基础设施
代理路由、浏览器执行、重试和用量控制都属于运维工作。
Skip brittle HTML parsing. Crawlora turns supported platforms into structured JSON that LLMs and AI agents can consume directly — over documented REST endpoints and hosted MCP tools.
问题所在
Teams building LLM apps and AI agents keep hitting the same wall: raw page HTML is noisy, token-heavy, and changes constantly, so AI web scraping turns into endless parser maintenance, anti-bot fights, and validation. For supported platforms, Crawlora removes that layer — call a documented endpoint or a hosted MCP tool and get normalized JSON that is ready to embed, summarize, rank, or hand to a tool call.
代理路由、浏览器执行、重试和用量控制都属于运维工作。
原始页面必须转化为稳定的数据记录，产品和数据团队才能加以利用。
应用场景落地页应直接对应买家的工作流程和内部数据模型。
结构化公开网络数据工作流程仍需要清晰的法律、隐私和平台边界。
可采集的数据
Example fields may include structured records from supported Crawlora platform APIs — already shaped for LLM and agent consumption.
相关 Crawlora API
从平台页面或端点文档入手，在正式集成前先在 Playground 中测试同一路由。
Structured search results for retrieval, research, and grounding workflows.
打开Local business and place records as clean JSON for agents.
打开Product and marketplace fields for shopping and pricing agents.
打开Video, comment, and transcript data for summarization pipelines.
打开Public community discussion records for listening and research agents.
打开搜索意图
将页面内容与买家实际搜索的任务对应起来，再打开每个工作流程背后相关的 Crawlora API。
Traditional web scraping fetches a page and parses HTML with selectors you maintain per site. AI web scraping usually means one of two things: using a model to extract fields from arbitrary pages, or feeding an AI system clean web data. Crawlora targets the second — for supported platforms it returns documented, normalized JSON, so your model spends tokens on reasoning, not on cleaning markup.
Structured records are easier to clean, dedupe, cite, and govern than scraped HTML. Crawlora responses can be stored as snapshots and routed into retrieval indexes, evaluation sets, or training datasets, with source context retained so you can track provenance. Use it within applicable laws, platform terms, and your own data-governance rules.
示例工作流程
Crawlora 将抓取执行层封装在有文档说明的 API 之后，让你的产品专注于存储、分析、告警和用户工作流程。
01
Choose supported platforms and fields instead of writing per-site parsers.
02
Use a documented REST endpoint or a hosted MCP tool from your agent or backend.
03
Crawlora returns normalized records that are cleaner for tool calls and embeddings than raw HTML.
04
Route records into RAG, summaries, evaluations, or agent actions with human oversight where appropriate.
API 示例
Illustrative example using a documented Crawlora route. Agents should use the current Docs catalog for supported tools and inputs.
GET https://api.crawlora.net/api/v1/google-search/search?keyword=best%20web%20scraping%20api&country=us
x-api-key: YOUR_API_KEY{
"code": 200,
"msg": "OK",
"data": [
{
"position": 1,
"title": "Example result",
"url": "https://example.com",
"snippet": "Clean field, not raw HTML"
}
]
}你可以构建什么
以下是适用于 SaaS 产品、数据团队、AI 智能体、代理机构、增长团队和内部情报工具的实用工作流程模式。
Pull structured web data and load it into a retrieval index for grounded answers.
Let an agent search, compare, and summarize supported sources with clean inputs.
Watch supported platforms and alert when fields change.
Assemble normalized snapshots for evaluation or training sets, used responsibly.
Feed product and price fields to a commerce assistant where supported.
Expose Crawlora's web-data tools to MCP-compatible clients like Claude or Cursor.
自建还是购买
自定义爬虫适合做原型验证。生产环境的网络数据工作流程则需要基础设施、监控、稳定的输出以及清晰的失败处理机制。
| 自建方案 | Crawlora 方案 |
|---|---|
| Prompt an LLM to parse raw HTML for every site | Get documented, normalized JSON for supported platforms |
| Burn tokens cleaning noisy markup | Spend tokens on reasoning over token-light fields |
| Maintain anti-bot, proxy, and retry logic | Use managed execution behind an API key |
| Wire a custom tool per source for your agent | Use one hosted MCP server for supported endpoints |
基础设施
Crawlora 将针对特定平台的 API 与托管代理路由、基于浏览器的渲染、自动重试、速率限制、用量跟踪和弹性扩展控制结合在一起。
负责任使用
AI web scraping must still comply with applicable laws, platform terms, copyright, privacy expectations, and third-party rights. Crawlora provides structured data infrastructure, not permission to use any content for any AI purpose, including training. Review outputs and retain data only as appropriate for your workflow. 阅读 Crawlora 条款.
相关应用场景
交叉链接那些通常共享相同数据基础设施和产品买家的实用工作流程。
常见问题
面向正在评估 Crawlora 是否适合该工作流程的开发者和产品团队的解答。
AI web scraping describes collecting web data for AI systems — either using models to extract fields from pages, or feeding AI clean, structured web data. Crawlora focuses on the second: documented APIs that return normalized JSON for supported platforms, so LLMs and agents skip HTML parsing.
A traditional scraper fetches HTML and relies on selectors you maintain per site. Crawlora returns documented, normalized JSON for supported platforms with managed execution, so there is no per-site parser to keep alive for those sources.
Yes, where lawful. Responses can be stored, embedded, and routed into retrieval or evaluation sets, with source context retained. Use it within applicable laws, platform terms, and your own data-governance rules.
Yes. Crawlora exposes a hosted MCP endpoint so MCP-compatible agents can call structured web data APIs directly, in addition to the REST API.
Scraping public data can be lawful, but legality depends on the data, the source's terms, jurisdiction, and how you use it — training and redistribution raise extra questions. Crawlora is data infrastructure, not legal advice; see our guide on whether web scraping is legal.
For supported endpoints, Crawlora returns normalized JSON fields rather than raw HTML, which is easier for tool calls, embeddings, and summaries.
No. Crawlora is strongest for documented, platform-specific endpoints. For arbitrary whole-site crawling or markdown extraction of unknown pages, pair it with a general crawling tool.
Crawlora uses credit-based pricing with API-key usage tracking. Estimate recurring agent or pipeline usage on the pricing page.
浏览 Crawlora 的 API，在 Playground 中测试请求，从抓取基础设施工作转向生产环境的数据工作流程。