task

langroid/agent/task.py

Task(agent=None, name='', llm_delegate=False, single_round=False, system_message='', user_message='', restart=True, default_human_response=None, interactive=True, only_user_quits_root=False, erase_substeps=False, allow_null_result=True, max_stalled_steps=5, done_if_no_response=[], done_if_response=[])

A Task wraps an Agent object, and sets up the Agent's goals and instructions. A Task maintains two key variables:

• self.pending_message, which is the message awaiting a response, and
• self.pending_sender, which is the entity that sent the pending message.

The possible responders to self.pending_message are the Agent's own "native" responders (agent_response, llm_response, and user_response), and the run() methods of any sub-tasks. All responders have the same type-signature (somewhat simplified):

str | ChatDocument -> ChatDocument

Responders may or may not specify an intended recipient of their generated response.

The main top-level method in the Task class is run(), which repeatedly calls step() until done() returns true. The step() represents a "turn" in the conversation: this method sequentially (in round-robin fashion) calls the responders until it finds one that generates a valid response to the pending_message (as determined by the valid() method). Once a valid response is found, step() updates the pending_message and pending_sender variables, and on the next iteration, step() re-starts its search for a valid response from the beginning of the list of responders (the exception being that the human user always gets a chance to respond after each non-human valid response). This process repeats until done() returns true, at which point run() returns the value of result(), which is the final result of the task.

Parameters:

Name	Type	Description	Default
agent	Agent	agent associated with the task	None
name	str	name of the task	''
llm_delegate	bool	[Deprecated, not used; use done_if_response, done_if_no_response instead] Whether to delegate control to LLM; conceptually, the "controlling entity" is the one "seeking" responses to its queries, and has a goal it is aiming to achieve. The "controlling entity" is either the LLM or the USER. (Note within a Task there is just one LLM, and all other entities are proxies of the "User" entity).	False
single_round	bool	[Deprecated: Use done_if_response, done_if_no_response instead]. If true, task runs until one message by controller, and subsequent response by non-controller. If false, runs for the specified number of turns in run, or until done() is true. One run of step() is considered a "turn".	False
system_message	str	if not empty, overrides agent's system_message	''
user_message	str	if not empty, overrides agent's user_message	''
restart	bool	if true, resets the agent's message history	True
default_human_response	str	default response from user; useful for testing, to avoid interactive input from user. [Instead of this, setting interactive usually suffices]	None
interactive	bool	if true, wait for human input after each non-human response (prevents infinite loop of non-human responses). Default is true. If false, then default_human_response is set to ""	True
only_user_quits_root	bool	if true, only user can quit the root task. [This param is ignored & deprecated; Keeping for backward compatibility. Instead of this, setting interactive suffices]	False
erase_substeps	bool	if true, when task completes, erase intermediate conversation with subtasks from this agent's message_history, and also erase all subtask agents' message_history. Note: erasing can reduce prompt sizes, but results in repetitive sub-task delegation.	False
allow_null_result	bool	[Deprecated, may be removed in future.] If true, allow null (empty or NO_ANSWER) as the result of a step or overall task result. Optional, default is True.	True
max_stalled_steps	int	task considered done after this many consecutive steps with no progress. Default is 3.	5
done_if_no_response	List[Responder]	consider task done if NULL response from any of these responders. Default is empty list.	[]
done_if_response	List[Responder]	consider task done if NON-NULL response from any of these responders. Default is empty list.	[]

Source code in langroid/agent/task.py

def __init__(
    self,
    agent: Optional[Agent] = None,
    name: str = "",
    llm_delegate: bool = False,
    single_round: bool = False,
    system_message: str = "",
    user_message: str | None = "",
    restart: bool = True,
    default_human_response: Optional[str] = None,
    interactive: bool = True,
    only_user_quits_root: bool = False,
    erase_substeps: bool = False,
    allow_null_result: bool = True,
    max_stalled_steps: int = 5,
    done_if_no_response: List[Responder] = [],
    done_if_response: List[Responder] = [],
):
    """
    A task to be performed by an agent.

