Upload agents.py

agents.py

@@ -0,0 +1,1778 @@
+#!/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 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,
+    TokenUsage,
+    ToolCall,
+)
+from .models import (
+    CODEAGENT_RESPONSE_FORMAT,
+    ChatMessage,
+    ChatMessageStreamDelta,
+    ChatMessageToolCall,
+    MessageRole,
+    Model,
+    agglomerate_stream_deltas,
+    parse_json_if_needed,
+)
+from .monitoring import (
+    YELLOW_HEX,
+    AgentLogger,
+    LogLevel,
+    Monitor,
+)
+from .remote_executors import 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 and the agent's memory 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.",
+                    },
+                }
+                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
+        yield FinalAnswerStep(handle_agent_output_types(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 | PlanningStep):
+        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,
+            "max_steps": self.max_steps,
+            "verbosity_level": int(self.logger.level),
+            "planning_interval": self.planning_interval,
+            "name": self.name,
+            "description": self.description,
+            "requirements": sorted(requirements),
+        }
+        return agent_dict
+
+    @classmethod
+    def from_dict(cls, agent_dict: dict[str, Any], **kwargs) -> "MultiStepAgent":
+        """Create agent from a dictionary representation.
+
+        Args:
+            agent_dict (`dict[str, Any]`): Dictionary representation of the agent.
+            **kwargs: Additional keyword arguments that will override agent_dict values.
+
+        Returns:
+            `MultiStepAgent`: Instance of the agent class.
+        """
+        # Load model
+        model_info = agent_dict["model"]
+        model_class = getattr(importlib.import_module("smolagents.models"), model_info["class"])
+        model = model_class.from_dict(model_info["data"])
+        # Load tools
+        tools = []
+        for tool_info in agent_dict["tools"]:
+            tools.append(Tool.from_code(tool_info["code"]))
+        # Load managed agents
+        managed_agents = []
+        for managed_agent_dict in agent_dict["managed_agents"]:
+            agent_class = getattr(importlib.import_module("smolagents.agents"), managed_agent_dict["class"])
+            managed_agent = agent_class.from_dict(managed_agent_dict, **kwargs)
+            managed_agents.append(managed_agent)
+        # Extract base agent parameters
+        agent_args = {
+            "model": model,
+            "tools": tools,
+            "managed_agents": managed_agents,
+            "prompt_templates": agent_dict.get("prompt_templates"),
+            "max_steps": agent_dict.get("max_steps"),
+            "verbosity_level": agent_dict.get("verbosity_level"),
+            "planning_interval": agent_dict.get("planning_interval"),
+            "name": agent_dict.get("name"),
+            "description": agent_dict.get("description"),
+        }
+        # Filter out None values to use defaults from __init__
+        agent_args = {k: v for k, v in agent_args.items() if v is not None}
+        # Update with any additional kwargs
+        agent_args.update(kwargs)
+        # Create agent instance
+        return cls(**agent_args)
+
+    @classmethod
+    def from_hub(
+        cls,
+        repo_id: str,
+        token: str | None = 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: 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.
+        """
+        # Load agent.json
+        folder = Path(folder)
+        agent_dict = json.loads((folder / "agent.json").read_text())
+
+        # Load managed agents from their respective folders, recursively
+        managed_agents = []
+        for managed_agent_name, managed_agent_class_name in agent_dict["managed_agents"].items():
+            agent_cls = getattr(importlib.import_module("smolagents.agents"), managed_agent_class_name)
+            managed_agents.append(agent_cls.from_folder(folder / "managed_agents" / managed_agent_name))
+        agent_dict["managed_agents"] = {}
+
+        # Load tools
+        tools = []
+        for tool_name in agent_dict["tools"]:
+            tool_code = (folder / "tools" / f"{tool_name}.py").read_text()
+            tools.append({"name": tool_name, "code": tool_code})
+        agent_dict["tools"] = tools
+
+        # Add managed agents to kwargs to override the empty list in from_dict
+        if managed_agents:
+            kwargs["managed_agents"] = managed_agents
+
+        return cls.from_dict(agent_dict, **kwargs)
+
+    def push_to_hub(
+        self,
+        repo_id: str,
+        commit_message: str = "Upload agent",
+        private: bool | None = None,
+        token: bool | str | None = 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 (`Model`): 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.
