langroid

langroid/init.py

Main langroid package

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
    self.interactive: bool | None = None
    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
    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,
        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.

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 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
    if not settings.quiet:
        results_str = (
            results if isinstance(results, str) else 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",
        )
    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
    )

    interactive = self.interactive or settings.interactive

    if not 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}Human "
                "(respond or q, x to exit current level, "
                f"or hit enter to continue)\n{self.indent}",
            ).strip()

    tool_ids = []
    if msg is not None and isinstance(msg, ChatDocument):
        tool_ids = msg.metadata.tool_ids
    # 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[6:].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.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.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(msg=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,
    msg: Optional[str | ChatDocument] = None,
) -> Optional[ChatDocument]:
    """
    Asynch version of `llm_response`. See there for details.
    """
    if msg is None or not self.llm_can_respond(msg):
        return None

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

    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(msg, str) else msg.metadata.tool_ids
    return cdoc

llm_response(msg=None)

LLM response to a prompt. Args: msg (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,
    msg: Optional[str | ChatDocument] = None,
) -> Optional[ChatDocument]:
    """
    LLM response to a prompt.
    Args:
        msg (str|ChatDocument): prompt string, or ChatDocument object

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

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

    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(msg, str) else msg.metadata.tool_ids
    return cdoc

has_tool_message_attempt(msg)

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

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)"""
    if msg is None:
        return False
    try:
        tools = self.get_tool_messages(msg)
        return len(tools) > 0
    except ValidationError:
        # 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.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. If all_tools is True: - return all tools, i.e. any tool in self.llm_tools_known, whether it is handled by this agent or not; - otherwise, return only the tools handled by this agent.

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.
    If all_tools is True:
    - return all tools, i.e. any tool in self.llm_tools_known,
        whether it is handled by this agent or not;
    - otherwise, return only the tools handled by this agent.
    """

    if msg is None:
        return []

    if isinstance(msg, str):
        json_tools = self.get_json_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_json_tool_messages(msg.content)
        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

    # 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_json_tool_messages(input_str)

Returns ToolMessage objects (tools) corresponding to JSON substrings, if any.

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_json_tool_messages(self, input_str: str) -> List[ToolMessage]:
    """
    Returns ToolMessage objects (tools) corresponding to JSON substrings, if any.

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

    Returns:
        List[ToolMessage]: list of ToolMessage objects
    """
    json_substrings = extract_top_level_json(input_str)
    if len(json_substrings) == 0:
        return []
    results = [self._get_one_tool_message(j) for j in json_substrings]
    return [r for r in results if r is not None]

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.
            """
        )
        return None
    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 = []
    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
        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)
    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 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) == 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 enabling 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 enabling 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.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 result  # 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

AgentConfig

Bases: BaseSettings

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

StatusCode

Bases: str, Enum

Codes meant to be returned by task.run(). Some are not used yet.

ChatDocument(**data)

Bases: Document

Represents a message in a conversation among agents. All responders of an agent have signature ChatDocument -> ChatDocument (modulo None, str, etc), and so does the Task.run() method.

Attributes:

Name	Type	Description
oai_tool_calls	Optional[List[OpenAIToolCall]]	Tool-calls from an OpenAI-compatible API
oai_tool_id2results	Optional[OrderedDict[str, str]]	Results of tool-calls from OpenAI (dict is a map of tool_id -> result)
oai_tool_choice	ToolChoiceTypes | Dict[str, Dict[str, str] | str]	ToolChoiceTypes | Dict[str, str]: Param controlling how the LLM should choose tool-use in its response (auto, none, required, or a specific tool)
function_call	Optional[LLMFunctionCall]	Function-call from an OpenAI-compatible API (deprecated by OpenAI, in favor of tool-calls)
tool_messages	List[ToolMessage]	Langroid ToolMessages extracted from - content field (via JSON parsing), - oai_tool_calls, or - function_call
metadata	ChatDocMetaData	Metadata for the message, e.g. sender, recipient.
attachment	None | ChatDocAttachment	Any additional data attached.

Source code in langroid/agent/chat_document.py

def __init__(self, **data: Any):
    super().__init__(**data)
    ObjectRegistry.register_object(self)

delete_id(id) staticmethod

Remove ChatDocument with given id from ObjectRegistry, and all its descendants.

Source code in langroid/agent/chat_document.py

@staticmethod
def delete_id(id: str) -> None:
    """Remove ChatDocument with given id from ObjectRegistry,
    and all its descendants.
    """
    chat_doc = ChatDocument.from_id(id)
    # first delete all descendants
    while chat_doc is not None:
        next_chat_doc = chat_doc.child
        ObjectRegistry.remove(chat_doc.id())
        chat_doc = next_chat_doc

get_json_tools()

Get names of attempted JSON tool usages in the content of the message. Returns: List[str]: list of JSON tool names

Source code in langroid/agent/chat_document.py

def get_json_tools(self) -> List[str]:
    """
    Get names of attempted JSON tool usages in the content
        of the message.
    Returns:
        List[str]: list of JSON tool names
    """
    jsons = extract_top_level_json(self.content)
    tools = []
    for j in jsons:
        json_data = json.loads(j)
        tool = json_data.get("request")
        if tool is not None:
            tools.append(str(tool))
    return tools

log_fields()

Fields for logging in csv/tsv logger Returns: List[str]: list of fields

Source code in langroid/agent/chat_document.py

def log_fields(self) -> ChatDocLoggerFields:
    """
    Fields for logging in csv/tsv logger
    Returns:
        List[str]: list of fields
    """
    tool_type = ""  # FUNC or TOOL
    tool = ""  # tool name or function name
    if self.function_call is not None:
        tool_type = "FUNC"
        tool = self.function_call.name
    elif self.get_json_tools() != []:
        tool_type = "TOOL"
        tool = self.get_json_tools()[0]
    recipient = self.metadata.recipient
    content = self.content
    sender_entity = self.metadata.sender
    sender_name = self.metadata.sender_name
    if tool_type == "FUNC":
        content += str(self.function_call)
    return ChatDocLoggerFields(
        sender_entity=sender_entity,
        sender_name=sender_name,
        recipient=recipient,
        block=self.metadata.block,
        tool_type=tool_type,
        tool=tool,
        content=content,
    )

pop_tool_ids()

Pop the last tool_id from the stack of tool_ids.

Source code in langroid/agent/chat_document.py

def pop_tool_ids(self) -> None:
    """
    Pop the last tool_id from the stack of tool_ids.
    """
    if len(self.metadata.tool_ids) > 0:
        self.metadata.tool_ids.pop()

from_LLMResponse(response, displayed=False) staticmethod

Convert LLMResponse to ChatDocument. Args: response (LLMResponse): LLMResponse to convert. displayed (bool): Whether this response was displayed to the user. Returns: ChatDocument: ChatDocument representation of this LLMResponse.

Source code in langroid/agent/chat_document.py

@staticmethod
def from_LLMResponse(
    response: LLMResponse,
    displayed: bool = False,
) -> "ChatDocument":
    """
    Convert LLMResponse to ChatDocument.
    Args:
        response (LLMResponse): LLMResponse to convert.
        displayed (bool): Whether this response was displayed to the user.
    Returns:
        ChatDocument: ChatDocument representation of this LLMResponse.
    """
    recipient, message = response.get_recipient_and_message()
    message = message.strip()
    if message in ["''", '""']:
        message = ""
    if response.function_call is not None:
        ChatDocument._clean_fn_call(response.function_call)
    if response.oai_tool_calls is not None:
        # there must be at least one if it's not None
        for oai_tc in response.oai_tool_calls:
            ChatDocument._clean_fn_call(oai_tc.function)
    return ChatDocument(
        content=message,
        content_any=message,
        oai_tool_calls=response.oai_tool_calls,
        function_call=response.function_call,
        metadata=ChatDocMetaData(
            source=Entity.LLM,
            sender=Entity.LLM,
            usage=response.usage,
            displayed=displayed,
            cached=response.cached,
            recipient=recipient,
        ),
    )

to_LLMMessage(message, oai_tools=None) staticmethod

Convert to list of LLMMessage, to incorporate into msg-history sent to LLM API. Usually there will be just a single LLMMessage, but when the ChatDocument contains results from multiple OpenAI tool-calls, we would have a sequence LLMMessages, one per tool-call result.

Parameters:

Name	Type	Description	Default
message	str | ChatDocument	Message to convert.	required
oai_tools	Optional[List[OpenAIToolCall]]	Tool-calls currently awaiting response, from the ChatAgent's latest message.	None

Returns: List[LLMMessage]: list of LLMMessages corresponding to this ChatDocument.

