Datacore

Datacore

AI second-brain engine: GTD, knowledge graph, and engram memory over MCP.

Category
Visit Server

README

Datacore

Own Your Intelligence.

An open-source framework for building AI-automated businesses. Datacore gives Claude (and other MCP-compatible agents) the context, structure, and autonomy to run day-to-day operations while you focus on strategy.

License: MIT Python 3.8+ Modules DIPs

Quick Start

Option 1: Let your AI install it

Tell Claude Code (or Cursor, Windsurf, OpenClaw):

"Go to datacore.one and install Datacore."

Option 2: CLI

npx @datacore-one/cli init

Sets up ~/Data, clones modules, and configures the MCP server automatically.

Option 3: MCP server only

npx @datacore-one/mcp init

Then add to .claude/mcp.json or .cursor/mcp.json:

"datacore": {
  "command": "npx",
  "args": ["-y", "@datacore-one/mcp"]
}

Then open Claude Code and try /today or /continue. See GETTING_STARTED.md for a full walkthrough.


What is Datacore?

It starts as an extended mind. It becomes an autonomous business.

Stage 1 — Extended mind        ← start here
  AI that knows your work, remembers your decisions, surfaces what matters.
  Persistent memory via PLUR. GTD task management. Zettelkasten knowledge base.

Stage 2 — Autonomous business  ← where most users end up
  Agents run day-to-day operations: content, research, outreach, coordination.
  Queued during the day. Executed overnight. Reviewed in your morning briefing.

Stage 3 — AI business network  ← the horizon
  Agents from different businesses collaborating and exchanging value.

Your data stays on your drive. You control the agents. You set the direction.

At its core, it provides:

  • Autonomous execution -- Delegate tasks to AI agents overnight; wake up to a quality-evaluated briefing
  • GTD task management -- Capture, organize, and delegate tasks using Getting Things Done methodology with org-mode
  • Knowledge management -- Zettelkasten-style notes, wiki-links, and semantic search across your knowledge base
  • Modular architecture -- Install only what you need; extend with community or custom modules
  • Persistent memory -- Powered by PLUR (preinstalled): corrections, preferences, and decisions survive across sessions

How It Works

You capture ideas and tasks
        |
Datacore organizes, links, and indexes them
        |
AI assistants access your knowledge and context via MCP
        |
Agents execute delegated work overnight
        |
You review results in your morning briefing

Prerequisites


Architecture

~/Data/
|
+-- .datacore/                    # System core
|   +-- agents/                   # AI agent definitions
|   +-- commands/                 # Slash commands (workflows)
|   +-- modules/                  # Installed modules
|   +-- lib/                      # Python utilities
|   +-- specs/                    # System specifications
|   +-- dips/                     # Design proposals
|   +-- registry/                 # Agent, command, source registries
|   +-- state/                    # Runtime state (gitignored)
|   \-- env/                      # Secrets (gitignored)
|
+-- 0-personal/                   # Personal space
|   +-- org/                      # GTD system (org-mode)
|   +-- notes/                    # PKM (Obsidian)
|   +-- code/                     # Personal projects
|   \-- content/                  # Generated content
|
+-- [N]-[name]/                   # Team spaces (separate repos)
|
+-- CLAUDE.md                     # AI context (layered, auto-generated)
+-- install.yaml                  # Installation manifest
\-- sync                          # Multi-repo sync script

Key Concepts

Spaces -- Isolated workspaces for different contexts (personal, teams, organizations). Each space has its own GTD system, knowledge base, and journal. Team spaces are separate git repos.

Agents -- AI agent definitions that handle specific types of work: inbox processing, content writing, data analysis, research orchestration, project management, and more.

Commands -- Slash commands that orchestrate multi-step workflows: /today (morning briefing), /continue (resume work), /tomorrow (end-of-day delegation), /wrap-up (session close).

Modules -- Optional extensions that add domain-specific functionality. Install only what you need.

Layered Context -- Configuration files use a four-layer privacy model (public, org, team, private) so you can contribute improvements upstream without exposing personal data.

Memory -- Persistent memory is handled by PLUR, an open-source engram engine that comes preinstalled. Corrections, preferences, and decisions survive across sessions and are injected automatically — no setup needed.


Modules

Public modules available for community use:

Module Description
gtd Getting Things Done -- task capture, inbox processing, org-mode management
nightshift Autonomous overnight task execution with multi-persona quality evaluation
research Automated research pipelines with knowledge extraction and podcast generation
outbox Content routing out of active workspaces -- archive, delivery, publish
datacortex Knowledge graph -- semantic search, graph statistics, link analysis
crm Network intelligence -- track entities, relationships, interaction history
meetings Meeting lifecycle -- standup generation, preparation, transcription processing
mail Email integration -- Gmail adapter, classification, processing

See the Module Catalog for installation instructions and the full list of available modules.


Documentation

Resource Description
Getting Started Quick walkthrough for new users
Installation Guide Complete setup instructions
Contributing How to contribute
Module Catalog Available modules and space templates
DIP Specifications System design documents
Agent Registry All registered agents
Command Registry All registered commands

Contributing

Datacore uses a fork-and-overlay contribution model. Fork the repo, make improvements to public layer files (.base.md), and submit a PR upstream. Your private configuration stays local and is never shared.

See CONTRIBUTING.md for full guidelines.


License

MIT License -- see LICENSE for details.


Datacore is built by Datacore. The AI system that bootstraps itself into existence.

datacore.one · github.com/datacore-one/datacore

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured