Engram Rs
@kael-bit
About Engram Rs
Hierarchical memory for AI agents. Three-layer (buffer/working/core) with automatic decay, promotion, and semantic search.
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": ""
}
}
}
}Tools
No tools detected
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
FAQ from Engram Rs
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.
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.
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.
What transport and authentication does it use?
It uses the MCP protocol over stdio transport. Authentication is not covered in the README.
What clients does Engram Rs work with?
It works with Claude Desktop, Cursor, Windsurf, and any MCP‑compatible client that supports stdio transport.
Frequently asked questions
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.
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.
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.
What transport and authentication does it use?
It uses the MCP protocol over stdio transport. Authentication is not covered in the README.
What clients does Engram Rs work with?
It works with Claude Desktop, Cursor, Windsurf, and any MCP‑compatible client that supports stdio transport.
Basic information
More Memory & Knowledge MCP servers

PLUR
plur-aiAI agents start every session with amnesia — you re-explain the project, repeat your preferences, and correct the same mistakes over and over. PLUR gives them a memory that persists. Your agent's corrections, preference

OpenLore
aakarimServe your docs to AI agents over SSH and MCP. Agents use ls, cat, grep, find, and pipes against a virtual filesystem backed by your documentation. Identity-scoped access, governed writing with human approval, and portab

Agimem
Persistent memory for AI agents. Hosted MCP server with capsule-scoped key/value storage, tags, TTL expiration, bulk ops, and RBAC.
Tempreon
TempreonPerson-owned, portable AI memory served over MCP. Your knowledge, preferences, and decisions — captured in one assistant, available in all of them.

Orcha
westonhancockOrcha is a unified context layer for AI tools and agents. It stores and indexes and exposes organizational, team, or individual knowledge. Files, structured databases, and connected sources live in one workspace, and age
Comments