remind — MCP Server by sandst1

by sandst1 · MCP Server · ★ 76

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About remind

Remind Generalization-capable memory layer for LLMs. Unlike simple RAG systems that store verbatim text, Remind extracts and maintains generalized concepts from experiences — mimicking how human memory consolidates specific events into abstract knowledge. Documentation · Examples · Changelog Quick start Configure a provider (): Use it: Episodes go in, consolidation runs, generalized concepts come out. Two ways to use Remind Skills + CLI (recommended) Project-local memory via composable skills. The database lives in your repo at . bash remind skill-install # In

agentic-aiagentsaicodingcontext-engineeringgenaillmlong-term-memorymcpmcp-server

Quick Facts

Stars76
Forks11
LanguagePython
CategoryMCP Server
LicenseApache-2.0
Quality Score61.5184917860935/100
Open Issues4
Last Updated2026-05-05
Created2025-11-26
Platformsmcp, python
Est. Tokens~906k

remind alternative? Top 6 similar tools

Looking for a remind alternative? If you're comparing remind with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

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Frequently Asked Questions

What is remind?

remind is A memory layer for AI Agents. It is categorized as a MCP Server with 76 GitHub stars.

What programming language is remind written in?

remind is primarily written in Python. It covers topics such as agentic-ai, agents, ai.

How do I install or use remind?

You can find installation instructions and usage details in the remind GitHub repository at github.com/sandst1/remind. The project has 76 stars and 11 forks, indicating an active community.

What license does remind use?

remind is released under the Apache-2.0 license, making it free to use and modify according to the license terms.

What are the best alternatives to remind?

The top alternatives to remind on Agent Skills Hub include agentic-design-patterns, mcp-server, context-engineering. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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