LangChain

• Pros

  • Interface to easily compose components
  • As one comment puts it, it’s a great way to let you realize what is possible with LLM workflows, before you build a more production grade one yourself

• Cons

  • Class and method sprawl
    • seems like there are multiple ways to do the same thing
      • .invoke, .predict, .run
      • Tool vs @tool + llm.bind
  • There’s a lot of encapsulation, but not enough abstraction imo
  • limited documentation
  • I’ve seen docs that talk about wrapping llm chains with other llm chains as backup
    • I think overall, I may not have the proper mental model yet
  • Online, people seem to be arguing about poor performance (ML- and engineering-wise)
  • Feels weird that some code is in langchain, others in langchain_core, and yet others have their own provider-specific package langchain_openai

• Questions

  • How can I handle errors if my tool raises one?