Repository: huggingface/smolagents

Issue:
[BUG] agent.replay errors
**Describe the bug**
A clear and concise description of what the bug is
.
There are a number of errors during a replay, 
step.facts missing 
step.model_output is None



**Code to reproduce the error**
The simplest code snippet that produces your bug.

```python
import os
import requests
from rich.console import Console
# from smolagents.agents import ToolCallingAgent
from smolagents import ToolCallingAgent, LiteLLMModel, tool
from smolagents.memory import SystemPromptStep, TaskStep, ActionStep, PlanningStep
from smolagents.monitoring import LogLevel

# For anthropic: change model_id below to 'anthropic/claude-3-5-sonnet-20240620'
model = LiteLLMModel(
    model_id="groq/deepseek-r1-distill-llama-70b",
    api_base="https://api.groq.com/openai/v1",
    api_key=os.environ.get("GROQ_API_KEY", "ERROR"),
    # max_completion_tokens=4096,
)

console = Console()

@tool
def get_joke() -> str:
    """
    Fetches a random joke from the JokeAPI.
    This function sends a GET request to the JokeAPI to retrieve a random joke.
    It handles both single jokes and two-part jokes (setup and delivery).
    If the request fails or the response does not contain a joke, an error message is returned.
    Returns:
        str: The joke as a string, or an error message if the joke could not be fetched.
    """
    url = "https://v2.jokeapi.dev/joke/Any?type=single"

    try:
        response = requests.get(url)
        response.raise_for_status()

        data = response.json()

        if "joke" in data:
            return data["joke"]
        elif "setup" in data and "delivery" in data:
            return f"{data['setup']} - {data['delivery']}"
        else:
            return "Error: Unable to fetch joke."

    except requests.exceptions.RequestException as e:
        return f"Error fetching joke: {str(e)}"


agent = ToolCallingAgent(
    tools=[
        get_joke,
    ],
    model=model,
)


agent.run("Tell me a joke")
agent.replay() # ERROR HERE 

# Soemthing like this might work, but might be missing data (tasks, model_output?)
# work 
console.print("[bold]Summary of agent actions:[/bold]")

for i, step in enumerate(agent.memory.steps):
    class_name = type(step).__name__
    if isinstance(step, SystemPromptStep):
        agent.logger.log_markdown(
            title="System prompt", content=step.system_prompt, level=LogLevel.ERROR
        )
    elif isinstance(step, TaskStep):
        agent.logger.log_task(step.task, "", level=LogLevel.ERROR)
    elif isinstance(step, ActionStep):
        agent.logger.log_rule(f"Step {step.step_number}", level=LogLevel.ERROR)
        agent.logger.log_messages(step.model_input_messages)

        agent.logger.log_markdown(
            title="Agent output:", content=(step.model_output if step.model_output is not None else ""), level=LogLevel.ERROR
        )
    elif isinstance(step, PlanningStep):
        agent.logger.log_rule("Planning step", level=LogLevel.ERROR)
        agent.logger.log_messages(step.model_input_messages, level=LogLevel.ERROR)
        agent.logger.log_markdown(
            title="Agent output:",
            content=step.plan,
            level=LogLevel.ERROR,
        )


```
**Error logs (if any)**
Provide error logs if there are any.


 ```
 File "~/pcode/tester/test.py", line 57, in <module>
    agent.replay()
  File "/opt/anaconda3/envs/tester/lib/python3.10/site-packages/smolagents/agents.py", line 586, in replay
    self.memory.replay(self.logger, detailed=detailed)
  File "/opt/anaconda3/envs/tester/lib/python3.10/site-packages/smolagents/memory.py", line 225, in replay
    logger.log_markdown(title="Agent output:", content=step.model_output, level=LogLevel.ERROR)
  File "/opt/anaconda3/envs/tester/lib/python3.10/site-packages/smolagents/monitoring.py", line 113, in log_markdown
    self.log(
  File "/opt/anaconda3/envs/tester/lib/python3.10/site-packages/smolagents/monitoring.py", line 100, in log
    self.console.print(*args, **kwargs)
  File "/opt/anaconda3/envs/tester/lib/python3.10/site-packages/rich/console.py", line 1705, in print
    extend(render(renderable, render_options))
  File "/opt/anaconda3/envs/tester/lib/python3.10/site-packages/rich/console.py", line 1330, in render
    yield from self.render(render_output, _options)
  File "/opt/anaconda3/envs/tester/lib/python3.10/site-packages/rich/console.py", line 1326, in render
    for render_output in iter_render:
  File "/opt/anaconda3/envs/tester/lib/python3.10/site-packages/rich/syntax.py", line 628, in __rich_console__
    segments = Segments(self._get_syntax(console, options))
  File "/opt/anaconda3/envs/tester/lib/python3.10/site-packages/rich/segment.py", line 681, in __init__
    self.segments = list(segments)
  File "/opt/anaconda3/envs/tester/lib/python3.10/site-packages/rich/syntax.py", line 653, in _get_syntax
    ends_on_nl, processed_code = self._process_code(self.code)
  File "/opt/anaconda3/envs/tester/lib/python3.10/site-packages/rich/syntax.py", line 813, in _process_code
    ends_on_nl = code.endswith("\n")
AttributeError: 'NoneType' object has no attribute 'endswith'

```
**Expected behavior**
A clear and concise description of what you expected to happen.

No errors 

**Packages version:**
Run `pip freeze | grep smolagents` and paste it here.
smolagents==1.13.0
**Additional context**
Add any other context about the problem here.
I put a potential fix in the include code, but I just started using this lib

Discussion:
@keetrap: `memory_step.model_output` is always `None` in case of `ToolCallingAgent`
@albertvillanova  would you like to see a PR fixing this issue.
We can set it like  `Called Tool: '{tool_name}' with arguments: {tool_arguments}`@aymeric-roucher: @keetrap Albert is on vacation: indeed such a PR would be great! Also could you add a simple test with a mock model?

