#!/usr/bin/env python

# coding=utf-8

# Copyright 2024 The HuggingFace Inc. team. All rights reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

import importlib

import json

import os

import tempfile

import textwrap

import time

import warnings

from abc import ABC, abstractmethod

from collections.abc import Callable, Generator

from concurrent.futures import ThreadPoolExecutor, as_completed

from contextvars import copy_context

from dataclasses import dataclass

from logging import getLogger

from pathlib import Path

from typing import TYPE_CHECKING, Any, Literal, Type, TypeAlias, TypedDict, Union

import yaml

from huggingface_hub import create_repo, metadata_update, snapshot_download, upload_folder

from jinja2 import StrictUndefined, Template

from rich.console import Group

from rich.live import Live

from rich.markdown import Markdown

from rich.panel import Panel

from rich.rule import Rule

from rich.text import Text

if TYPE_CHECKING:

import PIL.Image

from .agent_types import AgentAudio, AgentImage, handle_agent_output_types

from .default_tools import TOOL_MAPPING, FinalAnswerTool

from .local_python_executor import BASE_BUILTIN_MODULES, LocalPythonExecutor, PythonExecutor, fix_final_answer_code

from .memory import (

ActionStep,

AgentMemory,

CallbackRegistry,

FinalAnswerStep,

MemoryStep,

PlanningStep,

SystemPromptStep,

TaskStep,

Timing,

ToolCall,

)

from .models import (

CODEAGENT_RESPONSE_FORMAT,

MODEL_REGISTRY,

ChatMessage,

ChatMessageStreamDelta,

ChatMessageToolCall,

MessageRole,

Model,

agglomerate_stream_deltas,

parse_json_if_needed,

)

from .monitoring import (

YELLOW_HEX,

AgentLogger,

LogLevel,

Monitor,

TokenUsage,

)

from .remote_executors import BlaxelExecutor, DockerExecutor, E2BExecutor, ModalExecutor, WasmExecutor

from .tools import BaseTool, Tool, validate_tool_arguments

from .utils import (

AgentError,

AgentExecutionError,

AgentGenerationError,

AgentMaxStepsError,

AgentParsingError,

AgentToolCallError,

AgentToolExecutionError,

create_agent_gradio_app_template,

extract_code_from_text,

is_valid_name,

make_init_file,

parse_code_blobs,

truncate_content,

)

logger = getLogger(__name__)

def populate_template(template: str, variables: dict[str, Any]) -> str:

compiled_template = Template(template, undefined=StrictUndefined)

try:

return compiled_template.render(**variables)

except Exception as e:

raise Exception(f"Error during jinja template rendering: {type(e).__name__}: {e}")

@dataclass

class ActionOutput:

output: Any

is_final_answer: bool

@dataclass

class ToolOutput:

id: str

output: Any

is_final_answer: bool

observation: str

tool_call: ToolCall

class PlanningPromptTemplate(TypedDict):

"""

Prompt templates for the planning step.

Args:

plan (`str`): Initial plan prompt.

update_plan_pre_messages (`str`): Update plan pre-messages prompt.

update_plan_post_messages (`str`): Update plan post-messages prompt.

"""

initial_plan: str

update_plan_pre_messages: str

update_plan_post_messages: str

class ManagedAgentPromptTemplate(TypedDict):

"""

Prompt templates for the managed agent.

Args:

task (`str`): Task prompt.

report (`str`): Report prompt.

"""

task: str

report: str

class FinalAnswerPromptTemplate(TypedDict):

"""

Prompt templates for the final answer.

Args:

pre_messages (`str`): Pre-messages prompt.

post_messages (`str`): Post-messages prompt.

"""

pre_messages: str

post_messages: str

class PromptTemplates(TypedDict):

"""

Prompt templates for the agent.

Args:

system_prompt (`str`): System prompt.

planning ([`~agents.PlanningPromptTemplate`]): Planning prompt templates.

managed_agent ([`~agents.ManagedAgentPromptTemplate`]): Managed agent prompt templates.

final_answer ([`~agents.FinalAnswerPromptTemplate`]): Final answer prompt templates.

