MemoryRouter vs Entroly

A side-by-side look at the MemoryRouter and Entroly MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.

M
MemoryRouterKnowledge & Memory · Remote HTTP/SSE
Persistent AI memory shared across Claude, ChatGPT, coding agents, and compatible MCP clients.39FairAuth: no auth required
Entroly logoEntrolyKnowledge & Memory · Local subprocess (stdio)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 entroly65GreatAuth: no auth required

Verdict: choose MemoryRouter for knowledge & memory work over a hosted endpoint; choose Entroly for knowledge & memory as a local process. You can also run both side by side in one mcpServers block.

Which MCP server should you choose?

M
Choose MemoryRouter when

  • You need dedicated capabilities in the Knowledge & Memory domain.
  • You prefer a remote HTTP/SSE transport.
  • Its auth model fits you: No auth required.
Explore MemoryRouter

Entroly logoChoose Entroly when

  • You need dedicated capabilities in the Knowledge & Memory domain.
  • You prefer a local stdio subprocess transport.
  • Its auth model fits you: No auth required.
Explore Entroly

Feature & specification comparison

Rows where the two servers differ are marked.

Spec
M
MemoryRouter
Entroly logoEntroly
CategoryKnowledge & MemoryKnowledge & Memory
Quality signal39/100 · Fair65/100 · Great
Install commandConfirmedConfirmed
TransportRemote HTTP/SSELocal subprocess (stdio)
AuthNo auth requiredNo auth required
PricingNot declaredFree
Required env varsNone listedNone listed
Verified ownerCommunity listingCommunity listing
GitHub stars—479
Directory activity 2 views 0 installs 0 upvotes 4 views 0 installs 0 upvotes
ToolsNot listedNot listed

Tools & capabilities

MemoryRouter tools

No tool names declared yet. Check the project README from the listing page.

Entroly tools

No tool names declared yet. Check the project README from the listing page.

Ready-to-paste client configs

Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.

MemoryRouter

mcpServers (Claude Desktop / Cursor)
{
  "mcpServers": {
    "memoryrouter": {
      "command": "npx",
      "args": [
        "-y",
        "memoryrouter"
      ]
    }
  }
}

Entroly

mcpServers (Claude Desktop / Cursor)
{
  "mcpServers": {
    "juyterman1000-entroly": {
      "command": "npx",
      "args": [
        "-y",
        "entroly"
      ]
    }
  }
}

Frequently asked questions

MemoryRouter is categorized under Knowledge & Memory and uses a remote HTTP/SSE transport. Entroly belongs to Knowledge & Memory and uses a local stdio subprocess. Pick MemoryRouter for knowledge & memory work and Entroly for knowledge & memory.

Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.

MemoryRouter has no declared pricing — check the project's own site. Auth: no auth required. Entroly is listed as free. Auth: no auth required. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.

Yes. MCP clients support multiple servers: add memoryrouter and juyterman1000-entroly as separate keys in the same mcpServers object, and your assistant will call tools from either one.

On AllMCPs, MemoryRouter has a quality signal of 39/100 (Fair) with 2 views, 0 installs and 0 GitHub stars. Entroly has 65/100 (Great) with 4 views, 0 installs and 479 GitHub stars.