mirror of
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ef3f99f019
* refactor(packaging): reorganize published packages * fix(packaging): install AgentScope extra in wheel smoke * docs: align package guides and documentation site * ci(workflow): add core dependency verification step in Python package build - Add a workflow step to verify released core dependencies by installing the wheel with core extras - Assert the presence of the static index.html file to ensure proper package contents - Create and use a temporary virtual environment for isolation during verification - Keep existing artifacts upload step intact and conditional on inputs.upload_artifacts flag * fix(ci): update package installation dependencies in Windows workflow - Change pip install from editable reme_studio and core to only dev and as extras - Remove installation of reme_studio and core to streamline dependency setup - Ensure Windows CI uses the correct extras for testing environment * fix(tests): add missing commas in toml file reads in package version tests - Added trailing commas in the tomllib.loads calls for auto-fin and daily_paper configs - Ensured consistent syntax to prevent potential tuple misinterpretation - Improved readability and correctness of the test setup code * fix(packaging): protect qwenpaw releases and test Studio health
401 lines
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401 lines
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Markdown
<p align="center">
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<img src="https://raw.githubusercontent.com/agentscope-ai/ReMe/main/docs/figure/reme_logo.png" alt="ReMe Logo" width="50%">
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</p>
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<p align="center">
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<a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/badge/python-3.11+-blue" alt="Python Version"></a>
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<a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/pypi/v/reme-ai.svg?logo=pypi" alt="PyPI Version"></a>
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<a href="https://pepy.tech/project/reme-ai/"><img src="https://img.shields.io/pypi/dm/reme-ai" alt="PyPI Downloads"></a>
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<a href="https://github.com/agentscope-ai/ReMe"><img src="https://img.shields.io/github/commit-activity/m/agentscope-ai/ReMe?style=flat-square" alt="GitHub commit activity"></a>
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<a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache--2.0-black" alt="License"></a>
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<a href="https://reme.agentscope.io"><img src="https://img.shields.io/badge/docs-ReMe-blue" alt="Documentation"></a>
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<a href="./README.md"><img src="https://img.shields.io/badge/English-Click-yellow" alt="English"></a>
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<a href="./README_ZH.md"><img src="https://img.shields.io/badge/简体中文-点击查看-orange" alt="简体中文"></a>
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<a href="https://github.com/agentscope-ai/ReMe"><img src="https://img.shields.io/github/stars/agentscope-ai/ReMe?style=social" alt="GitHub Stars"></a>
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<a href="https://deepwiki.com/agentscope-ai/ReMe"><img src="https://img.shields.io/badge/DeepWiki-Ask_Devin-navy.svg" alt="DeepWiki"></a>
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</p>
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<p align="center">
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<a href="https://trendshift.io/repositories/20528" target="_blank"><img src="https://trendshift.io/api/badge/repositories/20528" alt="agentscope-ai%2FReMe | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
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</p>
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<p align="center">
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<strong>A local-first, self-evolving personal knowledge base for AI agents.</strong><br>
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</p>
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> Previous versions: [0.3.x](https://github.com/agentscope-ai/ReMe/tree/reme_v3) ·
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> [0.2.x](https://github.com/agentscope-ai/ReMe/tree/v0.2.0.6) ·
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> [MemoryScope](https://github.com/agentscope-ai/ReMe/tree/memoryscope_branch)
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## ✨ Why ReMe?
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🧠 ReMe turns conversations and resources into readable, editable, searchable, and interconnected Markdown memory. Agents
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such as QwenPaw and DeepSeek Harness can share the same workspace to retrieve, maintain, and evolve knowledge, while
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users retain control of the durable files.
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- **Memory as File, File as Memory**: ReMe stores durable memory as ordinary Markdown with frontmatter and wikilinks.
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Users and agents can inspect, edit, move, sync, and back it up with familiar tools, while indexes and generated
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metadata remain rebuildable.
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- **Self-evolving knowledge base**: ReMe progressively turns conversations and resources into daily notes and long-term
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knowledge, preserving sources while refining facts, preferences, procedures, and relationships over time.
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- **Recall is precise and context-aware.** BM25, optional embeddings, and wikilink expansion retrieve relevant
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line-level passages and their relationships without loading the entire knowledge base into the agent context.
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- **One memory workspace works across agents.** Personal assistants, coding agents, and other agent runtimes can share
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the same local workspace through native integrations, SKILL.md, CLI, HTTP, MCP, or Python APIs.
