How AI‑Generated Prompts Make Technical Architecture Diagrams Animate

Why drawing architecture diagrams is hard

When writing technical proposals, the most frustrating part is not the architecture itself but turning the call chain, domain boundaries and failure paths into a clear diagram that others can understand. Hand‑drawing is slow, and generic text‑to‑image tools often misplace arrows, endpoints, or text.

fireworks‑tech‑graph as an AI‑driven solution

fireworks‑tech‑graph implements an Agent Skill that accepts a Chinese description of a system and directly produces a verifiable SVG, high‑resolution PNG, and optionally an animated GIF. The agent first determines the diagram type and visual style, then structures the request into semantic components, plans nodes and edges, generates the SVG, performs XML, geometry, and layout checks, and finally exports the desired formats. Because the final delivery retains the SVG, titles, nodes, and colors can be edited without regenerating the whole image.

Supported visual styles

The repository offers twelve visual styles; eleven are generated automatically, while the "Dark Luxury" style is handcrafted by AI to follow a specific aesthetic. The tool covers fourteen UML categories and includes built‑in AI scenarios such as RAG, Mem0, multi‑agent collaboration, and tool invocation.

Testing the twelve styles

All twelve Chinese GIF variants were generated. Each GIF is 960 px wide, 20 fps, 115 frames, lasts 5.75 seconds, and stays under 500 KB. The repository’s test suite reported 129 passed cases and 3 skipped. The animation sequence is consistent: the diagram first appears without connections, then arrows draw the business‑order path, the full topology is displayed, data continues to flow along the arrows, and finally the animation briefly resets before looping. Nodes and text remain static, avoiding a chaotic shaking effect.

Examples of styles include:

Flat icons – suitable for product documentation.

Dark terminal – ideal for tool‑call and execution chains.

Engineering blueprint – fits microservice architectures.

Notion Clean – works for knowledge structures and lightweight explanations.

Glass‑morphic – emphasizes multi‑agent collaboration.

Claude Official – presents system architecture.

OpenAI Official – visualizes API integration flows.

Dark Luxury – resembles deep‑color tech talks.

C4 Review Canvas – forces clear responsibility, technology, and protocol definitions.

Cloud Fabric – highlights region, VPC, and cross‑region mechanisms.

Event Transit – draws event lines like subway routes.

Ops Pulse – combines golden‑signal metrics and trace visualizations.

Four real‑world scenarios with prompts and results

Scenario 1: Multi‑agent collaboration diagram

画一张“AI 研究团队多智能体协作图”。用户需求先进入协调智能体，由它把任务分派给研究智能体、编码智能体和评审智能体；研究结果与代码产物进入共享记忆，再由综合引擎汇总，评审通过后输出最终回复。使用玻璃拟态风格，深色背景，全部说明文字用中文。生成 SVG、PNG，并把箭头按任务交接顺序做成循环 GIF。

The resulting GIF uses blue for inputs/context, purple for task delegation, green for shared work, and orange for review output, making the hand‑off flow visually clearer than a static diagram.

Scenario 2: Dual‑region checkout system disaster‑recovery

画一张“双活结账系统部署图”。全局入口分别连接北美和欧洲边缘节点；每个区域都要画出 Region、VPC、结账应用和订单数据库，标明区域内写入与跨区域复制。使用 Cloud Fabric 风格，中性云图标，所有标题与说明用中文。导出 SVG、PNG 和带数据流箭头的循环 GIF。

The diagram separates region, VPC, application, and database boundaries with distinct colors, and shows global routing, regional writes, and cross‑region replication, providing a more reliable visual for disaster‑recovery discussions.

Scenario 3: Kafka event flow as a subway line

画一张“结账事件线路图”。主线是 checkout.events，从结账 API 依次经过 Schema 校验、欺诈评分、订单路由和履约服务；欺诈评分的异常事件进入结账 DLQ，订单路由把状态投影到 RocksDB 物化视图。使用 Event Transit 地铁线路风格，节点按处理顺序编号，全部说明文字用中文，并生成箭头流动的 GIF。

The red main track shows the primary event flow, the DLQ branch highlights exceptions, and the RocksDB projection is displayed separately, preventing visual clutter by using stations and branches to separate semantics.

Scenario 4: Checkout request reliability post‑mortem

画一张“结账请求可靠性排查图”。展示边缘网关、API 网关、结账服务和支付服务，每个服务显示延迟、流量、错误率、饱和度四个黄金信号；标出一次结账请求的关键路径，结账服务向 OTel Collector 导出遥测，并在下方画出同一 Trace 的瀑布图。使用 Ops Pulse 深色运维风格，全部说明文字用中文，导出循环 GIF。

The upper half presents the four golden‑signal metrics for each service, while the lower half shows the trace waterfall for a single request, directly linking high latency and degradation in the checkout service to observable metrics.

Installation and first‑run guide

The skill works with both Codex and Claude Code. Installation uses a nested skill path; the trailing /skills/fireworks-tech-graph must not be omitted:

npx -y [email protected] add \
  yizhiyanhua-ai/fireworks-tech-graph/skills/fireworks-tech-graph \
  --agent codex claude-code -g -y --copy

After installing, restart the agent and issue a clear scenario description, for example:

画一张中文的 RAG 系统架构图，包含用户、检索器、向量库、重排器、LLM 和引用输出；使用 OpenAI Official 风格，导出 SVG 和 1920px PNG。再把箭头按检索与生成顺序做成 GIF。

Effective prompts should specify the problem the diagram solves, required nodes, reading direction, chosen style, and desired output formats.

Runtime dependencies and limits

Static outputs (SVG, PNG, HTML) require Python 3.9+ with CairoSVG or rsvg-convert. GIF generation additionally needs Node.js 18+, Chrome/Chromium, FFmpeg, FFprobe, and Puppeteer. The default GIF size is 960 px, 5.75 s, 20 fps; arrows follow the semantic path while nodes and background stay still.

The tool focuses on well‑structured technical diagrams; it enforces constraints on line crossings, bend counts, spacing, container boundaries, and text placement, sacrificing freeform illustration for stable engineering expression.

Release notes and version considerations

Prefer installing from the GitHub release page; the npm package may lag. The repository’s README now lists twelve styles (previously seven), and the v1.2.0 release reflects this expanded capability.

Final thoughts

The author’s key observation is that the arrows truly move according to semantics: task capsules in multi‑agent diagrams, cross‑region replication in cloud deployments, exception branches in event lines, and telemetry/trace flow in operations diagrams each have distinct motion patterns. For anyone who frequently writes technical proposals, READMEs, architecture reviews, or incident post‑mortems, this skill is a valuable addition to an AI‑agent toolbox. Start with a simple, well‑bounded scenario and gradually add constraints to achieve higher success rates.