Description

This project explores the feasibility of using the open-source Grafana LLM plugin to enhance the Uyuni Health-check tool with LLM capabilities. The idea is to integrate a chat-based "AI Troubleshooter" directly into existing dashboards, allowing users to ask natural-language questions about errors, anomalies, or performance issues.

Goals

  • Investigate if and how the grafana-llm-app plug-in can be used within the Uyuni Health-check tool.
  • Investigate if this plug-in can be used to query LLMs for troubleshooting scenarios.
  • Evaluate support for local LLMs and external APIs through the plugin.
  • Evaluate if and how the Uyuni MCP server could be integrated as another source of information.

Resources

Grafana LMM plug-in

Uyuni Health-check

Looking for hackers with the skills:

uyuni grafana ai mcp

This project is part of:

Hack Week 25

Activity

  • about 2 months ago: j_renner liked this project.
  • about 2 months ago: deneb_alpha liked this project.
  • 2 months ago: juliogonzalezgil liked this project.
  • 2 months ago: paolodepa liked this project.
  • 2 months ago: ygutierrez added keyword "mcp" to this project.
  • 2 months ago: ygutierrez started this project.
  • 2 months ago: ygutierrez added keyword "uyuni" to this project.
  • 2 months ago: ygutierrez added keyword "grafana" to this project.
  • 2 months ago: ygutierrez added keyword "ai" to this project.
  • 2 months ago: ygutierrez originated this project.

  • Comments

    • lkocman
      about 2 months ago by lkocman | Reply

      Hello could you please write a summary for the Hackweek25? Thank you

    Similar Projects

    Uyuni read-only replica by cbosdonnat

    Description

    For now, there is no possible HA setup for Uyuni. The idea is to explore setting up a read-only shadow instance of an Uyuni and make it as useful as possible.

    Possible things to look at:

    • live sync of the database, probably using the WAL. Some of the tables may have to be skipped or some features disabled on the RO instance (taskomatic, PXT sessions…)
    • Can we use a load balancer that routes read-only queries to either instance and the other to the RW one? For example, packages or PXE data can be served by both, the API GET requests too. The rest would be RW.

    Goals

    • Prepare a document explaining how to do it.
    • PR with the needed code changes to support it


    Uyuni Saltboot rework by oholecek

    Description

    When Uyuni switched over to the containerized proxies we had to abandon salt based saltboot infrastructure we had before. Uyuni already had integration with a Cobbler provisioning server and saltboot infra was re-implemented on top of this Cobbler integration.

    What was not obvious from the start was that Cobbler, having all it's features, woefully slow when dealing with saltboot size environments. We did some improvements in performance, introduced transactions, and generally tried to make this setup usable. However the underlying slowness remained.

    Goals

    This project is not something trying to invent new things, it is just finally implementing saltboot infrastructure directly with the Uyuni server core.

    Instead of generating grub and pxelinux configurations by Cobbler for all thousands of systems and branches, we will provide a GET access point to retrieve grub or pxelinux file during the boot:

    /saltboot/group/grub/$fqdn and similar for systems /saltboot/system/grub/$mac

    Next we adapt our tftpd translator to query these points when asked for default or mac based config.

    Lastly similar thing needs to be done on our apache server when HTTP UEFI boot is used.

    Resources


    Enhance setup wizard for Uyuni by PSuarezHernandez

    Description

    This project wants to enhance the intial setup on Uyuni after its installation, so it's easier for a user to start using with it.

    Uyuni currently uses "uyuni-tools" (mgradm) as the installation entrypoint, to trigger the installation of Uyuni in the given host, but does not really perform an initial setup, for instance:

    • user creation
    • adding products / channels
    • generating bootstrap repos
    • create activation keys
    • ...

    Goals

    • Provide initial setup wizard as part of mgradm uyuni installation

    Resources


    Set Up an Ephemeral Uyuni Instance by mbussolotto

    Description

    To test, check, and verify the latest changes in the master branch, we want to easily set up an ephemeral environment.

    Goals

    • Create an ephemeral environment manually
    • Create an ephemeral environment automatically

      Resources

    • https://github.com/uyuni-project/uyuni

    • https://www.uyuni-project.org/uyuni-docs/en/uyuni/index.html


    Move Uyuni Test Framework from Selenium to Playwright + AI by oscar-barrios

    Description

    This project aims to migrate the existing Uyuni Test Framework from Selenium to Playwright. The move will improve the stability, speed, and maintainability of our end-to-end tests by leveraging Playwright's modern features. We'll be rewriting the current Selenium code in Ruby to Playwright code in TypeScript, which includes updating the test framework runner, step definitions, and configurations. This is also necessary because we're moving from Cucumber Ruby to CucumberJS.

