awesome-autonomous-gpt

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Basic Information

This repository is a curated, community-maintained collection of projects, tools, benchmarks, papers, blog posts and talks focused on autonomous GPT-style AI agents. It collects and organizes open-source frameworks, agent applications, evaluation suites and academic work to help developers, researchers and practitioners explore the space of language-model-based autonomous agents and tool-using LLMs. The README is structured with sections for developer tools, applications, benchmarks, research papers, blog articles and talks and highlights notable projects and recent additions. The maintainer frames the collection as a roadmap for building and studying autonomous agents and indicates ongoing updates and community inclusion in larger resource lists.

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Features
Organized sections covering developer tools, end-user applications, benchmarks and academic papers to give quick topical access. Includes a wide range of named projects and frameworks such as Langchain, LMQL, MetaGPT, Auto-GPT, SuperAGI and many application examples. Lists benchmarking efforts like AgentBench and Auto-GPT-Benchmarks and aggregates recent research papers and surveys on agent architectures and tool use. Contains curated blog articles and talks to provide context and practitioner perspectives. Entries often note release dates, demos or code repositories, and the README highlights community signals such as inclusion in other wikis and a recent self-developed agent release mentioned by the maintainer.
Use Cases
This collection saves time by aggregating prominent agent frameworks, example applications, benchmarks and relevant literature in one place so users can discover, compare and follow developments quickly. It is useful for developers seeking frameworks or demos to prototype autonomous agents, for researchers wanting to locate recent papers and benchmarks, and for educators or decision makers surveying the field. The curated links to projects, benchmarks and explanatory articles provide starting points for implementation, evaluation and further reading, and the README"s organization helps map the ecosystem of autonomous LLM agents.

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