Linggen Releases: Local-First MCP Memory Layer for AI Coding Assistant with Multi-Tool Integration and Shared Memory

Linggen Releases

Linggen is a locally-first RAG and MCP memory layer that provides cross-project shared memory, code repository navigation, and multi-tool connection capabilities for AI programming assistants. All data is processed locally by default.

Knowledge management and memory Developer tools #Local RAG #MCP Memory Layer #Code Navigation #Multi-Tool Connection

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update time : 2025-12-29

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What is Linggen?

Linggen is a locally-first memory and search layer designed specifically for AI programming assistants. It allows you to index multiple code repositories, documents, and notes, and then perform intelligent queries across this content through semantic search and chat functionality. Most importantly, it exposes your personal knowledge base to compatible AI programming tools (such as Cursor, Zed, Windsurf, etc.) via the Model Context Protocol (MCP), enabling AI assistants to access your entire working environment.

How to use Linggen?

Using Linggen is very simple: First, download and install the application, which will automatically start a local server. Then, configure the MCP connection in your preferred AI programming tool (such as Cursor) to point to Linggen's local address. Once configured, you can directly search for and reference all the indexed code and documents in the AI assistant.

Use Cases

Linggen is particularly suitable for the following scenarios: 1. Switching between multiple projects and needing to quickly recall relevant code. 2. Working with large code repositories and needing intelligent search and understanding of code relationships. 3. Team collaboration, sharing technical documents and code specifications. 4. Personal knowledge management, organizing study notes and technical documents. 5. Development environments requiring privacy protection, where all data remains local.

Main Features

Locally-First Architecture

All data processing, including text embedding, index building, and search queries, is done on your device. By default, no data leaves your computer, ensuring complete privacy protection.

Cross-Project Memory

Establish a unified memory layer that covers all your projects and documents. AI assistants can access your entire work history, not just the currently open files.

MCP Protocol Support

Integrate with various AI programming tools via the standardized Model Context Protocol. Once configured, it can be used in multiple tools such as Cursor, Zed, and Windsurf.

Semantic Search

Use advanced embedding models to understand the semantic meaning of queries, rather than just keyword matching. It can find conceptually relevant content even if the exact same terms are not used.

Team Sharing

It can be deployed on a shared machine, allowing the entire team to access the same knowledge base. It is free for both individuals and teams, with no subscription fees.

AI Chat Interface

The built-in chat interface allows you to directly interact with the indexed content, quickly obtaining information without leaving the application.

Advantages

Complete privacy protection: All data is processed locally and is not uploaded to the cloud.

Free to use: Both individuals and teams can use it for free, with no subscription fees.

Cross-tool compatibility: Supports multiple AI programming assistants via the MCP protocol.

Unified knowledge base: Centralizes knowledge scattered across different projects.

Easy to deploy: Download and use, with automatic configuration of the local server.

Team-friendly: Supports deployment in a shared environment for the entire team to use.

Limitations

Currently only supports the macOS system. Windows and Linux versions are still in development.

Requires local computing resources for embedding and searching, which may put some pressure on older devices.

Initial indexing of large code repositories may take some time.

Requires users to manually configure the MCP connection (although the process is simple).

Relies on local embedding models, which may not be as powerful as some cloud-based models.

How to Use

Download and Install the Application

Download the latest macOS version (.dmg file) from the GitHub Releases page, and then drag it to the Applications folder.

First Run and Model Download

When Linggen is launched for the first time, it will automatically download the required embedding model (about 100MB) and start the local backend server (by default at http://localhost:8787).

Add Your Knowledge Sources

Add the folders, code repositories, documents, or notes you want to index in the Linggen application. The application will automatically start the indexing process.

Configure Cursor MCP Connection

Edit Cursor's MCP configuration file and add the Linggen server address. If the file does not exist, create it.

Restart and Verify the Connection

Restart the Cursor application, and then check the MCP server connection status. You should see linggen as a connected server.

Usage Examples

Cross-Project Code Reuse

When you need to implement a feature in a new project and you remember having implemented a similar feature in another project, you can directly search for the relevant implementation through the AI assistant.

Navigation in Large Code Repositories

When you need to find the implementation of a specific function or understand the code structure in a large code repository, you can use semantic search to quickly locate it.

Team Knowledge Sharing

When new team members need to quickly understand the project architecture and coding specifications, they can access the team's shared knowledge base through Linggen.

Technical Document Query

When you need to refer to the documentation or API usage of a library but don't want to leave the coding environment, you can directly query it in the AI assistant.

Frequently Asked Questions

Is Linggen free?

Is my data secure? Will it be uploaded to the cloud?

Which operating systems are supported?

In addition to Cursor, which other AI programming tools are supported?

How long does it take to index a large code repository?

How can I share a Linggen instance with my team?

How much disk space does Linggen occupy?

What should I do if the Linggen application crashes?

Related Resources

Linggen Official Website

Complete product documentation, usage guides, and the latest information

GitHub Releases Page

Download the latest version of the Linggen application

Model Context Protocol (MCP) Documentation

Understand the technical details and working principles of the MCP protocol

Cursor Official Website

Understand the features and capabilities of the Cursor AI programming assistant

🚀 Linggen

Linggen is a local-first RAG + MCP memory layer designed for AI coding assistants. It offers a shared memory layer that runs entirely on your machine, is free for both individuals and teams, and is tailored to handle multiple projects, navigate large codebases and documentation sets, and connect various IDEs/agents via MCP (such as Cursor, Zed, and Windsurf). By default, all operations, including embeddings, indexing, and searching, are performed locally, ensuring data stays on your machine.

🚀 Quick Start

Get started with Linggen by exploring its key features and installation steps.

✨ Features

Index Your World: You can index local folders, codebases, documents, and notes, creating a unified knowledge base.
Semantic Search + AI Chat: Conduct semantic searches and engage in AI-powered chats across all your indexed content.
Expose an MCP Server: Linggen exposes an MCP server at http://localhost:8787/mcp/sse, which can be used by compatible tools.
Team Deployment: Run Linggen on a shared machine for a team and configure everyone's MCP settings to connect to it.

For detailed product documentation, visit linggen.dev.

📦 Installation

Install (App)

Download the latest macOS build from GitHub Releases: https://github.com/linggen/linggen-releases/releases
Open the .dmg file and drag Linggen into the Applications folder.
On the first launch, Linggen will download an embedding model (approximately 100MB) and start a local backend at http://localhost:8787.

Currently, Linggen only supports macOS. Windows and Linux support is planned for future releases.

MCP Setup (Cursor)

Linggen starts an MCP server at http://localhost:8787/mcp/sse. To connect Cursor, add the following configuration to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "linggen": {
      "url": "http://localhost:8787/mcp/sse"
    }
  }
}

Restart Cursor, and you should see linggen listed as a connected MCP server.

💻 Usage Examples

This Repo

This repository hosts the Linggen landing page and documentation site, which is built using React and Vite and deployed to Cloudflare Pages. To set up the development environment, run the following commands: