Looking for insights on LangChain/LangGraph drawbacks
I’ve been exploring various discussion forums and noticed several developers mentioning that LangChain and LangGraph struggle with complex scenarios. Many suggest using native SDKs instead for advanced implementations.
While I understand these frameworks are constantly improving and adding new features, I’m just getting started with LangChain and want to better grasp its current constraints.
Specific questions:
What are the main technical limitations you’ve encountered?
In which scenarios would you recommend avoiding these frameworks?
When does it make more sense to work directly with underlying APIs?
Any real-world examples or experiences would be greatly appreciated to help me make informed architectural decisions.
Honestly, version compatibility is the worst issue I’ve hit. LangChain updates break everything constantly and their migration docs are garbage. Memory usage also goes insane with bigger chains - watched our app devour 2GB of RAM for basic retrieval tasks. Fine for simple chatbots, but production scale? Just go native.
Hit this same problem 8 months back building a document analysis pipeline. Started with LangChain - seemed obvious - but ripped it out halfway through.
Debugging was hell. Something breaks in a complex chain? Good luck finding where. Those abstraction layers make it impossible to trace your actual API calls.
Performance sucked too. All that middleware adds serious latency when you’re hitting hundreds of requests. Got 40% faster just switching to direct OpenAI SDK calls.
LangGraph’s memory management is a mess. Had a conversational agent randomly losing context because the framework was doing its own state management thing.
Skip these frameworks when:
You need exact control over API parameters
Performance matters
You’re scaling past prototype
You want reliable errors and logging
They’re okay for quick demos or learning LLMs. But for production? Native SDKs give you way more control and transparency.
I just write simple wrapper functions now. Takes an extra day but saves weeks of debugging.
I’ve used both for about a year. The worst part? Vendor lock-in. Build your pipeline around LangChain’s abstractions and you’re stuck - switching LLM providers is way harder than they claim, even with their “provider-agnostic” design.
Their docs are a mess. Half the examples use deprecated methods, and the API changes constantly. Stack Overflow answers from 6 months ago? Worthless. I spend more time reading source code than docs.
LangGraph really struggles with complex branching. I had a multi-step reasoning task that needed conditional flows based on intermediate results. The framework kept forcing me into their patterns instead of letting me build what I actually needed.
For enterprise work, the observability is terrible. Can’t monitor token usage properly, can’t trace performance issues, debugging production problems is pure guesswork.
These frameworks work great for quick prototypes and learning, but when you need reliability and maintainability? Build a thin layer over native SDKs instead. Much better long-term.