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Welcome to the TinyTroupe wiki!

TinyTroupe is an experimental Python library that allows the simulation of people with specific personalities, interests, and goals. These artificial agents - TinyPersons - can listen to us and one another, reply back, and go about their lives in simulated TinyWorld environments. This is achieved by leveraging the power of Large Language Models (LLMs), notably GPT-4, to generate realistic simulated behavior. This allow us to investigate a wide range of convincing interactions and consumer types, with highly customizable personas, under conditions of our choosing. The focus is thus on understanding human behavior and not on directly supporting it (like, say, AI assistants do) -- this results in, among other things, specialized mechanisms that make sense only in a simulation setting. Further, unlike other game-like LLM-based simulation approaches, TinyTroupe aims at enlightening productivity and business scenarios, thereby contributing to more successful projects and products. Here are some application ideas to enhance human imagination:

• Advertisement: TinyTroupe can evaluate digital ads (e.g., Bing Ads) offline with a simulated audience before spending money on them!
• Software Testing: TinyTroupe can provide test input to systems (e.g., search engines, chatbots or copilots) and then evaluate the results.
• Training and exploratory data: TinyTroupe can generate realistic synthetic data that can be later used to train models or be subject to opportunity analyses.
• Product and project management: TinyTroupe can read project or product proposals and give feedback from the perspective of specific personas (e.g., physicians, lawyers, and knowledge workers in general).
• Brainstorming: TinyTroupe can simulate focus groups and deliver great product feedback at a fraction of the cost!

In all of the above, and many others, we hope users can gain insights about their domain of interest, and thus make better decisions.