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<p align="center">
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<img src="docs/figure/design-philosophy.svg" alt="ReMe Design Philosophy" width="92%">
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</p>
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## 📰 Latest Updates
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- [2026.08] - Published [`@agentscope-ai/reme`](https://www.npmjs.com/package/@agentscope-ai/reme), providing native
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ReMe memory integrations for DeepSeek Harness and OpenClaw plus a shared TypeScript HTTP client.
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- [2026.08] - Published the [ReMe blog](https://agentscope-ai.github.io/ReMe/?doc=en-reme-blog), an end-to-end introduction to its local-first memory
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architecture, self-evolving workflows, hybrid search, proactive discovery, and benchmark results.
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- [2026.08] - [Experience-driven enhancement method](https://reme.agentscope.io/?doc=toolmemory-en) of agent tool-use execution built
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on ReMe is available on [arXiv:2608.03403](https://arxiv.org/abs/2608.03403).
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- [2026.07] - Introduced optional plugins: [Daily Paper](https://reme.agentscope.io/?doc=daily-paper-en) for paper discovery and
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analysis, and [Auto Fin](https://reme.agentscope.io/?doc=auto-fin-en) for researching the latest 24 hours of topic-related CLS news
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with local-memory search and validated historical wikilinks.
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- [2026.07] - Our
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paper [Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution](https://aclanthology.org/2026.findings-acl.829/)
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has been accepted to Findings of ACL 2026.
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## 🚀 Quick Start
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### Installation
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ReMe requires Python 3.11+.
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Install from pip:
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```bash
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pip install "reme-ai[core]"
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```
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Install from source:
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```bash
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git clone https://github.com/agentscope-ai/ReMe.git
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cd ReMe
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pip install -e reme_studio -e ".[core]"
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cd reme_studio
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npm ci
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npm run build:static
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cd ..
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```
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The static build requires Node.js 22.13 or newer and makes Studio available from the source tree.
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### Start the Service
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```bash
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reme start
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```
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The default service address is `127.0.0.1:2333`. If the port is occupied, specify another port:
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```bash
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reme start service.port=8181
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# reme start workspace_dir=/tmp/reme-demo service.port=8181
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```
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```bash
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reme version
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reme health_check
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reme help
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curl -s http://127.0.0.1:2333/version -H 'Content-Type: application/json' -d '{}'
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```
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### 5-Minute Memory Demo
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With the service running, write a memory node, let ReMe index it, then retrieve it:
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```bash
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reme write \
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path=digest/wiki/quick-start-demo \
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name="Quick Start Demo" \
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description="A first ReMe memory node" \
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content="# Quick Start Demo
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ReMe stores agent memory as readable Markdown.
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Related: [[digest/wiki/memory-as-file.md]]"
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reme search query="agent memory markdown" limit=5
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reme read path=digest/wiki/quick-start-demo start_line=1 end_line=20
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```
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The generated file is ordinary Markdown with frontmatter:
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```markdown
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---
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name: Quick Start Demo
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description: A first ReMe memory node
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---
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# Quick Start Demo
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ReMe stores agent memory as readable Markdown.
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Related: [[digest/wiki/memory-as-file.md]]
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```
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### ReMe Studio (Optional)
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The `core` installation includes Studio. After starting ReMe, open <http://127.0.0.1:2333/> to browse, edit, and search
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the workspace. To add Studio to a base installation, use `pip install "reme-ai[web]"`. See the
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[ReMe Studio guide](https://reme.agentscope.io/?doc=studio-en) for source builds, configuration, and development.
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### Optional Model Configuration
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Configure environment variables when you want LLM-powered memory evolution or embedding retrieval. Embeddings are
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disabled by default, so the default setup does not start an embedding model or require an embedding API key.
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```bash
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cat > .env <<'EOF'
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# Optional: used only after embedding components are explicitly enabled in the config.
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# EMBEDDING_API_KEY=sk-xxx
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# EMBEDDING_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
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# Required for auto_memory, auto_resource, and auto_dream.
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LLM_API_KEY=sk-xxx
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LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
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EOF
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```
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Basic file operations, BM25 search, wikilink traversal, and reading proactive topics can run without LLM credentials.
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> [!NOTE]
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> To enable embedding-based semantic retrieval, uncomment `components.as_embedding` and
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> `components.embedding_store` in [`reme/config/default.yaml`](reme/config/default.yaml), then change
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> `components.file_store.default.embedding_store` from `""` to `default`. See the
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> [memory search guide](docs/en/memory_search.md) for details.
