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Engram Rs MCP Server

@kael-bit

Repository

Basic information

Category

Memory & Knowledge

Transports

stdio

Author

kael-bit

Submitted by

Kael

About Engram Rs MCP Server

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 config

Add this server to your MCP-compatible client using the configuration below.

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

Engram Rs MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

Fetch the live tool list by running this server in a temporary sandbox using the button above.

Overview

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

Frequently asked questions

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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