    If you're still curious about the AI in the title, it was just a way to grab your attention. Thanks for your understanding.

    Nah, let's be honest add-emoji AI helped a lot to vibe code a good part of the Ruby methods of the Test framework, moving them to Typescript, along with the migration from Capybara to Playwright. I've been using "Cline" as plugin for WebStorm IDE, using Gemini API behind it.


    Goals

    • Migrate Core tests including Onboarding of clients
    • Improve test reliabillity: Measure and confirm a significant reduction of flakiness.
    • Implement a robust framework: Establish a well-structured and reusable Playwright test framework using the CucumberJS

    Resources


    Flaky Tests AI Finder for Uyuni and MLM Test Suites by oscar-barrios

    Description

    Our current Grafana dashboards provide a great overview of test suite health, including a panel for "Top failed tests." However, identifying which of these failures are due to legitimate bugs versus intermittent "flaky tests" is a manual, time-consuming process. These flaky tests erode trust in our test suites and slow down development.

    This project aims to build a simple but powerful Python script that automates flaky test detection. The script will directly query our Prometheus instance for the historical data of each failed test, using the jenkins_build_test_case_failure_age metric. It will then format this data and send it to the Gemini API with a carefully crafted prompt, asking it to identify which tests show a flaky pattern.

    The final output will be a clean JSON list of the most probable flaky tests, which can then be used to populate a new "Top Flaky Tests" panel in our existing Grafana test suite dashboard.

    Goals

    By the end of Hack Week, we aim to have a single, working Python script that:

    1. Connects to Prometheus and executes a query to fetch detailed test failure history.
    2. Processes the raw data into a format suitable for the Gemini API.
    3. Successfully calls the Gemini API with the data and a clear prompt.
    4. Parses the AI's response to extract a simple list of flaky tests.
    5. Saves the list to a JSON file that can be displayed in Grafana.
    6. New panel in our Dashboard listing the Flaky tests

    Resources

    Outcome


    "what is it" file and directory analysis via MCP and local LLM, for console and KDE by rsimai

    Description

    Users sometimes wonder what files or directories they find on their local PC are good for. If they can't determine from the filename or metadata, there should an easy way to quickly analyze the content and at least guess the meaning. An LLM could help with that, through the use of a filesystem MCP and to-text-converters for typical file types. Ideally this is integrated into the desktop environment but works as well from a console. All data is processed locally or "on premise", no artifacts remain or leave the system.

    Goals

    • The user can run a command from the console, to check on a file or directory
    • The filemanager contains the "analyze" feature within the context menu
    • The local LLM could serve for other use cases where privacy matters

    TBD

    • Find or write capable one-shot and interactive MCP client
    • Find or write simple+secure file access MCP server
    • Create local LLM service with appropriate footprint, containerized
    • Shell command with options
    • KDE integration (Dolphin)
    • Package
    • Document

    Resources


    SUSE Observability MCP server by drutigliano

    Description

    The idea is to implement the SUSE Observability Model Context Protocol (MCP) Server as a specialized, middle-tier API designed to translate the complex, high-cardinality observability data from StackState (topology, metrics, and events) into highly structured, contextually rich, and LLM-ready snippets.

    This MCP Server abstract the StackState APIs. Its primary function is to serve as a Tool/Function Calling target for AI agents. When an AI receives an alert or a user query (e.g., "What caused the outage?"), the AI calls an MCP Server endpoint. The server then fetches the relevant operational facts, summarizes them, normalizes technical identifiers (like URNs and raw metric names) into natural language concepts, and returns a concise JSON or YAML payload. This payload is then injected directly into the LLM's prompt, ensuring the final diagnosis or action is grounded in real-time, accurate SUSE Observability data, effectively minimizing hallucinations.

    Goals

    • Grounding AI Responses: Ensure that all AI diagnoses, root cause analyses, and action recommendations are strictly based on verifiable, real-time data retrieved from the SUSE Observability StackState platform.
    • Simplifying Data Access: Abstract the complexity of StackState's native APIs (e.g., Time Travel, 4T Data Model) into simple, semantic functions that can be easily invoked by LLM tool-calling mechanisms.
    • Data Normalization: Convert complex, technical identifiers (like component URNs, raw metric names, and proprietary health states) into standardized, natural language terms that an LLM can easily reason over.
    • Enabling Automated Remediation: Define clear, action-oriented MCP endpoints (e.g., execute_runbook) that allow the AI agent to initiate automated operational workflows (e.g., restarts, scaling) after a diagnosis, closing the loop on observability.

