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.total_llm_token_cost = 0.0
    self.total_llm_token_usage = 0
    self.token_stats_str = ""
    self.default_human_response: Optional[str] = None
    self._indent = ""
    self.llm = LanguageModel.create(config.llm)
    self.vecdb = VectorStore.create(config.vecdb) if config.vecdb else None
    self.parser: Optional[Parser] = (
        Parser(config.parsing) if config.parsing else None
    )
    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,
    )

indent: str property writable

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

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_example()  # type: ignore
        for i, msg_cls in enumerate(enabled_classes)
        if i < 2
    ]
    return "\n\n".join(sample_convo)

agent_response_template()

Template for agent_response.

Source code in langroid/agent/base.py

def agent_response_template(self) -> ChatDocument:
    """Template for agent_response."""
    return self._response_template(Entity.AGENT)

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:
        console.print(f"[red]{self.indent}", end="")
        print(f"[red]Agent: {results}")
        maybe_json = len(extract_top_level_json(results)) > 0
        self.callbacks.show_agent_response(
            content=results,
            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 "request", i.e. name of the function_call
        sender_name = msg.function_call.name

    return ChatDocument(
        content=results,
        metadata=ChatDocMetaData(
            source=Entity.AGENT,
            sender=Entity.AGENT,
            sender_name=sender_name,
            # preserve trail of tool_ids for OpenAI Assistant fn-calls
            tool_ids=[] if isinstance(msg, str) else msg.metadata.tool_ids,
        ),
    )

user_response_template()

Template for user_response.

Source code in langroid/agent/base.py

def user_response_template(self) -> ChatDocument:
    """Template for user_response."""
    return self._response_template(Entity.USER)

user_response(msg=None)

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

Parameters:

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

Returns:

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

Source code in langroid/agent/base.py

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

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

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

    """
    if self.default_human_response is not None:
        # useful for automated testing
        user_msg = self.default_human_response
    elif not settings.interactive:
        user_msg = ""
    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

llm_response_template()

Template for llm_response.

Source code in langroid/agent/base.py

def llm_response_template(self) -> ChatDocument:
    """Template for llm_response."""
    return self._response_template(Entity.LLM)

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

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]

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 | ChatDocument	Optional[Str]: The result of the handler method in string form so it can
None | str | ChatDocument	be sent back to the LLM, or None if msg was not successfully
None | str | ChatDocument	handled by a method.

Source code in langroid/agent/base.py

def handle_message(self, msg: str | ChatDocument) -> None | 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:
        Optional[Str]: The result of the handler method in string form so it can
        be sent back to the LLM, or None if `msg` was not successfully
        handled by a method.
    """
    try:
        tools = self.get_tool_messages(msg)
    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:
        return self.handle_message_fallback(msg)

    results = [self.handle_tool_message(t) for t in tools]

    results_list = [r for r in results if r is not None]
    if len(results_list) == 0:
        return None  # self.handle_message_fallback(msg)
    # there was a non-None result
    chat_doc_results = [r for r in results_list if isinstance(r, ChatDocument)]
    if len(chat_doc_results) > 1:
        logger.warning(
            """There were multiple ChatDocument results from tools,
            which is unexpected. The first one will be returned, and the others
            will be ignored.
            """
        )
    if len(chat_doc_results) > 0:
        return chat_doc_results[0]

    str_doc_results = [r for r in results_list if isinstance(r, str)]
    final = "\n".join(str_doc_results)
    return final

handle_message_fallback(msg)

Fallback method to handle possible "tool" msg if no other method applies or if an error is thrown. This method can be overridden by subclasses.

Parameters:

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

Returns: str: The result of the handler method in string form so it can be sent back to the LLM.

Source code in langroid/agent/base.py

def handle_message_fallback(
    self, msg: str | ChatDocument
) -> str | ChatDocument | None:
    """
    Fallback method to handle possible "tool" msg if no other method applies
    or if an error is thrown.
    This method can be overridden by subclasses.

    Args:
        msg (str | ChatDocument): The input msg to handle
    Returns:
        str: The result of the handler method in string form so it can
            be sent back to the LLM.
    """
    return None

handle_tool_message(tool)

Respond to a tool request from the LLM, in the form of an ToolMessage object. Args: tool: ToolMessage object representing the tool request.

