Core Agent Classes

Base Types

Base agent types.

BaseAgent

Bases: BaseChatEngine, BaseQueryEngine

Base Agent.

Source code in llama-index-core/llama_index/core/base/agent/types.py

class BaseAgent(BaseChatEngine, BaseQueryEngine):
    """Base Agent."""

    def _get_prompts(self) -> PromptDictType:
        """Get prompts."""
        # TODO: the ReAct agent does not explicitly specify prompts, would need a
        # refactor to expose those prompts
        return {}

    def _get_prompt_modules(self) -> PromptMixinType:
        """Get prompt modules."""
        return {}

    def _update_prompts(self, prompts: PromptDictType) -> None:
        """Update prompts."""

    # ===== Query Engine Interface =====
    @trace_method("query")
    def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        agent_response = self.chat(
            query_bundle.query_str,
            chat_history=[],
        )
        return Response(
            response=str(agent_response), source_nodes=agent_response.source_nodes
        )

    @trace_method("query")
    async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        agent_response = await self.achat(
            query_bundle.query_str,
            chat_history=[],
        )
        return Response(
            response=str(agent_response), source_nodes=agent_response.source_nodes
        )

    def stream_chat(
        self, message: str, chat_history: Optional[List[ChatMessage]] = None
    ) -> StreamingAgentChatResponse:
        raise NotImplementedError("stream_chat not implemented")

    async def astream_chat(
        self, message: str, chat_history: Optional[List[ChatMessage]] = None
    ) -> StreamingAgentChatResponse:
        raise NotImplementedError("astream_chat not implemented")

BaseAgentWorker

Bases: PromptMixin, DispatcherSpanMixin

Base agent worker.

Source code in llama-index-core/llama_index/core/base/agent/types.py

class BaseAgentWorker(PromptMixin, DispatcherSpanMixin):
    """Base agent worker."""

    def _get_prompts(self) -> PromptDictType:
        """Get prompts."""
        # TODO: the ReAct agent does not explicitly specify prompts, would need a
        # refactor to expose those prompts
        return {}

    def _get_prompt_modules(self) -> PromptMixinType:
        """Get prompt modules."""
        return {}

    def _update_prompts(self, prompts: PromptDictType) -> None:
        """Update prompts."""

    @abstractmethod
    def initialize_step(self, task: Task, **kwargs: Any) -> TaskStep:
        """Initialize step from task."""

    @abstractmethod
    def run_step(self, step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput:
        """Run step."""

    @abstractmethod
    async def arun_step(
        self, step: TaskStep, task: Task, **kwargs: Any
    ) -> TaskStepOutput:
        """Run step (async)."""
        raise NotImplementedError

    @abstractmethod
    def stream_step(self, step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput:
        """Run step (stream)."""
        # TODO: figure out if we need a different type for TaskStepOutput
        raise NotImplementedError

    @abstractmethod
    async def astream_step(
        self, step: TaskStep, task: Task, **kwargs: Any
    ) -> TaskStepOutput:
        """Run step (async stream)."""
        raise NotImplementedError

    @abstractmethod
    def finalize_task(self, task: Task, **kwargs: Any) -> None:
        """Finalize task, after all the steps are completed."""

    def set_callback_manager(self, callback_manager: CallbackManager) -> None:
        """Set callback manager."""
        # TODO: make this abstractmethod (right now will break some agent impls)

    def as_agent(self, **kwargs: Any) -> "AgentRunner":
        """Return as an agent runner."""
        from llama_index.core.agent.runner.base import AgentRunner

        return AgentRunner(self, **kwargs)

initialize_step abstractmethod

initialize_step(task: Task, **kwargs: Any) -> TaskStep

Initialize step from task.

Source code in llama-index-core/llama_index/core/base/agent/types.py

@abstractmethod
def initialize_step(self, task: Task, **kwargs: Any) -> TaskStep:
    """Initialize step from task."""

run_step abstractmethod

run_step(step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput

Run step.

Source code in llama-index-core/llama_index/core/base/agent/types.py

@abstractmethod
def run_step(self, step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput:
    """Run step."""

arun_step abstractmethod async

arun_step(step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput

Run step (async).

Source code in llama-index-core/llama_index/core/base/agent/types.py

@abstractmethod
async def arun_step(
    self, step: TaskStep, task: Task, **kwargs: Any
) -> TaskStepOutput:
    """Run step (async)."""
    raise NotImplementedError

stream_step abstractmethod

stream_step(step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput

Run step (stream).

Source code in llama-index-core/llama_index/core/base/agent/types.py

@abstractmethod
def stream_step(self, step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput:
    """Run step (stream)."""
    # TODO: figure out if we need a different type for TaskStepOutput
    raise NotImplementedError

astream_step abstractmethod async

astream_step(step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput

Run step (async stream).

Source code in llama-index-core/llama_index/core/base/agent/types.py

@abstractmethod
async def astream_step(
    self, step: TaskStep, task: Task, **kwargs: Any
) -> TaskStepOutput:
    """Run step (async stream)."""
    raise NotImplementedError

finalize_task abstractmethod

finalize_task(task: Task, **kwargs: Any) -> None

Finalize task, after all the steps are completed.

Source code in llama-index-core/llama_index/core/base/agent/types.py

@abstractmethod
def finalize_task(self, task: Task, **kwargs: Any) -> None:
    """Finalize task, after all the steps are completed."""

set_callback_manager

set_callback_manager(callback_manager: CallbackManager) -> None

Set callback manager.

Source code in llama-index-core/llama_index/core/base/agent/types.py

def set_callback_manager(self, callback_manager: CallbackManager) -> None:
    """Set callback manager."""

as_agent

as_agent(**kwargs: Any) -> AgentRunner

Return as an agent runner.

Source code in llama-index-core/llama_index/core/base/agent/types.py

def as_agent(self, **kwargs: Any) -> "AgentRunner":
    """Return as an agent runner."""
    from llama_index.core.agent.runner.base import AgentRunner

    return AgentRunner(self, **kwargs)

Task

Bases: BaseModel

Agent Task.

Represents a "run" of an agent given a user input.

Source code in llama-index-core/llama_index/core/base/agent/types.py

class Task(BaseModel):
    """Agent Task.

    Represents a "run" of an agent given a user input.

    """

    model_config = ConfigDict(arbitrary_types_allowed=True)
    task_id: str = Field(
        default_factory=lambda: str(uuid.uuid4()), description="Task ID"
    )
    input: str = Field(..., description="User input")

    # NOTE: this is state that may be modified throughout the course of execution of the task
    memory: SerializeAsAny[BaseMemory] = Field(
        ...,
        description=(
            "Conversational Memory. Maintains state before execution of this task."
        ),
    )

    callback_manager: CallbackManager = Field(
        default_factory=lambda: CallbackManager([]),
        exclude=True,
        description="Callback manager for the task.",
    )

    extra_state: Dict[str, Any] = Field(
        default_factory=dict,
        description=(
            "Additional user-specified state for a given task. "
            "Can be modified throughout the execution of a task."
        ),
    )

TaskStep

Bases: BaseModel

Agent task step.

Represents a single input step within the execution run ("Task") of an agent given a user input.

The output is returned as a TaskStepOutput.

Source code in llama-index-core/llama_index/core/base/agent/types.py

class TaskStep(BaseModel):
    """Agent task step.

    Represents a single input step within the execution run ("Task") of an agent
    given a user input.

    The output is returned as a `TaskStepOutput`.

    """

    task_id: str = Field(..., description="Task ID")
    step_id: str = Field(..., description="Step ID")
    input: Optional[str] = Field(default=None, description="User input")
    # memory: BaseMemory = Field(
    #     ..., description="Conversational Memory"
    # )
    step_state: Dict[str, Any] = Field(
        default_factory=dict, description="Additional state for a given step."
    )

    # NOTE: the state below may change throughout the course of execution
    # this tracks the relationships to other steps
    next_steps: Dict[str, "TaskStep"] = Field(
        default_factory=dict, description="Next steps to be executed."
    )
    prev_steps: Dict[str, "TaskStep"] = Field(
        default_factory=dict,
        description="Previous steps that were dependencies for this step.",
    )
    is_ready: bool = Field(
        default=True, description="Is this step ready to be executed?"
    )

    def get_next_step(
        self,
        step_id: str,
        input: Optional[str] = None,
        step_state: Optional[Dict[str, Any]] = None,
    ) -> "TaskStep":
        """Convenience function to get next step.

        Preserve task_id, memory, step_state.

        """
        return TaskStep(
            task_id=self.task_id,
            step_id=step_id,
            input=input,
            # memory=self.memory,
            step_state=step_state or self.step_state,
        )

    def link_step(
        self,
        next_step: "TaskStep",
    ) -> None:
        """Link to next step.

        Add link from this step to next, and from next step to current.

        """
        self.next_steps[next_step.step_id] = next_step
        next_step.prev_steps[self.step_id] = self

get_next_step

get_next_step(step_id: str, input: Optional[str] = None, step_state: Optional[Dict[str, Any]] = None) -> TaskStep

Convenience function to get next step.

Preserve task_id, memory, step_state.

Source code in llama-index-core/llama_index/core/base/agent/types.py

def get_next_step(
    self,
    step_id: str,
    input: Optional[str] = None,
    step_state: Optional[Dict[str, Any]] = None,
) -> "TaskStep":
    """Convenience function to get next step.

    Preserve task_id, memory, step_state.

    """
    return TaskStep(
        task_id=self.task_id,
        step_id=step_id,
        input=input,
        # memory=self.memory,
        step_state=step_state or self.step_state,
    )

link_step

link_step(next_step: TaskStep) -> None

Link to next step.

Add link from this step to next, and from next step to current.

Source code in llama-index-core/llama_index/core/base/agent/types.py

def link_step(
    self,
    next_step: "TaskStep",
) -> None:
    """Link to next step.

    Add link from this step to next, and from next step to current.

    """
    self.next_steps[next_step.step_id] = next_step
    next_step.prev_steps[self.step_id] = self

TaskStepOutput

Bases: BaseModel

Agent task step output.

Source code in llama-index-core/llama_index/core/base/agent/types.py

class TaskStepOutput(BaseModel):
    """Agent task step output."""

    output: Any = Field(..., description="Task step output")
    task_step: TaskStep = Field(..., description="Task step input")
    next_steps: List[TaskStep] = Field(..., description="Next steps to be executed.")
    is_last: bool = Field(default=False, description="Is this the last step?")

    def __str__(self) -> str:
        """String representation."""
        return str(self.output)

Runners

AgentRunner

Bases: BaseAgentRunner

Agent runner.

Top-level agent orchestrator that can create tasks, run each step in a task, or run a task e2e. Stores state and keeps track of tasks.

Parameters:

Name	Type	Description	Default
agent_worker	BaseAgentWorker	step executor	required
chat_history	Optional[List[ChatMessage]]	chat history. Defaults to None.	None
state	Optional[AgentState]	agent state. Defaults to None.	None
memory	Optional[BaseMemory]	memory. Defaults to None.	None
llm	Optional[LLM]	LLM. Defaults to None.	None
callback_manager	Optional[CallbackManager]	callback manager. Defaults to None.	None
init_task_state_kwargs	Optional[dict]	init task state kwargs. Defaults to None.	None

Source code in llama-index-core/llama_index/core/agent/runner/base.py

class AgentRunner(BaseAgentRunner):
    """Agent runner.

    Top-level agent orchestrator that can create tasks, run each step in a task,
    or run a task e2e. Stores state and keeps track of tasks.

    Args:
        agent_worker (BaseAgentWorker): step executor
        chat_history (Optional[List[ChatMessage]], optional): chat history. Defaults to None.
        state (Optional[AgentState], optional): agent state. Defaults to None.
        memory (Optional[BaseMemory], optional): memory. Defaults to None.
        llm (Optional[LLM], optional): LLM. Defaults to None.
        callback_manager (Optional[CallbackManager], optional): callback manager. Defaults to None.
        init_task_state_kwargs (Optional[dict], optional): init task state kwargs. Defaults to None.

