fireworks-tech-graph landed 1,530 stars this week and I can see exactly why. Technical diagrams are one of those things that should've been automated years ago — instead we've all wasted hours in Lucidchart or draw.io nudging boxes around. This repo ships a Claude Code skill that takes a natural language description and spits out a 1920px SVG/PNG in seconds, with 7 visual styles and 14 diagram types including full UML support. If you're building any kind of AI app in Next.js, this slots straight in.
What the skill actually does
The core flow is simple: you describe a system in plain English (or Chinese), the skill classifies the diagram type and style, generates the SVG programmatically, then exports a high-res PNG via rsvg-convert. No Mermaid, no Graphviz DSL to learn — just a prompt.
The 7 styles cover real use cases: flat icon for docs, dark terminal for dev tooling, blueprint for infrastructure, Notion-clean for internal wikis, glassmorphism for landing pages, and Claude/OpenAI official styles if you're building integrations showcasing those platforms. The 14 diagram types lean heavily on AI/Agent patterns — RAG pipelines, multi-agent flows, Mem0 memory architectures, tool call sequences — plus the full UML set (sequence, class, state, component, etc.).
A stable prompt recipe looks like this:
Draw a Mem0 memory architecture diagram in style 2 (Dark Terminal).
Use swim lanes for: Input Layer, Memory Management, Storage Layer, Retrieval.
Show semantic arrows between components.
Export as mem0-dark.svg and mem0-dark.png.
The skill handles classification internally — you don't specify diagram type as a code enum, you just describe what you want.
Wiring it into a Next.js API route
The practical integration is a Next.js API route that shells out to the Claude Code skill, then serves the generated SVG/PNG back to the client. Here's the pattern:
// app/api/generate-diagram/route.ts
import { NextRequest, NextResponse } from 'next/server';
import { exec } from 'child_process';
import { promisify } from 'util';
import fs from 'fs/promises';
import path from 'path';
const execAsync = promisify(exec);
export async function POST(req: NextRequest) {
const { prompt, style = 1, outputName = 'diagram' } = await req.json();
const fullPrompt = `${prompt} Style ${style}. Export as ${outputName}.svg and ${outputName}.png.`;
// Run the Claude Code skill via CLI
const { stdout, stderr } = await execAsync(
`claude -p "${fullPrompt.replace(/"/g, '\\"')}" --allowedTools computer`,
{ cwd: process.env.FIREWORKS_SKILL_DIR }
);
const svgPath = path.join(process.env.FIREWORKS_SKILL_DIR!, `${outputName}.svg`);
const pngPath = path.join(process.env.FIREWORKS_SKILL_DIR!, `${outputName}.png`);
const [svgContent, pngBuffer] = await Promise.all([
fs.readFile(svgPath, 'utf-8'),
fs.readFile(pngPath),
]);
return NextResponse.json({
svg: svgContent,
png: `data:image/png;base64,${pngBuffer.toString('base64')}`,
log: stdout,
});
}
On the frontend, you render the SVG inline (fully scalable, copy-pasteable) and offer the PNG as a download:
// components/DiagramViewer.tsx
'use client';
import { useState } from 'react';
export function DiagramViewer() {
const [svg, setSvg] = useState<string | null>(null);
const [png, setPng] = useState<string | null>(null);
const [prompt, setPrompt] = useState('');
const [loading, setLoading] = useState(false);
async function generate() {
setLoading(true);
const res = await fetch('/api/generate-diagram', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ prompt, style: 3 }),
});
const data = await res.json();
setSvg(data.svg);
setPng(data.png);
setLoading(false);
}
return (
<div className="space-y-4">
<textarea
value={prompt}
onChange={e => setPrompt(e.target.value)}
placeholder="Describe your system architecture..."
className="w-full h-32 p-3 border rounded font-mono text-sm"
/>
<button onClick={generate} disabled={loading} className="px-4 py-2 bg-blue-600 text-white rounded">
{loading ? 'Generating...' : 'Generate Diagram'}
</button>
{svg && (
<div>
<div dangerouslySetInnerHTML={{ __html: svg }} className="border rounded p-4 bg-white" />
{png && (
<a href={png} download="diagram.png" className="mt-2 inline-block text-sm text-blue-600 underline">
Download PNG (1920px)
</a>
)}
</div>
)}
</div>
);
}
For production you'd want to write the files to a temp directory per request, clean them up after serving, and put this behind auth. But this is the working skeleton.
What I'd build with this
AI architecture documentation tool — users describe their system in a chat interface, the app generates the architecture diagram live, and they can toggle between styles (blueprint for the infrastructure team, Notion-clean for the PM). Export to Confluence or Notion via their APIs. Solves a real problem every team has.
GitHub README diagram bot — a GitHub Action or webhook that watches for a <!-- diagram: describe your system here --> comment in a PR, generates the SVG, commits it to the repo, and updates the README. No more stale diagrams that don't match the actual code.
AI agent visualiser — if you're building multi-agent systems with LangGraph or similar, wire this up to auto-render the agent graph topology from your code structure. Every time your agent config changes, regenerate the flow diagram. Pair it with the multi-agent and tool-call diagram types — they're clearly the most polished ones in the repo.
The thing I keep coming back to with fireworks-tech-graph is that it picked the right abstraction level. Natural language in, production SVG out, multiple styles so it fits different contexts. The 7 styles aren't arbitrary — each one maps to a real publishing target. I'm already using the blueprint style for infra docs and the Claude official style for anything going into an Anthropic integration showcase. Worth having in your toolkit.