base

langroid/agent/base.py

AgentConfig

Bases: BaseSettings

General config settings for an LLM agent. This is nested, combining configs of various components.

Agent(config=AgentConfig())

Bases: ABC

An Agent is an abstraction that encapsulates mainly two components:

• a language model (LLM)
• a vector store (vecdb)

plus associated components such as a parser, and variables that hold information about any tool/function-calling messages that have been defined.

Source code in langroid/agent/base.py

def __init__(self, config: AgentConfig = AgentConfig()):
    self.config = config
    self.lock = asyncio.Lock()  # for async access to update self.llm.usage_cost
    self.dialog: List[Tuple[str, str]] = []  # seq of LLM (prompt, response) tuples
    self.llm_tools_map: Dict[str, Type[ToolMessage]] = {}
    self.llm_tools_handled: Set[str] = set()
    self.llm_tools_usable: Set[str] = set()
    self.llm_tools_known: Set[str] = set()  # all known tools, handled/used or not
    # Indicates which tool-names are allowed to be inferred when
    # the LLM "forgets" to include the request field in its
    # tool-call.
    self.enabled_requests_for_inference: Optional[Set[str]] = (
        None  # If None, we allow all
    )
    self.interactive: bool = True  # may be modified by Task wrapper
    self.token_stats_str = ""
    self.default_human_response: Optional[str] = None
    self._indent = ""
    self.llm = LanguageModel.create(config.llm)
    self.vecdb = VectorStore.create(config.vecdb) if config.vecdb else None
    self.tool_error = False
    if config.parsing is not None and self.config.llm is not None:
        # token_encoding_model is used to obtain the tokenizer,
        # so in case it's an OpenAI model, we ensure that the tokenizer
        # corresponding to the model is used.
        if isinstance(self.llm, OpenAIGPT) and self.llm.is_openai_chat_model():
            config.parsing.token_encoding_model = self.llm.config.chat_model
    self.parser: Optional[Parser] = (
        Parser(config.parsing) if config.parsing else None
    )
    if config.add_to_registry:
        ObjectRegistry.register_object(self)

    self.callbacks = SimpleNamespace(
        start_llm_stream=lambda: noop_fn,
        start_llm_stream_async=async_noop_fn,
        cancel_llm_stream=noop_fn,
        finish_llm_stream=noop_fn,
        show_llm_response=noop_fn,
        show_agent_response=noop_fn,
        get_user_response=None,
        get_last_step=noop_fn,
        set_parent_agent=noop_fn,
        show_error_message=noop_fn,
        show_start_response=noop_fn,
    )
    Agent.init_state(self)

indent: str property writable

Indentation to print before any responses from the agent's entities.

all_llm_tools_known: set[str] property

All known tools; this may extend self.llm_tools_known.

init_state()

Initialize all state vars. Called by Task.run() if restart is True

Source code in langroid/agent/base.py

def init_state(self) -> None:
    """Initialize all state vars. Called by Task.run() if restart is True"""
    self.total_llm_token_cost = 0.0
    self.total_llm_token_usage = 0

entity_responders()

Sequence of (entity, response_method) pairs. This sequence is used in a Task to respond to the current pending message. See Task.step() for details. Returns: Sequence of (entity, response_method) pairs.

Source code in langroid/agent/base.py

def entity_responders(
    self,
) -> List[
    Tuple[Entity, Callable[[None | str | ChatDocument], None | ChatDocument]]
]:
    """
    Sequence of (entity, response_method) pairs. This sequence is used
        in a `Task` to respond to the current pending message.
        See `Task.step()` for details.
    Returns:
        Sequence of (entity, response_method) pairs.
    """
    return [
        (Entity.AGENT, self.agent_response),
        (Entity.LLM, self.llm_response),
        (Entity.USER, self.user_response),
    ]

entity_responders_async()

Async version of entity_responders. See there for details.

Source code in langroid/agent/base.py

def entity_responders_async(
    self,
) -> List[
    Tuple[
        Entity,
        Callable[
            [None | str | ChatDocument], Coroutine[Any, Any, None | ChatDocument]
        ],
    ]
]:
    """
    Async version of `entity_responders`. See there for details.
    """
    return [
        (Entity.AGENT, self.agent_response_async),
        (Entity.LLM, self.llm_response_async),
        (Entity.USER, self.user_response_async),
    ]

enable_message_handling(message_class=None)

Enable an agent to RESPOND (i.e. handle) a "tool" message of a specific type from LLM. Also "registers" (i.e. adds) the message_class to the self.llm_tools_map dict.

Parameters:

Name	Type	Description	Default
message_class	Optional[Type[ToolMessage]]	The message class to enable; Optional; if None, all known message classes are enabled for handling.	None

Source code in langroid/agent/base.py

def enable_message_handling(
    self, message_class: Optional[Type[ToolMessage]] = None
) -> None:
    """
    Enable an agent to RESPOND (i.e. handle) a "tool" message of a specific type
        from LLM. Also "registers" (i.e. adds) the `message_class` to the
        `self.llm_tools_map` dict.

    Args:
        message_class (Optional[Type[ToolMessage]]): The message class to enable;
            Optional; if None, all known message classes are enabled for handling.

    """
    for t in self._get_tool_list(message_class):
        self.llm_tools_handled.add(t)

disable_message_handling(message_class=None)

Disable a message class from being handled by this Agent.

Parameters:

Name	Type	Description	Default
message_class	Optional[Type[ToolMessage]]	The message class to disable. If None, all message classes are disabled.	None

Source code in langroid/agent/base.py

def disable_message_handling(
    self,
    message_class: Optional[Type[ToolMessage]] = None,
) -> None:
    """
    Disable a message class from being handled by this Agent.

    Args:
        message_class (Optional[Type[ToolMessage]]): The message class to disable.
            If None, all message classes are disabled.
    """
    for t in self._get_tool_list(message_class):
        self.llm_tools_handled.discard(t)

sample_multi_round_dialog()

Generate a sample multi-round dialog based on enabled message classes. Returns: str: The sample dialog string.

Source code in langroid/agent/base.py

def sample_multi_round_dialog(self) -> str:
    """
    Generate a sample multi-round dialog based on enabled message classes.
    Returns:
        str: The sample dialog string.
    """
    enabled_classes: List[Type[ToolMessage]] = list(self.llm_tools_map.values())
    # use at most 2 sample conversations, no need to be exhaustive;
    sample_convo = [
        msg_cls().usage_examples(random=True)  # type: ignore
        for i, msg_cls in enumerate(enabled_classes)
        if i < 2
    ]
    return "\n\n".join(sample_convo)

create_agent_response(content=None, content_any=None, tool_messages=[], oai_tool_calls=None, oai_tool_choice='auto', oai_tool_id2result=None, function_call=None, recipient='')

Template for agent_response.