"""

system_prompt: str

planning: PlanningPromptTemplate

managed_agent: ManagedAgentPromptTemplate

final_answer: FinalAnswerPromptTemplate

EMPTY_PROMPT_TEMPLATES = PromptTemplates(

system_prompt="",

planning=PlanningPromptTemplate(

initial_plan="",

update_plan_pre_messages="",

update_plan_post_messages="",

),

managed_agent=ManagedAgentPromptTemplate(task="", report=""),

final_answer=FinalAnswerPromptTemplate(pre_messages="", post_messages=""),

)

@dataclass

class RunResult:

"""Holds extended information about an agent run.

Attributes:

output (Any | None): The final output of the agent run, if available.

state (Literal["success", "max_steps_error"]): The final state of the agent after the run.

steps (list[dict]): The agent's memory, as a list of steps.

token_usage (TokenUsage | None): Count of tokens used during the run.

timing (Timing): Timing details of the agent run: start time, end time, duration.

messages (list[dict]): The agent's memory, as a list of messages.

<Deprecated version="1.22.0">

Parameter 'messages' is deprecated and will be removed in version 1.25. Please use 'steps' instead.

</Deprecated>

"""

output: Any | None

state: Literal["success", "max_steps_error"]

steps: list[dict]

token_usage: TokenUsage | None

timing: Timing

def __init__(self, output=None, state=None, steps=None, token_usage=None, timing=None, messages=None):

# Handle deprecated 'messages' parameter

if messages is not None:

if steps is not None:

raise ValueError("Cannot specify both 'messages' and 'steps' parameters. Use 'steps' instead.")

warnings.warn(

"Parameter 'messages' is deprecated and will be removed in version 1.25. Please use 'steps' instead.",

FutureWarning,

stacklevel=2,

)

steps = messages

# Initialize with dataclass fields

self.output = output

self.state = state

self.steps = steps

self.token_usage = token_usage

self.timing = timing

@property

def messages(self):

"""Backward compatibility property that returns steps."""

warnings.warn(

"Parameter 'messages' is deprecated and will be removed in version 1.25. Please use 'steps' instead.",

FutureWarning,

stacklevel=2,

)

return self.steps

def dict(self):

return {

"output": self.output,

"state": self.state,

"steps": self.steps,

"token_usage": self.token_usage.dict() if self.token_usage is not None else None,

"timing": self.timing.dict(),

}

StreamEvent: TypeAlias = Union[

ChatMessageStreamDelta,

ChatMessageToolCall,

ActionOutput,

ToolCall,

ToolOutput,

PlanningStep,

ActionStep,

FinalAnswerStep,

]

class MultiStepAgent(ABC):

"""

Agent class that solves the given task step by step, using the ReAct framework:

While the objective is not reached, the agent will perform a cycle of action (given by the LLM) and observation (obtained from the environment).

Args:

tools (`list[Tool]`): [`Tool`]s that the agent can use.

model (`Callable[[list[dict[str, str]]], ChatMessage]`): Model that will generate the agent's actions.

prompt_templates ([`~agents.PromptTemplates`], *optional*): Prompt templates.

instructions (`str`, *optional*): Custom instructions for the agent, will be inserted in the system prompt.

max_steps (`int`, default `20`): Maximum number of steps the agent can take to solve the task.

add_base_tools (`bool`, default `False`): Whether to add the base tools to the agent's tools.

verbosity_level (`LogLevel`, default `LogLevel.INFO`): Level of verbosity of the agent's logs.

managed_agents (`list`, *optional*): Managed agents that the agent can call.

step_callbacks (`list[Callable]` | `dict[Type[MemoryStep], Callable | list[Callable]]`, *optional*): Callbacks that will be called at each step.

planning_interval (`int`, *optional*): Interval at which the agent will run a planning step.

name (`str`, *optional*): Necessary for a managed agent only - the name by which this agent can be called.

description (`str`, *optional*): Necessary for a managed agent only - the description of this agent.

provide_run_summary (`bool`, *optional*): Whether to provide a run summary when called as a managed agent.

final_answer_checks (`list[Callable]`, *optional*): List of validation functions to run before accepting a final answer.

Each function should:

- Take the final answer, the agent's memory, and the agent itself as arguments.

- Return a boolean indicating whether the final answer is valid.

return_full_result (`bool`, default `False`): Whether to return the full [`RunResult`] object or just the final answer output from the agent run.

