---
title: "SikamikanikoBG/homelab-monitor | Repository | AtlasRepo"
description: "Catalog record for SikamikanikoBG/homelab-monitor. Inspect the source, license, requirements and unknowns before choosing."
image: "https://atlasrepo.com/og/atlasrepo.png?v=folded-map-20261108"
---

[AtlasRepo](/catalog)

# SikamikanikoBG/homelab-monitor

## Evidence and unknowns

**Evidence scope:** Catalog metadata only. Documentation review, source inspection and execution are not established by this record.

**Verification date:** Not provided

**Verification freshness and method:** No dated verification supplied.

**Catalog record date:** 2026-06-22

**Repository update date:** 2026-09-05

**Freshness:** Record older than 30 days. Recheck upstream.

**License identifier:** Not provided

**Requirements reported by catalog:** Looks straightforward to evaluate: it exposes implementation signals such as API/SDK/CLI, language metadata, or focused topics.

**Limitations reported by catalog:** No major metadata risk found, but license, security posture, and active maintainers still need final review.

[Inspect the primary source](https://github.com/SikamikanikoBG/homelab-monitor)

License source not supplied. Check the license and terms upstream.

Catalog descriptions and adoption notes are unverified reports. They do not establish a source review or a successful run. Confirm the license and requirements upstream.

## Description and use

**Summary**
SikamikanikoBG/homelab‑monitor is a single‑container, plug‑and‑play dashboard that aggregates GPU/VRAM usage, local‑AI stats, Docker, systemd, and overall host health, and also runs a read‑only Model Context Protocol (MCP) server so AI agents can query the same data. With 132 stars, recent commits (as of 2026‑06‑22) and a Python codebase, it offers a low‑friction way to expose a homelab’s telemetry to both humans and autonomous assistants.

**Value**
The project bridges the gap between AI assistants and real‑world infrastructure by providing a standard MCP endpoint that any MCP‑compatible agent can consume, turning raw system metrics into a structured, queryable knowledge source. This enables use‑cases such as “let an LLM diagnose a container failure,” “automatically scale GPU workloads,” or “ship a self‑hosting MCP server alongside a model for seamless tool use.”

**Practical adoption path**

- **Deploy** – Pull the Docker image and run the container on the target host; the dashboard and MCP server start automatically.
- **Configure** – Add optional environment variables to point to Docker, systemd, or GPU drivers if they are not auto‑detected.
- **Integrate** – Point existing AI agents (e.g., LangChain, AutoGPT, or custom MCP clients) at the exposed `/mcp` endpoint; the agents can now retrieve health metrics, GPU/VRAM stats, and container lists via the standard protocol.
- **Extend** – Use the provided Python SDK/CLI to add custom probes or expose additional host data without modifying the core container.

**Production readiness**

- **Activity & community**: recent commits, 132 ⭐, 22 forks, and a growing set of topics indicate an active project.
- **Stability**: the container is self‑contained, requires no external services, and the MCP server runs read‑only, reducing attack surface.
- **Risk**: licensing and long‑term maintainer commitment still need a final check, but no major security or metadata concerns have been identified. Overall, the project is mature enough for a serious pilot in a production homelab or edge‑AI environment.

## Use in a workflow

SikamikanikoBG/homelab-monitor helps connect AI assistants to real tools and data through a standard protocol.

- connect AI agents to tools
- ship Model Context Protocol servers
- standardize integrations

## Limitations and review notes

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.

- Source: github
- Language: Python

[Original source](https://github.com/SikamikanikoBG/homelab-monitor)

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