Source code in langroid/agent/base.py

def create_agent_response(
    self,
    content: str | None = None,
    content_any: Any = None,
    tool_messages: List[ToolMessage] = [],
    oai_tool_calls: Optional[List[OpenAIToolCall]] = None,
    oai_tool_choice: ToolChoiceTypes | Dict[str, Dict[str, str] | str] = "auto",
    oai_tool_id2result: OrderedDict[str, str] | None = None,
    function_call: LLMFunctionCall | None = None,
    recipient: str = "",
) -> ChatDocument:
    """Template for agent_response."""
    return self.response_template(
        Entity.AGENT,
        content=content,
        content_any=content_any,
        tool_messages=tool_messages,
        oai_tool_calls=oai_tool_calls,
        oai_tool_choice=oai_tool_choice,
        oai_tool_id2result=oai_tool_id2result,
        function_call=function_call,
        recipient=recipient,
    )

agent_response(msg=None)

Response from the "agent itself", typically (but not only) used to handle LLM's "tool message" or function_call (e.g. OpenAI function_call). Args: msg (str|ChatDocument): the input to respond to: if msg is a string, and it contains a valid JSON-structured "tool message", or if msg is a ChatDocument, and it contains a function_call. Returns: Optional[ChatDocument]: the response, packaged as a ChatDocument

Source code in langroid/agent/base.py

def agent_response(
    self,
    msg: Optional[str | ChatDocument] = None,
) -> Optional[ChatDocument]:
    """
    Response from the "agent itself", typically (but not only)
    used to handle LLM's "tool message" or `function_call`
    (e.g. OpenAI `function_call`).
    Args:
        msg (str|ChatDocument): the input to respond to: if msg is a string,
            and it contains a valid JSON-structured "tool message", or
            if msg is a ChatDocument, and it contains a `function_call`.
    Returns:
        Optional[ChatDocument]: the response, packaged as a ChatDocument

    """
    if msg is None:
        return None

    results = self.handle_message(msg)
    if results is None:
        return None
    if not settings.quiet:
        if isinstance(results, str):
            results_str = results
        elif isinstance(results, ChatDocument):
            results_str = results.content
        elif isinstance(results, dict):
            results_str = json.dumps(results, indent=2)
        console.print(f"[red]{self.indent}", end="")
        print(f"[red]Agent: {escape(results_str)}")
        maybe_json = len(extract_top_level_json(results_str)) > 0
        self.callbacks.show_agent_response(
            content=results_str,
            language="json" if maybe_json else "text",
        )
    if isinstance(results, ChatDocument):
        # Preserve trail of tool_ids for OpenAI Assistant fn-calls
        results.metadata.tool_ids = (
            [] if isinstance(msg, str) else msg.metadata.tool_ids
        )
        return results
    sender_name = self.config.name
    if isinstance(msg, ChatDocument) and msg.function_call is not None:
        # if result was from handling an LLM `function_call`,
        # set sender_name to name of the function_call
        sender_name = msg.function_call.name

    results_str, id2result, oai_tool_id = self.process_tool_results(
        results if isinstance(results, str) else "",
        id2result=None if isinstance(results, str) else results,
        tool_calls=(msg.oai_tool_calls if isinstance(msg, ChatDocument) else None),
    )
    return ChatDocument(
        content=results_str,
        oai_tool_id2result=id2result,
        metadata=ChatDocMetaData(
            source=Entity.AGENT,
            sender=Entity.AGENT,
            sender_name=sender_name,
            oai_tool_id=oai_tool_id,
            # preserve trail of tool_ids for OpenAI Assistant fn-calls
            tool_ids=[] if isinstance(msg, str) else msg.metadata.tool_ids,
        ),
    )

process_tool_results(results, id2result, tool_calls=None)

Process results from a response, based on whether they are results of OpenAI tool-calls from THIS agent, so that we can construct an appropriate LLMMessage that contains tool results.

Parameters:

Name	Type	Description	Default
results	str	A possible string result from handling tool(s)	required
id2result	OrderedDict[str, str] | None	A dict of OpenAI tool id -> result, if there are multiple tool results.	required
tool_calls	List[OpenAIToolCall] | None	List of OpenAI tool-calls that the results are a response to.	None

Return

• str: The response string
• Dict[str,str]|None: A dict of OpenAI tool id -> result, if there are multiple tool results.
• str|None: tool_id if there was a single tool result

Source code in langroid/agent/base.py

def process_tool_results(
    self,
    results: str,
    id2result: OrderedDict[str, str] | None,
    tool_calls: List[OpenAIToolCall] | None = None,
) -> Tuple[str, Dict[str, str] | None, str | None]:
    """
    Process results from a response, based on whether
    they are results of OpenAI tool-calls from THIS agent, so that
    we can construct an appropriate LLMMessage that contains tool results.

    Args:
        results (str): A possible string result from handling tool(s)
        id2result (OrderedDict[str,str]|None): A dict of OpenAI tool id -> result,
            if there are multiple tool results.
        tool_calls (List[OpenAIToolCall]|None): List of OpenAI tool-calls that the
            results are a response to.

    Return:
        - str: The response string
        - Dict[str,str]|None: A dict of OpenAI tool id -> result, if there are
            multiple tool results.
        - str|None: tool_id if there was a single tool result

    """
    id2result_ = copy.deepcopy(id2result) if id2result is not None else None
    results_str = ""
    oai_tool_id = None

    if results != "":
        # in this case ignore id2result
        assert (
            id2result is None
        ), "id2result should be None when results string is non-empty!"
        results_str = results
        if len(self.oai_tool_calls) > 0:
            # We only have one result, so in case there is a
            # "pending" OpenAI tool-call, we expect no more than 1 such.
            assert (
                len(self.oai_tool_calls) == 1
            ), "There are multiple pending tool-calls, but only one result!"
            # We record the tool_id of the tool-call that
            # the result is a response to, so that ChatDocument.to_LLMMessage
            # can properly set the `tool_call_id` field of the LLMMessage.
            oai_tool_id = self.oai_tool_calls[0].id
    elif id2result is not None and id2result_ is not None:  # appease mypy
        if len(id2result_) == len(self.oai_tool_calls):
            # if the number of pending tool calls equals the number of results,
            # then ignore the ids in id2result, and use the results in order,
            # which is preserved since id2result is an OrderedDict.
            assert len(id2result_) > 1, "Expected to see > 1 result in id2result!"
            results_str = ""
            id2result_ = OrderedDict(
                zip(
                    [tc.id or "" for tc in self.oai_tool_calls], id2result_.values()
                )
            )
        else:
            assert (
                tool_calls is not None
            ), "tool_calls cannot be None when id2result is not None!"
            # This must be an OpenAI tool id -> result map;
            # However some ids may not correspond to the tool-calls in the list of
            # pending tool-calls (self.oai_tool_calls).
            # Such results are concatenated into a simple string, to store in the
            # ChatDocument.content, and the rest
            # (i.e. those that DO correspond to tools in self.oai_tool_calls)
            # are stored as a dict in ChatDocument.oai_tool_id2result.

