I keep seeing people say that Langchain and Langgraph shouldn’t be used in production environments, but nobody really explains the specific reasons behind this claim.
I’m currently working on building AI-powered voice assistants that run serverlessly. These assistants need to handle things like scheduling appointments and bringing additional people into ongoing conversations. I’m planning to use Langgraph for this project.
Should I be worried about using a serverless architecture combined with Langgraph? If there are genuine issues with this tech stack, what would be a better alternative? Would it make more sense to create everything from the ground up instead?
If building from scratch is the way to go, what resources or learning paths would you recommend for someone starting this journey?
Been there with a customer service chatbot that handled appointment scheduling. Langchain’s production issues aren’t just hype - I’ve dealt with real memory leaks and those heavy dependencies create random latency spikes when traffic gets heavy. Serverless works, but you’ll need persistent storage for conversation state between calls. Database round trips kill response times, especially for voice apps where people expect instant replies. I’d go hybrid - use Langchain locally for prototyping your conversation flows, then build production with direct OpenAI API calls and custom state management. You’ll lose some convenience but get way better reliability and predictable performance. For learning, hit up OpenAI’s function calling docs and WebSocket handling for real-time voice. Steeper curve than frameworks, but pays off when you need rock-solid production performance.
langchain’s solid in production if you set it up right. i’ve been running it 8 months with voice bots - the framework isn’t the problem, it’s how you build around it. cold starts will kill you though. had to ditch lambda for fargate containers. langgraph saves your ass with conversation state, especially for group chats.
Yeah, Langchain and Langgraph are a pain in production. Too much abstraction makes debugging hell, and they’re way too heavy for serverless - cold starts kill you.
For voice assistants doing real-time conversations and appointment booking, you need something predictable and fast. Building from scratch gives you control but takes forever.
I’d skip the frameworks and use automation for orchestration instead. Set up workflows that trigger on voice inputs, route to AI endpoints, update calendars, and handle multi-party chats.
Did this for our support system. Instead of fighting Langchain’s abstractions, I automated everything - voice recognition to response generation to database updates. Works great and debugging’s actually possible.
You get all the orchestration benefits without the framework bloat. Serverless functions stay light, conversation flows stay reliable.
Latenode’s solid for AI workflow automation. Handles the complexity but keeps it simple and production-ready.
I’ve seen this exact scenario at multiple companies. The problem isn’t whether Langchain works in production - you’re fighting two different issues at once.
Serverless and conversational AI don’t play nice together. You’re constantly rebuilding context and dealing with connection drops. Plus frameworks like Langchain lock you into their patterns when you need flexibility for voice processing.
What actually works: treat your voice assistant as connected workflows, not one monolithic app. Break it down - speech to text triggers one workflow, intent processing triggers another, appointment booking hits your calendar API, bringing people into conversations fires off notifications.
I built something similar for customer escalations. Instead of wrestling with framework limitations, I automated the entire pipeline. Voice input comes in, gets processed through specialized endpoints, updates happen in parallel, responses get routed back. Each piece does one thing well.
You can swap out AI models, change calendar providers, or update conversation logic without touching the rest. Plus debugging becomes trivial because each step is isolated.
This scales way better than forcing everything through Langchain’s abstractions. You get microservices reliability with simple visual workflow management.
Latenode handles this AI orchestration perfectly - built specifically for complex automation like this.