Documentation Index Fetch the complete documentation index at: https://docs.upsonic.ai/llms.txt
Use this file to discover all available pages before exploring further.
Simulation enables running time-series simulations powered by LLMs to forecast metrics and analyze scenarios over time. Each simulation scenario models real-world dynamics using AI reasoning to predict future states.
Available Scenarios
Merchant Revenue Forecast Forecast e-commerce merchant revenue with customer behavior and market dynamics
How Simulations Work Simulations iterate through time steps, using LLMs to predict metric values based on previous state and contextual factors. Each simulation:
Builds prompts with previous state and business context
Calls the LLM to predict next-step metrics with structured output
Tracks metrics throughout the simulation timeline
Generates comprehensive reports in multiple formats
Using Simulations Simulations are created with a scenario object and configuration:
from upsonic.simulation import Simulation from upsonic.simulation.scenarios import MerchantRevenueForecastSimulation simulation = Simulation( MerchantRevenueForecastSimulation( merchant_name = "TechCo" , sector = "E-commerce" , location = "San Francisco" , current_monthly_revenue_usd = 50000 ), model = "anthropic/claude-sonnet-4-5" , time_step = "daily" , simulation_duration = 100 , metrics_to_track = [ "monthly recurring revenue" ] ) result = simulation.run() # Export reports with chainable methods result.report( "summary" ).to_json( "summary.json" ) # result.report("summary").to_csv("summary.csv") # result.report("summary").to_html("summary.html") # result.report("summary").to_pdf("summary.pdf") # result.report("summary").show() # Other report types: # result.report("detailed").to_json("detailed.json") # result.report("visual").to_html("charts.html") # result.report("statistical").to_json("stats.json") # Save all reports at once: # result.reports().save_all(directory="./reports", format="json")
