aworld/tools/README.md

Environments

Virtual environments for execution of various tools.

Running on the local, we assume that the virtual environment completes startup when the python application starts.

Be tool

You also can convert locally defined functions into tools for use in a task.

NOTE: The function must have a return value, preferably a string, as observed content.

from pydantic import Field

from aworld.core.tool.func_to_tool import be_tool


@be_tool(tool_name='example', tool_desc="example description")
def example_1() -> str:
    return "example_1"


@be_tool(tool_name='example')
def example_2(param: str) -> str:
    return f"example_2{param}"


@be_tool(tool_name='example', name="example_3_alias_name", desc="example_3 description")
def example_3(param_1: str = "param",
              param_2: str = Field(default="", description="param2 description")) -> str:
    return f"example_3{param_1}{param_2}"

The name of the tool is example, now, you can use these functions as tools in the framework.

Write tool

Detailed steps for building a tool:

1. Register action of your tool to action factory, and inherit ExecutableAction
2. Optional implement the act or async_act method
3. Register your tool to tool factory, and inherit Tool or AsyncTool
4. Write the step method to execute the abilities in the tool and generate observation, update finished Status.

from typing import List, Tuple, Dict, Any

from aworld.core.common import ActionModel, Observation
from aworld.core.tool.action import ExecutableAction
from aworld.core.tool.base import ActionFactory, ToolFactory, AgentInput
from aworld.tools.template_tool import TemplateTool

from examples.tools.tool_action import GymAction


@ToolFactory.register(name="openai_gym", desc="gym classic control game", supported_action=GymAction)
class OpenAIGym(TemplateTool):
    def step(self, action: List[ActionModel], **kwargs) -> Tuple[AgentInput, float, bool, bool, Dict[str, Any]]:
        ...
        state, reward, terminal, truncate, info = self.env.step(action)
        ...
        return (Observation(content=state),
                reward,
                terminal,
                truncate,
                info)


@ActionFactory.register(name=GymAction.PLAY.value.name,
                        desc=GymAction.PLAY.value.desc,
                        tool_name="openai_gym")
class Play(ExecutableAction):
    """There is only one Action, it can be implemented in the tool, registration is required here."""

You can view the example code to learn more.