ReMe

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 . 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; the curl example uses the default port 2333:

reme version
reme health_check
reme help
curl -s http://127.0.0.1:2333/version -H 'Content-Type: application/json' -d '{}'

5-Minute Memory Demo

With the service running, write a memory node, let ReMe index it, then retrieve it:

reme write \
  path=digest/wiki/quick-start-demo \
  name="Quick Start Demo" \
  description="A first ReMe memory node" \
  content="# Quick Start Demo

ReMe stores agent memory as readable Markdown.

Related: [[digest/wiki/memory-as-file.md]]"

reme search query="agent memory markdown" limit=5
reme read path=digest/wiki/quick-start-demo start_line=1 end_line=20

The generated file is ordinary Markdown with frontmatter:

---
name: Quick Start Demo
description: A first ReMe memory node
---

# Quick Start Demo

ReMe stores agent memory as readable Markdown.

Related: [[digest/wiki/memory-as-file.md]]

ReMe Studio (Optional)

The core installation includes Studio. After starting ReMe, open to browse, edit, and search the workspace. To add Studio to a base installation, use pip install "reme-ai[web]". See the ReMe Studio guide for source builds, configuration, and development.

🤝 Use ReMe with Your Agent

ReMe can run as a local memory service accessed through the CLI, HTTP API, or MCP server, or it can be embedded in the host process through its Python API. Host integrations can add memory guidance, recall, and capture to the agent lifecycle according to the capabilities of each runtime.

Agent · Recommended path · Available after integration DeepSeek Harness · Install @agentscope-ai/reme-dsh-plugin with dsh plugin --profile web add @agentscope-ai/reme-dsh-plugin. · Configurable memory guidance, reme_search, automatic turn capture, scheduled Auto Dream, and ReMe Status. OpenClaw · Install @agentscope-ai/reme-openclaw-plugin with openclaw plugins install clawhub:@agentscope-ai/reme-openclaw-plugin. · Native memory tools, recall before user-triggered runs, and automatic turn capture. QwenPaw · Embed ReMe in-process through its Python API. · Reuse the host lifecycle and model config while keeping memory local and file-based. Claude Code · Start ReMe, then install the Claude Code plugin. · Automatic recall, batched completed-turn recording, and MCP tools. Hermes · Install the ReMe provider and choose HTTP or embedded mode. · Recall before model calls and asynchronous auto_memory after each completed turn. Codex · Start ReMe, then install the Codex plugin. · Native hooks for automatic recall and batched recording, plus MCP tools. Other CLI agents · Install or copy the ReMe Memory skill. · Search, read, and write memory through the CLI.

Auto Memory Auto Dream QwenPaw Claude Code