{"name":"ReMe: An Advanced Memory Management Kit for AI Agents","description":"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","url":"https://osrepos.com/repo/agentscope-ai-reme","source":"osrepos.com","sourceDescription":"This repository profile is provided by osrepos.com, an open source repository discovery platform.","repositoryProfile":"https://osrepos.com/repo/agentscope-ai-reme","generatedFor":"open source discovery and AI-assisted research","markdown":"https://osrepos.com/repo/agentscope-ai-reme.md","json":"https://osrepos.com/repo/agentscope-ai-reme.json","topics":["agent","ai-agents","memory","rag","python","knowledge-management","llm-agents"],"keywords":["agent","ai-agents","memory","rag","python","knowledge-management","llm-agents"],"stars":null,"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.","content":"## Introduction\n\nReMe, 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.\n\n## Installation\n\nReMe requires Python 3.11 or newer. You can easily install it using pip:\n\nbash\npip install \"reme-ai[core]\"\n\n\nFor more advanced setups or to install from source, please refer to the official documentation.\n\n## Examples\n\nGetting started with ReMe is straightforward. After installing and starting the service, you can quickly write, search, and read memory nodes.\n\nFirst, start the ReMe service:\n\nbash\nreme start\n\n\nThen, write a memory node:\n\nbash\nreme write \\\n  path=digest/wiki/quick-start-demo \\\n  name=\"Quick Start Demo\" \\\n  description=\"A first ReMe memory node\" \\\n  content=\"# Quick Start Demo\\n\\nReMe stores agent memory as readable Markdown.\\n\\nRelated: [[digest/wiki/memory-as-file.md]]\"\n\n\nSearch for relevant memory:\n\nbash\nreme search query=\"agent memory markdown\" limit=5\n\n\nAnd read a specific memory node:\n\nbash\nreme read path=digest/wiki/quick-start-demo start_line=1 end_line=20\n\n\nThis demonstrates how ReMe stores agent memory as readable Markdown files, complete with frontmatter and wikilinks.\n\n## Why Use ReMe?\n\nReMe offers several compelling reasons for integration into your AI agent projects:\n\n*   **Memory as File**: It stores memory as Markdown files with frontmatter and wikilinks, making it directly readable and writable by both users and agents.\n*   **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.\n*   **Progressive Hybrid Search**: ReMe combines wikilinks, BM25, and embeddings for hybrid retrieval, ensuring comprehensive search capabilities across keyword matching, semantic recall, and relationship expansion.\n*   **Agent-friendly Integration**: With SKILL.md and CLI integration, ReMe simplifies how different agents can read, write, maintain, and reuse memory.\n*   **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.\n\n## Links\n\nExplore ReMe further through these official resources:\n\n*   **GitHub Repository**: <a href=\"https://github.com/agentscope-ai/ReMe\" target=\"_blank\">agentscope-ai/ReMe</a>\n*   **Official Documentation**: <a href=\"https://docs.agentscope.io/reme\" target=\"_blank\">ReMe Documentation</a>\n*   **PyPI Project**: <a href=\"https://pypi.org/project/reme-ai/\" target=\"_blank\">reme-ai on PyPI</a>","metrics":{"detailViews":3,"githubClicks":0},"dates":{"published":null,"modified":"2026-08-07T11:07:54.000Z"}}