Compartment vs Rag Kb

In-depth architectural comparison of the Compartment and Rag Kb MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.

Which MCP Server Should You Choose?

Choose Compartment when:

• You need dedicated capabilities in the Knowledge & Memory domain.
• You prefer local stdio subprocess transport architecture.
• Your security boundary fits: No auth required (Free / Open Source).

Explore Compartment Details

Choose Rag Kb when:

• You need dedicated capabilities in the Knowledge & Memory domain.
• You prefer local stdio subprocess transport architecture.
• Your security boundary fits: No auth required (Free / Open Source).

Explore Rag Kb Details

Feature & Specification Comparison

Specification	Compartment Knowledge & Memory	Rag Kb fish827-08 Knowledge & Memory
Summary	Fully offline, encrypted-at-rest vector memory for AI agents. AEAD-encrypted vectors, no cloud.	Local-first agent memory & knowledge service with hybrid retrieval (BGE-M3 dense vectors + BM25 via jieba, fused with RRF), document & webpage ingestion, and optional RAG answering via local Ollama. Single process, embedded ChromaDB, REST + native MCP server; fully functional offline without any LLM. Apache-2.0.
Category & Scope	Knowledge & Memory

Tools & Capabilities Breakdown

Ready-to-Paste Client Configurations

Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).

{
  "mcpServers": {
    "compartment": {
      "command": "uvx",
      "args": [
        "compartment"
      ]
    }
  }
}

{
  "mcpServers": {
    "fish827-08-rag-kb": {
      "command": "npx",
      "args": [
        "-y",
        "fish827-08-rag-kb"
      ]
    }
  }
}

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