src/smolagents/agents.py

#!/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 inspect
import json
import os
import re
import tempfile
import textwrap
import time
from collections import deque
from logging import getLogger
from pathlib import Path
from typing import Any, Callable, Dict, Generator, List, Optional, Set, Tuple, TypedDict, Union

import jinja2
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.panel import Panel
from rich.rule import Rule
from rich.text import Text

from .agent_types import AgentAudio, AgentImage, AgentType, 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, PlanningStep, SystemPromptStep, TaskStep, ToolCall
from .models import (
    ChatMessage,
    MessageRole,
    Model,
)
from .monitoring import (
    YELLOW_HEX,
    AgentLogger,
    LogLevel,
    Monitor,
)
from .remote_executors import DockerExecutor, E2BExecutor
from .tools import Tool
from .utils import (
    AgentError,
    AgentExecutionError,
    AgentGenerationError,
    AgentMaxStepsError,
    AgentParsingError,
    make_init_file,
    parse_code_blobs,
    parse_json_tool_call,
    truncate_content,
)


logger = getLogger(__name__)


def get_variable_names(self, template: str) -> Set[str]:
    pattern = re.compile(r"\{\{([^{}]+)\}\}")
    return {match.group(1).strip() for match in pattern.finditer(template)}


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}")


class PlanningPromptTemplate(TypedDict):
    """
    Prompt templates for the planning step.

    Args:
        initial_facts (`str`): Initial facts prompt.
        initial_plan (`str`): Initial plan prompt.
        update_facts_pre_messages (`str`): Update facts pre-messages prompt.
        update_facts_post_messages (`str`): Update facts post-messages prompt.
        update_plan_pre_messages (`str`): Update plan pre-messages prompt.
        update_plan_post_messages (`str`): Update plan post-messages prompt.
    """

    initial_facts: str
    initial_plan: str
    update_facts_pre_messages: str
    update_facts_post_messages: 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_facts="",
        initial_plan="",
        update_facts_pre_messages="",
        update_facts_post_messages="",
        update_plan_pre_messages="",
        update_plan_post_messages="",
    ),
    managed_agent=ManagedAgentPromptTemplate(task="", report=""),
    final_answer=FinalAnswerPromptTemplate(pre_messages="", post_messages=""),
)


class MultiStepAgent:
    """
    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.
        max_steps (`int`, default `20`): Maximum number of steps the agent can take to solve the task.
        tool_parser (`Callable`, *optional*): Function used to parse the tool calls from the LLM output.
        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.
        grammar (`dict[str, str]`, *optional*): Grammar used to parse the LLM output.
        managed_agents (`list`, *optional*): Managed agents that the agent can call.
        step_callbacks (`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`, *optional*): List of Callables to run before returning a final answer for checking validity.
    """

    def __init__(
        self,
        tools: List[Tool],
        model: Callable[[List[Dict[str, str]]], ChatMessage],
        prompt_templates: Optional[PromptTemplates] = None,
        max_steps: int = 20,
        tool_parser: Optional[Callable] = None,
        add_base_tools: bool = False,
        verbosity_level: LogLevel = LogLevel.INFO,
        grammar: Optional[Dict[str, str]] = None,
        managed_agents: Optional[List] = None,
        step_callbacks: Optional[List[Callable]] = None,
        planning_interval: Optional[int] = None,
        name: Optional[str] = None,
        description: Optional[str] = None,
        provide_run_summary: bool = False,
        final_answer_checks: Optional[List[Callable]] = None,
    ):
        self.agent_name = self.__class__.__name__
        self.model = model
        self.prompt_templates = prompt_templates or EMPTY_PROMPT_TEMPLATES
        self.max_steps = max_steps
        self.step_number = 0
        self.tool_parser = tool_parser or parse_json_tool_call
        self.grammar = grammar
        self.planning_interval = planning_interval
        self.state = {}
        self.name = name
        self.description = description
        self.provide_run_summary = provide_run_summary
        self.final_answer_checks = final_answer_checks

        self._setup_managed_agents(managed_agents)
        self._setup_tools(tools, add_base_tools)
        self._validate_tools_and_managed_agents(tools, managed_agents)

        self.system_prompt = self.initialize_system_prompt()
        self.input_messages = None
        self.task = None
        self.memory = AgentMemory(self.system_prompt)
        self.logger = AgentLogger(level=verbosity_level)
        self.monitor = Monitor(self.model, self.logger)
        self.step_callbacks = step_callbacks if step_callbacks is not None else []
        self.step_callbacks.append(self.monitor.update_metrics)

    def _setup_managed_agents(self, managed_agents):
        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}

    def _setup_tools(self, tools, add_base_tools):
        assert all(isinstance(tool, Tool) for tool in tools), "All elements must be instance of Tool (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 run(
        self,
        task: str,
        stream: bool = False,
        reset: bool = True,
        images: Optional[List[str]] = None,
        additional_args: Optional[Dict] = None,
        max_steps: Optional[int] = None,
    ):
        """
        Run the agent for the given task.

