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import os
from PIL import Image
from smolagents import CodeAgent, DuckDuckGoSearchTool, InferenceClientModel, VisitWebpageTool
from find_batman_mobile_agent import calculate_cargo_travel_time, check_reasoning_and_plot
def define_multi_agent():
example_model = InferenceClientModel(model_id="Qwen/Qwen2.5-Coder-32B-Instruct", provider="together")
task = """Find all Batman filming locations in the world, calculate the time to transfer via cargo plane to here (we're in Gotham, 40.7128, 74.0060), and return them to me as a pandas dataframe.
Also give me some supercar factories with the same cargo plane transfer time.
"""
example_multi_agent = CodeAgent(
model=example_model,
tools=[DuckDuckGoSearchTool(), VisitWebpageTool(), calculate_cargo_travel_time],
additional_authorized_imports=["pandas"],
max_steps=20
)
result = example_multi_agent.run(task)
example_multi_agent.planning_interval = 4
detailed_report = example_multi_agent.run(f"""
You're an expert analyst. You make comprehensive reports after visiting many websites.
Don't hesitate to search for many queries at once in a for loop.
For each data point that you find, visit the source url to confirm numbers.
""")
print(detailed_report)
example_web_model = InferenceClientModel(
"Qwen/Qwen2.5-Coder-32B-Instruct", provider="together", max_tokens=8096
)
example_web_agent = CodeAgent(
model=example_web_model,
tools=[
DuckDuckGoSearchTool(),
VisitWebpageTool(),
calculate_cargo_travel_time,
],
name="web_agent",
description="Browses the web to find information",
verbosity_level=0,
max_steps=10
)
manager_agent = CodeAgent(
model=InferenceClientModel("deepseek-ai/DeepSeek-R1", provider="together", max_tokens=8096),
tools=[calculate_cargo_travel_time],
managed_agents=[example_web_agent],
additional_authorized_imports=[
"geopandas",
"plotly",
"shapely",
"json",
"pandas",
"numpy"
],
planning_interval=5,
verbosity_level=2,
final_answer_checks=[check_reasoning_and_plot],
max_steps=15
)
return manager_agent
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