+        stream_outputs (`bool`, *optional*, default `False`): Whether to stream outputs during execution.
+        max_tool_threads (`int`, *optional*): Maximum number of threads for parallel tool calls.
+            Higher values increase concurrency but resource usage as well.
+            Defaults to `ThreadPoolExecutor`'s default.
+        **kwargs: Additional keyword arguments.
+    """
+
+    def __init__(
+        self,
+        tools: list[Tool],
+        model: Model,
+        prompt_templates: PromptTemplates | None = None,
+        planning_interval: int | None = None,
+        stream_outputs: bool = False,
+        max_tool_threads: int | None = 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,
+        )
+        # Streaming setup
+        self.stream_outputs = stream_outputs
+        if self.stream_outputs and not hasattr(self.model, "generate_stream"):
+            raise ValueError(
+                "`stream_outputs` is set to True, but the model class implements no `generate_stream` method."
+            )
+        # Tool calling setup
+        self.max_tool_threads = max_tool_threads
+
+    @property
+    def tools_and_managed_agents(self):
+        """Returns a combined list of tools and managed agents."""
+        return list(self.tools.values()) + list(self.managed_agents.values())
+
+    def initialize_system_prompt(self) -> str:
+        system_prompt = populate_template(
+            self.prompt_templates["system_prompt"],
+            variables={
+                "tools": self.tools,
+                "managed_agents": self.managed_agents,
+                "custom_instructions": self.instructions,
+            },
+        )
+        return system_prompt
+
+    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.
+        """
+        memory_messages = self.write_memory_to_messages()
+
+        input_messages = memory_messages.copy()
+
+        # Add new step in logs
+        memory_step.model_input_messages = input_messages
+
+        try:
+            if self.stream_outputs and hasattr(self.model, "generate_stream"):
+                output_stream = self.model.generate_stream(
+                    input_messages,
+                    stop_sequences=["Observation:", "Calling tools:"],
+                    tools_to_call_from=self.tools_and_managed_agents,
+                )
+
+                chat_message_stream_deltas: list[ChatMessageStreamDelta] = []
+                with Live("", console=self.logger.console, vertical_overflow="visible") as live:
+                    for event in output_stream:
+                        chat_message_stream_deltas.append(event)
+                        live.update(
+                            Markdown(agglomerate_stream_deltas(chat_message_stream_deltas).render_as_markdown())
+                        )
+                        yield event
+                chat_message = agglomerate_stream_deltas(chat_message_stream_deltas)
+            else:
+                chat_message: ChatMessage = self.model.generate(
+                    input_messages,
+                    stop_sequences=["Observation:", "Calling tools:"],
+                    tools_to_call_from=self.tools_and_managed_agents,
+                )
+                if chat_message.content is None and chat_message.raw is not None:
+                    log_content = str(chat_message.raw)
+                else:
+                    log_content = str(chat_message.content) or ""
+
+                self.logger.log_markdown(
+                    content=log_content,
+                    title="Output message of the LLM:",
+                    level=LogLevel.DEBUG,
+                )
+
+            # Record model output
+            memory_step.model_output_message = chat_message
+            memory_step.model_output = chat_message.content
+            memory_step.token_usage = chat_message.token_usage
+        except Exception as e:
+            raise AgentGenerationError(f"Error while generating output:\n{e}", self.logger) from e
+
+        if chat_message.tool_calls is None or len(chat_message.tool_calls) == 0:
+            try:
+                chat_message = self.model.parse_tool_calls(chat_message)
+            except Exception as e:
+                raise AgentParsingError(f"Error while parsing tool call from model output: {e}", self.logger)
+        else:
+            for tool_call in chat_message.tool_calls:
+                tool_call.function.arguments = parse_json_if_needed(tool_call.function.arguments)
+        final_answer, got_final_answer = None, False
+        for output in self.process_tool_calls(chat_message, memory_step):
+            yield output
+            if isinstance(output, ToolOutput):
+                if output.is_final_answer:
+                    if len(chat_message.tool_calls) > 1:
+                        raise AgentExecutionError(
+                            "If you want to return an answer, please do not perform any other tool calls than the final answer tool call!",
+                            self.logger,
+                        )
+                    if got_final_answer:
+                        raise AgentToolExecutionError(
+                            "You returned multiple final answers. Please return only one single final answer!",
+                            self.logger,
+                        )
+                    final_answer = output.output
+                    got_final_answer = True
+
+                    # Manage state variables
+                    if isinstance(final_answer, str) and final_answer in self.state.keys():
+                        final_answer = self.state[final_answer]
+        yield ActionOutput(
+            output=final_answer,
+            is_final_answer=got_final_answer,
+        )
+
+    def process_tool_calls(
+        self, chat_message: ChatMessage, memory_step: ActionStep
+    ) -> Generator[ToolCall | ToolOutput]:
+        """Process tool calls from the model output and update agent memory.
