Simulate buyer and user personas to pressure-test ideas and messaging with TinyTroupe

Use TinyTroupe when an agent should run simulated persona panels, synthetic interviews, or offline audience reactions before spending on campaigns, launches, or user research.

Prerequisites

Python, pip, access to a supported LLM provider

Installation

Use the upstream install or setup path that matches your environment:

• conda create -n tinytroupe python=3.10
• conda activate tinytroupe
• Use pip to install the library directly from this repository (we will not install from PyPI):
• pip install git+https://github.com/microsoft/TinyTroupe.git@main

Requirements and caveats from upstream:

• 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, repl...
• Internal LLM usage is now better supported via the LLMChat class, and also the @llm decorator, which transform any standard Python function into an LLM-based one (i.e., by using the docstring as part of the prompt, an...
• Python 3.10 or higher. We'll assume you are using Anaconda, but you can use other Python distributions.

Basic usage or getting-started notes:

• Take a look one example Vision for Product, Diagnosis and Appreciation Feedback (image modality) noteb...

• New example notebooks demonstrating empirical validation against real survey data.

• TinyWorld now run agents in parallel within each simulation step, allowing faster simulations.

• Source: https://github.com/microsoft/TinyTroupe

• Extracted from upstream docs: https://raw.githubusercontent.com/microsoft/TinyTroupe/HEAD/README.md

Documentation

• https://github.com/microsoft/TinyTroupe#readme

Source

• Agent Skill Exchange