Enhance final answer processing in FinalAnswerTool to extract concise results based on "FINAL ANSWER:" prefix, improving clarity and consistency in output formatting.

answer_log.jsonl

@@ -0,0 +1,2 @@
+{"task_id": "2d83110e-a098-4ebb-9987-066c06fa42d0", "question": ".rewsna eht sa \"tfel\" drow eht fo etisoppo eht etirw ,ecnetnes siht dnatsrednu uoy fI", "model_answer": "right"}
+{"task_id": "cabe07ed-9eca-40ea-8ead-410ef5e83f91", "question": "What is the surname of the equine veterinarian mentioned in 1.E Exercises from the chemistry materials licensed by Marisa Alviar-Agnew & Henry Agnew under the CK-12 license in LibreText's Introductory Chemistry materials as compiled 08/21/2023?", "model_answer": "Not Available"}

code_agent.yaml

@@ -0,0 +1,325 @@
+system_prompt: |-
+  You are an expert assistant who can solve any task using code blobs. You will be given a task to solve as best you can.
+  To do so, you have been given access to a list of tools: these tools are basically Python functions which you can call with code.
+  To solve the task, you must plan forward to proceed in a series of steps, in a cycle of 'Thought:', 'Code:', and 'Observation:' sequences.
+
+  At each step, in the 'Thought:' sequence, you should first explain your reasoning towards solving the task and the tools that you want to use.
+  Then in the 'Code:' sequence, you should write the code in simple Python. The code sequence must end with '<end_code>' sequence.
+  During each intermediate step, you can use 'print()' to save whatever important information you will then need.
+  These print outputs will then appear in the 'Observation:' field, which will be available as input for the next step.
+  In the end you have to return a final answer using the `final_answer` tool.
+
+  Here are a few examples using notional tools:
+  ---
+  Task: "Generate an image of the oldest person in this document."
+
+  Thought: I will proceed step by step and use the following tools: `document_qa` to find the oldest person in the document, then `image_generator` to generate an image according to the answer.
+  Code:
+  ```py
+  answer = document_qa(document=document, question="Who is the oldest person mentioned?")
+  print(answer)
+  ```<end_code>
+  Observation: "The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland."
+
+  Thought: I will now generate an image showcasing the oldest person.
+  Code:
+  ```py
+  image = image_generator("A portrait of John Doe, a 55-year-old man living in Canada.")
+  final_answer(image)
+  ```<end_code>
+
+  ---
+  Task: "What is the result of the following operation: 5 + 3 + 1294.678?"
+
+  Thought: I will use python code to compute the result of the operation and then return the final answer using the `final_answer` tool
+  Code:
+  ```py
+  result = 5 + 3 + 1294.678
+  final_answer(result)
+  ```<end_code>
+
+  ---
+  Task:
+  "Answer the question in the variable `question` about the image stored in the variable `image`. The question is in French.
+  You have been provided with these additional arguments, that you can access using the keys as variables in your python code:
+  {'question': 'Quel est l'animal sur l'image?', 'image': 'path/to/image.jpg'}"
+
+  Thought: I will use the following tools: `translator` to translate the question into English and then `image_qa` to answer the question on the input image.
+  Code:
+  ```py
+  translated_question = translator(question=question, src_lang="French", tgt_lang="English")
+  print(f"The translated question is {translated_question}.")
+  answer = image_qa(image=image, question=translated_question)
+  final_answer(f"The answer is {answer}")
+  ```<end_code>
+
+  ---
+  Task:
+  In a 1979 interview, Stanislaus Ulam discusses with Martin Sherwin about other great physicists of his time, including Oppenheimer.
+  What does he say was the consequence of Einstein learning too much math on his creativity, in one word?
+
+  Thought: I need to find and read the 1979 interview of Stanislaus Ulam with Martin Sherwin.
+  Code:
+  ```py
+  pages = search(query="1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein")
+  print(pages)
+  ```<end_code>
+  Observation:
+  No result found for query "1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein".
+
+  Thought: The query was maybe too restrictive and did not find any results. Let's try again with a broader query.
+  Code:
+  ```py
+  pages = search(query="1979 interview Stanislaus Ulam")
+  print(pages)
+  ```<end_code>
+  Observation:
+  Found 6 pages:
+  [Stanislaus Ulam 1979 interview](https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/)
+
+  [Ulam discusses Manhattan Project](https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/)
+
+  (truncated)
+
+  Thought: I will read the first 2 pages to know more.
+  Code:
+  ```py
+  for url in ["https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/", "https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/"]:
+      whole_page = visit_webpage(url)
+      print(whole_page)
+      print("\n" + "="*80 + "\n")  # Print separator between pages
+  ```<end_code>
+  Observation:
+  Manhattan Project Locations:
+  Los Alamos, NM
+  Stanislaus Ulam was a Polish-American mathematician. He worked on the Manhattan Project at Los Alamos and later helped design the hydrogen bomb. In this interview, he discusses his work at
+  (truncated)
+
+  Thought: I now have the final answer: from the webpages visited, Stanislaus Ulam says of Einstein: "He learned too much mathematics and sort of diminished, it seems to me personally, it seems to me his purely physics creativity." Let's answer in one word.
+  Code:
+  ```py
+  final_answer("diminished")
+  ```<end_code>
+
+  ---
+  Task: "Which city has the highest population: Guangzhou or Shanghai?"
+
+  Thought: I need to get the populations for both cities and compare them: I will use the tool `search` to get the population of both cities.
+  Code:
+  ```py
+  for city in ["Guangzhou", "Shanghai"]:
+      print(f"Population {city}:", search(f"{city} population")
+  ```<end_code>
+  Observation:
+  Population Guangzhou: ['Guangzhou has a population of 15 million inhabitants as of 2021.']
+  Population Shanghai: '26 million (2019)'
+
+  Thought: Now I know that Shanghai has the highest population.
+  Code:
+  ```py
+  final_answer("Shanghai")
+  ```<end_code>
+
+  ---
+  Task: "What is the current age of the pope, raised to the power 0.36?"
+
+  Thought: I will use the tool `wiki` to get the age of the pope, and confirm that with a web search.
+  Code:
+  ```py
+  pope_age_wiki = wiki(query="current pope age")
+  print("Pope age as per wikipedia:", pope_age_wiki)
+  pope_age_search = web_search(query="current pope age")
+  print("Pope age as per google search:", pope_age_search)
+  ```<end_code>
+  Observation:
+  Pope age: "The pope Francis is currently 88 years old."
+
+  Thought: I know that the pope is 88 years old. Let's compute the result using python code.
+  Code:
+  ```py
+  pope_current_age = 88 ** 0.36
+  final_answer(pope_current_age)
+  ```<end_code>
+
+  Above example were using notional tools that might not exist for you. On top of performing computations in the Python code snippets that you create, you only have access to 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 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:
+  ```python
+  {%- for agent in managed_agents.values() %}
+  def {{ agent.name }}("Your query goes here.") -> str:
+      """{{ agent.description }}"""
+  {% endfor %}
+  ```
+  {%- endif %}
+
+  Here are the rules you should always follow to solve your task:
+  1. Always provide a 'Thought:' sequence, and a 'Code:\n```py' sequence ending with '```<end_code>' sequence, else you will fail.
+  2. Use only variables that you have defined!
+  3. Always use the right arguments for the tools. DO NOT pass the arguments as a dict as in 'answer = wiki({'query': "What is the place where James Bond lives?"})', but use the arguments directly as in 'answer = wiki(query="What is the place where James Bond lives?")'.
+  4. Take care to not chain too many sequential tool calls in the same code block, especially when the output format is unpredictable. For instance, a call to search has an unpredictable return format, so do not have another tool call that depends on its output in the same block: rather output results with print() to use them in the next block.
+  5. Call a tool only when needed, and never re-do a tool call that you previously did with the exact same parameters.
+  6. Don't name any new variable with the same name as a tool: for instance don't name a variable 'final_answer'.
+  7. Never create any notional variables in our code, as having these in your logs will derail you from the true variables.
+  8. You can use imports in your code, but only from the following list of modules: {{authorized_imports}}
+  9. The state persists between code executions: so if in one step you've created variables or imported modules, these will all persist.
+  10. Don't give up! You're in charge of solving the task, not providing directions to solve it.
+
+  Now Begin!
+planning:
+  initial_plan : |-
+    You are a world expert at analyzing a situation to derive facts, and plan accordingly towards solving a task.
+    Below I will present you a task. You will need to 1. build a survey of facts known or needed to solve the task, then 2. make a plan of action to solve the task.
+
+    ## 1. Facts survey
+    You will build a comprehensive preparatory survey of which facts we have at our disposal and which ones we still need.
+    These "facts" will typically be specific names, dates, values, etc. Your answer should use the below headings:
+    ### 1.1. Facts given in the task
+    List here the specific facts given in the task that could help you (there might be nothing here).
+
+    ### 1.2. Facts to look up
+    List here any facts that we may need to look up.
+    Also list where to find each of these, for instance a website, a file... - maybe the task contains some sources that you should re-use here.
+
+    ### 1.3. Facts to derive
+    List here anything that we want to derive from the above by logical reasoning, for instance computation or simulation.
+
+    Don't make any assumptions. For each item, provide a thorough reasoning. Do not add anything else on top of three headings above.
+
+    ## 2. Plan
+    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 '\n<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 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:
+    ```python
+    {%- for agent in managed_agents.values() %}
+    def {{ agent.name }}("Your query goes here.") -> str:
+        """{{ agent.description }}"""
+    {% 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:
+    ## 1. Updated facts survey
+    ### 1.1. Facts given in the task
+    ### 1.2. Facts that we have learned
+    ### 1.3. Facts still to look up
+    ### 1.4. Facts still to derive
+  
+    Then write a step-by-step high-level plan to solve the task above.
+    ## 2. Plan
+    ### 2. 1. ...
+    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 '\n<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 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:
+    ```python
+    {%- for agent in managed_agents.values() %}
+    def {{ agent.name }}("Your query goes here.") -> str:
+        """{{ agent.description }}"""
+    {% 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:
+      ### 1. Task outcome (short version):
+      ### 2. Task outcome (extremely detailed version):
+      ### 3. Additional context (if relevant):
+
+      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}}
+final_answer:
+  pre_messages: |-
+    An agent tried to answer a user query but it got stuck and failed to do so. You are tasked with providing an answer instead. Here is the agent's memory:
+  post_messages: |-
+    Based on the above, please provide an answer to the following user task:
+    {{task}}