        Args:
            task (`str`): Task to perform.
            stream (`bool`): Whether to run in a streaming way.
            reset (`bool`): Whether to reset the conversation or keep it going from previous run.
            images (`list[str]`, *optional*): Paths to image(s).
            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.

        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
        if additional_args is not None:
            self.state.update(additional_args)
            self.task += f"""
You have been provided with these additional arguments, that you can access using the keys as variables in your python code:
{str(additional_args)}."""

        self.system_prompt = self.initialize_system_prompt()
        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(task=self.task, max_steps=max_steps, images=images)
        # Outputs are returned only at the end. We only look at the last step.
        return deque(self._run(task=self.task, max_steps=max_steps, images=images), maxlen=1)[0]

    def _run(
        self, task: str, max_steps: int, images: List[str] | None = None
    ) -> Generator[ActionStep | AgentType, None, None]:
        final_answer = None
        self.step_number = 1
        while final_answer is None and self.step_number <= max_steps:
            step_start_time = time.time()
            memory_step = self._create_memory_step(step_start_time, images)
            try:
                final_answer = self._execute_step(task, memory_step)
            except AgentError as e:
                memory_step.error = e
            finally:
                self._finalize_step(memory_step, step_start_time)
                yield memory_step
                self.step_number += 1

        if final_answer is None and self.step_number == max_steps + 1:
            final_answer = self._handle_max_steps_reached(task, images, step_start_time)
            yield memory_step
        yield handle_agent_output_types(final_answer)

    def _create_memory_step(self, step_start_time: float, images: List[str] | None) -> ActionStep:
        return ActionStep(step_number=self.step_number, start_time=step_start_time, observations_images=images)

    def _execute_step(self, task: str, memory_step: ActionStep) -> Union[None, Any]:
        if self.planning_interval is not None and self.step_number % self.planning_interval == 1:
            self.planning_step(task, is_first_step=(self.step_number == 1), step=self.step_number)
        self.logger.log_rule(f"Step {self.step_number}", level=LogLevel.INFO)
        final_answer = self.step(memory_step)
        if final_answer is not None and self.final_answer_checks:
            self._validate_final_answer(final_answer)
        return final_answer

    def _validate_final_answer(self, final_answer: Any):
        for check_function in self.final_answer_checks:
            try:
                assert check_function(final_answer, self.memory)
            except Exception as e:
                raise AgentError(f"Check {check_function.__name__} failed with error: {e}", self.logger)

    def _finalize_step(self, memory_step: ActionStep, step_start_time: float):
        memory_step.end_time = time.time()
        memory_step.duration = memory_step.end_time - step_start_time
        self.memory.steps.append(memory_step)
        for callback in self.step_callbacks:
            # For compatibility with old callbacks that don't take the agent as an argument
            callback(memory_step) if len(inspect.signature(callback).parameters) == 1 else callback(
                memory_step, agent=self
            )

    def _handle_max_steps_reached(self, task: str, images: List[str], step_start_time: float) -> Any:
        final_answer = self.provide_final_answer(task, images)
        final_memory_step = ActionStep(
            step_number=self.step_number, error=AgentMaxStepsError("Reached max steps.", self.logger)
        )
        final_memory_step.action_output = final_answer
        final_memory_step.end_time = time.time()
        final_memory_step.duration = final_memory_step.end_time - step_start_time
        self.memory.steps.append(final_memory_step)
        for callback in self.step_callbacks:
            callback(final_memory_step) if len(inspect.signature(callback).parameters) == 1 else callback(
                final_memory_step, agent=self
            )
        return final_answer

    def planning_step(self, task, is_first_step: bool, step: int) -> None:
        input_messages, facts_message, plan_message = (
            self._generate_initial_plan(task) if is_first_step else self._generate_updated_plan(task, step)
        )
        self._record_planning_step(input_messages, facts_message, plan_message, is_first_step)

