How is Auto-GPT different from LangChain #725
Replies: 6 comments 8 replies
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I could be wrong, but from my understanding it's different in that while it creates agents, AutoGPT executes codes and commands. While the concepts are similar, LangChain is basically just reasoning. So I think LangChain in conjunction with Reflextion is quite good at deducing steps in a plan, Auto-GPT isn't quite as strong at this. From my testing, Auto-GPT gets stuck in logic loops, repeats steps, and goes down these "rabbit holes". It's a great project and idea, but it could benefit greatly from say the implementation of BabyAGI (which uses LangChain and is a "better" reasoner, but can not execute code). |
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I'm still gettting a handle on the full capabilities of both Langchain and autogpt but it seems like there's value is using Langchain as an AI app constructor to autoGPT. Imagine giving the goal to autogpt to create an AI application using langchain that does x, y, z. TBH, the reason I got here was because I wanted to use a different (local) LLM with autogpt and thought about using langchain as an adapter somehow. |
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IMHO, LangChain is about process with control, while Auto-GPT is about result without control. LangChain is crazy, while Auto-GPT is insane. |
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Thanks guys for the discussion! The summary is that LangChain is better if you need to build your own application and control the process while AutoGPT is more about having autonomous agents. |
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So what about Langchain + AutoGPT? |
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Hi, I am working on a chat-based product that helps people develop business ideas. Correct me if I miss something, but I find that Auto-GPT is similar to LangChain in some ways. e.g. you can also add some tools like google and json parse as part of the agents in LangChain and build something similar to Auto-GPT. I am trying to decide which one to use. What are the main differences between these two? Thanks!
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