by BV-Venky
Turn your architecture into a living, queryable knowledge graph - and render it as beautiful auto-laid-out Excalidraw diagrams. An MCP server for Cursor, Claude Code & Windsurf. Offline, no API keys.
# Add to your Claude Code skills
git clone https://github.com/BV-Venky/excalidraw-architect-mcpGuides for using mcp servers skills like excalidraw-architect-mcp.
Last scanned: 6/9/2026
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}excalidraw-architect-mcp is an open-source mcp servers skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by BV-Venky. Turn your architecture into a living, queryable knowledge graph - and render it as beautiful auto-laid-out Excalidraw diagrams. An MCP server for Cursor, Claude Code & Windsurf. Offline, no API keys. It has 122 GitHub stars.
Yes. excalidraw-architect-mcp passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/BV-Venky/excalidraw-architect-mcp" and add it to your Claude Code skills directory (see the Installation section above).
excalidraw-architect-mcp is primarily written in Python. It is open-source under BV-Venky on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other MCP Servers skills you can browse and compare side by side. Open the MCP Servers category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh excalidraw-architect-mcp against similar tools.
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It's been a constant struggle trying to understand unfamiliar and complex codebases - managing cognitive overload and trying to imagine how everything fits together.
When you're onboarding onto a codebase, designing a new system, or documenting existing architecture, a visual diagram communicates in seconds what pages of text can't. But the options today aren't great. Mermaid diagrams are quick to generate but have limited capabilities - you can't drag a node to reposition it, group components visually. Excalidraw solves these problems, but when LLMs try to generate Excalidraw directly, they hallucinate coordinates - boxes overlap, arrows tangle, and you end up fixing the diagram manually.
excalidraw-architect-mcp separates the what from the where - the AI focuses on structure, the engine handles the pixel math.
Your LLM describes the components and connections, and the MCP handles layout, styling, and rendering using a proper graph layout algorithm. 50+ technologies (Kafka, PostgreSQL, Redis, etc.) get auto-styled, you can iteratively edit diagrams with natural language ("add a cache in front of the DB"), and it runs fully offline in Cursor/Windsurf - no API keys needed.
.excalidraw file to a portable image, no browser neededEvery frame below is generated entirely by AI using this MCP - zero manual positioning.


New in v1.0. Your diagrams were always backed by a graph. Now that graph is yours — a persistent, version-controlled model of your system that the AI builds once and reuses everywhere.
The diagram above was rendered from a knowledge graph — a single
.claude/architecture.mdfile. NoticeOrder Service → Payment Serviceappears twice: a solidREST /chargecall and a dashedKafka payment.requestedevent. Two communication modes, two arrows.
A one-off diagram goes stale the moment you close it. A knowledge graph is a living model:
The graph is a single markdown file (default .claude/architecture.md):
## Services
- order-service: Order Service [type: service] [domain: orders] [owner: @orders]
- payment-service: Payment Service [type: service] [domain: payments] [owner: @payments]
## Dependencies
- order-service -> payment-service : "REST /charge"
- order-service -> payment-service : "Kafka payment.requested" [style: dashed]
Edit it by hand or let the AI maintain it — it round-trips losslessly either way.
"Map this codebase into the architecture knowledge graph"
"Link the order service to payments over Kafka"
"What depends on the payment service? Render just its neighborhood"
"Render the orders domain as a focused diagram"
"Lint the architecture for cycles and single points of failure"
"Import my existing diagram.excalidraw into the knowledge graph"
"Generate an onboarding guide from the architecture"
See the Knowledge Graph tools for the full tool list.
.excalidraw file into your repo and update it with natural language as the system evolves. No more stale diagrams from six sprints ago.pip install excalidraw-architect-mcp
For PNG export support (SVG works out of the box):
pip install excalidraw-architect-mcp[png]
Or run without installing (requires uv):
uvx excalidraw-architect-mcp
Cursor - Add to .cursor/mcp.json:
{
"mcpServers": {
"excalidraw-architect": {
"command": "excalidraw-architect-mcp",
"transport": "stdio"
}
}
}
Claude Code - Run this one-liner:
claude mcp add-json excalidraw-architect '{"type":"stdio","command":"excalidraw-architect-mcp"}' --scope user
Or add manually to .mcp.json in your project root:
{
"mcpServers": {
"excalidraw-architect": {
"type": "stdio",
"command": "excalidraw-architect-mcp"
}
}
}
Windsurf / Other IDEs - Same pattern; point to the excalidraw-architect-mcp command over stdio.
This repo includes a Diagram Design Skill that teaches the AI how to structure diagrams for the best results - node count limits, topology rules, edge label guidelines, and common patterns.
For Cursor users:
mkdir -p ~/.cursor/skills/excalidraw-diagram-design && \
curl -o ~/.cursor/skills/excalidraw-diagram-design/SKILL.md \
https://raw.githubusercontent.com/BV-Venky/excalidraw-architect-mcp/main/.skills/excalidraw-diagram-design/SKILL.md
For other IDEs: Download the SKILL.md file and add it to your IDE's prompt context or system instructions.
The AI will automatically pick up the skill and apply it when generating diagrams. Feel free to modify the rules to suit your preferences - tweak node limits, add your own patterns, or adjust styling guidelines.
For the architecture knowledge graph, this repo also includes an Architecture Knowledge Graph Skill. It teaches the AI how to read a codebase well — identify service boundaries, map communication signals (HTTP / gRPC / Kafka / DB) to the right labelled links, match producers and consumers across repos, and keep the graph clean (stable ids, every edge labelled, lint before render).
For Cursor users:
mkdir -p ~/.cursor/skills/architecture-knowledge-graph && \
curl -o ~/.cursor/skills/architecture-knowledge-graph/SKILL.md \
https://raw.githubusercontent.com/BV-Venky/excalidraw-architect-mcp/main/.skills/architecture-knowledge-graph/SKILL.md
For other IDEs: Download the SKILL.md file and add it to your IDE's prompt context or system instructions.
A note on diagram complexity: As the number of components and connections grows, diagrams inevitably become harder to read - this is true for humans drawing by hand too, not just automated layout. For best results, aim for 6-15 nodes in architecture diagrams and 10-25 nodes in detailed flows. If your system is larger, split it into multiple focused diagrams rather than cramming everything into one.
Just ask your AI IDE naturally:
"Create a high-level architecture diagram of this codebase"
"Create an architecture diagram for a microservices system with an API Gateway, Auth Service, User Service, Order Service, PostgreSQL, Redis cache, and Kafka event bus"
"Convert this mermaid diagram to excalidraw diagram"
"Add a Caching layer to the Order Service in the High Level architecture diagram"
"Export the architecture diagram to SVG"
"Export the diagram as a PNG at 3x resolution"
The AI calls the MCP tool with the relationship map. The MCP handles layout, styling, and output. Open the resulting .excalidraw file with the [Excalidraw VS Code extension](https://marketplace.visualstudio.com/items?itemName=pomdtr.excalidraw-e