    def _generate_initial_plan(self, task: str) -> Tuple[ChatMessage, ChatMessage]:
        input_messages = [
            {
                "role": MessageRole.USER,
                "content": [
                    {
                        "type": "text",
                        "text": populate_template(
                            self.prompt_templates["planning"]["initial_facts"], variables={"task": task}
                        ),
                    }
                ],
            },
        ]
        facts_message = self.model(input_messages)

        message_prompt_plan = {
            "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,
                            "answer_facts": facts_message.content,
                        },
                    ),
                }
            ],
        }
        plan_message = self.model([message_prompt_plan], stop_sequences=["<end_plan>"])
        return input_messages, facts_message, plan_message

    def _generate_updated_plan(self, task: str, step: int) -> Tuple[ChatMessage, ChatMessage]:
        # Do not take the system prompt message from the memory
        # summary_mode=False: Do not take previous plan steps to avoid influencing the new plan
        memory_messages = self.write_memory_to_messages()[1:]
        facts_update_pre = {
            "role": MessageRole.SYSTEM,
            "content": [{"type": "text", "text": self.prompt_templates["planning"]["update_facts_pre_messages"]}],
        }
        facts_update_post = {
            "role": MessageRole.USER,
            "content": [{"type": "text", "text": self.prompt_templates["planning"]["update_facts_post_messages"]}],
        }
        input_messages = [facts_update_pre] + memory_messages + [facts_update_post]
        facts_message = self.model(input_messages)

        update_plan_pre = {
            "role": MessageRole.SYSTEM,
            "content": [
                {
                    "type": "text",
                    "text": populate_template(
                        self.prompt_templates["planning"]["update_plan_pre_messages"], variables={"task": task}
                    ),
                }
            ],
        }
        update_plan_post = {
            "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,
                            "facts_update": facts_message.content,
                            "remaining_steps": (self.max_steps - step),
                        },
                    ),
                }
            ],
        }
        plan_message = self.model(
            [update_plan_pre] + memory_messages + [update_plan_post], stop_sequences=["<end_plan>"]
        )
        return input_messages, facts_message, plan_message

    def _record_planning_step(
        self, input_messages: list, facts_message: ChatMessage, plan_message: ChatMessage, is_first_step: bool
    ) -> None:
        if is_first_step:
            facts = textwrap.dedent(f"""Here are the facts that I know so far:\n```\n{facts_message.content}\n```""")
            plan = textwrap.dedent(
                f"""Here is the plan of action that I will follow to solve the task:\n```\n{plan_message.content}\n```"""
            )
            log_message = "Initial plan"
        else:
            facts = textwrap.dedent(
                f"""Here is the updated list of the facts that I know:\n```\n{facts_message.content}\n```"""
            )
            plan = textwrap.dedent(
                f"""I still need to solve the task I was given:\n```\n{self.task}\n```\n\nHere is my new/updated plan of action to solve the task:\n```\n{plan_message.content}\n```"""
            )
            log_message = "Updated plan"
        self.memory.steps.append(
            PlanningStep(
                model_input_messages=input_messages,
                facts=facts,
                plan=plan,
                model_output_message_plan=plan_message,
                model_output_message_facts=facts_message,
            )
        )
        self.logger.log(Rule(f"[bold]{log_message}", style="orange"), Text(plan), level=LogLevel.INFO)

    @property
    def logs(self):
        logger.warning(
            "The 'logs' attribute is deprecated and will soon be removed. Please use 'self.memory.steps' instead."
        )
        return [self.memory.system_prompt] + self.memory.steps

    def initialize_system_prompt(self):
        """To be implemented in child classes"""
        pass

    def write_memory_to_messages(
        self,
        summary_mode: Optional[bool] = False,
    ) -> List[Dict[str, str]]:
        """
        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 visualize(self):
        """Creates a rich tree visualization of the agent's structure."""
        self.logger.visualize_agent_tree(self)

    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, images: Optional[list[str]]) -> str:
        """
        Provide the final answer to the task, based on the logs of the agent's interactions.

        Args:
            task (`str`): Task to perform.
            images (`list[str]`, *optional*): Paths to image(s).

        Returns:
            `str`: Final answer to the task.
        """
        messages = [
            {
                "role": MessageRole.SYSTEM,
                "content": [
                    {
                        "type": "text",
                        "text": self.prompt_templates["final_answer"]["pre_messages"],
                    }
                ],
            }
        ]
        if images:
            messages[0]["content"].append({"type": "image"})
        messages += self.write_memory_to_messages()[1:]
        messages += [
            {
                "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(messages)
            return chat_message.content
        except Exception as e:
            return f"Error in generating final LLM output:\n{e}"

    def execute_tool_call(self, tool_name: str, arguments: Union[Dict[str, str], str]) -> Any:
        """
        Execute tool with the provided input and returns the result.
        This method replaces arguments with the actual values from the state if they refer to state variables.

        Args:
            tool_name (`str`): Name of the Tool to execute (should be one from self.tools).
            arguments (Dict[str, str]): Arguments passed to the Tool.
        """
        available_tools = {**self.tools, **self.managed_agents}
        if tool_name not in available_tools:
            error_msg = f"Unknown tool {tool_name}, should be instead one of {list(available_tools.keys())}."
            raise AgentExecutionError(error_msg, self.logger)

        try:
            if isinstance(arguments, str):
                if tool_name in self.managed_agents:
                    observation = available_tools[tool_name].__call__(arguments)
                else:
                    observation = available_tools[tool_name].__call__(arguments, sanitize_inputs_outputs=True)
            elif isinstance(arguments, dict):
                for key, value in arguments.items():
                    if isinstance(value, str) and value in self.state:
                        arguments[key] = self.state[value]
                if tool_name in self.managed_agents:
                    observation = available_tools[tool_name].__call__(**arguments)
                else:
                    observation = available_tools[tool_name].__call__(**arguments, sanitize_inputs_outputs=True)
            else:
                error_msg = f"Arguments passed to tool should be a dict or string: got a {type(arguments)}."
                raise AgentExecutionError(error_msg, self.logger)
            return observation
        except Exception as e:
            if tool_name in self.tools:
                tool = self.tools[tool_name]
                error_msg = (
                    f"Error when executing tool {tool_name} with arguments {arguments}: {type(e).__name__}: {e}\nYou should only use this tool with a correct input.\n"
                    f"As a reminder, this tool's description is the following: '{tool.description}'.\nIt takes inputs: {tool.inputs} and returns output type {tool.output_type}"
                )
                raise AgentExecutionError(error_msg, self.logger)
            elif tool_name in self.managed_agents:
                error_msg = (
                    f"Error in calling team member: {e}\nYou should only ask this team member with a correct request.\n"
                    f"As a reminder, this team member's description is the following:\n{available_tools[tool_name]}"
                )
                raise AgentExecutionError(error_msg, self.logger)

    def step(self, memory_step: ActionStep) -> Union[None, Any]:
        """To be implemented in children classes. Should return either None if the step is not final."""
        pass

    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),
        )
        report = self.run(full_task, **kwargs)
        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, relative_path: Optional[str] = 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`): The folder in which you want to save your tool.
        """
        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()]
        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 = textwrap.dedent("""
            import yaml
            import os
            from smolagents import GradioUI, {{ class_name }}, {{ agent_dict['model']['class'] }}

            # Get current directory path
            CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))

            {% for tool in tools.values() -%}
            from {{managed_agent_relative_path}}tools.{{ tool.name }} import {{ tool.__class__.__name__ }} as {{ tool.name | camelcase }}
            {% endfor %}
            {% for managed_agent in managed_agents.values() -%}
            from {{managed_agent_relative_path}}managed_agents.{{ managed_agent.name }}.app import agent_{{ managed_agent.name }}
            {% endfor %}

            model = {{ agent_dict['model']['class'] }}(
            {% for key in agent_dict['model']['data'] if key not in ['class', 'last_input_token_count', 'last_output_token_count'] -%}
                {{ key }}={{ agent_dict['model']['data'][key]|repr }},
            {% endfor %})

            {% for tool in tools.values() -%}
            {{ tool.name }} = {{ tool.name | camelcase }}()
            {% endfor %}

            with open(os.path.join(CURRENT_DIR, "prompts.yaml"), 'r') as stream:
                prompt_templates = yaml.safe_load(stream)

