ReMe：为 AI Agent 管理记忆

ReMe: Memory Management Kit for Agents - Remember Me, Refine Me.

A local-first, self-evolving personal knowledge base for AI agents. Previous versions: 0.3.x · 0.2.x · MemoryScope ✨ Why ReMe? 🧠 ReMe turns conversations and resources into readable, editable, searchable, and interconnected Markdown memory. Agents such as QwenPaw and DeepSeek Harness can share the same workspace to retrieve, maintain, and evolve knowledge, while users retain control of the durable files. Memory as File, File as Memory: ReMe stores durable memory as ordinary Markdown with frontmatter and wikilinks. Users and agents can inspect, edit, move, sync, and back it up with familiar tools, while indexes and generated metadata remain rebuildable. Self-evolving knowledge base: ReMe progressively turns conversations and resources into daily notes and long-term knowledge, preserving sources while refining facts, preferences, procedures, and relationships over time. Recall is precise and context-aware. BM25, optional embeddings, and wikilink expansion retrieve relevant line-level passages and their relationships without loading the entire knowledge base into the agent context. One memory workspace works across agents. Personal assistants, coding agents, and other agent runtimes can share the same local workspace through native integrations, SKILL.md, CLI, HTTP, MCP, or Python APIs. 📰 Latest Updates [2026.10] - Decision-model context compaction: score old tool calls and results against the next action, preserve protected history, and return a recoverable compaction proposal. Includes an SVG walkthrough, two live-model integration scenarios, and a 14-query fixed-summary Top-N benchmark with a forced Hermes summary baseline. [2026.10] - ReMe Multimodal Memory update: Auto Memory reads images with their conversation context and preserves image sources; Auto Resource turns image files into searchable daily cards linked to the originals. [2026.10] - ReMe Studio Playground is live: explore example memory files, edit Markdown, and browse linked memory graphs right in your browser—no installation or backend required. Everyone is welcome to try it out! [2026.09] - ReMe Memory Tags published: an introduction to file-native entity tags, rebuildable tag indexes, and tag-filtered memory search. [2026.09] - Hermes Agent memory provider available: choose HTTP or embedded mode for automatic recall before model calls and asynchronous auto_memory after completed turns. The integration supports Hermes Agent 0.21+ and includes profile-aware background work. More updates [2026.09] - OpenClaw plugin released: install it from ClawHub or npm to add native memory recall, automatic conversation capture, and scheduled consolidation to OpenClaw. [2026.09] - DeepSeek Harness plugin released: install it from Awesome DSH Plugin or npm for long-term-memory guidance, reme_search, automatic memory, Auto Dream, and ReMe Status. [2026.08] - ReMe blog published: the ReMe blog introduces the local-first memory architecture, self-evolving workflows, hybrid search, proactive discovery, and benchmark results. [2026.08] - New ReMe ecosystem plugins: Daily Paper discovers and analyzes papers and generates file-native briefs, while Auto Fin researches the latest 24 hours of topic-related CLS news and builds traceable reports with local memory. Try them out. [2026.08] - Plugin development support released: use Plugin Development and Plugin Management to extend ReMe with Components, Steps, and Jobs. Contributions and new community plugins are welcome. [2026.08] - ReMe's experience-driven enhancement method for agent tool use is available on arXiv:2608.03403. [2026.07] - Our paper Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution has been accepted to Findings of ACL 2026. 🚀 Quick Start Installation ReMe requires Python 3.11+. Install from pip: pip install "reme-ai[core]" Install from source: git clone https://github.com/agentscope-ai/ReMe.git cd ReMe pip install -e reme_studio -e ".[core]" cd reme_studio npm ci npm run build:static cd .. The static build requires Node.js 22.13 or newer and makes Studio available from the source tree. Optional decision models use pip install "reme-ai[core,decision]". Configure a TypeSafe API key and run the service demo as described in Decision model configuration. Docker With Docker and Compose 2.24.0+, build and start ReMe with the bundled Studio: mkdir -p .reme docker compose up --build -d Open http://127.0.0.1:2333. The complete workspace persists in ./.reme. On Linux, set REME_UID and REME_GID to your user's IDs when they differ from 1000. See Docker deployment for model credentials, custom paths, published images, and upgrades. Environment Variables Configure environment variables when you want LLM-powered memory evolution or embedding retrieval. Embeddings are disabled by default, so the default setup does not start an embedding model or require an embedding API key. cat > .env <<'EOF' # Optional: used only after embedding components are explicitly enabled in the config. # EMBEDDING_API_KEY=sk-xxx # EMBEDDING_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1 # Required for auto_memory, auto_resource, auto_dream, and proactive refresh. LLM_API_KEY=sk-xxx LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1 EOF Basic file operations, BM25 search, wikilink traversal, and reading proactive topics can run without LLM credentials. Note To enable embedding-based semantic retrieval, uncomment components.as_embedding and components.embedding_store in reme/config/default.yaml, then change components.file_store.default.embedding_store from "" to default. See the memory search guide for details. Start the Service Start ReMe in one terminal and leave it running: reme start The default service address is 127.0.0.1:2333. If the port is occupied, specify another port: reme start service.port=8181 # reme start workspace_dir=/tmp/reme-demo service.port=8181 From another terminal, call the running service. The CLI discovers its configuration; t