+
+        Args:
+            chat_message (`ChatMessage`): Chat message containing tool calls from the model.
+            memory_step (`ActionStep)`: Memory ActionStep to update with results.
+
+        Yields:
+            `ToolCall | ToolOutput`: The tool call or tool output.
+        """
+        parallel_calls: dict[str, ToolCall] = {}
+        assert chat_message.tool_calls is not None
+        for chat_tool_call in chat_message.tool_calls:
+            tool_call = ToolCall(
+                name=chat_tool_call.function.name, arguments=chat_tool_call.function.arguments, id=chat_tool_call.id
+            )
+            yield tool_call
+            parallel_calls[tool_call.id] = tool_call
+
+        # Helper function to process a single tool call
+        def process_single_tool_call(tool_call: ToolCall) -> ToolOutput:
+            tool_name = tool_call.name
+            tool_arguments = tool_call.arguments or {}
+            self.logger.log(
+                Panel(Text(f"Calling tool: '{tool_name}' with arguments: {tool_arguments}")),
+                level=LogLevel.INFO,
+            )
+            tool_call_result = self.execute_tool_call(tool_name, tool_arguments)
+            tool_call_result_type = type(tool_call_result)
+            if tool_call_result_type in [AgentImage, AgentAudio]:
+                if tool_call_result_type == AgentImage:
+                    observation_name = "image.png"
+                elif tool_call_result_type == AgentAudio:
+                    observation_name = "audio.mp3"
+                # TODO: tool_call_result naming could allow for different names of same type
+                self.state[observation_name] = tool_call_result
+                observation = f"Stored '{observation_name}' in memory."
+            else:
+                observation = str(tool_call_result).strip()
+            self.logger.log(
+                f"Observations: {observation.replace('[', '|')}",  # escape potential rich-tag-like components
+                level=LogLevel.INFO,
+            )
+            is_final_answer = tool_name == "final_answer"
+
+            return ToolOutput(
+                id=tool_call.id,
+                output=tool_call_result,
+                is_final_answer=is_final_answer,
+                observation=observation,
+                tool_call=tool_call,
+            )
+
+        # Process tool calls in parallel
+        outputs = {}
+        if len(parallel_calls) == 1:
+            # If there's only one call, process it directly
+            tool_call = list(parallel_calls.values())[0]
+            tool_output = process_single_tool_call(tool_call)
+            outputs[tool_output.id] = tool_output
+            yield tool_output
+        else:
+            # If multiple tool calls, process them in parallel
+            with ThreadPoolExecutor(self.max_tool_threads) as executor:
+                futures = [
+                    executor.submit(process_single_tool_call, tool_call) for tool_call in parallel_calls.values()
+                ]
+                for future in as_completed(futures):
+                    tool_output = future.result()
+                    outputs[tool_output.id] = tool_output
+                    yield tool_output
+
+        memory_step.tool_calls = [parallel_calls[k] for k in sorted(parallel_calls.keys())]
+        memory_step.observations = memory_step.observations or ""
+        for tool_output in [outputs[k] for k in sorted(outputs.keys())]:
+            memory_step.observations += tool_output.observation + "\n"
+        memory_step.observations = (
+            memory_step.observations.rstrip("\n") if memory_step.observations else memory_step.observations
+        )
+
+    def _substitute_state_variables(self, arguments: dict[str, str] | str) -> dict[str, Any] | str:
+        """Replace string values in arguments with their corresponding state values if they exist."""
+        if isinstance(arguments, dict):
+            return {
+                key: self.state.get(value, value) if isinstance(value, str) else value
+                for key, value in arguments.items()
+            }
+        return arguments
+
+    def execute_tool_call(self, tool_name: str, arguments: dict[str, str] | str) -> Any:
+        """
+        Execute a tool or managed agent with the provided arguments.
+
+        The arguments are replaced with the actual values from the state if they refer to state variables.
+
+        Args:
+            tool_name (`str`): Name of the tool or managed agent to execute.
+            arguments (dict[str, str] | str): Arguments passed to the tool call.
+        """
+        # Check if the tool exists
+        available_tools = {**self.tools, **self.managed_agents}
+        if tool_name not in available_tools:
+            raise AgentToolExecutionError(
+                f"Unknown tool {tool_name}, should be one of: {', '.join(available_tools)}.", self.logger
+            )
+
+        # Get the tool and substitute state variables in arguments
+        tool = available_tools[tool_name]
+        arguments = self._substitute_state_variables(arguments)
+        is_managed_agent = tool_name in self.managed_agents
+
+        try:
+            validate_tool_arguments(tool, arguments)
+        except (ValueError, TypeError) as e:
+            raise AgentToolCallError(str(e), self.logger) from e
+        except Exception as e:
+            error_msg = f"Error executing tool '{tool_name}' with arguments {str(arguments)}: {type(e).__name__}: {e}"
+            raise AgentToolExecutionError(error_msg, self.logger) from e
+
+        try:
+            # Call tool with appropriate arguments
+            if isinstance(arguments, dict):
+                return tool(**arguments) if is_managed_agent else tool(**arguments, sanitize_inputs_outputs=True)
+            else:
+                return tool(arguments) if is_managed_agent else tool(arguments, sanitize_inputs_outputs=True)
+
+        except Exception as e:
+            # Handle execution errors
+            if is_managed_agent:
+                error_msg = (
+                    f"Error executing request to team member '{tool_name}' with arguments {str(arguments)}: {e}\n"
+                    "Please try again or request to another team member"
+                )
+            else:
+                error_msg = (
+                    f"Error executing tool '{tool_name}' with arguments {str(arguments)}: {type(e).__name__}: {e}\n"
+                    "Please try again or use another tool"
+                )
+            raise AgentToolExecutionError(error_msg, self.logger) from e
+
+
+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 (`Model`): Model that will generate the agent's actions.
+        prompt_templates ([`~agents.PromptTemplates`], *optional*): Prompt templates.
+        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 (`Literal["local", "e2b", "modal", "docker", "wasm"]`, default `"local"`): Type of code executor.
+        executor_kwargs (`dict`, *optional*): Additional arguments to pass to initialize the executor.
+        max_print_outputs_length (`int`, *optional*): Maximum length of the print outputs.
+        stream_outputs (`bool`, *optional*, default `False`): Whether to stream outputs during execution.
+        use_structured_outputs_internally (`bool`, default `False`): Whether to use structured generation at each action step: improves performance for many models.
+
+            <Added version="1.17.0"/>
+        code_block_tags (`tuple[str, str]` | `Literal["markdown"]`, *optional*): Opening and closing tags for code blocks (regex strings). Pass a custom tuple, or pass 'markdown' to use ("```(?:python|py)", "\\n```"), leave empty to use ("<code>", "</code>").
+        **kwargs: Additional keyword arguments.
+    """
+
+    def __init__(
+        self,
+        tools: list[Tool],
+        model: Model,
+        prompt_templates: PromptTemplates | None = None,
+        additional_authorized_imports: list[str] | None = None,
+        planning_interval: int | None = None,
+        executor_type: Literal["local", "e2b", "modal", "docker", "wasm"] = "local",
+        executor_kwargs: dict[str, Any] | None = None,
+        max_print_outputs_length: int | None = None,
+        stream_outputs: bool = False,
+        use_structured_outputs_internally: bool = False,
+        code_block_tags: str | tuple[str, str] | None = None,
+        **kwargs,
+    ):
+        self.additional_authorized_imports = additional_authorized_imports if additional_authorized_imports else []
+        self.authorized_imports = sorted(set(BASE_BUILTIN_MODULES) | set(self.additional_authorized_imports))
+        self.max_print_outputs_length = max_print_outputs_length
+        self._use_structured_outputs_internally = use_structured_outputs_internally
+        if self._use_structured_outputs_internally:
+            prompt_templates = prompt_templates or yaml.safe_load(
+                importlib.resources.files("smolagents.prompts").joinpath("structured_code_agent.yaml").read_text()
+            )
+        else:
+            prompt_templates = prompt_templates or yaml.safe_load(
+                importlib.resources.files("smolagents.prompts").joinpath("code_agent.yaml").read_text()
+            )
+
+        if isinstance(code_block_tags, str) and not code_block_tags == "markdown":
+            raise ValueError("Only 'markdown' is supported for a string argument to `code_block_tags`.")
+        self.code_block_tags = (
+            code_block_tags
+            if isinstance(code_block_tags, tuple)
+            else ("```python", "```")
+            if code_block_tags == "markdown"
+            else ("<code>", "</code>")
+        )
+
+        super().__init__(
+            tools=tools,
+            model=model,
+            prompt_templates=prompt_templates,
+            planning_interval=planning_interval,
+            **kwargs,
+        )
+        self.stream_outputs = stream_outputs
+        if self.stream_outputs and not hasattr(self.model, "generate_stream"):
+            raise ValueError(
+                "`stream_outputs` is set to True, but the model class implements no `generate_stream` method."
+            )
+        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.",
+                level=LogLevel.INFO,
+            )
+        if executor_type not in {"local", "e2b", "modal", "docker", "wasm"}:
+            raise ValueError(f"Unsupported executor type: {executor_type}")
+        self.executor_type = executor_type
+        self.executor_kwargs: dict[str, Any] = executor_kwargs or {}
+        self.python_executor = self.create_python_executor()
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.cleanup()
+
+    def cleanup(self):
+        """Clean up resources used by the agent, such as the remote Python executor."""
+        if hasattr(self.python_executor, "cleanup"):
+            self.python_executor.cleanup()
+
+    def create_python_executor(self) -> PythonExecutor:
+        if self.executor_type == "local":
+            return LocalPythonExecutor(
+                self.additional_authorized_imports,
+                **{"max_print_outputs_length": self.max_print_outputs_length} | self.executor_kwargs,
+            )
+        else:
+            if self.managed_agents:
+                raise Exception("Managed agents are not yet supported with remote code execution.")
+            remote_executors = {
+                "e2b": E2BExecutor,
+                "docker": DockerExecutor,
+                "wasm": WasmExecutor,
+                "modal": ModalExecutor,
+            }
+            return remote_executors[self.executor_type](
+                self.additional_authorized_imports, self.logger, **self.executor_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,
+                "authorized_imports": (
+                    "You can import from any package you want."
+                    if "*" in self.authorized_imports
+                    else str(self.authorized_imports)
+                ),
+                "custom_instructions": self.instructions,
+                "code_block_opening_tag": self.code_block_tags[0],
+                "code_block_closing_tag": self.code_block_tags[1],
+            },
+        )
+        return system_prompt
+
+    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.
+        """
+        memory_messages = self.write_memory_to_messages()
+
+        input_messages = memory_messages.copy()
+        ### Generate model output ###
+        memory_step.model_input_messages = input_messages
+        stop_sequences = ["Observation:", "Calling tools:"]
+        if self.code_block_tags[1] not in self.code_block_tags[0]:
+            # If the closing tag is contained in the opening tag, adding it as a stop sequence would cut short any code generation
+            stop_sequences.append(self.code_block_tags[1])
+        try:
+            additional_args: dict[str, Any] = {}
+            if self._use_structured_outputs_internally:
+                additional_args["response_format"] = CODEAGENT_RESPONSE_FORMAT
+            if self.stream_outputs:
+                output_stream = self.model.generate_stream(
+                    input_messages,
+                    stop_sequences=stop_sequences,
+                    **additional_args,
+                )
+                chat_message_stream_deltas: list[ChatMessageStreamDelta] = []
+                with Live("", console=self.logger.console, vertical_overflow="visible") as live:
+                    for event in output_stream:
+                        chat_message_stream_deltas.append(event)
+                        live.update(
+                            Markdown(agglomerate_stream_deltas(chat_message_stream_deltas).render_as_markdown())
+                        )
+                        yield event
+                chat_message = agglomerate_stream_deltas(chat_message_stream_deltas)
+                memory_step.model_output_message = chat_message
+                output_text = chat_message.content
+            else:
+                chat_message: ChatMessage = self.model.generate(
+                    input_messages,
+                    stop_sequences=stop_sequences,
+                    **additional_args,
+                )
+                memory_step.model_output_message = chat_message
+                output_text = chat_message.content
+                self.logger.log_markdown(
+                    content=output_text or "",
+                    title="Output message of the LLM:",
+                    level=LogLevel.DEBUG,
+                )
+
+            if not self._use_structured_outputs_internally:
+                # This adds the end code sequence (i.e. the closing code block tag) to the history.