            {{ agent_name }} = {{ class_name }}(
                model=model,
                tools=[{% for tool_name in tools.keys() if tool_name != "final_answer" %}{{ tool_name }}{% if not loop.last %}, {% endif %}{% endfor %}],
                managed_agents=[{% for subagent_name in managed_agents.keys() %}agent_{{ subagent_name }}{% if not loop.last %}, {% endif %}{% endfor %}],
                {% for attribute_name, value in agent_dict.items() if attribute_name not in ["model", "tools", "prompt_templates", "authorized_imports", "managed_agents", "requirements"] -%}
                {{ attribute_name }}={{ value|repr }},
                {% endfor %}prompt_templates=prompt_templates
            )
            if __name__ == "__main__":
                GradioUI({{ agent_name }}).launch()
            """).strip()
        template_env = jinja2.Environment(loader=jinja2.BaseLoader(), undefined=jinja2.StrictUndefined)
        template_env.filters["repr"] = repr
        template_env.filters["camelcase"] = lambda value: "".join(word.capitalize() for word in value.split("_"))
        template = template_env.from_string(app_template)

        # Render the app.py file from Jinja2 template
        app_text = 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]:
        """Converts agent into a dictionary."""
        # 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 = {
            "tools": tool_dicts,
            "model": {
                "class": self.model.__class__.__name__,
                "data": self.model.to_dict(),
            },
            "managed_agents": {
                managed_agent.name: managed_agent.__class__.__name__ for managed_agent in self.managed_agents.values()
            },
            "prompt_templates": self.prompt_templates,
            "max_steps": self.max_steps,
            "verbosity_level": int(self.logger.level),
            "grammar": self.grammar,
            "planning_interval": self.planning_interval,
            "name": self.name,
            "description": self.description,
            "requirements": list(requirements),
        }
        if hasattr(self, "authorized_imports"):
            agent_dict["authorized_imports"] = self.authorized_imports
        if hasattr(self, "executor_type"):
            agent_dict["executor_type"] = self.executor_type
            agent_dict["executor_kwargs"] = self.executor_kwargs
        if hasattr(self, "max_print_outputs_length"):
            agent_dict["max_print_outputs_length"] = self.max_print_outputs_length
        return agent_dict

    @classmethod
    def from_hub(
        cls,
        repo_id: str,
        token: Optional[str] = None,
        trust_remote_code: bool = False,
        **kwargs,
    ):
        """
        Loads an agent defined on the Hub.

        <Tip warning={true}>

        Loading a tool from the Hub means that you'll download the tool and execute it locally.
        ALWAYS inspect the tool you're downloading before loading it within your runtime, as you would do when
        installing a package using pip/npm/apt.

        </Tip>

        Args:
            repo_id (`str`):
                The name of the repo on the Hub where your tool is defined.
            token (`str`, *optional*):
                The token to identify you on hf.co. If unset, will use the token generated when running
                `huggingface-cli login` (stored in `~/.huggingface`).
            trust_remote_code(`bool`, *optional*, defaults to False):
                This flags marks that you understand the risk of running remote code and that you trust this tool.
                If not setting this to True, loading the tool from Hub will fail.
            kwargs (additional keyword arguments, *optional*):
                Additional keyword arguments that will be split in two: all arguments relevant to the Hub (such as
                `cache_dir`, `revision`, `subfolder`) will be used when downloading the files for your agent, and the
                others will be passed along to its init.
        """
        if not trust_remote_code:
            raise ValueError(
                "Loading an agent from Hub requires to acknowledge you trust its code: to do so, pass `trust_remote_code=True`."
            )

        # Get the agent's Hub folder.
        download_kwargs = {"token": token, "repo_type": "space"} | {
            key: kwargs.pop(key)
            for key in [
                "cache_dir",
                "force_download",
                "proxies",
                "revision",
                "local_files_only",
            ]
            if key in kwargs
        }

        download_folder = Path(snapshot_download(repo_id=repo_id, **download_kwargs))
        return cls.from_folder(download_folder, **kwargs)

    @classmethod
    def from_folder(cls, folder: Union[str, Path], **kwargs):
        """Loads an agent from a local folder.

        Args:
            folder (`str` or `Path`): The folder where the agent is saved.
            **kwargs: Additional keyword arguments that will be passed to the agent's init.
        """
        folder = Path(folder)
        agent_dict = json.loads((folder / "agent.json").read_text())

