I’m trying to understand why so many developers choose Langchain when working with LLM APIs seems pretty straightforward on its own.
I’ve been building apps with OpenAI’s API for a while now and honestly find it really easy to work with. Making chat completion requests, handling function calls, keeping track of conversation state - none of this feels complicated to me. The API structure is actually one of the cleaner ones I’ve used.
Recently I noticed that most people in the community seem to be using Langchain instead of just calling the APIs directly or using the official OpenAI libraries. This got me curious about what I might be missing.
From what I can tell, Langchain mainly provides two things. First, it lets you swap between different LLM providers without changing much code. Second, it wraps the basic API calls like chat completions and function calling.
The provider switching thing doesn’t seem that useful to me. Most projects stick with one LLM, and if you really need to switch, the APIs are similar enough that it’s not a big deal.
As for the wrapper functionality, I’m not sure I see the value. The function calling workflow is pretty simple once you read the docs. You send a prompt, maybe get function calls back, execute those functions, send the results back to the model, and repeat if needed.
What am I not seeing here? Are there real advantages to using Langchain that make it worth the extra dependency?
It’s a game-changer for production apps that need proper observability and error handling. I made the switch after weeks of debugging a customer-facing app where direct API calls kept failing silently or giving inconsistent results. The structured callbacks and retry mechanisms alone saved me tons of troubleshooting time. What people don’t realize is how much the standardized interfaces help later on - adding streaming responses or batch processing becomes dead simple with Langchain’s abstractions. The agent framework is great too when you’re building apps that need to decide which tools to use based on user input. Sure, it feels like overkill for basic stuff, but it stops technical debt from piling up as your app gets more complex.
I felt the same way about Langchain at first. What changed my mind was building a project that needed memory management across conversations and vector database integration for RAG. Sure, you can build this stuff from scratch with direct API calls, but Langchain’s already solved the edge cases you won’t think of upfront. Their memory classes handle token limits and conversation pruning automatically. The document loaders work with tons of formats and chunk things properly. I also found the prompt templates really helpful once I needed consistent prompting across different parts of my app. The debugging tools and callbacks were great for tracking token usage and performance in production. But honestly, if you’re just doing basic chat completions without complex workflows, stick with direct API calls - they’re clearer and easier to understand.
yeah, totally see your point! but langchain really helps when you start doing more complex stuff, like managing documents or chaining multiple tasks together. plus, the community has built a bunch of tools that can save u a lotta time when you scale up your projects.
I get it. Direct API calls seem clean and simple at first.
But after years building production systems, I’ve learned the pain comes later. You’ll need retries when APIs crash, rate limit management across providers, plus logging and monitoring for production debugging.
Then someone wants email notifications for specific conditions. Or your LLM needs database connections for real-time data. Now you’re writing tons of integration code that’s got nothing to do with your actual logic.
I’ve done this dance too many times. Start with a simple script, end up maintaining a nightmare of custom integration code just to connect services.
Now I automate all that upfront with Latenode. It handles API management, errors, and service connections without custom code for each integration. Your LLM triggers workflows, sends data to other apps, responds to external events. Visual and automated.
Saves weeks of dev time and keeps my code focused on business logic instead of plumbing.
depends on what ur building. langchain shines for rag stuff - embeddings, vector stores, retrieval chains. the abstraction pays off when u got multiple data sources and want consistent chunking across em.