NVIDIA has unveiled a log analysis agent that combines a retrieval-augmented generation (RAG) pipeline with a graph-based multi-agent workflow to automate log parsing, relevance grading, and self-correcting queries. The solution, introduced in NVIDIA’s Generative AI reference workflows and powered by NVIDIA Nemotron, is designed to help developers and operators quickly identify the causes behind system failures. This system benefits QA, DevOps, and CloudOps teams by automating log analysis and root cause detection. The log analysis agent is a self-corrective, multi-agent RAG system that extracts insights from logs using large language models (LLMs). It orchestrates a LangGraph workflow that includes:

Hybrid retrieval: Combines BM25 for lexical matching and FAISS vector store with NVIDIA NeMo Retriever embeddings for semantic similarity.
Reranking: Uses NeMo Retriever to rerank results and highlight relevant log lines.
Grading: Scores candidate snippets for contextual relevance.
Generation: Produces context-aware answers instead of raw log dumps.
Self-correction loop: Rewrites queries and retries if initial results are insufficient.

The system implements a directed graph where each node is a specialized agent (retrieval, reranking, grading, generation, or transformation), with edges encoding decision logic to dynamically steer the workflow. Key components include:

`bat_ai.py`: Defines the workflow graph using LangGraph.
`graphnodes.py`: Implements retrieval, reranking, grading, generation, and query transformation.
`graphedges.py`: Encodes transition logic.
`multiagent.py`: Implements the Hybrid Retriever, combining BM25 and FAISS retrieval.
`binary_score_models.py`: Defines structured outputs for grading.
`utils.py` and `prompt.json`: Provide prompts and NVIDIA AI endpoint integration.

The solution employs a hybrid retrieval approach using the `HybridRetriever` class in `multiagent.py`, combining `BM25Retriever` for lexical scoring and `FAISS Vectorstore` for semantic similarity, leveraging embeddings from an NVIDIA NeMo Retriever model (llama-3.2-nv-rerankqa-1b-v2). NVIDIA AI endpoints power embedding (llama-3.2-nv-embedqa-1b-v2), NeMo Retriever reranking (llama-3.2-nv-rerankqa-1b-v2), and generation (nvidia/llama-3.3-nemotron-super-49b-v1.5). To get started, users can clone the NVIDIA GenerativeAIExamples GitHub repository and run an example query to see the system in action. The log analysis agent can be customized for bug reproduction automation, observability dashboards, and cybersecurity pipelines.