Solution patch:
diff --git a/examples/open_deep_research/analysis.ipynb b/examples/open_deep_research/analysis.ipynb
index 04f315f..ccb6a1d 100644
--- a/examples/open_deep_research/analysis.ipynb
+++ b/examples/open_deep_research/analysis.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -11,19 +11,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/aymeric/venv/gaia/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n",
-      "Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "\n",
@@ -38,12 +28,12 @@
     "\n",
     "pd.set_option(\"max_colwidth\", None)\n",
     "\n",
-    "OUTPUT_DIR = \"output\""
+    "OUTPUT_DIR = \"../../output\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -52,29 +42,6 @@
     "eval_df = pd.DataFrame(eval_ds)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2    86\n",
-       "1    53\n",
-       "3    26\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series(eval_ds[\"task\"]).value_counts()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -84,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,254 +65,14 @@
     "    results.append(df)\n",
     "\n",
     "result_df = pd.concat(results)\n",
-    "result_df = result_df.drop(columns=[\"start_time\", \"end_time\"])\n",
     "result_df[\"prediction\"] = result_df[\"prediction\"].fillna(\"No prediction\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "String  cannot be normalized to number str.\n",
-      "String 2017 Komo Mai Drive 900000 cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String 2 High fantasy A Song of Ice and Fire cannot be normalized to number str.\n",
-      "String  cannot be normalized to number str.\n",
-      "String 94 CFM for Cheater cannot be normalized to number str.\n",
-      "String  93 CFM for Cheater beater cannot be normalized to number str.\n",
-      "String 2017 Komo Mai Drive 900000 cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String 2017 Komo Mai Drive 900000 cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String 3 or 4 cannot be normalized to number str.\n",
-      "String No year cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String 250 for Cheater cannot be normalized to number str.\n",
-      "String  220 for Cheater beater cannot be normalized to number str.\n",
-      "String  cannot be normalized to number str.\n",
-      "String 776 ft/min for Cheater cannot be normalized to number str.\n",
-      "String  768 ft/min for Cheater beater cannot be normalized to number str.\n",
-      "String  cannot be normalized to number str.\n",
-      "String 2017 Komo Mai Drive 900000 cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String CFM number for Cheater: not listed cannot be normalized to number str.\n",
-      "String  CFM number for Cheater beater: 665 ft/min cannot be normalized to number str.\n",
-      "String 2017 Komo Mai Drive 900000 cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String  cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String  cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String 1.46 Å cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String  cannot be normalized to number str.\n",
-      "String  cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String  cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String  cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String  cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String  cannot be normalized to number str.\n",
-      "String  cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String  cannot be normalized to number str.\n",
-      "String August 1: 0 August 2: 0 August 3: 0 August 4: 0 August 5: 0 August 6: 0 August 7: 0 August 8: 0 August 9: 0 August 10: 0 August 11: 0 August 12: 0 August 13: 0 August 14: 0 August 15: 0 August 16: 0 August 17: 0 August 18: 0 August 19: 0 August 20: 0 August 21: 0 August 22: 0 August 23: 0 August 24: 0 August 25: 0 August 26: 0 August 27: 0 August 28: 0 August 29: 0 August 30: 0 August 31: 0 cannot be normalized to number str.\n",
-      "String  cannot be normalized to number str.\n",
-      "String  cannot be normalized to number str.\n",
-      "String  cannot be normalized to number str.\n",
-      "String  cannot be normalized to number str.\n",
-      "String 120 for Cheater cannot be normalized to number str.\n",
-      "String  103 for Cheater beater cannot be normalized to number str.\n",
-      "String 2017 Komo Mai Drive 900000 cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String 120.28 for Cheater cannot be normalized to number str.\n",
-      "String  119.04 for Cheater beater cannot be normalized to number str.\n",
-      "String 3 or 4 cannot be normalized to number str.\n",
-      "String 2017 Komo Mai Drive 900000 cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String  cannot be normalized to number str.\n",
-      "String 2017 Komo Mai Drive sold for 900000 cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String 2730-2740 cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String 89706.00 USD cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String No prediction cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String 6 The Lord of the Rings (book) J. R. R. Tolkien Author American literature Fantasy literature Publishers A Song of Ice and Fire cannot be normalized to number str.\n",
-      "String  cannot be normalized to number str.\n",
-      "String  cannot be normalized to number str.\n",
-      "String 2017 Komo Mai Drive 900000 cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String 1.46 Å cannot be normalized to number str.\n",
-      "String  cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String 94.5 for Cheater cannot be normalized to number str.\n",
-      "String  93.5 for Cheater beater cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String 2017 Komo Mai Drive 900000 cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String 776 for Cheater cannot be normalized to number str.\n",
-      "String  Not specified for Cheater Beater cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String 5.75 for Cheater cannot be normalized to number str.\n",
-      "String  5.22 for Cheater Beater cannot be normalized to number str.\n",
-      "String 2017 Komo Mai Drive sold for 900000 cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String 33101 28557 cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "String Unable to determine cannot be normalized to number str.\n",
-      "Close call: Alfonso Cardinal Visconti vs Alfonso Visconti\n",
-      "Close call: Rockhopper Penguins vs Rockhopper penguin\n",
-      "Close call: INT. THE CASTLE vs THE CASTLE\n",
-      "Close call: to be or not to be that is the question whether tis nobler in the mind to suffer the slings and arrows of outrageous fortune or to take arms against a sea of troubles and by opposing end them vs To be or not to be that is the question whether tis nobler in the mind to suffer the slings and arrows of outrageous fortune\n",
-      "Close call: The World of the Twenty First Century 1994 vs The World of the Twenty First Century\n",
-      "Close call: Alfonso Cardinal Visconti vs Alfonso Visconti\n",
-      "Close call: Wes Craven's A Nightmare on Elm Street vs A Nightmare on Elm Street\n",
-      "Close call: God said let there be dragons vs Here be dragons\n",
-      "Close call: rockhopper penguins vs Rockhopper penguin\n",
-      "Close call: Harbinger, This Fire, Tidal vs Harbinger, Tidal\n",
-      "Close call: EC 3.1.3.1;EC 1.11.1.7 vs 3.1.3.1; 1.11.1.7\n",
-      "Close call: to be or not to be that is the question whether tis nobler in the mind to suffer the slings and arrows of outrageous fortune or to take arms against a sea of troubles and by opposing end them vs To be or not to be that is the question whether tis nobler in the mind to suffer the slings and arrows of outrageous fortune\n",
-      "Close call: Alfonso Cardinal Visconti vs Alfonso Visconti\n",
-      "Close call: to be or not to be that is the question whether tis nobler in the mind to suffer the slings and arrows of outrageous fortune or to take arms against a sea of troubles and by opposing end them vs To be or not to be that is the question whether tis nobler in the mind to suffer the slings and arrows of outrageous fortune\n",
-      "Close call: Out of the Silent Planet by C.S. Lewis vs Out of the Silent Planet\n",
-      "Close call: broccoli, celery, fresh basil, green beans, lettuce, sweet potatoes vs broccoli, celery, fresh basil, lettuce, sweet potatoes\n",
-      "Close call: To be or not to be that is the question whether tis nobler in the mind to suffer the slings and arrows of outrageous fortune or to take arms against a sea of troubles and by opposing end them vs To be or not to be that is the question whether tis nobler in the mind to suffer the slings and arrows of outrageous fortune\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/aymeric/Documents/Code/smolagents/examples/open_deep_research/scripts/gaia_scorer.py:52: UserWarning: Answer lists have different lengths, returning False.\n",