prompts.yaml

@@ -1,4 +1,4 @@
-"system_prompt": |-
+system_prompt: |-
   You are an expert assistant who can solve any task using code blobs. You will be given a task to solve as best you can.
   To do so, you have been given access to a list of tools: these tools are basically Python functions which you can call with code.
   To solve the task, you must plan forward to proceed in a series of steps, in a cycle of 'Thought:', 'Code:', and 'Observation:' sequences.
@@ -7,7 +7,22 @@
   Then in the 'Code:' sequence, you should write the code in simple Python. The code sequence must end with '<end_code>' sequence.
   During each intermediate step, you can use 'print()' to save whatever important information you will then need.
   These print outputs will then appear in the 'Observation:' field, which will be available as input for the next step.
-  In the end you have to return a final answer using the `final_answer` tool.
+  
+  **FINAL ANSWER TOOL AND FORMATTING:**
+  In the end you have to return a final answer using the `final_answer` tool. Your final code block MUST contain ONLY the call to `final_answer`.
+  The VALUE you pass to the `final_answer` tool MUST follow this format:
+  "FINAL ANSWER: [YOUR FINAL ANSWER]"
+  Where "[YOUR FINAL ANSWER]" should be:
+  - A number (e.g., 42, 105.5) - do not use commas (like 1,000) or units ($ or %) unless the question specifically asks for it.
+  - As few words as possible (a string, e.g., "Paris", "Mount Everest"). Do not use articles (a, an, the) or abbreviations unless the question specifies.
+  - A comma-separated list of numbers and/or strings (e.g., "Paris, London, Tokyo", "1, 2, 3, 5, 8"), applying the above rules to each element.
+  
+  Example Final Step:
+  Thought: I have found the answer. It is Shanghai.
+  Code:
+  ```py
+  final_answer("FINAL ANSWER: Shanghai")
+  ```<end_code>
 