Source code in langroid/agent/chat_document.py

@staticmethod
def to_LLMMessage(
    message: Union[str, "ChatDocument"],
    oai_tools: Optional[List[OpenAIToolCall]] = None,
) -> List[LLMMessage]:
    """
    Convert to list of LLMMessage, to incorporate into msg-history sent to LLM API.
    Usually there will be just a single LLMMessage, but when the ChatDocument
    contains results from multiple OpenAI tool-calls, we would have a sequence
    LLMMessages, one per tool-call result.

    Args:
        message (str|ChatDocument): Message to convert.
        oai_tools (Optional[List[OpenAIToolCall]]): Tool-calls currently awaiting
            response, from the ChatAgent's latest message.
    Returns:
        List[LLMMessage]: list of LLMMessages corresponding to this ChatDocument.
    """
    sender_name = None
    sender_role = Role.USER
    fun_call = None
    oai_tool_calls = None
    tool_id = ""  # for OpenAI Assistant
    chat_document_id: str = ""
    if isinstance(message, ChatDocument):
        content = message.content or to_string(message.content_any) or ""
        fun_call = message.function_call
        oai_tool_calls = message.oai_tool_calls
        if message.metadata.sender == Entity.USER and fun_call is not None:
            # This may happen when a (parent agent's) LLM generates a
            # a Function-call, and it ends up being sent to the current task's
            # LLM (possibly because the function-call is mis-named or has other
            # issues and couldn't be handled by handler methods).
            # But a function-call can only be generated by an entity with
            # Role.ASSISTANT, so we instead put the content of the function-call
            # in the content of the message.
            content += " " + str(fun_call)
            fun_call = None
        if message.metadata.sender == Entity.USER and oai_tool_calls is not None:
            # same reasoning as for function-call above
            content += " " + "\n\n".join(str(tc) for tc in oai_tool_calls)
            oai_tool_calls = None
        sender_name = message.metadata.sender_name
        tool_ids = message.metadata.tool_ids
        tool_id = tool_ids[-1] if len(tool_ids) > 0 else ""
        chat_document_id = message.id()
        if message.metadata.sender == Entity.SYSTEM:
            sender_role = Role.SYSTEM
        if (
            message.metadata.parent is not None
            and message.metadata.parent.function_call is not None
        ):
            # This is a response to a function call, so set the role to FUNCTION.
            sender_role = Role.FUNCTION
            sender_name = message.metadata.parent.function_call.name
        elif oai_tools is not None and len(oai_tools) > 0:
            pending_tool_ids = [tc.id for tc in oai_tools]
            # The ChatAgent has pending OpenAI tool-call(s),
            # so the current ChatDocument contains
            # results for some/all/none of them.

            if len(oai_tools) == 1:
                # Case 1:
                # There was exactly 1 pending tool-call, and in this case
                # the result would be a plain string in `content`
                return [
                    LLMMessage(
                        role=Role.TOOL,
                        tool_call_id=oai_tools[0].id,
                        content=content,
                        chat_document_id=chat_document_id,
                    )
                ]

            elif (
                message.metadata.oai_tool_id is not None
                and message.metadata.oai_tool_id in pending_tool_ids
            ):
                # Case 2:
                # ChatDocument.content has result of a single tool-call
                return [
                    LLMMessage(
                        role=Role.TOOL,
                        tool_call_id=message.metadata.oai_tool_id,
                        content=content,
                        chat_document_id=chat_document_id,
                    )
                ]
            elif message.oai_tool_id2result is not None:
                # Case 2:
                # There were > 1 tool-calls awaiting response,
                assert (
                    len(message.oai_tool_id2result) > 1
                ), "oai_tool_id2result must have more than 1 item."
                return [
                    LLMMessage(
                        role=Role.TOOL,
                        tool_call_id=tool_id,
                        content=result,
                        chat_document_id=chat_document_id,
                    )
                    for tool_id, result in message.oai_tool_id2result.items()
                ]
        elif message.metadata.sender == Entity.LLM:
            sender_role = Role.ASSISTANT
    else:
        # LLM can only respond to text content, so extract it
        content = message

    return [
        LLMMessage(
            role=sender_role,
            tool_id=tool_id,  # for OpenAI Assistant
            content=content,
            function_call=fun_call,
            tool_calls=oai_tool_calls,
            name=sender_name,
            chat_document_id=chat_document_id,
        )
    ]

ToolMessage

Bases: ABC, BaseModel

Abstract Class for a class that defines the structure of a "Tool" message from an LLM. Depending on context, "tools" are also referred to as "plugins", or "function calls" (in the context of OpenAI LLMs). Essentially, they are a way for the LLM to express its intent to run a special function or method. Currently these "tools" are handled by methods of the agent.

Attributes:

Name	Type	Description
request	str	name of agent method to map to.
purpose	str	purpose of agent method, expressed in general terms. (This is used when auto-generating the tool instruction to the LLM)

examples() classmethod

Examples to use in few-shot demos with JSON formatting instructions. Each example can be either: - just a ToolMessage instance, e.g. MyTool(param1=1, param2="hello"), or - a tuple (description, ToolMessage instance), where the description is a natural language "thought" that leads to the tool usage, e.g. ("I want to find the square of 5", SquareTool(num=5)) In some scenarios, including such a description can significantly enhance reliability of tool use. Returns:

Source code in langroid/agent/tool_message.py

@classmethod
def examples(cls) -> List["ToolMessage" | Tuple[str, "ToolMessage"]]:
    """
    Examples to use in few-shot demos with JSON formatting instructions.
    Each example can be either:
    - just a ToolMessage instance, e.g. MyTool(param1=1, param2="hello"), or
    - a tuple (description, ToolMessage instance), where the description is
        a natural language "thought" that leads to the tool usage,
        e.g. ("I want to find the square of 5",  SquareTool(num=5))
        In some scenarios, including such a description can significantly
        enhance reliability of tool use.
    Returns:
    """
    return []

usage_examples(random=False) classmethod

Instruction to the LLM showing examples of how to use the tool-message.

Parameters:

Name	Type	Description	Default
random	bool	whether to pick a random example from the list of examples. Set to true when using this to illustrate a dialog between LLM and user. (if false, use ALL examples)	False

Returns: str: examples of how to use the tool/function-call

Source code in langroid/agent/tool_message.py

@classmethod
def usage_examples(cls, random: bool = False) -> str:
    """
    Instruction to the LLM showing examples of how to use the tool-message.

    Args:
        random (bool): whether to pick a random example from the list of examples.
            Set to `true` when using this to illustrate a dialog between LLM and
            user.
            (if false, use ALL examples)
    Returns:
        str: examples of how to use the tool/function-call
    """
    # pick a random example of the fields
    if len(cls.examples()) == 0:
        return ""
    if random:
        examples = [choice(cls.examples())]
    else:
        examples = cls.examples()
    examples_jsons = [
        (
            f"EXAMPLE {i}: (THOUGHT: {ex[0]}) => \n{ex[1].json_example()}"
            if isinstance(ex, tuple)
            else f"EXAMPLE {i}:\n {ex.json_example()}"
        )
        for i, ex in enumerate(examples, 1)
    ]
    return "\n\n".join(examples_jsons)

get_value_of_type(target_type)

Try to find a value of a desired type in the fields of the ToolMessage.

Source code in langroid/agent/tool_message.py

def get_value_of_type(self, target_type: Type[Any]) -> Any:
    """Try to find a value of a desired type in the fields of the ToolMessage."""
    ignore_fields = self.Config.schema_extra["exclude"].union(["request"])
    for field_name in set(self.dict().keys()) - ignore_fields:
        value = getattr(self, field_name)
        if is_instance_of(value, target_type):
            return value
    return None

default_value(f) classmethod

Returns the default value of the given field, for the message-class Args: f (str): field name

Returns:

Name	Type	Description
Any	Any	default value of the field, or None if not set or if the field does not exist.

Source code in langroid/agent/tool_message.py

@classmethod
def default_value(cls, f: str) -> Any:
    """
    Returns the default value of the given field, for the message-class
    Args:
        f (str): field name

    Returns:
        Any: default value of the field, or None if not set or if the
            field does not exist.
    """
    schema = cls.schema()
    properties = schema["properties"]
    return properties.get(f, {}).get("default", None)

json_instructions(tool=False) classmethod

Default Instructions to the LLM showing how to use the tool/function-call. Works for GPT4 but override this for weaker LLMs if needed.

