[sgc]

I am very curious about this as well. I'm looking for something that does really well with workflows that require 20 plus steps including a couple while loops and user verifications, but also something simple like a chat bot with access to MCP servers and tools. I need to be able to use Gemini directly, openAI directly, and openrouter at various steps.

Right now I am having trouble deciding whether it's better to just write my own harness, or use langchain, or something else.

[reactordev]

You can do all of that with current frameworks depending on how deep you go. The issue people are finding is that one size does not fit all and your specific workflows may be better suited for a lower level.

[sgc]

What I find impossible to judge is whether me choosing the harness that works best for me and the way I like to work will limit the quality of the LLM output.

In this case, given the complexity of LangChain I don't know if it would burn a lot of tokens or confuse the LLMs with context creep due to the large env and tooling compared to something much simpler, or would something much simpler burn a lot of tokens and stutter in execution compared to LangChain, because they have a lot of middleware optimizations that I would have to relearn the hard way? Or are both those strategies off the mark and there is a better tool for the job I am not even thinking about?

It gets a bit expensive in terms of both time/effort and money to experiment on a full workflow rather than specific steps which is quite easy in openrouter, hence my curiosity as to others' experiences.