    """

    # # TODO: implement this in Pydantic

    def __init__(
        self,
        agent_worker: BaseAgentWorker,
        chat_history: Optional[List[ChatMessage]] = None,
        state: Optional[AgentState] = None,
        memory: Optional[BaseMemory] = None,
        llm: Optional[LLM] = None,
        callback_manager: Optional[CallbackManager] = None,
        init_task_state_kwargs: Optional[dict] = None,
        delete_task_on_finish: bool = False,
        default_tool_choice: str = "auto",
        verbose: bool = False,
    ) -> None:
        """Initialize."""
        self.agent_worker = agent_worker
        self.state = state or AgentState()
        self.memory = memory or ChatMemoryBuffer.from_defaults(chat_history, llm=llm)

        # get and set callback manager
        if callback_manager is not None:
            self.agent_worker.set_callback_manager(callback_manager)
            self.callback_manager = callback_manager
        else:
            # TODO: This is *temporary*
            # Stopgap before having a callback on the BaseAgentWorker interface.
            # Doing that requires a bit more refactoring to make sure existing code
            # doesn't break.
            if hasattr(self.agent_worker, "callback_manager"):
                self.callback_manager = (
                    self.agent_worker.callback_manager or CallbackManager()
                )
            else:
                self.callback_manager = CallbackManager()
        self.init_task_state_kwargs = init_task_state_kwargs or {}
        self.delete_task_on_finish = delete_task_on_finish
        self.default_tool_choice = default_tool_choice
        self.verbose = verbose

    @staticmethod
    def from_llm(
        tools: Optional[List[BaseTool]] = None,
        llm: Optional[LLM] = None,
        **kwargs: Any,
    ) -> "AgentRunner":
        from llama_index.core.agent import ReActAgent

        if os.getenv("IS_TESTING"):
            return ReActAgent.from_tools(
                tools=tools,
                llm=llm,
                **kwargs,
            )

        try:
            from llama_index.llms.openai import OpenAI  # pants: no-infer-dep
            from llama_index.llms.openai.utils import (
                is_function_calling_model,
            )  # pants: no-infer-dep
        except ImportError:
            raise ImportError(
                "`llama-index-llms-openai` package not found. Please "
                "install by running `pip install llama-index-llms-openai`."
            )

        if isinstance(llm, OpenAI) and is_function_calling_model(llm.model):
            from llama_index.agent.openai import OpenAIAgent  # pants: no-infer-dep

            return OpenAIAgent.from_tools(
                tools=tools,
                llm=llm,
                **kwargs,
            )
        else:
            return ReActAgent.from_tools(
                tools=tools,
                llm=llm,
                **kwargs,
            )

    @property
    def chat_history(self) -> List[ChatMessage]:
        return self.memory.get_all()

    def reset(self) -> None:
        self.memory.reset()
        self.state.reset()

    def create_task(self, input: str, **kwargs: Any) -> Task:
        """Create task."""
        if not self.init_task_state_kwargs:
            extra_state = kwargs.pop("extra_state", {})
        else:
            if "extra_state" in kwargs:
                raise ValueError(
                    "Cannot specify both `extra_state` and `init_task_state_kwargs`"
                )
            else:
                extra_state = self.init_task_state_kwargs

        callback_manager = kwargs.pop("callback_manager", self.callback_manager)
        task = Task(
            input=input,
            memory=self.memory,
            extra_state=extra_state,
            callback_manager=callback_manager,
            **kwargs,
        )
        # # put input into memory
        # self.memory.put(ChatMessage(content=input, role=MessageRole.USER))

        # get initial step from task, and put it in the step queue
        initial_step = self.agent_worker.initialize_step(task)
        task_state = TaskState(
            task=task,
            step_queue=deque([initial_step]),
        )
        # add it to state
        self.state.task_dict[task.task_id] = task_state

        return task

    def delete_task(
        self,
        task_id: str,
    ) -> None:
        """Delete task.

        NOTE: this will not delete any previous executions from memory.

        """
        self.state.task_dict.pop(task_id)

    def list_tasks(self, **kwargs: Any) -> List[Task]:
        """List tasks."""
        return [task_state.task for task_state in self.state.task_dict.values()]

    def get_task(self, task_id: str, **kwargs: Any) -> Task:
        """Get task."""
        return self.state.get_task(task_id)

    def get_upcoming_steps(self, task_id: str, **kwargs: Any) -> List[TaskStep]:
        """Get upcoming steps."""
        return list(self.state.get_step_queue(task_id))

    def get_completed_steps(self, task_id: str, **kwargs: Any) -> List[TaskStepOutput]:
        """Get completed steps."""
        return self.state.get_completed_steps(task_id)

    def get_task_output(self, task_id: str, **kwargs: Any) -> TaskStepOutput:
        """Get task output."""
        completed_steps = self.get_completed_steps(task_id)
        if len(completed_steps) == 0:
            raise ValueError(f"No completed steps for task_id: {task_id}")
        return completed_steps[-1]

    def get_completed_tasks(self, **kwargs: Any) -> List[Task]:
        """Get completed tasks."""
        task_states = list(self.state.task_dict.values())
        completed_tasks = []
        for task_state in task_states:
            completed_steps = self.get_completed_steps(task_state.task.task_id)
            if len(completed_steps) > 0 and completed_steps[-1].is_last:
                completed_tasks.append(task_state.task)

        return completed_tasks

    @dispatcher.span
    def _run_step(
        self,
        task_id: str,
        step: Optional[TaskStep] = None,
        input: Optional[str] = None,
        mode: ChatResponseMode = ChatResponseMode.WAIT,
        **kwargs: Any,
    ) -> TaskStepOutput:
        """Execute step."""
        task = self.state.get_task(task_id)
        step_queue = self.state.get_step_queue(task_id)
        step = step or step_queue.popleft()
        if input is not None:
            step.input = input

        dispatcher.event(
            AgentRunStepStartEvent(task_id=task_id, step=step, input=input)
        )

        if self.verbose:
            print(f"> Running step {step.step_id}. Step input: {step.input}")

        # TODO: figure out if you can dynamically swap in different step executors
        # not clear when you would do that by theoretically possible

        if mode == ChatResponseMode.WAIT:
            cur_step_output = self.agent_worker.run_step(step, task, **kwargs)
        elif mode == ChatResponseMode.STREAM:
            cur_step_output = self.agent_worker.stream_step(step, task, **kwargs)
        else:
            raise ValueError(f"Invalid mode: {mode}")
        # append cur_step_output next steps to queue
        next_steps = cur_step_output.next_steps
        step_queue.extend(next_steps)

        # add cur_step_output to completed steps
        completed_steps = self.state.get_completed_steps(task_id)
        completed_steps.append(cur_step_output)

        dispatcher.event(AgentRunStepEndEvent(step_output=cur_step_output))
        return cur_step_output

    @dispatcher.span
    async def _arun_step(
        self,
        task_id: str,
        step: Optional[TaskStep] = None,
        input: Optional[str] = None,
        mode: ChatResponseMode = ChatResponseMode.WAIT,
        **kwargs: Any,
    ) -> TaskStepOutput:
        """Execute step."""
        dispatcher.event(
            AgentRunStepStartEvent(task_id=task_id, step=step, input=input)
        )
        task = self.state.get_task(task_id)
        step_queue = self.state.get_step_queue(task_id)
        step = step or step_queue.popleft()
        if input is not None:
            step.input = input

        if self.verbose:
            print(f"> Running step {step.step_id}. Step input: {step.input}")

        # TODO: figure out if you can dynamically swap in different step executors
        # not clear when you would do that by theoretically possible
        if mode == ChatResponseMode.WAIT:
            cur_step_output = await self.agent_worker.arun_step(step, task, **kwargs)
        elif mode == ChatResponseMode.STREAM:
            cur_step_output = await self.agent_worker.astream_step(step, task, **kwargs)
        else:
            raise ValueError(f"Invalid mode: {mode}")
        # append cur_step_output next steps to queue
        next_steps = cur_step_output.next_steps
        step_queue.extend(next_steps)

        # add cur_step_output to completed steps
        completed_steps = self.state.get_completed_steps(task_id)
        completed_steps.append(cur_step_output)

        dispatcher.event(AgentRunStepEndEvent(step_output=cur_step_output))
        return cur_step_output

    @dispatcher.span
    def run_step(
        self,
        task_id: str,
        input: Optional[str] = None,
        step: Optional[TaskStep] = None,
        **kwargs: Any,
    ) -> TaskStepOutput:
        """Run step."""
        step = validate_step_from_args(task_id, input, step, **kwargs)
        return self._run_step(
            task_id, step, input=input, mode=ChatResponseMode.WAIT, **kwargs
        )

    @dispatcher.span
    async def arun_step(
        self,
        task_id: str,
        input: Optional[str] = None,
        step: Optional[TaskStep] = None,
        **kwargs: Any,
    ) -> TaskStepOutput:
        """Run step (async)."""
        step = validate_step_from_args(task_id, input, step, **kwargs)
        return await self._arun_step(
            task_id, step, input=input, mode=ChatResponseMode.WAIT, **kwargs
        )

    @dispatcher.span
    def stream_step(
        self,
        task_id: str,
        input: Optional[str] = None,
        step: Optional[TaskStep] = None,
        **kwargs: Any,
    ) -> TaskStepOutput:
        """Run step (stream)."""
        step = validate_step_from_args(task_id, input, step, **kwargs)
        return self._run_step(
            task_id, step, input=input, mode=ChatResponseMode.STREAM, **kwargs
        )

    @dispatcher.span
    async def astream_step(
        self,
        task_id: str,
        input: Optional[str] = None,
        step: Optional[TaskStep] = None,
        **kwargs: Any,
    ) -> TaskStepOutput:
        """Run step (async stream)."""
        step = validate_step_from_args(task_id, input, step, **kwargs)
        return await self._arun_step(
            task_id, step, input=input, mode=ChatResponseMode.STREAM, **kwargs
        )

    @dispatcher.span
    def finalize_response(
        self,
        task_id: str,
        step_output: Optional[TaskStepOutput] = None,
    ) -> AGENT_CHAT_RESPONSE_TYPE:
        """Finalize response."""
        if step_output is None:
            step_output = self.state.get_completed_steps(task_id)[-1]
        if not step_output.is_last:
            raise ValueError(
                "finalize_response can only be called on the last step output"
            )

        if not isinstance(
            step_output.output,
            (AgentChatResponse, StreamingAgentChatResponse),
        ):
            raise ValueError(
                "When `is_last` is True, cur_step_output.output must be "
                f"AGENT_CHAT_RESPONSE_TYPE: {step_output.output}"
            )

        # finalize task
        self.agent_worker.finalize_task(self.state.get_task(task_id))

        if self.delete_task_on_finish:
            self.delete_task(task_id)

        # Attach all sources generated across all steps
        step_output.output.sources = self.get_task(task_id).extra_state.get(
            "sources", []
        )
        step_output.output.set_source_nodes()

        return cast(AGENT_CHAT_RESPONSE_TYPE, step_output.output)

    @dispatcher.span
    def _chat(
        self,
        message: str,
        chat_history: Optional[List[ChatMessage]] = None,
        tool_choice: Union[str, dict] = "auto",
        mode: ChatResponseMode = ChatResponseMode.WAIT,
    ) -> AGENT_CHAT_RESPONSE_TYPE:
        """Chat with step executor."""
        if chat_history is not None:
            self.memory.set(chat_history)
        task = self.create_task(message)

        result_output = None
        dispatcher.event(AgentChatWithStepStartEvent(user_msg=message))
        while True:
            # pass step queue in as argument, assume step executor is stateless
            cur_step_output = self._run_step(
                task.task_id, mode=mode, tool_choice=tool_choice
            )

            if cur_step_output.is_last:
                result_output = cur_step_output
                break

            # ensure tool_choice does not cause endless loops
            tool_choice = "auto"

        result = self.finalize_response(
            task.task_id,
            result_output,
        )
        dispatcher.event(AgentChatWithStepEndEvent(response=result))
        return result

    @dispatcher.span
    async def _achat(
        self,
        message: str,
        chat_history: Optional[List[ChatMessage]] = None,
        tool_choice: Union[str, dict] = "auto",
        mode: ChatResponseMode = ChatResponseMode.WAIT,
    ) -> AGENT_CHAT_RESPONSE_TYPE:
        """Chat with step executor."""
        if chat_history is not None:
            self.memory.set(chat_history)
        task = self.create_task(message)

        result_output = None
        dispatcher.event(AgentChatWithStepStartEvent(user_msg=message))
        while True:
            # pass step queue in as argument, assume step executor is stateless
            cur_step_output = await self._arun_step(
                task.task_id, mode=mode, tool_choice=tool_choice
            )

            if cur_step_output.is_last:
                result_output = cur_step_output
                break

            # ensure tool_choice does not cause endless loops
            tool_choice = "auto"

        result = self.finalize_response(
            task.task_id,
            result_output,
        )
        dispatcher.event(AgentChatWithStepEndEvent(response=result))
        return result

    @dispatcher.span
    @trace_method("chat")
    def chat(
        self,
        message: str,
        chat_history: Optional[List[ChatMessage]] = None,
        tool_choice: Optional[Union[str, dict]] = None,
    ) -> AgentChatResponse:
        # override tool choice is provided as input.
        if tool_choice is None:
            tool_choice = self.default_tool_choice
        with self.callback_manager.event(
            CBEventType.AGENT_STEP,
            payload={EventPayload.MESSAGES: [message]},
        ) as e:
            chat_response = self._chat(
                message=message,
                chat_history=chat_history,
                tool_choice=tool_choice,
                mode=ChatResponseMode.WAIT,
            )
            assert isinstance(chat_response, AgentChatResponse)
            e.on_end(payload={EventPayload.RESPONSE: chat_response})
        return chat_response