Parameters:

Name	Type	Description	Default
tool	bool	instructions for Langroid-native tool use? (e.g. for non-OpenAI LLM) (or else it would be for OpenAI Function calls)	False

Returns: str: instructions on how to use the message

Source code in langroid/agent/tool_message.py

@classmethod
def json_instructions(cls, tool: bool = False) -> str:
    """
    Default Instructions to the LLM showing how to use the tool/function-call.
    Works for GPT4 but override this for weaker LLMs if needed.

    Args:
        tool: instructions for Langroid-native tool use? (e.g. for non-OpenAI LLM)
            (or else it would be for OpenAI Function calls)
    Returns:
        str: instructions on how to use the message
    """
    # TODO: when we attempt to use a "simpler schema"
    # (i.e. all nested fields explicit without definitions),
    # we seem to get worse results, so we turn it off for now
    param_dict = (
        # cls.simple_schema() if tool else
        cls.llm_function_schema(request=True).parameters
    )
    examples_str = ""
    if cls.examples():
        examples_str = "EXAMPLES:\n" + cls.usage_examples()
    return textwrap.dedent(
        f"""
        TOOL: {cls.default_value("request")}
        PURPOSE: {cls.default_value("purpose")} 
        JSON FORMAT: {
            json.dumps(param_dict, indent=4)
        }
        {examples_str}
        """.lstrip()
    )

json_group_instructions() staticmethod

Template for instructions for a group of tools. Works with GPT4 but override this for weaker LLMs if needed.

Source code in langroid/agent/tool_message.py

@staticmethod
def json_group_instructions() -> str:
    """Template for instructions for a group of tools.
    Works with GPT4 but override this for weaker LLMs if needed.
    """
    return textwrap.dedent(
        """
        === ALL AVAILABLE TOOLS and THEIR JSON FORMAT INSTRUCTIONS ===
        You have access to the following TOOLS to accomplish your task:

        {json_instructions}

        When one of the above TOOLs is applicable, you must express your 
        request as "TOOL:" followed by the request in the above JSON format.
        """
    )

llm_function_schema(request=False, defaults=True) classmethod

Clean up the schema of the Pydantic class (which can recursively contain other Pydantic classes), to create a version compatible with OpenAI Function-call API.

Adapted from this excellent library: https://github.com/jxnl/instructor/blob/main/instructor/function_calls.py

Parameters:

Name	Type	Description	Default
request	bool	whether to include the "request" field in the schema. (we set this to True when using Langroid-native TOOLs as opposed to OpenAI Function calls)	False
defaults	bool	whether to include fields with default values in the schema, in the "properties" section.	True

Returns:

Name	Type	Description
LLMFunctionSpec	LLMFunctionSpec	the schema as an LLMFunctionSpec

Source code in langroid/agent/tool_message.py

@classmethod
def llm_function_schema(
    cls,
    request: bool = False,
    defaults: bool = True,
) -> LLMFunctionSpec:
    """
    Clean up the schema of the Pydantic class (which can recursively contain
    other Pydantic classes), to create a version compatible with OpenAI
    Function-call API.

    Adapted from this excellent library:
    https://github.com/jxnl/instructor/blob/main/instructor/function_calls.py

    Args:
        request: whether to include the "request" field in the schema.
            (we set this to True when using Langroid-native TOOLs as opposed to
            OpenAI Function calls)
        defaults: whether to include fields with default values in the schema,
                in the "properties" section.

    Returns:
        LLMFunctionSpec: the schema as an LLMFunctionSpec

    """
    schema = cls.schema()
    docstring = parse(cls.__doc__ or "")
    parameters = {
        k: v for k, v in schema.items() if k not in ("title", "description")
    }
    for param in docstring.params:
        if (name := param.arg_name) in parameters["properties"] and (
            description := param.description
        ):
            if "description" not in parameters["properties"][name]:
                parameters["properties"][name]["description"] = description

    excludes = cls.Config.schema_extra["exclude"]
    if not request:
        excludes = excludes.union({"request"})
    # exclude 'excludes' from parameters["properties"]:
    parameters["properties"] = {
        field: details
        for field, details in parameters["properties"].items()
        if field not in excludes and (defaults or details.get("default") is None)
    }
    parameters["required"] = sorted(
        k
        for k, v in parameters["properties"].items()
        if ("default" not in v and k not in excludes)
    )
    if request:
        parameters["required"].append("request")

    if "description" not in schema:
        if docstring.short_description:
            schema["description"] = docstring.short_description
        else:
            schema["description"] = (
                f"Correctly extracted `{cls.__name__}` with all "
                f"the required parameters with correct types"
            )

    parameters.pop("exclude")
    _recursive_purge_dict_key(parameters, "title")
    _recursive_purge_dict_key(parameters, "additionalProperties")
    return LLMFunctionSpec(
        name=cls.default_value("request"),
        description=cls.default_value("purpose"),
        parameters=parameters,
    )

simple_schema() classmethod

Return a simplified schema for the message, with only the request and required fields. Returns: Dict[str, Any]: simplified schema

Source code in langroid/agent/tool_message.py

@classmethod
def simple_schema(cls) -> Dict[str, Any]:
    """
    Return a simplified schema for the message, with only the request and
    required fields.
    Returns:
        Dict[str, Any]: simplified schema
    """
    schema = generate_simple_schema(
        cls,
        exclude=list(cls.Config.schema_extra["exclude"]),
    )
    return schema

ChatAgent(config=ChatAgentConfig(), task=None)

Bases: Agent

Chat Agent interacting with external env (could be human, or external tools). The agent (the LLM actually) is provided with an optional "Task Spec", which is a sequence of LLMMessages. These are used to initialize the task_messages of the agent. In most applications we will use a ChatAgent rather than a bare Agent. The Agent class mainly exists to hold various common methods and attributes. One difference between ChatAgent and Agent is that ChatAgent's llm_response method uses "chat mode" API (i.e. one that takes a message sequence rather than a single message), whereas the same method in the Agent class uses "completion mode" API (i.e. one that takes a single message).

config: settings for the agent

Source code in langroid/agent/chat_agent.py

def __init__(
    self,
    config: ChatAgentConfig = ChatAgentConfig(),
    task: Optional[List[LLMMessage]] = None,
):
    """
    Chat-mode agent initialized with task spec as the initial message sequence
    Args:
        config: settings for the agent

    """
    super().__init__(config)
    self.config: ChatAgentConfig = config
    self.config._set_fn_or_tools(self._fn_call_available())
    self.message_history: List[LLMMessage] = []
    self.init_state()
    # An agent's "task" is defined by a system msg and an optional user msg;
    # These are "priming" messages that kick off the agent's conversation.
    self.system_message: str = self.config.system_message
    self.user_message: str | None = self.config.user_message

    if task is not None:
        # if task contains a system msg, we override the config system msg
        if len(task) > 0 and task[0].role == Role.SYSTEM:
            self.system_message = task[0].content
        # if task contains a user msg, we override the config user msg
        if len(task) > 1 and task[1].role == Role.USER:
            self.user_message = task[1].content

    # system-level instructions for using tools/functions:
    # We maintain these as tools/functions are enabled/disabled,
    # and whenever an LLM response is sought, these are used to
    # recreate the system message (via `_create_system_and_tools_message`)
    # each time, so it reflects the current set of enabled tools/functions.
    # (a) these are general instructions on using certain tools/functions,
    #   if they are specified in a ToolMessage class as a classmethod `instructions`
    self.system_tool_instructions: str = ""
    # (b) these are only for the builtin in Langroid TOOLS mechanism:
    self.system_json_tool_instructions: str = ""

    self.llm_functions_map: Dict[str, LLMFunctionSpec] = {}
    self.llm_functions_handled: Set[str] = set()
    self.llm_functions_usable: Set[str] = set()
    self.llm_function_force: Optional[Dict[str, str]] = None

    if self.config.enable_orchestration_tool_handling:
        # Only enable HANDLING by `agent_response`, NOT LLM generation of these.
        # This is useful where tool-handlers or agent_response generate these
        # tools, and need to be handled.
        # We don't want enable orch tool GENERATION by default, since that
        # might clutter-up the LLM system message unnecessarily.
        from langroid.agent.tools.orchestration import (
            AgentDoneTool,
            AgentSendTool,
            DonePassTool,
            DoneTool,
            ForwardTool,
            PassTool,
            ResultTool,
            SendTool,
        )

        self.enable_message(ForwardTool, use=False, handle=True)
        self.enable_message(DoneTool, use=False, handle=True)
        self.enable_message(AgentDoneTool, use=False, handle=True)
        self.enable_message(PassTool, use=False, handle=True)
        self.enable_message(DonePassTool, use=False, handle=True)
        self.enable_message(SendTool, use=False, handle=True)
        self.enable_message(AgentSendTool, use=False, handle=True)
        self.enable_message(ResultTool, use=False, handle=True)

task_messages: List[LLMMessage] property

The task messages are the initial messages that define the task of the agent. There will be at least a system message plus possibly a user msg. Returns: List[LLMMessage]: the task messages

init_state()

Initialize the state of the agent. Just conversation state here, but subclasses can override this to initialize other state.

