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An Agent Strategy Plugin gives an LLM the reasoning and decision-making logic to choose tools, call them, and handle their results, so it can solve problems autonomously. This guide walks through building a Function Calling strategy that lets the model fetch the current time on its own.

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Prerequisites

  • Dify plugin scaffolding tool
  • Python environment (version 3.12)
For details on preparing the plugin development tool, see CLI.
Run dify version in your terminal to confirm that the scaffolding tool is installed.

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1. Initialize the Plugin Template

Run the following command to create a development template for your Agent plugin: 
dify plugin init
Follow the on-screen prompts; the comments below explain each choice. 
➜  Dify Plugins Developing dify plugin init
Edit profile of the plugin
Plugin name (press Enter to next step): # Enter the plugin name
Author (press Enter to next step): Author name # Enter the plugin author
Description (press Enter to next step): Description # Enter the plugin description
---
Select the language you want to use for plugin development, and press Enter to con
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 type

- Tool: It's a tool provider, but not only limited to tools, you can implement an
- Model: Just a model provider, extending others is not allowed.
- Extension: Other times, you may only need a simple http service to extend the fu
- 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 b
  tool
-> agent-strategy # Select Agent strategy template
  llm
  text-embedding
---
Configure the permissions of the plugin, use up and down to navigate, tab to sel
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'
    Rerank: [✘]  You can invoke rerank models inside Dify if it's enabled
...
Initialization creates a folder with everything you need for plugin development: 
├── 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.

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2. Develop the Plugin

Agent Strategy Plugin development revolves around two files:
  • Plugin Declaration: strategies/basic_agent.yaml
  • Plugin Implementation: strategies/basic_agent.py

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2.1 Define Parameters

Start by declaring the plugin’s parameters in strategies/basic_agent.yaml. These parameters power the plugin’s core features, such as calling an LLM or using tools. We recommend starting with these four parameters:
  • model: The large language model to call (e.g., GPT-4, GPT-4o-mini).
  • tools: A list of tools that enhance your plugin’s functionality.
  • query: The user input or prompt content sent to the model.
  • maximum_iterations: The maximum iteration count, which prevents excessive computation.
Example: 
identity:
  name: basic_agent # the name of the agent_strategy
  author: novice # the author of the agent_strategy
  label:
    en_US: BasicAgent # the English 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
Dify automatically renders a configuration interface from these parameter declarations:
Agent Strategy Plugin UI

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2.2 Retrieve Parameters and Execute

When users fill out these fields, your plugin receives the submitted values. In strategies/basic_agent.py, define a Pydantic model that validates the 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
Then parse the parameters in _invoke and run your strategy logic: 
class BasicAgentAgentStrategy(AgentStrategy):
    def _invoke(self, parameters: dict[str, Any]) -> Generator[AgentInvokeMessage]:
        params = BasicParams(**parameters)

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3. Invoke the Model

Invoking the model is central to an Agent strategy. Use session.model.llm.invoke() from the SDK to call an LLM for text generation, dialogue, and similar tasks. For the LLM to drive tool calls, it must output structured arguments that match each tool’s interface—input the tool can accept, derived from the user’s instructions. The method takes the following parameters:
  • model
  • prompt_messages
  • tools
  • stop
  • stream
Method signature: 
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:...
For the full implementation, see the Invoke Model tab in the sample code below. With this in place, the plugin calls the LLM whenever a user enters a command, builds tool-invocation parameters from the model’s output, and lets the model dispatch the configured tools to complete complex tasks.
Request Parameters for Generating Tools

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4. Invoke Tools

Once the model has produced tool parameters, the plugin must actually call the tools. Use session.tool.invoke() to make those requests. The method takes the following parameters:
  • provider
  • tool_name
  • parameters
Method signature: 
 def invoke(
        self,
        provider_type: ToolProviderType,
        provider: str,
        tool_name: str,
        parameters: dict[str, Any],
    ) -> Generator[ToolInvokeMessage, None, None]:...
To let the LLM generate the tool-call parameters itself, feed the model’s extracted tool calls into your invocation 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},
    )
Your plugin can now perform Function Calling automatically—for instance, retrieving the current time.
Tool Invocation

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5. Create Logs

Complex tasks usually take multiple steps, and you need to track each step’s result to analyze decisions and refine your strategy. The SDK’s create_log_message and finish_log_message let you record state before and after each call, which speeds up problem diagnosis. For example:
  • Log a “starting model call” message before calling the model to show execution progress.
  • Log a “call succeeded” message once the model responds, so its 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,
    },
)
Once set up, the workflow log shows the execution results:
Agent Output Execution Results
When a task spans multiple rounds, set the parent parameter in your log calls to nest the logs hierarchically and keep them easy to follow: 
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

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Sample Code

The following code gives 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

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6. Debug the Plugin

With the declaration file and implementation code complete, verify that the plugin runs correctly. Dify supports remote debugging: go to Plugin Management to obtain your debug key and remote server address.
Debug Key and Remote Server Address in Plugin Management
In your plugin project, copy .env.example to .env and fill in the remote server address and debug key. 
INSTALL_METHOD=remote
REMOTE_INSTALL_URL=debug.dify.ai:5003
REMOTE_INSTALL_KEY=********-****-****-****-************
Then run: 
python -m main
The plugin appears in your workspace, where team members can also access it.
Browser Plugins

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Package the Plugin (Optional)

Once everything works, package your plugin by running: 
# Replace ./basic_agent/ with your actual plugin project path.

dify plugin package ./basic_agent/
A file named basic_agent.difypkg (matching your plugin name) appears in your current folder. This is your final plugin package. Congratulations! You’ve developed, tested, and packaged your Agent Strategy Plugin.

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Publish the Plugin (Optional)

You can now upload the package 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.

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Further Exploration

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