toapi vs deepscrape
Web scraping and structured data extraction compared
toapi and deepscrape turn web content into data that applications can use. toapi focuses on defining lightweight JSON endpoints with CSS selectors, while deepscrape offers a broader self-hosted service for scraping, crawling, and extraction workflows.
toapi: Turn Web Pages into JSON APIs
toapi converts web pages into JSON endpoints using declarative CSS selectors. It suits developers who need lightweight, on-demand data extraction without maintaining a separate crawler or database.
deepscrape: Scrape Websites and Extract Structured Data
DeepScrape is a self-hosted TypeScript service for scraping and crawling websites, returning clean content or structured data. It combines HTTP fetching, Playwright, optional LLM extraction, and APIs for building data pipelines and agent workflows.
| toapi | deepscrape | |
|---|---|---|
| Language | Python | TypeScript |
| License | MIT | MIT |
| Stars | 3.6k | 320 |
| Forks | 241 | 26 |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- toapi is a Python library that serves JSON through Flask; deepscrape is a TypeScript self-hosted service with APIs, workers, and an MCP server.
- toapi maps declared CSS selectors to fields and can combine sites behind API routes; deepscrape supports CSS extraction and optional schema-based LLM extraction, plus outputs such as Markdown, text, and screenshots.
- toapi fetches and parses pages on demand with caching; deepscrape also supports multi-page crawling, background jobs, and interactive browser sessions.
- toapi describes in-memory caching and may use a configured headless browser; deepscrape uses HTTP or Playwright and may require Redis and browser dependencies for deployment.
- Both projects use the MIT license and are not archived; the supplied metadata lists 3.6k stars and 241 forks for toapi, compared with 320 stars and 26 forks for deepscrape.
Choose toapi if you…
- need lightweight JSON endpoints from HTML using declarative CSS selectors.
- want to serve extracted fields through Flask without setting up a separate crawling service.
- prefer on-demand extraction with caching and the option to combine multiple sites behind one API.
Choose deepscrape if you…
- need crawling, background jobs, or multiple output formats in a self-hosted service.
- want CSS-based structured extraction alongside optional schema-based LLM extraction.
- need to connect scraping and browser workflows to AI tools through MCP or a Node.js SDK.
Compare the numbers side by side, or add more projects
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.