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Dify中的Agent策略插件开发例子：以Function Calling为例

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本文使用Dify v1.0.0-beta.1版本。Agent 策略插件能够帮助 LLM 执行推理或决策逻辑，包括工具选择、调用和结果处理，以更加自动化的方式处理问题。Dify官方实现的Agent策略插件包括Function Calling和Reason+ReAct(Reason+Act)。本文重点介绍Function Calling具体实现。

一.初始化插件模板

1.初始化Agent插件模板

运行以下命令，初始化 Agent 插件开发模板。如下所示：

2.填写或选择配置信息

按照页面提示，填写或选择对应信息。如下所示：

3.Agent插件模板代码结构

初始化插件模板后将生成一个代码文件夹，包含插件开发过程中所需的完整资源。熟悉 Agent 策略插件的整体代码结构有助于插件的开发过程。插件的功能代码集中在 strategies/ 目录内。

二.开发插件功能

Agent 策略插件的开发主要围绕以下两个文件展开：

  • 插件声明文件：strategies/function_calling.yaml
  • 插件功能代码：strategies/function_calling.py

1.定义参数

要创建一个Agent插件，首先需要在 strategies/function_calling.yaml 文件中定义插件所需的参数。这些参数决定了插件的核心功能，比如调用 LLM 模型和使用工具的能力。

官方建议优先配置以下五个基础参数：

  • model：指定要调用的大语言模型（LLM），如 GPT-4o、DeepSeek-V3 等。
  • tools：定义插件可以使用的工具列表，增强插件功能。
  • instruction：提供初始的系统提示消息内容，帮助模型理解和执行任务。
  • query：设置与模型交互的提示词或输入内容。
  • maximum_iterations：限制插件执行的最大迭代次数，避免过度计算。

完成参数配置后，插件将在自动生成相应的设置的使用页面，方便进行直观、便捷的调整和使用。

2.获取参数并执行

接下来需要先在 strategies/function_calling.py 文件内定义 Agent 参数类供后续使用。校验传入参数，如下所示：

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

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

获取参数后，执行具体的业务逻辑，如下所示：

`class FunctionCallingAgentStrategy(AgentStrategy):
    def init(self, session):
        super().init(session)
        self.query = ""

    def _invoke(self, parameters: dict[str, Any]) -> Generator[AgentInvokeMessage]:
        """
        Run FunctionCall agent application
        """
        fc_params = FunctionCallingParams(**parameters)
        ......
`

3.调用模型

在 Agent 策略插件中，调用模型是核心执行逻辑之一。可以通过 SDK 提供的 session.model.llm.invoke() 方法高效地调用 LLM 模型，实现文本生成、对话处理等功能。

def invoke(     self,     model_config: LLMModelConfig | dict,     prompt_messages: list[PromptMessage],     tools: list[PromptMessageTool] | None = None,     stop: list[str] | None = None,     stream: bool = True, ) -> Generator[LLMResultChunk, None, None] | LLMResult: ...

如果希望模型具备调用工具的能力，首先需要确保模型能够输出符合工具调用格式的输入参数。即模型需要根据用户指令生成符合工具接口要求的参数。构造参数如下所示：

  • model_config：模型配置信息
  • prompt_messages：提示词
  • tools：工具信息（Function Calling 相关）
  • stop：停止符
  • stream：是否支持流式输出

这样就实现了以下功能：用户输入指令后，Agent 策略插件会自动调用 LLM，根据生成结果构建并传递工具调用所需的参数，使模型能够灵活调度已接入的工具，高效完成复杂任务。

4.调用工具

填写工具参数后，需赋予 Agent 策略插件实际调用工具的能力。可以通过 SDK 中的session.tool.invoke() 函数进行工具调用。构造参数如下所示：

  • provider_type：提供商类型
  • provider：提供商
  • tool_name：工具名称
  • parameters：输入参数

def invoke(     self,     provider_type: ToolProviderType,     provider: str,     tool_name: str,     parameters: dict[str, Any], ) -> Generator[ToolInvokeMessage, None, None]:     """     Invoke tool     """     return self._backwards_invoke(         InvokeType.Tool,         ToolInvokeMessage,         {             "tool_type": provider_type.value,             "provider": provider,             "tool": tool_name,             "tool_parameters": parameters,         },     )

若希望通过 LLM 直接生成参数完成工具调用，可参考以下工具调用的示例代码：

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},     )

实现这部分功能代码后，Agent策略插件将具备自动Function Calling的能力，比如自动获取当前时间。如下所示：

5.日志创建

在 Agent 策略插件中，通常需要执行多轮操作才能完成复杂任务。记录每轮操作的执行结果对于开发者来说非常重要，有助于追踪 Agent 的执行过程、分析每一步的决策依据，从而更好地评估和优化策略效果。

