stashbase
★ 1.1k
vs
mcp-local-rag
★ 416
stashbase vs mcp-local-rag
stashbase: StashBase enables AI agents to search and utilize content from your local files, including PDFs, DOCX, images, audio, and video. It creates a searchable index from your folders, allowing agents to find relevant context by meaning rather than just keywords.; mcp-local-rag: MCP Local RAG provides a fully private, zero-setup Retrieval Augmented Generation (RAG) system for developers, enabling semantic search with keyword boosting on local documents. It integrates seamlessly with AI coding tools via MCP or offers direct command-line access, ensuring data privacy and offline functionality.
01
TL;DR
stashbaseRAG / Knowledge Base · ★ 1.1k
When to choose:
- You want a desktop app that indexes folders of PDFs, DOCX, images, audio and video and shares them with Claude, Codex and other MCP clients.
- Your files must stay the source of truth: the index can be rebuilt, and removing a folder never deletes files.
- You want exact text search with no key, and semantic search only if you opt in.
When to skip:
- Your documents cannot leave the machine and you need semantic search: it sends content to OpenAI or OpenRouter.
- You are on Linux and need official support: Linux builds are community-supported.
- You only need one file converted now and then: an MCP converter is lighter.
mcp-local-ragRAG / Knowledge Base · ★ 416
Searching internal technical specifications, research papers, or documentation privately
02
Side-by-Side Comparison
Field
stashbasemcp-local-ragCategory
RAG / Knowledge Base
RAG / Knowledge Base
Stars
★ 1.1k
★ 416
License
Apache-2.0
MIT
Install Command
brew install --cask liliu-z/stashbase/stashbase
npx -y mcp-local-rag
Updated
2d ago
2d ago
Open Source
Yes
Yes
Tags
Local Files, RAG, AI Agents
RAG, Semantic Search, Local AI
03
Features
stashbase01Extracts searchable text from various formats including PDFs, DOCX, images (OCR), audio, and video (transcription).
02Semantic search capabilities to find relevant context by meaning, supported by an AI Index.
03Connects and shares searchable context across Claude, Codex, and other MCP clients and built-in agents.
04Maintains local files as the source of truth, with indexes and derived data stored separately.
mcp-local-rag01Semantic search with keyword boost for exact technical terms
02Runs entirely locally, ensuring privacy and offline functionality
03Zero-friction setup with a single 'npx' command
04Smart semantic chunking based on meaning, not character count
05Quality-first result filtering based on relevance gaps
04
Use Cases
stashbase↳Providing AI agents with searchable access to research papers and contracts stored locally.
↳Enabling chatbots to answer questions based on internal documentation, scanned documents, and recordings.
↳Developers wanting to query project design documents and architecture notes using a built-in AI agent.
↳Organizing and searching personal knowledge bases that primarily consist of diverse local file types.
mcp-local-rag↳Searching internal technical specifications, research papers, or documentation privately
↳Enhancing AI assistant capabilities for query formulation and result interpretation
↳Ingesting and searching various document formats including PDF, DOCX, TXT, Markdown, and HTML
↳Local code search, accurately finding exact identifiers and error codes
05
Best For
stashbaseTrending
mcp-local-ragHidden Gem
FAQ
FAQ
What is the difference between stashbase and mcp-local-rag?
Both stashbase and mcp-local-rag are in the RAG / Knowledge Base category. stashbase has 1.1k stars, while mcp-local-rag has 416 stars.
Which is better, stashbase or mcp-local-rag?
The best choice depends on your use case. Choose stashbase if Providing AI agents with searchable access to research papers and contracts stored locally., and mcp-local-rag if Searching internal technical specifications, research papers, or documentation privately.
Is stashbase free or open source?
Yes, stashbase is open source on GitHub (Apache-2.0).
Is mcp-local-rag free or open source?
Yes, mcp-local-rag is open source on GitHub (MIT).
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