Human Intelligence (HI) excels at combining basic skills to solve complex tasks. This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive AI Agents, enabling them to harness expert models for complex task-solving towards Artificial General Intelligence (AGI). Large Language Models (LLMs) show promising learning and reasoning abilities, and can effectively use external models, tools, plugins, or APIs to tackle complex problems. In this work, we introduce OpenAGI, an open-source AGI research and development platform designed for solving multi-step, real-world tasks. Specifically, OpenAGI uses a dual strategy, integrating standard benchmark tasks for benchmarking and evaluation, and open-ended tasks including more expandable models, tools, plugins, or APIs for creative problem-solving. Tasks are presented as natural language queries to the LLM, which then selects and executes appropriate models. We also propose a Reinforcement Learning from Task Feedback (RLTF) mechanism that uses task results to improve the LLM's task-solving ability, which creates a self-improving AI feedback loop. While we acknowledge that AGI is a broad and multifaceted research challenge with no singularly defined solution path, the integration of LLMs with domain-specific expert models, inspired by mirroring the blend of general and specialized intelligence in humans, offers a promising approach towards AGI. We are open-sourcing the OpenAGI project's code, dataset, benchmarks, evaluation methods, and the UI demo to foster community involvement in AGI advancement: https://github.com/agiresearch/OpenAGI.

翻译：人类智能（HI）擅长通过组合基本技能解决复杂任务。这种能力对人工智能（AI）至关重要，应嵌入综合型AI代理中，使其能够利用专家模型解决复杂任务，迈向通用人工智能（AGI）。大语言模型（LLMs）展现出令人瞩目的学习与推理能力，并能有效运用外部模型、工具、插件或API处理复杂问题。本研究提出OpenAGI——一个面向多步骤现实世界任务的开源AGI研究与开发平台。具体而言，OpenAGI采用双轨策略：既集成标准基准任务用于评测，又包含可扩展模型、工具、插件或API的开放式任务以激发创造性问题解决。任务以自然语言查询形式提交给LLM，由LLM选择并执行相应模型。我们同时提出基于任务反馈的强化学习（RLTF）机制，利用任务结果提升LLM的任务解决能力，从而构建自我强化的AI反馈循环。尽管我们承认AGI是一个宏观且多维的研究挑战，无单一明确解决路径，但受人类通用与专用智能融合模式的启发，将LLM与领域专家模型相结合，为迈向AGI提供了富有前景的路径。我们已开源OpenAGI项目的代码、数据集、基准、评估方法及用户界面演示，以促进社区参与AGI发展：https://github.com/agiresearch/OpenAGI。