What is the key difference between Tempera and Entroly?

Tempera 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 Tempera 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 Tempera and Entroly free or paid?

Tempera 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?

Tempera lists 12 tools with 0 views and 6 GitHub stars. Entroly lists 6 tools with 3 views and 465 GitHub stars.

Can I use both Tempera 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.

Side-by-Side Model Context Protocol Comparison

Tempera vs Entroly

In-depth architectural comparison of the Tempera 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

Tempera

Knowledge & Memory · Local stdio

Quality: 45/100 (Fair) | Auth: No auth required

Entroly

Knowledge & Memory · Local stdio

Quality: 64/100 (Good) | Auth: No auth required

Verdict Summary: Choose Tempera 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 Tempera 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: tempera_session_start, tempera_brief, tempera_retrieve.

Explore Tempera 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: Budgeted evidence selection, Recoverable context compression, Content-addressed evidence recovery.

Explore Entroly Details

Feature & Specification Comparison

Specification	Tempera Knowledge & Memory	Entroly juyterman1000 Knowledge & Memory
Summary	Persistent episodic memory for AI coding: capture, retrieve, brief, dream cycle, cross-project.	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

Tools & Capabilities Breakdown

Tempera Tools (12)

tempera_session_start

Call ONCE at the very start. Returns any clarifying question tempera drafted after a previous failed/partial session in this project.

tempera_brief

Call once the file set is known. Joins pending ask-back, reasoning template, top correction categories for these files, should-have-asked triggers, and calibration warning into one response. Pass `task_type` + `domain` for richer output. Set `cross_project=true` to supplement with rows from other p…

tempera_retrieve

Search for similar past episodes. Set `scope="cross-project"` to include transferable claims from other projects.

tempera_template

Pull the reasoning template stored for a `(task_type, domain)` pair. The step sequence past wins followed.

tempera_log_correction

When the user corrects an assumption / decision / piece of code. Categorized log; the brief surface uses it.

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).

Tempera Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "tempera": {
      "command": "npx",
      "args": [
        "-y",
        "tempera"
      ]
    }
  }
}

Entroly Configuration

mcpServers (Claude Desktop / Cursor)

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

Frequently Asked Questions

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

More alternatives to Tempera More alternatives to Entroly Knowledge & Memory category hub Canonical compare URL