Remember, Don't Re-read: Stateful ReAct Agents for Token-Efficient Autonomous Experimentation

The autoresearch pattern enables autonomous experimentation by having a large language model (LLM) iteratively modify code to optimize a target metric. Its stateless design, however, reconstructs experimental context from scratch at every iteration, incurring O(n) token cost per iteration and O(n2) total. This work reformulates the pattern as a stateful ReAct agent using LangGraph, where typed persistent state carries experimental history across iterations via a tool-calling interface. Two benchmarks are evaluated: hyperparameter tuning (15 iterations, small per-iteration observations) and code performance optimization (40 iterations, large per-iteration observations containing full source code and benchmark results). On hyperparameter tuning, the stateful agent consumes 90% fewer tokens (2,492 vs. 24,465). On code optimization, the stateful agent consumes 52% fewer tokens (627K vs. 1,275K) while achieving comparable optimization quality on both tasks. The token reduction is structural: the stateless agent re-reads the full history at O(n) cost per iteration, while the stateful agent operates within a fixed-size conversation window at O(1) cost. This paper describes the architecture in sufficient detail for practitioners to implement a stateful autoresearch agent for their own workflows.