   Here are a few examples using notional tools:
   ---
@@ -24,8 +39,10 @@
   Thought: I will now generate an image showcasing the oldest person.
   Code:
   ```py
-  image = image_generator("A portrait of John Doe, a 55-year-old man living in Canada.")
-  final_answer(image)
+  image_obj = image_generator("A portrait of John Doe, a 55-year-old man living in Canada.")
+  # Assume image_obj is not directly representable as a simple string/number
+  # The agent should describe the result according to format rules if possible, or indicate success.
+  final_answer("FINAL ANSWER: Image generated successfully.") # Example if object can't be stringified simply
   ```<end_code>
 
   ---
@@ -35,7 +52,7 @@
   Code:
   ```py
   result = 5 + 3 + 1294.678
-  final_answer(result)
+  final_answer(f"FINAL ANSWER: {result}")
   ```<end_code>
 
   ---
@@ -49,10 +66,23 @@
   ```py
   translated_question = translator(question=question, src_lang="French", tgt_lang="English")
   print(f"The translated question is {translated_question}.")
-  answer = image_qa(image=image, question=translated_question)
-  final_answer(f"The answer is {answer}")
   ```<end_code>
-
+  Observation: The translated question is What animal is in the picture?.
+  
+  Thought: Now I can use image_qa.
+  Code:
+  ```py
+  animal = image_qa(image=image, question=translated_question)
+  print(f"The animal is {animal}")
+  ```<end_code>
+  Observation: The animal is cat.
+  
+  Thought: I have the answer.
+  Code:
+  ```py
+  final_answer(f"FINAL ANSWER: cat")
+  ```<end_code>
+  
   ---
   Task:
   In a 1979 interview, Stanislaus Ulam discusses with Martin Sherwin about other great physicists of his time, including Oppenheimer.
@@ -81,24 +111,20 @@
 
   (truncated)
 
-  Thought: I will read the first 2 pages to know more.
+  Thought: I will read the first page to find the relevant information.
   Code:
   ```py
-  for url in ["https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/", "https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/"]:
-      whole_page = visit_webpage(url)
-      print(whole_page)
-      print("\n" + "="*80 + "\n")  # Print separator between pages
+  url = "https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/"
+  page_content = visit_webpage(url)
+  print(page_content)
   ```<end_code>
   Observation:
-  Manhattan Project Locations:
-  Los Alamos, NM
-  Stanislaus Ulam was a Polish-American mathematician. He worked on the Manhattan Project at Los Alamos and later helped design the hydrogen bomb. In this interview, he discusses his work at
-  (truncated)
+  (Page content including the quote: "He learned too much mathematics and sort of diminished...")
 
-  Thought: I now have the final answer: from the webpages visited, Stanislaus Ulam says of Einstein: "He learned too much mathematics and sort of diminished, it seems to me personally, it seems to me his purely physics creativity." Let's answer in one word.
+  Thought: I now have the final answer. The interview states Einstein's creativity was "diminished". The question asks for one word.
   Code:
   ```py
-  final_answer("diminished")
+  final_answer("FINAL ANSWER: diminished")
   ```<end_code>
 
   ---
@@ -107,56 +133,76 @@
   Thought: I need to get the populations for both cities and compare them: I will use the tool `search` to get the population of both cities.
   Code:
   ```py
-  for city in ["Guangzhou", "Shanghai"]:
-      print(f"Population {city}:", search(f"{city} population")
+  guangzhou_pop_info = search(f"Guangzhou population")
+  print(f"Guangzhou info: {guangzhou_pop_info}")
+  shanghai_pop_info = search(f"Shanghai population")
+  print(f"Shanghai info: {shanghai_pop_info}")
   ```<end_code>
   Observation:
-  Population Guangzhou: ['Guangzhou has a population of 15 million inhabitants as of 2021.']
-  Population Shanghai: '26 million (2019)'
+  Guangzhou info: ['Guangzhou has a population of 15 million inhabitants as of 2021.']
+  Shanghai info: ['Shanghai population: 26.32 million (2019 est.)']
 
-  Thought: Now I know that Shanghai has the highest population.
+  Thought: Now I know that Shanghai has the highest population (26.32 million > 15 million).
   Code:
   ```py
-  final_answer("Shanghai")
+  final_answer("FINAL ANSWER: Shanghai")
   ```<end_code>
 
   ---
   Task: "What is the current age of the pope, raised to the power 0.36?"
 
