Containerlab

Containerlab is a network-lab orchestrator for engineers who want repeatable topologies for configuration work, testing, or telemetry workflows. Its code-defined approach and broad network OS support make it a strong fit for self-hosted labs; the Linux, Docker, and privilege requirements narrow its audience.

Overview

Topology files declare nodes, links, and lab settings, giving teams a consistent way to define and reproduce network environments. The CLI handles deployment, destruction, saving, inspection, and graph operations. This is a practical advantage when labs need to be recreated or incorporated into CI pipelines such as GitLab CI and GitHub Actions, rather than assembled by hand for each run.

Containerlab can also be paired with telemetry stacks to exercise collection, transport, and visualization workflows. It is focused on building and managing labs, so it is not the right choice for someone seeking a general-purpose network management interface without a lab environment to run.

Key features

Broad network OS support: Containerlab supports containerized and VM-based nodes from Nokia, Cisco, Juniper, Arista, and other vendors and projects. The supported range includes Nokia SR Linux, SR OS, and SR-SIM; Cisco XRd, XRv, XRv9k, Nexus 9000v, and others; Juniper cRPD, vMX, vQFX, and related platforms; as well as SONiC, Cumulus VX, VyOS, FRR, VPP, BSD systems, OpenWRT, Linux containers, and generic VMs. That breadth helps teams build labs around varied network stacks, though the actual mix depends on the node platforms they can run.

VM and Kubernetes integrations: vrnetlab and boxen can launch VM-based routers, while Clabernetes can run Containerlab-style topologies on Kubernetes. These integrations extend the ways teams can host labs, but add infrastructure choices beyond the core Linux-and-Docker setup.

Optional interfaces and API: A VS Code extension, desktop app, and web app provide graphical options. The desktop and web clients connect to a reachable clab-api-server, which performs lifecycle, file, terminal, capture, image, and inspection operations on the Linux host. These interfaces can help users who prefer more than a CLI, but the server must be reachable and the host remains central to lab operations.

Privilege model: The installer can configure sudo-less operation with a SUID binary and the clab_admins group. Group members effectively receive root-level privileges, so convenience comes with a consequential access-control decision for administrators.

Pricing

Containerlab is open source under the BSD-3-Clause license and costs nothing. There is no paid tier to unlock core capabilities and no free-trial clock to manage. The trade-off is operational rather than financial: users need a Linux server or VM, Docker, and sudo privileges for some operations.

The single Containerlab plan fits engineers and teams willing to manage their own lab host. It does not suit readers looking for a hosted, turnkey service that avoids Linux and Docker administration.

Platforms

Containerlab's deployment model is self-hosted on Linux, with Docker required. Its graphical options include web and desktop apps, as well as a VS Code extension, but desktop and web access depends on a reachable clab-api-server on the Linux host. The listed platform range also includes API, macOS, and Windows; these options do not remove the documented Linux host requirement.

Who it's for

Containerlab is best for network engineers, infrastructure teams, and educators who need repeatable topologies using real vendor or open-source network OS nodes. It is particularly well suited to configuration and network testing workflows that benefit from declarative definitions, CI execution, or telemetry validation. Teams without Docker and Linux administration capacity, or those unwilling to grant the required privileges, should look elsewhere.

Pros and cons

• Pro: Reproducible topology definitions. Nodes, links, and settings live in declarative files, supporting consistent lab deployment and CI workflows.
• Pro: Wide range of network nodes. Support spans major vendors and open projects, making it useful for labs that cross platform boundaries.
• Pro: Flexible operating options. VM-router integrations, Kubernetes support, and optional graphical interfaces extend beyond a CLI-only workflow.
• Con: Host requirements are substantial. Linux and Docker are prerequisites, and some operations require sudo privileges.
• Con: Sudo-less setup carries elevated risk. The clab_admins group effectively grants root-level privileges through the SUID configuration.
• Con: GUI access still depends on the host. Desktop and web clients require a reachable API server rather than replacing the Linux deployment.

Alternatives

For a different network software need, compare network automation software or compare network simulation software.

• ManageEngine Network Configuration Manager may suit readers seeking a configuration-management product with a free edition capped at two devices and a paid Professional Edition at $238 a year.
• ONAP is another free, open-source platform for teams looking for a Linux, self-hosted software platform rather than Containerlab's lab orchestration workflow.
• Arista NG Firewall is an alternative to consider for firewall software.
• Nautobot offers a free, self-managed Community edition for exploring, initial workflows, and limited production deployments; it is worth considering when that scope is a closer fit.
• Netmiko is a free, open-source Python library for readers building device automation in Python rather than orchestrating network labs.
• OpenDaylight is a free, open-source SDN platform for readers whose priority is an SDN platform instead of a lab orchestrator.
• Cisco pyATS is a free Apache 2.0 option to consider when a testing framework is a closer match than lab deployment and lifecycle management.
• NAPALM is a free Python library for device and driver workflows, with support varying by device and driver.

Verdict

Choose Containerlab if you need self-hosted, repeatable network labs across a broad range of vendor and open-source nodes, especially when code-based definitions can feed testing or CI. Its strongest reason to choose it is that topology and lifecycle management sit together in one workflow. Look elsewhere if Linux, Docker, and the associated privilege responsibilities are more infrastructure than your team wants to own.