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import json
import inspect
from pydantic import create_model, Field
from typing import Optional, Callable, Type, List, Any, Union, Dict
from .agent import Agent
from ..core.logging import logger
from ..core.registry import MODULE_REGISTRY, PARSE_FUNCTION_REGISTRY
from ..core.message import Message, MessageType
from ..models.model_configs import LLMConfig
from ..models.base_model import PARSER_VALID_MODE
from ..prompts.utils import DEFAULT_SYSTEM_PROMPT
from ..prompts.template import PromptTemplate
from ..actions.action import Action, ActionOutput
from ..utils.utils import generate_dynamic_class_name, make_parent_folder
from ..actions.customize_action import CustomizeAction
from ..actions.action import ActionInput
from ..tools.tool import Toolkit, Tool
class CustomizeAgent(Agent):
"""
CustomizeAgent provides a flexible framework for creating specialized LLM-powered agents without
writing custom code. It enables the creation of agents with well-defined inputs and outputs,
custom prompt templates, and configurable parsing strategies.
Attributes:
name (str): The name of the agent.
description (str): A description of the agent's purpose and capabilities.
prompt_template (PromptTemplate, optional): The prompt template that will be used for the agent's primary action.
prompt (str, optional): The prompt template that will be used for the agent's primary action.
Should contain placeholders in the format `{input_name}` for each input parameter.
llm_config (LLMConfig, optional): Configuration for the language model.
inputs (List[dict], optional): List of input specifications, where each dict (e.g., `{"name": str, "type": str, "description": str, ["required": bool]}`) contains:
- name (str): Name of the input parameter
- type (str): Type of the input
- description (str): Description of what the input represents
- required (bool, optional): Whether this input is required (default: True)
outputs (List[dict], optional): List of output specifications, where each dict (e.g., `{"name": str, "type": str, "description": str, ["required": bool]}`) contains:
- name (str): Name of the output field
- type (str): Type of the output
- description (str): Description of what the output represents
- required (bool, optional): Whether this output is required (default: True)
system_prompt (str, optional): The system prompt for the LLM. Defaults to DEFAULT_SYSTEM_PROMPT.
output_parser (Type[ActionOutput], optional): A custom class for parsing the LLM's output.
Must be a subclass of ActionOutput.
parse_mode (str, optional): Mode for parsing LLM output. Options are:
- "title": Parse outputs using section titles (default)
- "str": Parse as plain text
- "json": Parse as JSON
- "xml": Parse as XML
- "custom": Use a custom parsing function
parse_func (Callable, optional): Custom function for parsing LLM output when parse_mode is "custom".
Must accept a "content" parameter and return a dictionary.
title_format (str, optional): Format string for title parsing mode with {title} placeholder.
Default is "## {title}".
tools (list[Toolkit], optional): List of tools to be used by the agent.
max_tool_calls (int, optional): Maximum number of tool calls. Defaults to 5.
custom_output_format (str, optional): Specify the output format. Only used when `prompt_template` is used.
If not provided, the output format will be constructed from the `outputs` specification and `parse_mode`.
"""
def __init__(
self,
name: str,
description: str,
prompt: Optional[str] = None,
prompt_template: Optional[PromptTemplate] = None,
llm_config: Optional[LLMConfig] = None,
inputs: Optional[List[dict]] = None,
outputs: Optional[List[dict]] = None,
system_prompt: Optional[str] = None,
output_parser: Optional[Type[ActionOutput]] = None,
parse_mode: Optional[str] = "title",
parse_func: Optional[Callable] = None,
title_format: Optional[str] = None,
tools: Optional[List[Union[Toolkit, Tool]]] = None,
max_tool_calls: Optional[int] = 5,
custom_output_format: Optional[str] = None,
**kwargs
):
system_prompt = system_prompt or DEFAULT_SYSTEM_PROMPT
inputs = inputs or []
outputs = outputs or []
if tools is not None:
raw_tool_map = {tool.name: tool for tool in tools}
tools = [tool if isinstance(tool, Toolkit) else Toolkit(name=tool.name, tools=[tool]) for tool in tools]
else:
raw_tool_map = None
if prompt is not None and prompt_template is not None:
logger.warning("Both `prompt` and `prompt_template` are provided in `CustomizeAgent`. `prompt_template` will be used.")
prompt = None
if isinstance(parse_func, str):
if not PARSE_FUNCTION_REGISTRY.has_function(parse_func):
raise ValueError(f"parse function `{parse_func}` is not registered! To instantiate a CustomizeAgent from a file, you should use decorator `@register_parse_function` to register the parse function.")