+                # This will nudge subsequent LLM calls to finish with this end code sequence, thus efficiently stopping generation.
+                if output_text and not output_text.strip().endswith(self.code_block_tags[1]):
+                    output_text += self.code_block_tags[1]
+                    memory_step.model_output_message.content = output_text
+
+            memory_step.token_usage = chat_message.token_usage
+            memory_step.model_output = output_text
+        except Exception as e:
+            raise AgentGenerationError(f"Error in generating model output:\n{e}", self.logger) from e
+
+        ### Parse output ###
+        try:
+            if self._use_structured_outputs_internally:
+                code_action = json.loads(output_text)["code"]
+                code_action = extract_code_from_text(code_action, self.code_block_tags) or code_action
+            else:
+                code_action = parse_code_blobs(output_text, self.code_block_tags)
+            code_action = fix_final_answer_code(code_action)
+            memory_step.code_action = code_action
+        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)
+
+        tool_call = ToolCall(
+            name="python_interpreter",
+            arguments=code_action,
+            id=f"call_{len(self.memory.steps)}",
+        )
+        yield tool_call
+        memory_step.tool_calls = [tool_call]
+
+        ### Execute action ###
+        self.logger.log_code(title="Executing parsed code:", content=code_action, level=LogLevel.INFO)
+        try:
+            code_output = self.python_executor(code_action)
+            execution_outputs_console = []
+            if len(code_output.logs) > 0:
+                execution_outputs_console += [
+                    Text("Execution logs:", style="bold"),
+                    Text(code_output.logs),
+                ]
+            observation = "Execution logs:\n" + code_output.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(code_output.output))
+        observation += "Last output from code snippet:\n" + truncated_output
+        memory_step.observations = observation
+
+        if not code_output.is_final_answer:
+            execution_outputs_console += [
+                Text(
+                    f"Out: {truncated_output}",
+                ),
+            ]
+        self.logger.log(Group(*execution_outputs_console), level=LogLevel.INFO)
+        memory_step.action_output = code_output.output
+        yield ActionOutput(output=code_output.output, is_final_answer=code_output.is_final_answer)
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert the agent to a dictionary representation.
+
+        Returns:
+            `dict`: Dictionary representation of the agent.
+        """
+        agent_dict = super().to_dict()
+        agent_dict["authorized_imports"] = self.authorized_imports
+        agent_dict["executor_type"] = self.executor_type
+        agent_dict["executor_kwargs"] = self.executor_kwargs
+        agent_dict["max_print_outputs_length"] = self.max_print_outputs_length
+        return agent_dict
+
+    @classmethod
+    def from_dict(cls, agent_dict: dict[str, Any], **kwargs) -> "CodeAgent":
+        """Create CodeAgent from a dictionary representation.
+
+        Args:
+            agent_dict (`dict[str, Any]`): Dictionary representation of the agent.
+            **kwargs: Additional keyword arguments that will override agent_dict values.
+
+        Returns:
+            `CodeAgent`: Instance of the CodeAgent class.
+        """
+        # Add CodeAgent-specific parameters to kwargs
+        code_agent_kwargs = {
+            "additional_authorized_imports": agent_dict.get("authorized_imports"),
+            "executor_type": agent_dict.get("executor_type"),
+            "executor_kwargs": agent_dict.get("executor_kwargs"),
+            "max_print_outputs_length": agent_dict.get("max_print_outputs_length"),
+            "code_block_tags": agent_dict.get("code_block_tags"),
+        }
+        # Filter out None values
+        code_agent_kwargs = {k: v for k, v in code_agent_kwargs.items() if v is not None}
+        # Update with any additional kwargs
+        code_agent_kwargs.update(kwargs)
+        # Call the parent class's from_dict method
+        return super().from_dict(agent_dict, **code_agent_kwargs)