"""

def __init__(

self,

tools: list[Tool],

model: Model,

prompt_templates: PromptTemplates | None = None,

instructions: str | None = None,

max_steps: int = 20,

add_base_tools: bool = False,

verbosity_level: LogLevel = LogLevel.INFO,

managed_agents: list | None = None,

step_callbacks: list[Callable] | dict[Type[MemoryStep], Callable | list[Callable]] | None = None,

planning_interval: int | None = None,

name: str | None = None,

description: str | None = None,

provide_run_summary: bool = False,

final_answer_checks: list[Callable] | None = None,

return_full_result: bool = False,

logger: AgentLogger | None = None,

):

self.agent_name = self.__class__.__name__

self.model = model

self.prompt_templates = prompt_templates or EMPTY_PROMPT_TEMPLATES

if prompt_templates is not None:

missing_keys = set(EMPTY_PROMPT_TEMPLATES.keys()) - set(prompt_templates.keys())

assert not missing_keys, (

f"Some prompt templates are missing from your custom `prompt_templates`: {missing_keys}"

)

for key, value in EMPTY_PROMPT_TEMPLATES.items():

if isinstance(value, dict):

for subkey in value.keys():

assert key in prompt_templates.keys() and (subkey in prompt_templates[key].keys()), (

f"Some prompt templates are missing from your custom `prompt_templates`: {subkey} under {key}"

)

self.max_steps = max_steps

self.step_number = 0

self.planning_interval = planning_interval

self.state: dict[str, Any] = {}

self.name = self._validate_name(name)

self.description = description

self.provide_run_summary = provide_run_summary

self.final_answer_checks = final_answer_checks if final_answer_checks is not None else []

self.return_full_result = return_full_result

self.instructions = instructions

self._setup_managed_agents(managed_agents)

self._setup_tools(tools, add_base_tools)

self._validate_tools_and_managed_agents(tools, managed_agents)

self.task: str | None = None

self.memory = AgentMemory(self.system_prompt)

if logger is None:

self.logger = AgentLogger(level=verbosity_level)

else:

self.logger = logger

self.monitor = Monitor(self.model, self.logger)

self._setup_step_callbacks(step_callbacks)

self.stream_outputs = False

@property

def system_prompt(self) -> str:

return self.initialize_system_prompt()

@system_prompt.setter

def system_prompt(self, value: str):

raise AttributeError(

"""The 'system_prompt' property is read-only. Use 'self.prompt_templates["system_prompt"]' instead."""

)

def _validate_name(self, name: str | None) -> str | None:

if name is not None and not is_valid_name(name):

raise ValueError(f"Agent name '{name}' must be a valid Python identifier and not a reserved keyword.")

return name

def _setup_managed_agents(self, managed_agents: list | None = None) -> None:

"""Setup managed agents with proper logging."""

self.managed_agents = {}

if managed_agents:

assert all(agent.name and agent.description for agent in managed_agents), (

"All managed agents need both a name and a description!"

)

self.managed_agents = {agent.name: agent for agent in managed_agents}

# Ensure managed agents can be called as tools by the model: set their inputs and output_type

for agent in self.managed_agents.values():

agent.inputs = {

"task": {"type": "string", "description": "Long detailed description of the task."},

"additional_args": {

"type": "object",

"description": "Dictionary of extra inputs to pass to the managed agent, e.g. images, dataframes, or any other contextual data it may need.",

"nullable": True,

},

}

agent.output_type = "string"

def _setup_tools(self, tools, add_base_tools):

assert all(isinstance(tool, BaseTool) for tool in tools), (

"All elements must be instance of BaseTool (or a subclass)"

)

self.tools = {tool.name: tool for tool in tools}

if add_base_tools:

self.tools.update(

{

name: cls()

for name, cls in TOOL_MAPPING.items()

if name != "python_interpreter" or self.__class__.__name__ == "ToolCallingAgent"