parse_func = PARSE_FUNCTION_REGISTRY.get_function(parse_func)
if isinstance(output_parser, str):
output_parser = MODULE_REGISTRY.get_module(output_parser)
# set default title format
if parse_mode == "title" and title_format is None:
title_format = "## {title}"
# validate the data
self.validate_data(
prompt = prompt,
prompt_template = prompt_template,
inputs = inputs,
outputs = outputs,
output_parser = output_parser,
parse_mode = parse_mode,
parse_func = parse_func,
title_format = title_format
)
customize_action = self.create_customize_action(
name=name,
desc=description,
prompt=prompt,
prompt_template=prompt_template,
inputs=inputs,
outputs=outputs,
parse_mode=parse_mode,
parse_func=parse_func,
output_parser=output_parser,
title_format=title_format,
custom_output_format=custom_output_format ,
tools=tools,
max_tool_calls=max_tool_calls
)
super().__init__(
name=name,
description=description,
llm_config=llm_config,
system_prompt=system_prompt,
actions=[customize_action],
**kwargs
)
self._store_inputs_outputs_info(inputs, outputs, raw_tool_map)
self.output_parser = output_parser
self.parse_mode = parse_mode
self.parse_func = parse_func
self.title_format = title_format
self.tools = tools
self.max_tool_calls = max_tool_calls
self.custom_output_format = custom_output_format
def _add_tools(self, tools: List[Toolkit]):
self.get_action(self.customize_action_name).add_tools(tools)
@property
def customize_action_name(self) -> str:
"""
Get the name of the primary custom action for this agent.
Returns:
The name of the primary custom action
"""
for action in self.actions:
if action.name != self.cext_action_name:
return action.name
raise ValueError("Couldn't find the customize action name!")
@property
def action(self) -> Action:
"""
Get the primary custom action for this agent.
Returns:
The primary custom action
"""
return self.get_action(self.customize_action_name)
@property
def prompt(self) -> str:
"""
Get the prompt for the primary custom action.
Returns:
The prompt for the primary custom action
"""
return self.action.prompt
@property
def prompt_template(self) -> PromptTemplate:
"""
Get the prompt template for the primary custom action.
Returns:
The prompt template for the primary custom action
"""
return self.action.prompt_template
def validate_data(self, prompt: str, prompt_template: PromptTemplate, inputs: List[dict], outputs: List[dict], output_parser: Type[ActionOutput], parse_mode: str, parse_func: Callable, title_format: str):
# check if the prompt is provided
if prompt is None and prompt_template is None:
raise ValueError("`prompt` or `prompt_template` is required when creating a CustomizeAgent.")
# check if all the inputs are in the prompt (only used when prompt_template is not provided)
if prompt_template is None and inputs:
all_input_names = [input_item["name"] for input_item in inputs]
inputs_names_not_in_prompt = [name for name in all_input_names if f'{{{name}}}' not in prompt]
if inputs_names_not_in_prompt:
raise KeyError(f"The following inputs are not found in the prompt: {inputs_names_not_in_prompt}.")
# check if the output_parser is valid
if output_parser is not None:
self._check_output_parser(outputs, output_parser)
# check the parse_mode, parse_func, and title_format
if parse_mode not in PARSER_VALID_MODE:
raise ValueError(f"'{parse_mode}' is an invalid value for `parse_mode`. Available choices: {PARSER_VALID_MODE}.")
if parse_mode == "custom":
if parse_func is None:
raise ValueError("`parse_func` (a callable function with an input argument `content`) must be provided when `parse_mode` is 'custom'.")
if parse_func is not None:
if not callable(parse_func):
raise ValueError("`parse_func` must be a callable function with an input argument `content`.")
signature = inspect.signature(parse_func)
if "content" not in signature.parameters:
raise ValueError("`parse_func` must have an input argument `content`.")
if not PARSE_FUNCTION_REGISTRY.has_function(parse_func.__name__):
logger.warning(
f"parse function `{parse_func.__name__}` is not registered. This can cause issues when loading the agent from a file. "
f"It is recommended to register the parse function using `register_parse_function`:\n"
f"from evoagentx.core.registry import register_parse_function\n"
f"@register_parse_function\n"
f"def {parse_func.__name__}(content: str) -> dict:\n"
r" return {'output_name': output_value}"
)
if title_format is not None:
if parse_mode != "title":
logger.warning(f"`title_format` will not be used because `parse_mode` is '{parse_mode}', not 'title'. Set `parse_mode='title'` to use title formatting.")
if r'{title}' not in title_format:
raise ValueError(r"`title_format` must contain the placeholder `{title}`.")
def create_customize_action(
self,
name: str,
desc: str,
prompt: str,
prompt_template: PromptTemplate,
inputs: List[dict],
outputs: List[dict],
parse_mode: str,
parse_func: Optional[Callable] = None,
output_parser: Optional[ActionOutput] = None,
title_format: Optional[str] = "## {title}",
custom_output_format: Optional[str] = None,
tools: Optional[List[Toolkit]] = None,
max_tool_calls: Optional[int] = 5
) -> Action:
"""Create a custom action based on the provided specifications.
