SikamikanikoBG/homelab-monitor — Plug-and-play homelab dashboard in one container — GPU, local-AI VRAM, Docker, systemd, host health.

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**

1. **Deploy** – Pull the Docker image and run the container on the target host; the dashboard and MCP server start automatically.
2. **Configure** – Add optional environment variables to point to Docker, systemd, or GPU drivers if they are not auto‑detected.
3. **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.
4. **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.

Problem

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

Outcome

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

Workflow

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

Evidence

Plug-and-play homelab dashboard in one container — GPU, local-AI VRAM, Docker, systemd, host health. Built-in read-only MCP server so AI agents can explore it too.

Source