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
OpenDFM/MobA
MobA is an innovative mobile task automation system designed to manipulate mobile phones in a manner similar to human interaction. It addresses the limitations of current mobile assistants, which often rely heavily on system APIs and struggle with complex user instructions and diverse interfaces due to limited comprehension and decision-making capabilities. MobA leverages multimodal large language models (MLLM) to enhance understanding and planning through a sophisticated two-level agent architecture. This architecture consists of a high-level Global Agent (GA) that interprets user commands, tracks historical interactions, and plans tasks, and a low-level Local Agent (LA) that executes detailed actions via function calls based on sub-tasks and guidance from the GA. Additionally, MobA incorporates a Reflection Module that improves task completion efficiency and enables the system to handle previously unseen complex tasks effectively. The project demonstrates significant improvements in task execution efficiency and completion rates in real-world evaluations, highlighting the potential of MLLM-powered mobile assistants. MobA is still under active development, with ongoing updates to the codebase. It requires an Android device connected via Android Debug Bridge (ADB) and a Python environment with specific dependencies. The project also provides MobBench, a benchmark dataset of fifty tasks used for evaluation, available on Huggingface. The system is designed to run smoothly on Windows and includes detailed setup instructions. MobA has been recognized academically, accepted to the NAACL 2025 Demo Track, and its research paper is accessible on arXiv. The project encourages citation and offers contact information for further inquiries.
https://github.com/OpenDFM/MobA
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