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wet
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wet

Active·★ 19·Apache-2.0·Updated 2026-10-21

WET is an open-source MCP server designed to empower AI agents with advanced web capabilities. It provides web search, content extraction, and library documentation indexing, offering comprehensive data retrieval for agent-driven tasks.

#AI Agents#Web Search#Content Extraction#Library Docs#Academic Research#Web Scraping#RAG Tooling
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Guides
$ Install
$ pip install wet
↗ Visit site★ GitHub
01

Features

01Web Search: Embedded SearXNG metasearch (Google, Bing, DuckDuckGo, Brave) with query expansion, TTL cache, standardized citation format, and 200-token snippet cap. Optional cloud search backends as a fallback chain.
02Academic Research: Search across major academic databases including Google Scholar, Semantic Scholar, arXiv, PubMed, CrossRef, and BASE.
03Library Docs: Automatically discover and index library documentation using FTS5 hybrid search, HyDE-enhanced retrieval, and support for version-specific queries.
04Content Extraction: Utilizes a 5-strategy escalation chain for robust URL content extraction, producing smart chunks with structured output (text, markdown, JSON-LD, code blocks, metadata), supports batch processing, deep crawling, local file conversion, and includes anti-bot strategies.
05Zero Config Fallbacks: Offers out-of-the-box functionality with built-in local reference embedding and reranking via fastretrieval, requiring no API keys, while also supporting optional cloud providers for enhanced quality.
02

Compatibility

Python
Runtime
Verified via docs
Claude Code
Client
Verified via docs
Codex
Client
Verified via docs
Gemini CLI
Client
Verified via docs
Docker
Deployment
Verified via docs
Cloudflare Workers
Deployment
Verified via docs
03

Quick start

1
$ pip install wet
04

Use cases

↳AI Agent Web Exploration: Empower AI agents to conduct comprehensive web searches and retrieve relevant content for diverse tasks, facilitating informed decision-making.
↳Automated Research & Knowledge Gathering: Automate academic and library documentation research, efficiently gathering and providing structured information to AI agents for analysis and synthesis.
↳Content Analysis & Summarization: Facilitate advanced content extraction from URLs and local files, preparing data for in-depth analysis, summarization, or integration into Retrieval-Augmented Generation (RAG) systems.
↳Developer Documentation Lookup: Grant AI agents version-aware access to comprehensive library and project documentation, aiding in coding assistance, debugging, and issue resolution.
05

Alternatives

⚡Top VS:
VSolostep-mcp-server★24→VSwet-mcp★19→VSAwwwards-mcp★282→VS

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n24q02m /
wet
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Open-source MCP server for AI agents: web search, content extraction, and library docs -- 5-strategy scraping, runs without API keys.

19Apache-2.0
Last commit2d ago
StatusActive
CategoryRAG / Knowledge Base
Links
Documentation↗Discussion↗Issues↗Releases↗
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