jixiner/flinkagentmonitoring — explained in plain English
Analysis updated 2026-05-18
Watch live CPU, memory, disk, and network stats for your own machine and remote Linux servers on one dashboard.
Get automatic alerts and recovery notices from an AI assistant instead of manually checking metrics.
Monitor the health of shared infrastructure pieces like Kafka, Redis, MySQL, and Flink from a single view.
Run a scheduled health inspection every twelve hours and keep the reports it produces for later review.
| jixiner/flinkagentmonitoring | 95ge/cfquant | aevella/sky-pc-mcp-companion | |
|---|---|---|---|
| Stars | 26 | 26 | 26 |
| Language | Python | Python | Python |
| Setup difficulty | moderate | moderate | moderate |
| Complexity | 4/5 | 3/5 | 3/5 |
| Audience | ops devops | developer | vibe coder |
Figures from each repo's GitHub metadata at analysis time.
Needs a DeepSeek API key for the AI assistant features, plus SSH access to any remote servers you want to monitor.
Flink-Agent is a server monitoring dashboard built with Django and WebSocket, paired with an AI assistant called Jix that watches over the results. It collects CPU, memory, disk, network, and process data from the local machine every second, and can also connect to remote Linux servers over SSH to collect the same kind of data from them. All of this gets pushed live to a browser dashboard built with ECharts charts, so you can watch trends over the last minute update in real time without refreshing the page. The Jix assistant reads the current metrics and stays quiet while everything looks healthy. It only speaks up when a resource looks risky, when a problem has recovered, or during a scheduled check it runs every twelve hours, and it can also answer direct questions about whether a server needs attention or how a particular piece of infrastructure like Kafka, Redis, or MySQL is doing. Jix relies on the DeepSeek chat API for its answers, but if no DeepSeek key is configured, it falls back to answering with simple rule-based logic built from the live metrics instead. Beyond individual servers, the project includes a built in view for common big data infrastructure pieces such as Kafka, Redis, MySQL, Flink itself, HDFS, YARN, ZooKeeper, and Elasticsearch, showing their role, status, and key resource numbers in one place. Remote server passwords are encrypted before being stored, and the API never returns those sensitive fields back to the client. Setting it up means installing Python dependencies, running Django's database migrations, and adding a DeepSeek API key to a local environment file if you want the AI features. The whole thing runs as a single Django project using Daphne to serve both the regular web pages and the WebSocket connections that keep the dashboard updating live. The author notes production use would need a properly configured secret key, restricted allowed hosts, and a Redis backed channel layer, since the current defaults are meant for local development.
A Django dashboard that monitors local and remote Linux servers in real time, with an AI assistant that flags problems and answers ops questions.
Mainly Python. The stack also includes Python, Django, Django Channels.
No license is stated in the README, so reuse and redistribution terms are unclear.
Setup difficulty is rated moderate, with roughly 1h+ to a first successful run.
Mainly ops devops.
This repo across BitVibe Labs
Verify against the repo before relying on details.