Selectively extracts web content chunks relevant to a query for AI agents, optimizing context window usage.
Sponsored
Trawl addresses the challenge of providing AI agents with concise, relevant web information by taking a URL and a natural-language query. Unlike tools that dump entire web pages, Trawl fetches, extracts, chunks, and embeds content using a local bge-m3 model, returning only the most pertinent chunks. This method dramatically reduces token usage, allowing agents to efficiently "read" web pages by focusing on approximately 1,000 tokens of critical information, powered by query-aware dense retrieval rather than slow, LLM-driven full-page processing.
Key Features
01Heading-aware chunking that preserves context and table integrity.
02Adaptive fetcher routing for diverse web sources (Wikipedia, YouTube, Playwright fallback).
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04VLM-driven page profiling to optimize content extraction on repeated site visits.
05Provides an MCP server for `fetch_page` and `profile_page` tools.
06Query-aware dense retrieval using bge-m3 embeddings and cross-encoder reranking.
Use Cases
01Building RAG systems that require precise web content input.
02Reducing token cost and latency for web-aware LLM applications.
03Enabling AI agents to quickly understand specific information from web pages without context window overflow.
