n"
        )
        result = ""
        for tool_call_id, tool_call_name, tool_call_args in tool_calls:
            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,
                parameters={**tool_instance.runtime_parameters, **tool_call_args},
            )
            if not tool_instance:
                tool_invoke_responses = {
                    "tool_call_id": tool_call_id,
                    "tool_call_name": tool_call_name,
                    "tool_response": f"there is not a tool named {tool_call_name}",
                }
            else:
                # invoke tool
                tool_invoke_responses = self.session.tool.invoke(
                    provider_type=ToolProviderType.BUILT_IN,
                    provider=tool_instance.identity.provider,
                    tool_name=tool_instance.identity.name,
                    parameters={**tool_instance.runtime_parameters, **tool_call_args},
                )
                result = ""
                for tool_invoke_response in tool_invoke_responses:
                    if tool_invoke_response.type == ToolInvokeMessage.MessageType.TEXT:
                        result += cast(
                            ToolInvokeMessage.TextMessage, tool_invoke_response.message
                        ).text
                    elif (
                        tool_invoke_response.type == ToolInvokeMessage.MessageType.LINK
                    ):
                        result += (
                            f"result link: {cast(ToolInvokeMessage.TextMessage, tool_invoke_response.message).text}."
                            + " please tell user to check it."
                        )
                    elif tool_invoke_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 (
                        tool_invoke_response.type == ToolInvokeMessage.MessageType.JSON
                    ):
                        text = json.dumps(
                            cast(
                                ToolInvokeMessage.JsonMessage,
                                tool_invoke_response.message,
                            ).json_object,
                            ensure_ascii=False,
                        )
                        result += f"tool response: {text}."
                    else:
                        result += f"tool response: {tool_invoke_response.message!r}."

                tool_response = {
                    "tool_call_id": tool_call_id,
                    "tool_call_name": tool_call_name,
                    "tool_response": result,
                }
        yield self.create_text_message(result)

    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

    def check_tool_calls(self, llm_result_chunk: LLMResultChunk) -> bool:
        """
        Check if there is any tool call in llm result chunk
        """
        return bool(llm_result_chunk.delta.message.tool_calls)

    def extract_tool_calls(
        self, llm_result_chunk: LLMResultChunk
    ) -> list[tuple[str, str, dict[str, Any]]]:
        """
        Extract tool calls from llm result chunk

        Returns:
            List[Tuple[str, str, Dict[str, Any]]]: [(tool_call_id, tool_call_name, tool_call_args)]
        """
        tool_calls = []
        for prompt_message in llm_result_chunk.delta.message.tool_calls:
            args = {}
            if prompt_message.function.arguments != "":
                args = json.loads(prompt_message.function.arguments)

            tool_calls.append(
                (
                    prompt_message.id,
                    prompt_message.function.name,
                    args,
                )
            )

        return tool_calls

​
3. Debugging the Plugin

After finalizing the plugin’s declaration file and implementation code, run python -m main in the plugin directory to restart it. Next, confirm the plugin runs correctly. Dify offers remote debugging—go to “Plugin Management” to obtain your debug key and remote server address.

Back in your plugin project, copy .env.example to .env and insert the relevant remote server and debug key info.

INSTALL_METHOD=remote
REMOTE_INSTALL_URL=debug.dify.ai:5003
REMOTE_INSTALL_KEY=********-****-****-****-************

Then run:

python -m main

You’ll see the plugin installed in your Workspace, and team members can also access it.

​
Packaging the Plugin (Optional)

Once everything works, you can package your plugin by running:

# Replace ./basic_agent/ with your actual plugin project path.

dify plugin package ./basic_agent/

A file named google.difypkg (for example) appears in your current folder—this is your final plugin package.

Congratulations! You’ve fully developed, tested, and packaged your Agent Strategy Plugin.

​
Publishing the Plugin (Optional)

You can now upload it to the Dify Plugins repository. Before doing so, ensure it meets the Plugin Publishing Guidelines. Once approved, your code merges into the main branch, and the plugin automatically goes live on the Dify Marketplace.

​
Further Exploration

Complex tasks often need multiple rounds of thinking and tool calls, typically repeating model invoke → tool use until the task ends or a maximum iteration limit is reached. Managing prompts effectively is crucial in this process. Check out the complete Function Calling implementation for a standardized approach to letting models call external tools and handle their outputs.

testing

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