}

)

self.tools.setdefault("final_answer", FinalAnswerTool())

def _validate_tools_and_managed_agents(self, tools, managed_agents):

tool_and_managed_agent_names = [tool.name for tool in tools]

if managed_agents is not None:

tool_and_managed_agent_names += [agent.name for agent in managed_agents]

if self.name:

tool_and_managed_agent_names.append(self.name)

if len(tool_and_managed_agent_names) != len(set(tool_and_managed_agent_names)):

raise ValueError(

"Each tool or managed_agent should have a unique name! You passed these duplicate names: "

f"{[name for name in tool_and_managed_agent_names if tool_and_managed_agent_names.count(name) > 1]}"

)

def _setup_step_callbacks(self, step_callbacks):

# Initialize step callbacks registry

self.step_callbacks = CallbackRegistry()

if step_callbacks:

# Register callbacks list only for ActionStep for backward compatibility

if isinstance(step_callbacks, list):

for callback in step_callbacks:

self.step_callbacks.register(ActionStep, callback)

# Register callbacks dict for specific step classes

elif isinstance(step_callbacks, dict):

for step_cls, callbacks in step_callbacks.items():

if not isinstance(callbacks, list):

callbacks = [callbacks]

for callback in callbacks:

self.step_callbacks.register(step_cls, callback)

else:

raise ValueError("step_callbacks must be a list or a dict")

# Register monitor update_metrics only for ActionStep for backward compatibility

self.step_callbacks.register(ActionStep, self.monitor.update_metrics)

def run(

self,

task: str,

stream: bool = False,

reset: bool = True,

images: list["PIL.Image.Image"] | None = None,

additional_args: dict | None = None,

max_steps: int | None = None,

return_full_result: bool | None = None,

) -> Any | RunResult:

"""

Run the agent for the given task.

Args:

task (`str`): Task to perform.

stream (`bool`): Whether to run in streaming mode.

If `True`, returns a generator that yields each step as it is executed. You must iterate over this generator to process the individual steps (e.g., using a for loop or `next()`).

If `False`, executes all steps internally and returns only the final answer after completion.

reset (`bool`): Whether to reset the conversation or keep it going from previous run.

images (`list[PIL.Image.Image]`, *optional*): Image(s) objects.

additional_args (`dict`, *optional*): Any other variables that you want to pass to the agent run, for instance images or dataframes. Give them clear names!

max_steps (`int`, *optional*): Maximum number of steps the agent can take to solve the task. if not provided, will use the agent's default value.

return_full_result (`bool`, *optional*): Whether to return the full [`RunResult`] object or just the final answer output.

If `None` (default), the agent's `self.return_full_result` setting is used.

Example:

```py

from smolagents import CodeAgent

agent = CodeAgent(tools=[])

agent.run("What is the result of 2 power 3.7384?")

```

"""

max_steps = max_steps or self.max_steps

self.task = task

self.interrupt_switch = False

if additional_args:

self.state.update(additional_args)

self.task += f"""

You have been provided with these additional arguments, that you can access directly using the keys as variables:

{str(additional_args)}."""

self.memory.system_prompt = SystemPromptStep(system_prompt=self.system_prompt)

if reset:

self.memory.reset()

self.monitor.reset()

self.logger.log_task(

content=self.task.strip(),

subtitle=f"{type(self.model).__name__} - {(self.model.model_id if hasattr(self.model, 'model_id') else '')}",

level=LogLevel.INFO,

title=self.name if hasattr(self, "name") else None,

)

self.memory.steps.append(TaskStep(task=self.task, task_images=images))

if getattr(self, "python_executor", None):

self.python_executor.send_variables(variables=self.state)

self.python_executor.send_tools({**self.tools, **self.managed_agents})

if stream:

# The steps are returned as they are executed through a generator to iterate on.

return self._run_stream(task=self.task, max_steps=max_steps, images=images)

run_start_time = time.time()

steps = list(self._run_stream(task=self.task, max_steps=max_steps, images=images))

