I’m having problems with my LangChain AI chatbot that uses tool integration features. The bot keeps making wrong responses and doesn’t pick the correct tools when it should. This is really annoying me.
My setup uses FastAPI for the backend and MongoDB to save chat conversations with a sessionId. Every time someone sends a message, I get the old messages from the database and put them in memory. I use ConversationBufferMemory for recent stuff and ConversationSummaryMemory for older conversations. But it’s still not working well.
I’m thinking about moving to LangGraph because it might give me better control. Before I make this change, I need some help with these questions:
Is it better to use ready-made LangGraph agents or should I build my own?
What are the top ways to handle memory in LangGraph for both recent and old conversation data?
How can I manage context better in a FastAPI setup where each request starts fresh?
i totally get u! langgraph’s been way easyer for me too! the built-in state persistence saves so much hassle, and the tool selection is way better. i think you’ll really like it more than langchain!
Had this exact headache last year with a customer service bot that kept hitting wrong APIs. Wasn’t really LangChain’s fault - our tool selection logic was just structured poorly.
Debug your current setup before switching to LangGraph. Check if your tool descriptions are clear and you’re passing the right context. Most tool selection problems? Vague descriptions or missing context.
That said, LangGraph does give you way better control over execution flow. I’d build custom agents since you’ve got specific MongoDB and session handling needs. The prebuilt ones won’t play nice with your FastAPI setup.
For memory in LangGraph, ditch those LangChain memory classes completely. Just build a simple state manager that pulls your chat history from MongoDB into the graph state at startup. Let LangGraph’s built-in state management handle the rest during execution.
Biggest win with LangGraph? You can actually see where your agent makes decisions. Makes debugging tool selection way easier than LangChain’s black box.
One heads up though - migration takes time. Make sure this isn’t something simpler you can fix first.
LangGraph will fix your tool selection problems. I switched from LangChain six months ago and saw results right away. Skip the pre-made agents and build custom ones - you need specific memory handling anyway. The graph-based execution makes tool routing way more predictable than LangChain’s approach. For memory, use a hybrid setup: load your conversation history into the graph state when it starts, then let LangGraph handle everything else. With FastAPI, just build a state manager that pulls your MongoDB data and rebuilds the graph context on each request. Way cleaner than dealing with LangChain’s memory objects.
I’ve been fighting the same tool routing headaches lately. What you’re describing sounds exactly like tools not getting proper context from previous conversation turns - especially with that sessionId setup. Before you ditch everything, check how you’re formatting your tool descriptions and whether your conversation memory actually feeds relevant context to tool selection. Sometimes it’s not the framework, just how we structure the prompt templates. That said, LangGraph gives you way better visibility into decision-making. The graph structure lets you trace exactly where tool selection breaks, which is huge for debugging. For your FastAPI/MongoDB setup, you’d need a custom state loader that rebuilds conversation context at the start of each request cycle. LangGraph’s memory management works differently than LangChain’s memory classes though. You’re managing state transitions explicitly instead of relying on automatic memory buffering. More control, but more upfront design work. The migration’s a big job, so exhaust your debugging options first.