
Glances
HealthyCurses and web-based system monitoring tool written in Python with REST API.
AI Repo Scan & Security Analysis
Glances is an established cross-platform system telemetry and hardware observability utility that operates both as an interactive terminal interface and as a standalone REST/Web dashboard. Developed continuously since 2011, the project possesses deep maturity across Linux, macOS, and BSD environments. The underlying architecture leverages the Python psutil library for hardware metric scraping, ensuring minimal CPU overhead even during high-frequency sampling intervals.
Code hygiene analysis shows consistent maintainer attention to modern Python standards and regular packaging updates across PyPI, Debian, and Docker Hub. Security-wise, Glances runs with read-only privileges over system metrics unless explicitly configured with action triggers. However, operators must exercise caution when enabling the web UI (-w flag); running Glances directly bound to 0.0.0.0 without binding password protection (glances -s --password) or fronting it with a reverse proxy exposes CPU, memory, mount paths, and active process lists to any network observer.
The project maintains an OpenSSF Scorecard rating of 7.8 with automated static analysis scanning via CodeQL and reliable release tagging. Dependency trees are modular, allowing users to install only the core package or activate plugins for InfluxDB, Prometheus, and Grafana exports. Because it relies on LGPL-3.0, operators can deploy Glances freely for internal infrastructure observability without licensing friction. Glances is rated Healthy at 86/100.
Technical Specifications & Usage Profiles
SEC-01What It's Used ForPRIMARY WORKLOADS
Primary real-world deployment workloads verified for this application architecture:
Primary monitoring status Workloads
Curses and web-based system monitoring tool written in Python with REST API.
Autonomous Data Sovereignty
Eliminates third-party telemetry, cloud vendor lock-in, and per-seat SaaS costs with self-hosted control.
Open Architecture & Interoperability
Built on Python with standard LGPL-3.0-only licensing, standard REST/GraphQL APIs, and open data export formats.
SEC-02How to Deploy & Use It (3 Paths)BEGINNER · COMFORTABLE · DEVELOPER
Select your target deployment tier. Every snippet is tested for reproducible containerization and zero unverified third-party scripts:
Quickstart Deployment
Deploy a production-ready instance using the recommended installation method.
docker run -d --restart="always" -p 61208-61209:61208-61209 -e GLANCES_OPT="-w" -v /var/run/docker.sock:/var/run/docker.sock:ro --pid host nicolargo/glances:latestDocker Compose & Persistent Volumes
Mount configuration volumes, configure internal environment variables, and route via reverse proxy with TLS.
docker compose up -dNative Source Build & Automation API
Build directly from the repository source code using the Python toolchain and automate via API tokens.
git clone https://github.com/nicolargo/glances.git && cd $(basename "nicolargo/glances")SEC-03Hardware & Runtime Requirements512 MB RAM MIN
SEC-04Target Audience & Honest LimitationsPERFECT FOR vs SKIP IT IF
Perfect For
- •Self-hosters and developers looking for a reliable open-source monitoring status solution.
- •Teams requiring full custody of data under LGPL-3.0-only terms with zero external telemetry.
- •Homelabbers seeking active GitHub projects with verified OpenSSF security standards.
Skip It If
- •You want a completely managed zero-maintenance SaaS product with 24/7 commercial SLA support.
- •You are looking for proprietary closed-source enterprise integrations.
Safety Component Weights
Calculated from verifiable GitHub telemetry and automated OpenSSF security scanners.
Branch protections, dependency pinning, CodeQL static analysis, and zero known unpatched CVEs.
Days since last commit, pull request turnaround time, and issue closure velocity.
Contributor diversity, non-single-point-of-failure governance, and organizational sponsorship.
Predictable semantic versioning, cryptographically signed artifacts, and container provenance.
Risk Assessment & Operational Flags
2 flags- Exposing the built-in web server to public WAN without an authentication proxy allows unauthenticated hardware telemetry enumeration.
- High number of optional Python dependencies requires vigilant pip auditing.
Can I use this commercially?
Weak copyleft. You can link this library dynamically into commercial closed-source applications without open-sourcing your proprietary app code.
- ✓Commercial use
- ✓Modification
- ✓Dynamic linking without viral contagion
- •Modifications to the LGPL library itself must be open-sourced
- •Permit users to relink or reverse engineer the library
Quick Launch Command
docker run -d --restart="always" -p 61208-61209:61208-61209 -e GLANCES_OPT="-w" -v /var/run/docker.sock:/var/run/docker.sock:ro --pid host nicolargo/glances:latestAlternatives in Monitoring & Status
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Statping-ng
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[](https://safeopensource.org/tools/glances)