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 typically (but not necessarily) encapsulates an LLM.

Source code in langroid/agent/base.py

def __init__(self, config: AgentConfig = AgentConfig()):
    self.config = config
    self.id = ObjectRegistry.new_id()  # Initialize agent ID
    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
    self.search_for_tools = {
        SearchForTools.CONTENT.value,
        SearchForTools.TOOLS.value,
        SearchForTools.FUNCTIONS.value,
    }
    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_lambda_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_user_response_async=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 property writable

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

all_llm_tools_known 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, files=[], content_any=None, tool_messages=[], oai_tool_calls=None, oai_tool_choice='auto', oai_tool_id2result=None, function_call=None, recipient='', tainted=False)

Template for agent_response.

Source code in langroid/agent/base.py

def create_agent_response(
    self,
    content: str | None = None,
    files: List[FileAttachment] = [],
    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 = "",
    tainted: bool = False,
) -> ChatDocument:
    """Template for agent_response."""
    return self.response_template(
        Entity.AGENT,
        content=content,
        files=files,
        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,
        tainted=tainted,
    )

render_agent_response(results)

Render the response from the agent, typically from tool-handling. Args: results: results from tool-handling, which may be a string, a dict of tool results, or a ChatDocument.

Source code in langroid/agent/base.py

def render_agent_response(
    self,
    results: Optional[str | OrderedDict[str, str] | ChatDocument],
) -> None:
    """
    Render the response from the agent, typically from tool-handling.
    Args:
        results: results from tool-handling, which may be a string,
            a dict of tool results, or a ChatDocument.
    """
    if self.config.hide_agent_response or results is None:
        return
    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)
    if not settings.quiet:
        console.print(f"[red]{self.indent}", end="")
        print(f"[red]Agent: {escape(results_str)}")

agent_response_async(msg=None) async

Asynch version of agent_response. See there for details.

Source code in langroid/agent/base.py

async def agent_response_async(
    self,
    msg: Optional[str | ChatDocument] = None,
) -> Optional[ChatDocument]:
    """
    Asynch version of `agent_response`. See there for details.
    """
    if msg is None:
        return None

    results = await self.handle_message_async(msg)

    return self._agent_response_final(msg, results)

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)

    return self._agent_response_final(msg, results)

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, files=[], content_any=None, tool_messages=[], oai_tool_calls=None, oai_tool_choice='auto', oai_tool_id2result=None, function_call=None, recipient='', tainted=False)

Template for response from entity e.

Source code in langroid/agent/base.py

def response_template(
    self,
    e: Entity,
    content: str | None = None,
    files: List[FileAttachment] = [],
    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 = "",
    tainted: bool = False,
) -> ChatDocument:
    """Template for response from entity `e`."""
    return ChatDocument(
        content=content or "",
        files=files,
        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,
            # Trust tools structurally produced by an LLM or agent/handler
            # (explicit `tool_messages`): they must survive
            # _filter_user_origin_tools after a Task relabels the sender to
            # USER for a handoff. Content-only / echoed responses carry no
            # structured tool_messages, so they are NOT marked and stay
            # filtered (GHSA-gjgq-w2m6-wr5q). Known gap: tools repackaged
            # from untrusted content (e.g. via get_tool_messages) are still
            # trusted here -- see issue #1035 for the taint-propagation fix.
            tools_from_agent=bool(tool_messages)
            and e in (Entity.LLM, Entity.AGENT),
            # DISTRUST signal (#1035): set for any USER-origin response
            # (external user input -- create_user_response / to_ChatDocument
            # of a USER string), when the caller passes tainted=True, or when
            # the response repackages an already-tainted tool (e.g.
            # DonePassTool/AgentDoneTool re-emitting tools parsed from a USER
            # message). The Task result-wrap relabel builds ChatDocMetaData
            # directly (not via this template), so trusted agent->agent
            # handoffs are unaffected.
            tainted=tainted
            or e == Entity.USER
            or any(getattr(t, "_tainted", False) for t in tool_messages),
        ),
    )

create_user_response(content=None, files=[], 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,
    files: List[FileAttachment] = [],
    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,
        files=files,
        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_can_respond(msg=None)

Whether the user can respond to a message.

