Engram MCP Server

@tstockham96

代码仓库

基本信息

分类

记忆与知识

传输方式

stdio

作者

tstockham96

提交者

tstockham96

关于 Engram MCP Server

Universal memory layer for AI agents. Semantic recall, automatic consolidation, and bi-temporal knowledge — in SQLite. 80% on LOCOMO benchmark using 96% fewer tokens.

#memory #agents #mcp #knowledge-graph #sqlite #ai

Engram MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "engram": {
      "command": "npx",
      "args": [
        "-y",
        "engram-sdk",
        "mcp"
      ],
      "env": {
        "GEMINI_API_KEY": "<YOUR_GEMINI_API_KEY>"
      }
    }
  }
}

Engram MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

What is Engram?

Engram is an intelligence layer for AI agents that stores, learns, and surfaces memories—going beyond simple storage by consolidating patterns, detecting contradictions, and providing context you didn’t ask for. It integrates as an MCP server (for Claude Code, Cursor, or any MCP client), a REST API, and a TypeScript SDK.

How to use Engram?

Install globally with npm install -g engram-sdk, then run engram init to set up a local SQLite vault. For MCP clients, use the same command; for a REST API, run npx engram-serve after setting your GEMINI_API_KEY. Use the CLI, SDK, or HTTP endpoints to store and recall memories.

Key features of Engram

Three memory tiers: explicit, implicit, and synthesized.
Entity-aware recall and bi-temporal fact tracking.
LLM-powered consolidation and contradiction detection.
Works with Gemini, OpenAI, Ollama, and any OpenAI-compatible provider.
Zero‑infrastructure: SQLite, no Docker, no external services.
CLI, REST API, TypeScript SDK, and MCP server with 10 tools.

Use cases of Engram

Persist user preferences and conversation context across sessions.
Detect behavioral patterns from how users work (implicit memory).
Surface relevant context automatically via spreading activation.
Run enterprise‑grade memory benchmarks (LOCOMO, Letta, codebase navigation).
Build agents that learn and adapt without manual memory management.

常见问题

How does Engram compare to Mem0, Zep, or LangMem?

Engram invests intelligence at read time (when the query is known), not write time. Benchmarks on LOCOMO show 80.0% accuracy (19.6% relative improvement over Mem0) using 96.6% fewer tokens than full‑context retrieval.

What runtime/dependencies does Engram require?

Node.js (>=18) and npm. Zero infrastructure: only SQLite for storage. For consolidation/embeddings you need a Gemini API key (or custom LLM via `ENGRAM_LLM_BASE_URL`).

Where does Engram store memory data?

By default in `~/.engram/default.db` (SQLite). The path can be changed with `ENGRAM_DB_PATH`.

Are there any limits or licensing restrictions?

Engram is proprietary (BSL‑1.1). Self‑hosting is free. The hosted free tier allows 1,000 memories and 1 agent; paid plans scale to unlimited memories.

What transports and authentication does Engram support?

MCP (stdio and SSE), REST API (HTTP on port 3800), and CLI. API authentication is optional via `ENGRAM_AUTH_TOKEN` (Bearer token).

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