{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"}],"enrichment":{"capability":"ReMe is a local-first knowledge base that converts conversations and resources into searchable, interconnected Markdown files with wikilinks, designed to give AI agents and users a persistent, editable memory layer.","skillfed_tags":["agent-memory","knowledge-base","local-first"],"use_cases":["Give personal assistant agents (QwenPaw, OpenClaw, Hermes) a user-editable long-term memory layer that persists across sessions.","Preserve coding style, project decisions, and workflow experience for coding agents like Claude Code across multiple development sessions.","Build a searchable, traceable Markdown wiki from conversations and resources that both users and agents can maintain and query.","Enable agents to learn from experience by recording successful procedures, failed attempts, and periodic reflections as indexed memory nodes."],"what_it_does":"ReMe is a persistent memory system for AI agents that stores knowledge as ordinary Markdown files with frontmatter and wikilinks, keeping all data under user control in the local filesystem. It combines keyword search (BM25), optional semantic embeddings, and relationship traversal to retrieve relevant context without loading entire knowledge bases into memory. The system includes automated workflows\u2014Auto Memory distills conversations into daily notes, Auto Resource imports external documents, Auto Dream consolidates notes into long-term knowledge, and Auto Link writes relationships back into files.\n\nThe package integrates with agent frameworks through CLI commands, HTTP endpoints, Model Context Protocol (MCP), and embedded Python APIs. It runs as a local service (default port 2333) and includes an optional web UI (ReMe Studio) for browsing and editing. Core file operations, BM25 search, and wikilink traversal work without LLM credentials; AI-powered memory evolution requires OpenAI-compatible API keys. The 18 runtime dependencies include fastapi for the service layer, pydantic for configuration, and loguru for logging.","worth_installing":"Yes. ReMe is actively maintained, has no known vulnerabilities, uses a permissive license, and solves a concrete problem for agent developers: persistent, editable, searchable memory under user control. The low install friction and modular design (core features work without LLM keys) make it accessible. Install if you're building agents that need to retain and evolve knowledge across sessions, or if you want a local-first alternative to external memory services."},"id":"reme-ai","links":{"html":"https://skillfed.io/packages/reme-ai","md":"https://skillfed.io/packages/reme-ai.md","pypi":"https://pypi.org/project/reme-ai/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-13","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"reme-ai","python_support":"supports_current","summary":"Remember Me, Refine Me."},"popularity":{"monthly_downloads":188191,"position":9949,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.4.1.7"}