    @dispatcher.span
    @trace_method("chat")
    async def achat(
        self,
        message: str,
        chat_history: Optional[List[ChatMessage]] = None,
        tool_choice: Optional[Union[str, dict]] = None,
    ) -> AgentChatResponse:
        # override tool choice is provided as input.
        if tool_choice is None:
            tool_choice = self.default_tool_choice
        with self.callback_manager.event(
            CBEventType.AGENT_STEP,
            payload={EventPayload.MESSAGES: [message]},
        ) as e:
            chat_response = await self._achat(
                message=message,
                chat_history=chat_history,
                tool_choice=tool_choice,
                mode=ChatResponseMode.WAIT,
            )
            assert isinstance(chat_response, AgentChatResponse)
            e.on_end(payload={EventPayload.RESPONSE: chat_response})
        return chat_response

    @dispatcher.span
    @trace_method("chat")
    def stream_chat(
        self,
        message: str,
        chat_history: Optional[List[ChatMessage]] = None,
        tool_choice: Optional[Union[str, dict]] = None,
    ) -> StreamingAgentChatResponse:
        # override tool choice is provided as input.
        if tool_choice is None:
            tool_choice = self.default_tool_choice
        with self.callback_manager.event(
            CBEventType.AGENT_STEP,
            payload={EventPayload.MESSAGES: [message]},
        ) as e:
            chat_response = self._chat(
                message, chat_history, tool_choice, mode=ChatResponseMode.STREAM
            )
            assert isinstance(chat_response, StreamingAgentChatResponse) or (
                isinstance(chat_response, AgentChatResponse)
                and chat_response.is_dummy_stream
            )
            e.on_end(payload={EventPayload.RESPONSE: chat_response})

        assert isinstance(chat_response, StreamingAgentChatResponse)
        return chat_response

    @dispatcher.span
    @trace_method("chat")
    async def astream_chat(
        self,
        message: str,
        chat_history: Optional[List[ChatMessage]] = None,
        tool_choice: Optional[Union[str, dict]] = None,
    ) -> StreamingAgentChatResponse:
        # override tool choice is provided as input.
        if tool_choice is None:
            tool_choice = self.default_tool_choice
        with self.callback_manager.event(
            CBEventType.AGENT_STEP,
            payload={EventPayload.MESSAGES: [message]},
        ) as e:
            chat_response = await self._achat(
                message, chat_history, tool_choice, mode=ChatResponseMode.STREAM
            )
            assert isinstance(chat_response, StreamingAgentChatResponse) or (
                isinstance(chat_response, AgentChatResponse)
                and chat_response.is_dummy_stream
            )
            e.on_end(payload={EventPayload.RESPONSE: chat_response})

        assert isinstance(chat_response, StreamingAgentChatResponse)
        return chat_response

    def undo_step(self, task_id: str) -> None:
        """Undo previous step."""
        raise NotImplementedError("undo_step not implemented")

create_task

create_task(input: str, **kwargs: Any) -> Task

Create task.

Source code in llama-index-core/llama_index/core/agent/runner/base.py

def create_task(self, input: str, **kwargs: Any) -> Task:
    """Create task."""
    if not self.init_task_state_kwargs:
        extra_state = kwargs.pop("extra_state", {})
    else:
        if "extra_state" in kwargs:
            raise ValueError(
                "Cannot specify both `extra_state` and `init_task_state_kwargs`"
            )
        else:
            extra_state = self.init_task_state_kwargs

    callback_manager = kwargs.pop("callback_manager", self.callback_manager)
    task = Task(
        input=input,
        memory=self.memory,
        extra_state=extra_state,
        callback_manager=callback_manager,
        **kwargs,
    )
    # # put input into memory
    # self.memory.put(ChatMessage(content=input, role=MessageRole.USER))

    # get initial step from task, and put it in the step queue
    initial_step = self.agent_worker.initialize_step(task)
    task_state = TaskState(
        task=task,
        step_queue=deque([initial_step]),
    )
    # add it to state
    self.state.task_dict[task.task_id] = task_state

    return task

delete_task

delete_task(task_id: str) -> None

Delete task.

NOTE: this will not delete any previous executions from memory.

Source code in llama-index-core/llama_index/core/agent/runner/base.py

def delete_task(
    self,
    task_id: str,
) -> None:
    """Delete task.

    NOTE: this will not delete any previous executions from memory.

    """
    self.state.task_dict.pop(task_id)

list_tasks

list_tasks(**kwargs: Any) -> List[Task]

List tasks.

Source code in llama-index-core/llama_index/core/agent/runner/base.py

def list_tasks(self, **kwargs: Any) -> List[Task]:
    """List tasks."""
    return [task_state.task for task_state in self.state.task_dict.values()]

get_task

get_task(task_id: str, **kwargs: Any) -> Task

Get task.

Source code in llama-index-core/llama_index/core/agent/runner/base.py

def get_task(self, task_id: str, **kwargs: Any) -> Task:
    """Get task."""
    return self.state.get_task(task_id)

get_upcoming_steps

get_upcoming_steps(task_id: str, **kwargs: Any) -> List[TaskStep]

Get upcoming steps.

Source code in llama-index-core/llama_index/core/agent/runner/base.py

def get_upcoming_steps(self, task_id: str, **kwargs: Any) -> List[TaskStep]:
    """Get upcoming steps."""
    return list(self.state.get_step_queue(task_id))

get_completed_steps

get_completed_steps(task_id: str, **kwargs: Any) -> List[TaskStepOutput]

Get completed steps.

Source code in llama-index-core/llama_index/core/agent/runner/base.py

def get_completed_steps(self, task_id: str, **kwargs: Any) -> List[TaskStepOutput]:
    """Get completed steps."""
    return self.state.get_completed_steps(task_id)

get_task_output

get_task_output(task_id: str, **kwargs: Any) -> TaskStepOutput

Get task output.

Source code in llama-index-core/llama_index/core/agent/runner/base.py

def get_task_output(self, task_id: str, **kwargs: Any) -> TaskStepOutput:
    """Get task output."""
    completed_steps = self.get_completed_steps(task_id)
    if len(completed_steps) == 0:
        raise ValueError(f"No completed steps for task_id: {task_id}")
    return completed_steps[-1]

get_completed_tasks

get_completed_tasks(**kwargs: Any) -> List[Task]

Get completed tasks.

Source code in llama-index-core/llama_index/core/agent/runner/base.py

def get_completed_tasks(self, **kwargs: Any) -> List[Task]:
    """Get completed tasks."""
    task_states = list(self.state.task_dict.values())
    completed_tasks = []
    for task_state in task_states:
        completed_steps = self.get_completed_steps(task_state.task.task_id)
        if len(completed_steps) > 0 and completed_steps[-1].is_last:
            completed_tasks.append(task_state.task)

    return completed_tasks

run_step

run_step(task_id: str, input: Optional[str] = None, step: Optional[TaskStep] = None, **kwargs: Any) -> TaskStepOutput

Run step.

Source code in llama-index-core/llama_index/core/agent/runner/base.py

@dispatcher.span
def run_step(
    self,
    task_id: str,
    input: Optional[str] = None,
    step: Optional[TaskStep] = None,
    **kwargs: Any,
) -> TaskStepOutput:
    """Run step."""
    step = validate_step_from_args(task_id, input, step, **kwargs)
    return self._run_step(
        task_id, step, input=input, mode=ChatResponseMode.WAIT, **kwargs
    )

arun_step async

arun_step(task_id: str, input: Optional[str] = None, step: Optional[TaskStep] = None, **kwargs: Any) -> TaskStepOutput

Run step (async).

Source code in llama-index-core/llama_index/core/agent/runner/base.py

@dispatcher.span
async def arun_step(
    self,
    task_id: str,
    input: Optional[str] = None,
    step: Optional[TaskStep] = None,
    **kwargs: Any,
) -> TaskStepOutput:
    """Run step (async)."""
    step = validate_step_from_args(task_id, input, step, **kwargs)
    return await self._arun_step(
        task_id, step, input=input, mode=ChatResponseMode.WAIT, **kwargs
    )

stream_step

stream_step(task_id: str, input: Optional[str] = None, step: Optional[TaskStep] = None, **kwargs: Any) -> TaskStepOutput

Run step (stream).

Source code in llama-index-core/llama_index/core/agent/runner/base.py

@dispatcher.span
def stream_step(
    self,
    task_id: str,
    input: Optional[str] = None,
    step: Optional[TaskStep] = None,
    **kwargs: Any,
) -> TaskStepOutput:
    """Run step (stream)."""
    step = validate_step_from_args(task_id, input, step, **kwargs)
    return self._run_step(
        task_id, step, input=input, mode=ChatResponseMode.STREAM, **kwargs
    )

astream_step async

astream_step(task_id: str, input: Optional[str] = None, step: Optional[TaskStep] = None, **kwargs: Any) -> TaskStepOutput

Run step (async stream).

Source code in llama-index-core/llama_index/core/agent/runner/base.py

@dispatcher.span
async def astream_step(
    self,
    task_id: str,
    input: Optional[str] = None,
    step: Optional[TaskStep] = None,
    **kwargs: Any,
) -> TaskStepOutput:
    """Run step (async stream)."""
    step = validate_step_from_args(task_id, input, step, **kwargs)
    return await self._arun_step(
        task_id, step, input=input, mode=ChatResponseMode.STREAM, **kwargs
    )

finalize_response

finalize_response(task_id: str, step_output: Optional[TaskStepOutput] = None) -> AGENT_CHAT_RESPONSE_TYPE

Finalize response.

Source code in llama-index-core/llama_index/core/agent/runner/base.py

@dispatcher.span
def finalize_response(
    self,
    task_id: str,
    step_output: Optional[TaskStepOutput] = None,
) -> AGENT_CHAT_RESPONSE_TYPE:
    """Finalize response."""
    if step_output is None:
        step_output = self.state.get_completed_steps(task_id)[-1]
    if not step_output.is_last:
        raise ValueError(
            "finalize_response can only be called on the last step output"
        )

    if not isinstance(
        step_output.output,
        (AgentChatResponse, StreamingAgentChatResponse),
    ):
        raise ValueError(
            "When `is_last` is True, cur_step_output.output must be "
            f"AGENT_CHAT_RESPONSE_TYPE: {step_output.output}"
        )

    # finalize task
    self.agent_worker.finalize_task(self.state.get_task(task_id))

    if self.delete_task_on_finish:
        self.delete_task(task_id)

    # Attach all sources generated across all steps
    step_output.output.sources = self.get_task(task_id).extra_state.get(
        "sources", []
    )
    step_output.output.set_source_nodes()

    return cast(AGENT_CHAT_RESPONSE_TYPE, step_output.output)

undo_step

undo_step(task_id: str) -> None

Undo previous step.

Source code in llama-index-core/llama_index/core/agent/runner/base.py

def undo_step(self, task_id: str) -> None:
    """Undo previous step."""
    raise NotImplementedError("undo_step not implemented")

ParallelAgentRunner

Bases: BaseAgentRunner

Parallel agent runner.

Executes steps in queue in parallel. Requires async support.

Source code in llama-index-core/llama_index/core/agent/runner/parallel.py

class ParallelAgentRunner(BaseAgentRunner):
    """Parallel agent runner.

    Executes steps in queue in parallel. Requires async support.

    """

    def __init__(
        self,
        agent_worker: BaseAgentWorker,
        chat_history: Optional[List[ChatMessage]] = None,
        state: Optional[DAGAgentState] = None,
        memory: Optional[BaseMemory] = None,
        llm: Optional[LLM] = None,
        callback_manager: Optional[CallbackManager] = None,
        init_task_state_kwargs: Optional[dict] = None,
        delete_task_on_finish: bool = False,
    ) -> None:
        """Initialize."""
        self.memory = memory or ChatMemoryBuffer.from_defaults(chat_history, llm=llm)
        self.state = state or DAGAgentState()
        self.callback_manager = callback_manager or CallbackManager([])
        self.init_task_state_kwargs = init_task_state_kwargs or {}
        self.agent_worker = agent_worker
        self.delete_task_on_finish = delete_task_on_finish

    @property
    def chat_history(self) -> List[ChatMessage]:
        return self.memory.get_all()

    def reset(self) -> None:
        self.memory.reset()

    def create_task(self, input: str, **kwargs: Any) -> Task:
        """Create task."""
        task = Task(
            input=input,
            memory=self.memory,
            extra_state=self.init_task_state_kwargs,
            **kwargs,
        )
        # # put input into memory
        # self.memory.put(ChatMessage(content=input, role=MessageRole.USER))

        # add it to state
        # get initial step from task, and put it in the step queue
        initial_step = self.agent_worker.initialize_step(task)
        task_state = DAGTaskState(
            task=task,
            root_step=initial_step,
            step_queue=deque([initial_step]),
        )

        self.state.task_dict[task.task_id] = task_state

        return task

    def delete_task(
        self,
        task_id: str,
    ) -> None:
        """Delete task.

        NOTE: this will not delete any previous executions from memory.

