wet-mcp
★ 19
vs
gigaxity-deep-research
★ 62
wet-mcp vs gigaxity-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.; gigaxity-deep-research: Gigaxity Deep Research is an open-source multi-source synthesis pipeline providing six MCP tools for deep research with LLMs. It integrates parallel web search, RRF fusion, contradiction detection, and citation binding to offer robust, evidence-backed answers.
01
TL;DR
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.
gigaxity-deep-researchRAG / Knowledge Base · ★ 62
Empowering AI agents like Claude Code with external, citation-backed deep research capabilities to avoid hallucinations.
02
Side-by-Side Comparison
Field
wet-mcpgigaxity-deep-researchCategory
RAG / Knowledge Base
RAG / Knowledge Base
Stars
★ 19
★ 62
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
git clone https://github.com/yoloshii/gigaxity-deep-research.git && cd gigaxity-deep-research && python -m venv .venv && source .venv/bin/activate && pip install -e .
Updated
2d ago
3w ago
Open Source
Yes
Yes
Tags
Web Search, Content Extraction, AI Agent Tooling
RAG, Multi-Agent, Web Search
03
Features
wet-mcp01Web 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.
gigaxity-deep-research01Multi-source parallel search across various providers with Reciprocal Rank Fusion (RRF) for comprehensive result aggregation.
02Exposes six distinct deep-research tools via MCP and a FastAPI REST API: search, research, ask, discover, synthesize, reason.
03Advanced synthesis capabilities including CRAG-style quality gates, PaperQA2-style contradiction detection, and VeriCite-style citation binding.
04Adaptive query routing, HyDE-style query expansion, and multi-aspect query decomposition for complex research tasks.
05Support for various focus modes (e.g., general, academic, debugging) and SciRAG-style outline-guided synthesis.
04
Use Cases
wet-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.
gigaxity-deep-research↳Empowering AI agents like Claude Code with external, citation-backed deep research capabilities to avoid hallucinations.
↳Automating comprehensive, multi-source knowledge synthesis for factual queries across various domains.
↳Building custom RAG-powered LLM applications using a flexible FastAPI REST API or MCP server for integration.
↳Supporting multi-tenant LLM services with individual API key billing for research costs, such as via OpenRouter.
FAQ
FAQ
What is the difference between wet-mcp and gigaxity-deep-research?
Both wet-mcp and gigaxity-deep-research are in the RAG / Knowledge Base category. wet-mcp has 19 stars, while gigaxity-deep-research has 62 stars.
Which is better, wet-mcp or gigaxity-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 gigaxity-deep-research if Empowering AI agents like Claude Code with external, citation-backed deep research capabilities to avoid hallucinations..
Is wet-mcp free or open source?
Yes, wet-mcp is open source on GitHub (Apache-2.0).
Is gigaxity-deep-research free or open source?
Yes, gigaxity-deep-research is open source on GitHub (MIT).
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