为了实现这一功能，可以利用 SDK 中的 create_log_message 和 finish_log_message 方法记录日志。这种方式不仅可以在模型调用前后实时记录操作状态，还能帮助开发者快速定位问题。

日志创建具体使用场景示例，如下所示：

  • 在模型调用之前，记录一条”开始调用模型”的日志，帮助开发者明确任务执行进度。
  • 在模型调用成功后，记录一条”调用成功”的日志，方便追踪模型响应的完整性。

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,     }, )

设置完成后，工作流日志将输出执行结果，如下所示：

三.调试、打包和发布插件

1.调试插件

首先将插件运行起来，如下所示：

然后就可以看到带有DEBUGGING PLUGIN标识的function_calling插件，如下所示：

2.打包和发布插件

具体操作不再赘述，详细参考文献[4]。

四.FunctionCallingAgentStrategy类的方法

1.类的方法总结

方法名称 方法功能解析 输入参数及解释 输出参数及解释
__init__ 初始化FunctionCallingAgentStrategy类 session : 会话对象 无
_invoke 运行 FunctionCall 代理应用程序 parameters : 参数字典 Generator[AgentInvokeMessage] : 代理调用消息生成器
check_tool_calls 检查 LLM 结果块中是否有工具调用 llm_result_chunk : LLM 结果块 bool : 是否有工具调用
check_blocking_tool_calls 检查 LLM 结果中是否有阻塞工具调用 llm_result : LLM 结果 bool : 是否有阻塞工具调用
extract_tool_calls 从 LLM 结果块中提取工具调用 llm_result_chunk : LLM 结果块 list[tuple[str, str, dict[str, Any]]] : 工具调用列表
extract_blocking_tool_calls 从 LLM 结果中提取阻塞工具调用 llm_result : LLM 结果 list[tuple[str, str, dict[str, Any]]] : 阻塞工具调用列表
_init_system_message 初始化系统消息 prompt_template : 提示模板prompt_messages: 提示消息列表 list[PromptMessage] : 提示消息列表
_organize_user_query 组织用户查询 query : 查询字符串prompt_messages: 提示消息列表 list[PromptMessage] : 提示消息列表
_clear_user_prompt_image_messages 清除用户提示中的图像消息 prompt_messages : 提示消息列表 list[PromptMessage] : 提示消息列表
_organize_prompt_messages 组织提示消息 current_thoughts : 当前思路列表history_prompt_messages: 历史提示消息列表 list[PromptMessage] : 提示消息列表

2._invoke()执行过程

FunctionCallingAgentStrategy类中的_invoke方法是一个核心方法，用于实现基于大语言模型（LLM）的多轮工具调用代理策略。以下是该方法的详细执行过程分析：

2.1 参数解析与初始化

  • 输入参数：接收字典参数并转换为FunctionCallingParams对象，包含：
    • query: 用户原始查询
    • instruction: 系统级指令（可选）
    • model: 模型配置（如模型名称、供应商、参数等）
    • tools: 可用工具列表（每个工具包含名称、参数、供应商等信息）
    • maximum_iterations: 最大迭代轮次（默认3次）
  • 初始化：
    • 初始化迭代步数iteration_step和最大迭代步数max_iteration_steps。
    • current_thoughts: 存储每轮的模型响应和工具调用结果。
    • function_call_state: 标记是否仍需工具调用。
    • 初始化LLM使用量llm_usage和最终答案final_answer。
    • 构建初始系统提示消息（SystemPromptMessage）。
    • 将工具列表转换为字典格式，便于后续通过工具名称快速查找。
    • 转换工具信息为模型需要的格式（prompt_messages_tools），用于告知LLM可用工具。
    • 初始化关键变量：

2.2 多轮迭代处理

通过while循环进行多轮处理，直到无工具调用或达到最大轮次。

（1）单轮初始化
  • 生成本轮开始的日志消息（round_log）。
  • 若为最后一轮，清空工具列表，强制模型生成最终回答。
  • 组织当前提示消息（prompt_messages）：
    • 合并历史消息、用户查询和当前思考。
    • 首次迭代后，清理用户消息中的图片内容（仅保留文本占位符）。
（2）调用LLM模型
  • 调整模型的max_tokens参数，确保不超过模型限制。
  • 流式处理（stream=True）：
    • 逐块处理响应（LLMResultChunk），实时检测工具调用。
    • 收集响应内容（response）和工具调用信息（tool_calls）。
  • 非流式处理：
    • 直接处理完整响应（LLMResult），提取工具调用和内容。
  • 记录资源使用情况（llm_usage）。
（3）工具调用处理
  • 提取工具调用：
    • 解析LLM响应中的工具调用信息（tool_call_id, tool_call_name, tool_call_args）。
    • 若存在工具调用，设置function_call_state=True，触发后续处理。
  • 执行工具调用：
    • 遍历每个工具调用，生成工具调用日志。
    • 通过session.tool.invoke实际调用工具，处理可能的多种返回类型（文本、链接、图片等）。
    • 收集工具结果，构建ToolPromptMessage并添加到current_thoughts，供下轮使用。
（4）本轮收尾
  • 更新提示消息中的工具信息（如参数变化）。
  • 生成本轮结束日志，包含响应内容、工具结果、耗时和资源消耗。