        """
        self.state.task_dict.pop(task_id)

    def get_completed_tasks(self, **kwargs: Any) -> List[Task]:
        """Get completed tasks."""
        task_states = list(self.state.task_dict.values())
        return [
            task_state.task
            for task_state in task_states
            if len(task_state.completed_steps) > 0
            and task_state.completed_steps[-1].is_last
        ]

    def get_task_output(self, task_id: str, **kwargs: Any) -> TaskStepOutput:
        """Get task output."""
        task_state = self.state.task_dict[task_id]
        if len(task_state.completed_steps) == 0:
            raise ValueError(f"No completed steps for task_id: {task_id}")
        return task_state.completed_steps[-1]

    def list_tasks(self, **kwargs: Any) -> List[Task]:
        """List tasks."""
        task_states = list(self.state.task_dict.values())
        return [task_state.task for task_state in task_states]

    def get_task(self, task_id: str, **kwargs: Any) -> Task:
        """Get task."""
        return self.state.get_task(task_id)

    def get_upcoming_steps(self, task_id: str, **kwargs: Any) -> List[TaskStep]:
        """Get upcoming steps."""
        return list(self.state.get_step_queue(task_id))

    def get_completed_steps(self, task_id: str, **kwargs: Any) -> List[TaskStepOutput]:
        """Get completed steps."""
        return self.state.get_completed_steps(task_id)

    def run_steps_in_queue(
        self,
        task_id: str,
        mode: ChatResponseMode = ChatResponseMode.WAIT,
        **kwargs: Any,
    ) -> List[TaskStepOutput]:
        """Execute steps in queue.

        Run all steps in queue, clearing it out.

        Assume that all steps can be run in parallel.

        """
        return asyncio_run(self.arun_steps_in_queue(task_id, mode=mode, **kwargs))

    async def arun_steps_in_queue(
        self,
        task_id: str,
        mode: ChatResponseMode = ChatResponseMode.WAIT,
        **kwargs: Any,
    ) -> List[TaskStepOutput]:
        """Execute all steps in queue.

        All steps in queue are assumed to be ready.

        """
        # first pop all steps from step_queue
        steps: List[TaskStep] = []
        while len(self.state.get_step_queue(task_id)) > 0:
            steps.append(self.state.get_step_queue(task_id).popleft())

        # take every item in the queue, and run it
        tasks = []
        for step in steps:
            tasks.append(self._arun_step(task_id, step=step, mode=mode, **kwargs))

        return await asyncio.gather(*tasks)

    def _run_step(
        self,
        task_id: str,
        step: Optional[TaskStep] = None,
        mode: ChatResponseMode = ChatResponseMode.WAIT,
        **kwargs: Any,
    ) -> TaskStepOutput:
        """Execute step."""
        task = self.state.get_task(task_id)
        task_queue = self.state.get_step_queue(task_id)
        step = step or task_queue.popleft()

        if not step.is_ready:
            raise ValueError(f"Step {step.step_id} is not ready")

        if mode == ChatResponseMode.WAIT:
            cur_step_output: TaskStepOutput = self.agent_worker.run_step(
                step, task, **kwargs
            )
        elif mode == ChatResponseMode.STREAM:
            cur_step_output = self.agent_worker.stream_step(step, task, **kwargs)
        else:
            raise ValueError(f"Invalid mode: {mode}")

        for next_step in cur_step_output.next_steps:
            if next_step.is_ready:
                task_queue.append(next_step)

        # add cur_step_output to completed steps
        completed_steps = self.state.get_completed_steps(task_id)
        completed_steps.append(cur_step_output)

        return cur_step_output

    async def _arun_step(
        self,
        task_id: str,
        step: Optional[TaskStep] = None,
        mode: ChatResponseMode = ChatResponseMode.WAIT,
        **kwargs: Any,
    ) -> TaskStepOutput:
        """Execute step."""
        task = self.state.get_task(task_id)
        task_queue = self.state.get_step_queue(task_id)
        step = step or task_queue.popleft()

        if not step.is_ready:
            raise ValueError(f"Step {step.step_id} is not ready")

        if mode == ChatResponseMode.WAIT:
            cur_step_output = await self.agent_worker.arun_step(step, task, **kwargs)
        elif mode == ChatResponseMode.STREAM:
            cur_step_output = await self.agent_worker.astream_step(step, task, **kwargs)
        else:
            raise ValueError(f"Invalid mode: {mode}")

        for next_step in cur_step_output.next_steps:
            if next_step.is_ready:
                task_queue.append(next_step)

        # add cur_step_output to completed steps
        completed_steps = self.state.get_completed_steps(task_id)
        completed_steps.append(cur_step_output)

        return cur_step_output

    def run_step(
        self,
        task_id: str,
        input: Optional[str] = None,
        step: Optional[TaskStep] = None,
        **kwargs: Any,
    ) -> TaskStepOutput:
        """Run step."""
        return self._run_step(task_id, step, mode=ChatResponseMode.WAIT, **kwargs)

    async def arun_step(
        self,
        task_id: str,
        input: Optional[str] = None,
        step: Optional[TaskStep] = None,
        **kwargs: Any,
    ) -> TaskStepOutput:
        """Run step (async)."""
        return await self._arun_step(
            task_id, step, mode=ChatResponseMode.WAIT, **kwargs
        )

    def stream_step(
        self,
        task_id: str,
        input: Optional[str] = None,
        step: Optional[TaskStep] = None,
        **kwargs: Any,
    ) -> TaskStepOutput:
        """Run step (stream)."""
        return self._run_step(task_id, step, mode=ChatResponseMode.STREAM, **kwargs)

    async def astream_step(
        self,
        task_id: str,
        input: Optional[str] = None,
        step: Optional[TaskStep] = None,
        **kwargs: Any,
    ) -> TaskStepOutput:
        """Run step (async stream)."""
        return await self._arun_step(
            task_id, step, mode=ChatResponseMode.STREAM, **kwargs
        )

    def finalize_response(
        self,
        task_id: str,
        step_output: Optional[TaskStepOutput] = None,
    ) -> AGENT_CHAT_RESPONSE_TYPE:
        """Finalize response."""
        if step_output is None:
            step_output = self.state.get_completed_steps(task_id)[-1]
        if not step_output.is_last:
            raise ValueError(
                "finalize_response can only be called on the last step output"
            )

        if not isinstance(
            step_output.output,
            (AgentChatResponse, StreamingAgentChatResponse),
        ):
            raise ValueError(
                "When `is_last` is True, cur_step_output.output must be "
                f"AGENT_CHAT_RESPONSE_TYPE: {step_output.output}"
            )

        # finalize task
        self.agent_worker.finalize_task(self.state.get_task(task_id))

        if self.delete_task_on_finish:
            self.delete_task(task_id)

        return cast(AGENT_CHAT_RESPONSE_TYPE, step_output.output)

    def _chat(
        self,
        message: str,
        chat_history: Optional[List[ChatMessage]] = None,
        tool_choice: Union[str, dict] = "auto",
        mode: ChatResponseMode = ChatResponseMode.WAIT,
    ) -> AGENT_CHAT_RESPONSE_TYPE:
        """Chat with step executor."""
        if chat_history is not None:
            self.memory.set(chat_history)
        task = self.create_task(message)

        result_output = None
        while True:
            # pass step queue in as argument, assume step executor is stateless
            cur_step_outputs = self.run_steps_in_queue(task.task_id, mode=mode)

            # check if a step output is_last
            is_last = any(
                cur_step_output.is_last for cur_step_output in cur_step_outputs
            )
            if is_last:
                if len(cur_step_outputs) > 1:
                    raise ValueError(
                        "More than one step output returned in final step."
                    )
                cur_step_output = cur_step_outputs[0]
                result_output = cur_step_output
                break

        return self.finalize_response(
            task.task_id,
            result_output,
        )

    async def _achat(
        self,
        message: str,
        chat_history: Optional[List[ChatMessage]] = None,
        tool_choice: Union[str, dict] = "auto",
        mode: ChatResponseMode = ChatResponseMode.WAIT,
    ) -> AGENT_CHAT_RESPONSE_TYPE:
        """Chat with step executor."""
        if chat_history is not None:
            self.memory.set(chat_history)
        task = self.create_task(message)

        result_output = None
        while True:
            # pass step queue in as argument, assume step executor is stateless
            cur_step_outputs = await self.arun_steps_in_queue(task.task_id, mode=mode)

            # check if a step output is_last
            is_last = any(
                cur_step_output.is_last for cur_step_output in cur_step_outputs
            )
            if is_last:
                if len(cur_step_outputs) > 1:
                    raise ValueError(
                        "More than one step output returned in final step."
                    )
                cur_step_output = cur_step_outputs[0]
                result_output = cur_step_output
                break

        return self.finalize_response(
            task.task_id,
            result_output,
        )

    @trace_method("chat")
    def chat(
        self,
        message: str,
        chat_history: Optional[List[ChatMessage]] = None,
        tool_choice: Union[str, dict] = "auto",
    ) -> AgentChatResponse:
        with self.callback_manager.event(
            CBEventType.AGENT_STEP,
            payload={EventPayload.MESSAGES: [message]},
        ) as e:
            chat_response = self._chat(
                message, chat_history, tool_choice, mode=ChatResponseMode.WAIT
            )
            assert isinstance(chat_response, AgentChatResponse)
            e.on_end(payload={EventPayload.RESPONSE: chat_response})
        return chat_response

    @trace_method("chat")
    async def achat(
        self,
        message: str,
        chat_history: Optional[List[ChatMessage]] = None,
        tool_choice: Union[str, dict] = "auto",
    ) -> AgentChatResponse:
        with self.callback_manager.event(
            CBEventType.AGENT_STEP,
            payload={EventPayload.MESSAGES: [message]},
        ) as e:
            chat_response = await self._achat(
                message, chat_history, tool_choice, mode=ChatResponseMode.WAIT
            )
            assert isinstance(chat_response, AgentChatResponse)
            e.on_end(payload={EventPayload.RESPONSE: chat_response})
        return chat_response

    @trace_method("chat")
    def stream_chat(
        self,
        message: str,
        chat_history: Optional[List[ChatMessage]] = None,
        tool_choice: Union[str, dict] = "auto",
    ) -> StreamingAgentChatResponse:
        with self.callback_manager.event(
            CBEventType.AGENT_STEP,
            payload={EventPayload.MESSAGES: [message]},
        ) as e:
            chat_response = self._chat(
                message, chat_history, tool_choice, mode=ChatResponseMode.STREAM
            )
            assert isinstance(chat_response, StreamingAgentChatResponse)
            e.on_end(payload={EventPayload.RESPONSE: chat_response})
        return chat_response

    @trace_method("chat")
    async def astream_chat(
        self,
        message: str,
        chat_history: Optional[List[ChatMessage]] = None,
        tool_choice: Union[str, dict] = "auto",
    ) -> StreamingAgentChatResponse:
        with self.callback_manager.event(
            CBEventType.AGENT_STEP,
            payload={EventPayload.MESSAGES: [message]},
        ) as e:
            chat_response = await self._achat(
                message, chat_history, tool_choice, mode=ChatResponseMode.STREAM
            )
            assert isinstance(chat_response, StreamingAgentChatResponse)
            e.on_end(payload={EventPayload.RESPONSE: chat_response})
        return chat_response

    def undo_step(self, task_id: str) -> None:
        """Undo previous step."""
        raise NotImplementedError("undo_step not implemented")

create_task

create_task(input: str, **kwargs: Any) -> Task

Create task.

Source code in llama-index-core/llama_index/core/agent/runner/parallel.py

def create_task(self, input: str, **kwargs: Any) -> Task:
    """Create task."""
    task = Task(
        input=input,
        memory=self.memory,
        extra_state=self.init_task_state_kwargs,
        **kwargs,
    )
    # # put input into memory
    # self.memory.put(ChatMessage(content=input, role=MessageRole.USER))

    # add it to state
    # get initial step from task, and put it in the step queue
    initial_step = self.agent_worker.initialize_step(task)
    task_state = DAGTaskState(
        task=task,
        root_step=initial_step,
        step_queue=deque([initial_step]),
    )

    self.state.task_dict[task.task_id] = task_state

    return task

delete_task

delete_task(task_id: str) -> None

Delete task.

NOTE: this will not delete any previous executions from memory.

Source code in llama-index-core/llama_index/core/agent/runner/parallel.py

def delete_task(
    self,
    task_id: str,
) -> None:
    """Delete task.

    NOTE: this will not delete any previous executions from memory.

    """
    self.state.task_dict.pop(task_id)

get_completed_tasks

get_completed_tasks(**kwargs: Any) -> List[Task]

Get completed tasks.

Source code in llama-index-core/llama_index/core/agent/runner/parallel.py

def get_completed_tasks(self, **kwargs: Any) -> List[Task]:
    """Get completed tasks."""
    task_states = list(self.state.task_dict.values())
    return [
        task_state.task
        for task_state in task_states
        if len(task_state.completed_steps) > 0
        and task_state.completed_steps[-1].is_last
    ]

get_task_output

get_task_output(task_id: str, **kwargs: Any) -> TaskStepOutput

Get task output.

Source code in llama-index-core/llama_index/core/agent/runner/parallel.py

def get_task_output(self, task_id: str, **kwargs: Any) -> TaskStepOutput:
    """Get task output."""
    task_state = self.state.task_dict[task_id]
    if len(task_state.completed_steps) == 0:
        raise ValueError(f"No completed steps for task_id: {task_id}")
    return task_state.completed_steps[-1]

list_tasks

list_tasks(**kwargs: Any) -> List[Task]

List tasks.

