N2n Memory

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中文版

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Context as code. Memory as asset.

A specialized MCP server designed to solve "memory pollution" during AI-assisted cross-project development. It persists AI's cognitive fragments directly within each project's own directory.

🌟 Key Highlights

• Project-Level Physical Isolation: Memory files are stored at [Project Root]/.mcp/memory.json.
• Git-Friendly: JSON data is automatically sorted by key to generate clean and readable git diff.
• Tool Agnostic: Uses the .mcp naming convention, not tied to any specific AI brand or IDE plugin.
• Assets for Your Code: Memory stays with your code; team members can share AI's understanding of the architecture by simply pulling the repository.
• Universal Compatibility: Works with all MCP-enabled models including Claude 4.5, Gemini 3 Pro/Flash, GPT-5/5.2, and DeepSeek V3.2.
• Privacy-First: Built with security by design, keeping your data local and isolated.

🚀 Quick Start

1. Installation & Config (IDE / Claude Desktop)

The easiest way to use this is via npx:

Claude Desktop

File Path: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "n2n-memory": {
      "command": "npx",
      "args": ["-y", "@datafrog-io/n2n-memory"]
    }
  }
}

Cursor / VSCode (MCP Plugin)

Add in the MCP settings panel:

• Name: n2n-memory
• Type: command
• Command: npx -y @datafrog-io/n2n-memory

2. Usage Guide

This service is path-driven. AI assistants should pay attention to:

1. Absolute Paths: When calling any n2n_* tool, the absolute path of the current project root (projectPath) must be provided.
2. Auto Storage: Memory is automatically saved to [ProjectPath]/.mcp/memory.json.
3. Collaboration: It is recommended to commit .mcp/memory.json to your Git repository to share the knowledge graph with your team.

Available Tools:

• n2n_add_entities: Create new entities.
• n2n_add_observations: Append observations or facts.
• n2n_create_relations: Establish connections between entities.
• n2n_read_graph: Read project memory and active context (Supports summaryMode and pagination).
• n2n_get_graph_summary: Quickly fetch a lightweight index of all entities (Supports pagination).
• n2n_update_context: Update current task status and next steps.
• n2n_search: Search the graph via keywords (Supports pagination).
• n2n_open_nodes: Retrieve specific entities by name.

🗺️ Future Roadmap

• Semantic Search: Integration of minimalist Vector Embeddings for fuzzy memory retrieval.
• Ontology Enforcement: Optional schema for relation type consistency.
• Time Travel: Versioned snapshots for memory rollback.

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📖 Related Docs

• Design Solution: Why project-level isolation?
• API Reference: Tool descriptions and schema.
• Development: How to build, test and extend.
• Changelog: Version history and incident recovery.

📄 License

This project is licensed under the MIT License.

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N2N Studio — The AI Innovation Lab of DataFrog.io.