src/smolagents/prompts/code_agent.yaml [269:317]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    Etc.
    This plan should involve individual tasks based on the available tools, that if executed correctly will yield the correct answer.
    Beware that you have {remaining_steps} steps remaining.
    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 write your updated facts survey below, then your new plan.
managed_agent:
  task: |-
      You're a helpful agent named '{{name}}'.
      You have been submitted this task by your manager.
      ---
      Task:
      {{task}}
      ---
      You're helping your manager solve a wider task: so make sure to not provide a one-line answer, but give as much information as possible to give them a clear understanding of the answer.

      Your final_answer WILL HAVE to contain these parts:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



src/smolagents/prompts/structured_code_agent.yaml [214:262]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    Etc.
    This plan should involve individual tasks based on the available tools, that if executed correctly will yield the correct answer.
    Beware that you have {remaining_steps} steps remaining.
    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 write your updated facts survey below, then your new plan.
managed_agent:
  task: |-
    You're a helpful agent named '{{name}}'.
    You have been submitted this task by your manager.
    ---
    Task:
    {{task}}
    ---
    You're helping your manager solve a wider task: so make sure to not provide a one-line answer, but give as much information as possible to give them a clear understanding of the answer.

    Your final_answer WILL HAVE to contain these parts:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



