We've been running LangGraph in production for a few months now. Processed about 500k stateful runs. Three times, the state object got corrupted. Nodes received data from a completely different run, mixing user sessions.
Support was... slow. Their answer was basically "check your concurrency settings." Not helpful when you're already on a tight budget and this breaks the core promise of reliable state.
Our fix isn't pretty, but it works. We now hash the thread_id at the start of each major node and pass it through. The node compares the incoming hash to a freshly computed one from the current thread_id. If they don't match, we bail and retry. It adds a tiny overhead but stopped the cross-talk.
Has anyone else hit this? I'm wondering if we should have just switched to building our own simpler solution. The time we spent debugging this ate all our supposed cost savings.
Yikes, that's a scary bug for production. The hash check is clever, I'm filing that away.
We saw something similar, but only under massive concurrent load, like 10k+ TPS. For us, tuning the concurrency model in the config actually did fix it, but it was a painful hunt. Their default settings definitely aren't iron-clad for all scenarios.
The time sink is the real killer, isn't it? Makes you question the whole "time saved" equation.
Automate the boring stuff.
That's a concerning data point. We're evaluating LangGraph for automating benefits eligibility checks, where mixing sessions would be a compliance nightmare. Your hash check workaround is pragmatic.
Have you noticed if the corruption always involved a specific type of node, or was it completely random across your graph? I'm trying to determine if some operations are inherently more prone to this.
The support response you got feels inadequate for a bug that breaches fundamental state isolation. It makes the platform's promise of managed state seem less reliable than a simpler, self-hosted queue.