-      "  warnings.warn(\"Answer lists have different lengths, returning False.\", UserWarning)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import re\n",
     "from collections import Counter\n",
@@ -395,12 +122,21 @@
     "    return total_count\n",
     "\n",
     "\n",
+    "def get_durations(row):\n",
+    "    # start_datetime = datetime.strptime(row['start_time'], \"%Y-%m-%d %H:%M:%S\")\n",
+    "    # end_datetime = datetime.strptime(row['end_time'], \"%Y-%m-%d %H:%M:%S\")\n",
+    "\n",
+    "    duration_timedelta = row[\"end_time\"] - row[\"start_time\"]\n",
+    "    return int(duration_timedelta.total_seconds())\n",
+    "\n",
+    "\n",
+    "result_df[\"duration\"] = result_df.apply(get_durations, axis=1)\n",
     "# result_df[\"tool_calls\"] = result_df[\"intermediate_steps\"].apply(sum_tool_calls)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -425,43 +161,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "agent_name\n",
-       "code_gpt4o_03_february_text                         165\n",
-       "code_o1_03_february_ablation-toolcalling-manager    165\n",
-       "code_o1_01_february_text                            165\n",
-       "code_o3-mini_03_february_remove-navigational        165\n",
-       "code_o1_04_february_submission5                     165\n",
-       "code_o1_03_february_text_high-reasoning-effort      165\n",
-       "code_o1_03_february_remove-navigational             164\n",
-       "code_o1_03_february_fix-print-outputs               164\n",
-       "code_o1_04_february_submission                      162\n",
-       "code_o1_03_february_goodoldtext-unbroken            161\n",
-       "code_gpt4o_03_february_goodoldtext-unbroken         159\n",
-       "code_gpt4o_03_february_magenticbrowser              159\n",
-       "code_o1_03_february_fix-print-outputs2              156\n",
-       "code_gpt4o_03_february_magenticbrowser2             156\n",
-       "code_o1_04_february_submission-medium               125\n",
-       "code_o1_29-01_text                                  105\n",
-       "code_llama-3                                         90\n",
-       "code_o1_22-01_managedagent-summary_planning          67\n",
-       "code_o1_25-01_visioon                                53\n",
-       "code_o1_04_february_submission3                      49\n",
-       "code_qwen-coder-32B_03_february_text                 43\n",
-       "code_o1_04_february_submission4                       6\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "result_df[\"agent_name\"].value_counts()"
    ]
@@ -475,440 +177,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "agent_name\n",
-       "code_gpt4o_03_february_text                         165\n",
-       "code_o1_03_february_ablation-toolcalling-manager    165\n",
-       "code_o1_01_february_text                            165\n",
-       "code_o3-mini_03_february_remove-navigational        165\n",
-       "code_o1_04_february_submission5                     165\n",
-       "code_o1_03_february_text_high-reasoning-effort      165\n",
-       "code_o1_03_february_remove-navigational             164\n",
-       "code_o1_03_february_fix-print-outputs               164\n",
-       "code_o1_04_february_submission                      162\n",
-       "code_o1_03_february_goodoldtext-unbroken            161\n",
-       "code_gpt4o_03_february_goodoldtext-unbroken         159\n",
-       "code_gpt4o_03_february_magenticbrowser              159\n",
-       "code_o1_03_february_fix-print-outputs2              156\n",
-       "code_gpt4o_03_february_magenticbrowser2             156\n",
-       "code_o1_04_february_submission-medium               125\n",
-       "code_o1_29-01_text                                  105\n",
-       "code_llama-3                                         90\n",
-       "code_o1_22-01_managedagent-summary_planning          67\n",
-       "code_o1_25-01_visioon                                53\n",
-       "code_o1_04_february_submission3                      49\n",
-       "code_qwen-coder-32B_03_february_text                 43\n",
-       "code_o1_04_february_submission4                       6\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "agent_name                                    task\n",
-       "code_gpt4o_03_february_goodoldtext-unbroken   2       84\n",
-       "                                              1       53\n",
-       "                                              3       22\n",
-       "code_gpt4o_03_february_magenticbrowser        2       83\n",
-       "                                              1       52\n",
-       "                                                      ..\n",
-       "code_o3-mini_03_february_remove-navigational  1       53\n",
-       "                                              3       26\n",
-       "code_qwen-coder-32B_03_february_text          2       22\n",
-       "                                              1       14\n",
-       "                                              3        7\n",
-       "Name: count, Length: 65, dtype: int64"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total length: 2809 - is complete: False\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "o1_vision = \"code_o1_25-01_visioon\"\n",
-    "o1_next = \"code_o1_29-01_text\"\n",
-    "o1 = \"code_o1_01_february_text\"\n",
-    "\n",
-    "list_versions = [o1, o1_vision, o1_next]\n",
-    "\n",
-    "# submission_selection_name = \"react_code_llama3-70b_02-05_full-gaia-validation-code\"\n",
     "sel_df = result_df\n",
     "# sel_df = sel_df.loc[\n",
     "#     (result_df[\"agent_name\"].isin(list_versions))\n",
-    "#     # & (~result_df[\"question\"].isin(UNSOLVED_QUESTIONS))\n",
     "# ]\n",
     "sel_df = sel_df.reset_index(drop=True)\n",
     "display(sel_df[\"agent_name\"].value_counts())\n",
     "sel_df = sel_df.drop_duplicates(subset=[\"agent_name\", \"question\"])\n",
     "display(sel_df.groupby(\"agent_name\")[[\"task\"]].value_counts())\n",
-    "print(\"Total length:\", len(sel_df), \"- is complete:\", len(sel_df) == 165)\n",
-    "# assert sel_df[\"question\"].value_counts().max() == len(list_versions), \"Some questions are duplicate!\""
+    "print(\"Total length:\", len(sel_df), \"- is complete:\", len(sel_df) == 165)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Average score:'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>is_correct</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>agent_name</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>code_gpt4o_03_february_goodoldtext-unbroken</th>\n",
-       "      <td>0.384</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>code_gpt4o_03_february_magenticbrowser</th>\n",
-       "      <td>0.352</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>code_gpt4o_03_february_magenticbrowser2</th>\n",
-       "      <td>0.365</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>code_gpt4o_03_february_text</th>\n",
-       "      <td>0.376</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>code_llama-3</th>\n",
-       "      <td>0.078</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>code_o1_01_february_text</th>\n",
-       "      <td>0.491</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>code_o1_03_february_ablation-toolcalling-manager</th>\n",
-       "      <td>0.327</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>code_o1_03_february_fix-print-outputs</th>\n",
-       "      <td>0.518</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>code_o1_03_february_fix-print-outputs2</th>\n",
-       "      <td>0.558</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>code_o1_03_february_goodoldtext-unbroken</th>\n",
-       "      <td>0.534</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>code_o1_03_february_remove-navigational</th>\n",
-       "      <td>0.537</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>code_o1_03_february_text_high-reasoning-effort</th>\n",
-       "      <td>0.485</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>code_o1_04_february_submission</th>\n",
-       "      <td>0.494</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>code_o1_04_february_submission-medium</th>\n",
-       "      <td>0.488</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>code_o1_04_february_submission3</th>\n",
-       "      <td>0.490</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>code_o1_04_february_submission4</th>\n",
-       "      <td>0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>code_o1_04_february_submission5</th>\n",