     Hackweek STEP

    • Create a functional MCP endpoint exposing one (or more) tool(s) to answer queries like "What is the health of service X?") by fetching, normalizing, and returning live StackState data in an LLM-ready format.

     Scope

    • Implement read-only MCP server that can:
      • Connect to a live SUSE Observability instance and authenticate (with API token)
      • Use tools to fetch data for a specific component URN (e.g., current health state, metrics, possibly topology neighbors, ...).
      • Normalize response fields (e.g., URN to "Service Name," health state DEVIATING to "Unhealthy", raw metrics).
      • Return the data as a structured JSON payload compliant with the MCP specification.

    Deliverables

    • MCP Server v0.1 A running Golang MCP server with at least one tool.
    • A README.md and a test script (e.g., curl commands or a simple notebook) showing how an AI agent would call the endpoint and the resulting JSON payload.

    Outcome A functional and testable API endpoint that proves the core concept: translating complex StackState data into a simple, LLM-ready format. This provides the foundation for developing AI-driven diagnostics and automated remediation.

    Resources

    • https://www.honeycomb.io/blog/its-the-end-of-observability-as-we-know-it-and-i-feel-fine
    • https://www.datadoghq.com/blog/datadog-remote-mcp-server
    • https://modelcontextprotocol.io/specification/2025-06-18/index
    • https://modelcontextprotocol.io/docs/develop/build-server

     Basic implementation

    • https://github.com/drutigliano19/suse-observability-mcp-server

    Results

    Successfully developed and delivered a fully functional SUSE Observability MCP Server that bridges language models with SUSE Observability's operational data. This project demonstrates how AI agents can perform intelligent troubleshooting and root cause analysis using structured access to real-time infrastructure data.

    Example execution


    Exploring Modern AI Trends and Kubernetes-Based AI Infrastructure by jluo

    Description

    Build a solid understanding of the current landscape of Artificial Intelligence and how modern cloud-native technologies—especially Kubernetes—support AI workloads.

    Goals

    Use Gemini Learning Mode to guide the exploration, surface relevant concepts, and structure the learning journey:

    • Gain insight into the latest AI trends, tools, and architectural concepts.
    • Understand how Kubernetes and related cloud-native technologies are used in the AI ecosystem (model training, deployment, orchestration, MLOps).

    Resources

    • Red Hat AI Topic Articles

      • https://www.redhat.com/en/topics/ai
    • Kubeflow Documentation

      • https://www.kubeflow.org/docs/
    • Q4 2025 CNCF Technology Landscape Radar report:

      • https://www.cncf.io/announcements/2025/11/11/cncf-and-slashdata-report-finds-leading-ai-tools-gaining-adoption-in-cloud-native-ecosystems/
      • https://www.cncf.io/wp-content/uploads/2025/11/cncfreporttechradar_111025a.pdf
    • Agent-to-Agent (A2A) Protocol

      • https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/


    Try out Neovim Plugins supporting AI Providers by enavarro_suse

    Description

    Experiment with several Neovim plugins that integrate AI model providers such as Gemini and Ollama.

    Goals

    Evaluate how these plugins enhance the development workflow, how they differ in capabilities, and how smoothly they integrate into Neovim for day-to-day coding tasks.

    Resources


    Liz - Prompt autocomplete by ftorchia

    Description

    Liz is the Rancher AI assistant for cluster operations.

    Goals

    We want to help users when sending new messages to Liz, by adding an autocomplete feature to complete their requests based on the context.

    Example:

    • User prompt: "Can you show me the list of p"
    • Autocomplete suggestion: "Can you show me the list of p...od in local cluster?"

    Example:

    • User prompt: "Show me the logs of #rancher-"
    • Chat console: It shows a drop-down widget, next to the # character, with the list of available pod names starting with "rancher-".

    Technical Overview

    1. The AI agent should expose a new ws/autocomplete endpoint to proxy autocomplete messages to the LLM.
    2. The UI extension should be able to display prompt suggestions and allow users to apply the autocomplete to the Prompt via keyboard shortcuts.

    Resources

    GitHub repository


    Enable more features in mcp-server-uyuni by j_renner

    Description

    I would like to contribute to mcp-server-uyuni, the MCP server for Uyuni / Multi-Linux Manager) exposing additional features as tools. There is lots of relevant features to be found throughout the API, for example:

    • System operations and infos
    • System groups
    • Maintenance windows
    • Ansible
    • Reporting
    • ...