-  Thought: I will use the tool `wiki` to get the age of the pope, and confirm that with a web search.
+  Thought: I will use the tool `search` to get the age of the pope.
   Code:
   ```py
-  pope_age_wiki = wiki(query="current pope age")
-  print("Pope age as per wikipedia:", pope_age_wiki)
-  pope_age_search = web_search(query="current pope age")
-  print("Pope age as per google search:", pope_age_search)
+  pope_age_search = search(query="current pope age")
+  print("Pope age search result:", pope_age_search)
+  # Extract age
+  import re
+  try:
+    age_match = re.search(r'(\d+)\s*years? old', pope_age_search[0])
+    pope_age_int = int(age_match.group(1))
+    print(f"Extracted age: {pope_age_int}")
+  except Exception as e:
+    print(f"Failed to extract age: {e}")
+    pope_age_int = None
   ```<end_code>
   Observation:
-  Pope age: "The pope Francis is currently 88 years old."
+  Pope age search result: ['Pope Francis is 87 years old as of December 17, 2023.']
+  Extracted age: 87
 
-  Thought: I know that the pope is 88 years old. Let's compute the result using python code.
+  Thought: I know that the pope is 87 years old. Let's compute the result 87 ** 0.36.
   Code:
   ```py
-  pope_current_age = 88 ** 0.36
-  final_answer(pope_current_age)
+  result = 87 ** 0.36
+  print(f"Calculated result: {result}")
+  final_answer(f"FINAL ANSWER: {result}")
   ```<end_code>
 
-  Above example were using notional tools that might not exist for you. On top of performing computations in the Python code snippets that you create, you only have access to these tools:
+  Above example were using notional tools that might not exist for you. On top of performing computations in the Python code snippets that you create, you only have access to these tools, behaving like regular python functions:
+  ```python
   {%- for tool in tools.values() %}
-  - {{ tool.name }}: {{ tool.description }}
-      Takes inputs: {{tool.inputs}}
-      Returns an output of type: {{tool.output_type}}
-  {%- endfor %}
+  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 the same as for 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 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:
+  ```python
   {%- for agent in managed_agents.values() %}
-  - {{ agent.name }}: {{ agent.description }}
-  {%- endfor %}
-  {%- else %}
+  def {{ agent.name }}("Your query goes here.") -> str:
+      """{{ agent.description }}"""
+  {% endfor %}
+  ```
   {%- endif %}
 
   Here are the rules you should always follow to solve your task:
@@ -171,167 +217,179 @@
   9. The state persists between code executions: so if in one step you've created variables or imported modules, these will all persist.
   10. Don't give up! You're in charge of solving the task, not providing directions to solve it.
 
-  Now Begin! If you solve the task correctly, you will receive a reward of $1,000,000.
-"planning":
-  "initial_facts": |-
-    Below I will present you a task.
+  Now Begin!
+planning:
+  initial_plan : |-
+    You are a world expert at analyzing a situation to derive facts, and plan accordingly towards solving a task.
+    Below I will present you a task. You will need to 1. build a survey of facts known or needed to solve the task, then 2. make a plan of action to solve the task.
 
-    You will now build a comprehensive preparatory survey of which facts we have at our disposal and which ones we still need.
-    To do so, you will have to read the task and identify things that must be discovered in order to successfully complete it.
-    Don't make any assumptions. For each item, provide a thorough reasoning. Here is how you will structure this survey:
-
-    ---
-    ### 1. Facts given in the task
+    ## 1. Facts survey
+    You will build a comprehensive preparatory survey of which facts we have at our disposal and which ones we still need.
+    These "facts" will typically be specific names, dates, values, etc. Your answer should use the below headings:
+    ### 1.1. Facts given in the task
     List here the specific facts given in the task that could help you (there might be nothing here).
 
-    ### 2. Facts to look up
+    ### 1.2. Facts to look up
     List here any facts that we may need to look up.
     Also list where to find each of these, for instance a website, a file... - maybe the task contains some sources that you should re-use here.
 
-    ### 3. Facts to derive
+    ### 1.3. Facts to derive
     List here anything that we want to derive from the above by logical reasoning, for instance computation or simulation.
 
-    Keep in mind that "facts" will typically be specific names, dates, values, etc. Your answer should use the below headings:
-    ### 1. Facts given in the task
-    ### 2. Facts to look up
-    ### 3. Facts to derive
-    Do not add anything else.
-  "initial_plan": |-
-    You are a world expert at making efficient plans to solve any task using a set of carefully crafted tools.
+    Don't make any assumptions. For each item, provide a thorough reasoning. Do not add anything else on top of three headings above.
 
-    Now for the given task, develop a step-by-step high-level plan taking into account the above inputs and list of facts.
+    ## 2. Plan
+    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 '\n<end_plan>' tag and stop there.
 
-    Here is your task:
-
-    Task:
-    ```
-    {{task}}
-    ```
-    You can leverage these tools:
+    You can leverage these tools, behaving like regular python functions:
+    ```python
     {%- for tool in tools.values() %}
-    - {{ tool.name }}: {{ tool.description }}
-        Takes inputs: {{tool.inputs}}
-        Returns an output of type: {{tool.output_type}}
-    {%- endfor %}
+    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 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.
+    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:
+    ```python
     {%- for agent in managed_agents.values() %}
-    - {{ agent.name }}: {{ agent.description }}
-    {%- endfor %}
-    {%- else %}
-    {%- endif %}
-
-    List of facts that you know:
-    ```
-    {{answer_facts}}
+    def {{ agent.name }}("Your query goes here.") -> str:
+        """{{ agent.description }}"""
+    {% endfor %}
     ```
+    {%- endif %}
 
-    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:
+    ---
+    Now begin! Here is your 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:
+    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:
+    ## 1. Updated facts survey
+    ### 1.1. Facts given in the task
+    ### 1.2. Facts that we have learned
+    ### 1.3. Facts still to look up
+    ### 1.4. Facts still to derive
+  
+    Then write a step-by-step high-level plan to solve the task above.
+    ## 2. Plan
+    ### 2. 1. ...
+    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 '\n<end_plan>' tag and stop there.
 
