final push

app.py

@@ -1,19 +1,21 @@
-from smolagents import CodeAgent, tool, OpenAIServerModel
+import json
+from smolagents import CodeAgent, tool, LiteLLMModel
 import requests
 import yaml
 import os
 from collections import defaultdict
 from tools.final_answer import FinalAnswerTool
-from Gradio_UI import GradioUI
+import gradio as gr
+
 
 @tool
 def get_pokedex_entry(pokemon_name: str) -> str:
     """
     Fetches the English Pokédex entry (flavor text) for the given Pokémon.
-    
+
     Args:
         pokemon_name: Name or ID of the Pokémon (e.g., 'pikachu').
-    
+
     Returns:
         A string containing the Pokémon's English Pokédex entry or an error message.
     """
@@ -34,14 +36,15 @@ def get_pokedex_entry(pokemon_name: str) -> str:
     title = pokemon_name.strip().title()
     return f"{title}: {entry}"
 
+
 @tool
 def get_pokemon_basic_info(pokemon_name: str) -> str:
     """
     Fetches basic information about a Pokémon, such as its ID, height, weight, and types.
-    
+
     Args:
         pokemon_name: The name or ID of the Pokémon (e.g., 'pikachu').
-    
+
     Returns:
         A string summarizing the Pokémon's ID, height, weight, and types, or an error message.
     """
@@ -58,14 +61,15 @@ def get_pokemon_basic_info(pokemon_name: str) -> str:
         f"Types: {types}"
     )
 
+
 @tool
 def get_pokemon_abilities(pokemon_name: str) -> str:
     """
     Retrieves a list of abilities for a given Pokémon, distinguishing between normal and hidden abilities.
-    
+
     Args:
         pokemon_name: The name or ID of the Pokémon.
-    
+
     Returns:
         A string listing the Pokémon's abilities, labeled as Normal or Hidden, or an error message.
     """
@@ -84,14 +88,15 @@ def get_pokemon_abilities(pokemon_name: str) -> str:
         return f"No abilities found for '{pokemon_name}'."
     return f"{pokemon_name.title()}'s abilities:\n" + "\n".join(abilities)
 
+
 @tool
 def get_pokemon_base_stats(pokemon_name: str) -> str:
     """
     Fetches the base stats (HP, Attack, Defense, etc.) of a Pokémon.
-    
+
     Args:
         pokemon_name: The name or ID of the Pokémon.
-    
+
     Returns:
         A string summarizing the Pokémon's base stats or an error message.
     """
@@ -107,14 +112,15 @@ def get_pokemon_base_stats(pokemon_name: str) -> str:
     stats_str = ', '.join([f"{k}: {v}" for k, v in stats.items()])
     return f"{pokemon_name.title()}'s base stats: {stats_str}"
 
+
 @tool
 def get_pokemon_evolutions(pokemon_name: str) -> str:
     """
     Retrieves the evolution chain for a given Pokémon.
-    
+
     Args:
         pokemon_name: The name or ID of the Pokémon.
-    
+
     Returns:
         A string listing the evolution chain or an error message.
     """
@@ -130,23 +136,26 @@ def get_pokemon_evolutions(pokemon_name: str) -> str:
     if evo_response.status_code != 200:
         return f"Error fetching evolution chain (HTTP {evo_response.status_code})."
     evo_data = evo_response.json()
+
     def extract_chain(chain):
         names = [chain['species']['name'].title()]
         for evo in chain.get('evolves_to', []):
             names.extend(extract_chain(evo))
         return names
+
     evo_names = extract_chain(evo_data['chain'])
     return f"Evolution chain: {' → '.join(evo_names)}"
 