# Outputs are returned only at the end. We only look at the last step.

assert isinstance(steps[-1], FinalAnswerStep)

output = steps[-1].output

return_full_result = return_full_result if return_full_result is not None else self.return_full_result

if return_full_result:

total_input_tokens = 0

total_output_tokens = 0

correct_token_usage = True

for step in self.memory.steps:

if isinstance(step, (ActionStep, PlanningStep)):

if step.token_usage is None:

correct_token_usage = False

break

else:

total_input_tokens += step.token_usage.input_tokens

total_output_tokens += step.token_usage.output_tokens

if correct_token_usage:

token_usage = TokenUsage(input_tokens=total_input_tokens, output_tokens=total_output_tokens)

else:

token_usage = None

if self.memory.steps and isinstance(getattr(self.memory.steps[-1], "error", None), AgentMaxStepsError):

state = "max_steps_error"

else:

state = "success"

step_dicts = self.memory.get_full_steps()

return RunResult(

output=output,

token_usage=token_usage,

steps=step_dicts,

timing=Timing(start_time=run_start_time, end_time=time.time()),

state=state,

)

return output

def _run_stream(

self, task: str, max_steps: int, images: list["PIL.Image.Image"] | None = None

) -> Generator[ActionStep | PlanningStep | FinalAnswerStep | ChatMessageStreamDelta]:

self.step_number = 1

returned_final_answer = False

while not returned_final_answer and self.step_number <= max_steps:

if self.interrupt_switch:

raise AgentError("Agent interrupted.", self.logger)

# Run a planning step if scheduled

if self.planning_interval is not None and (

self.step_number == 1 or (self.step_number - 1) % self.planning_interval == 0

):

planning_start_time = time.time()

planning_step = None

for element in self._generate_planning_step(

task, is_first_step=len(self.memory.steps) == 1, step=self.step_number

): # Don't use the attribute step_number here, because there can be steps from previous runs

yield element

planning_step = element

assert isinstance(planning_step, PlanningStep) # Last yielded element should be a PlanningStep

planning_end_time = time.time()

planning_step.timing = Timing(

start_time=planning_start_time,

end_time=planning_end_time,

)

self._finalize_step(planning_step)

self.memory.steps.append(planning_step)

# Start action step!

action_step_start_time = time.time()

action_step = ActionStep(

step_number=self.step_number,

timing=Timing(start_time=action_step_start_time),

observations_images=images,

)

self.logger.log_rule(f"Step {self.step_number}", level=LogLevel.INFO)

try:

for output in self._step_stream(action_step):

# Yield all

yield output

if isinstance(output, ActionOutput) and output.is_final_answer:

final_answer = output.output

self.logger.log(

Text(f"Final answer: {final_answer}", style=f"bold {YELLOW_HEX}"),

level=LogLevel.INFO,

)

if self.final_answer_checks:

self._validate_final_answer(final_answer)

returned_final_answer = True

action_step.is_final_answer = True

except AgentGenerationError as e:

# Agent generation errors are not caused by a Model error but an implementation error: so we should raise them and exit.

raise e

except AgentError as e:

# Other AgentError types are caused by the Model, so we should log them and iterate.

action_step.error = e

finally:

self._finalize_step(action_step)

self.memory.steps.append(action_step)

yield action_step

self.step_number += 1

if not returned_final_answer and self.step_number == max_steps + 1:

final_answer = self._handle_max_steps_reached(task)

yield action_step

final_answer_step = FinalAnswerStep(handle_agent_output_types(final_answer))

self._finalize_step(final_answer_step)

yield final_answer_step

def _validate_final_answer(self, final_answer: Any):

for check_function in self.final_answer_checks:

try:

assert check_function(final_answer, self.memory, agent=self)

except Exception as e:

raise AgentError(f"Check {check_function.__name__} failed with error: {e}", self.logger)

def _finalize_step(self, memory_step: ActionStep | PlanningStep | FinalAnswerStep):

if not isinstance(memory_step, FinalAnswerStep):

memory_step.timing.end_time = time.time()

self.step_callbacks.callback(memory_step, agent=self)

def _handle_max_steps_reached(self, task: str) -> Any:

action_step_start_time = time.time()

final_answer = self.provide_final_answer(task)

final_memory_step = ActionStep(

step_number=self.step_number,

error=AgentMaxStepsError("Reached max steps.", self.logger),

timing=Timing(start_time=action_step_start_time, end_time=time.time()),

token_usage=final_answer.token_usage,

)

final_memory_step.action_output = final_answer.content

self._finalize_step(final_memory_step)

self.memory.steps.append(final_memory_step)

return final_answer.content

def _generate_planning_step(

self, task, is_first_step: bool, step: int

) -> Generator[ChatMessageStreamDelta | PlanningStep]:

start_time = time.time()

if is_first_step:

input_messages = [

ChatMessage(

role=MessageRole.USER,

content=[

{

"type": "text",

"text": populate_template(

self.prompt_templates["planning"]["initial_plan"],

variables={"task": task, "tools": self.tools, "managed_agents": self.managed_agents},