2.3 最终结果与资源统计

  • 当所有迭代完成，生成包含总资源消耗的JSON消息：
    • total_price: 累计费用。
    • currency: 费用单位。
    • total_tokens: 总令牌数。

3.关键辅助方法

  • check_tool_calls/check_blocking_tool_calls: 检测响应中是否存在工具调用。
  • extract_tool_calls/extract_blocking_tool_calls: 从响应中提取工具调用细节。
  • _init_system_message: 确保系统消息存在于提示中。
  • _organize_user_query: 将用户查询添加到提示消息。
  • _clear_user_prompt_image_messages: 清理用户消息中的图片内容（避免多轮迭代中的冗余）。

4.异常与边界处理

  • 工具不存在：返回错误信息，避免流程中断。
  • JSON解析错误：使用ensure_ascii=False处理非ASCII字符。
  • 资源统计：累计每轮的令牌数和费用，确保最终结果准确。

5.流程示意图

通过这种多轮迭代策略，FunctionCallingAgentStrategy能够动态结合LLM的推理能力和外部工具的执行，逐步解决复杂任务，同时记录详细的执行日志和资源消耗，适合需要多步交互的应用场景。

五.可能遇到的问题

1.Failed to transform agent message

因为dify-plugin-daemon源码跑在WSL2中，前端和后端源码跑在宿主机中，导致无法访问宿主机的5001端口。具体报错，如下所示：

解决方案是在WSL Settings中，设置网络模式为Mirrored，打开主机地址环回，如下所示：

六.Chatflow中的Agent执行过程

1.event流

`data: {"event": "workflow_started", "conversation_id": "c64faf26-0f38-4267-ba63-1392997e3319", "message_id": "c9a6f7ac-6814-49ce-bce1-7aaea3230603", "created_at": 1738740364, "task_id": "1934b64d-e761-4a14-961b-a0f25bc9eb24", "workflow_run_id": "ef93b6c1-4219-40e1-9253-e247ad188a3a", "data": {"id": "ef93b6c1-4219-40e1-9253-e247ad188a3a", "workflow_id": "98838bd9-e2a3-4c73-abf8-1d1f1327e9dd", "sequence_number": 77, "inputs": {"sys.query": "\u73b0\u5728\u51e0\u70b9\u4e86", "sys.files": [], "sys.conversation_id": "c64faf26-0f38-4267-ba63-1392997e3319", "sys.user_id": "b3d39b0d-f73f-40fe-b406-e3035c755d11", "sys.dialogue_count": 0, "sys.app_id": "bd18d1d5-8b35-4126-810a-a35748e8ef77", "sys.workflow_id": "98838bd9-e2a3-4c73-abf8-1d1f1327e9dd", "sys.workflow_run_id": "ef93b6c1-4219-40e1-9253-e247ad188a3a"}, "created_at": 1738740364}}

data: {"event": "node_started", "conversation_id": "c64faf26-0f38-4267-ba63-1392997e3319", "message_id": "c9a6f7ac-6814-49ce-bce1-7aaea3230603", "created_at": 1738740364, "task_id": "1934b64d-e761-4a14-961b-a0f25bc9eb24", "workflow_run_id": "ef93b6c1-4219-40e1-9253-e247ad188a3a", "data": {"id": "0ac54d2e-437a-4517-9405-12cf701d3078", "node_id": "1738648646598", "node_type": "start", "title": "\u5f00\u59cb", "index": 1, "predecessor_node_id": null, "inputs": null, "created_at": 1738740364, "extras": {}, "parallel_id": null, "parallel_start_node_id": null, "parent_parallel_id": null, "parent_parallel_start_node_id": null, "iteration_id": null, "parallel_run_id": null}}

