AutoHeal AI
An IT operations self-healing system based on local LLMs. Through monitoring metrics, anomaly detection, and local large-model analysis, it automatically explains the causes of failures and executes recovery actions within policy-permitted bounds, operating entirely offline.
Timeline (last 90 days)
AutoHeal AI is a self-healing operations system that continuously monitors infrastructure metrics, uses machine learning to identify anomalous patterns, has a locally running large model explain the causes, automatically executes recovery actions within the range allowed by policy, and generates incident reports, forming a closed loop.
AutoHeal AI is based on the Gemma 3 model and the Ollama local inference runtime, and after downloading the dependencies and model, it can work completely without an internet connection.
In AutoHeal AI, metrics such as CPU, memory, and disk I/O are fed into Isolation Forest to output anomaly scores, which are then handed to Gemma to provide a readable explanation of the causes combined with logs.
Gemma was developed by Google and in AutoHeal AI is responsible for analyzing server error logs, explaining possible causes of failures, recommending troubleshooting steps, answering IT operations questions, and generating incident reports.
In AutoHeal AI, Gemma does not directly execute system commands; recovery actions are handled by an independent recovery engine, which is responsible for validating permissions, executing predefined policies, and requesting manual approval when necessary.
AutoHeal AI adopts a three-layer architecture: React frontend dashboard → FastAPI backend → three core engines (monitoring engine, AI engine, recovery engine).
AutoHeal AI's tech stack includes: Gemma 3 4B as the language model, Ollama as the local runtime, Python + FastAPI for the backend, React for the frontend, psutil for monitoring collection, Scikit-learn for anomaly detection, SQLite for data storage, Docker for the containerized test environment, Recharts for visualization, and GitHub for open-source hosting.
AutoHeal AI's development cycle is estimated at 6 to 10 weeks, advanced through ten stages, and the initial version can be developed on a single Windows laptop.
To use AutoHeal AI to predict failures, a dataset containing actual failure history is needed to train the model, rather than relying solely on anomaly detection.
All Facts (9)
To use AutoHeal AI to predict failures, a dataset containing actual failure history is needed to train the model, rather than relying solely on anomaly detection.
50%UnverifiedAutoHeal AI is based on the Gemma 3 model and the Ollama local inference runtime, and after downloading the dependencies and model, it can work completely without an internet connection.
50%UnverifiedIn AutoHeal AI, metrics such as CPU, memory, and disk I/O are fed into Isolation Forest to output anomaly scores, which are then handed to Gemma to provide a readable explanation of the causes combined with logs.
50%UnverifiedGemma was developed by Google and in AutoHeal AI is responsible for analyzing server error logs, explaining possible causes of failures, recommending troubleshooting steps, answering IT operations questions, and generating incident reports.
50%UnverifiedIn AutoHeal AI, Gemma does not directly execute system commands; recovery actions are handled by an independent recovery engine, which is responsible for validating permissions, executing predefined policies, and requesting manual approval when necessary.
50%UnverifiedAutoHeal AI adopts a three-layer architecture: React frontend dashboard → FastAPI backend → three core engines (monitoring engine, AI engine, recovery engine).
50%UnverifiedAutoHeal AI's tech stack includes: Gemma 3 4B as the language model, Ollama as the local runtime, Python + FastAPI for the backend, React for the frontend, psutil for monitoring collection, Scikit-learn for anomaly detection, SQLite for data storage, Docker for the containerized test environment, Recharts for visualization, and GitHub for open-source hosting.
50%UnverifiedAutoHeal AI is a self-healing operations system that continuously monitors infrastructure metrics, uses machine learning to identify anomalous patterns, has a locally running large model explain the causes, automatically executes recovery actions within the range allowed by policy, and generates incident reports, forming a closed loop.
50%UnverifiedAutoHeal AI's development cycle is estimated at 6 to 10 weeks, advanced through ten stages, and the initial version can be developed on a single Windows laptop.
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