Returns:

Source code in langroid/agent/base.py

def handle_tool_message(self, tool: ToolMessage) -> 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.

    Returns:

    """
    tool_name = tool.default_value("request")
    handler_method = getattr(self, tool_name, None)
    if handler_method is None:
        return None

    try:
        result = handler_method(tool)
    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

Bases: Document

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 = ""
    return ChatDocument(
        content=message,
        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) staticmethod

Convert to LLMMessage for use with LLM.

Parameters:

Name	Type	Description	Default
message	str | ChatDocument	Message to convert.	required

Returns: LLMMessage: LLMMessage representation of this str or ChatDocument.

Source code in langroid/agent/chat_document.py

@staticmethod
def to_LLMMessage(message: Union[str, "ChatDocument"]) -> LLMMessage:
    """
    Convert to LLMMessage for use with LLM.

    Args:
        message (str|ChatDocument): Message to convert.
    Returns:
        LLMMessage: LLMMessage representation of this str or ChatDocument.

    """
    sender_name = None
    sender_role = Role.USER
    fun_call = None
    tool_id = ""
    if isinstance(message, ChatDocument):
        content = message.content
        fun_call = message.function_call
        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
        sender_name = message.metadata.sender_name
        tool_ids = message.metadata.tool_ids
        tool_id = tool_ids[-1] if len(tool_ids) > 0 else ""
        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
        ):
            sender_role = Role.FUNCTION
            sender_name = message.metadata.parent.function_call.name
        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,
        content=content,
        function_call=fun_call,
        name=sender_name,
    )

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)
result	str	example of result of agent method.

examples() classmethod

Examples to use in few-shot demos with JSON formatting instructions. Returns:

Source code in langroid/agent/tool_message.py

@classmethod
def examples(cls) -> List["ToolMessage"]:
    """
    Examples to use in few-shot demos with JSON formatting instructions.
    Returns:
    """
    return []

usage_example() classmethod

Instruction to the LLM showing an example of how to use the message. Returns: str: example of how to use the message

Source code in langroid/agent/tool_message.py

@classmethod
def usage_example(cls) -> str:
    """
    Instruction to the LLM showing an example of how to use the message.
    Returns:
        str: example of how to use the message
    """
    # pick a random example of the fields
    if len(cls.examples()) == 0:
        return ""
    ex = choice(cls.examples())
    return ex.json_example()

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
    )
    return textwrap.dedent(
        f"""
        TOOL: {cls.default_value("request")}
        PURPOSE: {cls.default_value("purpose")} 
        JSON FORMAT: {
            json.dumps(param_dict, indent=4)
        }
        {"EXAMPLE: " + cls.usage_example() if cls.examples() else ""}
        """.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 = (
        ["result", "purpose"] if request else ["request", "result", "purpose"]
    )
    # 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=["result", "purpose"])
    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.tool_instructions_added: bool = False
    # 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

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

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 close 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 close 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
    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)
    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
    """
    enabled_classes: List[Type[ToolMessage]] = list(self.llm_tools_map.values())
    if len(enabled_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 enumerate(enabled_classes)
            if msg_cls.default_value("request") in self.llm_tools_usable
        ]
    )
    # 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 enabled_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_example())
            if example != "":
                example = "EXAMPLE: " + example
            guidance = (
                ""
                if msg_cls.instructions() == ""
                else ("GUIDANCE: " + msg_cls.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`"""
    for i in range(len(self.message_history) - 1, -1, -1):
        if self.message_history[i].role == role:
            return self.message_history[i]
    return None

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

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]]	The ToolMessage class to enable, for USE, or HANDLING, or both. 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
require_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.)	required

