QuiverAI

QuiverAI是可通过API调用的AI矢量图形工具，核心功能为：由文本提示生成生产级SVG，将PNG/JPEG/WebP等栅格图转为SVG，适配logo、图标、插画制作场景。输入可提供文本描述、栅格图片（URL或base64），还能配置风格指令、参考图等参数；输出标准SVG代码。需API密钥调用，每个请求扣1积分，支持多结果生成、流式输出等配置。

charmmm718

热度 57.02

最近更新 2026-05-11 17:27:32

分类：创作依赖：第三方 API应用场景：图像生成 / 品牌素材 / 文件转换

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add quiverai

Metadata

name: quiverai
description: Generate and vectorize SVG graphics via the QuiverAI API (Arrow model). Use when the user asks to create logos, icons, or illustrations as SVG, convert raster images (PNG/JPEG/WebP) to SVG, or generate vector graphics from text prompts.
metadata: {"openclaw":{"emoji":"🖋️","requires":{"env":["QUIVERAI_API_KEY"]},"primaryEnv":"QUIVERAI_API_KEY"}}

QuiverAI — AI Vector Graphics

QuiverAI generates production-ready SVGs from text prompts or raster images.

Site: https://quiver.ai
Docs: https://docs.quiver.ai
API base: https://api.quiver.ai/v1
Model: arrow-preview
Auth: Bearer token via QUIVERAI_API_KEY
Billing: 1 credit per request (regardless of n).

Setup

Get an API key at https://app.quiver.ai/settings/api-keys (create account at https://quiver.ai/start first).

Text to SVG

Generate SVGs from a text description.

Endpoint: POST /v1/svgs/generations

curl -X POST https://api.quiver.ai/v1/svgs/generations \
  -H "Authorization: Bearer $QUIVERAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "arrow-preview",
    "prompt": "A minimalist monogram logo using the letter Q",
    "n": 1,
    "stream": false
  }'

Node.js SDK (npm install @quiverai/sdk):

import { QuiverAI } from "@quiverai/sdk";
const client = new QuiverAI({ bearerAuth: process.env.QUIVERAI_API_KEY });

const result = await client.createSVGs.generateSVG({
  model: "arrow-preview",
  prompt: "A minimalist monogram logo using the letter Q",
});
// result.data[0].svg contains the SVG markup

Parameters

Param	Type	Default	Description
`model`	string	—	Required. Use `arrow-preview`.
`prompt`	string	—	Required. Describes the desired SVG.
`instructions`	string	—	Additional style guidance (e.g. "flat monochrome, rounded corners").
`references`	array	—	Up to 4 reference images (`{ url }` or `{ base64 }`).
`n`	int	1	Number of outputs (1–16).
`temperature`	float	1	Sampling temperature (0–2). Lower = more deterministic.
`top_p`	float	1	Nucleus sampling (0–1).
`max_output_tokens`	int	—	Upper bound for output tokens (max 131072).
`stream`	bool	false	SSE streaming (events: `reasoning`, `draft`, `content`).

Response

{
  "id": "resp_01J...",
  "created": 1704067200,
  "data": [{ "svg": "<svg ...>...</svg>", "mime_type": "image/svg+xml" }],
  "usage": { "total_tokens": 1640, "input_tokens": 1200, "output_tokens": 440 }
}

Image to SVG (Vectorize)

Convert a raster image (PNG/JPEG/WebP) into SVG.

Endpoint: POST /v1/svgs/vectorizations

curl -X POST https://api.quiver.ai/v1/svgs/vectorizations \
  -H "Authorization: Bearer $QUIVERAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "arrow-preview",
    "stream": false,
    "image": { "url": "https://example.com/logo.png" }
  }'

SDK:

const result = await client.vectorizeSVG.vectorizeSVG({
  model: "arrow-preview",
  image: { url: "https://example.com/logo.png" },
});

Additional parameters (beyond Text-to-SVG shared ones)

Param	Type	Default	Description
`image`	object	—	Required. `{ url: "..." }` or `{ base64: "..." }`.
`auto_crop`	bool	false	Crop to dominant subject before vectorization.
`target_size`	int	—	Square resize target in px (128–4096) before inference.

Response format is identical to Text-to-SVG.

Error codes

Status	Code	Meaning
400	`invalid_request`	Malformed body or missing fields.
401	`unauthorized`	Bad or missing API key.
402	`insufficient_credits`	Out of credits.
429	`rate_limit_exceeded`	Too many requests; back off and retry.

Tips

Save SVG output to a .svg file for immediate use.
Use instructions to control style without changing the prompt.
For logos, try low temperature (0.3–0.5) for cleaner, more consistent results.
Use references to provide visual examples the model should match.
For vectorization, enable auto_crop: true when the source image has excess whitespace.