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from google.adk.agents import LlmAgent
from tools import csv_parser, plot_generator, forecaster
# Define the agents using raw function references as tools
trend_detector_agent = LlmAgent(
name="trend_detector_agent",
model="gemini-2.5-pro-exp-03-25",
description="Detects trends and anomalies in business data.",
instruction="""
Analyze the input table. Identify major trends, seasonal patterns,
and anomalies (spikes or drops). Return a concise summary.
""",
tools=[
csv_parser.parse_csv_tool,
plot_generator.plot_sales_tool
]
)
forecast_agent = LlmAgent(
name="forecast_agent",
model="gemini-2.5-pro-exp-03-25",
description="Forecasts future metrics from time series data.",
instruction="""
Forecast next 3 months of sales based on historical patterns.
Use the forecast tool to generate a visual chart.
""",
tools=[
forecaster.forecast_tool
]
)
strategy_agent = LlmAgent(
name="strategy_agent",
model="gemini-2.5-pro-exp-03-25",
description="Recommends strategic business decisions.",
instruction="""
Based on trends and forecasts, suggest optimization strategies
across marketing, operations, and finance (ROI, CAC, churn).
"""
)
# Parent coordinator that orchestrates all sub-agents
analytics_coordinator = LlmAgent(
name="analytics_coordinator",
model="gemini-2.5-pro-exp-03-25",
description="Coordinates full BI pipeline: trends, forecast, strategy.",
instruction="""
Run the following:
1. Analyze the CSV with trend_detector_agent
2. Forecast future metrics using forecast_agent
3. Recommend business strategies using strategy_agent
Return a full dashboard-style summary.
""",
sub_agents=[
trend_detector_agent,
forecast_agent,
strategy_agent
]
)
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