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#web scraping
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A Python crawler for unstable or JavaScript-heavy sites, with selector healing, structured-data extraction, adaptive rate limiting, and an MCP server for agent-driven crawling. Use only for authorized scraping. Anansi is a Python web scraping toolkit designed for sites that change often or need browser rendering. It combines adaptive parsing, structured-data extraction, incremental crawling, proxy support, and an MCP server so an LLM or agent workflow can drive fetch, extract, crawl, pause, resume, export, and metrics actions. Why it is useful Self-healing selectors: stores selector confidence and attempts fallback strategies when a layout changes. Structured extraction first: pulls JSON-LD, Open Graph, and Microdata before relying on brittle CSS selectors. Browser upgrade path: can switch from HTTP fetching to Playwright rendering for JavaScript-heavy pages. Crawler durability: includes an async crawler, SQLite-backed queue, incremental recrawls, ETag/Last-Modified handling, and resumable jobs. Agent-ready interface: ships with an MCP server so compatible LLM tools can operate crawls through tool calls. Best fit Use Anansi when you need a resilient research or data-extraction crawler for websites you are allowed to access, especially where pages change structure or require JavaScript rendering. It is most relevant for developers building data pipelines, monitoring workflows, competitive research dashboards, or agentic browsing systems. Quick evaluation checklist Confirm the target website permits your intended crawling use case. Start with structured data extraction before custom selectors. Enable browser rendering only where HTTP fetching is insufficient. Keep adaptive rate limiting active and respect Retry-After responses. Use the MCP server when you want an agent to orchestrate crawl tasks instead of manually scripting every step. Source notes The GitHub repository describes Anansi as a self-healing web scraper with selector repair, browser rendering fallback, Chrome-like TLS fingerprinting, Pydantic validation, incremental crawling, and an MCP server. The project is written primarily in Python and is licensed under Apache-2.0.
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Context.dev bundles clean Markdown scraping, crawling, structured extraction, screenshots, and brand intelligence behind one API, with a free test allowance and clear credit pricing. Context.dev is a practical Loot pick for builders who need fresh web context inside agents, RAG pipelines, enrichment flows, or onboarding products without maintaining a pile of scraping infrastructure. The useful angle is the bundle: clean Markdown or HTML from public URLs, sitemap crawls, screenshots, structured extraction, brand data, styleguide details, and logo delivery under one API key. Use it when the job is closer to product-grade web context than a one-off scrape. Good first tests: feed current docs into a support bot, enrich a company signup from a domain, pull pricing pages into a comparison workflow, or let an internal agent check live web pages before drafting a report. The official docs expose quickstart and API reference paths, and the public pricing page makes the free test limits and paid credit model visible before committing. Caveats: this is still an external data provider, so do not pipe sensitive customer prompts or private pages into it without checking your data controls. For heavy crawling, compare credit cost, rate limits, robots/compliance requirements, and failure behavior against open-source crawlers, Firecrawl, Apify, or your existing browser automation stack. Treat Product Hunt traction as launch context only; the decision should come from testing output quality on your real URLs.
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