Pulse8 AI Cortex Knowledge Vault

Name: Pulse8 AI Cortex Knowledge Vault
Author: synpulse8-opensource

作者：synpulse8-opensource版本 1.6.0i★ –i在你的电脑上运行

注册名称io.github.synpulse8-opensource/pulse8-ai-cortex-knowledge-vault

安装

概览

作者提供的说明（英文原文）

Agent-native knowledge OS on Markdown: typed graph, hybrid search, and compiler over MCP.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 搜索与 AI 记忆
你需要填写：CORTEX_VAULT_PATH, CORTEX_LLM_API_KEY

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
需要你自己填写： CORTEX_VAULT_PATH, CORTEX_LLM_API_KEY

标为机密的值是你账户的钥匙。千万不要分享给别人，也不要粘贴到聊天里。

你需要

PyPI已安装 Python 的 uv（用于 uvx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "pulse8-ai-cortex-knowledge-vault": {
      "command": "uvx",
      "args": [
        "pulse8-ai-cortex-knowledge-vault",
        "cortex"
      ],
      "env": {
        "CORTEX_VAULT_PATH": "your-value",
        "CORTEX_LLM_API_KEY": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add pulse8-ai-cortex-knowledge-vault --env CORTEX_VAULT_PATH=your-value --env CORTEX_LLM_API_KEY=your-value -- uvx pulse8-ai-cortex-knowledge-vault cortex

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "pulse8-ai-cortex-knowledge-vault": {
      "command": "uvx",
      "args": [
        "pulse8-ai-cortex-knowledge-vault",
        "cortex"
      ],
      "env": {
        "CORTEX_VAULT_PATH": "your-value",
        "CORTEX_LLM_API_KEY": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "pulse8-ai-cortex-knowledge-vault": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "pulse8-ai-cortex-knowledge-vault",
        "cortex"
      ],
      "env": {
        "CORTEX_VAULT_PATH": "your-value",
        "CORTEX_LLM_API_KEY": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

需要你填写的值

CORTEX_VAULT_PATH 必填 env
Absolute path to the Markdown vault directory the server reads and writes.
CORTEX_LLM_API_KEY 机密 env
Optional OpenRouter (or compatible) API key. Only needed for LLM cross-referencing during compile; file conversion and search work without it.

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 synpulse8-opensource 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“搜索与 AI 记忆”中的其他服务器比较i

星标数多于此分类中其他 7247 个服务器的 0%
发布到注册中心的时间比此分类中其他 7247 个服务器的 25% 更新
基础检查结果等于或优于此分类中其他 7247 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

Pulse8 AI Cortex Knowledge Vault 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

你需要填写：CORTEX_VAULT_PATH, CORTEX_LLM_API_KEY

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（1.6.0）于3个月前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。

有什么可以替代它？

同一分类中有：Context7, Codebase Memory, Exa

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Context7

作者：upstash

Up-to-date docs for AI agents

电脑或云端★ 64,224MIT今天更新

安装

Codebase Memory

作者：DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

在你的电脑上运行★ 48,148MIT20天前更新

安装

Exa

作者：exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

在云端运行★ 5,213MIT上个月更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

PULSE8.ai Cortex

The open-source knowledge layer for AI agents

Knowledge that compounds.

PULSE8.ai Cortex is the open-source knowledge layer for AI agents: Git-native memory, a typed knowledge graph, and MCP-powered retrieval on top of plain Markdown — so agents can build, evolve, and reuse persistent knowledge instead of re-deriving it on every query.

Under the hood it's a unified vault for AI agents and humans, backed by a typed knowledge graph, full-text + hybrid search, and a MarkItDown-powered file compiler. Drop files in (PDF, DOCX, PPTX, XLSX, HTML, images, and more), let agents read, write, search, link, and compile knowledge — no database required.

Inspired by Andrej Karpathy's LLM Wiki pattern — a persistent, compounding knowledge base maintained by LLMs instead of re-derived on every query. Search powered by Tobi Lütke's QMD.

Why PULSE8.ai Cortex?

Most AI agents can access tools, but they cannot accumulate knowledge.

Traditional RAG systems retrieve documents. PULSE8.ai Cortex builds a persistent, evolving knowledge layer that grows over time and becomes more valuable the more agents and humans interact with it.

With PULSE8.ai Cortex, agents can:

Learn from interactions — every read, write, ingest, and compile event is logged and graph-linked
Build and traverse knowledge graphs — wikilinks, tags, and typed edges, maintained automatically
Store structured insights — Markdown notes with typed nodes (note, agent_def, session, daily, feedback)
Retrieve context across projects — vault_context builds a ranked subgraph from any seed query
Share knowledge through MCP — one vault, every MCP-compatible client
Maintain long-term memory — files survive sessions, deployments, and model upgrades
Version knowledge through Git — the vault is a plain directory of Markdown, diff-friendly out of the box

Aspect	Traditional RAG	PULSE8.ai Cortex
Focus	Documents	Knowledge
Memory	Session-based	Persistent
Structure	Chunks	Markdown + typed graph
Evolution	Static index	Continuous, file-watched
Versioning	None	Git-native
Agent collaboration	Limited	First-class (MCP)

When to use PULSE8.ai Cortex

Ideal use cases

✅ Persistent memory for AI agents
✅ Shared knowledge across multiple agents
✅ Git-versioned organisational knowledge
✅ MCP-compatible knowledge retrieval
✅ Knowledge graphs without a dedicated graph database
✅ Long-term accumulation of institutional knowledge
✅ Human + AI collaborative knowledge management

