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.

Agentic Solver provides a comprehensive toolkit for developing autonomous AI agents that leverage large language models (LLMs) to tackle real-world problems. It offers components for task decomposition, planning, execution, and result evaluation, enabling agents to break down high-level objectives into sequenced actions. Users can integrate external APIs, custom functions, and memory stores to extend agent capabilities, while built-in logging and retry mechanisms ensure resilience. Written in Python, the framework supports modular pipelines and flexible prompt templates, facilitating rapid experimentation. Whether automating customer support, data analysis, or content generation, Agentic Solver streamlines the end-to-end lifecycle, from initial configuration and tool registration to continuous agent monitoring and performance optimization.

Agents-Prompts is a comprehensive GitHub repository offering developers a structured collection of customizable prompt templates for building intelligent AI agents. These templates cover core functions such as memory management, dynamic instruction updates, multi-agent orchestration, decision-making logic, and API integration. Users can mix and match templates to define agent roles, tasks, and conversation flows, enabling rapid experimentation and prototyping. The repository also includes code samples for interfacing with major LLM services, examples for chaining agent actions, and guidelines for best practices when designing autonomous workflows. By leveraging these reusable prompt patterns, teams can accelerate development, maintain consistency across agents, and focus on higher-level application logic rather than low-level prompt engineering.