Parameters:

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

Returns:

Source code in langroid/agent/base.py

def user_can_respond(self, msg: Optional[str | ChatDocument] = None) -> bool:
    """
    Whether the user can respond to a message.

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

    Returns:

    """
    # 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
    )

    if not self.interactive and not need_human_response:
        return False

    return True

user_response_async(msg=None) async

Asynch version of user_response. See there for details.

Source code in langroid/agent/base.py

async def user_response_async(
    self,
    msg: Optional[str | ChatDocument] = None,
) -> Optional[ChatDocument]:
    """
    Asynch version of `user_response`. See there for details.
    """
    if not self.user_can_respond(msg):
        return None

    if self.default_human_response is not None:
        user_msg = self.default_human_response
    else:
        if (
            self.callbacks.get_user_response_async is not None
            and self.callbacks.get_user_response_async is not async_noop_fn
        ):
            user_msg = await self.callbacks.get_user_response_async(prompt="")
        elif self.callbacks.get_user_response is not None:
            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}"
            )

    return self._user_response_final(msg, user_msg)

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

    """

    if not self.user_can_respond(msg):
        return None

    if 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}"
            )

    return self._user_response_final(msg, user_msg)

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='', tainted=False)

Template for llm_response.

Parameters:

Name	Type	Description	Default
tainted	bool	Mark the response as USER-derived. Handlers that re-emit attacker-influenceable content as an LLM-labelled message must pass the taint of the message they read it from, so the content stays vetoed by :meth:_filter_user_origin_tools (#1035).	False

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 = "",
    tainted: bool = False,
) -> ChatDocument:
    """Template for llm_response.

    Args:
        tainted: Mark the response as USER-derived. Handlers that re-emit
            attacker-influenceable content as an LLM-labelled message must
            pass the taint of the message they read it from, so the content
            stays vetoed by :meth:`_filter_user_origin_tools` (#1035).
    """
    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,
        tainted=tainted,
    )

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.model_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 or ""))
    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.model_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 = "[red](cached)[/red]" if response.cached else ""
        console.print(f"[green]{self.indent}", end="")
        print(cached + "[green]" + escape(response.message or ""))
    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
    if isinstance(msg, ChatDocument):
        if len(msg.tool_messages) > 0:
            return True
        if msg.metadata.sender != Entity.LLM:
            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 none of the tools in the msg are handleable 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 none of the tools in the msg are
    handleable 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, stamping each with the source doc's taint mark (see #1035).

NOTE: as a side-effect, this will update msg.tool_messages when msg is a ChatDocument and msg contains tool messages.

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,
    stamping each with the source doc's taint mark (see #1035).

    NOTE: as a side-effect, this will update msg.tool_messages
    when msg is a ChatDocument and msg contains tool messages.

    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
    """
    tools = self._get_tool_messages_inner(msg, all_tools)
    if isinstance(msg, ChatDocument) and msg.metadata.tainted and tools:
        # Taint rides the tool objects themselves (#1035): a tool parsed
        # out of (or attached to) a tainted doc keeps the mark wherever
        # it is later re-emitted or repackaged, so laundering it into a
        # fresh "trusted-looking" ChatDocument cannot wash it clean.
        # Copy-on-stamp: tools ATTACHED to the doc may be shared/reusable
        # objects, so mark deep copies, and swap the copies into the
        # doc's caches (doc-scoped state) so repeated calls agree.
        marked = {id(t): self._tainted_copy(t) for t in tools}
        tools = [marked[id(t)] for t in tools]
        msg.tool_messages = [marked.get(id(t), t) for t in msg.tool_messages]
        if msg.all_tool_messages is not None:
            msg.all_tool_messages = [
                marked.get(id(t), t) for t in msg.all_tool_messages
            ]
    return tools

get_formatted_tool_messages(input_str, from_llm=True)

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
from_llm	bool	whether the input was generated by the LLM. If so, we track malformed tool calls.	True

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, from_llm: bool = True
) -> 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
        from_llm (bool): whether the input was generated by the LLM. If so,
            we track malformed tool calls.

