Pulse8 AI Cortex Knowledge Vault

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

by synpulse8-opensourceversion 1.6.0i★ –iRuns on your computer

Registry nameio.github.synpulse8-opensource/pulse8-ai-cortex-knowledge-vault

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory
You need to fill in: CORTEX_VAULT_PATH, CORTEX_LLM_API_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: CORTEX_VAULT_PATH, CORTEX_LLM_API_KEY

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into 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"
      }
    }
  }
}

Run this in your terminal

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

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.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"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "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"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

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

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account synpulse8-opensource, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7247 servers in this category
Published to the registry more recently than 25% of the other 7247 servers in this category
Basic checks equal to or better than 100% of the other 7247 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Pulse8 AI Cortex Knowledge Vault safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: CORTEX_VAULT_PATH, CORTEX_LLM_API_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.6.0) reached the registry 3 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated today

Install

Codebase Memory

by DeusData

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

Runs on your computer★ 48,148MITUpdated 20 days ago

Install

Exa

by exa-labs

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

Runs in the cloud★ 5,213MITUpdated last month

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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

Only the beginning is shown; read the rest in the repo

Pulse8 AI Cortex Knowledge Vault

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

Why PULSE8.ai Cortex?

When to use PULSE8.ai Cortex

Ideal use cases

Not ideal for

PULSE8.ai Cortex vs alternatives

Works with

Get started

Native QMD mode (macOS / Metal GPU)

Start native QMD (background daemon) + Cortex in Docker

Cortex-only mode (external QMD)

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

Features

Benchmarks