Source code in llama-index-core/llama_index/core/agent/runner/parallel.py

def list_tasks(self, **kwargs: Any) -> List[Task]:
    """List tasks."""
    task_states = list(self.state.task_dict.values())
    return [task_state.task for task_state in task_states]

get_task

get_task(task_id: str, **kwargs: Any) -> Task

Get task.

Source code in llama-index-core/llama_index/core/agent/runner/parallel.py

def get_task(self, task_id: str, **kwargs: Any) -> Task:
    """Get task."""
    return self.state.get_task(task_id)

get_upcoming_steps

get_upcoming_steps(task_id: str, **kwargs: Any) -> List[TaskStep]

Get upcoming steps.

Source code in llama-index-core/llama_index/core/agent/runner/parallel.py

def get_upcoming_steps(self, task_id: str, **kwargs: Any) -> List[TaskStep]:
    """Get upcoming steps."""
    return list(self.state.get_step_queue(task_id))

get_completed_steps

get_completed_steps(task_id: str, **kwargs: Any) -> List[TaskStepOutput]

Get completed steps.

Source code in llama-index-core/llama_index/core/agent/runner/parallel.py

def get_completed_steps(self, task_id: str, **kwargs: Any) -> List[TaskStepOutput]:
    """Get completed steps."""
    return self.state.get_completed_steps(task_id)

run_steps_in_queue

run_steps_in_queue(task_id: str, mode: ChatResponseMode = ChatResponseMode.WAIT, **kwargs: Any) -> List[TaskStepOutput]

Execute steps in queue.

Run all steps in queue, clearing it out.

Assume that all steps can be run in parallel.

Source code in llama-index-core/llama_index/core/agent/runner/parallel.py

def run_steps_in_queue(
    self,
    task_id: str,
    mode: ChatResponseMode = ChatResponseMode.WAIT,
    **kwargs: Any,
) -> List[TaskStepOutput]:
    """Execute steps in queue.

    Run all steps in queue, clearing it out.

    Assume that all steps can be run in parallel.

    """
    return asyncio_run(self.arun_steps_in_queue(task_id, mode=mode, **kwargs))

arun_steps_in_queue async

arun_steps_in_queue(task_id: str, mode: ChatResponseMode = ChatResponseMode.WAIT, **kwargs: Any) -> List[TaskStepOutput]

Execute all steps in queue.

All steps in queue are assumed to be ready.

Source code in llama-index-core/llama_index/core/agent/runner/parallel.py

async def arun_steps_in_queue(
    self,
    task_id: str,
    mode: ChatResponseMode = ChatResponseMode.WAIT,
    **kwargs: Any,
) -> List[TaskStepOutput]:
    """Execute all steps in queue.

    All steps in queue are assumed to be ready.

    """
    # first pop all steps from step_queue
    steps: List[TaskStep] = []
    while len(self.state.get_step_queue(task_id)) > 0:
        steps.append(self.state.get_step_queue(task_id).popleft())

    # take every item in the queue, and run it
    tasks = []
    for step in steps:
        tasks.append(self._arun_step(task_id, step=step, mode=mode, **kwargs))

    return await asyncio.gather(*tasks)

run_step

run_step(task_id: str, input: Optional[str] = None, step: Optional[TaskStep] = None, **kwargs: Any) -> TaskStepOutput

Run step.

Source code in llama-index-core/llama_index/core/agent/runner/parallel.py

def run_step(
    self,
    task_id: str,
    input: Optional[str] = None,
    step: Optional[TaskStep] = None,
    **kwargs: Any,
) -> TaskStepOutput:
    """Run step."""
    return self._run_step(task_id, step, mode=ChatResponseMode.WAIT, **kwargs)

arun_step async

arun_step(task_id: str, input: Optional[str] = None, step: Optional[TaskStep] = None, **kwargs: Any) -> TaskStepOutput

Run step (async).

Source code in llama-index-core/llama_index/core/agent/runner/parallel.py

async def arun_step(
    self,
    task_id: str,
    input: Optional[str] = None,
    step: Optional[TaskStep] = None,
    **kwargs: Any,
) -> TaskStepOutput:
    """Run step (async)."""
    return await self._arun_step(
        task_id, step, mode=ChatResponseMode.WAIT, **kwargs
    )

stream_step

stream_step(task_id: str, input: Optional[str] = None, step: Optional[TaskStep] = None, **kwargs: Any) -> TaskStepOutput

Run step (stream).

Source code in llama-index-core/llama_index/core/agent/runner/parallel.py

def stream_step(
    self,
    task_id: str,
    input: Optional[str] = None,
    step: Optional[TaskStep] = None,
    **kwargs: Any,
) -> TaskStepOutput:
    """Run step (stream)."""
    return self._run_step(task_id, step, mode=ChatResponseMode.STREAM, **kwargs)

astream_step async

astream_step(task_id: str, input: Optional[str] = None, step: Optional[TaskStep] = None, **kwargs: Any) -> TaskStepOutput

Run step (async stream).

Source code in llama-index-core/llama_index/core/agent/runner/parallel.py

async def astream_step(
    self,
    task_id: str,
    input: Optional[str] = None,
    step: Optional[TaskStep] = None,
    **kwargs: Any,
) -> TaskStepOutput:
    """Run step (async stream)."""
    return await self._arun_step(
        task_id, step, mode=ChatResponseMode.STREAM, **kwargs
    )

finalize_response

finalize_response(task_id: str, step_output: Optional[TaskStepOutput] = None) -> AGENT_CHAT_RESPONSE_TYPE

Finalize response.

Source code in llama-index-core/llama_index/core/agent/runner/parallel.py

def finalize_response(
    self,
    task_id: str,
    step_output: Optional[TaskStepOutput] = None,
) -> AGENT_CHAT_RESPONSE_TYPE:
    """Finalize response."""
    if step_output is None:
        step_output = self.state.get_completed_steps(task_id)[-1]
    if not step_output.is_last:
        raise ValueError(
            "finalize_response can only be called on the last step output"
        )

    if not isinstance(
        step_output.output,
        (AgentChatResponse, StreamingAgentChatResponse),
    ):
        raise ValueError(
            "When `is_last` is True, cur_step_output.output must be "
            f"AGENT_CHAT_RESPONSE_TYPE: {step_output.output}"
        )

    # finalize task
    self.agent_worker.finalize_task(self.state.get_task(task_id))

    if self.delete_task_on_finish:
        self.delete_task(task_id)

    return cast(AGENT_CHAT_RESPONSE_TYPE, step_output.output)

undo_step

undo_step(task_id: str) -> None

Undo previous step.

Source code in llama-index-core/llama_index/core/agent/runner/parallel.py

def undo_step(self, task_id: str) -> None:
    """Undo previous step."""
    raise NotImplementedError("undo_step not implemented")

Workers

CustomSimpleAgentWorker

Bases: BaseModel, BaseAgentWorker

Custom simple agent worker.

This is "simple" in the sense that some of the scaffolding is setup already. Assumptions: - assumes that the agent has tools, llm, callback manager, and tool retriever - has a from_tools convenience function - assumes that the agent is sequential, and doesn't take in any additional intermediate inputs.

Parameters:

Name	Type	Description	Default
tools	Sequence[BaseTool]	Tools to use for reasoning	required
llm	LLM	LLM to use	required
callback_manager	CallbackManager	Callback manager	None
tool_retriever	Optional[ObjectRetriever[BaseTool]]	Tool retriever	None
verbose	bool	Whether to print out reasoning steps	False

Source code in llama-index-core/llama_index/core/agent/custom/simple.py

class CustomSimpleAgentWorker(BaseModel, BaseAgentWorker):
    """Custom simple agent worker.

    This is "simple" in the sense that some of the scaffolding is setup already.
    Assumptions:
    - assumes that the agent has tools, llm, callback manager, and tool retriever
    - has a `from_tools` convenience function
    - assumes that the agent is sequential, and doesn't take in any additional
    intermediate inputs.

    Args:
        tools (Sequence[BaseTool]): Tools to use for reasoning
        llm (LLM): LLM to use
        callback_manager (CallbackManager): Callback manager
        tool_retriever (Optional[ObjectRetriever[BaseTool]]): Tool retriever
        verbose (bool): Whether to print out reasoning steps

    """

    model_config = ConfigDict(arbitrary_types_allowed=True)
    tools: Sequence[BaseTool] = Field(..., description="Tools to use for reasoning")
    llm: LLM = Field(..., description="LLM to use")
    callback_manager: CallbackManager = Field(
        default_factory=lambda: CallbackManager([]), exclude=True
    )
    tool_retriever: Optional[ObjectRetriever[BaseTool]] = Field(
        default=None, description="Tool retriever"
    )
    verbose: bool = Field(False, description="Whether to print out reasoning steps")

    _get_tools: Callable[[str], Sequence[BaseTool]] = PrivateAttr()

    def __init__(
        self,
        tools: Sequence[BaseTool],
        llm: LLM,
        callback_manager: Optional[CallbackManager] = None,
        verbose: bool = False,
        tool_retriever: Optional[ObjectRetriever[BaseTool]] = None,
        **kwargs: Any,
    ) -> None:
        callback_manager = callback_manager or CallbackManager([])
        super().__init__(
            tools=tools,
            llm=llm,
            callback_manager=callback_manager or CallbackManager([]),
            tool_retriever=tool_retriever,
            verbose=verbose,
            **kwargs,
        )

        if len(tools) > 0 and tool_retriever is not None:
            raise ValueError("Cannot specify both tools and tool_retriever")
        elif len(tools) > 0:
            self._get_tools = lambda _: tools
        elif tool_retriever is not None:
            tool_retriever_c = cast(ObjectRetriever[BaseTool], tool_retriever)
            self._get_tools = lambda message: tool_retriever_c.retrieve(message)
        else:
            self._get_tools = lambda _: []

    @classmethod
    def from_tools(
        cls,
        tools: Optional[Sequence[BaseTool]] = None,
        tool_retriever: Optional[ObjectRetriever[BaseTool]] = None,
        llm: Optional[LLM] = None,
        callback_manager: Optional[CallbackManager] = None,
        verbose: bool = False,
        **kwargs: Any,
    ) -> "CustomSimpleAgentWorker":
        """Convenience constructor method from set of of BaseTools (Optional)."""
        llm = llm or Settings.llm
        if callback_manager is not None:
            llm.callback_manager = callback_manager
        return cls(
            tools=tools or [],
            tool_retriever=tool_retriever,
            llm=llm,
            callback_manager=callback_manager or CallbackManager([]),
            verbose=verbose,
            **kwargs,
        )

    @abstractmethod
    def _initialize_state(self, task: Task, **kwargs: Any) -> Dict[str, Any]:
        """Initialize state."""

    def initialize_step(self, task: Task, **kwargs: Any) -> TaskStep:
        """Initialize step from task."""
        sources: List[ToolOutput] = []
        # temporary memory for new messages
        new_memory = ChatMemoryBuffer.from_defaults()

        # initialize initial state
        initial_state = {
            "sources": sources,
            "memory": new_memory,
        }

        step_state = self._initialize_state(task, **kwargs)
        # if intersecting keys, error
        if set(step_state.keys()).intersection(set(initial_state.keys())):
            raise ValueError(
                f"Step state keys {step_state.keys()} and initial state keys {initial_state.keys()} intersect."
                f"*NOTE*: initial state keys {initial_state.keys()} are reserved."
            )
        step_state.update(initial_state)

        return TaskStep(
            task_id=task.task_id,
            step_id=str(uuid.uuid4()),
            input=task.input,
            step_state=step_state,
        )

    def get_tools(self, input: str) -> List[AsyncBaseTool]:
        """Get tools."""
        return [adapt_to_async_tool(t) for t in self._get_tools(input)]

    def _get_task_step_response(
        self, agent_response: AGENT_CHAT_RESPONSE_TYPE, step: TaskStep, is_done: bool
    ) -> TaskStepOutput:
        """Get task step response."""
        if is_done:
            new_steps = []
        else:
            new_steps = [
                step.get_next_step(
                    step_id=str(uuid.uuid4()),
                    # NOTE: input is unused
                    input=None,
                )
            ]

        return TaskStepOutput(
            output=agent_response,
            task_step=step,
            is_last=is_done,
            next_steps=new_steps,
        )

    @abstractmethod
    def _run_step(
        self, state: Dict[str, Any], task: Task, input: Optional[str] = None
    ) -> Tuple[AgentChatResponse, bool]:
        """Run step.

        Returns:
            Tuple of (agent_response, is_done)

        """

    async def _arun_step(
        self, state: Dict[str, Any], task: Task, input: Optional[str] = None
    ) -> Tuple[AgentChatResponse, bool]:
        """Run step (async).

        Can override this method if you want to run the step asynchronously.