Source code in langroid/agent/chat_agent.py

def enable_message(
    self,
    message_class: Optional[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 to enable,
            for USE, or HANDLING, or both.
            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).
        require_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.)
    """
    super().enable_message_handling(message_class)  # enables handling only
    tools = self._get_tool_list(message_class)
    if message_class is not None:
        if require_recipient:
            message_class = message_class.require_recipient()
        request = message_class.default_value("request")
        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:
        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:
            self.llm_tools_usable.add(t)
            self.llm_functions_usable.add(t)
        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
    with StreamingIfAllowed(self.llm, self.llm.get_stream()):
        response = self.llm_response_messages(hist, output_len)
    # TODO - when response contains function_call we should include
    # that (and related fields) in the message_history
    self.message_history.append(ChatDocument.to_LLMMessage(response))
    # 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)
    with StreamingIfAllowed(self.llm, self.llm.get_stream()):
        response = await self.llm_response_messages_async(hist, output_len)
    # TODO - when response contains function_call we should include
    # that (and related fields) in the message_history
    self.message_history.append(ChatDocument.to_LLMMessage(response))
    # 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_messages(messages, output_len=None)

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
) -> 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():
            # show rich spinner only if not streaming!
            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 = self._function_args()
        assert self.llm is not None
        response = self.llm.chat(
            messages,
            output_len,
            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()

    if not self.llm.get_stream() or response.cached:
        # 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 ""
        if not settings.quiet:
            print(cached + "[green]" + escape(str(response)))
            self.callbacks.show_llm_response(
                content=str(response),
                is_tool=self.has_tool_message_attempt(
                    ChatDocument.from_LLMResponse(response, displayed=True)
                ),
                cached=response.cached,
            )
    self.update_token_usage(
        response,
        messages,
        self.llm.get_stream(),
        chat=True,
        print_response_stats=self.config.show_stats and not settings.quiet,
    )
    return ChatDocument.from_LLMResponse(response, displayed=True)

llm_response_messages_async(messages, output_len=None) 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
) -> 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: Optional[List[LLMFunctionSpec]] = None
    fun_call: str | Dict[str, str] = "none"
    if self.config.use_functions_api and len(self.llm_functions_usable) > 0:
        functions = [self.llm_functions_map[f] for f in self.llm_functions_usable]
        fun_call = (
            "auto" if self.llm_function_force is None else self.llm_function_force
        )
    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,
        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()
    if not self.llm.get_stream() or response.cached:
        # 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 ""
        if not settings.quiet:
            print(cached + "[green]" + escape(str(response)))
            self.callbacks.show_llm_response(
                content=str(response),
                is_tool=self.has_tool_message_attempt(
                    ChatDocument.from_LLMResponse(response, displayed=True)
                ),
                cached=response.cached,
            )

    self.update_token_usage(
        response,
        messages,
        self.llm.get_stream(),
        chat=True,
        print_response_stats=self.config.show_stats and not settings.quiet,
    )
    return ChatDocument.from_LLMResponse(response, displayed=True)

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.
    self.message_history.pop() if len(self.message_history) > n_msgs else None
    self.message_history.pop() if len(self.message_history) > n_msgs else None
    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.
    self.message_history.pop() if len(self.message_history) > n_msgs else None
    self.message_history.pop() if len(self.message_history) > n_msgs else None
    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 native to the LLM API (e.g. OpenAI's function_call mechanism)

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

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

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

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

str | ChatDocument -> ChatDocument

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

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

Parameters:

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

Source code in langroid/agent/task.py

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

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

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

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

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

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

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

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

clone(i)

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

Source code in langroid/agent/task.py

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

kill_session(session_id='') classmethod

Kill the session with the given session_id.

Source code in langroid/agent/task.py

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

kill()

Kill the task run associated with the current session.

Source code in langroid/agent/task.py

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

add_sub_task(task)

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

Parameters:

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

Source code in langroid/agent/task.py

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

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

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

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

init(msg=None)

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

Returns:

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

Source code in langroid/agent/task.py

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

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

    self._show_pending_message_if_debug()

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

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

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

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

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

Source code in langroid/agent/task.py

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

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

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

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

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

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

Parameters:

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

Returns:

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

Source code in langroid/agent/task.py

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

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

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

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

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

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

step(turns=-1)

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

Source code in langroid/agent/task.py

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

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

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

        responders.insert(0, Entity.USER)

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

step_async(turns=-1) async

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

Parameters:

