We are broader. We have essential building blocks for RAG, Agents. But also made whatever you build possible to auto-optimize. You can think of us as the library to do in-context learning. Just like PyTorch is for model-training.
We have better accuracy, more token-efficient, and faster convergence speed. We are publishing three research papers to explain this better to researchers.
We will compare with these optimization libraries but wont compare with libraries like LangChain or LlamaIndex. As they simply dont have optimization and it is pain to build on them.
Our benchmark has compared with Dspy and Text-grad(https://github.com/zou-group/textgrad)
We have better accuracy, more token-efficient, and faster convergence speed. We are publishing three research papers to explain this better to researchers.
https://adalflow.sylph.ai/use_cases/question_answering.html
We will compare with these optimization libraries but wont compare with libraries like LangChain or LlamaIndex. As they simply dont have optimization and it is pain to build on them.
Hope this make sense