Name: albert-ying/agentic-cortex
Rating: 3.3 (1 reviews)
Author: albert-ying

albert-ying/agentic-cortex

Score: 57.9 Rank #6

A personal AI operating system built on OpenClaw/Claude Code, MCP servers, and structured markdown. Feedback RL, voice cloning, Screenpipe integration.

Overview

albert-ying/agentic-cortex is a Python MCP server licensed under MIT. A personal AI operating system built on OpenClaw/Claude Code, MCP servers, and structured markdown. Feedback RL, voice cloning, Screenpipe integration.

Ranked #6 out of 104 indexed tools.

In the top 6% of all indexed tools.

Actively maintained with commits in the last week.

Ecosystem

Python MIT

Score Breakdown

Stars 15% 6

6 stars → early stage

Freshness 25% 1d ago

Last commit 1d ago → actively maintained

Issue Health 25% 50%

No issues filed → no history to score

Contributors 10% 1

1 contributor → solo project

Dependents 25% 0

No dependents → no downstream usage

Forks 1

Description Detailed

License MIT

Weights: Freshness 25% · Issue Health 25% · Dependents 25% · Stars 15% · Contributors 10% · How we score →

How to Improve

Contributors medium impact

Single-contributor projects carry bus-factor risk — welcoming contributors boosts confidence

Dependents medium impact

No downstream dependents detected yet — adoption by other projects is the strongest trust signal

Badge

Markdown

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HTML

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Embed Widget docs →

HTML

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Details

Stars: 6
Forks: 1
Open Issues: 0
Closed Issues: 0
Contributors: 1
Dependents: —
Language: Python
License: MIT
Last Commit: 1d ago

Matched Queries

"mcp server""mcp-server"

From the README

# 🧠 Agentic Cortex

> 🦞 Built for [OpenClaw](https://github.com/nicobailon/openclaw) / [Claude Code](https://docs.anthropic.com/en/docs/claude-code) — a personal AI operating system that shares your memory, your voice, and your taste.

**[Quick Start](#-quick-start)** · **[Installation](#-installation)** · **[Architecture](#architecture)** · **[Chapters](#chapters)** · **[FAQ](#faq)**

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## What Makes This Different

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Storing notes in markdown is not new. Obsidian, Logseq, and Dendron all do it. What none of them do is close the loop: structured markdown as persistent AI memory + MCP integrations for live data + a structured feedback mechanism that reshapes AI behavior from natural language corrections + voice cloning extracted from your writing history + ambient intelligence from screen and audio recording.

The result is an AI agent that has the same memory as you — and even more precise memory. I

Read full README on GitHub →