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Basic Information

This repository is a small experiment and demonstration project using the Pydantic agent framework to build and test simple AI agents. It collects notes and example code that show how to install the Pydantic agent library, choose model providers, and run a minimal command-line agent. The repo includes a specific example called dice_game.py that implements a CLI number-guessing game based on the Pydantic examples and requires an OpenAI API key set in a .env file. The README documents available model choices and mentions support for local models such as Ollama. The project is framed as an exploratory starting point for further development, with future plans to add a web chat interface and a more complex decision-assisting agent. The repository is therefore intended for learning, prototyping, and testing how to wire Pydantic-based agent tools to different model providers.

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Features
Concise installation guidance for the Pydantic agent framework is provided, including the main package install command and a slim variant to install only specific model dependencies. The README outlines that the framework supports multiple model providers and local models, and it references the official model list and API metadata available from the framework documentation. An included example file, dice_game.py, demonstrates a working CLI agent that accepts user input and validates guesses. The repository documents environment setup for API access by instructing creation of a .env file containing an OpenAI API key. The project is intentionally minimal and focused on practical examples rather than a full product, making it easy to inspect and extend. The README also lists intended future work such as a web chat front end and more complex agent behaviors.
Use Cases
This repository helps developers and researchers get started quickly with the Pydantic agent framework by providing a concrete, runnable example and clear installation notes. The dice game example illustrates how to wire user input, agent logic, and model access in a minimal CLI, which is useful for learning how agents process prompts and return structured outputs. The README explains how to install only the dependencies needed for chosen models, which helps reduce overhead when experimenting with different providers or local runtimes. Mention of local model support and Ollama examples informs users who want to run models without remote APIs. The documented .env requirement and API key usage prepare users for common configuration steps. Overall, the repo is a lightweight scaffold for prototyping agent functionality and transitioning to a web chat or more complex decision-making agent in future work.

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