I have a full-on "The Problem With LangChain" blog post in the pipeline, and the reason I made a simple alternative (
https://news.ycombinator.com/item?id=36393782) because I spent a month working with LangChain and coming to the conclusion that it's just easier to make my own Python package than it is to hack LangChain to fit my needs.
A few bullet points:
- LangChain encourages tool lock-in for little developer benefit, as noted in the OP. There is no inherent advantage into using them, and some have suboptimal implementations.
- The current implementations of the ReAct workflow and prompt engineering are based on InstructGPT (text-davinci-003), and are extremely out of date compared to what you can do with ChatGPT/GPT-4.
- Debugging a LangChain error is near impossible, even with verbose=True.
- If you need anything outside the workflows in the documentation, it's extremely difficult to hack, even with Custom Agents.
- The documentation is missing a lot of relevant detail (e.g. the difference between Agent types) that you have to go diving into the codebase for.
- The extreme popularity of LangChain is warping the entire AI ecosystem around the workflows to the point of harming it. Recent releases by Hugging Face and OpenAI recontextualize themselves around LangChain's "it's just magic AI" to the point of hurting development and code clarity.
Part of the reason I'm hesitant to release said blog post is because I don't want to be that asshole who criticizes open source software that's operating in good faith.
A few bullet points:
- LangChain encourages tool lock-in for little developer benefit, as noted in the OP. There is no inherent advantage into using them, and some have suboptimal implementations.
- The current implementations of the ReAct workflow and prompt engineering are based on InstructGPT (text-davinci-003), and are extremely out of date compared to what you can do with ChatGPT/GPT-4.
- Debugging a LangChain error is near impossible, even with verbose=True.
- If you need anything outside the workflows in the documentation, it's extremely difficult to hack, even with Custom Agents.
- The documentation is missing a lot of relevant detail (e.g. the difference between Agent types) that you have to go diving into the codebase for.
- The extreme popularity of LangChain is warping the entire AI ecosystem around the workflows to the point of harming it. Recent releases by Hugging Face and OpenAI recontextualize themselves around LangChain's "it's just magic AI" to the point of hurting development and code clarity.
Part of the reason I'm hesitant to release said blog post is because I don't want to be that asshole who criticizes open source software that's operating in good faith.