This method dynamically generates an Action class and instance with:
- Input parameters defined by the inputs specification
- Output format defined by the outputs specification
- Custom execution logic using the customize_action_execute function
- If tools is provided, returns a CustomizeAction action instead
Args:
name: Base name for the action
desc: Description of the action
prompt: Prompt template for the action
prompt_template: Prompt template for the action
inputs: List of input field specifications
outputs: List of output field specifications
parse_mode: Mode to use for parsing LLM output
parse_func: Optional custom parsing function
output_parser: Optional custom output parser class
tools: Optional list of tools
Returns:
A newly created Action instance
"""
assert prompt is not None or prompt_template is not None, "must provide `prompt` or `prompt_template` when creating CustomizeAgent"
# create the action input type
action_input_fields = {}
for field in inputs:
required = field.get("required", True)
if required:
action_input_fields[field["name"]] = (str, Field(description=field["description"]))
else:
action_input_fields[field["name"]] = (Optional[str], Field(default=None, description=field["description"]))
action_input_type = create_model(
self._get_unique_class_name(
generate_dynamic_class_name(name+" action_input")
),
**action_input_fields,
__base__=ActionInput
)
# create the action output type
if output_parser is None:
action_output_fields = {}
for field in outputs:
required = field.get("required", True)
if required:
action_output_fields[field["name"]] = (Any, Field(description=field["description"]))
else:
action_output_fields[field["name"]] = (Optional[Any], Field(default=None, description=field["description"]))
action_output_type = create_model(
self._get_unique_class_name(
generate_dynamic_class_name(name+" action_output")
),
**action_output_fields,
__base__=ActionOutput,
# get_content_data=customize_get_content_data,
# to_str=customize_to_str
)
else:
# self._check_output_parser(outputs, output_parser)
action_output_type = output_parser
action_cls_name = self._get_unique_class_name(
generate_dynamic_class_name(name+" action")
)
# Create CustomizeAction-based action with parsing properties only
customize_action_cls = create_model(
action_cls_name,
__base__=CustomizeAction
)
customize_action = customize_action_cls(
name=action_cls_name,
description=desc,
prompt=prompt,
prompt_template=prompt_template,
inputs_format=action_input_type,
outputs_format=action_output_type,
parse_mode=parse_mode,
parse_func=parse_func,
title_format=title_format,
custom_output_format=custom_output_format,
max_tool_try=max_tool_calls,
tools=tools
)
return customize_action
def _check_output_parser(self, outputs: List[dict], output_parser: Type[ActionOutput]):
if output_parser is not None:
if not isinstance(output_parser, type):
raise TypeError(f"output_parser must be a class, but got {type(output_parser).__name__}")
if not issubclass(output_parser, ActionOutput):
raise ValueError(f"`output_parser` must be a class and a subclass of `ActionOutput`, but got `{output_parser.__name__}`.")
# check if the output parser is compatible with the outputs
output_parser_fields = output_parser.get_attrs()
all_output_names = [output_item["name"] for output_item in outputs]
for field in output_parser_fields:
if field not in all_output_names:
raise ValueError(
f"The output parser `{output_parser.__name__}` is not compatible with the `outputs`.\n"
f"The output parser fields: {output_parser_fields}.\n"
f"The outputs: {all_output_names}.\n"
f"All the fields in the output parser must be present in the outputs."
)
def _store_inputs_outputs_info(self, inputs: List[dict], outputs: List[dict], tool_map: Dict[str, Union[Toolkit, Tool]]):
self._action_input_types, self._action_input_required = {}, {}
for field in inputs:
required = field.get("required", True)
self._action_input_types[field["name"]] = field["type"]
self._action_input_required[field["name"]] = required
self._action_output_types, self._action_output_required = {}, {}
for field in outputs:
required = field.get("required", True)
self._action_output_types[field["name"]] = field["type"]
self._action_output_required[field["name"]] = required
self._raw_tool_map = tool_map
def __call__(self, inputs: dict = None, return_msg_type: MessageType = MessageType.UNKNOWN, **kwargs) -> Message:
"""
Call the customize action.