-       "      <td>0.552</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>code_o1_22-01_managedagent-summary_planning</th>\n",
-       "      <td>0.418</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>code_o1_25-01_visioon</th>\n",
-       "      <td>0.340</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>code_o1_29-01_text</th>\n",
-       "      <td>0.390</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>code_o3-mini_03_february_remove-navigational</th>\n",
-       "      <td>0.291</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>code_qwen-coder-32B_03_february_text</th>\n",
-       "      <td>0.209</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                  is_correct\n",
-       "agent_name                                                  \n",
-       "code_gpt4o_03_february_goodoldtext-unbroken            0.384\n",
-       "code_gpt4o_03_february_magenticbrowser                 0.352\n",
-       "code_gpt4o_03_february_magenticbrowser2                0.365\n",
-       "code_gpt4o_03_february_text                            0.376\n",
-       "code_llama-3                                           0.078\n",
-       "code_o1_01_february_text                               0.491\n",
-       "code_o1_03_february_ablation-toolcalling-manager       0.327\n",
-       "code_o1_03_february_fix-print-outputs                  0.518\n",
-       "code_o1_03_february_fix-print-outputs2                 0.558\n",
-       "code_o1_03_february_goodoldtext-unbroken               0.534\n",
-       "code_o1_03_february_remove-navigational                0.537\n",
-       "code_o1_03_february_text_high-reasoning-effort         0.485\n",
-       "code_o1_04_february_submission                         0.494\n",
-       "code_o1_04_february_submission-medium                  0.488\n",
-       "code_o1_04_february_submission3                        0.490\n",
-       "code_o1_04_february_submission4                        0.500\n",
-       "code_o1_04_february_submission5                        0.552\n",
-       "code_o1_22-01_managedagent-summary_planning            0.418\n",
-       "code_o1_25-01_visioon                                  0.340\n",
-       "code_o1_29-01_text                                     0.390\n",
-       "code_o3-mini_03_february_remove-navigational           0.291\n",
-       "code_qwen-coder-32B_03_february_text                   0.209"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>is_correct</th>\n",
-       "      <th>is_near_correct</th>\n",
-       "      <th>count_steps</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>agent_name</th>\n",
-       "      <th>task</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">code_gpt4o_03_february_goodoldtext-unbroken</th>\n",
-       "      <th>1</th>\n",
-       "      <td>0.452830</td>\n",
-       "      <td>0.452830</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>53</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.380952</td>\n",
-       "      <td>0.392857</td>\n",
-       "      <td>8.511905</td>\n",
-       "      <td>84</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.227273</td>\n",
-       "      <td>0.227273</td>\n",
-       "      <td>10.409091</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">code_gpt4o_03_february_magenticbrowser</th>\n",
-       "      <th>1</th>\n",
-       "      <td>0.480769</td>\n",
-       "      <td>0.480769</td>\n",
-       "      <td>7.153846</td>\n",
-       "      <td>52</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.349398</td>\n",
-       "      <td>0.361446</td>\n",
-       "      <td>8.168675</td>\n",
-       "      <td>83</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">code_o3-mini_03_february_remove-navigational</th>\n",
-       "      <th>2</th>\n",
-       "      <td>0.232558</td>\n",
-       "      <td>0.244186</td>\n",
-       "      <td>4.976744</td>\n",
-       "      <td>86</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.153846</td>\n",
-       "      <td>0.153846</td>\n",
-       "      <td>6.615385</td>\n",
-       "      <td>26</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">code_qwen-coder-32B_03_february_text</th>\n",
-       "      <th>1</th>\n",
-       "      <td>0.357143</td>\n",
-       "      <td>0.357143</td>\n",
-       "      <td>5.428571</td>\n",
-       "      <td>14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.136364</td>\n",
-       "      <td>0.136364</td>\n",
-       "      <td>6.409091</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.142857</td>\n",
-       "      <td>0.142857</td>\n",
-       "      <td>6.571429</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>65 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                   is_correct  \\\n",
-       "agent_name                                   task               \n",
-       "code_gpt4o_03_february_goodoldtext-unbroken  1       0.452830   \n",
-       "                                             2       0.380952   \n",
-       "                                             3       0.227273   \n",
-       "code_gpt4o_03_february_magenticbrowser       1       0.480769   \n",
-       "                                             2       0.349398   \n",
-       "...                                                       ...   \n",
-       "code_o3-mini_03_february_remove-navigational 2       0.232558   \n",
-       "                                             3       0.153846   \n",
-       "code_qwen-coder-32B_03_february_text         1       0.357143   \n",
-       "                                             2       0.136364   \n",
-       "                                             3       0.142857   \n",
-       "\n",
-       "                                                   is_near_correct  \\\n",
-       "agent_name                                   task                    \n",
-       "code_gpt4o_03_february_goodoldtext-unbroken  1            0.452830   \n",
-       "                                             2            0.392857   \n",
-       "                                             3            0.227273   \n",
-       "code_gpt4o_03_february_magenticbrowser       1            0.480769   \n",
-       "                                             2            0.361446   \n",
-       "...                                                            ...   \n",
-       "code_o3-mini_03_february_remove-navigational 2            0.244186   \n",
-       "                                             3            0.153846   \n",
-       "code_qwen-coder-32B_03_february_text         1            0.357143   \n",
-       "                                             2            0.136364   \n",
-       "                                             3            0.142857   \n",
-       "\n",
-       "                                                   count_steps  count  \n",
-       "agent_name                                   task                      \n",
-       "code_gpt4o_03_february_goodoldtext-unbroken  1        7.000000     53  \n",
-       "                                             2        8.511905     84  \n",
-       "                                             3       10.409091     22  \n",
-       "code_gpt4o_03_february_magenticbrowser       1        7.153846     52  \n",
-       "                                             2        8.168675     83  \n",
-       "...                                                        ...    ...  \n",
-       "code_o3-mini_03_february_remove-navigational 2        4.976744     86  \n",
-       "                                             3        6.615385     26  \n",
-       "code_qwen-coder-32B_03_february_text         1        5.428571     14  \n",
-       "                                             2        6.409091     22  \n",
-       "                                             3        6.571429      7  \n",
-       "\n",
-       "[65 rows x 4 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "display(\"Average score:\", sel_df.groupby(\"agent_name\")[[\"is_correct\"]].mean().round(3))\n",
     "display(\n",
-    "    sel_df.groupby([\"agent_name\", \"task\"])[[\"is_correct\", \"is_near_correct\", \"count_steps\", \"question\"]]\n",
+    "    sel_df.groupby([\"agent_name\", \"task\"])[[\"is_correct\", \"is_near_correct\", \"count_steps\", \"question\", \"duration\"]]\n",
     "    .agg(\n",
     "        {\n",
     "            \"is_correct\": \"mean\",\n",
     "            \"is_near_correct\": \"mean\",\n",
     "            \"count_steps\": \"mean\",\n",
     "            \"question\": \"count\",\n",
+    "            \"duration\": \"mean\",\n",
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-        "yaxis": {
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-          "text": "is_correct"
