I’m working on an application that currently uses OpenAI’s assistants API along with their vector storage system. While this setup has been working okay, I’ve noticed that newer and more powerful models are becoming available from different providers. The assistants API also seems to lag behind the chat completions API when it comes to getting the latest features and improvements.
Because of these limitations, I want to restructure my app to work with multiple LLM providers instead of being locked into just OpenAI. My plan involves using Langchain with specialized agents that can perform retrieval augmented generation against a Pinecone vector database. Each agent would handle different knowledge sources and feed the retrieved information to whatever LLM the user selects.
But when I look at discussions online, I keep seeing negative opinions about Langchain and stories of developers switching away from it. This makes me wonder if I’m heading down the wrong path.
So here’s what I need advice on: Is Langchain still worth using for this kind of multi-agent RAG setup in 2025? And if not, what other frameworks or libraries would you suggest for building a vendor-neutral LLM application?
Multi-LLM projects turn into a nightmare when you’re dealing with different APIs, rate limits, and response formats. Been there.
The problem isn’t finding the right framework - it’s managing all the chaos. Vector queries, model switching, error handling, keeping everything synced.
I solved this with automated workflows that run the entire pipeline. No framework bloat, no vendor lock-in. Workflows route requests to the right model, hit Pinecone, format responses, and handle crashes.
I’ve got one workflow for docs, another for picking models based on cost/speed, and one that runs the RAG flow. When OpenAI drops new features or I want Anthropic, I just tweak the workflow logic.
Way more flexible than frameworks and you’re not debugging someone else’s mess when it breaks. Scaling and monitoring come free.
For multi-agent stuff, you can test different LLM combos without rewriting code constantly. Check out this automation approach: https://latenode.com
From running multi-provider systems in production, I’d add LangSmith to whatever framework you pick. The monitoring is a lifesaver when you’re dealing with multiple LLM providers and debugging RAG issues. For your use case though, check out Haystack over LangChain. It’s built specifically for RAG and has much cleaner multi-provider abstractions. Easier learning curve and way better docs too. Hard lesson learned - build fallback chains from day one, no matter what framework you use. When Claude crashes or OpenAI hits rate limits, you need automatic provider switching. And budget extra time for prompt engineering since different models act totally different even with the same prompts.
Framework choice doesn’t matter - your architecture does. I’ve built several multi-LLM systems and the real problem isn’t Langchain vs whatever else. It’s dealing with how different providers work. Response formats, token limits, context windows, pricing - they’re all over the place. Build a solid abstraction layer for each model’s quirks, whatever framework you pick. I ended up writing custom prompts for each provider because what crushes it with GPT-4 completely bombs with Claude or Gemini. The hardest part? Keeping things consistent across models while still using what makes each one special. Skip the framework popularity contest and focus on bulletproof error handling and response validation. Users don’t care what’s running behind the scenes if it just works.
Yeah, I get the Langchain hesitation. It’s bloated and the complaints are legit.
But you don’t have to pick between heavy frameworks or building everything yourself. I’ve done similar multi-LLM setups and there’s a better way.
Skip Langchain’s abstractions and months of custom coding. Just automate the pipeline. Set up workflows for LLM routing, vector queries, and response formatting. No lock-in, no complex agent debugging.
I built something that switches between OpenAI, Claude, and local models based on request type and cost. Automation handles Pinecone queries, chunks results, and feeds the right LLM. Takes an afternoon vs weeks of coding.
Best part? New providers or API changes? Update the workflow, don’t refactor classes. Way cleaner than Langchain complexity or maintaining custom frameworks.
For vendor-neutral setups, automation beats coding. Check it out: https://latenode.com
Been there, done that. I went with a hybrid approach that worked great. Skip the full Langchain route - I built my own lightweight abstraction layer with simple Python classes instead. You get vendor neutrality without all the overhead and complexity everyone gripes about with Langchain. For RAG stuff, I kept it simple with direct Pinecone API calls and wrote my own retrieval logic. Here’s what I learned: most of Langchain’s features can be replicated with a few hundred lines of clean code that you actually understand and control. Debugging’s way easier when things break since you’re not wrestling with framework abstractions. Performance is noticeably better too - less middleware between your code and the actual APIs. If you build custom, just make sure you add proper retry logic and rate limiting from day one.
LangChain gets way too much hate. Sure, it’s bloated, but it saves tons of time for multi-LLM RAG projects. Most complaints are from older versions - it’s much better now. If you want something lighter, try LlamaIndex or use LiteLLM for provider switching with custom RAG logic. Really depends if you’d rather build from scratch or use existing tools.
langchain gets way more hate than it deserves for multi-llm projects. i used it last year on something similar - yeah, there’s a learning curve, but it’s solid once you figure it out. the abstractions are actually helpful when you’re switching between different providers. way less code than building everything from scratch. just don’t go crazy with all their features - use what you need and ignore the rest.