            # OAI tools from THIS agent, awaiting response
            pending_tool_ids = [tc.id for tc in self.oai_tool_calls]
            # tool_calls that the results are a response to
            # (but these may have been sent from another agent, hence may not be in
            # self.oai_tool_calls)
            parent_tool_id2name = {
                tc.id: tc.function.name
                for tc in tool_calls or []
                if tc.function is not None
            }

            # (id, result) for result NOT corresponding to self.oai_tool_calls,
            # i.e. these are results of EXTERNAL tool-calls from another agent.
            external_tool_id_results = []

            for tc_id, result in id2result.items():
                if tc_id not in pending_tool_ids:
                    external_tool_id_results.append((tc_id, result))
                    id2result_.pop(tc_id)
            if len(external_tool_id_results) == 0:
                results_str = ""
            elif len(external_tool_id_results) == 1:
                results_str = external_tool_id_results[0][1]
            else:
                results_str = "\n\n".join(
                    [
                        f"Result from tool/function "
                        f"{parent_tool_id2name[id]}: {result}"
                        for id, result in external_tool_id_results
                    ]
                )

            if len(id2result_) == 0:
                id2result_ = None
            elif len(id2result_) == 1 and len(external_tool_id_results) == 0:
                results_str = list(id2result_.values())[0]
                oai_tool_id = list(id2result_.keys())[0]
                id2result_ = None

    return results_str, id2result_, oai_tool_id

response_template(e, content=None, content_any=None, tool_messages=[], oai_tool_calls=None, oai_tool_choice='auto', oai_tool_id2result=None, function_call=None, recipient='')

Template for response from entity e.

Source code in langroid/agent/base.py

def response_template(
    self,
    e: Entity,
    content: str | None = None,
    content_any: Any = None,
    tool_messages: List[ToolMessage] = [],
    oai_tool_calls: Optional[List[OpenAIToolCall]] = None,
    oai_tool_choice: ToolChoiceTypes | Dict[str, Dict[str, str] | str] = "auto",
    oai_tool_id2result: OrderedDict[str, str] | None = None,
    function_call: LLMFunctionCall | None = None,
    recipient: str = "",
) -> ChatDocument:
    """Template for response from entity `e`."""
    return ChatDocument(
        content=content or "",
        content_any=content_any,
        tool_messages=tool_messages,
        oai_tool_calls=oai_tool_calls,
        oai_tool_id2result=oai_tool_id2result,
        function_call=function_call,
        oai_tool_choice=oai_tool_choice,
        metadata=ChatDocMetaData(
            source=e, sender=e, sender_name=self.config.name, recipient=recipient
        ),
    )

create_user_response(content=None, content_any=None, tool_messages=[], oai_tool_calls=None, oai_tool_choice='auto', oai_tool_id2result=None, function_call=None, recipient='')

Template for user_response.

Source code in langroid/agent/base.py

def create_user_response(
    self,
    content: str | None = None,
    content_any: Any = None,
    tool_messages: List[ToolMessage] = [],
    oai_tool_calls: List[OpenAIToolCall] | None = None,
    oai_tool_choice: ToolChoiceTypes | Dict[str, Dict[str, str] | str] = "auto",
    oai_tool_id2result: OrderedDict[str, str] | None = None,
    function_call: LLMFunctionCall | None = None,
    recipient: str = "",
) -> ChatDocument:
    """Template for user_response."""
    return self.response_template(
        e=Entity.USER,
        content=content,
        content_any=content_any,
        tool_messages=tool_messages,
        oai_tool_calls=oai_tool_calls,
        oai_tool_choice=oai_tool_choice,
        oai_tool_id2result=oai_tool_id2result,
        function_call=function_call,
        recipient=recipient,
    )

user_response(msg=None)

Get user response to current message. Could allow (human) user to intervene with an actual answer, or quit using "q" or "x"

Parameters:

Name	Type	Description	Default
msg	str | ChatDocument	the string to respond to.	None

Returns:

Type	Description
Optional[ChatDocument]	(str) User response, packaged as a ChatDocument

Source code in langroid/agent/base.py

def user_response(
    self,
    msg: Optional[str | ChatDocument] = None,
) -> Optional[ChatDocument]:
    """
    Get user response to current message. Could allow (human) user to intervene
    with an actual answer, or quit using "q" or "x"

    Args:
        msg (str|ChatDocument): the string to respond to.

    Returns:
        (str) User response, packaged as a ChatDocument

    """

    # When msg explicitly addressed to user, this means an actual human response
    # is being sought.
    need_human_response = (
        isinstance(msg, ChatDocument) and msg.metadata.recipient == Entity.USER
    )
    default_user_msg = (
        (self.default_human_response or "null") if need_human_response else ""
    )

    if not self.interactive and not need_human_response:
        return None
    elif self.default_human_response is not None:
        user_msg = self.default_human_response
    else:
        if self.callbacks.get_user_response is not None:
            # ask user with empty prompt: no need for prompt
            # since user has seen the conversation so far.
            # But non-empty prompt can be useful when Agent
            # uses a tool that requires user input, or in other scenarios.
            user_msg = self.callbacks.get_user_response(prompt="")
        else:
            user_msg = Prompt.ask(
                f"[blue]{self.indent}"
                + self.config.human_prompt
                + f"\n{self.indent}"
            ).strip()

    tool_ids = []
    if msg is not None and isinstance(msg, ChatDocument):
        tool_ids = msg.metadata.tool_ids

    user_msg = user_msg.strip() or default_user_msg.strip()
    # only return non-None result if user_msg not empty
    if not user_msg:
        return None
    else:
        if user_msg.startswith("SYSTEM"):
            user_msg = user_msg.replace("SYSTEM", "").strip()
            source = Entity.SYSTEM
            sender = Entity.SYSTEM
        else:
            source = Entity.USER
            sender = Entity.USER
        return ChatDocument(
            content=user_msg,
            metadata=ChatDocMetaData(
                source=source,
                sender=sender,
                # preserve trail of tool_ids for OpenAI Assistant fn-calls
                tool_ids=tool_ids,
            ),
        )

llm_can_respond(message=None)

Whether the LLM can respond to a message. Args: message (str|ChatDocument): message or ChatDocument object to respond to.

Returns:

Source code in langroid/agent/base.py

@no_type_check
def llm_can_respond(self, message: Optional[str | ChatDocument] = None) -> bool:
    """
    Whether the LLM can respond to a message.
    Args:
        message (str|ChatDocument): message or ChatDocument object to respond to.

