MaxKB
★ 23.0k
vs
wet
★ 19
MaxKB vs wet
MaxKB: MaxKB is an open-source platform for building enterprise-grade AI agents with integrated RAG pipelines, workflow orchestration, and MCP tool-use capabilities. It supports direct document upload, automatic web crawling, and multiple embedding models for knowledge base construction. Widely used for intelligent customer service, internal knowledge bases, and academic research applications.; wet: 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.
01
TL;DR
MaxKBRAG / Knowledge Base · ★ 23.0k
When to choose:
- You want an all-in-one knowledge-base and workflow agent platform that starts with a single Docker command and embeds easily into websites.
- You need broad support for domestic Chinese LLM providers alongside global models for internal enterprise Q&A.
When to skip:
- You cannot use GPL-3.0 licensed software in your commercial distribution; RAGFlow is Apache-2.0.
- Your primary bottleneck is extracting complex tables and layouts from messy scanned PDFs; RAGFlow's deep document parser goes further.
wetRAG / Knowledge Base · ★ 19
AI Agent Web Exploration: Empower AI agents to conduct comprehensive web searches and retrieve relevant content for diverse tasks, facilitating informed decision-making.
02
Side-by-Side Comparison
Field
MaxKBwetCategory
RAG / Knowledge Base
RAG / Knowledge Base
Stars
★ 23.0k
★ 19
License
GPL-3.0
Apache-2.0
Install Command
docker run -d --name=maxkb --restart=always -p 8080:8080 -v ~/.maxkb:/opt/maxkb 1panel/maxkb
pip install wet
Updated
1d ago
1d ago
Open Source
Yes
Yes
Tags
RAG, Agent, LLM
AI Agents, Web Search, Content Extraction
03
Features
MaxKB01RAG pipeline with document upload, web crawling, and automatic chunking
02Visual workflow builder for multi-step agent logic
03MCP tool-use capabilities for connecting to external services
04Supports multiple LLMs and embedding models
05Docker deployment with 1Panel integration for easy self-hosting
wet01Web 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.
04
Use Cases
MaxKB↳Building intelligent customer service bots with enterprise knowledge bases
↳Creating internal Q&A systems over company documents and wikis
↳Deploying research assistants with RAG over academic or technical document collections
wet↳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
Best For
MaxKBMost PopularEssentialTrending
wet—
FAQ
FAQ
What is the difference between MaxKB and wet?
Both MaxKB and wet are in the RAG / Knowledge Base category. MaxKB has 23.0k stars, while wet has 19 stars.
Which is better, MaxKB or wet?
The best choice depends on your use case. Choose MaxKB if Building intelligent customer service bots with enterprise knowledge bases, and wet if AI Agent Web Exploration: Empower AI agents to conduct comprehensive web searches and retrieve relevant content for diverse tasks, facilitating informed decision-making..
Is MaxKB free or open source?
Yes, MaxKB is open source on GitHub (GPL-3.0).
Is wet free or open source?
Yes, wet is open source on GitHub (Apache-2.0).
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