Source code in langroid/agent/chat_agent.py

def init_state(self) -> None:
    """
    Initialize the state of the agent. Just conversation state here,
    but subclasses can override this to initialize other state.
    """
    super().init_state()
    self.clear_history(0)
    self.clear_dialog()

from_id(id) staticmethod

Get an agent from its ID Args: agent_id (str): ID of the agent Returns: ChatAgent: The agent with the given ID

Source code in langroid/agent/chat_agent.py

@staticmethod
def from_id(id: str) -> "ChatAgent":
    """
    Get an agent from its ID
    Args:
        agent_id (str): ID of the agent
    Returns:
        ChatAgent: The agent with the given ID
    """
    return cast(ChatAgent, Agent.from_id(id))

clone(i=0)

Create i'th clone of this agent, ensuring tool use/handling is cloned. Important: We assume all member variables are in the init method here and in the Agent class. TODO: We are attempting to clone an agent after its state has been changed in possibly many ways. Below is an imperfect solution. Caution advised. Revisit later.

Source code in langroid/agent/chat_agent.py

def clone(self, i: int = 0) -> "ChatAgent":
    """Create i'th clone of this agent, ensuring tool use/handling is cloned.
    Important: We assume all member variables are in the __init__ method here
    and in the Agent class.
    TODO: We are attempting to clone an agent after its state has been
    changed in possibly many ways. Below is an imperfect solution. Caution advised.
    Revisit later.
    """
    agent_cls = type(self)
    config_copy = copy.deepcopy(self.config)
    config_copy.name = f"{config_copy.name}-{i}"
    new_agent = agent_cls(config_copy)
    new_agent.system_tool_instructions = self.system_tool_instructions
    new_agent.system_json_tool_instructions = self.system_json_tool_instructions
    new_agent.llm_tools_map = self.llm_tools_map
    new_agent.llm_functions_map = self.llm_functions_map
    new_agent.llm_functions_handled = self.llm_functions_handled
    new_agent.llm_functions_usable = self.llm_functions_usable
    new_agent.llm_function_force = self.llm_function_force
    # Caution - we are copying the vector-db, maybe we don't always want this?
    new_agent.vecdb = self.vecdb
    new_agent.id = ObjectRegistry.new_id()
    if self.config.add_to_registry:
        ObjectRegistry.register_object(new_agent)
    return new_agent

clear_history(start=-2)

Clear the message history, starting at the index start

Parameters:

Name	Type	Description	Default
start	int	index of first message to delete; default = -2 (i.e. delete last 2 messages, typically these are the last user and assistant messages)	-2

Source code in langroid/agent/chat_agent.py

def clear_history(self, start: int = -2) -> None:
    """
    Clear the message history, starting at the index `start`

    Args:
        start (int): index of first message to delete; default = -2
                (i.e. delete last 2 messages, typically these
                are the last user and assistant messages)
    """
    if start < 0:
        n = len(self.message_history)
        start = max(0, n + start)
    dropped = self.message_history[start:]
    # consider the dropped msgs in REVERSE order, so we are
    # carefully updating self.oai_tool_calls
    for msg in reversed(dropped):
        self._drop_msg_update_tool_calls(msg)
        # clear out the chat document from the ObjectRegistry
        ChatDocument.delete_id(msg.chat_document_id)
    self.message_history = self.message_history[:start]

update_history(message, response)

Update the message history with the latest user message and LLM response. Args: message (str): user message response: (str): LLM response

Source code in langroid/agent/chat_agent.py

def update_history(self, message: str, response: str) -> None:
    """
    Update the message history with the latest user message and LLM response.
    Args:
        message (str): user message
        response: (str): LLM response
    """
    self.message_history.extend(
        [
            LLMMessage(role=Role.USER, content=message),
            LLMMessage(role=Role.ASSISTANT, content=response),
        ]
    )

json_format_rules()

Specification of JSON formatting rules, based on the currently enabled usable ToolMessages

Returns:

Name	Type	Description
str	str	formatting rules

Source code in langroid/agent/chat_agent.py

def json_format_rules(self) -> str:
    """
    Specification of JSON formatting rules, based on the currently enabled
    usable `ToolMessage`s

    Returns:
        str: formatting rules
    """
    # ONLY Usable tools (i.e. LLM-generation allowed),
    usable_tool_classes: List[Type[ToolMessage]] = [
        t
        for t in list(self.llm_tools_map.values())
        if t.default_value("request") in self.llm_tools_usable
    ]

    if len(usable_tool_classes) == 0:
        return "You can ask questions in natural language."
    json_instructions = "\n\n".join(
        [
            msg_cls.json_instructions(tool=self.config.use_tools)
            for msg_cls in usable_tool_classes
        ]
    )
    # if any of the enabled classes has json_group_instructions, then use that,
    # else fall back to ToolMessage.json_group_instructions
    for msg_cls in usable_tool_classes:
        if hasattr(msg_cls, "json_group_instructions") and callable(
            getattr(msg_cls, "json_group_instructions")
        ):
            return msg_cls.json_group_instructions().format(
                json_instructions=json_instructions
            )
    return ToolMessage.json_group_instructions().format(
        json_instructions=json_instructions
    )

tool_instructions()

Instructions for tools or function-calls, for enabled and usable Tools. These are inserted into system prompt regardless of whether we are using our own ToolMessage mechanism or the LLM's function-call mechanism.

Returns:

Name	Type	Description
str	str	concatenation of instructions for all usable tools

Source code in langroid/agent/chat_agent.py

def tool_instructions(self) -> str:
    """
    Instructions for tools or function-calls, for enabled and usable Tools.
    These are inserted into system prompt regardless of whether we are using
    our own ToolMessage mechanism or the LLM's function-call mechanism.

    Returns:
        str: concatenation of instructions for all usable tools
    """
    enabled_classes: List[Type[ToolMessage]] = list(self.llm_tools_map.values())
    if len(enabled_classes) == 0:
        return ""
    instructions = []
    for msg_cls in enabled_classes:
        if (
            hasattr(msg_cls, "instructions")
            and inspect.ismethod(msg_cls.instructions)
            and msg_cls.default_value("request") in self.llm_tools_usable
        ):
            # example will be shown in json_format_rules() when using TOOLs,
            # so we don't need to show it here.
            example = "" if self.config.use_tools else (msg_cls.usage_examples())
            if example != "":
                example = "EXAMPLES:\n" + example
            class_instructions = msg_cls.instructions()
            guidance = (
                ""
                if class_instructions == ""
                else ("GUIDANCE: " + class_instructions)
            )
            if guidance == "" and example == "":
                continue
            instructions.append(
                textwrap.dedent(
                    f"""
                    TOOL: {msg_cls.default_value("request")}:
                    {guidance}
                    {example}
                    """.lstrip()
                )
            )
    if len(instructions) == 0:
        return ""
    instructions_str = "\n\n".join(instructions)
    return textwrap.dedent(
        f"""
        === GUIDELINES ON SOME TOOLS/FUNCTIONS USAGE ===
        {instructions_str}
        """.lstrip()
    )

augment_system_message(message)

Augment the system message with the given message. Args: message (str): system message

Source code in langroid/agent/chat_agent.py

def augment_system_message(self, message: str) -> None:
    """
    Augment the system message with the given message.
    Args:
        message (str): system message
    """
    self.system_message += "\n\n" + message

last_message_with_role(role)

from message_history, return the last message with role role

Source code in langroid/agent/chat_agent.py

def last_message_with_role(self, role: Role) -> LLMMessage | None:
    """from `message_history`, return the last message with role `role`"""
    n_role_msgs = len([m for m in self.message_history if m.role == role])
    if n_role_msgs == 0:
        return None
    idx = self.nth_message_idx_with_role(role, n_role_msgs)
    return self.message_history[idx]

nth_message_idx_with_role(role, n)

Index of nth message in message_history, with specified role. (n is assumed to be 1-based, i.e. 1 is the first message with that role). Return -1 if not found. Index = 0 is the first message in the history.