+
 @tool
 def get_pokemon_moves_grouped(pokemon_name: str) -> str:
     """
     Lists all moves a Pokémon can learn, grouped by move name, showing the minimum level,
     all learning methods, and all game versions. Egg moves are listed separately.
-    
+
     Args:
         pokemon_name: The name or ID of the Pokémon.
-    
+
     Returns:
         A string listing the Pokémon's moves, grouped by name, with levels, methods, and versions.
     """
@@ -169,7 +178,8 @@ def get_pokemon_moves_grouped(pokemon_name: str) -> str:
             else:
                 moves_dict[move_name]['methods'].add(method)
                 moves_dict[move_name]['versions'].add(version)
-                if method == "Level Up" and (moves_dict[move_name]['level'] is None or level < moves_dict[move_name]['level']):
+                if method == "Level Up" and (
+                        moves_dict[move_name]['level'] is None or level < moves_dict[move_name]['level']):
                     moves_dict[move_name]['level'] = level
     moves_lines = []
     for move_name, info in sorted(moves_dict.items()):
@@ -179,9 +189,10 @@ def get_pokemon_moves_grouped(pokemon_name: str) -> str:
             moves_lines.append(f"{move_name} (Level {info['level']}, Versions: {versions_str})")
         else:
             moves_lines.append(f"{move_name} (Methods: {methods_str}, Versions: {versions_str})")
-    egg_lines = [f"{move_name} (Egg Move in: {', '.join(sorted(versions))})" for move_name, versions in sorted(egg_moves_dict.items())]
+    egg_lines = [f"{move_name} (Egg Move in: {', '.join(sorted(versions))})" for move_name, versions in
+                 sorted(egg_moves_dict.items())]
     moves_preview = '\n'.join(moves_lines[:20])
-    more_moves = f"\n...and {len(moves_lines)-20} more moves." if len(moves_lines) > 20 else ""
+    more_moves = f"\n...and {len(moves_lines) - 20} more moves." if len(moves_lines) > 20 else ""
     egg_moves_output = '\n'.join(egg_lines) if egg_lines else 'None'
     return (
         f"{pokemon_name.title()} can learn these moves grouped by name with versions and levels:\n"
@@ -189,14 +200,15 @@ def get_pokemon_moves_grouped(pokemon_name: str) -> str:
         f"Egg moves:\n{egg_moves_output}"
     )
 
+
 @tool
 def get_pokemon_sprite(pokemon_name: str) -> str:
     """
     Fetches the default front sprite URL for a Pokémon.
-    
+
     Args:
         pokemon_name: The name or ID of the Pokémon.
-    
+
     Returns:
         A string containing the sprite URL or an error message.
     """
@@ -211,14 +223,15 @@ def get_pokemon_sprite(pokemon_name: str) -> str:
         return f"No sprite found for '{pokemon_name}'."
     return f"{pokemon_name.title()} sprite: {sprite_url}"
 
+
 @tool
 def get_pokemon_held_items(pokemon_name: str) -> str:
     """
     Fetches the items a Pokémon can hold in the wild, including rarity and game versions.
-    
+
     Args:
         pokemon_name: The name or ID of the Pokémon.
-    
+
     Returns:
         A string listing the Pokémon's held items, their rarity, and versions, or an error message.
     """
@@ -239,14 +252,15 @@ def get_pokemon_held_items(pokemon_name: str) -> str:
         item_lines.append(f"{item_name} (Rarity: {rarity}%, Versions: {versions})")
     return f"{pokemon_name.title()}'s held items:\n" + "\n".join(item_lines)
 
+
 @tool
 def get_pokemon_encounter_locations(pokemon_name: str) -> str:
     """
     Fetches the locations where a Pokémon can be encountered in different game versions.
-    
+
     Args:
         pokemon_name: The name or ID of the Pokémon.
-    
+
     Returns:
         A string listing the Pokémon's encounter locations by game version, or an error message.
     """
@@ -277,14 +291,15 @@ def get_pokemon_encounter_locations(pokemon_name: str) -> str:
         return f"No encounter locations found for '{pokemon_name}'."
     return f"{pokemon_name.title()}'s encounter locations:\n" + "\n".join(output)
 
+
 @tool
 def get_pokemon_type_effectiveness(pokemon_name: str) -> str:
     """
     Fetches the type effectiveness (weaknesses, resistances, immunities) for a Pokémon's types.
-    
+
     Args:
         pokemon_name: The name or ID of the Pokémon.
-    
+
     Returns:
         A string summarizing the Pokémon's type effectiveness, or an error message.
     """
@@ -316,14 +331,15 @@ def get_pokemon_type_effectiveness(pokemon_name: str) -> str:
         return f"No type effectiveness data found for '{pokemon_name}'."
     return f"{pokemon_name.title()}'s type effectiveness:\n" + "\n".join(output)
 
