TinyAuton provides a minimal, extensible architecture for building autonomous agents that plan, execute, and refine tasks using OpenAI’s GPT models. It offers built-in modules for defining objectives, managing conversation context, invoking custom tools, and logging agent decisions. Through iterative self-reflection loops, the agent can analyze outcomes, adjust plans, and retry failed steps. Developers can integrate external APIs or local scripts as tools, set up memory or state, and customize the agent’s reasoning pipeline. TinyAuton is optimized for rapid prototyping of AI-driven workflows, from data extraction to code generation, all within a few lines of Python.

Self-Determining AI Agents is a Python-based framework designed to simplify the creation of autonomous AI agents. It features a customizable planning loop where agents generate tasks, plan strategies, and execute actions using integrated tools. The framework includes persistent memory modules for context retention, a flexible task scheduling system, and hooks for custom tool integrations such as web APIs or database queries. Developers define agent goals via configuration files or code, and the library handles the iterative decision-making process. It supports logging, performance monitoring, and can be extended with new planning algorithms. Ideal for research, automating workflows, and prototyping intelligent multi-agent systems.