Source code in langroid/agent/chat_agent.py

def nth_message_idx_with_role(self, role: Role, n: int) -> int:
    """Index of `n`th message in message_history, with specified role.
    (n is assumed to be 1-based, i.e. 1 is the first message with that role).
    Return -1 if not found. Index = 0 is the first message in the history.
    """
    indices_with_role = [
        i for i, m in enumerate(self.message_history) if m.role == role
    ]

    if len(indices_with_role) < n:
        return -1
    return indices_with_role[n - 1]

update_last_message(message, role=Role.USER)

Update the last message that has role role in the message history. Useful when we want to replace a long user prompt, that may contain context documents plus a question, with just the question. Args: message (str): new message to replace with role (str): role of message to replace

Source code in langroid/agent/chat_agent.py

def update_last_message(self, message: str, role: str = Role.USER) -> None:
    """
    Update the last message that has role `role` in the message history.
    Useful when we want to replace a long user prompt, that may contain context
    documents plus a question, with just the question.
    Args:
        message (str): new message to replace with
        role (str): role of message to replace
    """
    if len(self.message_history) == 0:
        return
    # find last message in self.message_history with role `role`
    for i in range(len(self.message_history) - 1, -1, -1):
        if self.message_history[i].role == role:
            self.message_history[i].content = message
            break

unhandled_tools()

The set of tools that are known but not handled. Useful in task flow: an agent can refuse to accept an incoming msg when it only has unhandled tools.

Source code in langroid/agent/chat_agent.py

def unhandled_tools(self) -> set[str]:
    """The set of tools that are known but not handled.
    Useful in task flow: an agent can refuse to accept an incoming msg
    when it only has unhandled tools.
    """
    return self.llm_tools_known - self.llm_tools_handled

enable_message(message_class, use=True, handle=True, force=False, require_recipient=False, include_defaults=True)

Add the tool (message class) to the agent, and enable either - tool USE (i.e. the LLM can generate JSON to use this tool), - tool HANDLING (i.e. the agent can handle JSON from this tool),

Parameters:

Name	Type	Description	Default
message_class	Optional[Type[ToolMessage] | List[Type[ToolMessage]]]	The ToolMessage class OR List of such classes to enable, for USE, or HANDLING, or both. If this is a list of ToolMessage classes, then the remain args are applied to all classes. Optional; if None, then apply the enabling to all tools in the agent's toolset that have been enabled so far.	required
use	bool	IF True, allow the agent (LLM) to use this tool (or all tools), else disallow	True
handle	bool	if True, allow the agent (LLM) to handle (i.e. respond to) this tool (or all tools)	True
force	bool	whether to FORCE the agent (LLM) to USE the specific tool represented by message_class. force is ignored if message_class is None.	False
require_recipient	bool	whether to require that recipient be specified when using the tool message (only applies if use is True).	False
include_defaults	bool	whether to include fields that have default values, in the "properties" section of the JSON format instructions. (Normally the OpenAI completion API ignores these fields, but the Assistant fn-calling seems to pay attn to these, and if we don't want this, we should set this to False.)	True

Source code in langroid/agent/chat_agent.py

def enable_message(
    self,
    message_class: Optional[Type[ToolMessage] | List[Type[ToolMessage]]],
    use: bool = True,
    handle: bool = True,
    force: bool = False,
    require_recipient: bool = False,
    include_defaults: bool = True,
) -> None:
    """
    Add the tool (message class) to the agent, and enable either
    - tool USE (i.e. the LLM can generate JSON to use this tool),
    - tool HANDLING (i.e. the agent can handle JSON from this tool),

    Args:
        message_class: The ToolMessage class OR List of such classes to enable,
            for USE, or HANDLING, or both.
            If this is a list of ToolMessage classes, then the remain args are
            applied to all classes.
            Optional; if None, then apply the enabling to all tools in the
            agent's toolset that have been enabled so far.
        use: IF True, allow the agent (LLM) to use this tool (or all tools),
            else disallow
        handle: if True, allow the agent (LLM) to handle (i.e. respond to) this
            tool (or all tools)
        force: whether to FORCE the agent (LLM) to USE the specific
             tool represented by `message_class`.
             `force` is ignored if `message_class` is None.
        require_recipient: whether to require that recipient be specified
            when using the tool message (only applies if `use` is True).
        include_defaults: whether to include fields that have default values,
            in the "properties" section of the JSON format instructions.
            (Normally the OpenAI completion API ignores these fields,
            but the Assistant fn-calling seems to pay attn to these,
            and if we don't want this, we should set this to False.)
    """
    if message_class is not None and isinstance(message_class, list):
        for mc in message_class:
            self.enable_message(
                mc,
                use=use,
                handle=handle,
                force=force,
                require_recipient=require_recipient,
                include_defaults=include_defaults,
            )
        return None
    if require_recipient and message_class is not None:
        message_class = message_class.require_recipient()
    super().enable_message_handling(message_class)  # enables handling only
    tools = self._get_tool_list(message_class)
    if message_class is not None:
        request = message_class.default_value("request")
        if request == "":
            raise ValueError(
                f"""
                ToolMessage class {message_class} must have a non-empty 
                'request' field if it is to be enabled as a tool.
                """
            )
        llm_function = message_class.llm_function_schema(defaults=include_defaults)
        self.llm_functions_map[request] = llm_function
        if force:
            self.llm_function_force = dict(name=request)
        else:
            self.llm_function_force = None

    for t in tools:
        self.llm_tools_known.add(t)

        if handle:
            self.llm_tools_handled.add(t)
            self.llm_functions_handled.add(t)
        else:
            self.llm_tools_handled.discard(t)
            self.llm_functions_handled.discard(t)

        if use:
            tool_class = self.llm_tools_map[t]
            if tool_class._allow_llm_use:
                self.llm_tools_usable.add(t)
                self.llm_functions_usable.add(t)
            else:
                logger.warning(
                    f"""
                    ToolMessage class {tool_class} does not allow LLM use,
                    because `_allow_llm_use=False` either in the Tool or a 
                    parent class of this tool;
                    so not enabling LLM use for this tool!
                    If you intended an LLM to use this tool, 
                    set `_allow_llm_use=True` when you define the tool.
                    """
                )
        else:
            self.llm_tools_usable.discard(t)
            self.llm_functions_usable.discard(t)

    # Set tool instructions and JSON format instructions
    if self.config.use_tools:
        self.system_json_tool_instructions = self.json_format_rules()
    self.system_tool_instructions = self.tool_instructions()

disable_message_handling(message_class=None)

Disable this agent from RESPONDING to a message_class (Tool). If message_class is None, then disable this agent from responding to ALL. Args: message_class: The ToolMessage class to disable; Optional.

Source code in langroid/agent/chat_agent.py

def disable_message_handling(
    self,
    message_class: Optional[Type[ToolMessage]] = None,
) -> None:
    """
    Disable this agent from RESPONDING to a `message_class` (Tool). If
        `message_class` is None, then disable this agent from responding to ALL.
    Args:
        message_class: The ToolMessage class to disable; Optional.
    """
    super().disable_message_handling(message_class)
    for t in self._get_tool_list(message_class):
        self.llm_tools_handled.discard(t)
        self.llm_functions_handled.discard(t)

disable_message_use(message_class)

Disable this agent from USING a message class (Tool). If message_class is None, then disable this agent from USING ALL tools. Args: message_class: The ToolMessage class to disable. If None, disable all.

Source code in langroid/agent/chat_agent.py

def disable_message_use(
    self,
    message_class: Optional[Type[ToolMessage]],
) -> None:
    """
    Disable this agent from USING a message class (Tool).
    If `message_class` is None, then disable this agent from USING ALL tools.
    Args:
        message_class: The ToolMessage class to disable.
            If None, disable all.
    """
    for t in self._get_tool_list(message_class):
        self.llm_tools_usable.discard(t)
        self.llm_functions_usable.discard(t)

disable_message_use_except(message_class)

Disable this agent from USING ALL messages EXCEPT a message class (Tool) Args: message_class: The only ToolMessage class to allow

Source code in langroid/agent/chat_agent.py

def disable_message_use_except(self, message_class: Type[ToolMessage]) -> None:
    """
    Disable this agent from USING ALL messages EXCEPT a message class (Tool)
    Args:
        message_class: The only ToolMessage class to allow
    """
    request = message_class.__fields__["request"].default
    to_remove = [r for r in self.llm_tools_usable if r != request]
    for r in to_remove:
        self.llm_tools_usable.discard(r)
        self.llm_functions_usable.discard(r)

llm_response(message=None)

Respond to a single user message, appended to the message history, in "chat" mode Args: message (str|ChatDocument): message or ChatDocument object to respond to. If None, use the self.task_messages Returns: LLM response as a ChatDocument object

Source code in langroid/agent/chat_agent.py

def llm_response(
    self, message: Optional[str | ChatDocument] = None
) -> Optional[ChatDocument]:
    """
    Respond to a single user message, appended to the message history,
    in "chat" mode
    Args:
        message (str|ChatDocument): message or ChatDocument object to respond to.
            If None, use the self.task_messages
    Returns:
        LLM response as a ChatDocument object
    """
    if self.llm is None:
        return None
    hist, output_len = self._prep_llm_messages(message)
    if len(hist) == 0:
        return None
    tool_choice = (
        "auto"
        if isinstance(message, str)
        else (message.oai_tool_choice if message is not None else "auto")
    )
    with StreamingIfAllowed(self.llm, self.llm.get_stream()):
        response = self.llm_response_messages(hist, output_len, tool_choice)
    self.message_history.extend(ChatDocument.to_LLMMessage(response))
    response.metadata.msg_idx = len(self.message_history) - 1
    response.metadata.agent_id = self.id
    # Preserve trail of tool_ids for OpenAI Assistant fn-calls
    response.metadata.tool_ids = (
        []
        if isinstance(message, str)
        else message.metadata.tool_ids if message is not None else []
    )
    return response

llm_response_async(message=None) async

Async version of llm_response. See there for details.

