Awesome AGI

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Basic Information

Awesome-AGI is a curated, community-maintained collection of resources about Artificial General Intelligence and autonomous agents. The repository organizes and catalogs AGI frameworks, multi-agent platforms, working agent projects, research papers, datasets, tutorials, blogs, demos and websites. It highlights prominent projects and implementations such as Auto-GPT, AgentGPT, MetaGPT, MiniGPT-4 and many others, and groups entries into sections like Frameworks and Platforms, Agents, Papers, Datasets, Blogs and Online Demos. The README provides short introductions, notes and star counts for many projects and points to demos and marketplaces. The target audience includes developers, researchers and practitioners who want an overview of AGI tooling, example agents, evaluation benchmarks and learning materials. The list is intended to help users discover, compare and explore open-source AGI-related projects and community resources.

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Features
A structured, navigable README that categorizes AGI resources into clear sections such as Frameworks and Platforms, Agents, Papers, Datasets, Blogs, Courses and Websites. The list includes curated tables with project names, short introductions, and GitHub star indicators to surface notable repositories. It calls out popular multi-agent frameworks, autonomous agent projects, knowledge bases and local AGI efforts. The repository aggregates research papers and datasets relevant to AGI evaluation and development, and includes links to online demos and marketplaces for live experimentation. Notes and short descriptions provide context for each entry, and the collection references related curated lists and subprojects to aid deeper exploration.
Use Cases
This repository serves as a discovery and reference hub for anyone exploring AGI and autonomous agents. It helps developers and researchers quickly find frameworks to build or run agents, identify example agent implementations to study or extend, and locate evaluation benchmarks and datasets. Educators and learners can use the papers, blogs and course references to understand AGI concepts and recent work. The included demos and website pointers allow rapid hands-on evaluation of projects, while the curated listings and notes simplify comparison and prioritization of tools and platforms for experimentation, deployment or further research.

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