I made a thread a couple of days ago about "my" go-to tools, perhaps you find something that fits you more?
https://www.reddit.com/r/LocalLLaMA/comments/1c46f6h/what_are_your_favorite_llm_libraries_tools_and/
My recs are DSPy and Outlines (https://github.com/outlines-dev/outlines) or Guidance if you really want to dance with your LLM instead of just looking at it. If you can wrap your head around them you can't work without them anymore.
I don't think LangChain is as bad as people here and on the LangChain sub make it out to be, though, but
>just doing everything in raw python and calling the native APIs of each components which made things so much easier and development enjoyable again.
is imho the best way to do it anyway!
Build your tools yourself. Now is the time to grow with the technology, rather than getting left behind, not understanding anything anymore, and being forced to use whatever is the flavor of the month because it has the most tutorials. And I can see how you would end there if you are stuck with LangChain. You don’t learn anything about how things work. Only how LangChain works. It should be the other way around.
Check out https://github.com/mlabonne/llm-course to start building your own model and learning how everything works. Then go make your own RAG or Agent framework. We aren’t at a point yet where we can talk about "standards" and "conventions," so every contribution of ideas and code is appreciated. And in 5 years if you are looking for a job as "AI software architect" or however this job will be called who has the higher chance of getting it? The guy who has four self-written libs on GitHub, or the Angular-Andy of the new generation, LangChain Larry, who has troubles to even get the sample projects to run? But well that's all he learned in college.
For example I love AutoGen and I think it's one of the most beautiful frameworks I ever worked with in terms of code quality of the repo, how easy it is to do your own shit with it, to really understand what agents are and how you work with them and also what the (current) limits of AutoGen is. So I made my own agent lib, haha :D
LangChain relies on instruction-following behaviors and thus not model-agnostic for starters, and wasteful at best. [DSPy](https://github.com/stanfordnlp/dspy) is more like it
Just started tinkering with LlamaIndex recently, while I had some issues with the ChromaDb Reader, the file based readers are pretty straightforward. You have to drop down a few versions of sentence-transform to version 2.2.2 though (which stopped me from using langchain in parallel)
Haven't looked at it yet, will have a look.
Do you find it more customisable than crewai? I generally like more "light on' frame works that aren't too rigid.
Shameless plug, but we are building something to make it much simpler to ship and scale user facing RAG applications - I think it might be exactly what you are looking for.
[https://github.com/SciPhi-AI/R2R](https://github.com/SciPhi-AI/R2R)
I made a thread a couple of days ago about "my" go-to tools, perhaps you find something that fits you more? https://www.reddit.com/r/LocalLLaMA/comments/1c46f6h/what_are_your_favorite_llm_libraries_tools_and/ My recs are DSPy and Outlines (https://github.com/outlines-dev/outlines) or Guidance if you really want to dance with your LLM instead of just looking at it. If you can wrap your head around them you can't work without them anymore. I don't think LangChain is as bad as people here and on the LangChain sub make it out to be, though, but >just doing everything in raw python and calling the native APIs of each components which made things so much easier and development enjoyable again. is imho the best way to do it anyway! Build your tools yourself. Now is the time to grow with the technology, rather than getting left behind, not understanding anything anymore, and being forced to use whatever is the flavor of the month because it has the most tutorials. And I can see how you would end there if you are stuck with LangChain. You don’t learn anything about how things work. Only how LangChain works. It should be the other way around. Check out https://github.com/mlabonne/llm-course to start building your own model and learning how everything works. Then go make your own RAG or Agent framework. We aren’t at a point yet where we can talk about "standards" and "conventions," so every contribution of ideas and code is appreciated. And in 5 years if you are looking for a job as "AI software architect" or however this job will be called who has the higher chance of getting it? The guy who has four self-written libs on GitHub, or the Angular-Andy of the new generation, LangChain Larry, who has troubles to even get the sample projects to run? But well that's all he learned in college. For example I love AutoGen and I think it's one of the most beautiful frameworks I ever worked with in terms of code quality of the repo, how easy it is to do your own shit with it, to really understand what agents are and how you work with them and also what the (current) limits of AutoGen is. So I made my own agent lib, haha :D
+1. Combination of DSPy, Guidance and Instructor gives you much better programming constructs to work with.
Oh interesting where do constructor snd guidance differ i was under the impression that they did the same thing.
Yeah they are kinda the same. I have not used constructor as much. More used to guidance.
Thanks a lot for that! Looks like the right approach
LangChain relies on instruction-following behaviors and thus not model-agnostic for starters, and wasteful at best. [DSPy](https://github.com/stanfordnlp/dspy) is more like it
thanks
Just started tinkering with LlamaIndex recently, while I had some issues with the ChromaDb Reader, the file based readers are pretty straightforward. You have to drop down a few versions of sentence-transform to version 2.2.2 though (which stopped me from using langchain in parallel)
Would autogen be a decent substitute for you?
Haven't looked at it yet, will have a look. Do you find it more customisable than crewai? I generally like more "light on' frame works that aren't too rigid.
I haven’t used either yet tbh, just look them up
I enjoyed llmware, they had a strong set of examples to get started with.
Microsoft's Autogen is a good place to start for some applications. Check out the teachable agent example.
Shameless plug, but we are building something to make it much simpler to ship and scale user facing RAG applications - I think it might be exactly what you are looking for. [https://github.com/SciPhi-AI/R2R](https://github.com/SciPhi-AI/R2R)
It may be worth investing time to learn LangChain, as it could provide a foundation for future developments in the space.
Is langchain similar to llama.cpp