        # Recursively get managed agents
        managed_agents = []
        for managed_agent_name, managed_agent_class in agent_dict["managed_agents"].items():
            agent_cls = getattr(importlib.import_module("smolagents.agents"), managed_agent_class)
            managed_agents.append(agent_cls.from_folder(folder / "managed_agents" / managed_agent_name))

        tools = []
        for tool_name in agent_dict["tools"]:
            tool_code = (folder / "tools" / f"{tool_name}.py").read_text()
            tools.append(Tool.from_code(tool_code))

        model_class: Model = getattr(importlib.import_module("smolagents.models"), agent_dict["model"]["class"])
        model = model_class.from_dict(agent_dict["model"]["data"])

        args = dict(
            model=model,
            tools=tools,
            managed_agents=managed_agents,
            name=agent_dict["name"],
            description=agent_dict["description"],
            max_steps=agent_dict["max_steps"],
            planning_interval=agent_dict["planning_interval"],
            grammar=agent_dict["grammar"],
            verbosity_level=agent_dict["verbosity_level"],
        )
        if cls.__name__ == "CodeAgent":
            args["additional_authorized_imports"] = agent_dict["authorized_imports"]
            args["executor_type"] = agent_dict["executor_type"]
            args["executor_kwargs"] = agent_dict["executor_kwargs"]
            args["max_print_outputs_length"] = agent_dict["max_print_outputs_length"]
        args.update(kwargs)
        return cls(**args)

    def push_to_hub(
        self,
        repo_id: str,
        commit_message: str = "Upload agent",
        private: Optional[bool] = None,
        token: Optional[Union[bool, str]] = None,
        create_pr: bool = False,
    ) -> str:
        """
        Upload the agent to the Hub.

        Parameters:
            repo_id (`str`):
                The name of the repository you want to push to. It should contain your organization name when
                pushing to a given organization.
            commit_message (`str`, *optional*, defaults to `"Upload agent"`):
                Message to commit while pushing.
            private (`bool`, *optional*, defaults to `None`):
                Whether to make the repo private. If `None`, the repo will be public unless the organization's default is private. This value is ignored if the repo already exists.
            token (`bool` or `str`, *optional*):
                The token to use as HTTP bearer authorization for remote files. If unset, will use the token generated
                when running `huggingface-cli login` (stored in `~/.huggingface`).
            create_pr (`bool`, *optional*, defaults to `False`):
                Whether to create a PR with the uploaded files or directly commit.
        """
        repo_url = create_repo(
            repo_id=repo_id,
            token=token,
            private=private,
            exist_ok=True,
            repo_type="space",
            space_sdk="gradio",
        )
        repo_id = repo_url.repo_id
        metadata_update(
            repo_id,
            {"tags": ["smolagents", "agent"]},
            repo_type="space",
            token=token,
            overwrite=True,
        )

        with tempfile.TemporaryDirectory() as work_dir:
            self.save(work_dir)
            logger.info(f"Uploading the following files to {repo_id}: {','.join(os.listdir(work_dir))}")
            return upload_folder(
                repo_id=repo_id,
                commit_message=commit_message,
                folder_path=work_dir,
                token=token,
                create_pr=create_pr,
                repo_type="space",
            )


class ToolCallingAgent(MultiStepAgent):
    """
    This agent uses JSON-like tool calls, using method `model.get_tool_call` to leverage the LLM engine's tool calling capabilities.

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

    def __init__(
        self,
        tools: List[Tool],
        model: Callable[[List[Dict[str, str]]], ChatMessage],
        prompt_templates: Optional[PromptTemplates] = None,
        planning_interval: Optional[int] = None,
        **kwargs,
    ):
        prompt_templates = prompt_templates or yaml.safe_load(
            importlib.resources.files("smolagents.prompts").joinpath("toolcalling_agent.yaml").read_text()
        )
        super().__init__(
            tools=tools,
            model=model,
            prompt_templates=prompt_templates,
            planning_interval=planning_interval,
            **kwargs,
        )

    def initialize_system_prompt(self) -> str:
        system_prompt = populate_template(
            self.prompt_templates["system_prompt"],
            variables={"tools": self.tools, "managed_agents": self.managed_agents},
        )
        return system_prompt

    def step(self, memory_step: ActionStep) -> Union[None, Any]:
        """
        Perform one step in the ReAct framework: the agent thinks, acts, and observes the result.
        Returns None if the step is not final.
        """
        memory_messages = self.write_memory_to_messages()

        self.input_messages = memory_messages

        # Add new step in logs
        memory_step.model_input_messages = memory_messages.copy()

        try:
            model_message: ChatMessage = self.model(
                memory_messages,
                tools_to_call_from=list(self.tools.values()),
                stop_sequences=["Observation:"],
            )
            memory_step.model_output_message = model_message
            if model_message.tool_calls is None or len(model_message.tool_calls) == 0:
                raise Exception("Model did not call any tools. Call `final_answer` tool to return a final answer.")
            tool_call = model_message.tool_calls[0]
            tool_name, tool_call_id = tool_call.function.name, tool_call.id
            tool_arguments = tool_call.function.arguments

        except Exception as e:
            raise AgentGenerationError(f"Error in generating tool call with model:\n{e}", self.logger) from e

        memory_step.tool_calls = [ToolCall(name=tool_name, arguments=tool_arguments, id=tool_call_id)]

