> ## Documentation Index
> Fetch the complete documentation index at: https://docs.dify.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Agent 策略插件
> 从零开始构建一个 Function Calling Agent 策略，通过实战示例展示如何为 LLM 提供工具并让其自主获取当前时间
> 本文档由 AI 自动翻译。如有任何不准确之处，请参考 [英文原版](/en/develop-plugin/dev-guides-and-walkthroughs/agent-strategy-plugin)。
**Agent 策略插件**为 LLM 提供推理与决策逻辑，使其能够选择工具、调用工具并处理工具结果，从而自主解决问题。
本指南将带你构建一个 **Function Calling** 策略，让模型自行获取当前时间。
## 前置条件
* Dify 插件脚手架工具
* Python 环境（版本 3.12）
有关准备插件开发工具的详细信息，参见 [CLI](/zh/develop-plugin/getting-started/cli)。
在终端中运行 `dify version` 以确认脚手架工具已安装。
***
## 1. 初始化插件模板
运行以下命令为你的 Agent 插件创建开发模板：
```bash theme={null}
dify plugin init
```
按照屏幕提示操作，下方注释解释了每一项选择。
```bash theme={null}
➜ 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
...
```
初始化会创建一个文件夹，包含插件开发所需的全部资源：
```text theme={null}
├── 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
```
此插件的所有关键功能都在 `strategies/` 目录中。
***
## 2. 开发插件
Agent 策略插件的开发围绕两个文件展开：
* **插件声明**：`strategies/basic_agent.yaml`
* **插件实现**：`strategies/basic_agent.py`
### 2.1 定义参数
首先在 `strategies/basic_agent.yaml` 中声明插件的参数。这些参数驱动插件的核心功能，例如调用 LLM 或使用工具。
建议先从以下四个参数开始：
* **`model`**：要调用的大语言模型（例如 GPT-4、GPT-4o-mini）。
* **`tools`**：增强插件功能的工具列表。
* **`query`**：发送给模型的用户输入或提示词内容。
* **`maximum_iterations`**：最大迭代次数，用于防止过度计算。
示例：
```yaml theme={null}
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 会根据这些参数声明自动渲染配置界面：
### 2.2 获取参数并执行
用户填写这些字段后，插件会收到提交的值。在 `strategies/basic_agent.py` 中，定义一个 Pydantic 模型来校验传入的参数：
```python theme={null}
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
```
然后在 `_invoke` 中解析参数并运行你的策略逻辑：
```python theme={null}
class BasicAgentAgentStrategy(AgentStrategy):
def _invoke(self, parameters: dict[str, Any]) -> Generator[AgentInvokeMessage]:
params = BasicParams(**parameters)
```
## 3. 调用模型
调用模型是 Agent 策略的核心。使用 SDK 中的 `session.model.llm.invoke()` 调用 LLM，处理文本生成、对话等任务。
要让 LLM 驱动工具调用，它必须输出与各工具接口匹配的结构化参数，即根据用户指令推导出工具可接受的输入。
该方法接受以下参数：
* `model`
* `prompt_messages`
* `tools`
* `stop`
* `stream`
方法签名：
```python theme={null}
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:...
```
完整实现参见下方[示例代码](#示例代码)中的 **Invoke Model** 标签页。
完成上述设置后，每当用户输入命令，插件就会调用 LLM，根据模型输出构造工具调用参数，并让模型调度已配置的工具来完成复杂任务。
## 4. 调用工具
模型生成工具参数后，插件必须实际调用这些工具。使用 `session.tool.invoke()` 发起这些请求。
该方法接受以下参数：
* `provider`
* `tool_name`
* `parameters`
方法签名：
```python theme={null}
def invoke(
self,
provider_type: ToolProviderType,
provider: str,
tool_name: str,
parameters: dict[str, Any],
) -> Generator[ToolInvokeMessage, None, None]:...
```
要让 LLM 自行生成工具调用参数，将模型提取出的工具调用传入你的调用代码：
```python theme={null}
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},
)
```
现在，你的插件即可自动执行 Function Calling，例如获取当前时间。
## 5. 创建日志
复杂任务通常需要多个步骤，你需要跟踪每一步的结果，以分析决策并优化策略。SDK 的 `create_log_message` 和 `finish_log_message` 可在每次调用前后记录状态，从而加快问题诊断。
例如：
* 在调用模型之前记录一条「开始模型调用」消息，以显示执行进度。
* 模型响应后记录一条「调用成功」消息，使其输出可端到端追踪。
```python theme={null}
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，工作流日志会显示执行结果：
当任务跨越多轮时，在日志调用中设置 `parent` 参数，将日志嵌套为层级结构，便于跟踪：
```python theme={null}
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
```
### 示例代码
以下代码为 Agent 策略插件赋予调用模型的能力：
```python theme={null}
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
```
以下代码调用模型，并向模型所选的工具发送格式规范的请求：
```python theme={null}
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
```
一个完整示例，涵盖模型调用、工具处理和多轮日志记录：
```python theme={null}
import json
import time
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)
function_call_round_log = self.create_log_message(
label="Function Call Round1 ",
data={},
metadata={},
)
yield function_call_round_log
model_started_at = time.perf_counter()
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,
parent=function_call_round_log,
)
yield model_log
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 {}
)
tool_call_names = ""
tool_call_inputs = ""
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.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,
},
)
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
```
## 6. 调试插件
声明文件和实现代码完成后，验证插件能否正常运行。Dify 支持远程调试：前往 **插件管理** 获取调试密钥和远程服务器地址。
在插件项目中，将 `.env.example` 复制为 `.env`，并填入远程服务器地址和调试密钥。
```bash theme={null}
INSTALL_METHOD=remote
REMOTE_INSTALL_URL=debug.dify.ai:5003
REMOTE_INSTALL_KEY=********-****-****-****-************
```
然后运行：
```bash theme={null}
python -m main
```
插件会出现在你的工作空间中，团队成员也可以访问。
## 打包插件（可选）
一切正常后，运行以下命令打包你的插件：
```bash theme={null}
# Replace ./basic_agent/ with your actual plugin project path.
dify plugin package ./basic_agent/
```
当前文件夹中会出现一个名为 `basic_agent.difypkg`（与你的插件名称匹配）的文件。这就是你的最终插件包。
恭喜！你已经开发、测试并打包了你的 Agent 策略插件。
## 发布插件（可选）
你现在可以将插件包上传到 [Dify 插件仓库](https://github.com/langgenius/dify-plugins)。在此之前，确保它符合[插件发布指南](/zh/develop-plugin/publishing/marketplace-listing/release-to-dify-marketplace)。一旦获得批准，你的代码将合并到主分支，插件将自动在 [Dify 市场](https://marketplace.dify.ai/) 上线。
***
## 进一步探索
复杂任务通常需要多轮思考和工具调用，会重复「模型调用 → 工具使用」的循环，直到任务结束或达到迭代上限。在此过程中，妥善管理提示词至关重要。参见[完整的 Function Calling 实现](https://github.com/langgenius/dify-official-plugins/blob/main/agent-strategies/cot_agent/strategies/function_calling.py)，了解让模型调用外部工具并处理其输出的标准化方法。