With 7,300+ GitHub stars and active releases through version 3.17, Woodpecker CI is the community-driven fork of Drone CI that kept the entire platform open source under Apache 2.0 — delivering a lightweight, container-native CI/CD engine that runs on minimal resources while providing enterprise-grade pipeline automation. Every pipeline step executes in an isolated Docker container, ensuring reproducible builds with zero host contamination and clean teardown after each run. Define workflows in simple YAML configuration files with step dependencies, conditional execution via when filters, service containers for databases and caches, and matrix builds that test across multiple language versions, platforms, or database engines simultaneously. Deep forge integration connects to GitHub, GitLab, Gitea, Forgejo, and Bitbucket via OAuth and webhooks, triggering pipelines on push, pull request, tag, deployment, and cron events. The plugin ecosystem inherits compatibility with the Drone plugin library — over 100 plugins for Docker builds, S3 uploads, SSH deployment, Slack and Telegram notifications, Helm deployments, and more. The server-agent architecture separates the web UI, API, and scheduler from pipeline execution, enabling horizontal scaling by adding agents across multiple machines or architectures including ARM. Secrets management supports global, organization, and repository-level scopes with approval workflows for forked repository pipelines. The admin web UI provides user management, agent monitoring, and queue control. Docker Compose deployment provisions the complete stack in under five minutes. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

When a commit lands, a timer fires, or a pull request opens, Concourse runs the matching jobs in fresh containers and draws the entire CI/CD pipeline as a live graph you can read at a glance. Pipelines are plain YAML files that tie resources such as git repositories, registries, and buckets to jobs made of get, put, and task steps, so the same file behaves the same on every installation. Resources are checked continuously for new versions, webhooks can request an immediate check, and a version can be pinned when something needs to stay put. The dashboard lists every pipeline by team with status colors, a filter box, favorites, and a high-density view, while build pages stream colored logs and let you rerun a build with the exact same inputs. The fly command line sets pipelines, runs one-off tasks against local files from your laptop, and opens a shell inside the container of a failed step. A set_pipeline step lets pipelines configure other pipelines, and instance vars create a family of related pipelines, such as one per pull request. Teams keep projects separate and can be tied to a GitHub organization, credentials arrive at run time from Vault, CredHub, AWS, or Kubernetes, and policy rules can block disallowed images. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

With 16,100 GitHub stars and created by Solomon Hykes (co-founder of Docker), Dagger eliminates proprietary YAML DSLs from CI/CD by letting developers write pipelines as real programs in their preferred language — then executing them identically on a laptop, in GitHub Actions, in GitLab CI, or on any machine with a container runtime. The BuildKit-based Dagger Engine runs every pipeline operation inside OCI containers, constructing a directed acyclic graph where each node is cached by default, parallelized automatically, and produces bit-for-bit reproducible outputs regardless of execution environment. Native SDKs generated from the GraphQL API schema provide Go, Python, TypeScript, PHP, Java, .NET, Elixir, and Rust developers with full type safety, IDE autocomplete, and the ability to unit-test CI pipelines using the same testing frameworks as application code. The module ecosystem enables cross-language composition where a Python team can invoke a Go team's build functions without learning Go, while filesystems, secrets, git repositories, and network tunnels are passed between functions as strongly-typed objects. Pipeline operations cache at container-layer granularity with content-addressed deduplication, and the interactive REPL enables step-by-step pipeline debugging with live container inspection. Host dependencies are explicit and strictly typed, eliminating implicit environment coupling that causes "works on my machine" failures. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

GoCD treats delivery as a pipeline you can see: every commit enters a sequence of stages, passes or fails in the open, and travels from first build to production deployment along one visible, traceable path. Stages split work into jobs that run in parallel on a fleet of agents, so unit tests, integration suites, packaging, and security scans finish together instead of queueing behind each other. Pipelines connect with fan-in and fan-out dependencies, meaning a release pipeline waits for every upstream component to pass before it assembles them, and one library build can feed a dozen services. The value stream map draws that whole web on one screen, from the triggering commit through every stage to deployment, and any run can be compared with another to spot exactly what changed. Materials watch Git, Subversion, or Mercurial repositories and start pipelines on new commits, while artifacts produced by one stage are fetched, versioned, and promoted by the stages after it. Environments group pipelines and agents so test and production stay isolated, resources tag agents by capability, and elastic agents appear on Docker, Kubernetes, or cloud hosts only while work exists. Pipeline definitions can live in YAML or JSON inside a repository, changing through code review like the software they deliver, and templates keep repeated release patterns consistent across teams. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

