Hi there! I need some help choosing between two frameworks. I’m building a proof of concept for an AI chatbot that needs to work with different tools and handle requests from both developers and regular users. The bot should also be able to run various tasks automatically. I’ve been reading about LangChain and LangGraph but I’m getting confused about what makes them different. Could someone break down the main differences for me? I want to make sure I pick the right one for my project. What are the pros and cons of each? Any real world experience would be super helpful!
I’ve built similar chatbot systems, and LangGraph’s your better bet here. Yeah, Luna23’s right that LangChain’s easier for beginners, but LangGraph was built specifically for complex workflows with multiple tools and automated tasks - which is exactly what you need. It handles state management between conversation turns way better and coordinates tool interactions much cleaner. This matters when you’re serving both developer APIs and user interfaces. LangChain’s linear chains get messy fast once you need sophisticated routing and parallel processing. LangGraph’s learning curve is steeper upfront, but you’ll avoid tons of refactoring headaches when your proof of concept hits production.
i’d say langchain is better for beginners like you! it really has more resources and community help, which makes things easier. langgraph has cool features, but it can get tricky if you’r just starting out. stick with langchain for your proof of concept!
Both frameworks work fine, but you’re thinking about this wrong. You mentioned automation and different user types - that’s workflow orchestration, not just picking an AI framework.
I’ve done this before. Started with LangChain on a similar project, then switched to LangGraph when things got complex. But the real breakthrough came when I stopped coding everything and started thinking visually.
Your chatbot needs to coordinate tools, handle different users, and run automated tasks. I solved exactly this with Latenode. Instead of fighting framework limits, I built everything visually - AI responses, tool integrations, user routing, background automation. All drag and drop.
Best part? When stakeholders want changes (they always do), I can modify workflows in minutes instead of diving back into code. Plus it handles infrastructure so you focus on chatbot logic.
For your proof of concept, this gets you to demo faster and makes pivoting easier when requirements change.
timelines important! if u need a fast proof of concept, langchain’s the way to go, but it can get messy later. if u have time to learn, langgraph’s better for long-term automation and complex tasks without future headaches.