Wax — MCP Server by christopherkarani

Q: What is Wax?

Wax is Single-file memory layer for AI agents, sub mili-second RAG on Apple Silicon. Metal Optimized On-Device. No Server. No API. One File. Pure Swift. It is categorized as a MCP Server with 760 GitHub stars.

Q: What programming language is Wax written in?

Wax is primarily written in Swift. It covers topics such as ai-agents, cli, coreml.

Q: How do I install or use Wax?

You can find installation instructions and usage details in the Wax GitHub repository at github.com/christopherkarani/Wax. The project has 760 stars and 47 forks, indicating an active community.

Q: What license does Wax use?

Wax is released under the Apache-2.0 license, making it free to use and modify according to the license terms.

Q: What are the best alternatives to Wax?

The top alternatives to Wax on Agent Skills Hub include mcp-memory-service, ClawMem, SocratiCode. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

by christopherkarani · MCP Server · ★ 760

Last updated: 2026-06-14 · Indexed by AgentSkillsHub · Auto-synced every 8h

About Wax

Wax is a high-performance, single-file memory layer for AI agents on Apple platforms. On-device, private, and portable. No server and no cloud dependency. English · Español · Français · 日本語 · <a href="Resources/locales/R

ai-agents cli coreml coreml-framework data-science machine-learning mcp mcp-server memory memory-cache

Quick Facts

Stars	760
Forks	47
Language	Swift
Category	MCP Server
License	Apache-2.0
Quality Score	53.138/100
Last Updated	2026-06-14
Created	2026-01-20
Platforms	cli, mcp
Est. Tokens	~18k

Compatible Skills

These tools work well together with Wax for enhanced workflows:

lucid-memory — semantic(0.35)+complementary+rare_topics+similar_pop (51%)
maclocal-api — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (51%)
relevanceai — semantic(0.30)+complementary+rare_topics+similar_pop (49%)
InterposeKit — semantic(0.17)+complementary+same_lang+similar_pop (46%)
honcho — semantic(0.31)+complementary+rare_topics+similar_pop (46%)

Wax alternative? Top 6 similar tools

Looking for a Wax alternative? If you're comparing Wax with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

mcp-memory-service by doobidoo · ⭐ 1.9k
Open-source persistent memory for AI agent pipelines (LangGraph, CrewAI, AutoGen) and Claude. REST API + knowl
ClawMem by yoloshii · ⭐ 186
On-device memory layer for AI agents. Claude Code, Hermes and OpenClaw. Hooks + MCP server + hybrid RAG search
SocratiCode by giancarloerra · ⭐ 3.0k
Enterprise-grade (40m+ LOC) codebase intelligence, zero-setup, local & private Plugin/Skill/Extension or MCP:
mcp by MicrosoftDocs · ⭐ 1.7k
Official Microsoft Learn MCP Server and CLI tool – powering LLMs and AI agents with real-time, trusted Microso
ai-devkit by codeaholicguy · ⭐ 1.4k
The control plane for AI coding agents.
cupertino by mihaelamj · ⭐ 834
A local Apple Documentation crawler and MCP server. Written in Swift.

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Frequently Asked Questions

What is Wax?

Wax is Single-file memory layer for AI agents, sub mili-second RAG on Apple Silicon. Metal Optimized On-Device. No Server. No API. One File. Pure Swift. It is categorized as a MCP Server with 760 GitHub stars.

What programming language is Wax written in?

Wax is primarily written in Swift. It covers topics such as ai-agents, cli, coreml.

How do I install or use Wax?

You can find installation instructions and usage details in the Wax GitHub repository at github.com/christopherkarani/Wax. The project has 760 stars and 47 forks, indicating an active community.

What license does Wax use?

Wax is released under the Apache-2.0 license, making it free to use and modify according to the license terms.

What are the best alternatives to Wax?

The top alternatives to Wax on Agent Skills Hub include mcp-memory-service, ClawMem, SocratiCode. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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