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-        }
-       }
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import plotly.express as px\n",
     "\n",
@@ -10788,9 +245,6 @@
     "\n",
     "\n",
     "cumulative_df[\"question\"] = cumulative_df.apply(find_question, axis=1)\n",
-    "# cumulative_df[\"question\"] = [el[:50] for el in sel_df[\"question\"].values]\n",
-    "\n",
-    "# cumulative_df[\"is_correct\"] = cumulative_df[\"is_correct\"] * (165 - 68) / 165\n",
     "\n",
     "px.line(\n",
     "    cumulative_df,\n",
@@ -10810,19 +264,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "165\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "sel_df = result_df.loc[result_df[\"agent_name\"] == o1]\n",
+    "sel_df = result_df.loc[result_df[\"agent_name\"] == \"o1\"]\n",
     "print(len(sel_df))"
    ]
   },
@@ -10835,56 +281,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
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-      "\n",
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-      "\n",
-      "/var/folders/6m/9b1tts6d5w960j80wbw9tx3m0000gn/T/ipykernel_25011/2022001392.py:11: SettingWithCopyWarning:\n",
-      "\n",
-      "\n",
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-      "\n"
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+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "\n",
@@ -10916,890 +315,9 @@
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-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
-          }
-         }
-        },
-        "width": 800,
-        "xaxis": {
-         "anchor": "y",
-         "domain": [
-          0,
-          1
-         ],
-         "title": {
-          "text": "variable"
-         }
-        },
-        "yaxis": {
-         "anchor": "x",
-         "domain": [
-          0,
-          1
-         ],
-         "title": {
-          "text": "<b>Average count</b>"
-         }
-        }
-       }
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import plotly.express as px\n",
     "\n",
@@ -11841,153 +359,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>is_correct</th>\n",
-       "      <th>count_steps</th>\n",
-       "      <th>question</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>attachment_type</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>None</th>\n",
-       "      <td>0.423799</td>\n",
-       "      <td>4.959725</td>\n",
-       "      <td>2185</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>csv</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>7.750000</td>\n",
-       "      <td>16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>docx</th>\n",
-       "      <td>0.571429</td>\n",
-       "      <td>4.904762</td>\n",
-       "      <td>21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>jpg</th>\n",
-       "      <td>0.142857</td>\n",
-       "      <td>5.750000</td>\n",
-       "      <td>28</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>jsonld</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>6.600000</td>\n",
-       "      <td>15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mp3</th>\n",
-       "      <td>0.480000</td>\n",
-       "      <td>4.500000</td>\n",
-       "      <td>50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>pdb</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>4.444444</td>\n",
-       "      <td>18</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>pdf</th>\n",
-       "      <td>0.588235</td>\n",
-       "      <td>4.137255</td>\n",
-       "      <td>51</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>png</th>\n",
-       "      <td>0.216783</td>\n",
-       "      <td>4.412587</td>\n",
-       "      <td>143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>pptx</th>\n",
-       "      <td>0.882353</td>\n",
-       "      <td>4.058824</td>\n",
-       "      <td>17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>py</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>4.266667</td>\n",
-       "      <td>15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>txt</th>\n",
-       "      <td>0.705882</td>\n",
-       "      <td>4.764706</td>\n",
-       "      <td>17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>xlsx</th>\n",
-       "      <td>0.612745</td>\n",
-       "      <td>4.823529</td>\n",
-       "      <td>204</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>zip</th>\n",
-       "      <td>0.448276</td>\n",
-       "      <td>5.344828</td>\n",
-       "      <td>29</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 is_correct  count_steps  question\n",
-       "attachment_type                                   \n",
-       "None               0.423799     4.959725      2185\n",
-       "csv                0.000000     7.750000        16\n",
-       "docx               0.571429     4.904762        21\n",
-       "jpg                0.142857     5.750000        28\n",
-       "jsonld             0.000000     6.600000        15\n",
-       "mp3                0.480000     4.500000        50\n",
-       "pdb                0.000000     4.444444        18\n",
-       "pdf                0.588235     4.137255        51\n",
-       "png                0.216783     4.412587       143\n",
-       "pptx               0.882353     4.058824        17\n",
-       "py                 1.000000     4.266667        15\n",
-       "txt                0.705882     4.764706        17\n",
-       "xlsx               0.612745     4.823529       204\n",
-       "zip                0.448276     5.344828        29"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "display(\n",
     "    result_df.groupby([\"attachment_type\"])[[\"is_correct\", \"count_steps\", \"question\"]].agg(\n",
@@ -12005,7 +379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12015,52 +389,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "agent_name\n",
-       "code_gpt4o_03_february_goodoldtext-unbroken         38.36\n",
-       "code_gpt4o_03_february_magenticbrowser              35.22\n",
-       "code_gpt4o_03_february_magenticbrowser2             36.54\n",
-       "code_gpt4o_03_february_text                         37.58\n",
-       "code_o1_01_february_text                            49.09\n",
-       "code_o1_03_february_ablation-toolcalling-manager    32.73\n",
-       "code_o1_03_february_fix-print-outputs               51.83\n",
-       "code_o1_03_february_fix-print-outputs2              55.77\n",
-       "code_o1_03_february_goodoldtext-unbroken            53.42\n",
-       "code_o1_03_february_remove-navigational             53.66\n",
-       "code_o1_03_february_text_high-reasoning-effort      48.48\n",
-       "code_o1_04_february_submission                      49.38\n",
-       "code_o1_04_february_submission5                     55.15\n",
-       "code_o3-mini_03_february_remove-navigational        29.09\n",
-       "Name: is_correct, dtype: float64"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Majority score: 58.18\n",
-      "Oracle score: 72.73\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/6m/9b1tts6d5w960j80wbw9tx3m0000gn/T/ipykernel_25011/3287428472.py:20: DeprecationWarning:\n",
-      "\n",
-      "DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def majority_vote(df):\n",
     "    df = df[(df[\"prediction\"] != \"Unable to determine\") & (~df[\"prediction\"].isna()) & (df[\"prediction\"] != \"None\")]\n",
@@ -12100,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12112,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12129,9 +460,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "gaia",
+   "display_name": "test",
    "language": "python",
-   "name": "python3"
+   "name": "test"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/examples/open_deep_research/run_gaia.py b/examples/open_deep_research/run_gaia.py
index 52dd620..bee71f3 100644
--- a/examples/open_deep_research/run_gaia.py
+++ b/examples/open_deep_research/run_gaia.py
@@ -33,7 +33,6 @@ from tqdm import tqdm
 from smolagents import (
     CodeAgent,
     GoogleSearchTool,
-    # HfApiModel,
     LiteLLMModel,
     Model,
     ToolCallingAgent,
@@ -190,12 +189,7 @@ def answer_single_question(example, model_id, answers_file, visual_inspection_to
     else:
         model_params["max_tokens"] = 4096
     model = LiteLLMModel(**model_params)
-    # model = HfApiModel(model_id="Qwen/Qwen2.5-72B-Instruct", provider="together")
-    #     "https://lnxyuvj02bpe6mam.us-east-1.aws.endpoints.huggingface.cloud",
-    #     custom_role_conversions=custom_role_conversions,
-    #     # provider="sambanova",
-    #     max_tokens=8096,
-    # )
+    # model = HfApiModel(model_id="Qwen/Qwen2.5-Coder-32B-Instruct", provider="together", max_tokens=4096)
     document_inspection_tool = TextInspectorTool(model, 100000)
 
