From Logs to Knowledge: LLM-Powered Dynamic Knowledge Graphs for Real-Time Cloud Observability

Cloud platforms continuously generate vast amounts of logs, metrics, and traces that are vital for monitoring and debugging distributed systems. However, current observability solutions are often siloed, dashboard-centric, and limited to surface-level correlations, making it difficult to derive actionable insights in real time. In this work, we present Log2Graph, a novel framework that leverages large language models (LLMs) to transform heterogeneous telemetry into dynamic knowledge graphs that evolve alongside system state. Unlike traditional log analytics, Log2Graph unifies unstructured messages, distributed traces, and configuration data into a living graph representation, enabling real-time dependency mapping, causal chain analysis, and compliance monitoring. Furthermore, the framework supports natural language queries over the evolving graph, allowing operators to ask questions such as “what services will be impacted if this database fails?” and receive precise, graph-backed explanations. Our evaluation on multi-cloud testbeds shows that Log2Graph reduces incident resolution time, improves accuracy in dependency detection, and enhances operator productivity. This work introduces a new paradigm of LLM-augmented observability, bridging the gap between raw logs and actionable cloud intelligence.