Graph workflows vs. agent loops on a local 9B model: same accuracy, a lot less tokens

Reddit r/AI_Agents News
graph-workflows agent-loops token-efficiency local-model qwen-3.5 email-triage

Summary

An experiment comparing graph workflows and ReAct-style agent loops for email triage using a local 9B model found that graph workflows significantly reduce token usage with similar accuracy.

Hi all! I ran a small experiment comparing a standard ReAct-style loop against a graph workflow for an email triage task on an M1 Macbook Pro running Qwen 3.5 9B. Original hypothesis was that it'd lead to more consistency on repeated execution (passk), maybe improvement on accuracy, and be potentially more efficient in terms of token usage and time. Accuracy difference was small and not statistically significant. But the efficiency gap was quite significant - ReAct loops narrate a lot and generate quite a bit more (2.6x in our setup) output tokens (and by extension wall-clock time, about 1.5x longer), even with thinking off. Full write-up in comment. Curious whether these results still hold at different scale, e.g. larger tasks (in terms of input-token size), more states, more varied data points, etc. Would love to hear from those who have operated LangGraph beyond a toy setup/demo.
Original Article

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