    Returns:

    """
    if self.llm is None:
        return False

    if message is not None and len(self.try_get_tool_messages(message)) > 0:
        # if there is a valid "tool" message (either JSON or via `function_call`)
        # then LLM cannot respond to it
        return False

    return True

can_respond(message=None)

Whether the agent can respond to a message. Used in Task.py to skip a sub-task when we know it would not respond. Args: message (str|ChatDocument): message or ChatDocument object to respond to.

Source code in langroid/agent/base.py

def can_respond(self, message: Optional[str | ChatDocument] = None) -> bool:
    """
    Whether the agent can respond to a message.
    Used in Task.py to skip a sub-task when we know it would not respond.
    Args:
        message (str|ChatDocument): message or ChatDocument object to respond to.
    """
    tools = self.try_get_tool_messages(message)
    if len(tools) == 0 and self.config.respond_tools_only:
        return False
    if message is not None and self.has_only_unhandled_tools(message):
        # The message has tools that are NOT enabled to be handled by this agent,
        # which means the agent cannot respond to it.
        return False
    return True

create_llm_response(content=None, content_any=None, tool_messages=[], oai_tool_calls=None, oai_tool_choice='auto', oai_tool_id2result=None, function_call=None, recipient='')

Template for llm_response.

Source code in langroid/agent/base.py

def create_llm_response(
    self,
    content: str | None = None,
    content_any: Any = None,
    tool_messages: List[ToolMessage] = [],
    oai_tool_calls: None | List[OpenAIToolCall] = None,
    oai_tool_choice: ToolChoiceTypes | Dict[str, Dict[str, str] | str] = "auto",
    oai_tool_id2result: OrderedDict[str, str] | None = None,
    function_call: LLMFunctionCall | None = None,
    recipient: str = "",
) -> ChatDocument:
    """Template for llm_response."""
    return self.response_template(
        Entity.LLM,
        content=content,
        content_any=content_any,
        tool_messages=tool_messages,
        oai_tool_calls=oai_tool_calls,
        oai_tool_choice=oai_tool_choice,
        oai_tool_id2result=oai_tool_id2result,
        function_call=function_call,
        recipient=recipient,
    )

llm_response_async(message=None) async

Asynch version of llm_response. See there for details.

Source code in langroid/agent/base.py

@no_type_check
async def llm_response_async(
    self,
    message: Optional[str | ChatDocument] = None,
) -> Optional[ChatDocument]:
    """
    Asynch version of `llm_response`. See there for details.
    """
    if message is None or not self.llm_can_respond(message):
        return None

    if isinstance(message, ChatDocument):
        prompt = message.content
    else:
        prompt = message

    output_len = self.config.llm.max_output_tokens
    if self.num_tokens(prompt) + output_len > self.llm.completion_context_length():
        output_len = self.llm.completion_context_length() - self.num_tokens(prompt)
        if output_len < self.config.llm.min_output_tokens:
            raise ValueError(
                """
            Token-length of Prompt + Output is longer than the
            completion context length of the LLM!
            """
            )
        else:
            logger.warning(
                f"""
            Requested output length has been shortened to {output_len}
            so that the total length of Prompt + Output is less than
            the completion context length of the LLM. 
            """
            )

    with StreamingIfAllowed(self.llm, self.llm.get_stream()):
        response = await self.llm.agenerate(prompt, output_len)

    if not self.llm.get_stream() or response.cached and not settings.quiet:
        # We would have already displayed the msg "live" ONLY if
        # streaming was enabled, AND we did not find a cached response.
        # If we are here, it means the response has not yet been displayed.
        cached = f"[red]{self.indent}(cached)[/red]" if response.cached else ""
        print(cached + "[green]" + escape(response.message))
    async with self.lock:
        self.update_token_usage(
            response,
            prompt,
            self.llm.get_stream(),
            chat=False,  # i.e. it's a completion model not chat model
            print_response_stats=self.config.show_stats and not settings.quiet,
        )
    cdoc = ChatDocument.from_LLMResponse(response, displayed=True)
    # Preserve trail of tool_ids for OpenAI Assistant fn-calls
    cdoc.metadata.tool_ids = (
        [] if isinstance(message, str) else message.metadata.tool_ids
    )
    return cdoc

llm_response(message=None)

LLM response to a prompt. Args: message (str|ChatDocument): prompt string, or ChatDocument object

Returns:

Type	Description
Optional[ChatDocument]	Response from LLM, packaged as a ChatDocument

Source code in langroid/agent/base.py

@no_type_check
def llm_response(
    self,
    message: Optional[str | ChatDocument] = None,
) -> Optional[ChatDocument]:
    """
    LLM response to a prompt.
    Args:
        message (str|ChatDocument): prompt string, or ChatDocument object

    Returns:
        Response from LLM, packaged as a ChatDocument
    """
    if message is None or not self.llm_can_respond(message):
        return None

    if isinstance(message, ChatDocument):
        prompt = message.content
    else:
        prompt = message

    with ExitStack() as stack:  # for conditionally using rich spinner
        if not self.llm.get_stream():
            # show rich spinner only if not streaming!
            cm = status("LLM responding to message...")
            stack.enter_context(cm)
        output_len = self.config.llm.max_output_tokens
        if (
            self.num_tokens(prompt) + output_len
            > self.llm.completion_context_length()
        ):
            output_len = self.llm.completion_context_length() - self.num_tokens(
                prompt
            )
            if output_len < self.config.llm.min_output_tokens:
                raise ValueError(
                    """
                Token-length of Prompt + Output is longer than the
                completion context length of the LLM!
                """
                )
            else:
                logger.warning(
                    f"""
                Requested output length has been shortened to {output_len}
                so that the total length of Prompt + Output is less than
                the completion context length of the LLM. 
                """
                )
        if self.llm.get_stream() and not settings.quiet:
            console.print(f"[green]{self.indent}", end="")
        response = self.llm.generate(prompt, output_len)

    if not self.llm.get_stream() or response.cached and not settings.quiet:
        # we would have already displayed the msg "live" ONLY if
        # streaming was enabled, AND we did not find a cached response
        # If we are here, it means the response has not yet been displayed.
        cached = f"[red]{self.indent}(cached)[/red]" if response.cached else ""
        console.print(f"[green]{self.indent}", end="")
        print(cached + "[green]" + escape(response.message))
    self.update_token_usage(
        response,
        prompt,
        self.llm.get_stream(),
        chat=False,  # i.e. it's a completion model not chat model
        print_response_stats=self.config.show_stats and not settings.quiet,
    )
    cdoc = ChatDocument.from_LLMResponse(response, displayed=True)
    # Preserve trail of tool_ids for OpenAI Assistant fn-calls
    cdoc.metadata.tool_ids = (
        [] if isinstance(message, str) else message.metadata.tool_ids
    )
    return cdoc

has_tool_message_attempt(msg)

Check whether msg contains a Tool/fn-call attempt (by the LLM).

