An Agent Strategy is an extensible template that defines standard input content and output formats. By developing specific Agent strategy interface functionality, you can implement various Agent strategies such as CoT (Chain of Thought) / ToT (Tree of Thought) / GoT (Graph of Thought) / BoT (Backbone of Thought), and achieve complex strategies like Semantic Kernel.
Adding Fields in Manifest
To add Agent strategies in a plugin, add the plugins.agent_strategies field in the manifest.yaml file and define the Agent provider. Example code:
Create a function_calling.yaml file to define the Agent strategy code:
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.
parameters:
- name: model
type: model-selector
scope: tool-call&llm
required: true
label:
en_US: Model
- name: tools
type: array[tools]
required: true
label:
en_US: Tools list
- name: query
type: string
required: true
label:
en_US: Query
- name: max_iterations
type: number
required: false
default: 5
label:
en_US: Max Iterations
max: 50
min: 1
extra:
python:
source: strategies/function_calling.py
The code format is similar to the Tool standard format and defines four parameters: model, tools, query, and max_iterations to implement the most basic Agent strategy. This means that users can:
Select which model to use
Choose which tools to utilize
Configure the maximum number of iterations
Input a query to start executing the Agent
All these parameters work together to define how the Agent will process tasks and interact with the selected tools and models.
Functional Implementation Coding
Retrieving Parameters
Based on the four parameters defined earlier, the model type parameter is model-selector, and the tool type parameter is a special array[tools]. The retrieved parameters can be converted using the SDK’s built-in AgentModelConfig and list[ToolEntity].
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)
Invoking the Model
Invoking a specific model is an essential capability of the Agent plugin. Use the session.model.invoke() function from the SDK to call the model. The required input parameters can be obtained from the model.
You need to pass the model information (model_config), prompt information (prompt_messages), and tool information (tools). The prompt_messages parameter can be referenced using the example code below, while tool_messages require certain transformations.
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
Invoking Tools
Invoking tools is also a crucial capability of the Agent plugin. Use self.session.tool.invoke() to call a tool.
Required parameters include provider_type, provider, tool_name, and parameters. Typically, tool_name and parameters are generated by the LLM during Function Calling.
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 requires stream parsing.
Refer to the following function for parsing:
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'
Log
To view the Agent's thinking process, besides normal message returns, you can use specialized interfaces to display the entire Agent thought process in a tree structure.
Creating Logs
This interface creates and returns an AgentLogMessage, which represents a node in the log tree.
If a parent is passed in, it indicates this node has a parent node.
The default status is "Success". However, if you want to better display the task execution process, you can first set the status to "start" to show a "in progress" log, then update the log status to "Success" after the task is completed. This way, users can clearly see the entire process from start to finish.
The label will be used as the log title shown to users.
