
Webclaw
Turn any website into LLM-ready data
What it does
Webclaw turns websites into clean markdown, JSON, structured data, and LLM-ready content. Use it to scrape pages, crawl docs, extract fields, summarize, diff changes, and feed reliable web context into AI agents, RAG pipelines, CLI workflows, SDKs, and MCP clients. Designed for developers building with Claude Code, Cursor, LangChain, LlamaIndex, and custom agents.
Does the same job
all alternatives →- LSLLM Scraper – turn any webpage into structured data2024 · github.com · ▲88

- IWI wrote an open-source browser alternative for Computer Use for any LLM2024 · github.com · ▲180
Hey HN, I made Browser-Use, an open-source tool that lets (all Langchain supported) LLMs execute tasks directly in the browser just with function calling. It allows you to build agents that interact with web elements using natural language prompts. We created a layer that simplifies website interaction for LLMs by extracting xPaths and interactive elements like buttons and input fields (and other fancy things). This enables you to design custom web automation and scraping functions without manual inspection through DevTools. Hasn't this been done a lot of times? Good question, as a general…

- TATurn any website into a knowledge base for LLMs2024 · embedding.io · ▲305
I built this tool because I wanted a way to just take a bunch of URLs or domains, and query their content in RAG applications. It takes away the pain of crawling, extracting content, chunking, vectorizing, and updating periodically. I'm curious to see if it can be useful to others. I meant to launch this six months ago but life got in the way...

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- NW
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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