     agent = create_agent_team(model)
@@ -224,7 +218,7 @@ Here is the task:
         # Run agent 🚀
         final_result = agent.run(augmented_question)
 
-        agent_memory = agent.write_memory_to_messages(summary_mode=True)
+        agent_memory = agent.write_memory_to_messages()
 
         final_result = prepare_response(augmented_question, agent_memory, reformulation_model=model)
 
@@ -249,6 +243,12 @@ Here is the task:
         exception = e
         raised_exception = True
     end_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    token_counts_manager = agent.monitor.get_total_token_counts()
+    token_counts_web = list(agent.managed_agents.values())[0].monitor.get_total_token_counts()
+    total_token_counts = {
+        "input": token_counts_manager["input"] + token_counts_web["input"],
+        "output": token_counts_manager["output"] + token_counts_web["output"],
+    }
     annotated_example = {
         "agent_name": model.model_id,
         "question": example["question"],
@@ -258,11 +258,12 @@ Here is the task:
         "parsing_error": parsing_error,
         "iteration_limit_exceeded": iteration_limit_exceeded,
         "agent_error": str(exception) if raised_exception else None,
-        "start_time": start_time,
-        "end_time": end_time,
         "task": example["task"],
         "task_id": example["task_id"],
         "true_answer": example["true_answer"],
+        "start_time": start_time,
+        "end_time": end_time,
+        "token_counts": total_token_counts,
     }
     append_answer(annotated_example, answers_file)
 
diff --git a/pyproject.toml b/pyproject.toml
index 273d2c9..3f25ce2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,7 +7,7 @@ name = "smolagents"
 version = "1.14.0.dev0"
 description = "🤗 smolagents: a barebones library for agents. Agents write python code to call tools or orchestrate other agents."
 authors = [
-  { name="Aymeric Roucher", email="aymeric@hf.co" }, { name="Thomas Wolf"},
+  { name="Aymeric Roucher", email="aymeric@hf.co" },
 ]
 readme = "README.md"
 requires-python = ">=3.10"
diff --git a/src/smolagents/agents.py b/src/smolagents/agents.py
index 545f70c..78b1e03 100644
--- a/src/smolagents/agents.py
+++ b/src/smolagents/agents.py
@@ -93,18 +93,12 @@ class PlanningPromptTemplate(TypedDict):
     Prompt templates for the planning step.
 