    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, from_llm) 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 {}
    self.tool_error = False
    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
            and msg.metadata.sender == Entity.LLM
        ):
            self.tool_error = True
        return None
    tool_class = self.llm_tools_map[tool_name]
    tool_msg.update(dict(request=tool_name))
    try:
        tool = tool_class.model_validate(tool_msg)
    except ValidationError as ve:
        # Store tool class as an attribute on the exception
        ve.tool_class = tool_class  # type: ignore
        raise ve
    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))
        try:
            tool = tool_class.model_validate(tool_msg)
        except ValidationError as ve:
            # Store tool class as an attribute on the exception
            ve.tool_class = tool_class  # type: ignore
            raise ve
        tool.id = tc.id or ""
        tools.append(tool)
    # When no tool is valid and the message was produced
    # by the LLM, set the recovery flag
    self.tool_error = all_errors and msg.metadata.sender == Entity.LLM
    return tools

tool_validation_error(ve, tool_class=None)

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: ve (ValidationError): The exception raised tool_class (Optional[Type[ToolMessage]]): The tool class that failed validation

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, tool_class: Optional[Type[ToolMessage]] = None
) -> 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:
        ve (ValidationError): The exception raised
        tool_class (Optional[Type[ToolMessage]]): The tool class that
            failed validation

    Returns:
        str: The error message to send back to the LLM
    """
    # First try to get tool class from the exception itself
    if hasattr(ve, "tool_class") and ve.tool_class:
        tool_name = ve.tool_class.default_value("request")  # type: ignore
    elif tool_class is not None:
        tool_name = tool_class.default_value("request")
    else:
        # Fallback: try to extract from error context if available
        tool_name = "Unknown Tool"
    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_async(msg) async

Asynch version of handle_message. See there for details.

Source code in langroid/agent/base.py

async def handle_message_async(
    self, msg: str | ChatDocument
) -> None | str | OrderedDict[str, str] | ChatDocument:
    """
    Asynch version of `handle_message`. See there for details.
    """
    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
    # Security: USER-origin tool JSON must not be able to invoke tools that
    # the LLM itself is not allowed to use. ``enable_message(..., use=False,
    # handle=True)`` is intended for tools triggered by an LLM's output (this
    # agent's or, in multi-agent setups, another agent's), not by raw user
    # input. Filtering here ensures that an end user typing tool JSON cannot
    # bypass the LLM and directly invoke such a handler
    # (GHSA-gjgq-w2m6-wr5q).
    tools = self._filter_user_origin_tools(msg, tools)
    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,
        )
    chat_doc = msg if isinstance(msg, ChatDocument) else None

    results = self._get_multiple_orch_tool_errs(tools)
    if not results:
        results = [
            await self.handle_tool_message_async(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]

    return self._handle_message_final(tools, results)

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
    # Security: see ``handle_message_async`` -- the same USER-origin filter
    # also applies on the sync path (GHSA-gjgq-w2m6-wr5q).
    tools = self._filter_user_origin_tools(msg, tools)
    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,
        )

    results: List[str | ChatDocument | None] = []
    if len(tools) > 1 and not self.config.allow_multiple_tools:
        results = ["ERROR: Use ONE tool at a time!"] * len(tools)
    if not results:
        results = self._get_multiple_orch_tool_errs(tools)
    if not results:
        chat_doc = msg if isinstance(msg, ChatDocument) else None
        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]

    return self._handle_message_final(tools, results)

handle_message_fallback(msg)

Fallback method for the case where the msg has no tools that can be handled by this agent. 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 case where the msg has no tools that
    can be handled by this agent.
    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

