I just spent my entire weekend trying to get LangChain working with my proof of concept project and I’m honestly pretty frustrated. Maybe I’m doing something wrong but I wanted to ask if others have similar experiences.
My goal was pretty simple - build a basic RAG system using Python and LangChain. The idea is to take user questions, convert them to MongoDB queries, grab the data, and send back a natural language response through an LLM. Seemed straightforward enough.
But wow, what a nightmare this turned out to be:
The docs are confusing and missing important details
Can’t find any current tutorials that actually work
Everything I found online seems outdated
Even AI tools give me LangChain code that doesn’t run
The API docs have almost no examples, just bare descriptions
I tried using MongoDBDatabaseToolkit but had no clue how it actually behaves
Turns out it was retrying failed queries over and over, burning through my tokens
Only discovered this when I started logging events - nowhere was this documented
No idea how to limit retries or if that’s even possible
The abstraction layers make everything way more complex than needed
Breaking changes happen frequently which makes tutorials useless quickly
Take the MongoDBDatabaseToolkit docs for example. The description is just “MongoDBDatabaseToolkit for interacting with MongoDB databases.” That’s it. No mention of retry behavior, no explanation of events, nothing useful.
Am I missing something obvious here? Is there some secret documentation I should be looking at?
I eventually got something working with agents and LangGraph but it was so overly complicated that I scrapped it and built my own simple version. Now my code is actually readable and debuggable.
Anyone else struggle with LangChain like this? Would love to hear if I’m just approaching this wrong.
Been there. LangChain’s way too much for what you’re building.
I’ve watched developers waste weeks on frameworks when they could knock out exactly what they need in a day.
Your RAG system’s perfect for automation. Skip LangChain’s mess - just automate the whole thing: question comes in, converts to MongoDB query, grabs the data, sends it to your LLM.
Built something similar last month with Latenode. Took maybe 2 hours. No retry weirdness, no token waste, no docs deep-dives. Just clean automation that works.
Best part? You see every step. When it breaks, you know exactly where and why. No black box hiding what’s actually happening.
Easy to add error handling, logging, or tweak the flow without digging through someone else’s code.
Been using LangChain in production for about a year - your frustrations are spot on. It’s massively over-engineered. Simple stuff gets buried under layers of unnecessary abstractions. That MongoDB retry issue is the worst because it fails silently and burns through tokens. I started wrapping all LangChain components with custom monitoring after getting hit by similar problems. The docs aren’t just missing examples - the whole architecture changes so much that their own examples break between minor versions. It’s a mess. Here’s what worked for me: treat LangChain like a reference, not a real solution. Learn the patterns from it, then build your own lightweight version. You made the right call going custom for a basic RAG system.
honestly langchain feels like it’s built by devs for devs who already know everything. the retry stuff you mentioned is brutal - burned through my openai credits so fast before i figured out what was happening. stick with your custom solution, way less headache
i totally get it! langchain can be a real headache for newbies. it seems like it’s made for folks who already know this stuff. those api changes can really mess with your flow. building ur own version is smart - you’ll get it better. don’t lose hope, it gets easier with time!
Your experience is spot-on - most developers hit this same wall when they first try LangChain. The framework moves fast and the docs can’t keep up with new features or breaking changes. Those abstraction layers that make it powerful? They’re also what makes debugging a nightmare. I ran into the exact same MongoDB issues - tons of undocumented quirks. The toolkit approach technically works, but you’ll spend hours digging through source code just to figure out what’s actually happening. Set up proper logging from day one. Seriously, I wish someone had hammered that into my head earlier. You made the smart choice building your own version. Once you understand the core concepts, going back to LangChain becomes way easier. It’s useful for complex workflows, but starting simple and building up beats fighting the framework from the start.