    At the end of the week I managed to enable basic system group operations:

    • List all system groups visible to the user
    • Create new system groups
    • List systems assigned to a group
    • Add and remove systems from groups

    Goals

    • Set up test environment locally with the MCP server and client + a recent MLM server [DONE]
    • Identify features and use cases offering a benefit with limited effort required for enablement [DONE]
    • Create a PR to the repo [DONE]

    Resources


    SUSE Edge Image Builder MCP by eminguez

    Description

    Based on my other hackweek project, SUSE Edge Image Builder's Json Schema I would like to build also a MCP to be able to generate EIB config files the AI way.

    Realistically I don't think I'll be able to have something consumable at the end of this hackweek but at least I would like to start exploring MCPs, the difference between an API and MCP, etc.

    Goals

    • Familiarize myself with MCPs
    • Unrealistic: Have an MCP that can generate an EIB config file

    Resources

    Result

    https://github.com/e-minguez/eib-mcp

    I've extensively used antigravity and its agent mode to code this. This heavily uses https://hackweek.opensuse.org/25/projects/suse-edge-image-builder-json-schema for the MCP to be built.

    I've ended up learning a lot of things about "prompting", json schemas in general, some golang, MCPs and AI in general :)

    Example:

    Generate an Edge Image Builder configuration for an ISO image based on slmicro-6.2.iso, targeting x86_64 architecture. The output name should be 'my-edge-image' and it should install to /dev/sda. It should deploy a 3 nodes kubernetes cluster with nodes names "node1", "node2" and "node3" as: * hostname: node1, IP: 1.1.1.1, role: initializer * hostname: node2, IP: 1.1.1.2, role: agent * hostname: node3, IP: 1.1.1.3, role: agent The kubernetes version should be k3s 1.33.4-k3s1 and it should deploy a cert-manager helm chart (the latest one available according to https://cert-manager.io/docs/installation/helm/). It should create a user called "suse" with password "suse" and set ntp to "foo.ntp.org". The VIP address for the API should be 1.2.3.4

    Generates:

    ``` apiVersion: "1.0" image: arch: x86_64 baseImage: slmicro-6.2.iso imageType: iso outputImageName: my-edge-image kubernetes: helm: charts: - name: cert-manager repositoryName: jetstack


    Try to use AI and MCP for ACPI table analysis by joeyli

    Description

    Try to use AI and MCP if they can help with ACPI table analysis.

    Goals

    It's not easy for looking at ACPI tables even it be disassemble to ASL. I want to learn AI and MCP in Hackweek 25 to see if they can help ACPI table analysis.

    Resources

    Any resources about AI and MCP.


    Multi-agent AI assistant for Linux troubleshooting by doreilly

    Description

    Explore multi-agent architecture as a way to avoid MCP context rot.

    Having one agent with many tools bloats the context with low-level details about tool descriptions, parameter schemas etc which hurts LLM performance. Instead have many specialised agents, each with just the tools it needs for its role. A top level supervisor agent takes the user prompt and delegates to appropriate sub-agents.

    Goals

    Create an AI assistant with some sub-agents that are specialists at troubleshooting Linux subsystems, e.g. systemd, selinux, firewalld etc. The agents can get information from the system by implementing their own tools with simple function calls, or use tools from MCP servers, e.g. a systemd-agent can use tools from systemd-mcp.

    Example prompts/responses:

    user$ the system seems slow
    assistant$ process foo with pid 12345 is using 1000% cpu ...
    
    user$ I can't connect to the apache webserver
    assistant$ the firewall is blocking http ... you can open the port with firewall-cmd --add-port ...
    

    Resources

    Language Python. The Python ADK is more mature than Golang.

    https://google.github.io/adk-docs/

    https://github.com/djoreilly/linux-helper


    Bugzilla goes AI - Phase 1 by nwalter

    Description

    This project, Bugzilla goes AI, aims to boost developer productivity by creating an autonomous AI bug agent during Hackweek. The primary goal is to reduce the time employees spend triaging bugs by integrating Ollama to summarize issues, recommend next steps, and push focused daily reports to a Web Interface.

    Goals

    To reduce employee time spent on Bugzilla by implementing an AI tool that triages and summarizes bug reports, providing actionable recommendations to the team via Web Interface.

    Project Charter

    Bugzilla goes AI Phase 1

    Description

    Project Achievements during Hackweek

    In this file you can read about what we achieved during Hackweek.

    Project Achievements