-    You can leverage these tools:
+    You can leverage these tools, behaving like regular python functions:
+    ```python
     {%- for tool in tools.values() %}
-    - {{ tool.name }}: {{ tool.description }}
-        Takes inputs: {{tool.inputs}}
-        Returns an output of type: {{tool.output_type}}
-    {%- endfor %}
+    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 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:
+    ```python
     {%- for agent in managed_agents.values() %}
-    - {{ agent.name }}: {{ agent.description }}
-    {%- endfor %}
-    {%- else %}
-    {%- endif %}
-
-    Here is the up to date list of facts that you know:
-    ```
-    {{facts_update}}
+    def {{ agent.name }}("Your query goes here.") -> str:
+        """{{ agent.description }}"""
+    {% endfor %}
     ```
+    {%- endif %}
 
-    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.
-
-    Now write your new plan below.
-"managed_agent":
-  "task": |-
+    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 MUST contain these parts. Follow the formatting precisely:
+#       ### 1. Task outcome (short version):
+#       [CONCISE ANSWER ONLY - e.g., "Extremely.", "Paris", "b, e". NO introductory text.]
+#       
+#       ### 2. Task outcome (extremely detailed version):
+#       [Detailed explanation and steps taken]
+#       
+#       ### 3. Additional context (if relevant):
+#       [Any other relevant information or context]
+
+#       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: |-
+#       {{final_answer}}
+
+
+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:
-    ### 1. Task outcome (short version):
-    ### 2. Task outcome (extremely detailed version):
-    ### 3. Additional context (if relevant):
-
-    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 only with your FINAL ANSWER. 
+    YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. 
+    If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. 
+    If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. 
+    If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
+    
+    Put the answer 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 a short and concise answer at the best of your ability, so that your manager can act upon this feedback.
 
+  report: |- # This report template might need adjustment based on how GAIA expects reports, assuming it just wants the final answer.
+      {{final_answer}}
 
 
 final_answer:
   pre_messages: |-
-    You are about to provide a final answer to the task.
-    The answer should be clear, concise, and directly address the original question.
-    Here is your task: {{task}}
-    Based on everything you've learned, provide your final answer below.
+    # This template is used when the agent gets stuck and needs to generate a final answer based on memory.
+    # The primary instruction for final answer format is now in the system_prompt.
+    Based on the agent's memory, provide a final answer to the original task.
   post_messages: |-
-    your answer should be formatted with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. 
-    YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. 
-    If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. 
-    If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. 
-    If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
-    Here is your final answer:
+    # This template prompts for the final answer based on memory when the agent is stuck.
+    # The primary instruction for final answer format is now in the system_prompt.
+    Based on the above memory, please provide an answer to the following user task:
+    {{task}}

questions.json

@@ -0,0 +1,122 @@
+[
+    {
+        "task_id": "8e867cd7-cff9-4e6c-867a-ff5ddc2550be",
+        "question": "How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)? You can use the latest 2022 version of english wikipedia.",
+        "Level": "1",
+        "file_name": ""
+    },
+    {
+        "task_id": "a1e91b78-d3d8-4675-bb8d-62741b4b68a6",
+        "question": "In the video https://www.youtube.com/watch?v=L1vXCYZAYYM, what is the highest number of bird species to be on camera simultaneously?",
+        "Level": "1",
+        "file_name": ""
+    },
+    {
+        "task_id": "2d83110e-a098-4ebb-9987-066c06fa42d0",
+        "question": ".rewsna eht sa \"tfel\" drow eht fo etisoppo eht etirw ,ecnetnes siht dnatsrednu uoy fI",
+        "Level": "1",
+        "file_name": ""
+    },
+    {
+        "task_id": "cca530fc-4052-43b2-b130-b30968d8aa44",
+        "question": "Review the chess position provided in the image. It is black's turn. Provide the correct next move for black which guarantees a win. Please provide your response in algebraic notation.",
+        "Level": "1",
+        "file_name": "cca530fc-4052-43b2-b130-b30968d8aa44.png"
+    },
+    {
+        "task_id": "4fc2f1ae-8625-45b5-ab34-ad4433bc21f8",
+        "question": "Who nominated the only Featured Article on English Wikipedia about a dinosaur that was promoted in November 2016?",
+        "Level": "1",
+        "file_name": ""
+    },
+    {
+        "task_id": "6f37996b-2ac7-44b0-8e68-6d28256631b4",
+        "question": "Given this table defining * on the set S = {a, b, c, d, e}\n\n|*|a|b|c|d|e|\n|---|---|---|---|---|---|\n|a|a|b|c|b|d|\n|b|b|c|a|e|c|\n|c|c|a|b|b|a|\n|d|b|e|b|e|d|\n|e|d|b|a|d|c|\n\nprovide the subset of S involved in any possible counter-examples that prove * is not commutative. Provide your answer as a comma separated list of the elements in the set in alphabetical order.",
+        "Level": "1",
+        "file_name": ""
+    },
+    {
+        "task_id": "9d191bce-651d-4746-be2d-7ef8ecadb9c2",
+        "question": "Examine the video at https://www.youtube.com/watch?v=1htKBjuUWec.\n\nWhat does Teal'c say in response to the question \"Isn't that hot?\"",
+        "Level": "1",
+        "file_name": ""
+    },
+    {
+        "task_id": "cabe07ed-9eca-40ea-8ead-410ef5e83f91",
+        "question": "What is the surname of the equine veterinarian mentioned in 1.E Exercises from the chemistry materials licensed by Marisa Alviar-Agnew & Henry Agnew under the CK-12 license in LibreText's Introductory Chemistry materials as compiled 08/21/2023?",
+        "Level": "1",
+        "file_name": ""
+    },
+    {
+        "task_id": "3cef3a44-215e-4aed-8e3b-b1e3f08063b7",