Source code in langroid/agent/chat_agent.py

async def llm_response_async(
    self, message: Optional[str | ChatDocument] = None
) -> Optional[ChatDocument]:
    """
    Async version of `llm_response`. See there for details.
    """
    if self.llm is None:
        return None
    hist, output_len = self._prep_llm_messages(message)
    if len(hist) == 0:
        return None
    tool_choice = (
        "auto"
        if isinstance(message, str)
        else (message.oai_tool_choice if message is not None else "auto")
    )
    with StreamingIfAllowed(self.llm, self.llm.get_stream()):
        response = await self.llm_response_messages_async(
            hist, output_len, tool_choice
        )
    self.message_history.extend(ChatDocument.to_LLMMessage(response))
    response.metadata.msg_idx = len(self.message_history) - 1
    response.metadata.agent_id = self.id
    # Preserve trail of tool_ids for OpenAI Assistant fn-calls
    response.metadata.tool_ids = (
        []
        if isinstance(message, str)
        else message.metadata.tool_ids if message is not None else []
    )
    return response

init_message_history()

Initialize the message history with the system message and user message

Source code in langroid/agent/chat_agent.py

def init_message_history(self) -> None:
    """
    Initialize the message history with the system message and user message
    """
    self.message_history = [self._create_system_and_tools_message()]
    if self.user_message:
        self.message_history.append(
            LLMMessage(role=Role.USER, content=self.user_message)
        )

llm_response_messages(messages, output_len=None, tool_choice='auto')

Respond to a series of messages, e.g. with OpenAI ChatCompletion Args: messages: seq of messages (with role, content fields) sent to LLM output_len: max number of tokens expected in response. If None, use the LLM's default max_output_tokens. Returns: Document (i.e. with fields "content", "metadata")

Source code in langroid/agent/chat_agent.py

def llm_response_messages(
    self,
    messages: List[LLMMessage],
    output_len: Optional[int] = None,
    tool_choice: ToolChoiceTypes | Dict[str, str | Dict[str, str]] = "auto",
) -> ChatDocument:
    """
    Respond to a series of messages, e.g. with OpenAI ChatCompletion
    Args:
        messages: seq of messages (with role, content fields) sent to LLM
        output_len: max number of tokens expected in response.
                If None, use the LLM's default max_output_tokens.
    Returns:
        Document (i.e. with fields "content", "metadata")
    """
    assert self.config.llm is not None and self.llm is not None
    output_len = output_len or self.config.llm.max_output_tokens
    streamer = noop_fn
    if self.llm.get_stream():
        streamer = self.callbacks.start_llm_stream()
    self.llm.config.streamer = streamer
    with ExitStack() as stack:  # for conditionally using rich spinner
        if not self.llm.get_stream() and not settings.quiet:
            # show rich spinner only if not streaming!
            # (Why? b/c the intent of showing a spinner is to "show progress",
            # and we don't need to do that when streaming, since
            # streaming output already shows progress.)
            cm = status(
                "LLM responding to messages...",
                log_if_quiet=False,
            )
            stack.enter_context(cm)
        if self.llm.get_stream() and not settings.quiet:
            console.print(f"[green]{self.indent}", end="")
        functions, fun_call, tools, force_tool = self._function_args()
        assert self.llm is not None
        response = self.llm.chat(
            messages,
            output_len,
            tools=tools,
            tool_choice=force_tool or tool_choice,
            functions=functions,
            function_call=fun_call,
        )
    if self.llm.get_stream():
        self.callbacks.finish_llm_stream(
            content=str(response),
            is_tool=self.has_tool_message_attempt(
                ChatDocument.from_LLMResponse(response, displayed=True),
            ),
        )
    self.llm.config.streamer = noop_fn
    if response.cached:
        self.callbacks.cancel_llm_stream()
    self._render_llm_response(response)
    self.update_token_usage(
        response,  # .usage attrib is updated!
        messages,
        self.llm.get_stream(),
        chat=True,
        print_response_stats=self.config.show_stats and not settings.quiet,
    )
    chat_doc = ChatDocument.from_LLMResponse(response, displayed=True)
    self.oai_tool_calls = response.oai_tool_calls or []
    self.oai_tool_id2call.update(
        {t.id: t for t in self.oai_tool_calls if t.id is not None}
    )
    return chat_doc

llm_response_messages_async(messages, output_len=None, tool_choice='auto') async

Async version of llm_response_messages. See there for details.

Source code in langroid/agent/chat_agent.py

async def llm_response_messages_async(
    self,
    messages: List[LLMMessage],
    output_len: Optional[int] = None,
    tool_choice: ToolChoiceTypes | Dict[str, str | Dict[str, str]] = "auto",
) -> ChatDocument:
    """
    Async version of `llm_response_messages`. See there for details.
    """
    assert self.config.llm is not None and self.llm is not None
    output_len = output_len or self.config.llm.max_output_tokens
    functions, fun_call, tools, force_tool = self._function_args()
    assert self.llm is not None

    streamer = noop_fn
    if self.llm.get_stream():
        streamer = self.callbacks.start_llm_stream()
    self.llm.config.streamer = streamer

    response = await self.llm.achat(
        messages,
        output_len,
        tools=tools,
        tool_choice=force_tool or tool_choice,
        functions=functions,
        function_call=fun_call,
    )
    if self.llm.get_stream():
        self.callbacks.finish_llm_stream(
            content=str(response),
            is_tool=self.has_tool_message_attempt(
                ChatDocument.from_LLMResponse(response, displayed=True),
            ),
        )
    self.llm.config.streamer = noop_fn
    if response.cached:
        self.callbacks.cancel_llm_stream()
    self._render_llm_response(response)
    self.update_token_usage(
        response,  # .usage attrib is updated!
        messages,
        self.llm.get_stream(),
        chat=True,
        print_response_stats=self.config.show_stats and not settings.quiet,
    )
    chat_doc = ChatDocument.from_LLMResponse(response, displayed=True)
    self.oai_tool_calls = response.oai_tool_calls or []
    self.oai_tool_id2call.update(
        {t.id: t for t in self.oai_tool_calls if t.id is not None}
    )
    return chat_doc

llm_response_forget(message)

LLM Response to single message, and restore message_history. In effect a "one-off" message & response that leaves agent message history state intact.

Parameters:

Name	Type	Description	Default
message	str	user message	required

Returns:

Type	Description
ChatDocument	A Document object with the response.

Source code in langroid/agent/chat_agent.py

def llm_response_forget(self, message: str) -> ChatDocument:
    """
    LLM Response to single message, and restore message_history.
    In effect a "one-off" message & response that leaves agent
    message history state intact.

    Args:
        message (str): user message

    Returns:
        A Document object with the response.

    """
    # explicitly call THIS class's respond method,
    # not a derived class's (or else there would be infinite recursion!)
    n_msgs = len(self.message_history)
    with StreamingIfAllowed(self.llm, self.llm.get_stream()):  # type: ignore
        response = cast(ChatDocument, ChatAgent.llm_response(self, message))
    # If there is a response, then we will have two additional
    # messages in the message history, i.e. the user message and the
    # assistant response. We want to (carefully) remove these two messages.
    if len(self.message_history) > n_msgs:
        msg = self.message_history.pop()
        self._drop_msg_update_tool_calls(msg)

    if len(self.message_history) > n_msgs:
        msg = self.message_history.pop()
        self._drop_msg_update_tool_calls(msg)

    return response

llm_response_forget_async(message) async

Async version of llm_response_forget. See there for details.