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## 🤝 Use ReMe with Your Agent
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ReMe can run as a local memory service accessed through the CLI, HTTP API, or MCP server, or it can be embedded in the
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host process through its Python API. Host integrations can add memory guidance, recall, and capture to the agent
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lifecycle according to the capabilities of each runtime.
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| Agent | Recommended path | Available after integration |
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| ------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------- |
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| **DeepSeek Harness** | Install [`@agentscope-ai/reme`](typescript/README.md#deepseek-harness) with `dsh plugin --profile web add @agentscope-ai/reme`. | Long-term memory guidance, the `reme_search` tool, and automatic capture of completed main-agent turns. |
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| **OpenClaw** | Install [`@agentscope-ai/reme`](typescript/README.md#openclaw) with `openclaw plugins install @agentscope-ai/reme`. | Native memory tools, recall before user-triggered runs, and automatic turn capture. |
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| **QwenPaw** | Embed ReMe in-process through its Python API. | Reuse the host lifecycle and model config while keeping memory local and file-based. |
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| **Claude Code** | Start the streamable HTTP MCP service and install [the ReMe plugin](integrations/claude_code/reme). | MCP recall tools, the `reme-memory` skill, and a Stop hook that records sessions automatically. |
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| **Hermes** | Start the HTTP service and install [the ReMe provider](integrations/hermes_agent). | Recall before model calls and asynchronous `auto_memory` after each completed turn. |
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| **Codex and other CLI agents** | Install or copy the [ReMe Memory skill](skills/reme_memory/SKILL.md). | Search, read, and write memory through the CLI; automatic capture requires host lifecycle integration. |
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<p align="center"><b>Integration demos</b></p>
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<table>
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<tr>
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<td align="center"></td>
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<td width="45%" align="center"><b>Auto Memory</b></td>
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<td width="45%" align="center"><b>Auto Dream</b></td>
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</tr>
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<tr>
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<td align="center"><b>QwenPaw</b></td>
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<td width="45%">
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<img src="docs/figure/qwenpaw-auto-memory.gif" alt="QwenPaw Auto Memory demo" width="100%">
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</td>
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<td width="45%">
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<img src="docs/figure/qwenpaw-auto-dream.gif" alt="QwenPaw Auto Dream demo" width="100%">
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</td>
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</tr>
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<tr>
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<td align="center"><b>Claude Code</b></td>
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<td width="45%">
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<img src="docs/figure/cc-auto-memory.gif" alt="Claude Code Auto Memory demo" width="100%">
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</td>
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<td width="45%">
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<img src="docs/figure/cc-auto-dream.gif" alt="Claude Code Auto Dream demo" width="100%">
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</td>
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</tr>
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</table>
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## 🧠 How ReMe Works
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> Memory as File, File as Memory.
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ReMe treats **memory as files**, progressively processing filtered conversation source records and external resources
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from `session/` and `resource/` into `daily/`, then `digest/`. The default workspace is `.reme/` under the current
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directory; `workspace_dir=...` selects a different user-owned location.
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### Workspace Layout
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```text
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<workspace_dir>/
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├── metadata/ # Rebuildable indexes, graphs, catalogs, and caches
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├── session/ # Conversation source records and agent sessions
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│ ├── dialog/
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│ │ └── <session_id>.jsonl # Source messages saved by auto_memory
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│ └── claude_code/
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│ └── <session_id>.jsonl # ReMe copy used by auto_memory_cc
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├── mem_session/ # Generated agent-wrapper sessions/config, not user memory
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│ ├── agentscope/
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│ ├── claude_config/
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│ └── codex/
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├── resource/ # External raw materials
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│ ├── <resource>.<ext> # Root-level files enter today's daily layer
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│ └── YYYY-MM-DD/
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│ └── <resource>.<ext>
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├── daily/ # Lightly processed memory: daily facts, conversation summaries, resource readings
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│ ├── YYYY-MM-DD.md
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│ └── YYYY-MM-DD/
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│ ├── <generated_name>.md # Topic-named conversation or resource card
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│ └── interests.yaml
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└── digest/ # Long-term memory: personal facts, procedural experience, knowledge nodes
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├── personal/
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│ └── {topic/event}.md
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├── procedure/
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│ └── {topic/event}.md
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└── wiki/
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└── {topic/event}.md
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```
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<p align="center">
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<img src="docs/figure/reme-overview.svg" alt="ReMe file-based memory system overview" width="92%">
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</p>
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### Memory Lifecycle
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ReMe follows a capture → index → consolidate → recall loop. Workspace files remain the durable source of truth;
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everything under `metadata/` is rebuildable.
