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Memory and knowledge · MCP server

Knowledge rag: MCP server, setup and security

Knowledge rag is an open-source MCP server published on GitHub that lets an AI agent retrieve offline knowledge for Claude Code using hybrid search. Its repository has 300 stars and its last commit is from October 23, 2026 and it is licensed under MIT.

Actively maintained

What Knowledge rag does

According to its repository: “Offline RAG for Claude Code with hybrid BM25 and semantic search, cross-encoder reranking, Markdown-aware chunking and query expansion.”

The Model Context Protocol (MCP) is a standard plug between an AI agent and an outside service: once the server is connected, its tools show up in the agent, which decides when to call them. Knowledge rag is listed under Memory and knowledge, a category of 250 servers in the directory, where it ranks #38 by stars.

Topics declared by the repository: claude, claude-code, local-ai, rag, retrieval-augmented-generation, semantic-search.

Installing and connecting Knowledge rag

No package or remote server is declared in the official MCP registry: you install it from source, following the repository README.

It works with any MCP-compatible client (Claude Desktop, Claude Code, Cursor, VS Code, Codex and most recent agents). You add one entry to the client's MCP configuration: server name, start command or URL, environment variables. Project language: Python. Listed systems: macOS, Windows, Linux.

Security: what to check

Connecting an MCP server means installing software that talks straight to your agent: it runs with your rights and the model trusts its tool descriptions. The risk therefore depends on what it can touch and how far its code can be trusted.

None of the risk signals we track (system access, secrets, missing licence, abandonment) applies to Knowledge rag. That does not replace reading the code.

Good practice: grant only the rights needed (least privilege), require human approval for irreversible actions, and run the AI agent security check.

Project activity and maintenance

Created on February 5, 2026, the lyonzin/knowledge-rag repository has 300 stars and 49 forks, with 3 open issues. It has more stars than 83% of the servers in the directory. It received changes this month.

Stars measure attention, not quality: a recent, heavily promoted project can collect them fast, while a useful but niche server will have few.

Context7Context7 : 63.6K63.6KHindsightHindsight : 53.4K53.4KCogneeCognee : 32.7K32.7KSkill SeekersSkill Seekers : 15.2K15.2KHonchoHoncho : 7.8K7.8KBasic memoryBasic memory : 4.3K4.3KNoteDiscoveryNoteDiscovery : 2.8K2.8KMemantoMemanto : 2.3K2.3KKnowledge ragKnowledge rag : 300300
GitHub stars of the most followed servers in the category (log scale); Knowledge rag in orange. Checked October 25, 2026.

Verdict

Knowledge rag is a reasonable choice; check the points below before connecting it.

Strengths

  • Active project: recent commit
  • Widely followed: 300 stars
  • Permissive licence (MIT)

Limitations

  • Install from source

Frequently asked questions

How do I install Knowledge rag?

No package is declared in the MCP registry: clone the lyonzin/knowledge-rag repository, follow its README, then declare the start command in your client's MCP configuration.

Is Knowledge rag free?

The code is published under the MIT licence and can be used free of charge.

Is Knowledge rag safe?

No risk signal found (system access, secrets, licence, abandonment). As with any MCP server, keep rights minimal and read the code before sensitive use.

Is it an official server?

Nothing says so: it is a project published by lyonzin on GitHub. Check who maintains it before giving it access.

What are the alternatives to Knowledge rag?

In the same category: Context7, Basic memory, Hindsight, and Cognee. Context7 and Basic memory score higher on our grid.

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Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score