CAUTION: This uses self.get_tool_messages(msg) which as a side-effect may update msg.tool_messages when msg is a ChatDocument, if there are any tools in msg.

Source code in langroid/agent/base.py

def has_tool_message_attempt(self, msg: str | ChatDocument | None) -> bool:
    """
    Check whether msg contains a Tool/fn-call attempt (by the LLM).

    CAUTION: This uses self.get_tool_messages(msg) which as a side-effect
    may update msg.tool_messages when msg is a ChatDocument, if there are
    any tools in msg.
    """
    if msg is None:
        return False
    try:
        tools = self.get_tool_messages(msg)
        return len(tools) > 0
    except (ValidationError, XMLException):
        # there is a tool/fn-call attempt but had a validation error,
        # so we still consider this a tool message "attempt"
        return True
    return False

has_only_unhandled_tools(msg)

Does the msg have at least one tool, and ALL tools are disabled for handling by this agent?

Source code in langroid/agent/base.py

def has_only_unhandled_tools(self, msg: str | ChatDocument) -> bool:
    """
    Does the msg have at least one tool, and ALL tools are
    disabled for handling by this agent?
    """
    if msg is None:
        return False
    tools = self.try_get_tool_messages(msg, all_tools=True)
    if len(tools) == 0:
        return False
    return all(not self._tool_recipient_match(t) for t in tools)

get_tool_messages(msg, all_tools=False)

Get ToolMessages recognized in msg, handle-able by this agent. NOTE: as a side-effect, this will update msg.tool_messages when msg is a ChatDocument and msg contains tool messages. The intent here is that update=True should be set ONLY within agent_response() or agent_response_async() methods. In other words, we want to persist the msg.tool_messages only AFTER the agent has had a chance to handle the tools.

Parameters:

Name	Type	Description	Default
msg	str | ChatDocument	the message to extract tools from.	required
all_tools	bool	if True, return all tools, i.e. any recognized tool in self.llm_tools_known, whether it is handled by this agent or not; otherwise, return only the tools handled by this agent.	False

Returns:

Type	Description
List[ToolMessage]	List[ToolMessage]: list of ToolMessage objects

Source code in langroid/agent/base.py

def get_tool_messages(
    self,
    msg: str | ChatDocument | None,
    all_tools: bool = False,
) -> List[ToolMessage]:
    """
    Get ToolMessages recognized in msg, handle-able by this agent.
    NOTE: as a side-effect, this will update msg.tool_messages
    when msg is a ChatDocument and msg contains tool messages.
    The intent here is that update=True should be set ONLY within agent_response()
    or agent_response_async() methods. In other words, we want to persist the
    msg.tool_messages only AFTER the agent has had a chance to handle the tools.

    Args:
        msg (str|ChatDocument): the message to extract tools from.
        all_tools (bool):
            - if True, return all tools,
                i.e. any recognized tool in self.llm_tools_known,
                whether it is handled by this agent or not;
            - otherwise, return only the tools handled by this agent.

    Returns:
        List[ToolMessage]: list of ToolMessage objects
    """

    if msg is None:
        return []

    if isinstance(msg, str):
        json_tools = self.get_formatted_tool_messages(msg)
        if all_tools:
            return json_tools
        else:
            return [
                t
                for t in json_tools
                if self._tool_recipient_match(t) and t.default_value("request")
            ]

    if all_tools and len(msg.all_tool_messages) > 0:
        # We've already identified all_tool_messages in the msg;
        # return the corresponding ToolMessage objects
        return msg.all_tool_messages
    if len(msg.tool_messages) > 0:
        # We've already found tool_messages,
        # (either via OpenAI Fn-call or Langroid-native ToolMessage);
        # or they were added by an agent_response.
        # note these could be from a forwarded msg from another agent,
        # so return ONLY the messages THIS agent to enabled to handle.
        return msg.tool_messages
    assert isinstance(msg, ChatDocument)
    if (
        msg.content != ""
        and msg.oai_tool_calls is None
        and msg.function_call is None
    ):

        tools = self.get_formatted_tool_messages(msg.content)
        msg.all_tool_messages = tools
        # filter for actually handle-able tools, and recipient is this agent
        my_tools = [t for t in tools if self._tool_recipient_match(t)]
        msg.tool_messages = my_tools

        if all_tools:
            return tools
        else:
            return my_tools

    # otherwise, we look for `tool_calls` (possibly multiple)
    tools = self.get_oai_tool_calls_classes(msg)
    msg.all_tool_messages = tools
    my_tools = [t for t in tools if self._tool_recipient_match(t)]
    msg.tool_messages = my_tools

    if len(tools) == 0:
        # otherwise, we look for a `function_call`
        fun_call_cls = self.get_function_call_class(msg)
        tools = [fun_call_cls] if fun_call_cls is not None else []
        msg.all_tool_messages = tools
        my_tools = [t for t in tools if self._tool_recipient_match(t)]
        msg.tool_messages = my_tools
    if all_tools:
        return tools
    else:
        return my_tools

get_formatted_tool_messages(input_str)

Returns ToolMessage objects (tools) corresponding to tool-formatted substrings, if any. ASSUMPTION - These tools are either ALL JSON-based, or ALL XML-based (i.e. not a mix of both). Terminology: a "formatted tool msg" is one which the LLM generates as part of its raw string output, rather than within a JSON object in the API response (i.e. this method does not extract tools/fns returned by OpenAI's tools/fns API or similar APIs).

Parameters:

Name	Type	Description	Default
input_str	str	input string, typically a message sent by an LLM	required

Returns:

Type	Description
List[ToolMessage]	List[ToolMessage]: list of ToolMessage objects

Source code in langroid/agent/base.py

def get_formatted_tool_messages(self, input_str: str) -> List[ToolMessage]:
    """
    Returns ToolMessage objects (tools) corresponding to
    tool-formatted substrings, if any.
    ASSUMPTION - These tools are either ALL JSON-based, or ALL XML-based
    (i.e. not a mix of both).
    Terminology: a "formatted tool msg" is one which the LLM generates as
        part of its raw string output, rather than within a JSON object
        in the API response (i.e. this method does not extract tools/fns returned
        by OpenAI's tools/fns API or similar APIs).

    Args:
        input_str (str): input string, typically a message sent by an LLM

    Returns:
        List[ToolMessage]: list of ToolMessage objects
    """
    self.tool_error = False
    substrings = XMLToolMessage.find_candidates(input_str)
    is_json = False
    if len(substrings) == 0:
        substrings = extract_top_level_json(input_str)
        is_json = len(substrings) > 0
        if not is_json:
            return []

    results = [self._get_one_tool_message(j, is_json) for j in substrings]
    valid_results = [r for r in results if r is not None]
    # If any tool is correctly formed we do not set the flag
    if len(valid_results) > 0:
        self.tool_error = False
    return valid_results

get_function_call_class(msg)

From ChatDocument (constructed from an LLM Response), get the ToolMessage corresponding to the function_call if it exists.

