PharosRAG MCP Server
Laurent00TT/PharosRAG
[](https://agentrank-ai.com/tool/Laurent00TT--PharosRAG/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge) Pharos — local-first agentic RAG for your team's document library: multi-format ingest, hybrid retrieval, enterprise ACL, dual HTTP + MCP exits.
claude mcp add agentrank -- npx -y agentrank-mcp-server Overview
Laurent00TT/PharosRAG is a Python tool licensed under MIT. Pharos — local-first agentic RAG for your team's document library: multi-format ingest, hybrid retrieval, enterprise ACL, dual HTTP + MCP exits. Topics: agentic-rag, embeddings, fastapi, hybrid-search, knowledge-base, llm, local-first, mcp, model-context-protocol, python, qdrant, rag, retrieval-augmented-generation, self-hosted.
Ranked #24901 out of 24846 indexed tools.
Ecosystem
Score Breakdown
243 stars → growing community
Last commit 2mo ago → moderate activity
No issues filed → no history to score
1 contributor → solo project
No dependents → no downstream usage
Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →
How to Improve
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From the README
<div align="center"> # Pharos **Turn PDFs, scans, docx, pptx and xlsx into a local knowledge base you can ask questions of — with enterprise access control and citations you can trace back to the page.** Each RAG component was sharpened on its own, then folded into one repository: multi-identity auth, observability and systemd supervision come with it. </div> > **A note on language.** The code, CLI and commit history are in English; the > documentation — including the 12-part learning series — is written in Chinese. > This README is the English entry point and covers what the system is, how it is > put together and how to run it. For anything deeper, you will be reading Chinese > (or machine-translating it). --- > The Lighthouse of Alexandria stood beside the Library, guiding ships to shore. **Pharos does the same job, except what it lights up is your team's document library.** This is not another chunking toy. Every significant decision here is backed by a measurement, argued tRead full README on GitHub →
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