    Args:
        agent (Agent): agent associated with the task
        name (str): name of the task
        llm_delegate (bool):
            [Deprecated, not used; use `done_if_response`, `done_if_no_response`
            instead]
            Whether to delegate control to LLM; conceptually,
            the "controlling entity" is the one "seeking" responses to its queries,
            and has a goal it is aiming to achieve. The "controlling entity" is
            either the LLM or the USER. (Note within a Task there is just one
            LLM, and all other entities are proxies of the "User" entity).
        single_round (bool):
            [Deprecated: Use `done_if_response`, `done_if_no_response` instead].
            If true, task runs until one message by controller,
            and subsequent response by non-controller. If false, runs for the
            specified number of turns in `run`, or until `done()` is true.
            One run of step() is considered a "turn".
        system_message (str): if not empty, overrides agent's system_message
        user_message (str): if not empty, overrides agent's user_message
        restart (bool): if true, resets the agent's message history
        default_human_response (str): default response from user; useful for
            testing, to avoid interactive input from user.
            [Instead of this, setting `interactive` usually suffices]
        interactive (bool): if true, wait for human input after each non-human
            response (prevents infinite loop of non-human responses).
            Default is true. If false, then `default_human_response` is set to ""
        only_user_quits_root (bool): if true, only user can quit the root task.
            [This param is ignored & deprecated; Keeping for backward compatibility.
            Instead of this, setting `interactive` suffices]
        erase_substeps (bool): if true, when task completes, erase intermediate
            conversation with subtasks from this agent's `message_history`, and also
            erase all subtask agents' `message_history`.
            Note: erasing can reduce prompt sizes, but results in repetitive
            sub-task delegation.
        allow_null_result (bool): [Deprecated, may be removed in future.]
            If true, allow null (empty or NO_ANSWER)
            as the result of a step or overall task result.
            Optional, default is True.
        max_stalled_steps (int): task considered done after this many consecutive
            steps with no progress. Default is 3.
        done_if_no_response (List[Responder]): consider task done if NULL
            response from any of these responders. Default is empty list.
        done_if_response (List[Responder]): consider task done if NON-NULL
            response from any of these responders. Default is empty list.
    """
    if agent is None:
        agent = ChatAgent()
    self.callbacks = SimpleNamespace(
        show_subtask_response=noop_fn,
        set_parent_agent=noop_fn,
    )
    # copy the agent's config, so that we don't modify the original agent's config,
    # which may be shared by other agents.
    try:
        config_copy = copy.deepcopy(agent.config)
        agent.config = config_copy
    except Exception:
        logger.warning(
            """
            Failed to deep-copy Agent config during task creation, 
            proceeding with original config. Be aware that changes to 
            the config may affect other agents using the same config.
            """
        )

    if isinstance(agent, ChatAgent) and len(agent.message_history) == 0 or restart:
        agent = cast(ChatAgent, agent)
        agent.clear_history(0)
        agent.clear_dialog()
        # possibly change the system and user messages
        if system_message:
            # we always have at least 1 task_message
            agent.set_system_message(system_message)
        if user_message:
            agent.set_user_message(user_message)
    self.max_cost: float = 0
    self.max_tokens: int = 0
    self.session_id: str = ""
    self.logger: None | RichFileLogger = None
    self.tsv_logger: None | logging.Logger = None
    self.color_log: bool = False if settings.notebook else True
    self.agent = agent
    self.step_progress = False  # progress in current step?
    self.n_stalled_steps = 0  # how many consecutive steps with no progress?
    self.max_stalled_steps = max_stalled_steps
    self.done_if_response = [r.value for r in done_if_response]
    self.done_if_no_response = [r.value for r in done_if_no_response]
    self.is_done = False  # is task done (based on response)?
    self.is_pass_thru = False  # is current response a pass-thru?
    self.task_progress = False  # progress in current task (since run or run_async)?
    if name:
        # task name overrides name in agent config
        agent.config.name = name
    self.name = name or agent.config.name
    self.value: str = self.name
    self.default_human_response = default_human_response
    if default_human_response is not None and default_human_response == "":
        interactive = False
    self.interactive = interactive
    self.message_history_idx = -1
    if interactive:
        only_user_quits_root = True
    else:
        default_human_response = default_human_response or ""
        only_user_quits_root = False
    if default_human_response is not None:
        self.agent.default_human_response = default_human_response
    if self.interactive:
        self.agent.default_human_response = None
    self.only_user_quits_root = only_user_quits_root
    # set to True if we want to collapse multi-turn conversation with sub-tasks into
    # just the first outgoing message and last incoming message.
    # Note this also completely erases sub-task agents' message_history.
    self.erase_substeps = erase_substeps
    self.allow_null_result = allow_null_result

    agent_entity_responders = agent.entity_responders()
    agent_entity_responders_async = agent.entity_responders_async()
    self.responders: List[Responder] = [e for e, _ in agent_entity_responders]
    self.responders_async: List[Responder] = [
        e for e, _ in agent_entity_responders_async
    ]
    self.non_human_responders: List[Responder] = [
        r for r in self.responders if r != Entity.USER
    ]
    self.non_human_responders_async: List[Responder] = [
        r for r in self.responders_async if r != Entity.USER
    ]

    self.human_tried = False  # did human get a chance to respond in last step?
    self._entity_responder_map: Dict[
        Entity, Callable[..., Optional[ChatDocument]]
    ] = dict(agent_entity_responders)

    self._entity_responder_async_map: Dict[
        Entity, Callable[..., Coroutine[Any, Any, Optional[ChatDocument]]]
    ] = dict(agent_entity_responders_async)

    self.name_sub_task_map: Dict[str, Task] = {}
    # latest message in a conversation among entities and agents.
    self.pending_message: Optional[ChatDocument] = None
    self.pending_sender: Responder = Entity.USER
    self.single_round = single_round
    self.turns = -1  # no limit
    self.llm_delegate = llm_delegate
    if llm_delegate:
        self.controller = Entity.LLM
        if self.single_round:
            # 0: User instructs (delegating to LLM);
            # 1: LLM asks;
            # 2: user replies.
            self.turns = 2
    else:
        self.controller = Entity.USER
        if self.single_round:
            self.turns = 1  # 0: User asks, 1: LLM replies.

    # other sub_tasks this task can delegate to
    self.sub_tasks: List[Task] = []
    self.parent_task: Set[Task] = set()
    self.caller: Task | None = None  # which task called this task's `run` method

clone(i)

Returns a copy of this task, with a new agent.

Source code in langroid/agent/task.py

def clone(self, i: int) -> "Task":
    """
    Returns a copy of this task, with a new agent.
    """
    assert isinstance(self.agent, ChatAgent), "Task clone only works for ChatAgent"
    agent: ChatAgent = self.agent.clone(i)
    return Task(
        agent,
        name=self.name + f"-{i}",
        llm_delegate=self.llm_delegate,
        single_round=self.single_round,
        system_message=self.agent.system_message,
        user_message=self.agent.user_message,
        restart=False,
        default_human_response=self.default_human_response,
        interactive=self.interactive,
        erase_substeps=self.erase_substeps,
        allow_null_result=self.allow_null_result,
        max_stalled_steps=self.max_stalled_steps,
        done_if_no_response=[Entity(s) for s in self.done_if_no_response],
        done_if_response=[Entity(s) for s in self.done_if_response],
    )

kill_session(session_id='') classmethod

Kill the session with the given session_id.

Source code in langroid/agent/task.py

@classmethod
def kill_session(cls, session_id: str = "") -> None:
    """
    Kill the session with the given session_id.
    """
    session_id_kill_key = f"{session_id}:kill"
    cls.cache.store(session_id_kill_key, "1")

kill()

Kill the task run associated with the current session.

Source code in langroid/agent/task.py

def kill(self) -> None:
    """
    Kill the task run associated with the current session.
    """
    self._cache_session_store("kill", "1")

add_sub_task(task)

Add a sub-task (or list of subtasks) that this task can delegate (or fail-over) to. Note that the sequence of sub-tasks is important, since these are tried in order, as the parent task searches for a valid response.

Parameters:

Name	Type	Description	Default
task	Task | List[Task]	sub-task(s) to add	required

Source code in langroid/agent/task.py

def add_sub_task(self, task: Task | List[Task]) -> None:
    """
    Add a sub-task (or list of subtasks) that this task can delegate
    (or fail-over) to. Note that the sequence of sub-tasks is important,
    since these are tried in order, as the parent task searches for a valid
    response.

    Args:
        task (Task|List[Task]): sub-task(s) to add
    """

    if isinstance(task, list):
        for t in task:
            self.add_sub_task(t)
        return
    assert isinstance(task, Task), f"added task must be a Task, not {type(task)}"

    task.parent_task.add(self)  # add myself to set of parent tasks of `task`
    self.sub_tasks.append(task)
    self.name_sub_task_map[task.name] = task
    self.responders.append(cast(Responder, task))
    self.responders_async.append(cast(Responder, task))
    self.non_human_responders.append(cast(Responder, task))
    self.non_human_responders_async.append(cast(Responder, task))

init(msg=None)

Initialize the task, with an optional message to start the conversation. Initializes self.pending_message and self.pending_sender. Args: msg (str|ChatDocument): optional message to start the conversation.

Returns:

Type	Description
ChatDocument | None	the initialized self.pending_message.
ChatDocument | None	Currently not used in the code, but provided for convenience.

Source code in langroid/agent/task.py

def init(self, msg: None | str | ChatDocument = None) -> ChatDocument | None:
    """
    Initialize the task, with an optional message to start the conversation.
    Initializes `self.pending_message` and `self.pending_sender`.
    Args:
        msg (str|ChatDocument): optional message to start the conversation.

