During the software operation phase, automated log analysis is crucial for the early detection of anomalies to prevent critical incidents, like system failure. Learning-based anomaly detection techniques have shown the potential for real-time anomaly detection from trace logs through learning the execution patterns. However, extracting features from raw text format log files of diversified structures has been challenging and tackled in different ways. With the recent advancements in large language models (LLM), several LLM-based parsing methods have been proposed, where most of these methods struggled with uncertain output from LLM or manual rules set requirements for the parsing. To address these challenges, we have proposed a hybrid framework leveraging LLM in parsing and embedding. Our proposed approach uses the LLM to generate Regular expressions (REGEX) for the parser, along with parsing and event embedding (EM) using a pre-trained LLM model. Then, this framework leverages the reconstructive capacity of the autoencoder with attention mechanism (AM) for unsupervised learning of log patterns. The experimental case study shows the model’s effectiveness in anomaly detection using a public dataset with 96% accuracy. This framework will provide flexibility to pre-process different text-based log structures without human involvement in parsing.