+
 @tool
 def get_pokemon_ability_description(pokemon_name: str) -> str:
     """
     Fetches the descriptions of a Pokémon's abilities from the ability endpoint.
-    
+
     Args:
         pokemon_name: The name or ID of the Pokémon.
-    
+
     Returns:
         A string listing each ability and its English description, or an error message.
     """
@@ -343,21 +359,23 @@ def get_pokemon_ability_description(pokemon_name: str) -> str:
         if response.status_code != 200:
             continue
         ability_data = response.json()
-        effect = next((e['effect'] for e in ability_data['effect_entries'] if e['language']['name'] == 'en'), 'No description available.')
+        effect = next((e['effect'] for e in ability_data['effect_entries'] if e['language']['name'] == 'en'),
+                      'No description available.')
         output.append(f"{ability_name} ({label}): {effect}")
     if not output:
         return f"No ability descriptions found for '{pokemon_name}'."
     return f"{pokemon_name.title()}'s ability descriptions:\n" + "\n".join(output)
 
+
 @tool
 def list_pokemon_by_type(type_name: str, limit: int = 10) -> str:
     """
     Lists Pokémon that have a specific type, up to a specified limit.
-    
+
     Args:
         type_name: The name of the type (e.g., 'normal').
         limit: Maximum number of Pokémon to list (default: 10).
-    
+
     Returns:
         A string listing Pokémon with the specified type, or an error message.
     """
@@ -372,42 +390,241 @@ def list_pokemon_by_type(type_name: str, limit: int = 10) -> str:
         return f"No Pokémon found with type '{type_name}'."
     return f"Pokémon with {type_name.title()} type (up to {limit}):\n" + "\n".join(pokemon_list)
 
+
 @tool
 def get_pokemon_full_profile(pokemon_name: str) -> str:
     """
     Fetches a complete profile for a Pokémon, including all available data.
-    
+
     Args:
         pokemon_name: The name or ID of the Pokémon.
-    
+
     Returns:
         A string with a comprehensive Pokémon profile.
     """
     name = pokemon_name.strip().lower()
-    profile = []
-    profile.append(get_pokedex_entry(pokemon_name=name))
-    profile.append(get_pokemon_basic_info(pokemon_name=name))
-    profile.append(get_pokemon_abilities(pokemon_name=name))
-    profile.append(get_pokemon_base_stats(pokemon_name=name))
-    profile.append(get_pokemon_evolutions(pokemon_name=name))
-    profile.append(get_pokemon_moves_grouped(pokemon_name=name))
-    profile.append(get_pokemon_held_items(pokemon_name=name))
-    profile.append(get_pokemon_encounter_locations(pokemon_name=name))
-    profile.append(get_pokemon_type_effectiveness(pokemon_name=name))
-    profile.append(get_pokemon_ability_description(pokemon_name=name))
-    profile.append(get_pokemon_sprite(pokemon_name=name))
+    profile = [
+        get_pokedex_entry(pokemon_name=name),
+        get_pokemon_basic_info(pokemon_name=name),
+        get_pokemon_abilities(pokemon_name=name),
+        get_pokemon_base_stats(pokemon_name=name),
+        get_pokemon_evolutions(pokemon_name=name),
+        get_pokemon_moves_grouped(pokemon_name=name),
+        get_pokemon_held_items(pokemon_name=name),
+        get_pokemon_encounter_locations(pokemon_name=name),
+        get_pokemon_type_effectiveness(pokemon_name=name),
+        get_pokemon_ability_description(pokemon_name=name),
+        get_pokemon_sprite(pokemon_name=name)
+    ]
     return f"{name.title()} Profile:\n\n" + "\n\n".join(profile)
 
+
+@tool
+def get_pokemon_evolution_visual(pokemon_name: str) -> str:
+    """
+    Fetches the evolution chain for a Pokémon and generates an HTML visualization with sprites and basic info.
+    This tool should be called directly without any code generation.
+
+    Args:
+        pokemon_name: The name or ID of the Pokémon.
+
+    Returns:
+        A string with the evolution chain and instructions to view the HTML visualization.
+    """
+
+    def generate_evolution_html(pokemon_name: str, evolution_data: list) -> str:
+        html_template = """
+        <!DOCTYPE html>
+        <html lang="en">
+        <head>
+            <meta charset="UTF-8">
+            <meta name="viewport" content="width=device-width, initial-scale=1.0">
+            <title>{title} Evolution Chain</title>
+            <style>
+                body {{
+                    font-family: Arial, sans-serif;
+                    text-align: center;
+                    background-color: #f0f0f0;
+                }}
+                h1 {{
+                    color: #333;
+                }}
+                .evolution-container {{
+                    display: flex;
+                    justify-content: center;
+                    gap: 20px;
+                    margin: 20px;
+                }}
+                .pokemon {{
+                    background-color: #fff;
+                    padding: 10px;
+                    border-radius: 10px;
+                    box-shadow: 0 0 10px rgba(0,0,0,0.1);
+                }}
+                .pokemon img {{
+                    width: 100px;
+                    height: 100px;
+                }}
+                .pokemon p {{
+                    margin: 5px 0;
+                    font-weight: bold;
+                }}
+            </style>
+        </head>
+        <body>
+            <h1>{title} Evolution Chain</h1>
+            <div class="evolution-container">
+                {pokemon_entries}
+            </div>
+        </body>
+        </html>
+        """
+        pokemon_entries = ""
+        for name, sprite_url, info in evolution_data:
+            pokemon_entries += f"""
+                <div class="pokemon">
+                    <p>{name}</p>
+                    <img src="{sprite_url}" alt="{name}">
+                    <p>{info}</p>
+                </div>
+            """
+        return html_template.format(title=pokemon_name.title(), pokemon_entries=pokemon_entries)
+
+    evolution_chain = get_pokemon_evolutions(pokemon_name=pokemon_name)
+    if "Error" in evolution_chain or "No evolution chain" in evolution_chain:
+        return evolution_chain
+
+    names = [name.strip().title() for name in evolution_chain.replace("Evolution chain: ", "").split("→")]
+    evolution_data = []
+    for name in names:
+        sprite_result = get_pokemon_sprite(pokemon_name=name.lower())
+        sprite_url = sprite_result.split(": ")[1] if "sprite:" in sprite_result else "https://via.placeholder.com/100"
+        info_result = get_pokemon_basic_info(pokemon_name=name.lower())
+        info = info_result.replace(f"{name} (", "(").replace("\n",
+                                                             ", ") if "Error" not in info_result else "No info available"
+        evolution_data.append((name, sprite_url, info))
+
+    html_content = generate_evolution_html(pokemon_name, evolution_data)
+    html_filename = f"{pokemon_name.lower()}_evolution.html"
+    with open(html_filename, "w", encoding="utf-8") as f:
+        f.write(html_content)
+
+    return (
+        f"{evolution_chain}\n\n"
+        f"Evolution visualization saved to '{html_filename}'.\n"
+        f"The HTML shows each stage with its sprite and basic info (ID, height, weight, types)."
+    )
+
+
+@tool
+def get_evolution_stats_chart(pokemon_name: str) -> str:
+    """
+    Generates a Chart.js radar chart comparing base stats for a Pokémon's evolution chain.
+    This tool should be called directly without any code generation.
+
+    Args:
+        pokemon_name: The name or ID of the Pokémon.
+
+    Returns:
+        A string with instructions to view the chart JSON.
+    """
+    evolution_chain = get_pokemon_evolutions(pokemon_name=pokemon_name)
+    if "Error" in evolution_chain or "No evolution chain" in evolution_chain:
+        return evolution_chain
+
+    names = [name.strip().title() for name in evolution_chain.replace("Evolution chain: ", "").split("→")]
+    datasets = []
+    colors = [
+        {"bg": "rgba(255, 206, 86, 0.2)", "border": "rgba(255, 206, 86, 1)"},
+        {"bg": "rgba(255, 99, 132, 0.2)", "border": "rgba(255, 99, 132, 1)"},
+        {"bg": "rgba(54, 162, 235, 0.2)", "border": "rgba(54, 162, 235, 1)"}
+    ]
+
+    for idx, name in enumerate(names):
+        stats_result = get_pokemon_base_stats(pokemon_name=name.lower())
+        if "Error" in stats_result:
+            continue
+        stats_dict = {k.split(": ")[0]: int(k.split(": ")[1]) for k in stats_result.split(": ", 1)[1].split(", ")}
+        stats_data = [
+            stats_dict.get("Hp", 0),
+            stats_dict.get("Attack", 0),
+            stats_dict.get("Defense", 0),
+            stats_dict.get("Special Attack", 0),
+            stats_dict.get("Special Defense", 0),
+            stats_dict.get("Speed", 0)
+        ]
+        datasets.append({
+            "label": name,
+            "data": stats_data,
+            "backgroundColor": colors[idx % len(colors)]["bg"],
+            "borderColor": colors[idx % len(colors)]["border"],
+            "borderWidth": 1
+        })
+
+    chart_config = {
+        "type": "radar",
+        "data": {
+            "labels": ["HP", "Attack", "Defense", "Special Attack", "Special Defense", "Speed"],
+            "datasets": datasets
+        },
+        "options": {
+            "scales": {
+                "r": {
+                    "beginAtZero": True,
+                    "max": 150
+                }
+            },
+            "plugins": {
+                "title": {
+                    "display": True,
+                    "text": f"{pokemon_name.title()} Evolution Chain Base Stats"
+                }
+            }
+        }
+    }
+
+    chart_filename = f"{pokemon_name.lower()}_evolution_stats.json"
+    with open(chart_filename, "w") as f:
+        json.dump(chart_config, f, indent=2)
+
+    return f"Radar chart for {pokemon_name.title()}'s evolution chain stats saved to '{chart_filename}'."
+
+
 final_answer = FinalAnswerTool()
 