data: {"event": "node_finished", "conversation_id": "c64faf26-0f38-4267-ba63-1392997e3319", "message_id": "c9a6f7ac-6814-49ce-bce1-7aaea3230603", "created_at": 1738740364, "task_id": "1934b64d-e761-4a14-961b-a0f25bc9eb24", "workflow_run_id": "ef93b6c1-4219-40e1-9253-e247ad188a3a", "data": {"id": "0ac54d2e-437a-4517-9405-12cf701d3078", "node_id": "1738648646598", "node_type": "start", "title": "\u5f00\u59cb", "index": 1, "predecessor_node_id": null, "inputs": {"sys.query": "\u73b0\u5728\u51e0\u70b9\u4e86", "sys.files": [], "sys.conversation_id": "c64faf26-0f38-4267-ba63-1392997e3319", "sys.user_id": "b3d39b0d-f73f-40fe-b406-e3035c755d11", "sys.dialogue_count": 0, "sys.app_id": "bd18d1d5-8b35-4126-810a-a35748e8ef77", "sys.workflow_id": "98838bd9-e2a3-4c73-abf8-1d1f1327e9dd", "sys.workflow_run_id": "ef93b6c1-4219-40e1-9253-e247ad188a3a"}, "process_data": null, "outputs": {"sys.query": "\u73b0\u5728\u51e0\u70b9\u4e86", "sys.files": [], "sys.conversation_id": "c64faf26-0f38-4267-ba63-1392997e3319", "sys.user_id": "b3d39b0d-f73f-40fe-b406-e3035c755d11", "sys.dialogue_count": 0, "sys.app_id": "bd18d1d5-8b35-4126-810a-a35748e8ef77", "sys.workflow_id": "98838bd9-e2a3-4c73-abf8-1d1f1327e9dd", "sys.workflow_run_id": "ef93b6c1-4219-40e1-9253-e247ad188a3a"}, "status": "succeeded", "error": null, "elapsed_time": 0.034015, "execution_metadata": null, "created_at": 1738740364, "finished_at": 1738740364, "files": [], "parallel_id": null, "parallel_start_node_id": null, "parent_parallel_id": null, "parent_parallel_start_node_id": null, "iteration_id": null}}

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`

2.Agent节点输入和输出

Agent节点输入，如下所示：

{   "model": {     "provider": "langgenius/openai/openai",     "model": "gpt-4o",     "model_type": "llm",     "mode": "chat",     "type": "model-selector",     "completion_params": {       "temperature": 0.2,       "top_p": 0.75,       "presence_penalty": 0.5,       "frequency_penalty": 0.5     }   },   "tools": [     {       "provider_name": "time",       "tool_name": "current_time",       "parameters": {         "format": "%Y-%m-%d %H:%M:%S",         "timezone": "Asia/Shanghai"       },       "enabled": true,       "extra": {         "description": "获取当前时间"       }     }   ],   "instruction": "获取当前时间",   "query": "现在几点了",   "maximum_iterations": "3" }

Agent节点输出，如下所示：

{   "text": "当前时间是2025年2月5日，23:26。",   "files": [],   "json": [     {}   ] }

3.Agent策略function_calling的Round1

Agent策略function_calling的Round1-GPT-4o Thought：

{   "output": "",   "tool_input": "{\"current_time\": {}}",   "tool_name": "current_time" }

Agent策略function_calling的Round1-CALL current_time：

{   "output": {     "meta": {       "error": "",       "time_cost": 0,       "tool_config": {}     },     "tool_call_id": "current_time",     "tool_call_name": "current_time",     "tool_response": "2025-02-05 23:26:06"   } }

Agent策略function_calling的Round1输出：

{   "output": {     "llm_response": {       "message": {         "text": "2025-02-05 23:26:06"       },       "meta": null,       "type": "text"     },     "tool_responses": [       {         "meta": {           "error": "",           "time_cost": 0,           "tool_config": {}         },         "tool_call_id": "current_time",         "tool_call_name": "current_time",         "tool_response": "2025-02-05 23:26:06"       }     ]   } }

4.Agent策略function_calling的Round2

Agent策略function_calling的Round2-GPT-4o Thought：

{   "output": "当前时间是2025年2月5日，23:26。",   "tool_input": "",   "tool_name": "" }

Agent策略function_calling的Round2输出：

{   "output": {     "llm_response": "当前时间是2025年2月5日，23:26。",     "tool_responses": []   } }

参考文献

[1] Agent策略插件：https://docs.dify.ai/zh-hans/plugins/quick-start/develop-plugins/agent-strategy

[2] https://github.com/langgenius/dify-official-plugins/tree/main/agent-strategies/cot\_agent/strategies

[3] Dify Agent策略：https://marketplace.dify.ai/plugins/langgenius/agent

[4] Dify中的GoogleSearch工具插件开发例子：https://z0yrmerhgi8.feishu.cn/wiki/Ib15wh1rSi8mWckvWROckoT2n6g

[5] Dify中的Agent策略插件开发例子：以Function Calling为例(原文链接)：https://z0yrmerhgi8.feishu.cn/wiki/SjUuwiRk6imo4Dkn4BBcZoCOn1f

（文：NLP工程化）

Dify中的Agent策略插件开发例子：以Function Calling为例最先出现在每时AI。

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