This paper introduces **ReasoningBank**, a novel memory framework designed to enhance Large Language Model (LLM) agents by distilling and structuring reasoning patterns from both successful and failed task trajectories. Traditional memory systems typically overlook failure experiences and lack the ability to abstract high-level reasoning, a limitation ReasoningBank addresses by creating **structured memory items** (title, description, content) that capture transferable insights. Furthermore, the paper proposes **Memory-aware Test-Time Scaling (MaTTS)**, which leverages this high-quality memory to guide diverse exploration, forming a positive feedback loop where memory improves scaling, and scaling enriches memory. Experimental results across multiple benchmarks, including WebArena and SWE-Bench-Verified, demonstrate that ReasoningBank significantly **improves success rates** and **enhances efficiency** by reducing the average number of steps required to complete tasks compared to existing memory approaches and memory-free agents.