Datacore

Datacore

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

Category
访问服务器

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

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选