-model = OpenAIServerModel(
-    model_id="meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
-    api_base="https://api.together.xyz/v1/",
-    api_key=os.environ["TOGETHER_API_KEY"],
+model = LiteLLMModel(
+    model_id="ollama_chat/qwen2:7b",
+    api_base="http://127.0.0.1:11434",
+    num_ctx=8192,
 )
 
-with open("prompts.yaml", 'r') as stream:
-    prompt_templates = yaml.safe_load(stream)
+# Create improved prompt templates
+improved_prompts = {
+    "system_prompt": """You are a helpful Pokémon information assistant. You have access to many tools to get Pokémon data.
+
+IMPORTANT INSTRUCTIONS:
+1. Always use the available tools directly - never try to write code or import modules
+2. When asked to call get_pokemon_evolution_visual or get_evolution_stats_chart, call those tools directly
+3. Never attempt to write Python code, import statements, or create visualizations yourself
+4. Simply call the appropriate tool function with the pokemon name as argument
+5. Return the result from the tool call as your final answer
+
+Available tools:
+- get_pokemon_evolution_visual: Creates HTML evolution visualization
+- get_evolution_stats_chart: Creates radar chart comparing evolution stats
+- get_pokemon_basic_info: Gets basic Pokémon info
+- get_pokemon_evolutions: Gets evolution chain
+- get_pokemon_base_stats: Gets base stats
+- And many more Pokémon data tools
+
+When a user asks you to use a specific tool, call that tool directly.""",
+
+    "user_prompt": """Human request: {task}
+
+Remember: Use the available tools directly. Do not write code or try to import anything.""",
+
+    "code_execution_system_prompt": """You are a helpful assistant that can execute code to help users. When executing code, be direct and use available tools without attempting to import external modules."""
+}
 
 agent = CodeAgent(
     model=model,
@@ -425,11 +642,61 @@ agent = CodeAgent(
         get_pokemon_type_effectiveness,
         get_pokemon_ability_description,
         list_pokemon_by_type,
-        get_pokemon_full_profile
+        get_pokemon_full_profile,
+        get_pokemon_evolution_visual,
+        get_evolution_stats_chart
     ],
-    max_steps=6,
+    max_steps=3,  # Reduced from 6 to force more direct tool usage
     verbosity_level=2,
-    prompt_templates=prompt_templates
+    prompt_templates=improved_prompts
 )
 