Source code in langroid/agent/base.py

def get_function_call_class(self, msg: ChatDocument) -> Optional[ToolMessage]:
    """
    From ChatDocument (constructed from an LLM Response), get the `ToolMessage`
    corresponding to the `function_call` if it exists.
    """
    if msg.function_call is None:
        return None
    tool_name = msg.function_call.name
    tool_msg = msg.function_call.arguments or {}
    if tool_name not in self.llm_tools_handled:
        logger.warning(
            f"""
            The function_call '{tool_name}' is not handled 
            by the agent named '{self.config.name}'!
            If you intended this agent to handle this function_call,
            either the fn-call name is incorrectly generated by the LLM,
            (in which case you may need to adjust your LLM instructions),
            or you need to enable this agent to handle this fn-call.
            """
        )
        if tool_name not in self.all_llm_tools_known:
            self.tool_error = True
        return None
    self.tool_error = False
    tool_class = self.llm_tools_map[tool_name]
    tool_msg.update(dict(request=tool_name))
    tool = tool_class.parse_obj(tool_msg)
    return tool

get_oai_tool_calls_classes(msg)

From ChatDocument (constructed from an LLM Response), get a list of ToolMessages corresponding to the tool_calls, if any.

Source code in langroid/agent/base.py

def get_oai_tool_calls_classes(self, msg: ChatDocument) -> List[ToolMessage]:
    """
    From ChatDocument (constructed from an LLM Response), get
     a list of ToolMessages corresponding to the `tool_calls`, if any.
    """

    if msg.oai_tool_calls is None:
        return []
    tools = []
    all_errors = True
    for tc in msg.oai_tool_calls:
        if tc.function is None:
            continue
        tool_name = tc.function.name
        tool_msg = tc.function.arguments or {}
        if tool_name not in self.llm_tools_handled:
            logger.warning(
                f"""
                The tool_call '{tool_name}' is not handled 
                by the agent named '{self.config.name}'!
                If you intended this agent to handle this function_call,
                either the fn-call name is incorrectly generated by the LLM,
                (in which case you may need to adjust your LLM instructions),
                or you need to enable this agent to handle this fn-call.
                """
            )
            continue
        all_errors = False
        tool_class = self.llm_tools_map[tool_name]
        tool_msg.update(dict(request=tool_name))
        tool = tool_class.parse_obj(tool_msg)
        tool.id = tc.id or ""
        tools.append(tool)
    # When no tool is valid, set the recovery flag
    self.tool_error = all_errors
    return tools

tool_validation_error(ve)

Handle a validation error raised when parsing a tool message, when there is a legit tool name used, but it has missing/bad fields. Args: tool (ToolMessage): The tool message that failed validation ve (ValidationError): The exception raised

Returns:

Name	Type	Description
str	str	The error message to send back to the LLM

Source code in langroid/agent/base.py

def tool_validation_error(self, ve: ValidationError) -> str:
    """
    Handle a validation error raised when parsing a tool message,
        when there is a legit tool name used, but it has missing/bad fields.
    Args:
        tool (ToolMessage): The tool message that failed validation
        ve (ValidationError): The exception raised

    Returns:
        str: The error message to send back to the LLM
    """
    tool_name = cast(ToolMessage, ve.model).default_value("request")
    bad_field_errors = "\n".join(
        [f"{e['loc']}: {e['msg']}" for e in ve.errors() if "loc" in e]
    )
    return f"""
    There were one or more errors in your attempt to use the 
    TOOL or function_call named '{tool_name}': 
    {bad_field_errors}
    Please write your message again, correcting the errors.
    """

handle_message(msg)

Handle a "tool" message either a string containing one or more valid "tool" JSON substrings, or a ChatDocument containing a function_call attribute. Handle with the corresponding handler method, and return the results as a combined string.

Parameters:

Name	Type	Description	Default
msg	str | ChatDocument	The string or ChatDocument to handle	required

Returns:

Type

Description

None | str | OrderedDict[str, str] | ChatDocument

The result of the handler method can be: - None if no tools successfully handled, or no tools present - str if langroid-native JSON tools were handled, and results concatenated, OR there's a SINGLE OpenAI tool-call. (We do this so the common scenario of a single tool/fn-call has a simple behavior). - Dict[str, str] if multiple OpenAI tool-calls were handled (dict is an id->result map) - ChatDocument if a handler returned a ChatDocument, intended to be the final response of the agent_response method.

Source code in langroid/agent/base.py

def handle_message(
    self, msg: str | ChatDocument
) -> None | str | OrderedDict[str, str] | ChatDocument:
    """
    Handle a "tool" message either a string containing one or more
    valid "tool" JSON substrings,  or a
    ChatDocument containing a `function_call` attribute.
    Handle with the corresponding handler method, and return
    the results as a combined string.

    Args:
        msg (str | ChatDocument): The string or ChatDocument to handle

    Returns:
        The result of the handler method can be:
         - None if no tools successfully handled, or no tools present
         - str if langroid-native JSON tools were handled, and results concatenated,
             OR there's a SINGLE OpenAI tool-call.
            (We do this so the common scenario of a single tool/fn-call
             has a simple behavior).
         - Dict[str, str] if multiple OpenAI tool-calls were handled
             (dict is an id->result map)
         - ChatDocument if a handler returned a ChatDocument, intended to be the
             final response of the `agent_response` method.
    """
    try:
        tools = self.get_tool_messages(msg)
        tools = [t for t in tools if self._tool_recipient_match(t)]
    except ValidationError as ve:
        # correct tool name but bad fields
        return self.tool_validation_error(ve)
    except XMLException as xe:  # from XMLToolMessage parsing
        return str(xe)
    except ValueError:
        # invalid tool name
        # We return None since returning "invalid tool name" would
        # be considered a valid result in task loop, and would be treated
        # as a response to the tool message even though the tool was not intended
        # for this agent.
        return None
    if len(tools) > 1 and not self.config.allow_multiple_tools:
        return self.to_ChatDocument("ERROR: Use ONE tool at a time!")
    if len(tools) == 0:
        fallback_result = self.handle_message_fallback(msg)
        if fallback_result is None:
            return None
        return self.to_ChatDocument(
            fallback_result,
            chat_doc=msg if isinstance(msg, ChatDocument) else None,
        )
    has_ids = all([t.id != "" for t in tools])
    chat_doc = msg if isinstance(msg, ChatDocument) else None

    # check whether there are multiple orchestration-tools (e.g. DoneTool etc),
    # in which case set result to error-string since we don't yet support
    # multi-tools with one or more orch tools.
    from langroid.agent.tools.orchestration import (
        AgentDoneTool,
        AgentSendTool,
        DonePassTool,
        DoneTool,
        ForwardTool,
        PassTool,
        SendTool,
    )
    from langroid.agent.tools.recipient_tool import RecipientTool