    Returns:
        (ChatDocument|None): the initialized `self.pending_message`.
        Currently not used in the code, but provided for convenience.
    """
    self.pending_sender = Entity.USER
    if isinstance(msg, str):
        self.pending_message = ChatDocument(
            content=msg,
            metadata=ChatDocMetaData(
                sender=Entity.USER,
            ),
        )
    else:
        self.pending_message = msg
        if self.pending_message is not None and self.caller is not None:
            # msg may have come from `caller`, so we pretend this is from
            # the CURRENT task's USER entity
            self.pending_message.metadata.sender = Entity.USER

    self._show_pending_message_if_debug()

    if self.caller is not None and self.caller.logger is not None:
        self.logger = self.caller.logger
    else:
        self.logger = RichFileLogger(f"logs/{self.name}.log", color=self.color_log)

    if self.caller is not None and self.caller.tsv_logger is not None:
        self.tsv_logger = self.caller.tsv_logger
    else:
        self.tsv_logger = setup_file_logger("tsv_logger", f"logs/{self.name}.tsv")
        header = ChatDocLoggerFields().tsv_header()
        self.tsv_logger.info(f" \tTask\tResponder\t{header}")

    self.log_message(Entity.USER, self.pending_message)
    return self.pending_message

run(msg=None, turns=-1, caller=None, max_cost=0, max_tokens=0, session_id='')

Synchronous version of run_async(). See run_async() for details.

Source code in langroid/agent/task.py

def run(
    self,
    msg: Optional[str | ChatDocument] = None,
    turns: int = -1,
    caller: None | Task = None,
    max_cost: float = 0,
    max_tokens: int = 0,
    session_id: str = "",
) -> Optional[ChatDocument]:
    """Synchronous version of `run_async()`.
    See `run_async()` for details."""
    self.task_progress = False
    self.n_stalled_steps = 0
    self.max_cost = max_cost
    self.max_tokens = max_tokens
    self.session_id = session_id
    self._set_alive()

    assert (
        msg is None or isinstance(msg, str) or isinstance(msg, ChatDocument)
    ), f"msg arg in Task.run() must be None, str, or ChatDocument, not {type(msg)}"

    if (
        isinstance(msg, ChatDocument)
        and msg.metadata.recipient != ""
        and msg.metadata.recipient != self.name
    ):
        # this task is not the intended recipient so return None
        return None
    self._pre_run_loop(
        msg=msg,
        caller=caller,
        is_async=False,
    )
    # self.turns overrides if it is > 0 and turns not set (i.e. = -1)
    turns = self.turns if turns < 0 else turns
    i = 0
    while True:
        self.step()
        done, status = self.done()
        if done:
            if self._level == 0 and not settings.quiet:
                print("[magenta]Bye, hope this was useful!")
            break
        i += 1
        if turns > 0 and i >= turns:
            status = StatusCode.MAX_TURNS
            break

    final_result = self.result()
    if final_result is not None:
        final_result.metadata.status = status
    self._post_run_loop()
    return final_result

run_async(msg=None, turns=-1, caller=None, max_cost=0, max_tokens=0, session_id='') async

Loop over step() until task is considered done or turns is reached. Runs asynchronously.

Parameters:

Name	Type	Description	Default
msg	str | ChatDocument	initial message to process; if None, the LLM will respond to its initial self.task_messages which set up and kick off the overall task. The agent tries to achieve this goal by looping over self.step() until the task is considered done; this can involve a series of messages produced by Agent, LLM or Human (User).	None
turns	int	number of turns to run the task for; default is -1, which means run until task is done.	-1
caller	Task | None	the calling task, if any	None
max_cost	float	max cost allowed for the task (default 0 -> no limit)	0
max_tokens	int	max tokens allowed for the task (default 0 -> no limit)	0
session_id	str	session id for the task	''

Returns:

Type	Description
Optional[ChatDocument]	Optional[ChatDocument]: valid result of the task.

Source code in langroid/agent/task.py

async def run_async(
    self,
    msg: Optional[str | ChatDocument] = None,
    turns: int = -1,
    caller: None | Task = None,
    max_cost: float = 0,
    max_tokens: int = 0,
    session_id: str = "",
) -> Optional[ChatDocument]:
    """
    Loop over `step()` until task is considered done or `turns` is reached.
    Runs asynchronously.

    Args:
        msg (str|ChatDocument): initial message to process; if None,
            the LLM will respond to its initial `self.task_messages`
            which set up and kick off the overall task.
            The agent tries to achieve this goal by looping
            over `self.step()` until the task is considered
            done; this can involve a series of messages produced by Agent,
            LLM or Human (User).
        turns (int): number of turns to run the task for;
            default is -1, which means run until task is done.
        caller (Task|None): the calling task, if any
        max_cost (float): max cost allowed for the task (default 0 -> no limit)
        max_tokens (int): max tokens allowed for the task (default 0 -> no limit)
        session_id (str): session id for the task

    Returns:
        Optional[ChatDocument]: valid result of the task.
    """

