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nicolargo/glances

Glances an Eye on your system. A top/htop alternative for GNU/Linux, BSD, macOS and Windows operating systems.

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nicolargo/glances has 33k+ stars and 1.5k+ forks on GitHub, is written in Python, and was last pushed on Oct 17, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 43 minutes ago
Type:CLI ToolCategory(s):Observability & MonitoringCLI UtilitiesDevOps & Observability
Added to GitGenius on May 16th, 2023
Created on December 4th, 2011
Open Issues & Pull Requests: 105 (+0)
GitHub issues: Enabled
Number of forks: 1,830
Total Stargazers: 33,800 (+0)
Total Subscribers: 496 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 2.9 days
Mean response time: 469.2 days
90th percentile: 1974.0 days
Tracked items: 625

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Maintainer activity

11 people did triage or write work on this repository in the last 12 months.

Counts unlabeled, assigned, unassigned, milestoned, demilestoned, locked, unlocked over the last 12 months. These are issue and pull request events that require triage or write permission. Commits and code review are not counted. labeled and renamed are excluded because GitHub issue forms record the issue author as the actor. Figures from October 18, 2026. This count is not comparable across projects: each project's automation decides which of these events a person emits. How this is counted.

How this project is maintained

About 9% of issues opened in the past year have never received a reply. 51% of open issues come from outside the core team, a mix of external reports and the maintainers' own roadmap. Work labelled "regression" is answered fastest, typically in about 4 hours, while "needs more info" waits about 19 months. 84% of issues opened in the past year have been closed, leaving a working backlog. Three people close 93% of everything that gets resolved.

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Issue Activity (beta)

Open issues: 96
New in 7 days: 5
Closed in 7 days: 1
Avg open age: 998 days
Stale 30+ days: 82
Stale 90+ days: 68

Recent activity

Opened in 7 days: 4
Closed in 7 days: 1
Comments in 7 days: 7
Events in 7 days: 25

Top labels

  • bug (487)
  • enhancement (362)
  • needs discussion (160)
  • new feature (154)
  • needs test (140)
  • inactive (115)
  • needs more info (101)
  • needs contributor (94)

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Detailed Description

Glances is a cross-platform system monitoring tool written in Python that serves as a modern alternative to traditional utilities like top and htop. It provides real-time monitoring of CPU, memory, disk, and network usage across GNU/Linux, BSD, macOS, and Windows operating systems. The tool displays system information in an easy-to-read dashboard format and extends beyond basic resource monitoring to include process tracking, logged-in users, temperature readings, voltages, and fan speeds. It also supports container monitoring for Docker and LXC systems.

The project shares contributors with major repositories including microsoft/vscode, microsoft/typescript, and rust-lang/rust.

Glances operates in multiple modes to suit different use cases. The standalone terminal mode provides immediate system monitoring by running a single command. The web server mode exposes a responsive web interface accessible at a configurable port and includes a RESTful API for programmatic access. The tool supports client-server architecture using XML-RPC for remote monitoring via terminal interfaces. A browser mode allows discovery and monitoring of multiple Glances servers across a network. Additionally, Glances can output raw statistics to stdout in plain text, CSV, or JSON formats, enabling integration with other tools and scripts.

The tool includes a fetch mode for quick system snapshots and supports an MCP server for integration with AI assistants like Claude and Cursor, available in version 4.5.1 and higher. Glances can be used as a Python library by importing the glances.api module, allowing developers to programmatically access system metrics through a GlancesAPI class that provides access to all plugins and their fields.

Export capabilities are extensive, supporting multiple destinations including CSV and JSON files, time-series databases like InfluxDB, ElasticSearch, PostgreSQL, Cassandra, ClickHouse, and Prometheus, message brokers including RabbitMQ, NATS, ZeroMQ, and Kafka, and other services like StatsD, Graphite, and RESTful endpoints.

Installation is straightforward through PyPI using pip, with optional feature bundles available for web interface, GPU monitoring, container support, sensors, RAID, SNMP, and other specialized functionality. The project also supports installation via uvx for immediate execution without manual setup. The codebase is built on an open architecture allowing developers to create custom plugins and export modules, making Glances highly extensible for specific monitoring needs.