Tools
ReMe
By ReMe: Memory Management Kit for Agents - Remember Me, Refine Me.
Install
pip install "reme-ai[core]"README
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<strong>A local-first, self-evolving personal knowledge base for AI agents.</strong><br>
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> Previous versions: [0.3.x](https://github.com/agentscope-ai/ReMe/tree/reme_v3) ·
> [0.2.x](https://github.com/agentscope-ai/ReMe/tree/v0.2.0.6) ·
> [MemoryScope](https://github.com/agentscope-ai/ReMe/tree/memoryscope_branch)
## ✨ 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.
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<img src="docs/figure/design-philosophy.svg" alt="ReMe Design Philosophy" width="92%">
</p>
## 📰 Latest Updates
- [2026.09] - **[OpenClaw plugin](https://reme.agentscope.io/en/integrations/openclaw) released**: install it from
[ClawHub](https://clawhub.ai/agentscope-ai/plugins/reme-openclaw-plugin) or
[npm](https://www.npmjs.com/package/@agentscope-ai/reme-openclaw-plugin) to add native memory recall, automatic
conversation capture, and scheduled consolidation to OpenClaw.
- [2026.09] - **[DeepSeek Harness plugin](https://reme.agentscope.io/en/integrations/dsh) released**: install it from
[Awesome DSH Plugin](https://awesome-dsh-plugin.com/p/agentscope-ai/ReMe--integrations-dsh/) or
[npm](https://www.npmjs.com/package/@agentscope-ai/reme-dsh-plugin) for long-term-memory guidance, `reme_search`,
automatic memory, Auto Dream, and ReMe Status.
- [2026.08] - **ReMe blog published**: the [ReMe blog](https://reme.agentscope.io/en/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](https://reme.agentscope.io/en/plugins/daily-paper)
discovers and analyzes papers and generates file-native briefs, while
[Auto Fin](https://reme.agentscope.io/en/plugins/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](docs/en/plugin_development.md) and
[Plugin Management](docs/en/plugin_management.md) to extend ReMe with Components, Steps, and Jobs. Contributions and
new community plugins are welcome.
- [2026.08] - ReMe's [experience-driven enhancement method](https://reme.agentscope.io/en/benchmarks/toolmemory) for
agent tool use is available on [arXiv:2608.03403](https://arxiv.org/abs/2608.03403).
- [2026.07] - Our
paper [Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution](https://aclanthology.org/2026.findings-acl.829/)
has been accepted to Findings of ACL 2026.
## 🚀 Quick Start
### Installation
ReMe requires Python 3.11+.
Install from pip:
```bash
pip install "reme-ai[core]"
```
Install from source:
```bash
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.
### 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.
```bash
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`](reme/config/default.yaml), then change
> `components.file_store.default.embedding_store` from `""` to `default`. See the
> [memory search guide](docs/en/memory_search.md) for details.
### Start the Service
```bash
reme start
```
The default service address is `127.0.0.1:2333`. If the port is occupied, specify another port:
```bash
reme start service.port=8181
# reme start workspace_dir=/tmp/reme-demo service.port=8181
```
```bash
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:
```bash
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:
```markdown
---
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 <http://127.0.0.1:2333/> 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](https://reme.agentscope.io/en/workspace/studio) 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`](integrations/dsh/README.md) 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`](integrations/openclaw/README.md) 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-pro
... (truncated)
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