    # Even if the initial "sender" is not literally the USER (since the task could
    # have come from another LLM), as far as this agent is concerned, the initial
    # message can be considered to be from the USER
    # (from the POV of this agent's LLM).
    self.task_progress = False
    self.n_stalled_steps = 0
    self.max_cost = max_cost
    self.max_tokens = max_tokens
    self.session_id = session_id
    self._set_alive()

    if (
        isinstance(msg, ChatDocument)
        and msg.metadata.recipient != ""
        and msg.metadata.recipient != self.name
    ):
        # this task is not the intended recipient so return None
        return None
    self._pre_run_loop(
        msg=msg,
        caller=caller,
        is_async=True,
    )
    # self.turns overrides if it is > 0 and turns not set (i.e. = -1)
    turns = self.turns if turns < 0 else turns
    i = 0
    while True:
        await self.step_async()
        done, status = self.done()
        if done:
            if self._level == 0 and not settings.quiet:
                print("[magenta]Bye, hope this was useful!")
            break
        i += 1
        if turns > 0 and i >= turns:
            status = StatusCode.MAX_TURNS
            break

    final_result = self.result()
    if final_result is not None:
        final_result.metadata.status = status
    self._post_run_loop()
    return final_result

step(turns=-1)

Synchronous version of step_async(). See step_async() for details. TODO: Except for the self.response() calls, this fn should be identical to step_async(). Consider refactoring to avoid duplication.

Source code in langroid/agent/task.py

def step(self, turns: int = -1) -> ChatDocument | None:
    """
    Synchronous version of `step_async()`. See `step_async()` for details.
    TODO: Except for the self.response() calls, this fn should be identical to
    `step_async()`. Consider refactoring to avoid duplication.
    """
    self.is_done = False
    self.step_progress = False
    parent = self.pending_message
    recipient = (
        ""
        if self.pending_message is None
        else self.pending_message.metadata.recipient
    )
    if not self._valid_recipient(recipient):
        logger.warning(f"Invalid recipient: {recipient}")
        error_doc = ChatDocument(
            content=f"Invalid recipient: {recipient}",
            metadata=ChatDocMetaData(
                sender=Entity.AGENT,
                sender_name=Entity.AGENT,
            ),
        )
        self._process_valid_responder_result(Entity.AGENT, parent, error_doc)
        return error_doc

    responders: List[Responder] = self.non_human_responders.copy()

    if (
        Entity.USER in self.responders
        and not self.human_tried
        and not self.agent.has_tool_message_attempt(self.pending_message)
    ):
        # Give human first chance if they haven't been tried in last step,
        # and the msg is not a tool-call attempt;
        # This ensures human gets a chance to respond,
        #   other than to a LLM tool-call.
        # When there's a tool msg attempt we want the
        #  Agent to be the next responder; this only makes a difference in an
        #  interactive setting: LLM generates tool, then we don't want user to
        #  have to respond, and instead let the agent_response handle the tool.

        responders.insert(0, Entity.USER)

    found_response = False
    for r in responders:
        self.is_pass_thru = False
        if not self._can_respond(r):
            # create dummy msg for logging
            log_doc = ChatDocument(
                content="[CANNOT RESPOND]",
                function_call=None,
                metadata=ChatDocMetaData(
                    sender=r if isinstance(r, Entity) else Entity.USER,
                    sender_name=str(r),
                    recipient=recipient,
                ),
            )
            self.log_message(r, log_doc)
            continue
        self.human_tried = r == Entity.USER
        result = self.response(r, turns)
        self.is_done = self._is_done_response(result, r)
        self.is_pass_thru = PASS in result.content if result else False
        if self.valid(result, r):
            found_response = True
            assert result is not None
            self._process_valid_responder_result(r, parent, result)
            break
        else:
            self.log_message(r, result)
        if self.is_done:
            # skip trying other responders in this step
            break
    if not found_response:
        self._process_invalid_step_result(parent)
    self._show_pending_message_if_debug()
    return self.pending_message

step_async(turns=-1) async

A single "turn" in the task conversation: The "allowed" responders in this turn (which can be either the 3 "entities", or one of the sub-tasks) are tried in sequence, until a valid response is obtained; a valid response is one that contributes to the task, either by ending it, or producing a response to be further acted on. Update self.pending_message to the latest valid response (or NO_ANSWER if no valid response was obtained from any responder).

Parameters:

Name	Type	Description	Default
turns	int	number of turns to process. Typically used in testing where there is no human to "quit out" of current level, or in cases where we want to limit the number of turns of a delegated agent.	-1

Returns (ChatDocument|None): Updated self.pending_message. Currently the return value is not used by the task.run() method, but we return this as a convenience for other use-cases, e.g. where we want to run a task step by step in a different context.