),

}

],

)

]

if self.stream_outputs and hasattr(self.model, "generate_stream"):

plan_message_content = ""

output_stream = self.model.generate_stream(input_messages, stop_sequences=["<end_plan>"]) # type: ignore

input_tokens, output_tokens = 0, 0

with Live("", console=self.logger.console, vertical_overflow="visible") as live:

for event in output_stream:

if event.content is not None:

plan_message_content += event.content

live.update(Markdown(plan_message_content))

if event.token_usage:

input_tokens = event.token_usage.input_tokens

output_tokens += event.token_usage.output_tokens

yield event

else:

plan_message = self.model.generate(input_messages, stop_sequences=["<end_plan>"])

plan_message_content = plan_message.content

input_tokens, output_tokens = 0, 0

if plan_message.token_usage:

input_tokens = plan_message.token_usage.input_tokens

output_tokens = plan_message.token_usage.output_tokens

plan = textwrap.dedent(

f"""Here are the facts I know and the plan of action that I will follow to solve the task:\n```\n{plan_message_content}\n```"""

)

else:

# Summary mode removes the system prompt and previous planning messages output by the model.

# Removing previous planning messages avoids influencing too much the new plan.

memory_messages = self.write_memory_to_messages(summary_mode=True)

plan_update_pre = ChatMessage(

role=MessageRole.SYSTEM,

content=[

{

"type": "text",

"text": populate_template(

self.prompt_templates["planning"]["update_plan_pre_messages"], variables={"task": task}

),

}

],

)

plan_update_post = ChatMessage(

role=MessageRole.USER,

content=[

{

"type": "text",

"text": populate_template(

self.prompt_templates["planning"]["update_plan_post_messages"],

variables={

"task": task,

"tools": self.tools,

"managed_agents": self.managed_agents,

"remaining_steps": (self.max_steps - step),

},

),

}

],

)

input_messages = [plan_update_pre] + memory_messages + [plan_update_post]

if self.stream_outputs and hasattr(self.model, "generate_stream"):

plan_message_content = ""

input_tokens, output_tokens = 0, 0

with Live("", console=self.logger.console, vertical_overflow="visible") as live:

for event in self.model.generate_stream(

input_messages,

stop_sequences=["<end_plan>"],

): # type: ignore

if event.content is not None:

plan_message_content += event.content

live.update(Markdown(plan_message_content))

if event.token_usage:

input_tokens = event.token_usage.input_tokens

output_tokens += event.token_usage.output_tokens

yield event

else:

plan_message = self.model.generate(input_messages, stop_sequences=["<end_plan>"])

plan_message_content = plan_message.content

input_tokens, output_tokens = 0, 0

if plan_message.token_usage:

input_tokens = plan_message.token_usage.input_tokens

output_tokens = plan_message.token_usage.output_tokens

plan = textwrap.dedent(

f"""I still need to solve the task I was given:\n```\n{self.task}\n```\n\nHere are the facts I know and my new/updated plan of action to solve the task:\n```\n{plan_message_content}\n```"""

)

log_headline = "Initial plan" if is_first_step else "Updated plan"

self.logger.log(Rule(f"[bold]{log_headline}", style="orange"), Text(plan), level=LogLevel.INFO)

yield PlanningStep(

model_input_messages=input_messages,

plan=plan,

model_output_message=ChatMessage(role=MessageRole.ASSISTANT, content=plan_message_content),

token_usage=TokenUsage(input_tokens=input_tokens, output_tokens=output_tokens),

timing=Timing(start_time=start_time, end_time=time.time()),

)

@abstractmethod

def initialize_system_prompt(self) -> str:

"""To be implemented in child classes"""

...

def interrupt(self):

"""Interrupts the agent execution."""

self.interrupt_switch = True

def write_memory_to_messages(

self,

summary_mode: bool = False,

) -> list[ChatMessage]:

"""

Reads past llm_outputs, actions, and observations or errors from the memory into a series of messages

that can be used as input to the LLM. Adds a number of keywords (such as PLAN, error, etc) to help

the LLM.