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

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

Source code in langroid/agent/task.py

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

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

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

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

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

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

response(e, turns=-1)

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

Source code in langroid/agent/task.py

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

response_async(e, turns=-1) async

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

Returns:

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

Source code in langroid/agent/task.py

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

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

result()

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

Source code in langroid/agent/task.py

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

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

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

done(result=None, r=None)

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

Source code in langroid/agent/task.py

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

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

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

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

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

valid(result, r)

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

Source code in langroid/agent/task.py

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

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

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

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

Parameters:

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

Source code in langroid/agent/task.py

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

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

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

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

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

set_color_log(enable=True)

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

Source code in langroid/agent/task.py

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

    """
    self.color_log = enable

DocMetaData

Bases: BaseModel

Metadata for a document.

dict_bool_int(*args, **kwargs)

Special dict method to convert bool fields to int, to appease some downstream libraries, e.g. Chroma which complains about bool fields in metadata.

Source code in langroid/mytypes.py

def dict_bool_int(self, *args: Any, **kwargs: Any) -> Dict[str, Any]:
    """
    Special dict method to convert bool fields to int, to appease some
    downstream libraries,  e.g. Chroma which complains about bool fields in
    metadata.
    """
    original_dict = super().dict(*args, **kwargs)

    for key, value in original_dict.items():
        if isinstance(value, bool):
            original_dict[key] = 1 * value

    return original_dict

Document

Bases: BaseModel

Interface for interacting with a document.

Entity

Bases: str, Enum

Enum for the different types of entities that can respond to the current message.

run_batch_tasks(task, items, input_map=lambda x: str(x), output_map=lambda x: x, sequential=True, batch_size=None, turns=-1, max_cost=0.0, max_tokens=0)

Run copies of task async/concurrently one per item in items list. For each item, apply input_map to get the initial message to process. For each result, apply output_map to get the final result. Args: task (Task): task to run items (list[T]): list of items to process input_map (Callable[[T], str|ChatDocument]): function to map item to initial message to process output_map (Callable[[ChatDocument|str], U]): function to map result to final result sequential (bool): whether to run sequentially (e.g. some APIs such as ooba don't support concurrent requests) batch_size (Optional[int]): The number of tasks to run at a time, if None, unbatched turns (int): number of turns to run, -1 for infinite max_cost: float: maximum cost to run the task (default 0.0 for unlimited) max_tokens: int: maximum token usage (in and out) (default 0 for unlimited)

Returns:

Type	Description
List[U]	list[Any]: list of final results

Source code in langroid/agent/batch.py

def run_batch_tasks(
    task: Task,
    items: list[T],
    input_map: Callable[[T], str | ChatDocument] = lambda x: str(x),
    output_map: Callable[[ChatDocument | None], U] = lambda x: x,  # type: ignore
    sequential: bool = True,
    batch_size: Optional[int] = None,
    turns: int = -1,
    max_cost: float = 0.0,
    max_tokens: int = 0,
) -> List[U]:
    """
    Run copies of `task` async/concurrently one per item in `items` list.
    For each item, apply `input_map` to get the initial message to process.
    For each result, apply `output_map` to get the final result.
    Args:
        task (Task): task to run
        items (list[T]): list of items to process
        input_map (Callable[[T], str|ChatDocument]): function to map item to
            initial message to process
        output_map (Callable[[ChatDocument|str], U]): function to map result
            to final result
        sequential (bool): whether to run sequentially
            (e.g. some APIs such as ooba don't support concurrent requests)
        batch_size (Optional[int]): The number of tasks to run at a time,
            if None, unbatched
        turns (int): number of turns to run, -1 for infinite
        max_cost: float: maximum cost to run the task (default 0.0 for unlimited)
        max_tokens: int: maximum token usage (in and out) (default 0 for unlimited)

    Returns:
        list[Any]: list of final results
    """
    message = f"[bold green]Running {len(items)} copies of {task.name}..."
    return run_batch_task_gen(
        lambda i: task.clone(i),
        items,
        input_map,
        output_map,
        sequential,
        batch_size,
        turns,
        message,
        max_cost=max_cost,
        max_tokens=max_tokens,
    )