Code-based diagrams in Mermaid and PlantUML. Version control your architecture alongside your code. AI generates, you iterate.
Flowcharts, sequence, ER, class, state, Gantt - all in familiar Mermaid code syntax.
Component, deployment, activity, use case - full PlantUML support for detailed architectures.
Describe your system and AI generates diagram code. Edit the code to refine.
Export architecture as a scaffolded project with Docker, API routes, and migrations.
See diagram updates in real-time as you edit the code. Instant visual feedback.
Diagrams are text-based. Store them in git, diff changes, and track evolution.
A practical rollout path for engineering teams of any size.
Pick the service that causes the most confusion during onboarding or incident response. Generate its architecture diagram in Cybewave and add it to the repo. This becomes the proof of concept that shows the team the value.
Update your pull request template to include an 'architecture impact' section. For PRs that change service boundaries, data flows, or integrations, require an updated diagram. This embeds diagram maintenance into the existing workflow.
Create a top-level ARCHITECTURE.md in your monorepo that links to all service diagrams. As teams add diagrams for their services, the index grows into a navigable map of your entire system that new engineers use from day one.
Common workflows where visual documentation accelerates the whole team.
Instead of a week-long onboarding where senior engineers explain the system verbally, new hires read the architecture index. They see service boundaries, data flows, and deployment topology before writing their first line of code. Questions become specific ('how does auth propagate between these two services?') instead of vague ('how does the system work?').
When estimating a feature that touches multiple services, pull up the system diagram. Engineers can trace the exact components affected, identify which teams need to coordinate, and spot dependencies that would otherwise surface mid-sprint as blockers.
Every significant architectural change gets an ADR with before/after diagrams. Future teams can search the ADR history to understand why the system looks the way it does today — not just what it looks like, but the reasoning behind each evolution.
During incidents, the on-call engineer references the system topology to isolate the failure. During post-mortems, the team updates the diagram to reflect what they learned — adding monitoring points, documenting failure modes, or restructuring to prevent recurrence.
A PR that refactors service communication is nearly impossible to review from code diffs alone. When the PR includes an updated architecture diagram showing the old and new communication patterns, reviewers understand the intent and can evaluate whether the change is sound.
Visualize the current architecture alongside the desired target state. The gaps between the two diagrams become a concrete technical debt backlog. Leadership can see the complexity cost of current shortcuts and make informed decisions about when to invest in cleanup.
Engineering teams scale in two dimensions: people and systems. Adding engineers makes the team bigger; adding services makes the system more complex. Without diagrams, both dimensions create the same problem — knowledge silos. The person who designed the payment service is the only one who understands it. The team that built the notification pipeline is the only one who knows its failure modes.
Architecture diagrams break these silos by making system knowledge explicit and shareable. When diagrams live in the repository as Mermaid or PlantUML code, they become democratic — anyone can read them, anyone can update them, and changes are visible in pull requests. The architecture stops being tribal knowledge and becomes documented knowledge.
Teams that adopt diagram-as-code practices consistently report faster onboarding, fewer cross-team misunderstandings, and more productive design reviews. The investment is small — a few minutes per PR to update the relevant diagram — but the compound returns are significant as the team and system grow together.