+        "question": "I'm making a grocery list for my mom, but she's a professor of botany and she's a real stickler when it comes to categorizing things. I need to add different foods to different categories on the grocery list, but if I make a mistake, she won't buy anything inserted in the wrong category. Here's the list I have so far:\n\nmilk, eggs, flour, whole bean coffee, Oreos, sweet potatoes, fresh basil, plums, green beans, rice, corn, bell pepper, whole allspice, acorns, broccoli, celery, zucchini, lettuce, peanuts\n\nI need to make headings for the fruits and vegetables. Could you please create a list of just the vegetables from my list? If you could do that, then I can figure out how to categorize the rest of the list into the appropriate categories. But remember that my mom is a real stickler, so make sure that no botanical fruits end up on the vegetable list, or she won't get them when she's at the store. Please alphabetize the list of vegetables, and place each item in a comma separated list.",
+        "Level": "1",
+        "file_name": ""
+    },
+    {
+        "task_id": "99c9cc74-fdc8-46c6-8f8d-3ce2d3bfeea3",
+        "question": "Hi, I'm making a pie but I could use some help with my shopping list. I have everything I need for the crust, but I'm not sure about the filling. I got the recipe from my friend Aditi, but she left it as a voice memo and the speaker on my phone is buzzing so I can't quite make out what she's saying. Could you please listen to the recipe and list all of the ingredients that my friend described? I only want the ingredients for the filling, as I have everything I need to make my favorite pie crust. I've attached the recipe as Strawberry pie.mp3.\n\nIn your response, please only list the ingredients, not any measurements. So if the recipe calls for \"a pinch of salt\" or \"two cups of ripe strawberries\" the ingredients on the list would be \"salt\" and \"ripe strawberries\".\n\nPlease format your response as a comma separated list of ingredients. Also, please alphabetize the ingredients.",
+        "Level": "1",
+        "file_name": "99c9cc74-fdc8-46c6-8f8d-3ce2d3bfeea3.mp3"
+    },
+    {
+        "task_id": "305ac316-eef6-4446-960a-92d80d542f82",
+        "question": "Who did the actor who played Ray in the Polish-language version of Everybody Loves Raymond play in Magda M.? Give only the first name.",
+        "Level": "1",
+        "file_name": ""
+    },
+    {
+        "task_id": "f918266a-b3e0-4914-865d-4faa564f1aef",
+        "question": "What is the final numeric output from the attached Python code?",
+        "Level": "1",
+        "file_name": "f918266a-b3e0-4914-865d-4faa564f1aef.py"
+    },
+    {
+        "task_id": "3f57289b-8c60-48be-bd80-01f8099ca449",
+        "question": "How many at bats did the Yankee with the most walks in the 1977 regular season have that same season?",
+        "Level": "1",
+        "file_name": ""
+    },
+    {
+        "task_id": "1f975693-876d-457b-a649-393859e79bf3",
+        "question": "Hi, I was out sick from my classes on Friday, so I'm trying to figure out what I need to study for my Calculus mid-term next week. My friend from class sent me an audio recording of Professor Willowbrook giving out the recommended reading for the test, but my headphones are broken :(\n\nCould you please listen to the recording for me and tell me the page numbers I'm supposed to go over? I've attached a file called Homework.mp3 that has the recording. Please provide just the page numbers as a comma-delimited list. And please provide the list in ascending order.",
+        "Level": "1",
+        "file_name": "1f975693-876d-457b-a649-393859e79bf3.mp3"
+    },
+    {
+        "task_id": "840bfca7-4f7b-481a-8794-c560c340185d",
+        "question": "On June 6, 2023, an article by Carolyn Collins Petersen was published in Universe Today. This article mentions a team that produced a paper about their observations, linked at the bottom of the article. Find this paper. Under what NASA award number was the work performed by R. G. Arendt supported by?",
+        "Level": "1",
+        "file_name": ""
+    },
+    {
+        "task_id": "bda648d7-d618-4883-88f4-3466eabd860e",
+        "question": "Where were the Vietnamese specimens described by Kuznetzov in Nedoshivina's 2010 paper eventually deposited? Just give me the city name without abbreviations.",
+        "Level": "1",
+        "file_name": ""
+    },
+    {
+        "task_id": "cf106601-ab4f-4af9-b045-5295fe67b37d",
+        "question": "What country had the least number of athletes at the 1928 Summer Olympics? If there's a tie for a number of athletes, return the first in alphabetical order. Give the IOC country code as your answer.",
+        "Level": "1",
+        "file_name": ""
+    },
+    {
+        "task_id": "a0c07678-e491-4bbc-8f0b-07405144218f",
+        "question": "Who are the pitchers with the number before and after Taishō Tamai's number as of July 2023? Give them to me in the form Pitcher Before, Pitcher After, use their last names only, in Roman characters.",
+        "Level": "1",
+        "file_name": ""
+    },
+    {
+        "task_id": "7bd855d8-463d-4ed5-93ca-5fe35145f733",
+        "question": "The attached Excel file contains the sales of menu items for a local fast-food chain. What were the total sales that the chain made from food (not including drinks)? Express your answer in USD with two decimal places.",
+        "Level": "1",
+        "file_name": "7bd855d8-463d-4ed5-93ca-5fe35145f733.xlsx"
+    },
+    {
+        "task_id": "5a0c1adf-205e-4841-a666-7c3ef95def9d",
+        "question": "What is the first name of the only Malko Competition recipient from the 20th Century (after 1977) whose nationality on record is a country that no longer exists?",
+        "Level": "1",
+        "file_name": ""
+    }
+]

test.py

@@ -0,0 +1,210 @@
+import os
+import json
+import random
+import requests
+import yaml
+import pprint
+from dotenv import load_dotenv
+
+from smolagents import CodeAgent, HfApiModel
+from tools.final_answer import FinalAnswerTool
+from tools.visit_webpage import VisitWebpageTool
+from tools.web_search import DuckDuckGoSearchTool # Note: app.py imports this from tools.web_search and smolagents
+
+# Load environment variables from .env file
+load_dotenv()
+hf_token = os.getenv('HUGGINGFACE_TOKEN')
+if not hf_token:
