# StorageXTuner: An LLM Agent-Driven Automatic Tuning Framework for Heterogeneous Storage Systems

arXiv: https://arxiv.org/abs/2510.25017 · HTML: https://mlsanity.com/paper/2510.25017/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Agent memory systems, one of 16 directions in LLM agents and tool use.
- Builds on 29 of its 92 references that are in the map.
- First posted on arXiv 28 Oct 2025.

## Placed in

- LLM agents and tool use / [Agent memory systems](https://mlsanity.com/agents/agent-memory-systems/)

## Builds on (most cited first, 29 of 29)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting](https://mlsanity.com/paper/2310.11324/) (2023)
- [SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization](https://mlsanity.com/paper/2109.09831/) (2021)
- [MLLM-Tool: A Multimodal Large Language Model For Tool Agent Learning](https://mlsanity.com/paper/2401.10727/) (2024)
- [MMTok: Multimodal Coverage Maximization for Efficient Inference of VLMs](https://mlsanity.com/paper/2508.18264/) (2025)
- [GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization](https://mlsanity.com/paper/2311.03157/) (2023)
- [Evolutionary Large Language Model for Automated Feature Transformation](https://mlsanity.com/paper/2405.16203/) (2024)
- [ALR2: A Retrieve-then-Reason Framework for Long-context Question Answering](https://mlsanity.com/paper/2410.03227/) (2024)
- [λ-Tune: Harnessing Large Language Models for Automated Database System Tuning](https://mlsanity.com/paper/2411.03500/) (2024)
- [Unsupervised Feature Transformation via In-context Generation, Generator-critic LLM Agents, and Duet-play Teaming](https://mlsanity.com/paper/2504.21304/) (2025)
- [Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories](https://mlsanity.com/paper/2505.15076/) (2025)
- [LLLMs: A Data-Driven Survey of Evolving Research on Limitations of Large Language Models](https://mlsanity.com/paper/2505.19240/) (2025)
- [Table as Thought: Exploring Structured Thoughts in LLM Reasoning](https://mlsanity.com/paper/2501.02152/) (2025)
- [Efficient Post-Training Refinement of Latent Reasoning in Large Language Models](https://mlsanity.com/paper/2506.08552/) (2025)
- [DDPG++: Striving for Simplicity in Continuous-control Off-Policy Reinforcement Learning](https://mlsanity.com/paper/2006.15199/) (2020)
- [LLM-ML Teaming: Integrated Symbolic Decoding and Gradient Search for Valid and Stable Generative Feature Transformation](https://mlsanity.com/paper/2506.09085/) (2025)
- [Endure: A Robust Tuning Paradigm for LSM Trees Under Workload Uncertainty](https://mlsanity.com/paper/2110.13801/) (2021)
- [ELMo-Tune-V2: LLM-Assisted Full-Cycle Auto-Tuning to Optimize LSM-Based Key-Value Stores](https://mlsanity.com/paper/2502.17606/) (2025)
- [Learning-Aided Heuristics Design for Storage System](https://mlsanity.com/paper/2106.07288/) (2021)
- [CAMAL: Optimizing LSM-trees via Active Learning](https://mlsanity.com/paper/2409.15130/) (2024)

## Built on by (most cited first, 1 of 1)

- [IDP AutoOpt: Agent-Driven Optimization of Document Processing Pipeline Configurations](https://mlsanity.com/paper/2607.26075/) (2026)

## Abstract (arXiv)

Automatically configuring storage systems is hard: parameter spaces are large and conditions vary across workloads, deployments, and versions. Heuristic and ML tuners are often system specific, require manual glue, and degrade under changes. Recent LLM-based approaches help but usually treat tuning as a single-shot, system-specific task, which limits cross-system reuse, constrains exploration, and weakens validation. We present StorageXTuner, an LLM agent-driven auto-tuning framework for heterogeneous storage engines. StorageXTuner separates concerns across four agents - Executor (sandboxed benchmarking), Extractor (performance digest), Searcher (insight-guided configuration exploration), and Reflector (insight generation and management). The design couples an insight-driven tree search with layered memory that promotes empirically validated insights and employs lightweight checkers to guard against unsafe actions. We implement a prototype and evaluate it on RocksDB, LevelDB, CacheLib, and MySQL InnoDB with YCSB, MixGraph, and TPC-H/C. Relative to out-of-the-box settings and to ELMo-Tune, StorageXTuner reaches up to 575% and 111% higher throughput, reduces p99 latency by as much as 88% and 56%, and converges with fewer trials.