# ReMe: An Advanced Memory Management Kit for AI Agents

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ReMe is an innovative, local-first memory layer designed for AI agents, transforming conversations and resources into file-based long-term memory. It continuously indexes, links, and consolidates this information, enabling advanced recall and supporting the development of self-evolving agents. This kit helps agents remember and refine their experiences effectively.

GitHub: https://github.com/agentscope-ai/ReMe
OSRepos URL: https://osrepos.com/repo/agentscope-ai-reme

## Summary

ReMe is an innovative, local-first memory layer designed for AI agents, transforming conversations and resources into file-based long-term memory. It continuously indexes, links, and consolidates this information, enabling advanced recall and supporting the development of self-evolving agents. This kit helps agents remember and refine their experiences effectively.

## Topics

- agent
- ai-agents
- memory
- rag
- python
- knowledge-management
- llm-agents

## Repository Information

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## Content

## Introduction

ReMe, short for "Remember Me, Refine Me," is a powerful Memory Management Kit for AI Agents. This Python-based, local-first memory layer is designed to transform conversations and resources into file-based long-term memory. With over 3,200 stars and 280 forks on GitHub, ReMe provides a robust solution for agents to continuously index, link, and consolidate information for future recall. Its core philosophy revolves around treating memory as editable Markdown files, fostering a self-evolving knowledge base for intelligent agents.

## Installation

ReMe requires Python 3.11 or newer. You can easily install it using pip:

bash
pip install "reme-ai[core]"


For more advanced setups or to install from source, please refer to the official documentation.

## Examples

Getting started with ReMe is straightforward. After installing and starting the service, you can quickly write, search, and read memory nodes.

First, start the ReMe service:

bash
reme start


Then, write a memory node:

bash
reme write \
  path=digest/wiki/quick-start-demo \
  name="Quick Start Demo" \
  description="A first ReMe memory node" \
  content="# Quick Start Demo\n\nReMe stores agent memory as readable Markdown.\n\nRelated: [[digest/wiki/memory-as-file.md]]"


Search for relevant memory:

bash
reme search query="agent memory markdown" limit=5


And read a specific memory node:

bash
reme read path=digest/wiki/quick-start-demo start_line=1 end_line=20


This demonstrates how ReMe stores agent memory as readable Markdown files, complete with frontmatter and wikilinks.

## Why Use ReMe?

ReMe offers several compelling reasons for integration into your AI agent projects:

*   **Memory as File**: It stores memory as Markdown files with frontmatter and wikilinks, making it directly readable and writable by both users and agents.
*   **Self-evolving Knowledge Base**: Features like Auto Memory, Auto Resource, and Auto Dream progressively transform conversations and resources into long-term memories, automatically building wikilink relationships.
*   **Progressive Hybrid Search**: ReMe combines wikilinks, BM25, and embeddings for hybrid retrieval, ensuring comprehensive search capabilities across keyword matching, semantic recall, and relationship expansion.
*   **Agent-friendly Integration**: With SKILL.md and CLI integration, ReMe simplifies how different agents can read, write, maintain, and reuse memory.
*   **Diverse Use Cases**: From powering personal assistants and coding agents to building LLM wikis and supporting self-evolving agents, ReMe provides a versatile memory layer. Its design philosophy emphasizes capturing raw dialogs and resources, then refining them into long-term preferences, reusable experience, and valuable knowledge, all while keeping the result editable by humans and agents.

## Links

Explore ReMe further through these official resources:

*   **GitHub Repository**: <a href="https://github.com/agentscope-ai/ReMe" target="_blank">agentscope-ai/ReMe</a>
*   **Official Documentation**: <a href="https://docs.agentscope.io/reme" target="_blank">ReMe Documentation</a>
*   **PyPI Project**: <a href="https://pypi.org/project/reme-ai/" target="_blank">reme-ai on PyPI</a>