Used by over 100,000 organizations and ranked first in the Gartner Magic Quadrant for DevOps Platforms, GitLab is the open-core DevSecOps platform that delivers the complete software development lifecycle — from planning and source code management through CI/CD, security scanning, and monitoring — in a single self-managed application. The built-in CI/CD engine executes pipelines defined in YAML with parallel jobs, directed acyclic graph scheduling, multi-project pipelines, and auto-scaling runners on Docker, Kubernetes, or bare metal. Merge requests provide inline code review with approval rules, code owners, merge trains for serialized merging, and five merge strategies including fast-forward and semi-linear history. The integrated container registry stores Docker images alongside code, while the package registry supports npm, Maven, NuGet, PyPI, Conan, Go, and generic packages. Issue boards with epics, milestones, and labels enable agile planning, while the built-in wiki and GitLab Pages provide documentation hosting and static site publishing. Security scanning in the Community Edition includes basic SAST, secret detection, and container scanning running as pipeline jobs. The Omnibus installer bundles Rails, Puma, Gitaly, Workhorse, Nginx, PostgreSQL, Redis, Sidekiq, and Prometheus into a single package installable in minutes, while Docker and Helm chart options support containerized and Kubernetes deployments. GitLab Duo AI assists with code suggestions, merge request summaries, and vulnerability resolution. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

With 37,400+ GitHub stars and active daily development by the Harness engineering team, Gitness is the open-source developer platform that ships Git hosting, container-native CI/CD pipelines, a Docker-compatible artifact registry, and cloud developer environments in a single Go binary consuming approximately 100 MB of RAM — compared to GitLab's 4+ GB minimum footprint. Born as the next generation of Drone CI, Gitness inherits Drone's battle-tested pipeline engine while adding complete source code management with pull requests featuring threaded code review comments, suggested changes, required reviewer approvals, and merge strategies including squash and rebase. Branch protection rules enforce mandatory pull requests, minimum peer reviews, passing status checks, and force-push blocking. Pipelines use Drone-compatible YAML syntax with stages, steps, and service containers, triggered automatically on code changes with results displayed directly in pull request checks. The built-in container registry eliminates the need for separate Harbor or Docker Registry deployments, supporting Docker image push and pull plus Helm chart storage. Gitspaces provide on-demand remote development environments configured via industry-standard devcontainer.json files. Encrypted secrets management with Gitleaks-powered secret scanning blocks commits containing hardcoded credentials. One-click migration imports existing GitHub Organizations and GitLab Groups, automatically converting GitHub Actions and GitLab CI pipelines to Gitness YAML. The REST API with OpenAPI specification enables programmatic management of all resources. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

With over 15,100 GitHub stars and seven years of battle-tested production use, OneDev replaces entire DevOps toolchains — GitLab, Jenkins, Jira, Artifactory — with a single Java application that runs comfortably on a 1-core 2GB server for medium-sized projects. The platform unifies Git hosting with ANTLR-powered language-aware code search and symbol navigation, pull request workflows with configurable branch protection rules and required reviewers, and customizable issue tracking with scripted state transitions that automatically advance issues when code is committed, builds pass, or deployments complete. The GUI-based CI/CD editor creates pipelines without writing YAML, supporting typed parameters, matrix builds, job templates, cache management, and artifact publishing in a single visual interface. Jobs execute via Docker containers, bare metal shell, Kubernetes pods, or distributed agent pools, with web terminal access for live debugging and the ability to pause running jobs for inspection. Built-in package registries handle Docker images, npm packages, Maven artifacts, NuGet libraries, PyPI wheels, and RubyGems without external infrastructure. AI users can be assigned to issues and pull requests to autonomously implement features, review code, fix CI/CD failures, and resolve merge conflicts. The automated Kanban board supports rule-based card movement, time tracking, timesheets, and service desk ticket creation via email. Cluster deployment enables project replication across servers for high availability and horizontal distribution for scalability. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Pipedash unifies pipelines from twelve CI/CD providers into one self-hosted dashboard for watching statuses, reading job logs, and triggering, rerunning, or cancelling runs without opening each provider's site. Connect GitHub Actions, Gitea Actions, GitLab CI, Azure DevOps, Bitbucket, Buildkite, Jenkins, Tekton, ArgoCD, FluxCD, Komodo, or Prow, and every workflow lands in one list grouped by repository. Filters narrow by status, provider, organization, or time range, and saved views with rules, sub views, and pins give each team its own shortlist. Run history records each attempt with its commit, branch, actor, and duration, and opening a run reveals its jobs and a searchable log viewer that streams live output. Trigger forms load the workflow's own inputs, pipeline variables, or job parameters from the provider, so parameterized runs start with real values. Reruns reuse the original inputs, GitHub runs can restart only failed jobs, and stuck builds can be cancelled singly, in bulk, or by repository. Metrics charts track duration, success rate, and run counts per pipeline by hour, day, week, or month with P95 and P99 values. A notifications inbox gathers failures, degraded workloads, long running builds, and provider outages, while health panels place status pages beside the pipelines they describe. Every dashboard action is a REST API route under /api/v1 with bearer authentication, provider tokens stay encrypted at rest, and the server sends no telemetry. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. GPL-3.0 licensed.