"""

messages = self.memory.system_prompt.to_messages(summary_mode=summary_mode)

for memory_step in self.memory.steps:

messages.extend(memory_step.to_messages(summary_mode=summary_mode))

return messages

def _step_stream(

self, memory_step: ActionStep

) -> Generator[ChatMessageStreamDelta | ToolCall | ToolOutput | ActionOutput]:

"""

Perform one step in the ReAct framework: the agent thinks, acts, and observes the result.

Yields ChatMessageStreamDelta during the run if streaming is enabled.

At the end, yields either None if the step is not final, or the final answer.

"""

raise NotImplementedError("This method should be implemented in child classes")

def step(self, memory_step: ActionStep) -> Any:

"""

Perform one step in the ReAct framework: the agent thinks, acts, and observes the result.

Returns either None if the step is not final, or the final answer.

"""

return list(self._step_stream(memory_step))[-1]

def extract_action(self, model_output: str, split_token: str) -> tuple[str, str]:

"""

Parse action from the LLM output

Args:

model_output (`str`): Output of the LLM

split_token (`str`): Separator for the action. Should match the example in the system prompt.

"""

try:

split = model_output.split(split_token)

rationale, action = (

split[-2],

split[-1],

) # NOTE: using indexes starting from the end solves for when you have more than one split_token in the output

except Exception:

raise AgentParsingError(

f"No '{split_token}' token provided in your output.\nYour output:\n{model_output}\n. Be sure to include an action, prefaced with '{split_token}'!",

self.logger,

)

return rationale.strip(), action.strip()

def provide_final_answer(self, task: str) -> ChatMessage:

"""

Provide the final answer to the task, based on the logs of the agent's interactions.

Args:

task (`str`): Task to perform.

images (`list[PIL.Image.Image]`, *optional*): Image(s) objects.

Returns:

`str`: Final answer to the task.

"""

messages = [

ChatMessage(

role=MessageRole.SYSTEM,

content=[

{

"type": "text",

"text": self.prompt_templates["final_answer"]["pre_messages"],

}

],

)

]

messages += self.write_memory_to_messages()[1:]

messages.append(

ChatMessage(

role=MessageRole.USER,

content=[

{

"type": "text",

"text": populate_template(

self.prompt_templates["final_answer"]["post_messages"], variables={"task": task}

),

}

],

)

)

try:

chat_message: ChatMessage = self.model.generate(messages)

return chat_message

except Exception as e:

return ChatMessage(

role=MessageRole.ASSISTANT,

content=[{"type": "text", "text": f"Error in generating final LLM output: {e}"}],

)

def visualize(self):

"""Creates a rich tree visualization of the agent's structure."""

self.logger.visualize_agent_tree(self)

def replay(self, detailed: bool = False):

"""Prints a pretty replay of the agent's steps.

Args:

detailed (bool, optional): If True, also displays the memory at each step. Defaults to False.

Careful: will increase log length exponentially. Use only for debugging.

"""

self.memory.replay(self.logger, detailed=detailed)

def __call__(self, task: str, **kwargs):

"""Adds additional prompting for the managed agent, runs it, and wraps the output.

This method is called only by a managed agent.

"""

full_task = populate_template(

self.prompt_templates["managed_agent"]["task"],

variables=dict(name=self.name, task=task),

)

result = self.run(full_task, **kwargs)

if isinstance(result, RunResult):

report = result.output

else:

report = result

answer = populate_template(

self.prompt_templates["managed_agent"]["report"], variables=dict(name=self.name, final_answer=report)

)

if self.provide_run_summary:

answer += "\n\nFor more detail, find below a summary of this agent's work:\n<summary_of_work>\n"

for message in self.write_memory_to_messages(summary_mode=True):

content = message.content

answer += "\n" + truncate_content(str(content)) + "\n---"

answer += "\n</summary_of_work>"

return answer

def save(self, output_dir: str | Path, relative_path: str | None = None):

"""

Saves the relevant code files for your agent. This will copy the code of your agent in `output_dir` as well as autogenerate:

- a `tools` folder containing the logic for each of the tools under `tools/{tool_name}.py`.

