do LLM Powered Autonomous Agents operate?\n']    INFO:langchain.retrievers.web_research:Searching for relevant urls ...    INFO:langchain.retrievers.web_research:Searching for relevant urls ...    INFO:langchain.retrievers.web_research:Search results: [{'title': 'LLM Powered Autonomous Agents | Hacker News', 'link': 'https://news.ycombinator.com/item?id=36488871', 'snippet': 'Jun 26, 2023 ... Exactly. A temperature of 0 means you always pick the highest probability token (i.e. the "max" function), while a temperature of 1 means you\xa0...'}]    INFO:langchain.retrievers.web_research:Searching for relevant urls ...    INFO:langchain.retrievers.web_research:Search results: [{'title': "LLM Powered Autonomous Agents | Lil'Log", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Task decomposition can be done (1) by LLM with simple prompting like "Steps for XYZ.\\n1." , "What are the subgoals for achieving XYZ?" , (2) by\xa0...'}]    INFO:langchain.retrievers.web_research:New URLs to load: []    INFO:langchain.retrievers.web_research:Grabbing most relevant splits from urls...    {'question': 'How do LLM Powered Autonomous Agents work?',     'answer': "LLM-powered autonomous agents work by using LLM as the agent's brain, complemented by several key components such as planning, memory, and tool use. In terms of planning, the agent breaks down large tasks into smaller subgoals and can reflect and refine its actions based on past experiences. Memory is divided into short-term memory, which is used for in-context learning, and long-term memory, which allows the agent to retain and recall information over extended periods. Tool use involves the agent calling external APIs for additional information. These agents have been used in various applications, including scientific discovery and generative agents simulation.",     'sources': ''}Going deeper‚ÄãHere's a app that wraps this retriever with a lighweight UI.Question answering over a
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Open In Collab ->: do LLM Powered Autonomous Agents operate?\n']    INFO:langchain.retrievers.web_research:Searching for relevant urls ...    INFO:langchain.retrievers.web_research:Searching for relevant urls ...    INFO:langchain.retrievers.web_research:Search results: [{'title': 'LLM Powered Autonomous Agents | Hacker News', 'link': 'https://news.ycombinator.com/item?id=36488871', 'snippet': 'Jun 26, 2023 ... Exactly. A temperature of 0 means you always pick the highest probability token (i.e. the "max" function), while a temperature of 1 means you\xa0...'}]    INFO:langchain.retrievers.web_research:Searching for relevant urls ...    INFO:langchain.retrievers.web_research:Search results: [{'title': "LLM Powered Autonomous Agents | Lil'Log", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Task decomposition can be done (1) by LLM with simple prompting like "Steps for XYZ.\\n1." , "What are the subgoals for achieving XYZ?" , (2) by\xa0...'}]    INFO:langchain.retrievers.web_research:New URLs to load: []    INFO:langchain.retrievers.web_research:Grabbing most relevant splits from urls...    {'question': 'How do LLM Powered Autonomous Agents work?',     'answer': "LLM-powered autonomous agents work by using LLM as the agent's brain, complemented by several key components such as planning, memory, and tool use. In terms of planning, the agent breaks down large tasks into smaller subgoals and can reflect and refine its actions based on past experiences. Memory is divided into short-term memory, which is used for in-context learning, and long-term memory, which allows the agent to retain and recall information over extended periods. Tool use involves the agent calling external APIs for additional information. These agents have been used in various applications, including scientific discovery and generative agents simulation.",     'sources': ''}Going deeper‚ÄãHere's a app that wraps this retriever with a lighweight UI.Question answering over a