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OpenDAN Personal AI OS

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Basic Information

OpenDAN is an open source Personal AI Operating System designed to consolidate diverse AI modules into a single local platform for personal use. It lets users create and run AI Agents and multi-agent Workflows such as personal assistants, tutors, and content-generation teams while retaining local control and privacy. The project includes built-in agents (Jarvis, Mia, Tracy, ai_bash), a personal knowledge base fed by file and email spiders, AIGC workflows for creative tasks, and the ability to switch language models including locally hosted open models. It provides Docker-based rapid deployment and an option to run from source for developers. OpenDAN aims to enable agent collaboration, integrate with services and IoT devices, and offer a development framework and marketplace for installing agents, models, and workflows. The repo documents an MVP release and a roadmap for kernel, marketplace, and expanded KB and AIGC features.

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App URL
https://github.com/fiatrete/OpenDAN-Personal-AI-OS
Github Repository
https://github.com/fiatrete/OpenDAN-Personal-AI-OS/blob/main/README.md

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App Details

Features
Key features include Docker-first rapid installation and deployment to run on varied hardware including PCs and Raspberry Pi, and a source-install path for development. The system supports switching large language models with local execution of open models such as LLaMa. Several built-in AI Agents are provided (personal assistant Jarvis, information manager Mia, private English teacher Tracy, developer ai_bash) and agents can be organized into Workflows to tackle complex tasks. OpenDAN offers a local private Knowledge Base populated by file and email spiders, AIGC workflows like story_maker for collaborative content creation, connectivity via Telegram and Email, a distributed AI computing core for heavy tasks, hardware-specific optimizations, privacy and access control, and a development framework for creating and installing new agents and workflows.
Use Cases
OpenDAN helps individuals and developers run personalized AI assistants and multi-agent workflows locally to manage schedules, personal data, learning, and content generation while keeping data on local storage. It simplifies deployment with Docker for heterogeneous hardware and allows switching to local open models to reduce reliance on external APIs. Built-in spiders and a knowledge base let agents access personal files and emails for context-aware assistance. AIGC workflows and support for training private models enable custom voice and LoRA models for creative projects. Integration options like Telegram and Email provide remote access and notifications. The platform also targets developers with a framework to extend agents, integrate new LLM cores, and contribute to an evolving marketplace and kernel architecture.

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