- a `managed_agents` folder containing the logic for each of the managed agents.

- an `agent.json` file containing a dictionary representing your agent.

- a `prompt.yaml` file containing the prompt templates used by your agent.

- an `app.py` file providing a UI for your agent when it is exported to a Space with `agent.push_to_hub()`

- a `requirements.txt` containing the names of the modules used by your tool (as detected when inspecting its

code)

Args:

output_dir (`str` or `Path`): The folder in which you want to save your agent.

"""

make_init_file(output_dir)

# Recursively save managed agents

if self.managed_agents:

make_init_file(os.path.join(output_dir, "managed_agents"))

for agent_name, agent in self.managed_agents.items():

agent_suffix = f"managed_agents.{agent_name}"

if relative_path:

agent_suffix = relative_path + "." + agent_suffix

agent.save(os.path.join(output_dir, "managed_agents", agent_name), relative_path=agent_suffix)

class_name = self.__class__.__name__

# Save tools to different .py files

for tool in self.tools.values():

make_init_file(os.path.join(output_dir, "tools"))

tool.save(os.path.join(output_dir, "tools"), tool_file_name=tool.name, make_gradio_app=False)

# Save prompts to yaml

yaml_prompts = yaml.safe_dump(

self.prompt_templates,

default_style="|", # This forces block literals for all strings

default_flow_style=False,

width=float("inf"),

sort_keys=False,

allow_unicode=True,

indent=2,

)

with open(os.path.join(output_dir, "prompts.yaml"), "w", encoding="utf-8") as f:

f.write(yaml_prompts)

# Save agent dictionary to json

agent_dict = self.to_dict()

agent_dict["tools"] = [tool.name for tool in self.tools.values()]

agent_dict["managed_agents"] = {agent.name: agent.__class__.__name__ for agent in self.managed_agents.values()}

with open(os.path.join(output_dir, "agent.json"), "w", encoding="utf-8") as f:

json.dump(agent_dict, f, indent=4)

# Save requirements

with open(os.path.join(output_dir, "requirements.txt"), "w", encoding="utf-8") as f:

f.writelines(f"{r}\n" for r in agent_dict["requirements"])

# Make agent.py file with Gradio UI

agent_name = f"agent_{self.name}" if getattr(self, "name", None) else "agent"

managed_agent_relative_path = relative_path + "." if relative_path is not None else ""

app_template = create_agent_gradio_app_template()

# Render the app.py file from Jinja2 template

app_text = app_template.render(

{

"agent_name": agent_name,

"class_name": class_name,

"agent_dict": agent_dict,

"tools": self.tools,

"managed_agents": self.managed_agents,

"managed_agent_relative_path": managed_agent_relative_path,

}

)

with open(os.path.join(output_dir, "app.py"), "w", encoding="utf-8") as f:

f.write(app_text + "\n") # Append newline at the end

def to_dict(self) -> dict[str, Any]:

"""Convert the agent to a dictionary representation.

Returns:

`dict`: Dictionary representation of the agent.

"""

# TODO: handle serializing step_callbacks and final_answer_checks

for attr in ["final_answer_checks", "step_callbacks"]:

if getattr(self, attr, None):

self.logger.log(f"This agent has {attr}: they will be ignored by this method.", LogLevel.INFO)

tool_dicts = [tool.to_dict() for tool in self.tools.values()]

tool_requirements = {req for tool in self.tools.values() for req in tool.to_dict()["requirements"]}

managed_agents_requirements = {

req for managed_agent in self.managed_agents.values() for req in managed_agent.to_dict()["requirements"]

}

requirements = tool_requirements | managed_agents_requirements

if hasattr(self, "authorized_imports"):

requirements.update(

{package.split(".")[0] for package in self.authorized_imports if package not in BASE_BUILTIN_MODULES}

)

agent_dict = {

"class": self.__class__.__name__,

"tools": tool_dicts,

"model": {

"class": self.model.__class__.__name__,

"data": self.model.to_dict(),

},

"managed_agents": [managed_agent.to_dict() for managed_agent in self.managed_agents.values()],

"prompt_templates": self.prompt_templates,