Source code in langroid/agent/task.py

async def step_async(self, turns: int = -1) -> ChatDocument | None:
    """
    A single "turn" in the task conversation: The "allowed" responders in this
    turn (which can be either the 3 "entities", or one of the sub-tasks) are
    tried in sequence, until a _valid_ response is obtained; a _valid_
    response is one that contributes to the task, either by ending it,
    or producing a response to be further acted on.
    Update `self.pending_message` to the latest valid response (or NO_ANSWER
    if no valid response was obtained from any responder).

    Args:
        turns (int): number of turns to process. Typically used in testing
            where there is no human to "quit out" of current level, or in cases
            where we want to limit the number of turns of a delegated agent.

    Returns (ChatDocument|None):
        Updated `self.pending_message`. Currently the return value is not used
            by the `task.run()` method, but we return this as a convenience for
            other use-cases, e.g. where we want to run a task step by step in a
            different context.
    """
    self.is_done = False
    self.step_progress = False
    parent = self.pending_message
    recipient = (
        ""
        if self.pending_message is None
        else self.pending_message.metadata.recipient
    )
    if not self._valid_recipient(recipient):
        logger.warning(f"Invalid recipient: {recipient}")
        error_doc = ChatDocument(
            content=f"Invalid recipient: {recipient}",
            metadata=ChatDocMetaData(
                sender=Entity.AGENT,
                sender_name=Entity.AGENT,
            ),
        )
        self._process_valid_responder_result(Entity.AGENT, parent, error_doc)
        return error_doc

    responders: List[Responder] = self.non_human_responders_async.copy()

    if (
        Entity.USER in self.responders
        and not self.human_tried
        and not self.agent.has_tool_message_attempt(self.pending_message)
    ):
        # Give human first chance if they haven't been tried in last step,
        # and the msg is not a tool-call attempt;
        # This ensures human gets a chance to respond,
        #   other than to a LLM tool-call.
        # When there's a tool msg attempt we want the
        #  Agent to be the next responder; this only makes a difference in an
        #  interactive setting: LLM generates tool, then we don't want user to
        #  have to respond, and instead let the agent_response handle the tool.
        responders.insert(0, Entity.USER)

    found_response = False
    for r in responders:
        if not self._can_respond(r):
            # create dummy msg for logging
            log_doc = ChatDocument(
                content="[CANNOT RESPOND]",
                function_call=None,
                metadata=ChatDocMetaData(
                    sender=r if isinstance(r, Entity) else Entity.USER,
                    sender_name=str(r),
                    recipient=recipient,
                ),
            )
            self.log_message(r, log_doc)
            continue
        self.human_tried = r == Entity.USER
        result = await self.response_async(r, turns)
        self.is_done = self._is_done_response(result, r)
        self.is_pass_thru = PASS in result.content if result else False
        if self.valid(result, r):
            found_response = True
            assert result is not None
            self._process_valid_responder_result(r, parent, result)
            break
        else:
            self.log_message(r, result)
        if self.is_done:
            # skip trying other responders in this step
            break
    if not found_response:
        self._process_invalid_step_result(parent)
    self._show_pending_message_if_debug()
    return self.pending_message

response(e, turns=-1)

Sync version of response_async(). See response_async() for details.

Source code in langroid/agent/task.py

def response(
    self,
    e: Responder,
    turns: int = -1,
) -> Optional[ChatDocument]:
    """
    Sync version of `response_async()`. See `response_async()` for details.
    """
    if isinstance(e, Task):
        actual_turns = e.turns if e.turns > 0 else turns
        e.agent.callbacks.set_parent_agent(self.agent)
        # e.callbacks.set_parent_agent(self.agent)
        result = e.run(
            self.pending_message,
            turns=actual_turns,
            caller=self,
            max_cost=self.max_cost,
            max_tokens=self.max_tokens,
        )
        result_str = str(ChatDocument.to_LLMMessage(result))
        maybe_tool = len(extract_top_level_json(result_str)) > 0
        self.callbacks.show_subtask_response(
            task=e,
            content=result_str,
            is_tool=maybe_tool,
        )
    else:
        response_fn = self._entity_responder_map[cast(Entity, e)]
        result = response_fn(self.pending_message)
    return self._process_result_routing(result)

response_async(e, turns=-1) async

Get response to self.pending_message from a responder. If response is valid (i.e. it ends the current turn of seeking responses): -then return the response as a ChatDocument object, -otherwise return None. Args: e (Responder): responder to get response from. turns (int): number of turns to run the task for. Default is -1, which means run until task is done.

Returns:

Type	Description
Optional[ChatDocument]	Optional[ChatDocument]: response to self.pending_message from entity if
Optional[ChatDocument]	valid, None otherwise

Source code in langroid/agent/task.py

async def response_async(
    self,
    e: Responder,
    turns: int = -1,
) -> Optional[ChatDocument]:
    """
    Get response to `self.pending_message` from a responder.
    If response is __valid__ (i.e. it ends the current turn of seeking
    responses):
        -then return the response as a ChatDocument object,
        -otherwise return None.
    Args:
        e (Responder): responder to get response from.
        turns (int): number of turns to run the task for.
            Default is -1, which means run until task is done.