        Returns:
            Tuple of (agent_response, is_done)

        """
        raise NotImplementedError(
            "This agent does not support async." "Please implement _arun_step."
        )

    @trace_method("run_step")
    def run_step(self, step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput:
        """Run step."""
        agent_response, is_done = self._run_step(
            step.step_state, task, input=step.input
        )
        response = self._get_task_step_response(agent_response, step, is_done)
        # sync step state with task state
        task.extra_state.update(step.step_state)
        return response

    @trace_method("run_step")
    async def arun_step(
        self, step: TaskStep, task: Task, **kwargs: Any
    ) -> TaskStepOutput:
        """Run step (async)."""
        agent_response, is_done = await self._arun_step(
            step.step_state, task, input=step.input
        )
        response = self._get_task_step_response(agent_response, step, is_done)
        task.extra_state.update(step.step_state)
        return response

    @trace_method("run_step")
    def stream_step(self, step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput:
        """Run step (stream)."""
        raise NotImplementedError("This agent does not support streaming.")

    @trace_method("run_step")
    async def astream_step(
        self, step: TaskStep, task: Task, **kwargs: Any
    ) -> TaskStepOutput:
        """Run step (async stream)."""
        raise NotImplementedError("This agent does not support streaming.")

    @abstractmethod
    def _finalize_task(self, state: Dict[str, Any], **kwargs: Any) -> None:
        """Finalize task, after all the steps are completed.

        State is all the step states.

        """

    def finalize_task(self, task: Task, **kwargs: Any) -> None:
        """Finalize task, after all the steps are completed."""
        # add new messages to memory
        task.memory.set(task.memory.get() + task.extra_state["memory"].get_all())
        # reset new memory
        task.extra_state["memory"].reset()
        self._finalize_task(task.extra_state, **kwargs)

    def set_callback_manager(self, callback_manager: CallbackManager) -> None:
        """Set callback manager."""
        # TODO: make this abstractmethod (right now will break some agent impls)
        self.callback_manager = callback_manager

from_tools classmethod

from_tools(tools: Optional[Sequence[BaseTool]] = None, tool_retriever: Optional[ObjectRetriever[BaseTool]] = None, llm: Optional[LLM] = None, callback_manager: Optional[CallbackManager] = None, verbose: bool = False, **kwargs: Any) -> CustomSimpleAgentWorker

Convenience constructor method from set of of BaseTools (Optional).

Source code in llama-index-core/llama_index/core/agent/custom/simple.py

@classmethod
def from_tools(
    cls,
    tools: Optional[Sequence[BaseTool]] = None,
    tool_retriever: Optional[ObjectRetriever[BaseTool]] = None,
    llm: Optional[LLM] = None,
    callback_manager: Optional[CallbackManager] = None,
    verbose: bool = False,
    **kwargs: Any,
) -> "CustomSimpleAgentWorker":
    """Convenience constructor method from set of of BaseTools (Optional)."""
    llm = llm or Settings.llm
    if callback_manager is not None:
        llm.callback_manager = callback_manager
    return cls(
        tools=tools or [],
        tool_retriever=tool_retriever,
        llm=llm,
        callback_manager=callback_manager or CallbackManager([]),
        verbose=verbose,
        **kwargs,
    )

initialize_step

initialize_step(task: Task, **kwargs: Any) -> TaskStep

Initialize step from task.

Source code in llama-index-core/llama_index/core/agent/custom/simple.py

def initialize_step(self, task: Task, **kwargs: Any) -> TaskStep:
    """Initialize step from task."""
    sources: List[ToolOutput] = []
    # temporary memory for new messages
    new_memory = ChatMemoryBuffer.from_defaults()

    # initialize initial state
    initial_state = {
        "sources": sources,
        "memory": new_memory,
    }

    step_state = self._initialize_state(task, **kwargs)
    # if intersecting keys, error
    if set(step_state.keys()).intersection(set(initial_state.keys())):
        raise ValueError(
            f"Step state keys {step_state.keys()} and initial state keys {initial_state.keys()} intersect."
            f"*NOTE*: initial state keys {initial_state.keys()} are reserved."
        )
    step_state.update(initial_state)

    return TaskStep(
        task_id=task.task_id,
        step_id=str(uuid.uuid4()),
        input=task.input,
        step_state=step_state,
    )

get_tools

get_tools(input: str) -> List[AsyncBaseTool]

Get tools.

Source code in llama-index-core/llama_index/core/agent/custom/simple.py

def get_tools(self, input: str) -> List[AsyncBaseTool]:
    """Get tools."""
    return [adapt_to_async_tool(t) for t in self._get_tools(input)]

run_step

run_step(step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput

Run step.

Source code in llama-index-core/llama_index/core/agent/custom/simple.py

@trace_method("run_step")
def run_step(self, step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput:
    """Run step."""
    agent_response, is_done = self._run_step(
        step.step_state, task, input=step.input
    )
    response = self._get_task_step_response(agent_response, step, is_done)
    # sync step state with task state
    task.extra_state.update(step.step_state)
    return response

arun_step async

arun_step(step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput

Run step (async).

Source code in llama-index-core/llama_index/core/agent/custom/simple.py

@trace_method("run_step")
async def arun_step(
    self, step: TaskStep, task: Task, **kwargs: Any
) -> TaskStepOutput:
    """Run step (async)."""
    agent_response, is_done = await self._arun_step(
        step.step_state, task, input=step.input
    )
    response = self._get_task_step_response(agent_response, step, is_done)
    task.extra_state.update(step.step_state)
    return response

stream_step

stream_step(step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput

Run step (stream).

Source code in llama-index-core/llama_index/core/agent/custom/simple.py

@trace_method("run_step")
def stream_step(self, step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput:
    """Run step (stream)."""
    raise NotImplementedError("This agent does not support streaming.")

astream_step async

astream_step(step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput

Run step (async stream).

Source code in llama-index-core/llama_index/core/agent/custom/simple.py

@trace_method("run_step")
async def astream_step(
    self, step: TaskStep, task: Task, **kwargs: Any
) -> TaskStepOutput:
    """Run step (async stream)."""
    raise NotImplementedError("This agent does not support streaming.")

finalize_task

finalize_task(task: Task, **kwargs: Any) -> None

Finalize task, after all the steps are completed.

Source code in llama-index-core/llama_index/core/agent/custom/simple.py

def finalize_task(self, task: Task, **kwargs: Any) -> None:
    """Finalize task, after all the steps are completed."""
    # add new messages to memory
    task.memory.set(task.memory.get() + task.extra_state["memory"].get_all())
    # reset new memory
    task.extra_state["memory"].reset()
    self._finalize_task(task.extra_state, **kwargs)

set_callback_manager

set_callback_manager(callback_manager: CallbackManager) -> None

Set callback manager.

Source code in llama-index-core/llama_index/core/agent/custom/simple.py

def set_callback_manager(self, callback_manager: CallbackManager) -> None:
    """Set callback manager."""
    # TODO: make this abstractmethod (right now will break some agent impls)
    self.callback_manager = callback_manager

MultimodalReActAgentWorker

Bases: BaseAgentWorker

Multimodal ReAct Agent worker.

NOTE: This is a BETA feature.

Source code in llama-index-core/llama_index/core/agent/react_multimodal/step.py

class MultimodalReActAgentWorker(BaseAgentWorker):
    """Multimodal ReAct Agent worker.

    **NOTE**: This is a BETA feature.

    """

    def __init__(
        self,
        tools: Sequence[BaseTool],
        multi_modal_llm: MultiModalLLM,
        max_iterations: int = 10,
        react_chat_formatter: Optional[ReActChatFormatter] = None,
        output_parser: Optional[ReActOutputParser] = None,
        callback_manager: Optional[CallbackManager] = None,
        verbose: bool = False,
        tool_retriever: Optional[ObjectRetriever[BaseTool]] = None,
    ) -> None:
        self._multi_modal_llm = multi_modal_llm
        self.callback_manager = callback_manager or CallbackManager([])
        self._max_iterations = max_iterations
        self._react_chat_formatter = react_chat_formatter or ReActChatFormatter(
            system_header=REACT_MM_CHAT_SYSTEM_HEADER
        )
        self._output_parser = output_parser or ReActOutputParser()
        self._verbose = verbose

        try:
            from llama_index.multi_modal_llms.openai.utils import (
                generate_openai_multi_modal_chat_message,
            )  # pants: no-infer-dep

            self._add_user_step_to_reasoning = partial(
                add_user_step_to_reasoning,
                generate_chat_message_fn=generate_openai_multi_modal_chat_message,  # type: ignore
            )
        except ImportError:
            raise ImportError(
                "`llama-index-multi-modal-llms-openai` package cannot be found. "
                "Please install it by using `pip install `llama-index-multi-modal-llms-openai`"
            )

        if len(tools) > 0 and tool_retriever is not None:
            raise ValueError("Cannot specify both tools and tool_retriever")
        elif len(tools) > 0:
            self._get_tools = lambda _: tools
        elif tool_retriever is not None:
            tool_retriever_c = cast(ObjectRetriever[BaseTool], tool_retriever)
            self._get_tools = lambda message: tool_retriever_c.retrieve(message)
        else:
            self._get_tools = lambda _: []

    @classmethod
    def from_tools(
        cls,
        tools: Optional[Sequence[BaseTool]] = None,
        tool_retriever: Optional[ObjectRetriever[BaseTool]] = None,
        multi_modal_llm: Optional[MultiModalLLM] = None,
        max_iterations: int = 10,
        react_chat_formatter: Optional[ReActChatFormatter] = None,
        output_parser: Optional[ReActOutputParser] = None,
        callback_manager: Optional[CallbackManager] = None,
        verbose: bool = False,
        **kwargs: Any,
    ) -> "MultimodalReActAgentWorker":
        """Convenience constructor method from set of of BaseTools (Optional).

        NOTE: kwargs should have been exhausted by this point. In other words
        the various upstream components such as BaseSynthesizer (response synthesizer)
        or BaseRetriever should have picked up off their respective kwargs in their
        constructions.

        Returns:
            ReActAgent
        """
        if multi_modal_llm is None:
            try:
                from llama_index.multi_modal_llms.openai import (
                    OpenAIMultiModal,
                )  # pants: no-infer-dep

                multi_modal_llm = multi_modal_llm or OpenAIMultiModal(
                    model="gpt-4-vision-preview", max_new_tokens=1000
                )
            except ImportError:
                raise ImportError(
                    "`llama-index-multi-modal-llms-openai` package cannot be found. "
                    "Please install it by using `pip install `llama-index-multi-modal-llms-openai`"
                )
        return cls(
            tools=tools or [],
            tool_retriever=tool_retriever,
            multi_modal_llm=multi_modal_llm,
            max_iterations=max_iterations,
            react_chat_formatter=react_chat_formatter,
            output_parser=output_parser,
            callback_manager=callback_manager,
            verbose=verbose,
        )

    def initialize_step(self, task: Task, **kwargs: Any) -> TaskStep:
        """Initialize step from task."""
        sources: List[ToolOutput] = []
        current_reasoning: List[BaseReasoningStep] = []
        # temporary memory for new messages
        new_memory = ChatMemoryBuffer.from_defaults()

        # validation
        if "image_docs" not in task.extra_state:
            raise ValueError("Image docs not found in task extra state.")

        # initialize task state
        task_state = {
            "sources": sources,
            "current_reasoning": current_reasoning,
            "new_memory": new_memory,
        }
        task.extra_state.update(task_state)

        return TaskStep(
            task_id=task.task_id,
            step_id=str(uuid.uuid4()),
            input=task.input,
            step_state={"is_first": True, "image_docs": task.extra_state["image_docs"]},
        )

    def get_tools(self, input: str) -> List[AsyncBaseTool]:
        """Get tools."""
        return [adapt_to_async_tool(t) for t in self._get_tools(input)]

    def _extract_reasoning_step(
        self, output: ChatResponse, is_streaming: bool = False
    ) -> Tuple[str, List[BaseReasoningStep], bool]:
        """
        Extracts the reasoning step from the given output.