Source code in langroid/agent/chat_agent.py

async def llm_response_forget_async(self, message: str) -> ChatDocument:
    """
    Async version of `llm_response_forget`. See there for details.
    """
    # explicitly call THIS class's respond method,
    # not a derived class's (or else there would be infinite recursion!)
    n_msgs = len(self.message_history)
    with StreamingIfAllowed(self.llm, self.llm.get_stream()):  # type: ignore
        response = cast(
            ChatDocument, await ChatAgent.llm_response_async(self, message)
        )
    # If there is a response, then we will have two additional
    # messages in the message history, i.e. the user message and the
    # assistant response. We want to (carefully) remove these two messages.
    if len(self.message_history) > n_msgs:
        msg = self.message_history.pop()
        self._drop_msg_update_tool_calls(msg)

    if len(self.message_history) > n_msgs:
        msg = self.message_history.pop()
        self._drop_msg_update_tool_calls(msg)
    return response

chat_num_tokens(messages=None)

Total number of tokens in the message history so far.

Parameters:

Name	Type	Description	Default
messages	Optional[List[LLMMessage]]	if provided, compute the number of tokens in this list of messages, rather than the current message history.	None

Returns: int: number of tokens in message history

Source code in langroid/agent/chat_agent.py

def chat_num_tokens(self, messages: Optional[List[LLMMessage]] = None) -> int:
    """
    Total number of tokens in the message history so far.

    Args:
        messages: if provided, compute the number of tokens in this list of
            messages, rather than the current message history.
    Returns:
        int: number of tokens in message history
    """
    if self.parser is None:
        raise ValueError(
            "ChatAgent.parser is None. "
            "You must set ChatAgent.parser "
            "before calling chat_num_tokens()."
        )
    hist = messages if messages is not None else self.message_history
    return sum([self.parser.num_tokens(m.content) for m in hist])

message_history_str(i=None)

Return a string representation of the message history Args: i: if provided, return only the i-th message when i is postive, or last k messages when i = -k. Returns:

Source code in langroid/agent/chat_agent.py

def message_history_str(self, i: Optional[int] = None) -> str:
    """
    Return a string representation of the message history
    Args:
        i: if provided, return only the i-th message when i is postive,
            or last k messages when i = -k.
    Returns:
    """
    if i is None:
        return "\n".join([str(m) for m in self.message_history])
    elif i > 0:
        return str(self.message_history[i])
    else:
        return "\n".join([str(m) for m in self.message_history[i:]])

ChatAgentConfig

Bases: AgentConfig

Configuration for ChatAgent Attributes: system_message: system message to include in message sequence (typically defines role and task of agent). Used only if task is not specified in the constructor. user_message: user message to include in message sequence. Used only if task is not specified in the constructor. use_tools: whether to use our own ToolMessages mechanism use_functions_api: whether to use functions/tools native to the LLM API (e.g. OpenAI's function_call or tool_call mechanism) use_tools_api: When use_functions_api is True, if this is also True, the OpenAI tool-call API is used, rather than the older/deprecated function-call API. However the tool-call API has some tricky aspects, hence we set this to False by default. enable_orchestration_tool_handling: whether to enable handling of orchestration tools, e.g. ForwardTool, DoneTool, PassTool, etc.

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

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

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

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

str | ChatDocument -> ChatDocument

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

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

Parameters:

Name	Type	Description	Default
agent	Agent	agent associated with the task	None
name	str	name of the task	''
llm_delegate	bool	Whether to delegate "control" to LLM; conceptually, the "controlling entity" is the one "seeking" responses to its queries, and has a goal it is aiming to achieve, and decides when a task is done. The "controlling entity" is either the LLM or the USER. (Note within a Task there is just one LLM, and all other entities are proxies of the "User" entity). See also: done_if_response, done_if_no_response for more granular control of task termination.	False
single_round	bool	If true, task runs until one message by "controller" (i.e. LLM if llm_delegate is true, otherwise USER) and subsequent response by non-controller [When a tool is involved, this will not give intended results. See done_if_response, done_if_no_response below]. termination]. If false, runs for the specified number of turns in run, or until done() is true. One run of step() is considered a "turn". See also: done_if_response, done_if_no_response for more granular control of task termination.	False
system_message	str	if not empty, overrides agent's system_message	''
user_message	str	if not empty, overrides agent's user_message	''
restart	bool	if true, resets the agent's message history at every run.	True
default_human_response	str | None	default response from user; useful for testing, to avoid interactive input from user. [Instead of this, setting interactive usually suffices]	None
default_return_type	Optional[type]	if not None, extracts a value of this type from the result of self.run()	None
interactive	bool	if true, wait for human input after each non-human response (prevents infinite loop of non-human responses). Default is true. If false, then default_human_response is set to "" Note: When interactive = False, the one exception is when the user is explicitly addressed, via "@user" or using RecipientTool, in which case the system will wait for a user response. In other words, use interactive=False when you want a "largely non-interactive" run, with the exception of explicit user addressing.	True
only_user_quits_root	bool	if true, when interactive=True, only user can quit the root task (Ignored when interactive=False).	True
erase_substeps	bool	if true, when task completes, erase intermediate conversation with subtasks from this agent's message_history, and also erase all subtask agents' message_history. Note: erasing can reduce prompt sizes, but results in repetitive sub-task delegation.	False
allow_null_result	bool	If true, create dummy NO_ANSWER response when no valid response is found in a step. Optional, default is False. Note: In non-interactive mode, when this is set to True, you can have a situation where an LLM generates (non-tool) text, and no other responders have valid responses, and a "Null result" is inserted as a dummy response from the User entity, so the LLM will now respond to this Null result, and this will continue until the LLM emits a DONE signal (if instructed to do so), otherwise langroid detects a potential infinite loop after a certain number of such steps (= TaskConfig.inf_loop_wait_factor) and will raise an InfiniteLoopException.	False
max_stalled_steps	int	task considered done after this many consecutive steps with no progress. Default is 3.	5
done_if_no_response	List[Responder]	consider task done if NULL response from any of these responders. Default is empty list.	[]
done_if_response	List[Responder]	consider task done if NON-NULL response from any of these responders. Default is empty list.	[]

Source code in langroid/agent/task.py

def __init__(
    self,
    agent: Optional[Agent] = None,
    name: str = "",
    llm_delegate: bool = False,
    single_round: bool = False,
    system_message: str = "",
    user_message: str | None = "",
    restart: bool = True,
    default_human_response: Optional[str] = None,
    interactive: bool = True,
    only_user_quits_root: bool = True,
    erase_substeps: bool = False,
    allow_null_result: bool = False,
    max_stalled_steps: int = 5,
    default_return_type: Optional[type] = None,
    done_if_no_response: List[Responder] = [],
    done_if_response: List[Responder] = [],
    config: TaskConfig = TaskConfig(),
    **kwargs: Any,  # catch-all for any legacy params, for backwards compatibility
):
    """
    A task to be performed by an agent.

    Args:
        agent (Agent): agent associated with the task
        name (str): name of the task
        llm_delegate (bool):
            Whether to delegate "control" to LLM; conceptually,
            the "controlling entity" is the one "seeking" responses to its queries,
            and has a goal it is aiming to achieve, and decides when a task is done.
            The "controlling entity" is either the LLM or the USER.
            (Note within a Task there is just one
            LLM, and all other entities are proxies of the "User" entity).
            See also: `done_if_response`, `done_if_no_response` for more granular
            control of task termination.
        single_round (bool):
            If true, task runs until one message by "controller"
            (i.e. LLM if `llm_delegate` is true, otherwise USER)
            and subsequent response by non-controller [When a tool is involved,
            this will not give intended results. See `done_if_response`,
            `done_if_no_response` below].
            termination]. If false, runs for the specified number of turns in
            `run`, or until `done()` is true.
            One run of step() is considered a "turn".
            See also: `done_if_response`, `done_if_no_response` for more granular
            control of task termination.
        system_message (str): if not empty, overrides agent's system_message
        user_message (str): if not empty, overrides agent's user_message
        restart (bool): if true, resets the agent's message history *at every run*.
        default_human_response (str|None): default response from user; useful for
            testing, to avoid interactive input from user.
            [Instead of this, setting `interactive` usually suffices]
        default_return_type: if not None, extracts a value of this type from the
            result of self.run()
        interactive (bool): if true, wait for human input after each non-human
            response (prevents infinite loop of non-human responses).
            Default is true. If false, then `default_human_response` is set to ""
            Note: When interactive = False, the one exception is when the user
            is explicitly addressed, via "@user" or using RecipientTool, in which
            case the system will wait for a user response. In other words, use
            `interactive=False` when you want a "largely non-interactive"
            run, with the exception of explicit user addressing.
        only_user_quits_root (bool): if true, when interactive=True, only user can
            quit the root task (Ignored when interactive=False).
        erase_substeps (bool): if true, when task completes, erase intermediate
            conversation with subtasks from this agent's `message_history`, and also
            erase all subtask agents' `message_history`.
            Note: erasing can reduce prompt sizes, but results in repetitive
            sub-task delegation.
        allow_null_result (bool):
            If true, create dummy NO_ANSWER response when no valid response is found
            in a step.
            Optional, default is False.
            *Note:* In non-interactive mode, when this is set to True,
            you can have a situation where an LLM generates (non-tool) text,
            and no other responders have valid responses, and a "Null result"
            is inserted as a dummy response from the User entity, so the LLM
            will now respond to this Null result, and this will continue
            until the LLM emits a DONE signal (if instructed to do so),
            otherwise langroid detects a potential infinite loop after
            a certain number of such steps (= `TaskConfig.inf_loop_wait_factor`)
            and will raise an InfiniteLoopException.
        max_stalled_steps (int): task considered done after this many consecutive
            steps with no progress. Default is 3.
        done_if_no_response (List[Responder]): consider task done if NULL
            response from any of these responders. Default is empty list.
        done_if_response (List[Responder]): consider task done if NON-NULL
            response from any of these responders. Default is empty list.
    """
    if agent is None:
        agent = ChatAgent()
    self.callbacks = SimpleNamespace(
        show_subtask_response=noop_fn,
        set_parent_agent=noop_fn,
    )
    self.config = config
    # how to behave as a sub-task; can be overridden by `add_sub_task()`
    self.config_sub_task = copy.deepcopy(config)
    # counts of distinct pending messages in history,
    # to help detect (exact) infinite loops
    self.message_counter: Counter[str] = Counter()
    self._init_message_counter()

