career-ops-hq/career-ops
Open-source multi-agent job search pipeline that runs inside AI coding CLIs, scoring listings, tailoring CVs, and tracking every application.
Awesome AI Agents › Job Automation Tools
francedot/Interface-Agent
InterfaceAgent is a versatile and advanced framework designed to create intelligent system and interface agents that can manage and automate tasks across mobile and desktop applications. It enables the development of agents capable of multi-step planning and goal refinement, allowing them to construct complex workflows across different applications to fulfill user requests. These agents can adapt and refine their plans dynamically based on user feedback, enhancing their effectiveness and responsiveness. The framework employs sophisticated action prediction techniques, including pure visual coordinate-based methods, textual analysis of the DOM, and set-of-mark visual prompting, to accurately predict the next likely action an agent should take. It supports a mixture of AI models, notably GPT-4V and Claude, which excel at interpreting page screenshots to determine subsequent steps, making the agents highly capable in diverse environments. InterfaceAgent incorporates resilient error handling with a robust retry mechanism and exponential backoff strategy, ensuring that temporary failures do not disrupt the agent's progress. This reliability is crucial for maintaining smooth automation workflows. The framework extends its core capabilities with OS-specific agents that provide advanced automation tailored to particular platforms. Currently, it offers previews for iOS and Windows agents, enabling AI agents to interact deeply with the ecosystem of apps and functionalities on these devices. Upcoming expansions include Android agents based on Appium and web agents using Playwright, broadening the scope of automation possibilities. InterfaceAgent is designed for ease of use with clear installation instructions for its core and platform-specific packages. It also provides demos showcasing practical applications, such as downloading apps on Windows or preparing fitness challenges on iOS, demonstrating its real-world utility. The project is actively focused on overcoming challenges related to long-horizon planning and selector inference accuracy, aiming to improve the stability and performance of its agents. It is open to contributions and licensed under the MIT License, encouraging community involvement and collaboration.
https://github.com/francedot/Interface-Agent
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