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OpenDiagram is my attempt to make AI-generated diagrams more useful by separating what the AI decides from how the diagram is laid out. Instead of asking a model to emit Mermaid text or an image, the project turns a typed description of system components and relationships into editable Excalidraw elements.
I built it after running into a familiar problem: Mermaid output can fail to parse, and diagrams that do render can become crowded and difficult to edit. OpenDiagram’s approach addresses those problems in a different way, but it does not remove the need to check a generated diagram for missing or incorrect details.
Why I built OpenDiagram
AI can produce a quick first draft of a system diagram, but the route from prompt to usable canvas is not always smooth. In one example, I gave ChatGPT and Gemini the same URL-shortener prompt and received five Mermaid outputs; two did not parse. The reported cause was an edge label containing {shortCode}. That is one small, self-reported example from Rupam Golui, not a benchmark or a general failure rate.
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Even valid diagram code can leave a second problem: the resulting diagram may be crowded, with awkward connections and labels, and editing the source can be less convenient than changing the diagram itself. OpenDiagram is designed to produce an editable canvas rather than stop at diagram text or a rendered image.
#1 Best Overall
How OpenDiagram is described to work
The project’s central idea is to separate semantic decisions from visual layout. The AI describes what belongs in the system; deterministic code handles placement and rendering. As Golui puts it, “The AI decides what’s in the system. It never decides where anything goes.”
- Generate a semantic specification. The model identifies entities, components, and relationships in a typed structure rather than writing Mermaid syntax.
- Arrange the diagram. The project names ELK as its layout engine. A custom edge router handles connections between elements.
- Render editable elements. A themed renderer creates native Excalidraw elements. The project description says, “The model never outputs Mermaid, and it never outputs pixels.”
This division is intended to make diagram construction more controllable: the model contributes system meaning, while software determines where shapes and connections appear. It does not guarantee that every requested component is included or that every technical relationship is correct.
What kinds of diagrams can it make?
OpenDiagram describes support for several software and systems diagram categories:
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- Software architecture and cloud architecture
- Sequence diagrams, including an OAuth authorization-code example
- Entity-relationship diagrams, including an e-commerce example
- Flowcharts
- Network and infrastructure diagrams, also listed by the project repository
The author’s examples also include a URL shortener, an AWS image-processing pipeline, and a larger commerce architecture prompt. These are examples presented by the project author, not independently reproduced or evaluated results.
Can you edit the diagram after the AI draws it?
OpenDiagram describes its output as native Excalidraw canvas elements. That means the intended workflow is to move shapes, restyle them, add notes, and request revisions, rather than treat the result as a fixed picture. The repository also describes a persistent project workspace.
That editability is useful for turning a generated draft into a diagram that fits your team’s conventions. It does not make the diagram authoritative: review the components and connections against the system you mean to document.
Rank #2
Why generated diagrams still need review
In the author’s example involving a large commerce architecture prompt—described as having around 30 components—the generated diagram omitted disaster recovery, cross-region failover, and circuit breakers. The omission illustrates a practical limitation: a diagram can look readable while leaving out important requirements.
Before sharing or relying on a generated diagram, check it against the prompt and the underlying design. In particular, verify that:
- Every required component and external service appears.
- Connections have the correct direction and meaning.
- Important operational concerns, such as failover or recovery, are represented when relevant.
- Labels, boundaries, and groupings reflect the architecture rather than merely making the canvas look tidy.
Is OpenDiagram open source, and do you need an API key?
The article and project repository describe OpenDiagram as open source under the AGPL-3.0 license and self-hostable. The repository lists Bun 1.3 or later and PostgreSQL as self-hosting prerequisites. The article also describes hosted access as free during beta and says guest mode is available; hosted access and terms may change, so check the project’s current site and repository before relying on those details.
The project FAQ says users can optionally supply their own keys for OpenAI, Anthropic, Google, or OpenRouter. Provider support and configuration details can change; consult the project’s current documentation for the available options and setup instructions. The article and repository do not establish a current hosted price or provide enough detail here to give deployment steps.
Who might find this approach useful?
OpenDiagram may suit someone who wants an AI-assisted starting point but expects to edit the result on an Excalidraw canvas, or a self-hoster who wants to inspect and run an open-source project. Its described diagram types span architecture, cloud, sequence, ER, flowchart, network, and infrastructure work.
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