+    raise ValueError("HUGGINGFACE_TOKEN not found in environment variables. Make sure a .env file exists.")
+
+# --- Constants ---
+API_URL = os.getenv("API_URL", "https://agents-course-unit4-scoring.hf.space") # Use env var or default
+QUESTIONS_URL = f"{API_URL}/questions"
+QUESTIONS_FILE = "questions.json"
+ANSWERS_LOG_FILE = "answer_log.jsonl"
+PROMPTS_FILE = "prompts.yaml"
+
+# --- Function to Fetch Questions ---
+def fetch_and_save_questions(url: str, filename: str):
+    """Fetches questions from the API and saves them to a local JSON file."""
+    if os.path.exists(filename):
+        print(f"Questions file '{filename}' already exists. Skipping download.")
+        return True
+    
+    print(f"Fetching questions from: {url}")
+    try:
+        response = requests.get(url, timeout=30) # Increased timeout
+        response.raise_for_status()
+        questions_data = response.json()
+        if not questions_data:
+             print("Fetched questions list is empty.")
+             return False
+
+        with open(filename, 'w', encoding='utf-8') as f:
+            json.dump(questions_data, f, indent=4, ensure_ascii=False)
+        print(f"Successfully fetched {len(questions_data)} questions and saved to '{filename}'.")
+        return True
+    except requests.exceptions.RequestException as e:
+        print(f"Error fetching questions: {e}")
+        return False
+    except requests.exceptions.JSONDecodeError as e:
+         print(f"Error decoding JSON response from questions endpoint: {e}")
+         if 'response' in locals():
+             print(f"Response text: {response.text[:500]}")
+         return False
+    except Exception as e:
+        print(f"An unexpected error occurred fetching questions: {e}")
+        return False
+
+# --- Function to Load Questions ---
+def load_questions(filename: str) -> list:
+    """Loads questions from a local JSON file."""
+    try:
+        with open(filename, 'r', encoding='utf-8') as f:
+            questions_data = json.load(f)
+        print(f"Successfully loaded {len(questions_data)} questions from '{filename}'.")
+        return questions_data
+    except FileNotFoundError:
+        print(f"Error: Questions file '{filename}' not found.")
+        return []
+    except json.JSONDecodeError:
+        print(f"Error: Could not decode JSON from '{filename}'.")
+        return []
+    except Exception as e:
+        print(f"An unexpected error occurred loading questions: {e}")
+        return []
+
+# --- Function to Instantiate Agent ---
+def create_agent():
+    """Instantiates the CodeAgent with configuration similar to app.py."""
+    try:
+        # Load prompts
+        with open(PROMPTS_FILE, 'r') as stream:
+            prompt_templates = yaml.safe_load(stream)
+    except FileNotFoundError:
+        print(f"Error: Prompts file '{PROMPTS_FILE}' not found. Using default prompts.")
+        prompt_templates = None # Or handle differently
+    except yaml.YAMLError as e:
+        print(f"Error parsing prompts file '{PROMPTS_FILE}': {e}. Using default prompts.")
+        prompt_templates = None
+
+    # Configure model
+    model = HfApiModel(
+        max_tokens=2096,
+        temperature=0.5,
+        model_id='Qwen/Qwen2.5-Coder-32B-Instruct',
+        # custom_role_conversions=None, # Optional, kept default
+        token=hf_token, 
+    )
+
+    # Create agent instance
+    try:
+        agent = CodeAgent(
+            model=model,
+            tools=[
+                FinalAnswerTool(), 
+                DuckDuckGoSearchTool(),
+                VisitWebpageTool(),
+            ],
+            max_steps=6,
+            verbosity_level=1, # Set higher (e.g., 2 or 3) to potentially see reasoning in stdout
+            # grammar=None, # Optional, kept default
+            # planning_interval=None, # Optional, kept default
+            name="SmolAgentTester",
+            description="An AI coding assistant for testing.",
+            prompt_templates=prompt_templates, 
+        )
+        print("CodeAgent instantiated successfully.")
+        return agent
+    except Exception as e:
+        print(f"Error instantiating CodeAgent: {e}")
+        return None
+
+# --- Main Execution Logic ---
+if __name__ == "__main__":
+    print("Starting test script...")
+    
+    # Step 1: Fetch and save questions
+    if not fetch_and_save_questions(QUESTIONS_URL, QUESTIONS_FILE):
+        print("Failed to fetch questions. Exiting.")
+        exit(1)
+
+    # Step 2: Load questions
+    all_questions = load_questions(QUESTIONS_FILE)
+    if not all_questions:
+        print("Failed to load questions. Exiting.")
+        exit(1)
+
+    # Step 3: Randomly pick 2 questions
+    if len(all_questions) < 2:
+        print("Warning: Fewer than 2 questions available. Testing with all available questions.")
+        selected_questions = all_questions
+    else:
+        selected_questions = random.sample(all_questions, 2)
+
+    print(f"\nSelected {len(selected_questions)} questions for testing:")
+    pprint.pprint(selected_questions)
+    print("-"*50)
+
+    # Step 4: Instantiate agent
+    agent = create_agent()
+    if agent is None:
+        print("Failed to create agent. Exiting.")
+        exit(1)
+
+    # Step 5: Run agent and log results
+    print(f"Running agent on {len(selected_questions)} questions...")
+    results_log = []
+    
+    # Clear or create the log file
+    with open(ANSWERS_LOG_FILE, 'w', encoding='utf-8') as log_f:
+        pass # Just to clear the file initially
+
+    for item in selected_questions:
+        task_id = item.get("task_id")
+        question_text = item.get("question")
+        if not task_id or question_text is None:
+            print(f"Skipping item with missing task_id or question: {item}")
+            continue
+        
+        print(f"\n--- Running Task ID: {task_id} ---")
+        print(f"Question: {question_text}")
+        
+        try:
+            # Run the agent
+            # Note: The agent call might print its own reasoning steps depending on verbosity
+            model_answer = agent(question_text) # This now holds the CONCISE answer from FinalAnswerTool
+            print(f"\nAgent Final Answer: {model_answer}") # Renamed print for clarity
+
+            # Prepare result for logging
+            result = {
+                "task_id": task_id,
+                "question": question_text,