        # Execute
        self.logger.log(
            Panel(Text(f"Calling tool: '{tool_name}' with arguments: {tool_arguments}")),
            level=LogLevel.INFO,
        )
        if tool_name == "final_answer":
            if isinstance(tool_arguments, dict):
                if "answer" in tool_arguments:
                    answer = tool_arguments["answer"]
                else:
                    answer = tool_arguments
            else:
                answer = tool_arguments
            if (
                isinstance(answer, str) and answer in self.state.keys()
            ):  # if the answer is a state variable, return the value
                final_answer = self.state[answer]
                self.logger.log(
                    f"[bold {YELLOW_HEX}]Final answer:[/bold {YELLOW_HEX}] Extracting key '{answer}' from state to return value '{final_answer}'.",
                    level=LogLevel.INFO,
                )
            else:
                final_answer = answer
                self.logger.log(
                    Text(f"Final answer: {final_answer}", style=f"bold {YELLOW_HEX}"),
                    level=LogLevel.INFO,
                )

            memory_step.action_output = final_answer
            return final_answer
        else:
            if tool_arguments is None:
                tool_arguments = {}
            observation = self.execute_tool_call(tool_name, tool_arguments)
            observation_type = type(observation)
            if observation_type in [AgentImage, AgentAudio]:
                if observation_type == AgentImage:
                    observation_name = "image.png"
                elif observation_type == AgentAudio:
                    observation_name = "audio.mp3"
                # TODO: observation naming could allow for different names of same type

                self.state[observation_name] = observation
                updated_information = f"Stored '{observation_name}' in memory."
            else:
                updated_information = str(observation).strip()
            self.logger.log(
                f"Observations: {updated_information.replace('[', '|')}",  # escape potential rich-tag-like components
                level=LogLevel.INFO,
            )
            memory_step.observations = updated_information
            return None


class CodeAgent(MultiStepAgent):
    """
    In this agent, the tool calls will be formulated by the LLM in code format, then parsed and executed.

    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.
        grammar (`dict[str, str]`, *optional*): Grammar used to parse the LLM output.
        additional_authorized_imports (`list[str]`, *optional*): Additional authorized imports for the agent.
        planning_interval (`int`, *optional*): Interval at which the agent will run a planning step.
        executor_type (`str`, default `"local"`): Which executor type to use between `"local"`, `"e2b"`, or `"docker"`.
        executor_kwargs (`dict`, *optional*): Additional arguments to pass to initialize the executor.
        max_print_outputs_length (`int`, *optional*): Maximum length of the print outputs.
        **kwargs: Additional keyword arguments.

    """

    def __init__(
        self,
        tools: List[Tool],
        model: Callable[[List[Dict[str, str]]], ChatMessage],
        prompt_templates: Optional[PromptTemplates] = None,
        grammar: Optional[Dict[str, str]] = None,
        additional_authorized_imports: Optional[List[str]] = None,
        planning_interval: Optional[int] = None,
        executor_type: str = "local",
        executor_kwargs: Optional[Dict[str, Any]] = None,
        max_print_outputs_length: Optional[int] = None,
        **kwargs,
    ):
        self.additional_authorized_imports = additional_authorized_imports if additional_authorized_imports else []
        self.authorized_imports = list(set(BASE_BUILTIN_MODULES) | set(self.additional_authorized_imports))
        self.max_print_outputs_length = max_print_outputs_length
        prompt_templates = prompt_templates or yaml.safe_load(
            importlib.resources.files("smolagents.prompts").joinpath("code_agent.yaml").read_text()
        )
        super().__init__(
            tools=tools,
            model=model,
            prompt_templates=prompt_templates,
            grammar=grammar,
            planning_interval=planning_interval,
            **kwargs,
        )
        if "*" in self.additional_authorized_imports:
            self.logger.log(
                "Caution: you set an authorization for all imports, meaning your agent can decide to import any package it deems necessary. This might raise issues if the package is not installed in your environment.",
                0,
            )
        self.executor_type = executor_type
        self.executor_kwargs = executor_kwargs or {}
        self.python_executor = self.create_python_executor(executor_type, self.executor_kwargs)

