This review explores the integration of Large Language Models (LLMs) into autonomous agents, highlighting their transformative potential in various domains. LLMs, with their ability to process and generate human-like text, are reshaping autonomous agents, enabling them to perform complex tasks across multiple fields. However, challenges such as multimodality, human alignment, hallucinations, and evaluation remain significant hurdles. Techniques like prompting, reasoning, tool utilization, and in-context learning are being explored to enhance LLM capabilities. Evaluation platforms like AgentBench, WebArena, and ToolLLM provide robust methods for assessing these agents in complex scenarios. These advancements are leading to the development of more resilient and capable autonomous agents, anticipated to become integral in our digital lives, assisting in tasks from email responses to disease diagnosis. The future of AI, with LLMs at the forefront, is promising. LLMs, trained on extensive internet data, encapsulate a substantial corpus of human knowledge, mirroring the Semantic Web's objective of rendering internet data machine-readable. The interaction dynamics between humans and LLMs are crucial, as the manner in which we query or prompt these models can significantly shape the responses. This brings into focus the strategy of prompt tuning, a technique employed to enhance LLM performance by carefully selecting and adjusting prompts or seed texts to guide the model's generated text. The learning process of LLMs, driven by interaction with data, offers a pathway to deciphering human cognition. Researchers are exploring whether these computational models can serve as proxies for language processing in the human brain. The emergence of LLMs has provided a window into the world of general-purpose autonomous agents. These agents, powered by LLMs, demonstrate robust generalization capabilities across a range of applications, functioning as autonomous general-purpose task assistants. The integration of LLMs and multimodal models not only augments the agent's capabilities but also bestows upon it the semblance of a silicon lifeform. For embodied tasks, where robots interact with complex environments, text-only LLMs often encounter challenges due to a lack of compatibility with robotic visual perception. However, the fusion of LLMs and multimodal models into various robotic tasks offers a holistic solution. The review also discusses the limitations and challenges of employing different types of LLMs in agent construction, as well as the possibilities they present. For instance, LLaMA, an open-source LLM, has found application in agent construction. Conversely, closed-source models have also been deployed for similar purposes. The promise of open-source LLMs is noteworthy, as they democratize tool access, foster transparency, and stimulate innovation. Drawing an analogy to operating systems, the open-source design of Linux is often deemed more efficient than its counterparts, Windows and Mac, in terms of performance. This suggests that open-source LLMs, leveraging their design and community support, could eventually outperform their closed-source counterparts. Despite the challenges that persist, the continuous advancements and theThis review explores the integration of Large Language Models (LLMs) into autonomous agents, highlighting their transformative potential in various domains. LLMs, with their ability to process and generate human-like text, are reshaping autonomous agents, enabling them to perform complex tasks across multiple fields. However, challenges such as multimodality, human alignment, hallucinations, and evaluation remain significant hurdles. Techniques like prompting, reasoning, tool utilization, and in-context learning are being explored to enhance LLM capabilities. Evaluation platforms like AgentBench, WebArena, and ToolLLM provide robust methods for assessing these agents in complex scenarios. These advancements are leading to the development of more resilient and capable autonomous agents, anticipated to become integral in our digital lives, assisting in tasks from email responses to disease diagnosis. The future of AI, with LLMs at the forefront, is promising. LLMs, trained on extensive internet data, encapsulate a substantial corpus of human knowledge, mirroring the Semantic Web's objective of rendering internet data machine-readable. The interaction dynamics between humans and LLMs are crucial, as the manner in which we query or prompt these models can significantly shape the responses. This brings into focus the strategy of prompt tuning, a technique employed to enhance LLM performance by carefully selecting and adjusting prompts or seed texts to guide the model's generated text. The learning process of LLMs, driven by interaction with data, offers a pathway to deciphering human cognition. Researchers are exploring whether these computational models can serve as proxies for language processing in the human brain. The emergence of LLMs has provided a window into the world of general-purpose autonomous agents. These agents, powered by LLMs, demonstrate robust generalization capabilities across a range of applications, functioning as autonomous general-purpose task assistants. The integration of LLMs and multimodal models not only augments the agent's capabilities but also bestows upon it the semblance of a silicon lifeform. For embodied tasks, where robots interact with complex environments, text-only LLMs often encounter challenges due to a lack of compatibility with robotic visual perception. However, the fusion of LLMs and multimodal models into various robotic tasks offers a holistic solution. The review also discusses the limitations and challenges of employing different types of LLMs in agent construction, as well as the possibilities they present. For instance, LLaMA, an open-source LLM, has found application in agent construction. Conversely, closed-source models have also been deployed for similar purposes. The promise of open-source LLMs is noteworthy, as they democratize tool access, foster transparency, and stimulate innovation. Drawing an analogy to operating systems, the open-source design of Linux is often deemed more efficient than its counterparts, Windows and Mac, in terms of performance. This suggests that open-source LLMs, leveraging their design and community support, could eventually outperform their closed-source counterparts. Despite the challenges that persist, the continuous advancements and the