We present Exp-SWE-Agent, our submission to the FSE-AIWare 2026 MemComp challenge. Exp-SWE-Agent augments the Mini-SWE-Agent framework with a lightweight persistent memory module. Through a continuous retrieve-act-distill cycle, the agent extracts structured factual and experiential knowledge from past issue-resolution trajectories and uses them as strategic hints for subsequent tasks. Evaluated on a sequential 8 × 25 subset of SWE-Bench Pro, Exp-SWE-Agent increases the issue resolution rate (Pass%) from 42.7% to 47.5% while consistently reducing the number of interaction steps. These results demonstrate that structured persistent memory effectively enhances both the accuracy and efficiency of agents in long-horizon, repository-level software engineering workflows.