    Returns:
        Optional[ChatDocument]: response to `self.pending_message` from entity if
        valid, None otherwise
    """
    if isinstance(e, Task):
        actual_turns = e.turns if e.turns > 0 else turns
        e.agent.callbacks.set_parent_agent(self.agent)
        # e.callbacks.set_parent_agent(self.agent)
        result = await e.run_async(
            self.pending_message,
            turns=actual_turns,
            caller=self,
            max_cost=self.max_cost,
            max_tokens=self.max_tokens,
        )
        result_str = str(ChatDocument.to_LLMMessage(result))
        maybe_tool = len(extract_top_level_json(result_str)) > 0
        self.callbacks.show_subtask_response(
            task=e,
            content=result_str,
            is_tool=maybe_tool,
        )
    else:
        response_fn = self._entity_responder_async_map[cast(Entity, e)]
        result = await response_fn(self.pending_message)
    return self._process_result_routing(result)

result()

Get result of task. This is the default behavior. Derived classes can override this. Returns: ChatDocument: result of task

Source code in langroid/agent/task.py

def result(self) -> ChatDocument:
    """
    Get result of task. This is the default behavior.
    Derived classes can override this.
    Returns:
        ChatDocument: result of task
    """
    result_msg = self.pending_message

    content = result_msg.content if result_msg else ""
    if DONE in content:
        # assuming it is of the form "DONE: <content>"
        content = content.replace(DONE, "").strip()
    fun_call = result_msg.function_call if result_msg else None
    tool_messages = result_msg.tool_messages if result_msg else []
    block = result_msg.metadata.block if result_msg else None
    recipient = result_msg.metadata.recipient if result_msg else None
    responder = result_msg.metadata.parent_responder if result_msg else None
    tool_ids = result_msg.metadata.tool_ids if result_msg else []
    status = result_msg.metadata.status if result_msg else None

    # regardless of which entity actually produced the result,
    # when we return the result, we set entity to USER
    # since to the "parent" task, this result is equivalent to a response from USER
    return ChatDocument(
        content=content,
        function_call=fun_call,
        tool_messages=tool_messages,
        metadata=ChatDocMetaData(
            source=Entity.USER,
            sender=Entity.USER,
            block=block,
            status=status,
            parent_responder=responder,
            sender_name=self.name,
            recipient=recipient,
            tool_ids=tool_ids,
        ),
    )

done(result=None, r=None)

Check if task is done. This is the default behavior. Derived classes can override this. Args: result (ChatDocument|None): result from a responder r (Responder|None): responder that produced the result Not used here, but could be used by derived classes. Returns: bool: True if task is done, False otherwise StatusCode: status code indicating why task is done

Source code in langroid/agent/task.py

def done(
    self, result: ChatDocument | None = None, r: Responder | None = None
) -> Tuple[bool, StatusCode]:
    """
    Check if task is done. This is the default behavior.
    Derived classes can override this.
    Args:
        result (ChatDocument|None): result from a responder
        r (Responder|None): responder that produced the result
            Not used here, but could be used by derived classes.
    Returns:
        bool: True if task is done, False otherwise
        StatusCode: status code indicating why task is done
    """
    if self._is_kill():
        return (True, StatusCode.KILL)
    result = result or self.pending_message
    user_quit = (
        result is not None
        and result.content in USER_QUIT_STRINGS
        and result.metadata.sender == Entity.USER
    )
    if self._level == 0 and self.only_user_quits_root:
        # for top-level task, only user can quit out
        return (user_quit, StatusCode.USER_QUIT if user_quit else StatusCode.OK)

    if self.is_done:
        return (True, StatusCode.DONE)

    if self.n_stalled_steps >= self.max_stalled_steps:
        # we are stuck, so bail to avoid infinite loop
        logger.warning(
            f"Task {self.name} stuck for {self.max_stalled_steps} steps; exiting."
        )
        return (True, StatusCode.STALLED)

    if self.max_cost > 0 and self.agent.llm is not None:
        try:
            if self.agent.llm.tot_tokens_cost()[1] > self.max_cost:
                logger.warning(
                    f"Task {self.name} cost exceeded {self.max_cost}; exiting."
                )
                return (True, StatusCode.MAX_COST)
        except Exception:
            pass

    if self.max_tokens > 0 and self.agent.llm is not None:
        try:
            if self.agent.llm.tot_tokens_cost()[0] > self.max_tokens:
                logger.warning(
                    f"Task {self.name} uses > {self.max_tokens} tokens; exiting."
                )
                return (True, StatusCode.MAX_TOKENS)
        except Exception:
            pass
    final = (
        # no valid response from any entity/agent in current turn
        result is None
        # An entity decided task is done
        or DONE in result.content
        or (  # current task is addressing message to caller task
            self.caller is not None
            and self.caller.name != ""
            and result.metadata.recipient == self.caller.name
        )
        # or (
        #     # Task controller is "stuck", has nothing to say
        #     NO_ANSWER in result.content
        #     and result.metadata.sender == self.controller
        # )
        or user_quit
    )
    return (final, StatusCode.OK)

valid(result, r)

Is the result from a Responder (i.e. an entity or sub-task) such that we can stop searching for responses in this step?

