text: Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKUse casesQA and Chat over DocumentsAnalyzing structured dataExtractionInteracting with APIsChatbotsSummarizationCode UnderstandingAgent simulationsAgentsAutonomous (long-running) agentsAutoGPTBabyAGI User GuideBabyAGI with ToolsHuggingGPTmarathon_timesMeta-PromptMulti-modalUse casesAutonomous (long-running) agentsMeta-PromptOn this pageMeta-PromptThis is a LangChain implementation of Meta-Prompt, by Noah Goodman, for building self-improving agents.The key idea behind Meta-Prompt is to prompt the agent to reflect on its own performance and modify its own instructions.Here is a description from the original blog post:The agent is a simple loop that starts with no instructions and follows these steps:Engage in conversation with a user, who may provide requests, instructions, or feedback.At the end of the episode, generate self-criticism and a new instruction using the meta-promptAssistant has just had the below interactions with a User. Assistant followed their "system: Instructions" closely. Your job is to critique the Assistant's performance and then revise the Instructions so that Assistant would quickly and correctly respond in the future. ####{hist}#### Please reflect on these interactions.You should first critique Assistant's performance. What could Assistant have done better? What should the Assistant remember about this user? Are there things this user always wants? Indicate this with "Critique: ...".You should next revise the Instructions so that Assistant would quickly and correctly respond in the future. Assistant's goal is to satisfy the user in as few interactions as possible. Assistant will only see the new Instructions, not the interaction history, so anything important must be summarized in the Instructions. Don't forget any important details in the current Instructions! Indicate the new Instructions by "Instructions: ...".Repeat.The