I’ve been hearing a lot about LangChain lately and it seems to be everywhere. But I’m wondering what other tools or frameworks are out there for building LLM applications. Someone mentioned LlamaIndex to me but I don’t know much about it. I’m also curious about whether it’s worth building something from scratch instead of using these frameworks. Can anyone share their experience with different alternatives? What are the main advantages and disadvantages of each approach? I’d really appreciate any insights or recommendations from people who have tried different solutions.
i’ve tried Haystack too, it’s pretty great! not as popular as LangChain, but gets the job done without all the fluff. also, if ur into MS products, i found Semantic Kernel to be surprisingly good and way lighter than a lot of others.
I’ve been using LlamaIndex for six months - it’s solid for RAG tasks. Way cleaner docs than LangChain and better performance with document processing. Check out Guidance from Microsoft Research too. It gives you tight control over model outputs through templating, super useful when you need consistent formatting. For simple stuff, I just use OpenAI’s API with custom code - skips all the framework bloat. Main thing is frameworks get you up and running fast, but you’ll hit walls when you need something custom that doesn’t fit their boxes.
Been down this rabbit hole too many times. Everyone mentions the usual suspects but misses the real problem.
These frameworks lock you into their thinking. LangChain breaks at scale. LlamaIndex works great until you need something it doesn’t support. Haystack looks nice but debugging is hell.
What actually works: ditch frameworks, think workflows instead.
I dropped all of them for Latenode after hitting the same walls. Chain multiple AI calls? Visual workflow. Mix GPT-4 with local models? Easy. Database lookups between AI steps? Connect the nodes.
My last project was a customer support bot needing sentiment analysis, knowledge base lookup, and response generation. Would’ve been a nightmare in LangChain with their abstractions. Latenode made it simple - each step’s a node, data flows between them, done.
No vendor lock-in. No fighting documentation. No rebuilds when requirements change.
The secret isn’t the right framework. It’s the right approach.
AutoGPT and LangFlow are both solid options. LangFlow’s visual interface makes building chains dead simple - no coding required. Also consider the transformers library directly. More setup work, but you skip all the framework bloat and get exactly what you need.
I’ve used most of these frameworks and they all suck at the same thing - zero flexibility when you need to scale or customize anything.
Dropped frameworks completely and switched to Latenode for my LLM stuff. Way more flexible since you can hook up any AI service (OpenAI, Claude, local models) with databases, APIs, whatever - all through visual workflows.
Built a document processing system last month that would’ve been weeks of pain with LangChain. Latenode? Drag and drop. PDF processing, embeddings, vector storage, chat interface - all connected in one flow.
Best part? You’re not stuck with their opinions. Need to switch from OpenAI to Claude? Swap the node. Want custom logic? Done.
Frameworks promise everything, deliver mediocrity. Latenode builds exactly what you want without the garbage.
Vercel AI SDK is worth checking out if you’re building web apps. Streaming works great and plays nice with React components. I’ve had good luck with Chroma for vector databases, though it’s more niche than the full frameworks others mentioned. Here’s what I learned the hard way: don’t go completely custom. I started a project from scratch to avoid framework limits but just ended up rebuilding stuff these tools already do well. Best approach? Use frameworks for quick prototyping, then swap out pieces when you need to scale. Also, Pinecone with basic API calls often beats heavy frameworks for simple RAG apps.