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| Capability | Entry point | What it does | Output |
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| ------------------------------------------- | ----------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------- |
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| [`auto_memory`](docs/en/auto_memory.md) | Agent hook or `reme auto_memory` | Distills useful conversation facts while preserving a filtered conversation source record. | `session/dialog/*.jsonl`, `daily/<date>/<generated-name>.md` |
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| [`auto_resource`](docs/en/auto_resource.md) | Resource watcher or `reme auto_resource` | Turns files under `resource/` into source-linked, content-named daily cards. | `daily/<date>/<resource-card>.md` |
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| [`auto_index`](docs/en/memory_search.md) | Background watcher or `reme reindex` | Live-indexes Markdown in `daily/` and `digest/`; a full rebuild also scans `resource/` and JSONL. | Searchable chunks, BM25, wikilink graph, and optional vectors |
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| [`auto_dream`](docs/en/auto_dream.md) | `dream_cron` or `reme auto_dream` | By default, extracts up to five reusable units from changed files in the latest two-day window, then creates, corroborates, refines, or corrects digest nodes. | `digest/**`, `daily/<date>/interests.yaml` |
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| [`proactive`](docs/en/proactive.md) | `reme proactive` before an agent decides to act | Reads topics generated by `auto_dream`; the host agent decides whether and how to mention them. | Structured topics from `daily/<date>/interests.yaml` |
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<table>
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<tr>
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<td align="center" width="50%">
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<img src="docs/figure/memory-as-file.svg" alt="Memory as File" width="92%">
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</td>
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<td align="center" width="50%">
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<img src="docs/figure/auto-memory-resource.svg" alt="Auto Memory and Resource" width="92%">
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</td>
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</tr>
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<tr>
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<td align="center" width="50%">
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<img src="docs/figure/auto-dream-and-proactive.svg" alt="Auto Dream and Proactive" width="92%">
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</td>
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<td align="center" width="50%">
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<img src="docs/figure/auto-index-and-memory-search.svg" alt="Auto Index and Memory Search" width="92%">
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</td>
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</tr>
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</table>
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Search returns matching chunks with line ranges and bounded wikilink neighbors. Optional vector results are fused with
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BM25 through reciprocal rank fusion (RRF).
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> [!IMPORTANT]
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>
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> `proactive` only reads and exposes interest topics produced by Auto Dream. It does not independently browse the web,
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> send notifications, or rewrite the knowledge base; the host agent decides whether and how to act on a topic.
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## 📊 Benchmarks
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ReMe evaluates multi-session and long-context memory with agentic search-and-read workflows. The figures below are the
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published reference runs in this repository; model, prompt, dataset, and judging details are documented with each
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benchmark.
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| Benchmark | Setting | Sample size | Agentic score | Focus |
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| --------------------------------------------------------------------------- | ------------ | -----------------------: | ------------: | ------------------------------------------------------------------ |
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| **[LongMemEval cleaned-s](https://reme.agentscope.io/?doc=longmemeval-en)** | **Overall** | **500 questions** | **89.4%** | Cross-session retrieval, knowledge updates, and temporal reasoning |
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| [BEAM](https://reme.agentscope.io/?doc=beam-en) | 100K context | 20 cases / 400 questions | 66.1% | Ten types of long-context memory tasks |
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| [BEAM](https://reme.agentscope.io/?doc=beam-en) | 1M context | 35 cases / 700 questions | 65.0% | Ultra-long conversation settings |
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ReMe also achieved a **0.580 PROC score across five user personas** in the repository's
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[π-Bench evaluation](https://reme.agentscope.io/?doc=pibench-en), 2.4% above NanoBot under the same test-model configuration. PROC
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measures proactive handling of hidden intent, clarification, cross-session preferences and conventions, task
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dependencies, and underspecified requests.
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## 🧩 Extensions and Plugins
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Plugins are optional Python distributions that contribute Component, Step, or Job backends and configuration. They are
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installed separately and enabled explicitly by configuration. Daily Paper and Auto Fin are independently packaged
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plugins; see the source distributions and their documentation for [Daily Paper](plugins/daily_paper/README.md) and
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[Auto Fin](plugins/auto-fin/README.md).
