# Container maintenance tools

> AI-ranked search results for `container maintenance utilities` on awesome-repositories.com — ordered by an LLM for relevance, best match first. 119 total matches; showing the top 6.

Explore on the web: https://awesome-repositories.com/q/container-maintenance-utilities

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## Results

- [moncho/dry](https://awesome-repositories.com/repository/moncho-dry.md) (3,225 ⭐) — Dry is a container lifecycle manager and cluster orchestrator designed to control groups of containers, services, and stacks across multiple nodes. It functions as a resource manager for inspecting and cleaning up images, persistent volumes, and network configurations, while providing a terminal-based dashboard for monitoring system events and resource usage.

The project enables the coordination of distributed workloads through service-stack grouping and cluster orchestration. It provides a centralized control plane to synchronize the deployment and scaling of complex application architecture
- [spotify/docker-gc](https://awesome-repositories.com/repository/spotify-docker-gc.md) (5,019 ⭐) — docker-gc is a garbage collection tool for Docker hosts designed to reclaim disk space by automatically removing exited containers, unused images, and dangling volumes. It functions as a resource cleaner that optimizes host storage through the lifecycle management of orphaned or inactive container resources.

The utility manages image retention by preserving a minimum number of the most recent images per repository while deleting older versions. It employs configurable grace periods to determine when images and containers become eligible for deletion and supports pattern-based exclusion to pre
- [wagoodman/dive](https://awesome-repositories.com/repository/wagoodman-dive.md) (54,242 ⭐) — Dive is a command-line tool designed for the analysis and optimization of container images. It functions as a layered storage inspector, allowing users to decompose image manifests to examine individual filesystem layers and identify opportunities to reduce total image size.

The tool features a filesystem diffing engine that calculates net changes between sequential layers to highlight redundant data and storage inefficiencies. Users interact with this data through a terminal-based dashboard that provides keyboard-driven navigation of complex file structures and layer metadata. By abstracting
- [jesseduffield/lazydocker](https://awesome-repositories.com/repository/jesseduffield-lazydocker.md) (51,376 ⭐) — Lazydocker is a terminal-based command-line utility that provides an interactive dashboard for monitoring and controlling containerized environments. It functions as a text-based user interface, allowing users to manage containers, images, and volumes directly within a terminal emulator through keyboard-driven navigation.

The tool distinguishes itself by replacing manual command-line sequences with a unified workspace that communicates directly with the Docker daemon via the local Unix domain socket. It maintains state synchronization by listening to real-time container events and utilizes co
- [chadoe/docker-cleanup-volumes](https://awesome-repositories.com/repository/chadoe-docker-cleanup-volumes.md) (1,437 ⭐) — Docker cleanup volumes is a containerized system maintenance tool and shell script designed to scan host systems and delete unused Docker storage volumes. It reclaims disk space by identifying leftover data assets that are no longer attached to any active containers. 

The utility inspects container-graph dependencies to distinguish between active and orphaned storage items. It supports dry-run preview execution to simulate cleanup operations and review target volumes before any actual data deletion occurs. The software can be executed directly as a command-line shell script or run as a contai
- [slimtoolkit/slim](https://awesome-repositories.com/repository/slimtoolkit-slim.md) (22,977 ⭐) — Slim is a comprehensive suite for container lifecycle management, providing tools for image inspection, optimization, security hardening, and service troubleshooting. It functions as a platform for analyzing containerized applications through both static metadata review and dynamic behavioral probing, enabling users to understand image composition and runtime dependencies.

The project distinguishes itself by automating the creation of minimal, production-ready container images. It achieves this by removing unnecessary files and components, flattening image layers, and synthesizing restrictive