Zoomies converts servers you already own into an ephemeral GitHub Actions runner fleet, creating a clean runner for every queued job and destroying it the moment that job ends. Why rent CI minutes by the second while those machines sit idle? Point it at a GitHub organisation or a repository on a personal account and it watches for queued work through workflow_job webhooks, with polling as a fallback, then provisions capacity only on hosts matching the labels your workflow requested. One controller drives any number of agents across Ubuntu, Debian, Fedora, and Rocky Linux on amd64 and arm64, and agents connect outbound only, so a host behind NAT needs no inbound firewall rule. Registration needs no pasted tokens: Zoomies signs in as a GitHub App and mints single-use just-in-time runner registrations itself. Busy runners can borrow idle CPU and memory from quieter neighbours under rules you set. The live web UI shows pools, jobs, queue waits, and streaming logs in place, with a command palette for jumping anywhere and an audit row recorded for every change. Nightly backups seal themselves with your passphrase and ship to S3-compatible storage, restores are staged from the same tab, and you can ask the built-in assistant about your builds or connect your own tools through MCP. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

Stop hoping your AI coding assistant remembers to plan before it codes, test after it implements, and review before it ships. Archon wraps Claude Code, OpenAI Codex, and other AI agents inside structured YAML workflows that enforce the same development process every single time. Define your pipeline as a directed acyclic graph of nodes (AI tasks, shell scripts, approval gates, loops) and Archon handles the orchestration: resolving dependencies, running independent nodes in parallel, passing artifacts between steps, and isolating every run in its own git worktree so five bug fixes can proceed simultaneously without conflicts. Ship with 17 pre-built workflows covering everything from "idea to merged PR" to automated conflict resolution, or author your own by committing YAML files to your repository's .archon/workflows/ directory. The web dashboard, launched with archon serve, provides a conversation interface with real-time streaming, a visual drag-and-drop workflow builder for creating DAG pipelines, step-by-step progress monitoring for every run, and a unified sidebar aggregating conversations from CLI, Slack, Telegram, and GitHub into one view. An NLP router parses natural language requests and automatically selects the right workflow. Structured JSON output schemas let you enforce typed responses from AI nodes, with validation and auto-repair for providers that lack native schema support. Every workflow file is version-controlled, portable, and reviewable in pull requests, so your entire team runs identical processes from day one. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

SuperPlane is an open-source engineering automation platform that turns routine backlog tickets into verified, review-ready pull requests while orchestrating complex multi-system workflows across your developer tools. Engineers use the visual infinite canvas to assemble event-driven pipelines with trigger nodes, conditional routing, loops, and human-in-the-loop approval gates. You can delegate pull request code reviews, automated QA testing, documentation generation, and release management to coding agents operating within strictly defined policy boundaries. Custom operational consoles let teams build live monitoring dashboards displaying key delivery metrics, active incident states, and execution histories from connected tools. Integrations connect natively with GitHub, GitLab, Datadog, PagerDuty, Slack, AWS, and leading AI models through webhook events and bi-directional actions. Every automation run maintains durable state persistence across process restarts, ensuring failed steps resume with full input payloads and debugging context intact. Developers can define reusable work orders, configure canvas memory for long-term state retention, and execute fleet commands over SSH or containerized runners. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

Komodo puts every server, container, and deployment pipeline behind a single dashboard where you build, ship, and monitor Docker workloads across unlimited hosts. Lightweight stateless agents install on each connected server and report CPU, memory, and disk metrics back to the Rust-powered core, giving you real-time visibility without heavyweight monitoring stacks. Docker Compose stacks deploy directly from the UI or link to Git repositories with webhook-triggered automatic redeploys on push. A built-in CI pipeline compiles versioned Docker images from source, with optional AWS spot instances for burst build capacity. For orchestration at scale, Docker Swarm management handles node configuration, service deployment, and multi-node stack orchestration from the same control plane. Browser terminal sessions open persistent named shells on servers and inside containers, complete with shared team access and scriptable Actions that chain executions into multi-stage procedures. Infrastructure-as-code support defines all resources declaratively in TOML files within a Git repository, keeping production state version-controlled and auditable. Granular role-based access control with user groups, per-resource permissions, and OAuth through GitHub and Google keeps teams operating within defined boundaries. A full OpenAPI specification, dedicated CLI, and typesafe client libraries for Rust and TypeScript make programmatic integration straightforward. With 12,000+ stars and active development, the community continues expanding multi-architecture builds and Swarm tooling.