        This method parses the message content from the output,
        extracts the reasoning step, and determines whether the processing is
        complete. It also performs validation checks on the output and
        handles possible errors.
        """
        if output.message.content is None:
            raise ValueError("Got empty message.")
        message_content = output.message.content
        current_reasoning = []
        try:
            reasoning_step = self._output_parser.parse(message_content, is_streaming)
        except BaseException as exc:
            raise ValueError(f"Could not parse output: {message_content}") from exc
        if self._verbose:
            print_text(f"{reasoning_step.get_content()}\n", color="pink")
        current_reasoning.append(reasoning_step)

        if reasoning_step.is_done:
            return message_content, current_reasoning, True

        reasoning_step = cast(ActionReasoningStep, reasoning_step)
        if not isinstance(reasoning_step, ActionReasoningStep):
            raise ValueError(f"Expected ActionReasoningStep, got {reasoning_step}")

        return message_content, current_reasoning, False

    def _process_actions(
        self,
        task: Task,
        tools: Sequence[AsyncBaseTool],
        output: ChatResponse,
        is_streaming: bool = False,
    ) -> Tuple[List[BaseReasoningStep], bool]:
        tools_dict: Dict[str, AsyncBaseTool] = {
            tool.metadata.get_name(): tool for tool in tools
        }
        _, current_reasoning, is_done = self._extract_reasoning_step(
            output, is_streaming
        )

        if is_done:
            return current_reasoning, True

        # call tool with input
        reasoning_step = cast(ActionReasoningStep, current_reasoning[-1])
        tool = tools_dict[reasoning_step.action]
        with self.callback_manager.event(
            CBEventType.FUNCTION_CALL,
            payload={
                EventPayload.FUNCTION_CALL: reasoning_step.action_input,
                EventPayload.TOOL: tool.metadata,
            },
        ) as event:
            tool_output = tool.call(**reasoning_step.action_input)
            event.on_end(payload={EventPayload.FUNCTION_OUTPUT: str(tool_output)})

        task.extra_state["sources"].append(tool_output)

        observation_step = ObservationReasoningStep(
            observation=str(tool_output), return_direct=tool.metadata.return_direct
        )
        current_reasoning.append(observation_step)
        if self._verbose:
            print_text(f"{observation_step.get_content()}\n", color="blue")
        return current_reasoning, tool.metadata.return_direct

    async def _aprocess_actions(
        self,
        task: Task,
        tools: Sequence[AsyncBaseTool],
        output: ChatResponse,
        is_streaming: bool = False,
    ) -> Tuple[List[BaseReasoningStep], bool]:
        tools_dict = {tool.metadata.name: tool for tool in tools}
        _, current_reasoning, is_done = self._extract_reasoning_step(
            output, is_streaming
        )

        if is_done:
            return current_reasoning, True

        # call tool with input
        reasoning_step = cast(ActionReasoningStep, current_reasoning[-1])
        tool = tools_dict[reasoning_step.action]
        with self.callback_manager.event(
            CBEventType.FUNCTION_CALL,
            payload={
                EventPayload.FUNCTION_CALL: reasoning_step.action_input,
                EventPayload.TOOL: tool.metadata,
            },
        ) as event:
            tool_output = await tool.acall(**reasoning_step.action_input)
            event.on_end(payload={EventPayload.FUNCTION_OUTPUT: str(tool_output)})

        task.extra_state["sources"].append(tool_output)

        observation_step = ObservationReasoningStep(
            observation=str(tool_output), return_direct=tool.metadata.return_direct
        )
        current_reasoning.append(observation_step)
        if self._verbose:
            print_text(f"{observation_step.get_content()}\n", color="blue")
        return current_reasoning, tool.metadata.return_direct

    def _get_response(
        self,
        current_reasoning: List[BaseReasoningStep],
        sources: List[ToolOutput],
    ) -> AgentChatResponse:
        """Get response from reasoning steps."""
        if len(current_reasoning) == 0:
            raise ValueError("No reasoning steps were taken.")
        elif len(current_reasoning) == self._max_iterations:
            raise ValueError("Reached max iterations.")

        if isinstance(current_reasoning[-1], ResponseReasoningStep):
            response_step = cast(ResponseReasoningStep, current_reasoning[-1])
            response_str = response_step.response
        elif (
            isinstance(current_reasoning[-1], ObservationReasoningStep)
            and current_reasoning[-1].return_direct
        ):
            response_str = current_reasoning[-1].observation
        else:
            response_str = current_reasoning[-1].get_content()

        # TODO: add sources from reasoning steps
        return AgentChatResponse(response=response_str, sources=sources)

    def _get_task_step_response(
        self, agent_response: AGENT_CHAT_RESPONSE_TYPE, step: TaskStep, is_done: bool
    ) -> TaskStepOutput:
        """Get task step response."""
        if is_done:
            new_steps = []
        else:
            new_steps = [
                step.get_next_step(
                    step_id=str(uuid.uuid4()),
                    # NOTE: input is unused
                    input=None,
                )
            ]

        return TaskStepOutput(
            output=agent_response,
            task_step=step,
            is_last=is_done,
            next_steps=new_steps,
        )

    def _run_step(
        self,
        step: TaskStep,
        task: Task,
    ) -> TaskStepOutput:
        """Run step."""
        # This is either not None on the first step or if the user specifies
        # an intermediate step in the middle
        if step.input is not None:
            self._add_user_step_to_reasoning(
                step=step,
                memory=task.extra_state["new_memory"],
                current_reasoning=task.extra_state["current_reasoning"],
                verbose=self._verbose,
            )
        # TODO: see if we want to do step-based inputs
        tools = self.get_tools(task.input)

        input_chat = self._react_chat_formatter.format(
            tools,
            chat_history=task.memory.get_all()
            + task.extra_state["new_memory"].get_all(),
            current_reasoning=task.extra_state["current_reasoning"],
        )

        # send prompt
        chat_response = self._multi_modal_llm.chat(input_chat)
        # given react prompt outputs, call tools or return response
        reasoning_steps, is_done = self._process_actions(
            task, tools, output=chat_response
        )
        task.extra_state["current_reasoning"].extend(reasoning_steps)
        agent_response = self._get_response(
            task.extra_state["current_reasoning"], task.extra_state["sources"]
        )
        if is_done:
            task.extra_state["new_memory"].put(
                ChatMessage(content=agent_response.response, role=MessageRole.ASSISTANT)
            )

        return self._get_task_step_response(agent_response, step, is_done)

    async def _arun_step(
        self,
        step: TaskStep,
        task: Task,
    ) -> TaskStepOutput:
        """Run step."""
        if step.input is not None:
            self._add_user_step_to_reasoning(
                step=step,
                memory=task.extra_state["new_memory"],
                current_reasoning=task.extra_state["current_reasoning"],
                verbose=self._verbose,
            )
        # TODO: see if we want to do step-based inputs
        tools = self.get_tools(task.input)

        input_chat = self._react_chat_formatter.format(
            tools,
            chat_history=task.memory.get_all()
            + task.extra_state["new_memory"].get_all(),
            current_reasoning=task.extra_state["current_reasoning"],
        )
        # send prompt
        chat_response = await self._multi_modal_llm.achat(input_chat)
        # given react prompt outputs, call tools or return response
        reasoning_steps, is_done = await self._aprocess_actions(
            task, tools, output=chat_response
        )
        task.extra_state["current_reasoning"].extend(reasoning_steps)
        agent_response = self._get_response(
            task.extra_state["current_reasoning"], task.extra_state["sources"]
        )
        if is_done:
            task.extra_state["new_memory"].put(
                ChatMessage(content=agent_response.response, role=MessageRole.ASSISTANT)
            )

        return self._get_task_step_response(agent_response, step, is_done)

    def _run_step_stream(
        self,
        step: TaskStep,
        task: Task,
    ) -> TaskStepOutput:
        """Run step."""
        raise NotImplementedError("Stream step not implemented yet.")

    async def _arun_step_stream(
        self,
        step: TaskStep,
        task: Task,
    ) -> TaskStepOutput:
        """Run step."""
        raise NotImplementedError("Stream step not implemented yet.")

    @trace_method("run_step")
    def run_step(self, step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput:
        """Run step."""
        return self._run_step(step, task)

    @trace_method("run_step")
    async def arun_step(
        self, step: TaskStep, task: Task, **kwargs: Any
    ) -> TaskStepOutput:
        """Run step (async)."""
        return await self._arun_step(step, task)

    @trace_method("run_step")
    def stream_step(self, step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput:
        """Run step (stream)."""
        # TODO: figure out if we need a different type for TaskStepOutput
        return self._run_step_stream(step, task)

    @trace_method("run_step")
    async def astream_step(
        self, step: TaskStep, task: Task, **kwargs: Any
    ) -> TaskStepOutput:
        """Run step (async stream)."""
        return await self._arun_step_stream(step, task)

    def finalize_task(self, task: Task, **kwargs: Any) -> None:
        """Finalize task, after all the steps are completed."""
        # add new messages to memory
        task.memory.set(
            task.memory.get_all() + task.extra_state["new_memory"].get_all()
        )
        # reset new memory
        task.extra_state["new_memory"].reset()

    def set_callback_manager(self, callback_manager: CallbackManager) -> None:
        """Set callback manager."""
        # TODO: make this abstractmethod (right now will break some agent impls)
        self.callback_manager = callback_manager

from_tools classmethod

from_tools(tools: Optional[Sequence[BaseTool]] = None, tool_retriever: Optional[ObjectRetriever[BaseTool]] = None, multi_modal_llm: Optional[MultiModalLLM] = None, max_iterations: int = 10, react_chat_formatter: Optional[ReActChatFormatter] = None, output_parser: Optional[ReActOutputParser] = None, callback_manager: Optional[CallbackManager] = None, verbose: bool = False, **kwargs: Any) -> MultimodalReActAgentWorker

Convenience constructor method from set of of BaseTools (Optional).

NOTE: kwargs should have been exhausted by this point. In other words the various upstream components such as BaseSynthesizer (response synthesizer) or BaseRetriever should have picked up off their respective kwargs in their constructions.

Returns:

Type	Description
MultimodalReActAgentWorker	ReActAgent

Source code in llama-index-core/llama_index/core/agent/react_multimodal/step.py

@classmethod
def from_tools(
    cls,
    tools: Optional[Sequence[BaseTool]] = None,
    tool_retriever: Optional[ObjectRetriever[BaseTool]] = None,
    multi_modal_llm: Optional[MultiModalLLM] = None,
    max_iterations: int = 10,
    react_chat_formatter: Optional[ReActChatFormatter] = None,
    output_parser: Optional[ReActOutputParser] = None,
    callback_manager: Optional[CallbackManager] = None,
    verbose: bool = False,
    **kwargs: Any,
) -> "MultimodalReActAgentWorker":
    """Convenience constructor method from set of of BaseTools (Optional).

    NOTE: kwargs should have been exhausted by this point. In other words
    the various upstream components such as BaseSynthesizer (response synthesizer)
    or BaseRetriever should have picked up off their respective kwargs in their
    constructions.

    Returns:
        ReActAgent
    """
    if multi_modal_llm is None:
        try:
            from llama_index.multi_modal_llms.openai import (
                OpenAIMultiModal,
            )  # pants: no-infer-dep

            multi_modal_llm = multi_modal_llm or OpenAIMultiModal(
                model="gpt-4-vision-preview", max_new_tokens=1000
            )
        except ImportError:
            raise ImportError(
                "`llama-index-multi-modal-llms-openai` package cannot be found. "
                "Please install it by using `pip install `llama-index-multi-modal-llms-openai`"
            )
    return cls(
        tools=tools or [],
        tool_retriever=tool_retriever,
        multi_modal_llm=multi_modal_llm,
        max_iterations=max_iterations,
        react_chat_formatter=react_chat_formatter,
        output_parser=output_parser,
        callback_manager=callback_manager,
        verbose=verbose,
    )

initialize_step

initialize_step(task: Task, **kwargs: Any) -> TaskStep

Initialize step from task.

Source code in llama-index-core/llama_index/core/agent/react_multimodal/step.py

def initialize_step(self, task: Task, **kwargs: Any) -> TaskStep:
    """Initialize step from task."""
    sources: List[ToolOutput] = []
    current_reasoning: List[BaseReasoningStep] = []
    # temporary memory for new messages
    new_memory = ChatMemoryBuffer.from_defaults()

    # validation
    if "image_docs" not in task.extra_state:
        raise ValueError("Image docs not found in task extra state.")

    # initialize task state
    task_state = {
        "sources": sources,
        "current_reasoning": current_reasoning,
        "new_memory": new_memory,
    }
    task.extra_state.update(task_state)

    return TaskStep(
        task_id=task.task_id,
        step_id=str(uuid.uuid4()),
        input=task.input,
        step_state={"is_first": True, "image_docs": task.extra_state["image_docs"]},
    )

get_tools

get_tools(input: str) -> List[AsyncBaseTool]

Get tools.

Source code in llama-index-core/llama_index/core/agent/react_multimodal/step.py

def get_tools(self, input: str) -> List[AsyncBaseTool]:
    """Get tools."""
    return [adapt_to_async_tool(t) for t in self._get_tools(input)]

run_step

run_step(step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput

Run step.

Source code in llama-index-core/llama_index/core/agent/react_multimodal/step.py

@trace_method("run_step")
def run_step(self, step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput:
    """Run step."""
    return self._run_step(step, task)

arun_step async

arun_step(step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput

Run step (async).

Source code in llama-index-core/llama_index/core/agent/react_multimodal/step.py

@trace_method("run_step")
async def arun_step(
    self, step: TaskStep, task: Task, **kwargs: Any
) -> TaskStepOutput:
    """Run step (async)."""
    return await self._arun_step(step, task)

stream_step

stream_step(step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput

Run step (stream).

Source code in llama-index-core/llama_index/core/agent/react_multimodal/step.py

@trace_method("run_step")
def stream_step(self, step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput:
    """Run step (stream)."""
    # TODO: figure out if we need a different type for TaskStepOutput
    return self._run_step_stream(step, task)

astream_step async

astream_step(step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput

Run step (async stream).

