I’m searching for a solid RAG framework to use in my company’s production system. I’ve tried many different options but keep running into problems.
So far I’ve tested:
LlamaIndex - seems limited in customization options compared to what I need
LightRAG - couldn’t get it working properly, maybe I’m missing something
Various other tools including RAGAS, FlashRAG, ragbuilder, R2R, RAGFlow, Dify, and several smaller libraries
LangChain works okay for my current setup, but I keep hearing concerns about its stability for production use and issues with version updates breaking things. The code structure also feels messy sometimes.
Has anyone successfully deployed any of these frameworks in a real business environment? I need something that won’t break when I update it and can handle the configuration complexity my project requires. Any recommendations or experiences with these tools would be really helpful.
Same stability nightmare here. Been down the framework rabbit hole way too many times.
Most RAG frameworks bundle everything together - when one thing breaks, everything crashes. LangChain updates killed my production system twice last year.
Treat RAG like any data pipeline instead. Split document processing, embedding generation, vector storage, and retrieval into separate services. One piece fails? The rest keeps running.
I ditched monolithic frameworks and built RAG as workflow pipelines. Document ingestion runs on schedule, embeddings update in batches, queries hit optimized endpoints. No more random framework failures.
You get real error handling, retry logic, and monitoring that enterprise needs. Swapping components is easy when you want to test new models or databases.
Latenode makes this approach simple. Build your RAG pipeline as connected workflows instead of hoping frameworks stay stable: https://latenode.com
I’ve been running enterprise RAG systems for years, and honestly? The framework choice isn’t your biggest problem. Whether you pick LlamaIndex or LangChain doesn’t matter much - it’s all the other stuff that’ll kill you.
Vector databases need syncing, embeddings need updates, documents need processing, API calls need orchestrating, monitoring needs setup. That’s where production deployments actually break.
I quit fighting framework limitations and just automated the whole RAG pipeline instead. Built workflows for document ingestion, chunk processing, vector updates, and query routing without getting locked into any framework’s weird quirks.
Best part? You can swap components without rewriting everything. Testing a new embedding model? Update one node. Different vector database? Change the connection, leave your business logic alone.
You also get monitoring, error handling, and scaling that most RAG frameworks don’t include. Way more reliable than crossing your fingers every time your framework updates.
Check out Latenode for this approach - it handles the orchestration mess so you can focus on making your RAG system actually work: https://latenode.com
Honestly, Microsoft’s Semantic Kernel has been rock solid for our enterprise setup. We ditched LangChain after dealing with too many breaking changes - this thing actually stays stable between updates. The docs could use work, but if you’re in the .NET ecosystem, the integration is fantastic.
After dealing with similar headaches, I went back to basics with a custom setup using Weaviate and OpenAI embeddings. Been running this for 14 months across three client deployments without major issues. Enterprise RAG needs predictable behavior more than fancy features. Most frameworks try doing too much and break easily. I use FastAPI for the service layer, handle chunking with simple Python scripts, and keep everything modular. Version control’s way easier when you’re not stuck with someone else’s breaking changes. Document retrieval stays consistent, and troubleshooting’s straightforward since you know every component. Takes longer upfront, but maintenance headaches basically disappear. If you really need a framework, evaluate based on your specific use case, not general recommendations. What works for one company’s documents and queries might suck for yours.
haystack’s been a game changer for me! had it in production for 8 months and no issues at all. way better than langchain, and custom builds are super easy with the pipeline. yeah, the docs are a bit lacking, but once set up, updates have been smooth.