     Args:
-        initial_facts (`str`): Initial facts prompt.
-        initial_plan (`str`): Initial plan prompt.
-        update_facts_pre_messages (`str`): Update facts pre-messages prompt.
-        update_facts_post_messages (`str`): Update facts post-messages prompt.
+        plan (`str`): Initial plan prompt.
         update_plan_pre_messages (`str`): Update plan pre-messages prompt.
         update_plan_post_messages (`str`): Update plan post-messages prompt.
     """
 
-    initial_facts: str
-    initial_plan: str
-    update_facts_pre_messages: str
-    update_facts_post_messages: str
+    plan: str
     update_plan_pre_messages: str
     update_plan_post_messages: str
 
@@ -155,10 +149,7 @@ class PromptTemplates(TypedDict):
 EMPTY_PROMPT_TEMPLATES = PromptTemplates(
     system_prompt="",
     planning=PlanningPromptTemplate(
-        initial_facts="",
         initial_plan="",
-        update_facts_pre_messages="",
-        update_facts_post_messages="",
         update_plan_pre_messages="",
         update_plan_post_messages="",
     ),
@@ -210,6 +201,16 @@ class MultiStepAgent:
         self.agent_name = self.__class__.__name__
         self.model = model
         self.prompt_templates = prompt_templates or EMPTY_PROMPT_TEMPLATES
+        if prompt_templates is not None:
+            missing_keys = set(EMPTY_PROMPT_TEMPLATES.keys()) - set(prompt_templates.keys())
+            assert not missing_keys, (
+                f"Some prompt templates are missing from your custom `prompt_templates`: {missing_keys}"
+            )
+            for key in EMPTY_PROMPT_TEMPLATES.keys():
+                for subkey in EMPTY_PROMPT_TEMPLATES[key]:
+                    assert subkey in prompt_templates[key], (
+                        f"Some prompt templates are missing from your custom `prompt_templates`: {subkey} under {key}"
+                    )
         self.max_steps = max_steps
         self.step_number = 0
         self.grammar = grammar
@@ -993,7 +994,7 @@ class ToolCallingAgent(MultiStepAgent):
         tool_call = model_message.tool_calls[0]
         tool_name, tool_call_id = tool_call.function.name, tool_call.id
         tool_arguments = tool_call.function.arguments
-
+        memory_step.model_output = str(f"Called Tool: '{tool_name}' with arguments: {tool_arguments}")
         memory_step.tool_calls = [ToolCall(name=tool_name, arguments=tool_arguments, id=tool_call_id)]
 
         # Execute
diff --git a/src/smolagents/memory.py b/src/smolagents/memory.py
index 5d667bd..520e20a 100644
--- a/src/smolagents/memory.py
+++ b/src/smolagents/memory.py
@@ -153,7 +153,7 @@ class PlanningStep(MemoryStep):
             return []
         return [
             Message(role=MessageRole.ASSISTANT, content=[{"type": "text", "text": self.plan.strip()}]),
-            Message(role=MessageRole.USER, content=[{"type": "text", "text": "Now go on and execute this plan."}]),
+            Message(role=MessageRole.USER, content=[{"type": "text", "text": "Now proceed and carry out this plan."}]),
             # This second message creates a role change to prevent models models from simply continuing the plan message
         ]
 
diff --git a/src/smolagents/prompts/code_agent.yaml b/src/smolagents/prompts/code_agent.yaml
index 57e2570..2929460 100644
--- a/src/smolagents/prompts/code_agent.yaml
+++ b/src/smolagents/prompts/code_agent.yaml
@@ -141,21 +141,31 @@ system_prompt: |-
   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:
+  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 %}
+  def {{ agent.name }}("Your query goes here.") -> str:
+      """{{ agent.description }}"""
+  {% endfor %}
+  ```
   {%- endif %}
 
   Here are the rules you should always follow to solve your task:
@@ -170,18 +180,15 @@ system_prompt: |-
   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.
+  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. You will 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:
-
-    ---
-    ## Facts survey
+    ## 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).
 
@@ -192,99 +199,104 @@ planning:
     ### 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:
-    ### 1.1. Facts given in the task
-    ### 1.2. Facts to look up
-    ### 1.3. Facts to derive
-    Do not add anything else.
+    Don't make any assumptions. For each item, provide a thorough reasoning. Do not add anything else on top of three headings above.
 
-    ## Plan
+    ## 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 '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 %}
+    def {{ agent.name }}("Your query goes here.") -> str:
+        """{{ agent.description }}"""
+    {% endfor %}
+    ```
     {%- endif %}
 