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| Plugin | Capability |
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| ------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
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| [Daily Paper](https://reme.agentscope.io/?doc=daily-paper-en) | Discover and rank papers, analyze PDFs with an agent, and generate file-native notes and a five-minute brief. |
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| [Auto Fin](https://reme.agentscope.io/?doc=auto-fin-en) | Fetch topic-related CLS news, search ReMe history, and generate wikilink-backed Markdown reports. |
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See [Plugin Management](docs/en/plugin_management.md) to install, inspect, validate, enable, and uninstall ReMe plugins.
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## 📚 Documentation
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These guides cover the main user workflows and the runtime contracts implemented by the current code.
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| Guide | What you will learn |
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| ------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------- |
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| [Quick Start](docs/en/quick_start.md) | Install ReMe, start the service, and run the first file and memory operations. |
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| [Memory as File](docs/en/memory_as_file.md) | Understand workspace layers, frontmatter, wikilinks, chunks, and the file-as-source-of-truth model. |
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| [Auto Memory](docs/en/auto_memory.md) | Preserve source conversations and distill reusable daily memory cards. |
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| [Auto Resource](docs/en/auto_resource.md) | Import supported text resources and turn them into source-linked daily cards. |
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| [Auto Dream](docs/en/auto_dream.md) and [Auto Link](docs/en/auto_link.md) | Consolidate daily notes into evolving digest nodes and readable wikilink relationships. |
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| [Memory Search](docs/en/memory_search.md) | Use BM25, optional vectors, RRF fusion, line-range recall, and progressive link expansion. |
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| [Proactive](docs/en/proactive.md) | Read interest topics safely and integrate them into a host agent's decision flow. |
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| [Application Scenarios](docs/en/reme_scene.md) | Follow concrete financial research, coding-memory, and personal knowledge-base examples. |
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| [Framework](docs/en/framework.md) | Understand Application, Job, Step, Component, service, configuration, and lifecycle boundaries. |
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| [TypeScript integrations](typescript/README.md) | Configure the shared client and native DeepSeek Harness and OpenClaw adapters. |
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| [ReMe Blog](https://agentscope-ai.github.io/ReMe/?doc=en-reme-blog) | Read the product story, design rationale, examples, and benchmark summary. |
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## 🛠️ Common Commands
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Run `reme help` for the full job list. Common workspace and maintenance commands are:
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| Command | Purpose |
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| ----------------------------------------- | --------------------------------------------------------------------------------- |
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| `reme status` | Show stateful data-component memory estimates and process RSS. |
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| [`reme search`](docs/en/memory_search.md) | Retrieve memory with BM25 and wikilinks by default, plus vectors when enabled. |
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| `reme read` / `reme write` / `reme edit` | Inspect and maintain Markdown memory files. |
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| `reme traverse` / `reme graph_snapshot` | Explore wikilink neighborhoods or the category-rooted digest graph. |
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| `reme chat` | Stream a read-only, workspace-aware agent conversation. Requires LLM credentials. |
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| `reme reindex` | Rebuild search and wikilink indexes from existing files. |
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## 🤝 Community and Contributing
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- **Issues, requests, and help**: Check [Open Issues](https://github.com/agentscope-ai/ReMe/issues) first. If there is no
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related discussion, open one with the background, expected behavior, and impact scope.
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- **Code contributions**: Before making changes, read the repository's
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[contribution guide](docs/en/contributing.md). Source, schemas, and tests are the authoritative architecture and
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extension guide.
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- **Documentation contributions**: Update the canonical files under `docs/en/`, `docs/zh/`, or the relevant package
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|
directory in this repository. The documentation site is generated from these files.
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- **Commit convention**: Conventional Commits are recommended, for example `feat(search): add link expansion option` or
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`docs(zh): update quick start`.
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- **Pre-submit checks**: Before submitting a PR, try to run `pre-commit run --all-files` and `pytest`. If tests that
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depend on LLMs, embeddings, or external services cannot run, explain that in the PR.
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- **Documentation**: Visit [reme.agentscope.io](https://reme.agentscope.io).
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### Contributors
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|
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Thanks to everyone who has contributed to ReMe:
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<a href="https://github.com/agentscope-ai/ReMe/graphs/contributors">
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<img src="https://contrib.rocks/image?repo=agentscope-ai/ReMe" alt="Contributors" />
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</a>
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## 📄 Citation
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```bibtex
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@software{ReMe2026,
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title = {Remember me, Refine me: Memory Management Kit for Agents},
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author = {ReMe Team},
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url = {https://reme.agentscope.io},
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year = {2026}
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}
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```
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## ⚖️ License
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This project is open source under the Apache License 2.0. See [LICENSE](./LICENSE) for details.
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