Not ideal for

❌ Simple full-text document search (use a search engine)
❌ Pure vector-only retrieval with no graph (use a vector DB)
❌ Short-lived, stateless conversations
❌ Workflows that don't need persistent knowledge evolution

PULSE8.ai Cortex vs alternatives

Capability	PULSE8.ai Cortex	Traditional RAG	GraphRAG
Persistent knowledge	✅	❌	⚠️
Markdown-native storage	✅	❌	❌
MCP-compatible out of the box	✅	❌	❌
Knowledge graph	✅	❌	✅
Git versioning	✅	❌	❌
Agent memory layer	✅	❌	⚠️
Human + AI collaboration	✅	❌	⚠️
Continuous knowledge evolution	✅	❌	⚠️
Zero database required	✅	❌	❌

Works with

PULSE8.ai Cortex speaks MCP — so it plugs into any AI client that does. The same vault is reachable over streamable HTTP or stdio, and mirrored 1:1 by a REST API at /api/v1/.

Category	Compatible with
AI agents	Claude Desktop, Claude Code, OpenAI Agents, Gemini, custom agent frameworks
Development tools	Cursor, VS Code, JetBrains IDEs
Agent frameworks	LangGraph, LangChain, CrewAI, AutoGen
MCP ecosystem	MCP clients, MCP servers, MCP tool registries
Human tools	Obsidian, any Markdown editor, any Git client

Because the vault is just files, humans and agents collaborate on the same knowledge — no proprietary format, no lock-in.

Get started

[!NOTE]

PULSE8.ai Cortex requires Docker. An OpenRouter API key is optional — needed only for LLM-powered cross-referencing between wiki articles. File conversion works out of the box without any API key.

Clone the repository:

    git clone https://github.com/synpulse8-opensource/pulse8-ai-cortex-knowledge-vault.git

cd cortex-knowledge-vault

2. Launch PULSE8.ai Cortex:

    ./scripts/start.sh

This builds and starts both PULSE8.ai Cortex (API + MCP on :8420) and QMD (search on :3100), waits for health checks, and you're ready to go.

Connect your MCP client (e.g. Claude Desktop) to http://localhost:8420/mcp/.

To stop: ./scripts/stop.sh

Native QMD mode (macOS / Metal GPU)

Docker Desktop on macOS cannot expose the Metal GPU to containers, so containerized QMD embeds on CPU only — over an order of magnitude slower on non-trivial vaults. Run QMD natively instead; the qmd binary uses Metal automatically:

# One-time: install the qmd binary

brew install tobi/tap/qmd # or: npm install -g @tobilu/qmd

Start native QMD (background daemon) + Cortex in Docker

./scripts/start.sh --native-qmd

The QMD daemon's pid and log are kept in .qmd-native.pid / .qmd-native.log. To stop both: ./scripts/stop.sh --native-qmd (a plain ./scripts/stop.sh also cleans up a native QMD if one is running).

Cortex-only mode (external QMD)

If you manage QMD yourself (already running elsewhere), start only the Cortex container:

./scripts/start.sh --cortex-only   # set QMD_URL in .env if not http://host.docker.internal:3100

To stop: ./scripts/stop.sh --cortex-only

GPU-accelerated QMD (EC2 / Linux with NVIDIA GPU)

For production deployments with NVIDIA GPU acceleration:

docker compose -f docker-compose.yml -f docker-compose.gpu.yml up --build -d

See docs/ec2-gpu-setup.md for a full guide on instance selection, NVIDIA toolkit installation, and cost estimates.

Features


Knowledge Graph	Typed graph engine (NetworkX) — wikilinks, tags, and custom edges, auto-maintained on every file change
Full-Text Search	QMD search with hybrid (BM25 + vector + re-ranking) by default; keyword and semantic modes selectable. Results cached with a configurable TTL.
File Compiler	Converts raw sources (PDF, DOCX, PPTX, XLSX, HTML, images, etc.) to Markdown via MarkItDown. LLM used only for cross-referencing.
MCP Server	Streamable HTTP + stdio transport — works with Claude Desktop, Cursor, and any MCP client
Feedback & Notifications	`vault_feedback` captures quality feedback as notes; optional Microsoft Teams webhook posts an adaptive card per submission
Daily Activity Log	Every write/ingest/compile is mirrored into `daily/ .md` as a greppable, wikilinked timeline
Bulk Ingest	Ingest dozens or hundreds of files at once from a local directory with SHA-256 dedup and bounded concurrency
REST API	FastAPI endpoints mirroring all MCP tools at `/api/v1/`, including multipart file upload and bulk ingest
Vault Watcher	Real-time filesystem monitoring — graph stays in sync automatically
Lineage & Audit	Every edge labeled `extracted` / `inferred` / `manual`; `vault_trace` answers "why does the vault say X" back to the source document
Graph Queries	`vault_path` (what connects X to Y), `vault_impact` (what's downstream of this note), `vault_explain` (entity summary with provenance)
Curation Report	Read counters + outcome feedback (`useful` / `dead-end` / `corrected`) surface stale, contradicted, and never-read notes at `GET /api/v1/curation/report`
Zero Database	Everything persists as Markdown + JSON on your filesystem

Benchmarks

45.0% overall accuracy on [LongMemEval-S](https://github.com/xiaowu0162/LongMemEval) (500 questions, full haystacks, hybrid search) with 65.6% evidence recall@8 and zero judge errors — measured end-to-end through the pu

只显示了开头部分；其余内容请在仓库中阅读