-    Now begin! First in part 1, list the facts that you have at your disposal, then in part 2, make a plan to solve the task.
+    ---
+    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 to derive facts, and plan accordingly towards solving a task.
-    You have been given a task:
+    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 the task. You will first have to produce a survey of known and unknown facts:
-
-    ## Facts survey
-    ### 1. Facts given in the task
-    ### 2. Facts that we have learned
-    ### 3. Facts still to look up
-    ### 4. Facts still to derive
-
-    Then you will have to propose 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.
+  
+    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:
-
-    ## Updated facts survey
-    ### 1. Facts given in the task
-    ### 2. Facts that we have learned
-    ### 3. Facts still to look up
-    ### 4. Facts still to derive
-
+    ## 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.
-    ## Plan
-    ### 1. ...
-    Etc
-
+    ## 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 %}
+    def {{ agent.name }}("Your query goes here.") -> str:
+        """{{ agent.description }}"""
+    {% endfor %}
+    ```
     {%- endif %}
 
-    Now write your new plan below.
+    Now write your updated facts survey below, then your new plan.
 managed_agent:
   task: |-
       You're a helpful agent named '{{name}}'.
diff --git a/src/smolagents/prompts/toolcalling_agent.yaml b/src/smolagents/prompts/toolcalling_agent.yaml
index 024c339..3e99fb4 100644
--- a/src/smolagents/prompts/toolcalling_agent.yaml
+++ b/src/smolagents/prompts/toolcalling_agent.yaml
@@ -113,18 +113,15 @@ system_prompt: |-
   If no tool call is needed, use final_answer tool to return your answer.
   4. Never re-do a tool call that you previously did with the exact same parameters.
 
-  Now Begin! If you solve the task correctly, you will receive a reward of $1,000,000.
+  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. You will 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:
-
-    ---
-    ## Facts survey
+    ## 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).
 
@@ -135,25 +132,14 @@ planning:
     ### 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:
-    ### 1.1. Facts given in the task
-    ### 1.2. Facts to look up
-    ### 1.3. Facts to derive
-    Do not add anything else.
+    Don't make any assumptions. For each item, provide a thorough reasoning. Do not add anything else on top of three headings above.
 
-    ## Plan
+    ## 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:
     {%- for tool in tools.values() %}
     - {{ tool.name }}: {{ tool.description }}
@@ -171,40 +157,37 @@ planning:
     {%- endfor %}
     {%- endif %}
 
-    Now begin! First in part 1, list the facts that you have at your disposal, then in part 2, make a plan to solve the task.
+    ---
+    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 to derive facts, and plan accordingly towards solving a task.
-    You have been given a task:
+    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 the task. You will first have to produce a survey of known and unknown facts:
-
-    ## Facts survey
-    ### 1. Facts given in the task
-    ### 2. Facts that we have learned
-    ### 3. Facts still to look up
-    ### 4. Facts still to derive
-
-    Then you will have to propose 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.
+  
+    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:
-
-    ## Updated facts survey
-    ### 1. Facts given in the task
-    ### 2. Facts that we have learned
-    ### 3. Facts still to look up
-    ### 4. Facts still to derive
-
+    ## 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.
-    ## Plan
-    ### 1. ...
-    Etc
-
+    ## 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.
diff --git a/src/smolagents/utils.py b/src/smolagents/utils.py
index d36194b..a381e0c 100644
--- a/src/smolagents/utils.py
+++ b/src/smolagents/utils.py
@@ -158,7 +158,7 @@ def parse_json_blob(json_blob: str) -> Tuple[Dict[str, str], str]:
         json_data = json.loads(json_data, strict=False)
         return json_data, json_blob[:first_accolade_index]
     except IndexError:
-        raise ValueError("The JSON blob you used is invalid")
+        raise ValueError("The model output does not contain any JSON blob.")
     except json.JSONDecodeError as e:
         place = e.pos
         if json_blob[place - 1 : place + 2] == "},\n":


Test patch:
diff --git a/tests/test_agents.py b/tests/test_agents.py
index 4b84304..218f887 100644
--- a/tests/test_agents.py
+++ b/tests/test_agents.py
@@ -317,7 +317,7 @@ class TestAgent:
         assert "7.2904" in output
         assert agent.memory.steps[0].task == "What is 2 multiplied by 3.6452?"
         assert "7.2904" in agent.memory.steps[1].observations
-        assert agent.memory.steps[2].model_output is None
+        assert agent.memory.steps[2].model_output == "Called Tool: 'final_answer' with arguments: {'answer': '7.2904'}"
 
     def test_toolcalling_agent_handles_image_tool_outputs(self, shared_datadir):
         import PIL.Image
@@ -421,8 +421,9 @@ class TestAgent:
         tool.description = "fake_tool_description"
         agent = CodeAgent(tools=[tool], model=fake_code_model)
         agent.run("Empty task")
-        assert tool.name in agent.system_prompt
-        assert tool.description in agent.system_prompt
+        assert agent.system_prompt is not None
+        assert f"def {tool.name}(" in agent.system_prompt
+        assert f'"""{tool.description}' in agent.system_prompt
 
     def test_module_imports_get_baked_in_system_prompt(self):
         agent = CodeAgent(tools=[], model=fake_code_model)
@@ -494,6 +495,15 @@ class TestAgent:
         assert 'final_answer("got' in str_output
         assert "```<end_code>" in str_output
 
+        agent = ToolCallingAgent(tools=[PythonInterpreterTool()], model=FakeToolCallModel(), verbosity_level=0)
+        agent.run("What is 2 multiplied by 3.6452?")
+        with agent.logger.console.capture() as capture:
+            agent.replay()
+        str_output = capture.get().replace("\n", "")
+        assert "Called" in str_output
+        assert "Tool" in str_output
+        assert "arguments" in str_output
+
     def test_code_nontrivial_final_answer_works(self):
         def fake_code_model_final_answer(messages, stop_sequences=None, grammar=None):
             return ChatMessage(
@@ -532,8 +542,9 @@ nested_answer()
             do_sample=False,
         )
         agent = ToolCallingAgent(model=model, tools=[weather_api], max_steps=1, verbosity_level=10)
-        agent.run("What's the weather in Paris?")
-        assert agent.memory.steps[0].task == "What's the weather in Paris?"
+        task = "Use your weather api to tell me the weather in Paris."
+        agent.run(task)
+        assert agent.memory.steps[0].task == task
         assert agent.memory.steps[1].tool_calls[0].name == "weather_api"
         step_memory_dict = agent.memory.get_succinct_steps()[1]
         assert step_memory_dict["model_output_message"].tool_calls[0].function.name == "weather_api"
@@ -1277,6 +1288,12 @@ def prompt_templates():
     return {
         "system_prompt": "This is a test system prompt.",
         "managed_agent": {"task": "Task for {{name}}: {{task}}", "report": "Report for {{name}}: {{final_answer}}"},
+        "planning": {
+            "initial_plan": "The plan.",
+            "update_plan_pre_messages": "custom",
+            "update_plan_post_messages": "custom",
+        },
+        "final_answer": {"pre_messages": "custom", "post_messages": "custom"},
     }
 
 
diff --git a/tests/test_monitoring.py b/tests/test_monitoring.py
index 32cd537..324e0c2 100644
--- a/tests/test_monitoring.py
+++ b/tests/test_monitoring.py
@@ -160,7 +160,7 @@ class MonitoringTester(unittest.TestCase):
             )
         )
 
-        self.assertEqual(len(outputs), 5)
+        self.assertEqual(len(outputs), 6)
         final_message = outputs[-1]
         self.assertEqual(final_message.role, "assistant")
         self.assertIsInstance(final_message.content, dict)