src/smolagents/prompts/code_agent.yaml [202:259]:
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    Then for the given task, develop a step-by-step high-level plan taking into account the above inputs and list of facts.
    This plan should involve individual tasks based on the available tools, that if executed correctly will yield the correct answer.
    Do not skip steps, do not add any superfluous steps. Only write the high-level plan, DO NOT DETAIL INDIVIDUAL TOOL CALLS.
    After writing the final step of the plan, write the '<end_plan>' tag and stop there.

    You can leverage these tools, behaving like regular python functions:
    ```python
    {%- for tool in tools.values() %}
    def {{ tool.name }}({% for arg_name, arg_info in tool.inputs.items() %}{{ arg_name }}: {{ arg_info.type }}{% if not loop.last %}, {% endif %}{% endfor %}) -> {{tool.output_type}}:
        """{{ tool.description }}

        Args:
        {%- for arg_name, arg_info in tool.inputs.items() %}
            {{ arg_name }}: {{ arg_info.description }}
        {%- endfor %}
        """
    {% endfor %}
    ```

    {%- if managed_agents and managed_agents.values() | list %}
    You can also give tasks to team members.
    Calling a team member works similarly to calling a tool: provide the task description as the 'task' argument. Since this team member is a real human, be as detailed and verbose as necessary in your task description.
    You can also include any relevant variables or context using the 'additional_args' argument.
    Here is a list of the team members that you can call:
    ```python
    {%- for agent in managed_agents.values() %}
    def {{ agent.name }}(task: str, additional_args: dict[str, Any]) -> str:
        """{{ agent.description }}

        Args:
            task: Long detailed description of the task.
            additional_args: Dictionary of extra inputs to pass to the managed agent, e.g. images, dataframes, or any other contextual data it may need.
        """
    {% endfor %}
    ```
    {%- endif %}

    ---
    Now begin! Here is your task:
    ```
    {{task}}
    ```
    First in part 1, write the facts survey, then in part 2, write your plan.
  update_plan_pre_messages: |-
    You are a world expert at analyzing a situation, and plan accordingly towards solving a task.
    You have been given the following task:
    ```
    {{task}}
    ```
  
    Below you will find a history of attempts made to solve this task.
    You will first have to produce a survey of known and unknown facts, then propose a step-by-step high-level plan to solve the task.
    If the previous tries so far have met some success, your updated plan can build on these results.
    If you are stalled, you can make a completely new plan starting from scratch.

    Find the task and history below:
  update_plan_post_messages: |-
    Now write your updated facts below, taking into account the above history:
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src/smolagents/prompts/structured_code_agent.yaml [147:204]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    Then for the given task, develop a step-by-step high-level plan taking into account the above inputs and list of facts.
    This plan should involve individual tasks based on the available tools, that if executed correctly will yield the correct answer.
    Do not skip steps, do not add any superfluous steps. Only write the high-level plan, DO NOT DETAIL INDIVIDUAL TOOL CALLS.
    After writing the final step of the plan, write the '<end_plan>' tag and stop there.

    You can leverage these tools, behaving like regular python functions:
    ```python
    {%- for tool in tools.values() %}
    def {{ tool.name }}({% for arg_name, arg_info in tool.inputs.items() %}{{ arg_name }}: {{ arg_info.type }}{% if not loop.last %}, {% endif %}{% endfor %}) -> {{tool.output_type}}:
        """{{ tool.description }}

        Args:
        {%- for arg_name, arg_info in tool.inputs.items() %}
            {{ arg_name }}: {{ arg_info.description }}
        {%- endfor %}
        """
    {% endfor %}
    ```

    {%- if managed_agents and managed_agents.values() | list %}
    You can also give tasks to team members.
    Calling a team member works similarly to calling a tool: provide the task description as the 'task' argument. Since this team member is a real human, be as detailed and verbose as necessary in your task description.
    You can also include any relevant variables or context using the 'additional_args' argument.
    Here is a list of the team members that you can call:
    ```python
    {%- for agent in managed_agents.values() %}
    def {{ agent.name }}(task: str, additional_args: dict[str, Any]) -> str:
        """{{ agent.description }}

        Args:
            task: Long detailed description of the task.
            additional_args: Dictionary of extra inputs to pass to the managed agent, e.g. images, dataframes, or any other contextual data it may need.
        """
    {% endfor %}
    ```
    {%- endif %}

    ---
    Now begin! Here is your task:
    ```
    {{task}}
    ```
    First in part 1, write the facts survey, then in part 2, write your plan.
  update_plan_pre_messages: |-
    You are a world expert at analyzing a situation, and plan accordingly towards solving a task.
    You have been given the following task:
    ```
    {{task}}
    ```

    Below you will find a history of attempts made to solve this task.
    You will first have to produce a survey of known and unknown facts, then propose a step-by-step high-level plan to solve the task.
    If the previous tries so far have met some success, your updated plan can build on these results.
    If you are stalled, you can make a completely new plan starting from scratch.

    Find the task and history below:
  update_plan_post_messages: |-
    Now write your updated facts below, taking into account the above history:
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