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n2n-memory

Project-local MCP memory server for AI coding agents. Knowledge graph stored in .mcp/ inside each repo — isolated per project, Git-friendly JSON, dual-buffer (durable graph + active context).

Install / Use

claude mcp add n2ns -- npx -y github:n2ns/n2n-memory

If the server publishes to npm under a different name, use that package instead — check the repo README.

About this skill
🔌

MCP Server

Model Context Protocol server

Quality Score

81/100

Supported Platforms

Claude Code
Claude Desktop
Cursor

Our assessment of n2n-memory

n2n-memory scores 81/100 on our quality scale, 3499th of 4,597 Development & Engineering skills we index.

Its MCP Server is 6.7 KB long, well organised into 14 sections with 3 code examples: a thorough specification that gives an agent plenty to work with.

It has 10 GitHub stars, so there is little community track record yet; judge it on its content.

Substance
29/30
Structure
18/20
Description
15/15
Adoption
4/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 5 days ago, so n2n-memory is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 97/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

n2n-memory compared with similar skills

All 4 of these similar skills score higher than n2n-memory; compare them before choosing.

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Frequently asked questions

How do I install n2n-memory?
Run claude mcp add n2ns -- npx -y github:n2ns/n2n-memory. The install tabs above show the steps for each supported agent.
Which AI agents does n2n-memory work with?
It is written for Claude Code, Claude Desktop and Cursor, as a MCP Server file. Other agents that read the same format can often use it too.
Is n2n-memory safe to use?
It is MIT-licensed and scores 97/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is n2n-memory still maintained?
The repository was last updated 5 days ago, so n2n-memory is actively maintained.
<p align="center"> <img src="./assets/n2n-memory-logo.png" width="128" alt="n2n-memory logo"> </p>

n2n-memory

Project-local knowledge-graph memory MCP server from N2NS Lab for AI coding agents.

npm version npm total downloads license MCP Protocol node version

中文版


Context as code. Memory as asset.

n2n-memory is an open-source, local-first Model Context Protocol (MCP) memory server for AI coding assistants. It prevents cross-project memory pollution by storing durable project knowledge in .mcp/memory.json and active task context in .mcp/context.json inside each repository.

📚 Contents

💡 What is n2n-memory?

n2n-memory gives AI coding tools a project-local knowledge graph they can read and update through MCP. Memory and context are stored inside each repository under .mcp/, keeping them isolated, Git-friendly, and reviewable.

  • Restore project context at the start of an AI coding session.
  • Preserve architecture decisions, implementation notes, and known pitfalls.
  • Share durable project memory with teammates through Git.
  • Keep memory isolated per repository when working across many projects.

🚀 Quick start

1. Configure your MCP client

Claude Desktop

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

{
  "mcpServers": {
    "n2n-memory": {
      "command": "npx",
      "args": ["-y", "n2n-memory"]
    }
  }
}

Cursor / VS Code

Add in the MCP settings panel:

  • Name: n2n-memory
  • Type: command
  • Command: npx -y n2n-memory

Other MCP clients

  • The server uses stdio MCP and can be wired to any MCP client that supports local command execution.
  • If your client supports it, set N2N_LOG_LEVEL=debug only when troubleshooting local path resolution.

2. Usage guide

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

  1. Absolute Project Root: When calling any n2n_* tool, provide the absolute path of the current project root or workspace top-level directory (projectPath).
  2. Initialization Handshake: The first call in a project that has no .mcp/ yet does not create anything. The server detects the project root and replies with status: "AWAITING_CONFIRMATION" and a detectedRoot. Call the same tool again with confirmNewProjectRoot set to that exact detectedRoot to initialize memory. Folders without a real project marker (.git, package.json, language build files, …) are rejected outright, so memory is never created in an arbitrary directory.
  3. Auto Storage: Durable memory is saved to [ProjectPath]/.mcp/memory.json; active task context is saved to [ProjectPath]/.mcp/context.json.
  4. Collaboration: Commit .mcp/memory.json when the knowledge graph should be shared with the team. Commit context.json only if your workflow wants active task state shared.

Storage layout inside each project:

<project-root>/
└── .mcp/
    ├── memory.json    # durable knowledge graph — commit to share with the team
    └── context.json   # active task context — usually kept local

Recommended .gitignore policy for teams that want to share durable memory:

.mcp/context.json
!.mcp/
!.mcp/memory.json

If your project memory may contain private implementation details, keep the whole .mcp/ directory ignored.

Available tools

  • Graph writes (n2n_add_entities, n2n_add_observations, n2n_create_relations): add entities, observations, and relations to build the knowledge graph.
  • Graph reads (n2n_read_graph, n2n_get_graph_summary, n2n_search, n2n_open_nodes): read the full graph, get a lightweight summary, search, or open specific entities.
  • Context (n2n_update_context): update active task state before commits and handoffs.
  • Maintenance (n2n_delete_entities, n2n_delete_observations, n2n_delete_relations, n2n_export_markdown): delete entities, observations, or relations; export the graph to Markdown.

See Tools reference for parameter schemas and usage notes.

⚙️ Configuration

n2n-memory runs as a stdio MCP server with no CLI subcommands — start it with npx -y n2n-memory (or n2n-memory once installed). Storage location is derived from the projectPath you pass to each tool, not from configuration.

| Variable | Description | Default | | --- | --- | --- | | N2N_LOG_LEVEL | Set to debug to enable verbose logs when diagnosing local path resolution. Any other value (or unset) uses normal logging with full local paths hidden. | unset |

🔐 Security and governance notes

  • n2n_delete_entities, n2n_delete_observations, and n2n_delete_relations are destructive and should be governed by review workflows.
  • Keep context.json uncommitted by default, and commit .mcp/memory.json only when team sharing is intentional.
  • Existing but unreadable JSON files are treated as data integrity errors, not as empty memory.
  • Export paths must be relative and must stay inside the project root.
  • Relations that point to missing entities are rejected.
  • Generic README-only folders are not treated as project roots; use a real project marker such as .git, package.json, or language-specific build files.
  • Full local paths are hidden from server logs by default. Set N2N_LOG_LEVEL=debug when diagnosing path issues.
  • See SECURITY.md for vulnerability reporting.

📖 Related docs

📄 License

This project is licensed under the MIT License.


Built by N2NS Lab, the open-source lab of datafrog.io for practical AI developer tools.

Related Skills

View on GitHub
GitHub Stars10
CategoryDevelopment
Updated5d ago
Forks2

Languages

TypeScript

Trust signals

97/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

1 info
n2n-memory — MCP Server: Install & Safety Check | SkillAgent