by microsoft · LLM Plugin · ★ 318
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OpenRCA     OpenRCA is a benchmark for assessing LLMs' root cause analysis ability in a software operating scenario. When given a natural language query, LLMs need to analyze large volumes of telemetry data to identify the relevant root cause elements. This process requires the models to understand complex system dependencies and perform comprehensive reasoning across various types of telemetry data, including KPI time series, dependency trace graphs, and semi-structured log text. We also introduce RCA-agent as a baseline for OpenRCA. By using Python for data retrieval and analysis, the model avoids processing overly long contexts, enabling it to focus on reasoning and scalable for extensive telemetry. ✨ Quick Start ⚠️ Since the OpenRCA dataset includes a large amount of telemetry and RCA-agent requires extensive memory operations, we recommend using a device with at least 80GB of storage space and 32GB of memory. 🛠️ Installation OpenRCA requires Python = 3.10. It can be installed by running the following command: bash [optional to create c
| Stars | 318 |
| Forks | 42 |
| Language | Python |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 40.75/100 |
| Open Issues | 9 |
| Last Updated | 2026-04-14 |
| Created | 2024-10-30 |
| Platforms | python |
| Est. Tokens | ~174k |
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OpenRCA is [ICLR'25] OpenRCA: Can Large Language Models Locate the Root Cause of Software Failures?. It is categorized as a LLM Plugin with 318 GitHub stars.
OpenRCA is primarily written in Python. It covers topics such as benchmark, large-language-models, llm.
You can find installation instructions and usage details in the OpenRCA GitHub repository at github.com/microsoft/OpenRCA. The project has 318 stars and 42 forks, indicating an active community.
OpenRCA is released under the MIT license, making it free to use and modify according to the license terms.
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