    ORCHESTRATION_TOOLS = (
        AgentDoneTool,
        DoneTool,
        PassTool,
        DonePassTool,
        ForwardTool,
        RecipientTool,
        SendTool,
        AgentSendTool,
    )

    has_orch = any(isinstance(t, ORCHESTRATION_TOOLS) for t in tools)
    results: List[str | ChatDocument | None]
    if has_orch and len(tools) > 1:
        err_str = "ERROR: Use ONE tool at a time!"
        results = [err_str for _ in tools]
    else:
        results = [self.handle_tool_message(t, chat_doc=chat_doc) for t in tools]
        # if there's a solitary ChatDocument|str result, return it as is
        if len(results) == 1 and isinstance(results[0], (str, ChatDocument)):
            return results[0]
        # extract content from ChatDocument results so we have all str|None
        results = [r.content if isinstance(r, ChatDocument) else r for r in results]

    # now all results are str|None
    tool_names = [t.default_value("request") for t in tools]
    if has_ids:
        id2result = OrderedDict(
            (t.id, r)
            for t, r in zip(tools, results)
            if r is not None and isinstance(r, str)
        )
        result_values = list(id2result.values())
        if len(id2result) > 1 and any(
            orch_str in r
            for r in result_values
            for orch_str in ORCHESTRATION_STRINGS
        ):
            # Cannot support multi-tool results containing orchestration strings!
            # Replace results with err string to force LLM to retry
            err_str = "ERROR: Please use ONE tool at a time!"
            id2result = OrderedDict((id, err_str) for id in id2result.keys())

    name_results_list = [
        (name, r) for name, r in zip(tool_names, results) if r is not None
    ]
    if len(name_results_list) == 0:
        return None

    # there was a non-None result

    if has_ids and len(id2result) > 1:
        # if there are multiple OpenAI Tool results, return them as a dict
        return id2result

    # multi-results: prepend the tool name to each result
    str_results = [f"Result from {name}: {r}" for name, r in name_results_list]
    final = "\n\n".join(str_results)
    return final

handle_message_fallback(msg)

Fallback method for the "no-tools" scenario. This method can be overridden by subclasses, e.g., to create a "reminder" message when a tool is expected but the LLM "forgot" to generate one.

Parameters:

Name	Type	Description	Default
msg	str | ChatDocument	The input msg to handle	required

Returns: Any: The result of the handler method

Source code in langroid/agent/base.py

def handle_message_fallback(self, msg: str | ChatDocument) -> Any:
    """
    Fallback method for the "no-tools" scenario.
    This method can be overridden by subclasses, e.g.,
    to create a "reminder" message when a tool is expected but the LLM "forgot"
    to generate one.

    Args:
        msg (str | ChatDocument): The input msg to handle
    Returns:
        Any: The result of the handler method
    """
    return None

to_ChatDocument(msg, orig_tool_name=None, chat_doc=None, author_entity=Entity.AGENT)

Convert result of a responder (agent_response or llm_response, or task.run()), or tool handler, or handle_message_fallback, to a ChatDocument, to enable handling by other responders/tasks in a task loop possibly involving multiple agents.

Parameters:

Name	Type	Description	Default
msg	Any	The result of a responder or tool handler or task.run()	required
orig_tool_name	str	The original tool name that generated the response, if any.	None
chat_doc	ChatDocument	The original ChatDocument object that msg is a response to.	None
author_entity	Entity	The intended author of the result ChatDocument	AGENT

Source code in langroid/agent/base.py

def to_ChatDocument(
    self,
    msg: Any,
    orig_tool_name: str | None = None,
    chat_doc: Optional[ChatDocument] = None,
    author_entity: Entity = Entity.AGENT,
) -> Optional[ChatDocument]:
    """
    Convert result of a responder (agent_response or llm_response, or task.run()),
    or tool handler, or handle_message_fallback,
    to a ChatDocument, to enable handling by other
    responders/tasks in a task loop possibly involving multiple agents.

    Args:
        msg (Any): The result of a responder or tool handler or task.run()
        orig_tool_name (str): The original tool name that generated the response,
            if any.
        chat_doc (ChatDocument): The original ChatDocument object that `msg`
            is a response to.
        author_entity (Entity): The intended author of the result ChatDocument
    """
    if msg is None or isinstance(msg, ChatDocument):
        return msg

    is_agent_author = author_entity == Entity.AGENT

    if isinstance(msg, str):
        return self.response_template(author_entity, content=msg, content_any=msg)
    elif isinstance(msg, ToolMessage):
        # result is a ToolMessage, so...
        result_tool_name = msg.default_value("request")
        if (
            is_agent_author
            and result_tool_name in self.llm_tools_handled
            and (orig_tool_name is None or orig_tool_name != result_tool_name)
        ):
            # TODO: do we need to remove the tool message from the chat_doc?
            # if (chat_doc is not None and
            #     msg in chat_doc.tool_messages):
            #    chat_doc.tool_messages.remove(msg)
            # if we can handle it, do so
            result = self.handle_tool_message(msg, chat_doc=chat_doc)
            if result is not None and isinstance(result, ChatDocument):
                return result
        else:
            # else wrap it in an agent response and return it so
            # orchestrator can find a respondent
            return self.response_template(author_entity, tool_messages=[msg])
    else:
        result = to_string(msg)

    return (
        None
        if result is None
        else self.response_template(author_entity, content=result, content_any=msg)
    )

from_ChatDocument(msg, output_type)

Extract a desired output_type from a ChatDocument object. We use this fallback order: - if msg.content_any exists and matches the output_type, return it - if msg.content exists and output_type is str return it - if output_type is a ToolMessage, return the first tool in msg.tool_messages - if output_type is a list of ToolMessage, return all tools in msg.tool_messages - search for a tool in msg.tool_messages that has a field of output_type, and if found, return that field value - return None if all the above fail

Source code in langroid/agent/base.py

def from_ChatDocument(self, msg: ChatDocument, output_type: Type[T]) -> Optional[T]:
    """
    Extract a desired output_type from a ChatDocument object.
    We use this fallback order:
    - if `msg.content_any` exists and matches the output_type, return it
    - if `msg.content` exists and output_type is str return it
    - if output_type is a ToolMessage, return the first tool in `msg.tool_messages`
    - if output_type is a list of ToolMessage,
        return all tools in `msg.tool_messages`
    - search for a tool in `msg.tool_messages` that has a field of output_type,
         and if found, return that field value
    - return None if all the above fail
    """
    content = msg.content
    if output_type is str and content != "":
        return cast(T, content)
    content_any = msg.content_any
    if content_any is not None and isinstance(content_any, output_type):
        return cast(T, content_any)

    tools = self.try_get_tool_messages(msg, all_tools=True)

    if get_origin(output_type) is list:
        list_element_type = get_args(output_type)[0]
        if issubclass(list_element_type, ToolMessage):
            # list_element_type is a subclass of ToolMessage:
            # We output a list of objects derived from list_element_type
            return cast(
                T,
                [t for t in tools if isinstance(t, list_element_type)],
            )
    elif get_origin(output_type) is None and issubclass(output_type, ToolMessage):
        # output_type is a subclass of ToolMessage:
        # return the first tool that has this specific output_type
        for tool in tools:
            if isinstance(tool, output_type):
                return cast(T, tool)
        return None
    elif get_origin(output_type) is None and output_type in (str, int, float, bool):
        # attempt to get the output_type from the content,
        # if it's a primitive type
        primitive_value = from_string(content, output_type)  # type: ignore
        if primitive_value is not None:
            return cast(T, primitive_value)

