What is the key difference between Engram and Entroly?

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

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

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

Which MCP server has more tools and community activity?

Engram lists 6 tools with 1 views and 7 GitHub stars. Entroly lists 6 tools with 1 views and 435 GitHub stars.

Can I use both Engram and Entroly 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 Engram or Entroly 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 Engram and Entroly free to use, or do they require API keys?

Engram is listed under a Free / Open Source model with No auth required . Entroly 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 Engram and Entroly at the same time in my AI client?

Yes! MCP clients support multi-server orchestration. You can include both hbarefoot-engram and juyterman1000-entroly 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, Engram has a Quality Score of 56 /100 ( Good ) with 1 views, 0 installs, and 7 GitHub stars. Entroly holds a Quality Score of 56 /100 ( Good ) with 1 views, 0 installs, and 435 GitHub stars.

Side-by-Side Model Context Protocol Comparison

Engram vs Entroly

In-depth architectural comparison of the Engram and Entroly 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

Engram

Knowledge & Memory · Local stdio

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

Entroly

Knowledge & Memory · Local stdio

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

Verdict Summary: Choose Engram if you need specialized Knowledge & Memory tools running via a local process. Choose Entroly 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 Engram 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: engram_remember, engram_recall, engram_forget.

Explore Engram Details

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

Feature & Specification Comparison

Specification	Engram HBarefoot Knowledge & Memory	Entroly juyterman1000 Knowledge & Memory
Summary	Local-first persistent memory for AI agents. SQLite + local embeddings (all-MiniLM-L6-v2), hybrid semantic + FTS5 recall, secret detection, and contradiction handling. 6 MCP tools (remember, recall, forget, feedback, context, status) over stdio. Zero cloud, no API keys, fully offline. Works with Claude Desktop/Code, Cursor, and Windsurf. npm install -g @hbarefoot/engram	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
Category & Scope	Knowledge & Memory	Knowledge & Memory
Quality signal	56/100 (Good)	56/100 (Good)
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	`npx · high`	`uvx · high`
Engagement & Health	1 views 0 copies 0 upvotes 7 stars	1 views 0 copies 0 upvotes 435 stars
Verified / Official	Community Listing	Community Listing
Open full listing	View Engram Listing	View Entroly Listing

Tools & Capabilities Breakdown

Engram Tools (6)

engram_remember

Store a memory with category, entity, confidence, namespace, tags. Auto-runs secret detection.

engram_recall

Hybrid semantic + FTS5 search. Supports `category`, `namespace`, `threshold`, and `time_filter`.

engram_forget

Delete a specific memory by ID.

engram_feedback

Vote a memory helpful/unhelpful. Drives the feedback loop above.

engram_context

Pre-formatted context block (`markdown` / `xml` / `json` / `plain`) with a token budget for system-prompt injection.

engram_status

Health check: memory count, model status, configuration.

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

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).

Engram Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "hbarefoot-engram": {
      "command": "npx",
      "args": [
        "-y",
        "package"
      ]
    }
  }
}

Entroly Configuration

mcpServers (Claude Desktop / Cursor)

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

Frequently Asked Questions

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

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