engrim MCP Server
timgordontg/engrim
[](https://agentrank-ai.com/tool/timgordontg--engrim/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge) The Universal Cross-Model Episodic Memory Standard. Local-first, project-scoped SQLite engine for Google Antigravity, Claude Code, Cursor, Codex CLI, GitHub Copilot CLI, and OpenCode. Zero cloud lock-in.
claude mcp add agentrank -- npx -y agentrank-mcp-server Overview
timgordontg/engrim is a Python tool licensed under MIT. The Universal Cross-Model Episodic Memory Standard. Local-first, project-scoped SQLite engine for Google Antigravity, Claude Code, Cursor, Codex CLI, GitHub Copilot CLI, and OpenCode. Zero cloud lock-in. Topics: ai-agents, claude-code, context-management, developer-tools, llm, memory, rag, sqlite, local-first, antigravity, cursor, episodic-memory, mcp, model-context-protocol, windsurf, codex, opencode, copilot, copilot-cli, github-copilot.
Ranked #6206 out of 24846 indexed tools.
Ecosystem
Score Breakdown
298 stars → growing community
Last commit 1mo ago → moderate activity
0/4 issues closed → many open issues
6 contributors → small team
No dependents → no downstream usage
588 weekly installs → early adoption
Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →
How to Improve
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From the README
# engrim **The Universal Cross-Model & Cross-Agent Episodic Memory Standard.** A local-first, project-scoped SQLite memory engine that allows developers to freely switch between models and environments (**Google Antigravity**, **Claude Code**, **Cursor**, **Codex CLI**, **GitHub Copilot CLI**, **OpenCode**, and **Windsurf**) on the **same codebase** without losing architectural decisions, user constraints, or project state. --- ## 1. Overview & Core Value Proposition > **"Why pay for 200,000 tokens of forgotten noise on every turn? The models are disposable utilities; your project's decisions are not."** **Engrim** is the universal, cross-model episodic memory standard engineered specifically for autonomous AI coding agents and heavy software development. It solves the **"200,000-token context window trap"** (where coding models suffer from severe attention dilution, compounding per-turn token costs, and the loss of prior architectural decisions whenever a chat session is cleareRead full README on GitHub →
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