What is the key difference between Knowledge Rag and Engram Rs?

Knowledge Rag belongs to the Knowledge & Memory category with local stdio execution, while Engram Rs belongs to the Knowledge & Memory category with local stdio execution.

How do I install Knowledge Rag or Engram Rs in Claude Desktop or Cursor?

Copy the mcpServers JSON configuration snippet provided on this page into your claude_desktop_config.json or ~/.cursor/mcp.json file and restart your AI client.

Are Knowledge Rag and Engram Rs free or paid?

Knowledge Rag operates under a Free / Open Source model (No auth required). Engram Rs operates under a BYOK (Pay Provider Direct) model (API Key required).

Which MCP server has more tools and community activity?

Knowledge Rag lists 13 tools with 3 views and 280 GitHub stars. Engram Rs lists 16 tools with 2 views and 27 GitHub stars.

Can I use both Knowledge Rag and Engram Rs together in the same MCP host?

Yes! MCP hosts like Claude Desktop, Cursor, Windsurf, Cline, and VS Code support configuring multiple servers under the mcpServers object simultaneously.

Side-by-Side Model Context Protocol Comparison

Knowledge Rag vs Engram Rs

In-depth architectural comparison of the Knowledge Rag and Engram Rs 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.

At a Glance & Executive Verdict

Knowledge Rag

Knowledge & Memory · Local stdio

Quality: 63/100 (Good) | Auth: No auth required

Engram Rs

Knowledge & Memory · Local stdio

Quality: 60/100 (Good) | Auth: API Key required

Verdict Summary: Choose Knowledge Rag if you need specialized Knowledge & Memory tools running via a local process. Choose Engram Rs if your workspace requires Knowledge & Memory integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.

Which MCP Server Should You Choose?

Choose Knowledge Rag 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).
Primary tools included: search_knowledge, get_document, reindex_documents.

Explore Knowledge Rag Details

Choose Engram Rs when:

You need dedicated capabilities in the Knowledge & Memory domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: API Key required (BYOK (Pay Provider Direct)).
You have access to required keys: ENGRAM_EMBEDDING_PROVIDER, ENGRAM_EMBEDDING_API_KEY.
Primary tools included: engram_store, engram_recall, engram_recent.

Feature & Specification Comparison

Specification	Knowledge Rag lyonzin Knowledge & Memory	Engram Rs kael-bit Knowledge & Memory
Summary	Local RAG system for Claude Code with hybrid search (BM25 + semantic), cross-encoder reranking, markdown-aware chunking, query expansion, and 28 MCP tools. Runs entirely offline with zero external servers.	Hierarchical memory engine for AI agents with automatic decay, promotion, semantic dedup, and self-organizing topic tree. Single Rust binary, zero external dependencies.
Category & Scope

Tools & Capabilities Breakdown

Knowledge Rag Tools (13)

search_knowledge

Hybrid search combining semantic search + BM25 keyword search with cross-encoder reranking. Read-only. No side effects. Args: query: Search query text (1–3 keywords recommended; phrase queries also work) max_results: Maximum number of results (default: 5, max: 20) category: Optional category filter — one of: security, ctf, logscale, development, general, redteam, blueteam. Call list_categories() first to see available categories and counts. hybrid_alpha: Balance between semantic and keyword search. 0.0 = keyword-only (best for exact technical terms like CVE IDs or tool names), 0.3 = balanced default, 1.0 = semantic-only (best for conceptual or natural-language queries). min_score: Minimum normalized relevance score (0.0–1.0) to include a result. Results scoring below this threshold are discarded. Default 0.0 returns all results. Use 0.2–0.4 to cut low-relevance noise. snippet_mode: When true (default), truncates content to ~500 characters at a natural break point and adds a content_length field with the original size. Use get_document() to fetch full content when needed. Set to false to return full chunk content. search_method: Dispatch selector (v4.8.2+). One of ``"auto"`` (router picks FTS5 fast-path for lexical queries when enabled, hybrid otherwise), ``"hybrid"`` (force hybrid path — kill switch for suspected router misclassification), or ``"fts5"`` (force FTS5 fast-path — debug/testing; errors out when the feature is disabled or the index is not ready). Default ``"auto"`` preserves pre-v4.8.2 behavior byte-for-byte when the fast-path is disabled in config. Returns: JSON string with results including content chunks, source filepath, relevance score, and search method used. Returns chunks, not full document content. Usage: Primary search tool — use for any topic or keyword lookup. Prefer search_similar() when you already have a reference document and want more like it. Prefer get_document() when you already know the exact filepath and need the full content.

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).

Knowledge Rag Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "lyonzin-knowledge-rag": {
      "command": "uvx",
      "args": [
        "knowledge-rag"
      ]
    }
  }
}

Engram Rs Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "kael-bit-engram-rs": {
      "command": "npx",
      "args": [
        "-y",
        "engram-rs-mcp"
      ],
      "env": {
        "ENGRAM_EMBEDDING_PROVIDER": "YOUR_ENGRAM_EMBEDDING_PROVIDER_HERE",
        "ENGRAM_EMBEDDING_API_KEY": "YOUR_ENGRAM_EMBEDDING_API_KEY_HERE"
      }
    }
  }
}

Frequently Asked Questions

Knowledge Rag is categorized under Knowledge & Memory and uses a local stdio subprocess. In contrast, Engram Rs belongs to Knowledge & Memory using local stdio subprocess. Select Knowledge Rag when you need capabilities focused on knowledge & memory and Engram Rs when you require tools for knowledge & memory.

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