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AstronRPA: Open-Source Enterprise RPA Suite with AI Agent Integration
AstronRPA is an open-source, enterprise-grade Robotic Process Automation (RPA) desktop application. It features a low-code, no-code visual designer for building automation workflows across desktop software and web pages. Deeply integrated with AI agents, AstronRPA empowers broader business automation scenarios for both individuals and enterprises.
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AstronRPA: Open-Source Enterprise RPA Suite with AI Agent Integration
AstronRPA is an open-source, enterprise-grade Robotic Process Automation (RPA) desktop application. It features a low-code, no-code visual designer for building automation workflows across desktop software and web pages. Deeply integrated with AI agents, AstronRPA empowers broader business automation scenarios for both individuals and enterprises.

ADL CLI: Scaffold Enterprise-Ready AI Agents with A2A Protocol
The ADL CLI is a powerful command-line tool designed to rapidly scaffold and manage enterprise-ready AI Agents. It leverages the YAML-based Agent Definition Language (ADL) to generate complete project structures, eliminating boilerplate and ensuring consistent patterns. This tool significantly accelerates the development of AI agents powered by the A2A (Agent-to-Agent) protocol.
Qovira: A Private, Self-Hostable AI Personal Assistant in Go
Qovira is an ambitious project aiming to deliver a private, self-hostable AI personal assistant. Built with Go and SvelteKit, it will organize reminders, notes, and schedules using AI, all on infrastructure you control. Currently in early development, Qovira promises a secure and private alternative to cloud-based AI tools.

Goon: Autonomous AI Worker for Software Development and Custom Workflows
Goon is a self-hosted, autonomous AI worker designed to streamline software development and automate custom workflows. Built with Go and having zero dependencies, it operates as a daemon, capable of tasks from writing code and opening PRs to summarizing emails and monitoring logs. It learns from your context and asks for human approval before acting, ensuring controlled and intelligent automation.
Awesome-Self-Evolving-Agents: A Curated List for AI Agent Research
Awesome-Self-Evolving-Agents is a comprehensive GitHub repository offering a curated collection of resources on self-evolving agents. It includes a systematic survey, research papers, benchmarks, and open-source projects, providing valuable insights into this rapidly advancing field of AI. This repository serves as an essential guide for researchers and developers exploring model-centric, environment-centric, and co-evolutionary approaches.

Awesome-Self-Improving-Agents: A Curated List for Agentic AI Self-Improvement
Awesome-Self-Improving-Agents is a comprehensive GitHub repository featuring a curated and continuously updated list of resources on self-improvement in foundation model-based agentic systems. It serves as a central hub for researchers and practitioners, offering papers, benchmarks, and various media. This resource is essential for anyone exploring the cutting edge of self-evolving AI agents.
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Selenium: The Leading Framework for Browser Automation
Selenium is a comprehensive open-source project that provides tools and libraries for automating web browsers. It offers a platform and language-neutral coding interface, adhering to the W3C WebDriver specification, making it compatible with all major web browsers. This powerful framework is widely used for functional testing, regression testing, and general web automation.
Red-Team-Playbooks: Comprehensive Open-Source Security Notes and Tools
The `Red-Team-Playbooks` repository by 0xsyr0 is an invaluable open-source resource for cybersecurity professionals. It provides a comprehensive collection of cutting-edge security notes, tools, techniques, and procedures specifically designed to aid in Red Team assessments. This repository serves as a practical guide, covering various stages of offensive operations.

Judgy: Correcting LLM Judge Bias for Reliable AI Model Evaluation
Judgy is a Python package designed to improve the reliability of evaluations performed by LLM-as-Judges. It provides tools to estimate the true success rate of a system by correcting for LLM judge bias and generating confidence intervals through bootstrapping. This helps ensure more accurate and trustworthy assessments of AI model performance.
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