What is the key difference between Entroly and Kage?

Entroly belongs to the Knowledge & Memory category with local stdio execution, while Kage belongs to the Knowledge & Memory category with local stdio execution.

How do I install Entroly or Kage 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 Entroly and Kage free or paid?

Entroly operates under a Free / Open Source model (No auth required). Kage operates under a Free / Open Source model (No auth required).

Which MCP server has more tools and community activity?

Entroly lists 6 tools with 1 views and 435 GitHub stars. Kage lists 6 tools with 0 views and 32 GitHub stars.

Can I use both Entroly and Kage 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.

How do I install Entroly or Kage in Claude Desktop, Cursor, or Windsurf?

Both servers follow the standard Model Context Protocol configuration format. Simply copy the JSON block from the configuration section above and paste it into your AI client's mcpServers configuration object (for instance in claude_desktop_config.json or ~/.cursor/mcp.json ), then completely restart or refresh the application.

Are Entroly and Kage free to use, or do they require API keys?

Entroly is listed under a Free / Open Source model with No auth required . Kage operates under a Free / Open Source model with No auth required . If environment variables are required (such as API keys), be sure to define them under the env key in your MCP client's configuration file.

Can I run both Entroly and Kage at the same time in my AI client?

Yes! MCP clients support multi-server orchestration. You can include both juyterman1000-entroly and kage-core-kage as distinct keys inside the single mcpServers object in your configuration file. Your AI assistant will automatically select and call the appropriate tool from either server during chat sessions.

Which MCP server has higher directory engagement and quality ratings?

On AllMCPs, Entroly has a Quality Score of 56 /100 ( Good ) with 1 views, 0 installs, and 435 GitHub stars. Kage holds a Quality Score of 53 /100 ( Fair ) with 0 views, 0 installs, and 32 GitHub stars.

Side-by-Side Model Context Protocol Comparison

Entroly vs Kage

In-depth architectural comparison of the Entroly and Kage 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

Entroly

Knowledge & Memory · Local stdio

Quality: 56/100 (Good) | Auth: No auth required

Kage

Knowledge & Memory · Local stdio

Quality: 53/100 (Fair) | Auth: No auth required

Verdict Summary: Choose Entroly if you need specialized Knowledge & Memory tools running via a local process. Choose Kage 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 Entroly 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: Recoverable compression using BM25, entropy, and dependency graph knapsack, Auditable receipts detailing context kept and discarded, Local hallucination detection with WITNESS guard.

Explore Entroly Details

Choose Kage 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: Memory stored as plain JSON in git following Open Knowledge Format, Deterministic verification rejecting stale or hallucinated citations, Local-only operation with no account or API key required.

Explore Kage Details

Feature & Specification Comparison

Specification	Entroly juyterman1000 Knowledge & Memory	Kage kage-core Knowledge & Memory
Summary	Auditable context control plane and MCP server for AI coding agents. Compresses context 70–95% (BM25 + entropy + dep-graph knapsack), stabilizes prompt prefixes for provider cache discounts, routes easy tasks to cheaper models (RAVS Bayesian router), and verifies answers locally with WITNESS hallucination guard (0.844 AUROC, $0, 3 ms). MemoryOS adds local budget-aware working/episodic/semantic memory with decay, safety scanning, and durable persistence. 38 agent integrations (Cursor, Claude Code, Codex, Aider, and more). Ships as MCP server (entroly serve), HTTP proxy, or Python/Rust library. Apache-2.0, local-first. pip install entroly	Verified, git-native memory for coding agents. Memory is plain JSON packets committed in your repo, each checked against the code it cites — hallucinated citations rejected at write, stale or changed memory withheld at recall, plus diff-time stale-catch. Local-only (BM25 + vectors), no account, no API key. npx -y @kage-core/kage-graph-mcp install
Category & Scope	Knowledge & Memory	Knowledge & Memory
Quality signal	56/100 (Good)	53/100 (Fair)
Transport Protocol	Local Subprocess (stdio)	Local Subprocess (stdio)
Auth Requirement	No auth required	No auth required
Pricing Model	Free / Open Source	Free / Open Source
Required Env Vars	None required	None required
Compatible Clients	Claude DesktopCursorWindsurfClineVS Code	Claude DesktopCursorWindsurfClineVS Code
Install path signal	`uvx · high`	`npx · high`
Engagement & Health	1 views 0 copies 0 upvotes 435 stars	0 views 0 copies 0 upvotes 32 stars
Verified / Official	Community Listing	Community Listing
Open full listing	View Entroly Listing	View Kage Listing

Tools & Capabilities Breakdown

Entroly Tools (6)

Recoverable compression using BM25, entropy, and dependency graph knapsack

Auditable receipts detailing context kept and discarded

Local hallucination detection with WITNESS guard

Budget-aware memory with decay and durable persistence

Routing tasks to cheaper models via Bayesian router

Supports 38+ AI agent integrations and multiple deployment modes

Kage Tools (6)

Memory stored as plain JSON in git following Open Knowledge Format

Deterministic verification rejecting stale or hallucinated citations

Local-only operation with no account or API key required

Background proxy for zero-wiring integration with Anthropic agents

Per-directory auto-attach configuration for seamless agent routing

Compatibility with multiple coding agents including Claude Code and Codex

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).

Entroly Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "juyterman1000-entroly": {
      "command": "uvx",
      "args": [
        "entroly"
      ]
    }
  }
}

Kage Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "kage-core-kage": {
      "command": "npx",
      "args": [
        "-y",
        "@kage-core/kage-graph-mcp"
      ]
    }
  }
}

Frequently Asked Questions

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

More alternatives to Entroly More alternatives to Kage Knowledge & Memory category hub