    # Taint of the source doc rides mechanical derivations (#1035): a
    # handler result (str or arbitrary obj) derived from a tainted msg
    # yields a tainted doc. ToolMessage results instead carry their own
    # per-tool ``_tainted`` mark, checked in ``response_template``.
    src_tainted = chat_doc is not None and chat_doc.metadata.tainted
    if isinstance(msg, str):
        # USER-author strings (e.g. Task.run user input) are tainted by
        # response_template (#1035).
        return self.response_template(
            author_entity, content=msg, content_any=msg, tainted=src_tainted
        )
    elif isinstance(msg, ToolMessage):
        # A tool returned by a fallback/handler while processing a tainted
        # doc repackages untrusted fields (#1035); stamp it before it
        # enters the dispatch below, so the recursion veto, the handler-hop
        # threading, and response_template's tool check all see the mark.
        # LLM-AUTHORED tool results (e.g. a custom llm_response returning a
        # ToolMessage, converted by Task.response with author_entity=LLM)
        # are the trusted LLM-generation boundary and are NOT stamped.
        # Copy-on-stamp: msg may be a shared/config-held instance (e.g.
        # a reusable handle_llm_no_tool ToolMessage) -- never mutate it.
        if src_tainted and is_agent_author:
            msg = self._tainted_copy(msg)
        # 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)
            # Recursive-hop veto (#1035): a handler-returned tool marked
            # _tainted must get the same treatment as the filter's drop --
            # if it is handle-only, do NOT execute it; fall through to the
            # packaging branch below, which yields a tainted doc carrying
            # the (unexecuted) tool.
            and not (msg._tainted and result_tool_name not in self.llm_tools_usable)
        ):
            # 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, tainted=src_tainted
        )
    )

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_async(tool, chat_doc=None) async

Asynch version of handle_tool_message. See there for details.

Source code in langroid/agent/base.py

async def handle_tool_message_async(
    self,
    tool: ToolMessage,
    chat_doc: Optional[ChatDocument] = None,
) -> None | str | ChatDocument:
    """
    Asynch version of `handle_tool_message`. See there for details.
    """
    tool_name = tool.default_value("request")
    # resolve from the CLASS: an LLM-injected `_handler` key would
    # otherwise redirect dispatch to an arbitrary method (issue #1106)
    handler = handler_name(type(tool), tool_name)
    handler_method = getattr(self, handler + "_async", None)
    if handler_method is None:
        return self.handle_tool_message(tool, chat_doc=chat_doc)
    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 = await handler_method(tool, chat_doc=chat_doc)
        else:
            maybe_result = await handler_method(tool)
        maybe_result = self._taint_tool_result(tool, maybe_result)
        result = self.to_ChatDocument(maybe_result, tool_name, chat_doc)
        if (
            isinstance(result, ChatDocument)
            and not isinstance(maybe_result, (ChatDocument, ToolMessage))
            and getattr(tool, "_tainted", False)
        ):
            # Mechanical conversion of a plain str/object result from a
            # tainted tool (#1035): taint the framework-built doc. A
            # handler-RETURNED ChatDocument keeps its trusted label, and
            # a returned ToolMessage already carries its stamped copy.
            result.metadata.tainted = True
    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

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")
    # resolve from the CLASS: an LLM-injected `_handler` key would
    # otherwise redirect dispatch to an arbitrary method (issue #1106)
    handler = handler_name(type(tool), tool_name)
    handler_method = getattr(self, handler, 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)
        maybe_result = self._taint_tool_result(tool, maybe_result)
        result = self.to_ChatDocument(maybe_result, tool_name, chat_doc)
        if (
            isinstance(result, ChatDocument)
            and not isinstance(maybe_result, (ChatDocument, ToolMessage))
            and getattr(tool, "_tainted", False)
        ):
            # Mechanical conversion of a plain str/object result from a
            # tainted tool (#1035): taint the framework-built doc. A
            # handler-RETURNED ChatDocument keeps its trusted label, and
            # a returned ToolMessage already carries its stamped copy.
            result.metadata.tainted = True
    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) if needed. An update is needed only if: - stream is True (i.e. streaming was enabled), and - the response was NOT obtained from cached, and - the API did NOT provide the usage/cost fields during streaming (As of Sep 2024, the OpenAI API started providing these; for other APIs this may not necessarily be the case).

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) if needed.
    An update is needed only if:
    - stream is True (i.e. streaming was enabled), and
    - the response was NOT obtained from cached, and
    - the API did NOT provide the usage/cost fields during streaming
      (As of Sep 2024, the OpenAI API started providing these; for other APIs
        this may not necessarily be the case).

    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

    no_usage_info = response.usage is None or response.usage.prompt_tokens == 0
    # 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 and no_usage_info:
        # 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 or "")
            if response.function_call is not None:
                completion_tokens += self.num_tokens(str(response.function_call))
            cost = self.compute_token_cost(prompt_tokens, 0, 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