Update prompts.yaml

prompts.yaml

@@ -155,8 +155,8 @@ system_prompt: |-
 
   {%- if managed_agents and managed_agents.values() | list %}
   You can also give tasks to team members.
-  Calling a team member works the same as calling a tool: simply, the only argument you can give in the call is 'task', a long string explaining your task.
-  Given that this team member is a real human, you should be very verbose in your task.
+  Calling a team member works the same as calling a tool: simply, the only argument you can give in the call is 'task', a long string explaining your request.
+  Given that this team member is a real human, you should be very verbose in your request.
   Here is a list of the team members that you can call:
   {%- for agent in managed_agents.values() %}
   - {{ agent.name }}: {{ agent.description }}
@@ -208,102 +208,6 @@ planning:
     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 '\n<end_plan>' tag and stop there.
 
-    Here is your task:
-
-    Task:
-    ```
-    {{ task }}
-    ```
-    You can leverage these tools:
-    {%- for tool in tools.values() %}
-    - {{ tool.name }}: {{ tool.description }}
-        Takes inputs: {{ tool.inputs }}
-        Returns an output of type: {{ tool.output_type }}
-    {%- endfor %}
-
-    {%- if managed_agents and managed_agents.values() | list %}
-    You can also give tasks to team members.
-    Calling a team member works the same as for calling a tool: simply, the only argument you can give in the call is 'request', a long string explaining your request.
-    Given that this team member is a real human, you should be very verbose in your request.
-    Here is a list of the team members that you can call:
-    {%- for agent in managed_agents.values() %}
-    - {{ agent.name }}: {{ agent.description }}
-    {%- endfor %}
-    {%- endif %}
-
-    List of facts that you know:
-    ```
-    {{ answer_facts }}
-    ```
-
-    Now begin! Write your plan below.
-
-  update_facts_pre_messages: |-
-    You are a world expert at gathering known and unknown facts based on a conversation.
-    Below you will find a task, and a history of attempts made to solve the task. You will have to produce a list of these:
-    ### 1. Facts given in the task
-    ### 2. Facts that we have learned
-    ### 3. Facts still to look up
-    ### 4. Facts still to derive
-    Find the task and history below:
-
-  update_facts_post_messages: |-
-    Earlier we've built a list of facts.
-    But since in your previous steps you may have learned useful new facts or invalidated some false ones.
-    Please update your list of facts based on the previous history, and provide these headings:
-    ### 1. Facts given in the task
-    ### 2. Facts that we have learned
-    ### 3. Facts still to look up
-    ### 4. Facts still to derive
-
-    Now write your new list of facts below.
-
-  update_plan_pre_messages: |-
-    You are a world expert at making efficient plans to solve any task using a set of carefully crafted tools.
-
-    You have been given a task:
-    ```
-    {{ task }}
-    ```
-
-    Find below the record of what has been tried so far to solve it. Then you will be asked to make an updated plan to solve the task.
-    If the previous tries so far have met some success, you can make an updated plan based on these actions.
-    If you are stalled, you can make a completely new plan starting from scratch.
-
-  update_plan_post_messages: |-
-    You're still working towards solving this task:
-    ```
-    {{ task }}
-    ```
-
-    You can leverage these tools:
-    {%- for tool in tools.values() %}
-    - {{ tool.name }}: {{ tool.description }}
-        Takes inputs: {{ tool.inputs }}
-        Returns an output of type: {{ tool.output_type }}
-    {%- endfor %}
-
-    {%- if managed_agents and managed_agents.values() | list %}
-    You can also give tasks to team members.
-    Calling a team member works the same as for calling a tool: simply, the only argument you can give in the call is 'task'.
-    Given that this team member is a real human, you should be very verbose in your task, it should be a long string providing informations as detailed as necessary.
-    Here is a list of the team members that you can call:
-    {%- for agent in managed_agents.values() %}
-    - {{ agent.name }}: {{ agent.description }}
-    {%- endfor %}
-    {%- endif %}
-
-    Here is the up to date list of facts that you know:
-    ```
-    {{ facts_update }}
-    ```
-
-    Now 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.
-    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 '\n<end_plan>' tag and stop there.
-
 managed_agent:
   task: |-
     You're a helpful agent named '{{ name }}'.
@@ -322,9 +226,9 @@ managed_agent:
     Put all these in your final_answer tool, everything that you do not pass as an argument to final_answer will be lost.
     And even if your task resolution is not successful, please return as much context as possible, so that your manager can act upon this feedback.
 
-  report: |-
-    Here is the final answer from your managed agent '{{ name }}':
-    {{ final_answer }}
+report: |-
+  Here is the final answer from your managed agent '{{ name }}':
+  {{ final_answer }}
 
 # -------------------------------------------------------
 # This is your *hard* RAG lookup map – ensure the key