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Engram Rs MCP Server

@kael-bit

リポジトリ

基本情報

カテゴリ

メモリとナレッジ

トランスポート

stdio

作者

kael-bit

投稿者

Kael

Engram Rs MCP Server について

Hierarchical memory for AI agents. Three-layer (buffer/working/core) with automatic decay, promotion, and semantic search.

#memory#ai-agent#mcp#semantic-search#vector-database#rust#sqlite#llm#hierarchical-memory#context-management

Engram Rs MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "engram": {
      "command": "npx",
      "args": [
        "-y",
        "engram-rs-mcp"
      ],
      "env": {
        "ENGRAM_URL": "http://localhost:3917",
        "ENGRAM_API_KEY": "",
        "ENGRAM_NAMESPACE": ""
      }
    }
  }
}

Engram Rs MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

上のボタンで一時的なサンドボックスでこのサーバーを実行し、ツール一覧を取得できます。

概要

What is Engram Rs?

Engram Rs is a Model Context Protocol (MCP) server that gives AI agents persistent, human-like memory. It organizes memories through three cognitive layers — buffer (short-term), working (active knowledge), and core (long-term) — with automatic decay, promotion, and consolidation. Built as a local-first, single-binary Rust server using SQLite, it is designed for developers adding long-term memory to MCP-compatible AI clients.

How to use Engram Rs?

Download the platform-specific Rust binary and run it with any MCP client that supports stdio transport, such as Claude Desktop, Cursor, or Windsurf. The server exposes tools for storing, searching, deduplicating, and managing memories across isolated namespaces, and it can extract session context from LLM conversations.

Key features of Engram Rs

  • Three-tier memory: buffer, working, and core layers
  • Semantic search via HNSW vector index
  • Automatic deduplication and merging of memories
  • Namespace isolation for multi-agent setups
  • Session context extraction from LLM conversations
  • Local-first, single-binary Rust server with SQLite

Use cases of Engram Rs

  • Give conversational AI agents persistent, long-term memory across sessions
  • Manage multiple agent identities with isolated memory namespaces
  • Extract and consolidate key information from ongoing LLM conversations
  • Maintain context and knowledge without relying on proprietary cloud services

よくある質問

1

What makes Engram Rs different from other memory systems?

It uses a three-layer cognitive architecture with automatic decay, promotion, and consolidation, mimicking human memory dynamics rather than a flat storage approach.

2

What runtime or dependencies are required?

None beyond the compiled Rust binary. It is a local-first, single‑binary server that uses SQLite – no external databases, cloud services, or additional runtimes needed.

3

Where are memories stored?

All memories are stored locally in an SQLite database managed by the Rust server. No data is sent to external services.

4

What transport and authentication does it use?

It uses the MCP protocol over stdio transport. Authentication is not covered in the README.

5

What clients does Engram Rs work with?

It works with Claude Desktop, Cursor, Windsurf, and any MCP‑compatible client that supports stdio transport.

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