The agent can use internet access to search for relevant information and gather resources to aid in task decomposition. This allows the agent to access a vast amount of information and utilize it in the decomposition process.\n\n3. GPT-3.5 Powered Agents: The agent can delegate simple tasks to GPT-3.5 powered agents. These agents can perform specific tasks or provide assistance in task decomposition, allowing the main agent to focus on higher-level planning and decision-making.\n\n4. File Output: The agent can store the results of task decomposition in files or documents. This allows for easy retrieval and reference during the execution of the task.\n\nThese memory resources help the agent in organizing and managing information, making informed decisions, and effectively decomposing complex tasks into smaller, manageable subgoals.'}Again, we can use the LangSmith trace to explore the prompt structure.Going deeper‚ÄãAgents, such as the conversational retrieval agent, can be used for retrieval when necessary while also holding a conversation.PreviousInteracting with APIsNextExtractionUse caseOverviewQuickstartMemoryConversationChat RetrievalGoing deeperCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
Open In Colab
Open In Colab ->: The agent can use internet access to search for relevant information and gather resources to aid in task decomposition. This allows the agent to access a vast amount of information and utilize it in the decomposition process.\n\n3. GPT-3.5 Powered Agents: The agent can delegate simple tasks to GPT-3.5 powered agents. These agents can perform specific tasks or provide assistance in task decomposition, allowing the main agent to focus on higher-level planning and decision-making.\n\n4. File Output: The agent can store the results of task decomposition in files or documents. This allows for easy retrieval and reference during the execution of the task.\n\nThese memory resources help the agent in organizing and managing information, making informed decisions, and effectively decomposing complex tasks into smaller, manageable subgoals.'}Again, we can use the LangSmith trace to explore the prompt structure.Going deeper‚ÄãAgents, such as the conversational retrieval agent, can be used for retrieval when necessary while also holding a conversation.PreviousInteracting with APIsNextExtractionUse caseOverviewQuickstartMemoryConversationChat RetrievalGoing deeperCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.