    def create_python_executor(self, executor_type: str, kwargs: Dict[str, Any]) -> PythonExecutor:
        match executor_type:
            case "e2b" | "docker":
                if self.managed_agents:
                    raise Exception("Managed agents are not yet supported with remote code execution.")
                if executor_type == "e2b":
                    return E2BExecutor(self.additional_authorized_imports, self.logger, **kwargs)
                else:
                    return DockerExecutor(self.additional_authorized_imports, self.logger, **kwargs)
            case "local":
                return LocalPythonExecutor(
                    self.additional_authorized_imports,
                    max_print_outputs_length=self.max_print_outputs_length,
                )
            case _:  # if applicable
                raise ValueError(f"Unsupported executor type: {executor_type}")

    def initialize_system_prompt(self) -> str:
        system_prompt = populate_template(
            self.prompt_templates["system_prompt"],
            variables={
                "tools": self.tools,
                "managed_agents": self.managed_agents,
                "authorized_imports": (
                    "You can import from any package you want."
                    if "*" in self.authorized_imports
                    else str(self.authorized_imports)
                ),
            },
        )
        return system_prompt

    def step(self, memory_step: ActionStep) -> Union[None, Any]:
        """
        Perform one step in the ReAct framework: the agent thinks, acts, and observes the result.
        Returns None if the step is not final.
        """
        memory_messages = self.write_memory_to_messages()

        self.input_messages = memory_messages.copy()

        # Add new step in logs
        memory_step.model_input_messages = memory_messages.copy()
        try:
            additional_args = {"grammar": self.grammar} if self.grammar is not None else {}
            chat_message: ChatMessage = self.model(
                self.input_messages,
                stop_sequences=["<end_code>", "Observation:"],
                **additional_args,
            )
            memory_step.model_output_message = chat_message
            model_output = chat_message.content
            memory_step.model_output = model_output
        except Exception as e:
            raise AgentGenerationError(f"Error in generating model output:\n{e}", self.logger) from e

        self.logger.log_markdown(
            content=model_output,
            title="Output message of the LLM:",
            level=LogLevel.DEBUG,
        )

        # Parse
        try:
            code_action = fix_final_answer_code(parse_code_blobs(model_output))
        except Exception as e:
            error_msg = f"Error in code parsing:\n{e}\nMake sure to provide correct code blobs."
            raise AgentParsingError(error_msg, self.logger)

        memory_step.tool_calls = [
            ToolCall(
                name="python_interpreter",
                arguments=code_action,
                id=f"call_{len(self.memory.steps)}",
            )
        ]

        # Execute
        self.logger.log_code(title="Executing parsed code:", content=code_action, level=LogLevel.INFO)
        is_final_answer = False
        try:
            output, execution_logs, is_final_answer = self.python_executor(code_action)
            execution_outputs_console = []
            if len(execution_logs) > 0:
                execution_outputs_console += [
                    Text("Execution logs:", style="bold"),
                    Text(execution_logs),
                ]
            observation = "Execution logs:\n" + execution_logs
        except Exception as e:
            if hasattr(self.python_executor, "state") and "_print_outputs" in self.python_executor.state:
                execution_logs = str(self.python_executor.state["_print_outputs"])
                if len(execution_logs) > 0:
                    execution_outputs_console = [
                        Text("Execution logs:", style="bold"),
                        Text(execution_logs),
                    ]
                    memory_step.observations = "Execution logs:\n" + execution_logs
                    self.logger.log(Group(*execution_outputs_console), level=LogLevel.INFO)
            error_msg = str(e)
            if "Import of " in error_msg and " is not allowed" in error_msg:
                self.logger.log(
                    "[bold red]Warning to user: Code execution failed due to an unauthorized import - Consider passing said import under `additional_authorized_imports` when initializing your CodeAgent.",
                    level=LogLevel.INFO,
                )
            raise AgentExecutionError(error_msg, self.logger)

        truncated_output = truncate_content(str(output))
        observation += "Last output from code snippet:\n" + truncated_output
        memory_step.observations = observation

        execution_outputs_console += [
            Text(
                f"{('Out - Final answer' if is_final_answer else 'Out')}: {truncated_output}",
                style=(f"bold {YELLOW_HEX}" if is_final_answer else ""),
            ),
        ]
        self.logger.log(Group(*execution_outputs_console), level=LogLevel.INFO)
        memory_step.action_output = output
        return output if is_final_answer else None