Python logging missing console output (solved: workaround with Rich)

### Description:
I ran into an issue while trying to capture console output into a log.log file for manual inspection. I initially assumed that interfacing with the Python logging module in the library would be sufficient, but I noticed that most of the console output—particularly the "thinking" process—was missing from my logs. Only a few API calls and minimal details were being logged.

After investigating, I realized that most of the console output is handled by Rich (monitoring.py) rather than the standard logging module. As a result, simply configuring logging didn’t capture all the relevant information.

### Workaround: Dual Console Logging with Rich
To solve this, you can create a dual-output system using Rich’s Console class. This setup allows logging to both log.log and the terminal while maintaining proper formatting. I needed this solution because I'm still getting familiar with LLM/agentic frameworks and am not yet ready to dive into an observability platform. Just needed a simple way to test things.

Here is a full working script for reference (simple web search agent writing to both console and log.log file):

```python
from smolagents import HfApiModel, LiteLLMModel, TransformersModel, DuckDuckGoSearchTool
from smolagents.agents import CodeAgent
from smolagents.monitoring import LogLevel
from rich.console import Console

available_inferences = ["hf_api", "transformers", "ollama", "litellm"]
chosen_inference = "litellm"

print(f"Chose model: '{chosen_inference}'")

if chosen_inference == "hf_api":
model = HfApiModel(model_id="meta-llama/Llama-3.3-70B-Instruct")
elif chosen_inference == "transformers":
model = TransformersModel(
model_id="HuggingFaceTB/SmolLM2-1.7B-Instruct",
device_map="auto",
max_new_tokens=1000,
)
elif chosen_inference == "ollama":
model = LiteLLMModel(
model_id="ollama_chat/llama3.2",
api_base="http://localhost:11434",
api_key="your-api-key",
num_ctx=8192,
)
elif chosen_inference == "litellm":
model = LiteLLMModel(model_id="gpt-4o-mini")

# Create dual console that writes to both file and stdout
class DualConsole:
def __init__(self, filename):
self.file_console = Console(
file=open(filename, "a", encoding="utf-8", errors="replace"),
force_terminal=False, # Disable terminal sequences for file
width=120, # Wider width for file output
)
# Keep terminal console with default settings
self.std_console = Console()

def print(self, *args, **kwargs):
try:
self.file_console.print(*args, **kwargs, highlight=False)
self.std_console.print(*args, **kwargs)
except UnicodeEncodeError as e:
error_msg = f"Unicode handling error: {str(e)}"
self.file_console.print(error_msg, style="bold red")
self.std_console.print(error_msg, style="bold red")

# Initialize dual console before creating agent
dual_console = DualConsole("log.log")

# Initialize CodeAgent with DuckDuckGo search capability
agent = CodeAgent(
tools=[DuckDuckGoSearchTool()], # Web search tool for research
model=model,
add_base_tools=True,
verbosity_level=LogLevel.DEBUG,
)

# Override agent's console with our dual logger
agent.logger.console = dual_console
agent.monitor.logger.console = dual_console # Also update monitor's console

print(
"Research Results:",
agent.run(
"What are the latest developments in humanoid robot technology as of early 2025? "
"Find and summarize three key advancements from reliable sources."
),
)

```

Start by reading monitoring.py and the console and logging paths used by the agent. Reproduce the reported behavior with Python logging and compare it with the Rich-based output shown in the issue. The issue does not define a desired change beyond the provided workaround, so completion criteria need clarification.

Written by the indexing model from the issue text.