Tasks

Previous

To ask GPT-4o to mark water plants as done in your todo list:

    # export OPENAI_API_KEY=""
    uv run launch_agent.py agent=GPT-4o task_name=mark_water_plants_as_done

task_name specifies the task. Tasks are defined in config/tasks/all_tasks.yaml. For example,

mark_water_plants_as_done:
  # Indicates class where reward logic is defined
  _target_: open_apps.tasks.tasks.MarkToDoDoneTask
  goal: Mark 'Water plants' as done in my todo list.
  todo_name: "Water plants"

Adding New Tasks

To add a new task using an existing reward function, simply add a new entry to the config/tasks/all_tasks.yaml:

add_my_special_item_to_todo:
  # _target_ defines the class containing the task reward logic
  _target_: open_apps.tasks.tasks.AddToDoTask
  goal: ENTER YOUR GOAL
  todo_name: ENTER TITLE of TODO
  is_done: false

You can select this new task by specifying the task_name=add_my_special_item_todo.

New custom tasks

To add a custom task with its own reward logic, create a new class in src/open_apps/tasks/tasks.py.

Your new class should inherit Task and implement a reward function, check_if_task_is_complete, indicating whether the task is complete:

@dataclass
class MyCustomTask(Task):
    def check_if_task_is_complete(
        self, 
        initiate_state: dict, 
        current_state: dict) -> bool:
        # we handle providing the initial and current states for you!
        # write your custom reward logic
        ...

Then create a corresponding entry in config/tasks/all_tasks.yaml:

my_custom_task:
    _target_: open_apps.tasks.tasks.MyCustomTask
    goal: ENTER

Finally, ask your agent to solve the task by specifying task_name=my_custom_task.

Agents