Diagrams as Code

MinJae Kwon created Diagrams in 2020 to sketch cloud system architectures directly in Python without touching a drawing canvas. By compiling declarative code into Graphviz graphs decorated with official provider icons, the project made infrastructure blueprints auditable in version control. Engineering teams run it in CI pipelines to keep architectural diagrams synchronized with code.

Why Diagrams as Code instead of Lucidchart

Lucidchart relies on a hosted canvas where operators drag components, align connectors, and export static snapshots. That model creates friction for automated operations: documentation sits in an external silo, changes cannot be diffed in pull requests, and diagrams quietly drift as infrastructure evolves. Diagrams as Code treats architecture as software. Engineers write Python scripts alongside Terraform templates, commit them to git, and compile fresh diagrams during CI runs without manual drafting.

The tradeoff centers on interface flexibility versus automation. Lucidchart provides an interactive canvas with real-time multi-user editing, making it accessible to non-technical stakeholders mapping general business flows. Diagrams as Code eliminates per-seat fees and fits developer workflows, but requires familiarity with Python and an external Graphviz system binary. Teams trade pixel-level manual positioning for automated layout generation and guaranteed consistency across deployments.

What it does well

• Version control integration: Architecture scripts live in code repositories, allowing peer review in pull requests and historical diffing.
• Broad provider coverage: Ships with pre-built classes and icons for AWS, Azure, Google Cloud, Kubernetes, and on-premises environments.
• Automated pipeline rendering: Compiles headlessly into PNG, SVG, or PDF formats inside Docker containers and CI workflows.
• Programmatic generation: Uses standard Python loops to construct repetitive clusters like multi-region zones or worker pools.
• Directional flow modeling: Connects nodes and groups using Python shift operators to declare upstream and downstream relationships.
• Context grouping: Nests related services inside cluster blocks to depict virtual networks, subnets, and security perimeters.

Limitations

• No direct cluster-to-cluster edges: Connections cannot link cluster boundaries directly or point from a node to a cluster (GitHub issue #17), requiring workarounds with individual nodes.