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Currently working on a SaaS app that could be called an "AI Wrapper". One thing I picked up on is once you start using AI tools programmatically, you can start

Currently working on a SaaS app that could be called an "AI Wrapper". One thing I picked up on is once you start using AI tools programmatically, you can start doing far more complex things than what you can with ChatGPT or Claude. One thing we've leaned heavily into was using Langgraph for agentic workflows and it's really opened the door to cool ways you can use AI. These days the way I tell apart an AI "Wrappers" vs "Tools" is what is the underlying paradigm. Most "wrappers" just copy the paradigm of ChatGPT/Claude where you have a conversation with an agent, the "tools" are where you take the ability to generate content and then plug that into a broader workflow.

embedding-shape 11mo ago
> One thing we've leaned heavily into was using Langgraph for agentic workflows Probably my single biggest mistake so far with developing LLM tooling so far has been to try to use Langgraph even after inspecting the codebase, because people I thought were smarter than me hyped it up. Do yourself a favor and just write the plumbing yourself, it's a lot easier than one might think before digging into it, and tool calling is literally a loop passing tool requests and responses back and forth until the model responds, and having your own abstractions will make it a lot easier to build proper workflows. Plus you get to use whatever language you want and don't have to deal with Python.
- CjHuber 11mo ago
  I can recommend looking into DSpy, I haven’t felt the need to use any other LLM based frameworks though I‘m open to suggestions
  - embedding-shape 11mo ago
    There really isn't need, all they add is additional code to be responsible for, building the same abstractions yourself but focused on your use case will be something like 50-100 lines of code, hard to beat the simplicity, and the understanding you'll get.

embedding-shape 11mo ago

> One thing we've leaned heavily into was using Langgraph for agentic workflows Probably my single biggest mistake so far with developing LLM tooling so far has been to try to use Langgraph even after inspecting the codebase, because people I thought were smarter than me hyped it up. Do yourself a favor and just write the plumbing yourself, it's a lot easier than one might think before digging into it, and tool calling is literally a loop passing tool requests and responses back and forth until the model responds, and having your own abstractions will make it a lot easier to build proper workflows. Plus you get to use whatever language you want and don't have to deal with Python.

CjHuber 11mo ago
I can recommend looking into DSpy, I haven’t felt the need to use any other LLM based frameworks though I‘m open to suggestions
- embedding-shape 11mo ago
  There really isn't need, all they add is additional code to be responsible for, building the same abstractions yourself but focused on your use case will be something like 50-100 lines of code, hard to beat the simplicity, and the understanding you'll get.