# TopoEvo: A Topology-Aware Self-Evolving Multi-Agent Framework for Root Cause Analysis in Microservices

arXiv: https://arxiv.org/abs/2605.15611 · HTML: https://mlsanity.com/paper/2605.15611/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 15 May 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)
- Synthetic data and self-improvement for LLMs / [Self-evolving LLM agents](https://mlsanity.com/synth/self-evolving-llm-agents/)

## Builds on (most cited first, 14 of 14)

- [RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models](https://mlsanity.com/paper/2310.16340/) (2023)
- [Anomaly Detection and Failure Root Cause Analysis in (Micro)Service-Based Cloud Applications: A Survey](https://mlsanity.com/paper/2105.12378/) (2021)
- [Flow-of-Action: SOP Enhanced LLM-Based Multi-Agent System for Root Cause Analysis](https://mlsanity.com/paper/2502.08224/) (2025)
- [RCAEval: A Benchmark for Root Cause Analysis of Microservice Systems with Telemetry Data](https://mlsanity.com/paper/2412.17015/) (2024)
- [mABC: multi-Agent Blockchain-Inspired Collaboration for root cause analysis in micro-services architecture](https://mlsanity.com/paper/2404.12135/) (2024)
- [BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point Detection](https://mlsanity.com/paper/2405.09330/) (2024)
- [Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Data](https://mlsanity.com/paper/2302.05092/) (2023)
- [Groot: An Event-graph-based Approach for Root Cause Analysis in Industrial Settings](https://mlsanity.com/paper/2108.00344/) (2021)
- [TAMO: Fine-Grained Root Cause Analysis via Tool-Assisted LLM Agent with Multi-Modality Observation Data in Cloud-Native Systems](https://mlsanity.com/paper/2504.20462/) (2025)
- [Adaptive Root Cause Localization for Microservice Systems with Multi-Agent Recursion-of-Thought](https://mlsanity.com/paper/2508.20370/) (2025)
- [A Comprehensive Survey on Root Cause Analysis in (Micro) Services: Methodologies, Challenges, and Trends](https://mlsanity.com/paper/2408.00803/) (2024)
- [Agentic Memory Enhanced Recursive Reasoning for Root Cause Localization in Microservices](https://mlsanity.com/paper/2601.02732/) (2026)
- [MicroRCA-Agent: Microservice Root Cause Analysis Method Based on Large Language Model Agents](https://mlsanity.com/paper/2509.15635/) (2025)
- [Online Multi-modal Root Cause Identification in Microservice Systems](https://mlsanity.com/paper/2410.10021/) (2024)

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

- [Self-Evolving Agents as Dynamic Graph Transformation: A Survey and New Perspective](https://mlsanity.com/paper/2608.18104/) (2026)

## Abstract (arXiv)

Root cause analysis (RCA) in microservices is challenging due to (i) noisy and heterogeneous multimodal observability (metrics, logs, traces), (ii) cascading failure propagation that amplifies downstream symptoms, and (iii) non-stationary topology drift induced by autoscaling and rolling updates. Recent LLM-based RCA agents can generate tool-grounded explanations, yet they often remain topology-agnostic and suffer from symptom-amplification bias, misattributing the root cause to salient downstream victims. We propose TopoEvo, a topology-aware self-evolving multi-agent framework that couples graph representation learning with structured, topology-constrained reasoning. TopoEvo first introduces Metric-orthogonal Multimodal Alignment (MOMA), which decomposes metric embeddings into complementary subspaces and contrastively aligns logs and traces to reduce modality redundancy and sparsity, yielding stable node representations for graph encoding. It then applies Vector Quantization (VQ) to discretize topology-enhanced states into auditable symptom tokens with a symptom lexicon, enabling reliable retrieval and token-level evidence grounding. On top of these discrete topology cues, TopoEvo performs a multi-agent Hypothesis--Evidence--Test (HET) workflow to explicitly verify propagation-consistent explanations and separate initiating anomalies from amplified downstream symptoms. Finally, a Self-Evolving Mechanism refreshes hierarchical incident memory and performs conservative test-time adaptation with high-confidence pseudo-labels to maintain robustness under drift.