AgentIndex icon
AgentIndex
wet-mcp logo
wet-mcp
★ 19
vs
deep-research logo
deep-research
★ 4.7k

wet-mcp vs deep-research

wet-mcp: WET - Web Extended Toolkit MCP Server provides AI agents with comprehensive capabilities for web search, content extraction, and library documentation retrieval. It features a 5-strategy scraping chain, integrated metasearch, and zero-configuration local models for embeddings and reranking, operating without requiring API keys.; deep-research: Deep Research is an AI-powered tool designed to generate comprehensive research reports rapidly, leveraging advanced AI models and internet connectivity. It prioritizes user privacy by processing and storing all data locally.

01

TL;DR

wet-mcp logo
wet-mcpRAG / Knowledge Base · ★ 19

AI Agent Research: Enabling AI agents to perform comprehensive web research and academic literature reviews to gather information for complex tasks.

Read wet-mcp full review →
deep-research logo
deep-researchRAG / Knowledge Base · ★ 4.7k

Rapidly generate in-depth research reports for personal and professional use.

Read deep-research full review →
02

Side-by-Side Comparison

Field
wet-mcp logowet-mcp
deep-research logodeep-research
Category
RAG / Knowledge Base
RAG / Knowledge Base
Stars
★ 19
★ 4.7k
License
Apache-2.0
MIT
Install Command
docker run -d --name wet-mcp-http -p 8084:8080 -v wet-data:/data -e MCP_TRANSPORT=http -e PUBLIC_URL=https://wet.example.com n24q02m/wet-mcp:latest
docker run -d --name deep-research -p 3333:3000 xiangfa/deep-research
Updated
1d ago
4mo ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
Web Search, Content Extraction, AI Agent Tooling
AI Research, LLM, Privacy-focused
03

Features

wet-mcp logowet-mcp
01Web Search: Embedded SearXNG metasearch (Google, Bing, DuckDuckGo, Brave) with query expansion, TTL cache, and standardized citation format. Optional cloud search backends.
02Content Extract: 5-strategy escalation chain for URL content extraction, providing smart chunks structured output (clean text, markdown, JSON-LD, code blocks, metadata), batch processing, deep crawling, and local file conversion.
03Library Docs: Auto-discover and index documentation with FTS5 hybrid search, HyDE-enhanced retrieval, and version-specific docs for AI agents.
04Zero Config: Built-in local Qwen3 embedding + reranking, no API keys needed. Optional cloud providers can be selected per task for higher-quality results.
deep-research logodeep-research
01Rapidly generates comprehensive research reports in minutes.
02Ensures privacy by processing and storing all data locally in the browser.
03Supports a wide range of mainstream large language models (LLMs).
04Integrates with various web search engines for enhanced information gathering.
05Allows uploading and processing local files to build a knowledge base.
04

Use Cases

wet-mcp logowet-mcp
↳AI Agent Research: Enabling AI agents to perform comprehensive web research and academic literature reviews to gather information for complex tasks.
↳Structured Data Extraction: Automatically extracting structured and unstructured data from web pages, including clean text, markdown, JSON-LD, and code blocks, for further processing by agents.
↳Developer Documentation Access: Providing AI agents with version-aware and topic-specific access to software library documentation for code generation, debugging, or learning.
↳Offline Content Processing: Converting local files (PDF, DOCX, HTML, etc.) to Markdown for AI agent consumption, enabling analysis of local knowledge bases.
deep-research logodeep-research
↳Rapidly generate in-depth research reports for personal and professional use.
↳Serve as a deep research as a service (SaaS) platform via SSE API.
↳Integrate deep research functionality into other AI applications using MCP.
FAQ

FAQ

What is the difference between wet-mcp and deep-research?
Both wet-mcp and deep-research are in the RAG / Knowledge Base category. wet-mcp has 19 stars, while deep-research has 4.7k stars.
Which is better, wet-mcp or deep-research?
The best choice depends on your use case. Choose wet-mcp if AI Agent Research: Enabling AI agents to perform comprehensive web research and academic literature reviews to gather information for complex tasks., and deep-research if Rapidly generate in-depth research reports for personal and professional use..
Is wet-mcp free or open source?
Yes, wet-mcp is open source on GitHub (Apache-2.0).
Is deep-research free or open source?
Yes, deep-research is open source on GitHub (MIT).
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