    self.history: Deque[str] = deque(
        maxlen=self.config.inf_loop_cycle_len * self.config.inf_loop_wait_factor
    )
    # copy the agent's config, so that we don't modify the original agent's config,
    # which may be shared by other agents.
    try:
        config_copy = copy.deepcopy(agent.config)
        agent.config = config_copy
    except Exception:
        logger.warning(
            """
            Failed to deep-copy Agent config during task creation, 
            proceeding with original config. Be aware that changes to 
            the config may affect other agents using the same config.
            """
        )
    self.restart = restart
    agent = cast(ChatAgent, agent)
    self.agent: ChatAgent = agent
    if isinstance(agent, ChatAgent) and len(agent.message_history) == 0 or restart:
        self.agent.init_state()
        # possibly change the system and user messages
        if system_message:
            # we always have at least 1 task_message
            self.agent.set_system_message(system_message)
        if user_message:
            self.agent.set_user_message(user_message)
    self.max_cost: float = 0
    self.max_tokens: int = 0
    self.session_id: str = ""
    self.logger: None | RichFileLogger = None
    self.tsv_logger: None | logging.Logger = None
    self.color_log: bool = False if settings.notebook else True

    self.n_stalled_steps = 0  # how many consecutive steps with no progress?
    # how many 2-step-apart alternations of no_answer step-result have we had,
    # i.e. x1, N/A, x2, N/A, x3, N/A ...
    self.n_no_answer_alternations = 0
    self._no_answer_step: int = -5
    self._step_idx = -1  # current step index
    self.max_stalled_steps = max_stalled_steps
    self.done_if_response = [r.value for r in done_if_response]
    self.done_if_no_response = [r.value for r in done_if_no_response]
    self.is_done = False  # is task done (based on response)?
    self.is_pass_thru = False  # is current response a pass-thru?
    if name:
        # task name overrides name in agent config
        agent.config.name = name
    self.name = name or agent.config.name
    self.value: str = self.name

    self.default_human_response = default_human_response
    if default_human_response is not None:
        # only override agent's default_human_response if it is explicitly set
        self.agent.default_human_response = default_human_response
    self.interactive = interactive
    self.agent.interactive = interactive
    self.only_user_quits_root = only_user_quits_root
    self.message_history_idx = -1
    self.default_return_type = default_return_type

    # set to True if we want to collapse multi-turn conversation with sub-tasks into
    # just the first outgoing message and last incoming message.
    # Note this also completely erases sub-task agents' message_history.
    self.erase_substeps = erase_substeps
    self.allow_null_result = allow_null_result

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

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

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

    self.name_sub_task_map: Dict[str, Task] = {}
    # latest message in a conversation among entities and agents.
    self.pending_message: Optional[ChatDocument] = None
    self.pending_sender: Responder = Entity.USER
    self.single_round = single_round
    self.turns = -1  # no limit
    self.llm_delegate = llm_delegate
    if llm_delegate:
        if self.single_round:
            # 0: User instructs (delegating to LLM);
            # 1: LLM (as the Controller) asks;
            # 2: user replies.
            self.turns = 2
    else:
        if self.single_round:
            # 0: User (as Controller) asks,
            # 1: LLM replies.
            self.turns = 1
    # other sub_tasks this task can delegate to
    self.sub_tasks: List[Task] = []
    self.caller: Task | None = None  # which task called this task's `run` method

clone(i)

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

Source code in langroid/agent/task.py

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

kill_session(session_id='') classmethod

Kill the session with the given session_id.

Source code in langroid/agent/task.py

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

kill()

Kill the task run associated with the current session.

Source code in langroid/agent/task.py

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

add_sub_task(task)

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

Parameters:

Name	Type	Description	Default
task	Task | List[Task] | Tuple[Task, TaskConfig] | List[Tuple[Task, TaskConfig]]	A task, or list of tasks, or a tuple of task and task config, or a list of tuples of task and task config. These tasks are added as sub-tasks of the current task. The task configs (if any) dictate how the tasks are run when invoked as sub-tasks of other tasks. This allows users to specify behavior applicable only in the context of a particular task-subtask combination.	required

Source code in langroid/agent/task.py

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

    Args:
        task: A task, or list of tasks, or a tuple of task and task config,
            or a list of tuples of task and task config.
            These tasks are added as sub-tasks of the current task.
            The task configs (if any) dictate how the tasks are run when
            invoked as sub-tasks of other tasks. This allows users to specify
            behavior applicable only in the context of a particular task-subtask
            combination.
    """
    if isinstance(task, list):
        for t in task:
            self.add_sub_task(t)
        return

    if isinstance(task, tuple):
        task, config = task
    else:
        config = TaskConfig()
    task.config_sub_task = config
    self.sub_tasks.append(task)
    self.name_sub_task_map[task.name] = task
    self.responders.append(cast(Responder, task))
    self.responders_async.append(cast(Responder, task))
    self.non_human_responders.append(cast(Responder, task))
    self.non_human_responders_async.append(cast(Responder, task))

init(msg=None)

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

Returns:

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

Source code in langroid/agent/task.py

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

    Returns:
        (ChatDocument|None): the initialized `self.pending_message`.
        Currently not used in the code, but provided for convenience.
    """
    self.pending_sender = Entity.USER
    if isinstance(msg, str):
        self.pending_message = ChatDocument(
            content=msg,
            metadata=ChatDocMetaData(
                sender=Entity.USER,
            ),
        )
    elif msg is None and len(self.agent.message_history) > 1:
        # if agent has a history beyond system msg, set the
        # pending message to the ChatDocument linked from
        # last message in the history
        last_agent_msg = self.agent.message_history[-1]
        self.pending_message = ChatDocument.from_id(last_agent_msg.chat_document_id)
        if self.pending_message is not None:
            self.pending_sender = self.pending_message.metadata.sender
    else:
        if isinstance(msg, ChatDocument):
            # carefully deep-copy: fresh metadata.id, register
            # as new obj in registry
            self.pending_message = ChatDocument.deepcopy(msg)
        if self.pending_message is not None and self.caller is not None:
            # msg may have come from `caller`, so we pretend this is from
            # the CURRENT task's USER entity
            self.pending_message.metadata.sender = Entity.USER
            # update parent, child, agent pointers
            if msg is not None:
                msg.metadata.child_id = self.pending_message.metadata.id
                self.pending_message.metadata.parent_id = msg.metadata.id
            self.pending_message.metadata.agent_id = self.agent.id

    self._show_pending_message_if_debug()

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

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

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

reset_all_sub_tasks()

Recursively reset message history & state of own agent and those of all sub-tasks.

Source code in langroid/agent/task.py

def reset_all_sub_tasks(self) -> None:
    """
    Recursively reset message history & state of own agent and
    those of all sub-tasks.
    """
    self.agent.init_state()
    for t in self.sub_tasks:
        t.reset_all_sub_tasks()

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

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

Source code in langroid/agent/task.py

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