Source code in llama-index-core/llama_index/core/agent/react_multimodal/step.py

@trace_method("run_step")
async def astream_step(
    self, step: TaskStep, task: Task, **kwargs: Any
) -> TaskStepOutput:
    """Run step (async stream)."""
    return await self._arun_step_stream(step, task)

finalize_task

finalize_task(task: Task, **kwargs: Any) -> None

Finalize task, after all the steps are completed.

Source code in llama-index-core/llama_index/core/agent/react_multimodal/step.py

def finalize_task(self, task: Task, **kwargs: Any) -> None:
    """Finalize task, after all the steps are completed."""
    # add new messages to memory
    task.memory.set(
        task.memory.get_all() + task.extra_state["new_memory"].get_all()
    )
    # reset new memory
    task.extra_state["new_memory"].reset()

set_callback_manager

set_callback_manager(callback_manager: CallbackManager) -> None

Set callback manager.

Source code in llama-index-core/llama_index/core/agent/react_multimodal/step.py

def set_callback_manager(self, callback_manager: CallbackManager) -> None:
    """Set callback manager."""
    # TODO: make this abstractmethod (right now will break some agent impls)
    self.callback_manager = callback_manager

QueryPipelineAgentWorker

Bases: BaseModel, BaseAgentWorker

Query Pipeline agent worker.

NOTE: This is now deprecated. Use FnAgentWorker instead to build a stateful agent.

Barebones agent worker that takes in a query pipeline.

Default Workflow: The default workflow assumes that you compose a query pipeline with StatefulFnComponent objects. This allows you to store, update and retrieve state throughout the executions of the query pipeline by the agent.

The task and step state of the agent are stored in this state variable via a special key. Of course you can choose to store other variables in this state as well.

Deprecated Workflow: The deprecated workflow assumes that the first component in the query pipeline is an AgentInputComponent and last is AgentFnComponent.

Parameters:

Name	Type	Description	Default
pipeline	QueryPipeline	Query pipeline	required

Source code in llama-index-core/llama_index/core/agent/custom/pipeline_worker.py

@deprecated("Use `FnAgentWorker` instead to build a stateful agent.")
class QueryPipelineAgentWorker(BaseModel, BaseAgentWorker):
    """Query Pipeline agent worker.

    NOTE: This is now deprecated. Use `FnAgentWorker` instead to build a stateful agent.

    Barebones agent worker that takes in a query pipeline.

    **Default Workflow**: The default workflow assumes that you compose
    a query pipeline with `StatefulFnComponent` objects. This allows you to store, update
    and retrieve state throughout the executions of the query pipeline by the agent.

    The task and step state of the agent are stored in this `state` variable via a special key.
    Of course you can choose to store other variables in this state as well.

    **Deprecated Workflow**: The deprecated workflow assumes that the first component in the
    query pipeline is an `AgentInputComponent` and last is `AgentFnComponent`.

    Args:
        pipeline (QueryPipeline): Query pipeline

    """

    model_config = ConfigDict(arbitrary_types_allowed=True)
    pipeline: QueryPipeline = Field(..., description="Query pipeline")
    callback_manager: CallbackManager = Field(..., exclude=True)
    task_key: str = Field("task", description="Key to store task in state")
    step_state_key: str = Field("step_state", description="Key to store step in state")

    def __init__(
        self,
        pipeline: QueryPipeline,
        callback_manager: Optional[CallbackManager] = None,
        **kwargs: Any,
    ) -> None:
        """Initialize."""
        if callback_manager is not None:
            # set query pipeline callback
            pipeline.set_callback_manager(callback_manager)
        else:
            callback_manager = pipeline.callback_manager
        super().__init__(
            pipeline=pipeline,
            callback_manager=callback_manager,
            **kwargs,
        )
        # validate query pipeline
        # self.agent_input_component
        self.agent_components

    @property
    def agent_input_component(self) -> AgentInputComponent:
        """Get agent input component.

        NOTE: This is deprecated and will be removed in the future.

        """
        root_key = self.pipeline.get_root_keys()[0]
        if not isinstance(self.pipeline.module_dict[root_key], AgentInputComponent):
            raise ValueError(
                "Query pipeline first component must be AgentInputComponent, got "
                f"{self.pipeline.module_dict[root_key]}"
            )

        return cast(AgentInputComponent, self.pipeline.module_dict[root_key])

    @property
    def agent_components(self) -> Sequence[BaseAgentComponent]:
        """Get agent output component."""
        return _get_agent_components(self.pipeline)

    def preprocess(self, task: Task, step: TaskStep) -> None:
        """Preprocessing flow.

        This runs preprocessing to propagate the task and step as variables
        to relevant components in the query pipeline.

        Contains deprecated flow of updating agent components.
        But also contains main flow of updating StatefulFnComponent components.

        """
        # NOTE: this is deprecated
        # partial agent output component with task and step
        for agent_fn_component in self.agent_components:
            agent_fn_component.partial(task=task, state=step.step_state)

        # update stateful components
        self.pipeline.update_state(
            {self.task_key: task, self.step_state_key: step.step_state}
        )

    def initialize_step(self, task: Task, **kwargs: Any) -> TaskStep:
        """Initialize step from task."""
        sources: List[ToolOutput] = []
        # temporary memory for new messages
        new_memory = ChatMemoryBuffer.from_defaults()

        # initialize initial state
        initial_state = {
            "sources": sources,
            "memory": new_memory,
        }

        return TaskStep(
            task_id=task.task_id,
            step_id=str(uuid.uuid4()),
            input=task.input,
            step_state=initial_state,
        )

    def _get_task_step_response(
        self, agent_response: AGENT_CHAT_RESPONSE_TYPE, step: TaskStep, is_done: bool
    ) -> TaskStepOutput:
        """Get task step response."""
        if is_done:
            new_steps = []
        else:
            new_steps = [
                step.get_next_step(
                    step_id=str(uuid.uuid4()),
                    # NOTE: input is unused
                    input=None,
                )
            ]

        return TaskStepOutput(
            output=agent_response,
            task_step=step,
            is_last=is_done,
            next_steps=new_steps,
        )

    @trace_method("run_step")
    def run_step(self, step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput:
        """Run step."""
        self.preprocess(task, step)

        # HACK: do a try/except for now. Fine since old agent components are deprecated
        try:
            self.agent_input_component
            uses_deprecated = True
        except ValueError:
            uses_deprecated = False

        if uses_deprecated:
            agent_response, is_done = self.pipeline.run(
                state=step.step_state, task=task
            )
        else:
            agent_response, is_done = self.pipeline.run()
        response = self._get_task_step_response(agent_response, step, is_done)
        # sync step state with task state
        task.extra_state.update(step.step_state)
        return response

    @trace_method("run_step")
    async def arun_step(
        self, step: TaskStep, task: Task, **kwargs: Any
    ) -> TaskStepOutput:
        """Run step (async)."""
        self.preprocess(task, step)

        # HACK: do a try/except for now. Fine since old agent components are deprecated
        try:
            self.agent_input_component
            uses_deprecated = True
        except ValueError:
            uses_deprecated = False

        if uses_deprecated:
            agent_response, is_done = await self.pipeline.arun(
                state=step.step_state, task=task
            )
        else:
            agent_response, is_done = await self.pipeline.arun()
        response = self._get_task_step_response(agent_response, step, is_done)
        task.extra_state.update(step.step_state)
        return response

    @trace_method("run_step")
    def stream_step(self, step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput:
        """Run step (stream)."""
        raise NotImplementedError("This agent does not support streaming.")

    @trace_method("run_step")
    async def astream_step(
        self, step: TaskStep, task: Task, **kwargs: Any
    ) -> TaskStepOutput:
        """Run step (async stream)."""
        raise NotImplementedError("This agent does not support streaming.")

    def finalize_task(self, task: Task, **kwargs: Any) -> None:
        """Finalize task, after all the steps are completed."""
        # add new messages to memory
        task.memory.set(task.memory.get() + task.extra_state["memory"].get_all())
        # reset new memory
        task.extra_state["memory"].reset()

    def set_callback_manager(self, callback_manager: CallbackManager) -> None:
        """Set callback manager."""
        # TODO: make this abstractmethod (right now will break some agent impls)
        self.callback_manager = callback_manager
        self.pipeline.set_callback_manager(callback_manager)

agent_input_component property

agent_input_component: AgentInputComponent

Get agent input component.

NOTE: This is deprecated and will be removed in the future.

agent_components property

agent_components: Sequence[BaseAgentComponent]

Get agent output component.

preprocess

preprocess(task: Task, step: TaskStep) -> None

Preprocessing flow.

This runs preprocessing to propagate the task and step as variables to relevant components in the query pipeline.

Contains deprecated flow of updating agent components. But also contains main flow of updating StatefulFnComponent components.

Source code in llama-index-core/llama_index/core/agent/custom/pipeline_worker.py

def preprocess(self, task: Task, step: TaskStep) -> None:
    """Preprocessing flow.

    This runs preprocessing to propagate the task and step as variables
    to relevant components in the query pipeline.

    Contains deprecated flow of updating agent components.
    But also contains main flow of updating StatefulFnComponent components.

    """
    # NOTE: this is deprecated
    # partial agent output component with task and step
    for agent_fn_component in self.agent_components:
        agent_fn_component.partial(task=task, state=step.step_state)

    # update stateful components
    self.pipeline.update_state(
        {self.task_key: task, self.step_state_key: step.step_state}
    )

initialize_step

initialize_step(task: Task, **kwargs: Any) -> TaskStep

Initialize step from task.

Source code in llama-index-core/llama_index/core/agent/custom/pipeline_worker.py

def initialize_step(self, task: Task, **kwargs: Any) -> TaskStep:
    """Initialize step from task."""
    sources: List[ToolOutput] = []
    # temporary memory for new messages
    new_memory = ChatMemoryBuffer.from_defaults()

    # initialize initial state
    initial_state = {
        "sources": sources,
        "memory": new_memory,
    }

    return TaskStep(
        task_id=task.task_id,
        step_id=str(uuid.uuid4()),
        input=task.input,
        step_state=initial_state,
    )

run_step

run_step(step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput

Run step.

Source code in llama-index-core/llama_index/core/agent/custom/pipeline_worker.py

@trace_method("run_step")
def run_step(self, step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput:
    """Run step."""
    self.preprocess(task, step)

    # HACK: do a try/except for now. Fine since old agent components are deprecated
    try:
        self.agent_input_component
        uses_deprecated = True
    except ValueError:
        uses_deprecated = False

    if uses_deprecated:
        agent_response, is_done = self.pipeline.run(
            state=step.step_state, task=task
        )
    else:
        agent_response, is_done = self.pipeline.run()
    response = self._get_task_step_response(agent_response, step, is_done)
    # sync step state with task state
    task.extra_state.update(step.step_state)
    return response

arun_step async

arun_step(step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput

Run step (async).

Source code in llama-index-core/llama_index/core/agent/custom/pipeline_worker.py

@trace_method("run_step")
async def arun_step(
    self, step: TaskStep, task: Task, **kwargs: Any
) -> TaskStepOutput:
    """Run step (async)."""
    self.preprocess(task, step)

    # HACK: do a try/except for now. Fine since old agent components are deprecated
    try:
        self.agent_input_component
        uses_deprecated = True
    except ValueError:
        uses_deprecated = False

    if uses_deprecated:
        agent_response, is_done = await self.pipeline.arun(
            state=step.step_state, task=task
        )
    else:
        agent_response, is_done = await self.pipeline.arun()
    response = self._get_task_step_response(agent_response, step, is_done)
    task.extra_state.update(step.step_state)
    return response

stream_step

stream_step(step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput

Run step (stream).

Source code in llama-index-core/llama_index/core/agent/custom/pipeline_worker.py

@trace_method("run_step")
def stream_step(self, step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput:
    """Run step (stream)."""
    raise NotImplementedError("This agent does not support streaming.")

astream_step async

astream_step(step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput

Run step (async stream).

Source code in llama-index-core/llama_index/core/agent/custom/pipeline_worker.py

@trace_method("run_step")
async def astream_step(
    self, step: TaskStep, task: Task, **kwargs: Any
) -> TaskStepOutput:
    """Run step (async stream)."""
    raise NotImplementedError("This agent does not support streaming.")

finalize_task

finalize_task(task: Task, **kwargs: Any) -> None

Finalize task, after all the steps are completed.

Source code in llama-index-core/llama_index/core/agent/custom/pipeline_worker.py

def finalize_task(self, task: Task, **kwargs: Any) -> None:
    """Finalize task, after all the steps are completed."""
    # add new messages to memory
    task.memory.set(task.memory.get() + task.extra_state["memory"].get_all())
    # reset new memory
    task.extra_state["memory"].reset()

set_callback_manager

set_callback_manager(callback_manager: CallbackManager) -> None

Set callback manager.

Source code in llama-index-core/llama_index/core/agent/custom/pipeline_worker.py

def set_callback_manager(self, callback_manager: CallbackManager) -> None:
    """Set callback manager."""
    # TODO: make this abstractmethod (right now will break some agent impls)
    self.callback_manager = callback_manager
    self.pipeline.set_callback_manager(callback_manager)