-GradioUI(agent).launch()
+
+def pokemon_ui(pokemon_name):
+    if not pokemon_name:
+        return "Please enter a Pokémon name (e.g., pikachu).", "", ""
+
+    try:
+        # Use more direct tool calls
+        evo_result = get_pokemon_evolution_visual(pokemon_name)
+        chart_result = get_evolution_stats_chart(pokemon_name)
+
+        html_filename = f"{pokemon_name.lower()}_evolution.html"
+        evolution_html = ""
+        if os.path.exists(html_filename):
+            with open(html_filename, "r", encoding="utf-8") as f:
+                evolution_html = f.read()
+
+        chart_filename = f"{pokemon_name.lower()}_evolution_stats.json"
+        chart_html = ""
+        if os.path.exists(chart_filename):
+            with open(chart_filename, "r") as f:
+                chart_config = json.load(f)
+            chart_html = f"""
+            <div style="width: 500px; margin: auto;">
+                <canvas id="statsChart"></canvas>
+            </div>
+            <script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.0/dist/chart.umd.min.js"></script>
+            <script>
+                const ctx = document.getElementById('statsChart').getContext('2d');
+                new Chart(ctx, {json.dumps(chart_config)});
+            </script>
+            """
+
+        return evo_result, evolution_html, chart_html
+
+    except Exception as e:
+        return f"Error processing {pokemon_name}: {str(e)}", "", ""
+
+
+if __name__ == "__main__":
+    iface = gr.Interface(
+        fn=pokemon_ui,
+        inputs=gr.Textbox(label="Pokemon Name"),
+        outputs=[
+            gr.HTML(label="Evolution HTML"),
+            gr.HTML(label="Evolution Chart HTML"),
+            gr.HTML(label="Evolution Stats Chart HTML"),
+        ],
+    )
+    iface.launch(share=True)

prompts.yaml

@@ -1,321 +1,326 @@
-"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:
-  {%- 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', a long string explaining your task.
-  Given that this team member is a real human, you should be very verbose in your task.
-  Here is a list of the team members that you can call:
-  {%- for agent in managed_agents.values() %}
-  - {{ agent.name }}: {{ agent.description }}
-  {%- endfor %}
-  {%- else %}
-  {%- 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! 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.
-
-    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
-    List here the specific facts given in the task that could help you (there might be nothing here).
-
-    ### 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
-    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.
-
-    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.
-    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 %}
-    {%- else %}
-    {%- 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 %}
-    {%- else %}
-    {%- 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.
-
-    Now write your new plan below.
-"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}}
+#"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:
+#  {%- 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', a long string explaining your task.
+#  Given that this team member is a real human, you should be very verbose in your task.
+#  Here is a list of the team members that you can call:
+#  {%- for agent in managed_agents.values() %}
+#  - {{ agent.name }}: {{ agent.description }}
+#  {%- endfor %}
+#  {%- else %}
+#  {%- 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! 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.
+#
+#    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
+#    List here the specific facts given in the task that could help you (there might be nothing here).
+#
+#    ### 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
+#    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.
+#
+#    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.
+#    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 %}
+#    {%- else %}
+#    {%- 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 %}
+#    {%- else %}
+#    {%- 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.
+#
+#    Now write your new plan below.
+#"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}}
+
+system_prompt: |
+  You are a specialized Pokémon assistant.
+  Use the provided tools to answer Pokémon-related queries.
+  For visualization requests, use get_pokemon_evolution_visual and get_evolution_stats_chart.

requirements.txt

@@ -1,5 +1,8 @@
-markdownify
+markdownify~=1.1.0
 smolagents==1.13.0
-requests
+requests~=2.32.4
 duckduckgo_search
 pandas
+
+duckduckgo_search~=8.0.3
+PyYAML~=6.0.2