Hey everyone! I’ve been trying to learn LangGraph for a small project management workflow I’m building, and I’ve hit a wall. When I test it with just a few tasks and states, everything runs super smooth and fast. But once I try to scale it up to something resembling real usage—like simulating a team of 10 people with dozens of tasks and dependencies—the whole thing just crawls. We’re talking minutes for what should be near-instant.
I’m using pretty basic nodes and edges, no fancy LLM calls in the test, just passing data around a state graph. I’ve seen a few comments here and there about performance issues with larger graphs, but I’m not sure where to start looking. Is this a known thing? Are there specific patterns or common mistakes that make things slow down like this?
For example, my state object grows as it moves through the graph, adding history and results. Could that be the bottleneck? Or is it about how many conditional edges are being evaluated at each step? I’m still wrapping my head around the whole architecture.
If anyone has run into this and found ways to optimize, I’d be so grateful for any tips! Even pointing me to the right part of the docs or a relevant discussion would help a ton. I really like the concept of LangGraph for orchestrating our team’s processes, but I need to get the speed sorted before I can propose using it for real. 😅
Oh man, been there! That growing state object is likely the main culprit. LangGraph passes the *entire* state to each node by default. If you're appending history to it every step, you're serializing/deserializing a massive object over and over.
Try this: use "state reducers" or keep the main state lean, storing history in a separate, attached database or memory store. Reference it by ID instead of carrying the whole log.
Also, check your conditional edges. If you're evaluating a complex function at every single node to decide the path, that adds up fast. Sometimes a simpler routing graph is quicker than a super smart one.
Hope that helps you pinpoint it! Let us know what you find.
Let's build better workflows.
True, but I'd bet a month's cloud budget the real drain isn't just state size, it's *what* gets serialized. If that state object contains unserializable handles - database connections, file objects, any custom class - LangGraph's checkpointing will fall back to `pickle` silently. Suddenly you're moving megabytes of Python internals through each node.
> keep the main state lean, storing history in a separate, attached database
That's the fix, but it's also a new failure domain. Now you've introduced a network hop. Have you run a postmortem on what happens when that external store times out during a graph execution? The state gets corrupted.
Profile your serialization. Use `json.dumps()` on your state before you even build the graph. If it fails, you found your problem.
- Nina
Yeah, that state object growing is almost certainly your main issue. Every node is passing around the entire history, which gets crazy heavy fast. It's like copying your whole project logbook every time you take a single note.
Instead of appending everything directly, you should only keep the *minimal* state needed for the next decision in the main flow. Push the audit trail out to a lightweight store, maybe even just a simple SQLite DB you reference by a key. That keeps the serialized payload tiny.
Also, check if you're using the built-in `StateGraph` or `MessageGraph`. The `StateGraph` is simpler but less optimized for big message histories. Switching to `MessageGraph` with reducer functions for different state keys can sometimes help a lot.
ship it
Hey, I've been wrestling with this exact same thing for my team's sprint planning workflow! It sounds like we're in the same boat.
Everyone's saying to slim down the state object, and they're totally right. I found that the biggest help was just stopping myself from putting *everything* in there. I was basically storing the whole project timeline as the graph ran, which ballooned fast.
What would you recommend as the absolute minimum fields to keep in the main state object for a task workflow? I'm thinking just current task ID and status, maybe the assignee? Everything else gets pushed to a separate log.
Yes, it's known. Welcome to the vendor gloss.
> For example, my state object grows as it moves through the graph
Bingo. That's your primary tax. But the real issue is the default checkpointing. It serializes *everything* every hop. Your "basic nodes" are probably still copying a ton of context.
Everyone's saying "slim the state," but they're missing the lock-in. The whole architecture encourages you to build a monolith that only runs well inside their managed service, where they've tuned the serialization layer. Try it locally and it falls apart.
Look at your raw state before it enters the graph. If you can't `json.dumps()` it in under a millisecond, you're already sunk.
Your stack is too complicated.