Source code in langroid/agent/task.py

def valid(
    self,
    result: Optional[ChatDocument],
    r: Responder,
) -> bool:
    """
    Is the result from a Responder (i.e. an entity or sub-task)
    such that we can stop searching for responses in this step?
    """
    # TODO caution we should ensure that no handler method (tool) returns simply
    # an empty string (e.g when showing contents of an empty file), since that
    # would be considered an invalid response, and other responders will wrongly
    # be given a chance to respond.

    # if task would be considered done given responder r's `result`,
    # then consider the result valid.
    if result is not None and self.done(result, r)[0]:
        return True
    return (
        result is not None
        and not self._is_empty_message(result)
        and result.content.strip() != NO_ANSWER
    )

log_message(resp, msg=None, mark=False)

Log current pending message, and related state, for lineage/debugging purposes.

Parameters:

Name	Type	Description	Default
resp	Responder	Responder that generated the msg	required
msg	ChatDocument	Message to log. Defaults to None.	None
mark	bool	Whether to mark the message as the final result of a task.step() call. Defaults to False.	False

Source code in langroid/agent/task.py

def log_message(
    self,
    resp: Responder,
    msg: ChatDocument | None = None,
    mark: bool = False,
) -> None:
    """
    Log current pending message, and related state, for lineage/debugging purposes.

    Args:
        resp (Responder): Responder that generated the `msg`
        msg (ChatDocument, optional): Message to log. Defaults to None.
        mark (bool, optional): Whether to mark the message as the final result of
            a `task.step()` call. Defaults to False.
    """
    default_values = ChatDocLoggerFields().dict().values()
    msg_str_tsv = "\t".join(str(v) for v in default_values)
    if msg is not None:
        msg_str_tsv = msg.tsv_str()

    mark_str = "*" if mark else " "
    task_name = self.name if self.name != "" else "root"
    resp_color = "white" if mark else "red"
    resp_str = f"[{resp_color}] {resp} [/{resp_color}]"

    if msg is None:
        msg_str = f"{mark_str}({task_name}) {resp_str}"
    else:
        color = {
            Entity.LLM: "green",
            Entity.USER: "blue",
            Entity.AGENT: "red",
            Entity.SYSTEM: "magenta",
        }[msg.metadata.sender]
        f = msg.log_fields()
        tool_type = f.tool_type.rjust(6)
        tool_name = f.tool.rjust(10)
        tool_str = f"{tool_type}({tool_name})" if tool_name != "" else ""
        sender = f"[{color}]" + str(f.sender_entity).rjust(10) + f"[/{color}]"
        sender_name = f.sender_name.rjust(10)
        recipient = "=>" + str(f.recipient).rjust(10)
        block = "X " + str(f.block or "").rjust(10)
        content = f"[{color}]{f.content}[/{color}]"
        msg_str = (
            f"{mark_str}({task_name}) "
            f"{resp_str} {sender}({sender_name}) "
            f"({recipient}) ({block}) {tool_str} {content}"
        )

    if self.logger is not None:
        self.logger.log(msg_str)
    if self.tsv_logger is not None:
        resp_str = str(resp)
        self.tsv_logger.info(f"{mark_str}\t{task_name}\t{resp_str}\t{msg_str_tsv}")

set_color_log(enable=True)

Flag to enable/disable color logging using rich.console. In some contexts, such as Colab notebooks, we may want to disable color logging using rich.console, since those logs show up in the cell output rather than in the log file. Turning off this feature will still create logs, but without the color formatting from rich.console Args: enable (bool): value of self.color_log to set to, which will enable/diable rich logging

Source code in langroid/agent/task.py

def set_color_log(self, enable: bool = True) -> None:
    """
    Flag to enable/disable color logging using rich.console.
    In some contexts, such as Colab notebooks, we may want to disable color logging
    using rich.console, since those logs show up in the cell output rather than
    in the log file. Turning off this feature will still create logs, but without
    the color formatting from rich.console
    Args:
        enable (bool): value of `self.color_log` to set to,
            which will enable/diable rich logging

    """
    self.color_log = enable