multi agent test

cargo_tool.py

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+import math
+from typing import Optional, Tuple
+
+from smolagents import tool
+
+
+@tool
+def calculate_cargo_travel_time(
+    origin_coords: Tuple[float, float],
+    destination_coords: Tuple[float, float],
+    cruising_speed_kmh: Optional[float] = 750.0,  # Average speed for cargo planes
+) -> float:
+    """
+    Calculate the travel time for a cargo plane between two points on Earth using great-circle distance.
+
+    Args:
+        origin_coords: Tuple of (latitude, longitude) for the starting point
+        destination_coords: Tuple of (latitude, longitude) for the destination
+        cruising_speed_kmh: Optional cruising speed in km/h (defaults to 750 km/h for typical cargo planes)
+
+    Returns:
+        float: The estimated travel time in hours
+
+    Example:
+        >>> # Chicago (41.8781° N, 87.6298° W) to Sydney (33.8688° S, 151.2093° E)
+        >>> result = calculate_cargo_travel_time((41.8781, -87.6298), (-33.8688, 151.2093))
+    """
+
+    def to_radians(degrees: float) -> float:
+        return degrees * (math.pi / 180)
+
+    # Extract coordinates
+    lat1, lon1 = map(to_radians, origin_coords)
+    lat2, lon2 = map(to_radians, destination_coords)
+
+    # Earth's radius in kilometers
+    EARTH_RADIUS_KM = 6371.0
+
+    # Calculate great-circle distance using the haversine formula
+    dlon = lon2 - lon1
+    dlat = lat2 - lat1
+
+    a = (
+        math.sin(dlat / 2) ** 2
+        + math.cos(lat1) * math.cos(lat2) * math.sin(dlon / 2) ** 2
+    )
+    c = 2 * math.asin(math.sqrt(a))
+    distance = EARTH_RADIUS_KM * c
+
+    # Add 10% to account for non-direct routes and air traffic controls
+    actual_distance = distance * 1.1
+
+    # Calculate flight time
+    # Add 1 hour for takeoff and landing procedures
+    flight_time = (actual_distance / cruising_speed_kmh) + 1.0
+
+    # Format the results
+    return round(flight_time, 2)
+
+

multiAgent.py

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+from smolagents import CodeAgent, DuckDuckGoSearchTool, VisitWebpageTool
+
+from cargo_tool import calculate_cargo_travel_time
+from model import model
+
+# TASK for single agent:
+
+# 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° N, 74.0060° W), and return them to me as a pandas dataframe.
+# Also give me some supercar factories with the same cargo plane transfer time."""
+
+# def run_single_agent() -> None:
+#     agent = CodeAgent(
+#         model=model,
+#         tools=[DuckDuckGoSearchTool(), VisitWebpageTool(), calculate_cargo_travel_time],
+#         additional_authorized_imports=["pandas"],
+#         max_steps=20,
+#     )
+#     print(agent.run(TASK))
+
+
+# if __name__ == "__main__":
+#     run_single_agent()
+
+# TASK for multi-agent:
+
+def build_web_agent() -> CodeAgent:
+    return CodeAgent(
+        model=model,
+        tools=[DuckDuckGoSearchTool(), VisitWebpageTool(), calculate_cargo_travel_time],
+        name="web_agent",
+        description="Browses the web to find information",
+        verbosity_level=0,
+        max_steps=10,
+    )
+
+
+def build_manager_agent() -> CodeAgent:
+    return CodeAgent(
+        model=model,
+        tools=[calculate_cargo_travel_time],
+        managed_agents=[build_web_agent()],
+        additional_authorized_imports=["geopandas", "plotly", "shapely", "json", "pandas", "numpy"],
+        planning_interval=5,
+        verbosity_level=2,
+        max_steps=15,
+    )
+
+MAP_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° N, 74.0060° W).
+Also give me some supercar factories with the same cargo plane transfer time. You need at least 6 points in total.
+Represent this as spatial map of the world, with the locations represented as scatter points with a color that depends on the travel time, and save it to saved_map.png!
+
+Here's an example of how to plot and return a map:
+import plotly.express as px
+df = px.data.carshare()
+fig = px.scatter_map(df, lat="centroid_lat", lon="centroid_lon", text="name", color="peak_hour", size=100,
+     color_continuous_scale=px.colors.sequential.Magma, size_max=15, zoom=1)
+fig.show()
+fig.write_image("saved_image.png")
+final_answer(fig)
+
+Never try to process strings using code: when you have a string to read, just print it and you'll see it.
+"""
+
+if __name__ == "__main__":
+    manager = build_manager_agent()
+    manager.run(MAP_TASK)