Args:
inputs (dict): The inputs to the customize action.
**kwargs (Any): Additional keyword arguments.
Returns:
ActionOutput: The output of the customize action.
"""
# return self.execute(action_name=self.customize_action_name, action_input_data=inputs, **kwargs)
inputs = inputs or {}
return super().__call__(action_name=self.customize_action_name, action_input_data=inputs, return_msg_type=return_msg_type, **kwargs)
def get_customize_agent_info(self) -> dict:
"""
Get the information of the customize agent.
"""
customize_action = self.get_action(self.customize_action_name)
action_input_params = customize_action.inputs_format.get_attrs()
action_output_params = customize_action.outputs_format.get_attrs()
config = {
"class_name": "CustomizeAgent",
"name": self.name,
"description": self.description,
"prompt": customize_action.prompt,
"prompt_template": customize_action.prompt_template.to_dict() if customize_action.prompt_template is not None else None,
# "llm_config": self.llm_config.to_dict(exclude_none=True),
"inputs": [
{
"name": field,
"type": self._action_input_types[field],
"description": field_info.description,
"required": self._action_input_required[field]
}
for field, field_info in customize_action.inputs_format.model_fields.items() if field in action_input_params
],
"outputs": [
{
"name": field,
"type": self._action_output_types[field],
"description": field_info.description,
"required": self._action_output_required[field]
}
for field, field_info in customize_action.outputs_format.model_fields.items() if field in action_output_params
],
"system_prompt": self.system_prompt,
"output_parser": self.output_parser.__name__ if self.output_parser is not None else None,
"parse_mode": self.parse_mode,
"parse_func": self.parse_func.__name__ if self.parse_func is not None else None,
"title_format": self.title_format,
"tool_names": [tool.name for tool in customize_action.tools] if customize_action.tools else [],
"max_tool_calls": self.max_tool_calls,
"custom_output_format": self.custom_output_format
}
return config
@classmethod
def load_module(cls, path: str, llm_config: LLMConfig = None, tools: List[Union[Toolkit, Tool]] = None, **kwargs) -> "CustomizeAgent":
"""
load the agent from local storage. Must provide `llm_config` when loading the agent from local storage.
If tools is provided, tool_names must also be provided.
Args:
path: The path of the file
llm_config: The LLMConfig instance
tool_names: List of tool names to be used by the agent. If provided,
tool_dict: Dictionary mapping tool names to Tool instances. Required when tool_names is provided.
Returns:
CustomizeAgent: The loaded agent instance
"""
match_dict = {}
agent = super().load_module(path=path, llm_config=llm_config, **kwargs)
if tools:
match_dict = {tool.name:tool for tool in tools}
if agent.get("tool_names", None):
assert tools is not None, "must provide `tools: List[Union[Toolkit, Tool]]` when using `load_module` or `from_file` to load the agent from local storage and `tool_names` is not None or empty"
added_tools = [match_dict[tool_name] for tool_name in agent["tool_names"]]
agent["tools"] = [tool if isinstance(tool, Toolkit) else Toolkit(name=tool.name, tools=[tool]) for tool in added_tools]
return agent
def save_module(self, path: str, ignore: List[str] = [], **kwargs)-> str:
"""Save the customize agent's configuration to a JSON file.
Args:
path: File path where the configuration should be saved
ignore: List of keys to exclude from the saved configuration
**kwargs (Any): Additional parameters for the save operation
Returns:
The path where the configuration was saved
"""
config = self.get_customize_agent_info()
for ignore_key in ignore:
config.pop(ignore_key, None)
# Save to JSON file
make_parent_folder(path)
with open(path, 'w', encoding='utf-8') as f:
json.dump(config, f, indent=4, ensure_ascii=False)
return path
def _get_unique_class_name(self, candidate_name: str) -> str:
"""
Get a unique class name by checking if it already exists in the registry.
If it does, append "Vx" to make it unique.
"""
if not MODULE_REGISTRY.has_module(candidate_name):
return candidate_name
i = 1
while True:
unique_name = f"{candidate_name}V{i}"
if not MODULE_REGISTRY.has_module(unique_name):
break
i += 1
return unique_name
def get_config(self) -> dict:
"""
Get a dictionary containing all necessary configuration to recreate this agent.
Returns:
dict: A configuration dictionary that can be used to initialize a new Agent instance
with the same properties as this one.
"""
config = self.get_customize_agent_info()
config["llm_config"] = self.llm_config.to_dict()
tool_names = config.pop("tool_names", None)
if tool_names:
config["tools"] = [self._raw_tool_map[name] for name in tool_names]
return config
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