Human intelligence excels at combining basic skills to solve complex tasks. This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive intelligent models, 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 or APIs to tackle complex problems. In this work, we introduce OpenAGI, an open-source AGI research platform designed for 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 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 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 demo to foster community involvement in AGI advancement: https://github.com/agiresearch/OpenAGI.

翻译：人类智能擅长通过组合基础技能来解决复杂任务。这一能力对人工智能（AI）至关重要，应被嵌入综合智能模型中，使其能够利用专家模型解决复杂任务，从而迈向通用人工智能（AGI）。大语言模型（LLMs）展现出令人瞩目的学习与推理能力，并能有效使用外部模型、工具或应用程序接口（API）来处理复杂问题。本文介绍了OpenAGI，一个面向多步骤、真实世界任务的开源AGI研究平台。具体而言，OpenAGI采用双轨策略：既整合标准基准任务用于测试与评估，也包含更具扩展性的模型、工具或API的开放式任务以激发创造性问题解决能力。任务以自然语言查询形式呈现给大语言模型，模型据此选择并执行恰当的模型。我们还提出一种基于任务反馈的强化学习（RLTF）机制，利用任务结果提升大语言模型的能力，从而形成自我进化的AI反馈循环。尽管我们承认AGI是一个宽泛且多层面的研究挑战，尚无单一明确的解决路径，但受人类通用智能与专用智能融合的启发，将大语言模型与领域专家模型相结合，为通往AGI提供了一条有前景的路径。我们已将OpenAGI项目的代码、数据集、基准测试、评估方法及演示程序开源，以促进社区参与AGI的进步：https://github.com/agiresearch/OpenAGI。