+                "model_answer": model_answer, # Directly use the concise answer
+                # "reasoning_trace": "TODO" # Add if agent provides trace separately
+            }
+            results_log.append(result)
+
+            # Append result to log file (JSON Lines format)
+            with open(ANSWERS_LOG_FILE, 'a', encoding='utf-8') as log_f:
+                json.dump(result, log_f, ensure_ascii=False)
+                log_f.write('\n')
+                
+        except Exception as e:
+             print(f"\nAGENT ERROR on task {task_id}: {e}")
+             # Optionally log errors too
+             error_result = {"task_id": task_id, "model_answer": f"AGENT_ERROR: {e}"}
+             results_log.append(error_result)
+             with open(ANSWERS_LOG_FILE, 'a', encoding='utf-8') as log_f:
+                 json.dump(error_result, log_f, ensure_ascii=False)
+                 log_f.write('\n')
+
+    print("-"*50)
+    print(f"\nTest script finished. {len(results_log)} results logged to '{ANSWERS_LOG_FILE}'.")
+    print("Summary of results:")
+    pprint.pprint(results_log)
+
+# Ensure prompts.yaml and .env exist in the same directory or adjust paths.
+# Ensure necessary packages are installed: pip install requests pyyaml python-dotenv python-pprint smol-agents
+
+# ... rest of the script to be added ... 

tools/final_answer.py

@@ -1,14 +1,81 @@
-from typing import Any, Optional
+import re
+from typing import Any
 from smolagents.tools import Tool
 
 class FinalAnswerTool(Tool):
     name = "final_answer"
-    description = "Provides a final answer to the given problem."
-    inputs = {'answer': {'type': 'any', 'description': 'The final answer to the problem'}}
+    description = "Processes and returns the final, concise answer provided by the agent."
+    inputs = {'answer': {'type': 'any', 'description': 'The final answer value, potentially structured with sections.'}}
     output_type = "any"
 
     def forward(self, answer: Any) -> Any:
+        """
+        Passes the agent's final answer through without modification.
+        """
+        # Check if answer is a string and contains "FINAL ANSWER:" (case insensitive)
+        if isinstance(answer, str):
+            answer_text = answer.strip()
+            final_answer_prefix = "FINAL ANSWER:"
+            
+            # Remove any leading/trailing newlines and normalize internal whitespace
+            answer_text = '\n'.join(line.strip() for line in answer_text.splitlines()).strip()
+            
+            # Case insensitive check for prefix anywhere in the text
+            if final_answer_prefix.upper() in answer_text.upper():
+                # Split on the prefix (case insensitive) and take the last part
+                # This handles cases where the prefix might appear multiple times
+                parts = re.split(re.escape(final_answer_prefix), answer_text, flags=re.IGNORECASE)
+                parsed_answer = parts[-1].strip()
+                print(f"[FinalAnswerTool] Extracted answer after prefix: {parsed_answer[:100]}...")
+                return parsed_answer
+
+        # For non-string inputs or answers without the prefix, return as-is
+        print(f"[FinalAnswerTool] Passing through raw answer: {str(answer)[:100]}...")
         return answer
 
+        # --- PREVIOUS IMPLEMENTATION (COMMENTED OUT) ---
+        # """
+        # Receives the agent's final answer string, extracts the concise result
+        # specifically from the '### 1. Task outcome (short version):' section if present.
+        # Falls back to previous behavior or raw input otherwise.
+        # """
+        # import re # Ensure re is imported if uncommenting
+        # if not isinstance(answer, str):
+        #     print(f"[FinalAnswerTool] Warning: Input is not a string ('{type(answer)}'). Returning raw value: {str(answer)[:100]}...")
+        #     return answer # Return non-strings directly
+
+        # text = answer.strip()
+        # original_text_preview = text[:100].replace('\n', '\\n') # For logging
+
+        # # Pattern to capture content after "### 1. ...:" until the next "###" or end of string
+        # # Handles variations in spacing and capitalization of the section header.
+        # # Makes the "### " prefix optional.
+        # # Allows one or more newlines before the next section header.
+        # pattern = re.compile(r"^(?:###\s*)?1\.\s*Task outcome \(short version\):([\s\S]*?)(?=\n+(?:###\s*)?2\.|\Z)", re.IGNORECASE | re.MULTILINE)
+        # match = pattern.search(text)
+
+        # if match:
+        #     # Extract the content, strip leading/trailing whitespace and newlines
+        #     parsed_answer = match.group(1).strip()
+        #     print(f"[FinalAnswerTool] Extracted from section 1: Raw input: '{original_text_preview}...' -> Parsed output: '{parsed_answer[:100]}...'")
+        #     # Return the original full text if extraction results in an empty string (unlikely with [\s\S]*?)
+        #     return parsed_answer if parsed_answer else text
+        # else:
+        #     # Fallback 1: Check for "FINAL ANSWER:" prefix (original behavior)
+        #     print(f"[FinalAnswerTool] Info: Section '1. Task outcome (short version):' not found in '{original_text_preview}...'. Trying fallback.")
+        #     final_answer_prefix = "FINAL ANSWER:"
+        #     # Check from the beginning of the string for the prefix
+        #     if text.upper().strip().startswith(final_answer_prefix):
+        #         parsed_answer = text.strip()[len(final_answer_prefix):].strip()
+        #         parsed_answer = parsed_answer if parsed_answer else text # Avoid returning empty string
+        #         print(f"[FinalAnswerTool] Fallback (FINAL ANSWER:): Raw input: '{original_text_preview}...' -> Parsed output: '{parsed_answer[:100]}...'")
+        #         return parsed_answer
+        #     else:
+        #         # Fallback 2: Return the original text if no known format is matched
+        #         print(f"[FinalAnswerTool] Warning: Input missing '### 1.' section and 'FINAL ANSWER:' prefix: '{original_text_preview}...'. Returning raw value.")
+        #         return text
+        # --- END PREVIOUS IMPLEMENTATION ---
+
     def __init__(self, *args, **kwargs):
-        self.is_initialized = False
+        super().__init__(*args, **kwargs)
+        self.is_initialized = True