    # then search for output_type as a field in a tool
    for tool in tools:
        value = tool.get_value_of_type(output_type)
        if value is not None:
            return cast(T, value)
    return None

handle_tool_message(tool, chat_doc=None)

Respond to a tool request from the LLM, in the form of an ToolMessage object. Args: tool: ToolMessage object representing the tool request. chat_doc: Optional ChatDocument object containing the tool request. This is passed to the tool-handler method only if it has a chat_doc argument.

Returns:

Source code in langroid/agent/base.py

def handle_tool_message(
    self,
    tool: ToolMessage,
    chat_doc: Optional[ChatDocument] = None,
) -> None | str | ChatDocument:
    """
    Respond to a tool request from the LLM, in the form of an ToolMessage object.
    Args:
        tool: ToolMessage object representing the tool request.
        chat_doc: Optional ChatDocument object containing the tool request.
            This is passed to the tool-handler method only if it has a `chat_doc`
            argument.

    Returns:

    """
    tool_name = tool.default_value("request")
    handler_method = getattr(self, tool_name, None)
    if handler_method is None:
        return None
    has_chat_doc_arg = (
        chat_doc is not None
        and "chat_doc" in inspect.signature(handler_method).parameters
    )
    try:
        if has_chat_doc_arg:
            maybe_result = handler_method(tool, chat_doc=chat_doc)
        else:
            maybe_result = handler_method(tool)
        result = self.to_ChatDocument(maybe_result, tool_name, chat_doc)
    except Exception as e:
        # raise the error here since we are sure it's
        # not a pydantic validation error,
        # which we check in `handle_message`
        raise e
    return self._maybe_truncate_result(
        result, tool._max_result_tokens
    )  # type: ignore

update_token_usage(response, prompt, stream, chat=True, print_response_stats=True)

Updates response.usage obj (token usage and cost fields).the usage memebr It updates the cost after checking the cache and updates the tokens (prompts and completion) if the response stream is True, because OpenAI doesn't returns these fields.

Parameters:

Name	Type	Description	Default
response	LLMResponse	LLMResponse object	required
prompt	str | List[LLMMessage]	prompt or list of LLMMessage objects	required
stream	bool	whether to update the usage in the response object if the response is not cached.	required
chat	bool	whether this is a chat model or a completion model	True
print_response_stats	bool	whether to print the response stats	True

Source code in langroid/agent/base.py

def update_token_usage(
    self,
    response: LLMResponse,
    prompt: str | List[LLMMessage],
    stream: bool,
    chat: bool = True,
    print_response_stats: bool = True,
) -> None:
    """
    Updates `response.usage` obj (token usage and cost fields).the usage memebr
    It updates the cost after checking the cache and updates the
    tokens (prompts and completion) if the response stream is True, because OpenAI
    doesn't returns these fields.

    Args:
        response (LLMResponse): LLMResponse object
        prompt (str | List[LLMMessage]): prompt or list of LLMMessage objects
        stream (bool): whether to update the usage in the response object
            if the response is not cached.
        chat (bool): whether this is a chat model or a completion model
        print_response_stats (bool): whether to print the response stats
    """
    if response is None or self.llm is None:
        return

    # Note: If response was not streamed, then
    # `response.usage` would already have been set by the API,
    # so we only need to update in the stream case.
    if stream:
        # usage, cost = 0 when response is from cache
        prompt_tokens = 0
        completion_tokens = 0
        cost = 0.0
        if not response.cached:
            prompt_tokens = self.num_tokens(prompt)
            completion_tokens = self.num_tokens(response.message)
            if response.function_call is not None:
                completion_tokens += self.num_tokens(str(response.function_call))
            cost = self.compute_token_cost(prompt_tokens, completion_tokens)
        response.usage = LLMTokenUsage(
            prompt_tokens=prompt_tokens,
            completion_tokens=completion_tokens,
            cost=cost,
        )

    # update total counters
    if response.usage is not None:
        self.total_llm_token_cost += response.usage.cost
        self.total_llm_token_usage += response.usage.total_tokens
        self.llm.update_usage_cost(
            chat,
            response.usage.prompt_tokens,
            response.usage.completion_tokens,
            response.usage.cost,
        )
        chat_length = 1 if isinstance(prompt, str) else len(prompt)
        self.token_stats_str = self._get_response_stats(
            chat_length, self.total_llm_token_cost, response
        )
        if print_response_stats:
            print(self.indent + self.token_stats_str)

ask_agent(agent, request, no_answer=NO_ANSWER, user_confirm=True)

Send a request to another agent, possibly after confirming with the user. This is not currently used, since we rely on the task loop and RecipientTool to address requests to other agents. It is generally best to avoid using this method.

Parameters:

Name	Type	Description	Default
agent	Agent	agent to ask	required
request	str	request to send	required
no_answer	str	expected response when agent does not know the answer	NO_ANSWER
user_confirm	bool	whether to gate the request with a human confirmation	True

Returns:

Name	Type	Description
str	Optional[str]	response from agent

Source code in langroid/agent/base.py

def ask_agent(
    self,
    agent: "Agent",
    request: str,
    no_answer: str = NO_ANSWER,
    user_confirm: bool = True,
) -> Optional[str]:
    """
    Send a request to another agent, possibly after confirming with the user.
    This is not currently used, since we rely on the task loop and
    `RecipientTool` to address requests to other agents. It is generally best to
    avoid using this method.

    Args:
        agent (Agent): agent to ask
        request (str): request to send
        no_answer (str): expected response when agent does not know the answer
        user_confirm (bool): whether to gate the request with a human confirmation

    Returns:
        str: response from agent
    """
    agent_type = type(agent).__name__
    if user_confirm:
        user_response = Prompt.ask(
            f"""[magenta]Here is the request or message:
            {request}
            Should I forward this to {agent_type}?""",
            default="y",
            choices=["y", "n"],
        )
        if user_response not in ["y", "yes"]:
            return None
    answer = agent.llm_response(request)
    if answer != no_answer:
        return (f"{agent_type} says: " + str(answer)).strip()
    return None