An agent memory layer that turns conversations and resources into readable, editable, searchable Markdown memory.

Previous versions: 0.3.x · 0.2.x · MemoryScope

🧠 ReMe is a local-first memory layer for AI agents. It turns conversations and resources into file-based long-term memory, then continuously indexes, links, and consolidates that memory for future recall.

✨ Core Ideas

• Memory as File: Markdown files with frontmatter and wikilinks serve as memory nodes that both users and agents can read and write directly.
• Self-evolving knowledge base: Auto Memory, Auto Resource, and Auto Dream progressively transform conversations and resources into long-term memories, while automatically building wikilink relationships.
• Progressive hybrid search: ReMe combines wikilinks, BM25, and embeddings for hybrid retrieval across keyword matching, semantic recall, and relationship expansion.
• Agent-friendly integration: SKILL.md + CLI integration makes it easy for different agents to read, write, maintain, and reuse memory.

🔭 Use Cases

• Personal assistants: Give personal assistants such as QwenPaw, OpenClaw, and Hermes a user-editable long-term memory layer.
• Coding agents: Preserve coding style, project background, repository decisions, and workflow experience across sessions when integrating with coding agents such as Claude Code.
• LLM Wiki: Turn conversations, notes, and resources into a searchable, traceable, and linked Markdown knowledge base that both users and agents can maintain.
• Self-evolving agents: Support agents that learn from experience by saving successful paths, failed attempts, reusable procedures, and periodic reflections as memory.

📰 News

• [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 ".[core]"

Environment Variables

Configure environment variables when you want LLM-powered memory evolution or embedding retrieval:

cat > .env <<'EOF'
# Optional: enables semantic retrieval when the embedding store is configured.
EMBEDDING_API_KEY=sk-xxx
EMBEDDING_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1

# Required for auto_memory, auto_resource, and auto_dream.
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.

Start the Service

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

After startup, check the service status. If you use a custom port, replace 2333 in the URL below with that port.

reme version
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]]

📁 Memory System

Memory as File, File as Memory.

ReMe treats memory as files, progressively processing raw conversations and external resources from session/ and resource/ into daily/, then consolidating them into reusable long-term memory nodes under digest/.

Directory Structure

<workspace_dir>/
├── metadata/       # Persistent system state such as indexes, graphs, and catalogs
├── session/        # Raw conversations and agent sessions
│   ├── dialog/
│   │   └── <session_id>.jsonl
│   ├── agentscope/
│   └── claude_code/
├── resource/            # External raw materials
│   └── YYYY-MM-DD/
│       └── <resource>.<ext>
├── daily/               # Lightly processed memory: daily facts, conversation summaries, resource readings
│   ├── YYYY-MM-DD.md
│   └── YYYY-MM-DD/
│       ├── <session_event>.md
│       ├── <resource_stem>.md
│       └── interests.yaml
└── digest/              # Long-term memory: personal facts, procedural experience, knowledge nodes
    ├── personal/
    │   └── {topic/event}.md
    ├── procedure/
    │   └── {topic/event}.md
    └── wiki/
        └── {topic/event}.md

🧭 Memory Design Philosophy

Capture raw dialogs and resources, refine them into long-term preferences, reusable experience, and valuable knowledge, while keeping the result editable by humans and agents.

Automatic Memory Flow

ReMe follows a capture → index → consolidate → recall loop. Conversations and resources first become daily memory cards; background jobs keep files searchable; auto_dream distills stable knowledge into digest/; agents recall memory through search, wikilinks, or proactive topics.

Capability	Entry point	What it does	Output
auto_memory	Agent hook or reme auto_memory	Distills useful conversation facts while preserving the raw session.	session/dialog/*.jsonl, daily/<date>/<session>.md
auto_resource	Resource watcher or reme auto_resource	Turns files under resource/<date>/ into source-linked daily cards.	daily/<date>/<resource-card>.md
auto_index	Background watcher or reme reindex	Maintains chunks, BM25/embedding indexes, and the wikilink graph.	Searchable daily/, digest/, and resource/ content
auto_dream	dream_cron or reme auto_dream	Consolidates changed daily cards into long-term personal, procedure, and wiki memory.	digest/**, daily/<date>/interests.yaml
proactive	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

🤝 Agent-friendly Integration

ReMe runs as a local memory service and offers multiple integration paths: CLI, HTTP API, MCP server, and SDK. Different agents can choose the path that fits their runtime while sharing the same local memory workspace.

Agents	Recommended path	What works out of the box
QwenPaw	Embed ReMe via the Python SDK.	Reuse the app's own lifecycle and model config while keeping memory local and file-based.
Claude Code	Start ReMe as an MCP service and install plugins/reme.	MCP recall tools, a reme-memory skill, and a Stop hook that records sessions automatically.
Other CLI-capable agents (OpenClaw/Hermes/Codex)	Copy or install skills/reme_memory/SKILL.md.	Search/read/write memory and call auto_memory, auto_dream, and proactive via the CLI.

Integration demos

	Auto Memory	Auto Dream
QwenPaw
Claude Code

🛠️ ReMe Operations

ReMe operates the workspace through a unified job interface exposed by the CLI. Agents usually only need retrieval, reading, writing, editing, and automatic memory commands. Lower-level indexing, frontmatter, and file operation commands are mainly for maintenance, debugging, or advanced integration. Run reme help for the full job list.

Command	Purpose
reme start	Start the local ReMe service.
reme version / reme health_check	Check package and component status.
reme search	Retrieve memory with hybrid search.
reme read / reme write / reme edit	Inspect and maintain Markdown memory files.
reme auto_memory	Turn conversation messages into daily memory cards. Requires LLM credentials.
reme auto_resource	Interpret files under resource/ into daily resource cards. Requires LLM credentials.
reme auto_dream / reme proactive	Consolidate daily memory into long-term digest and surface topics worth attention.
reme reindex	Rebuild search and wikilink indexes from existing files.

🤝 Community and Support

• Issues and requests: Check Open Issues first. If there is no related discussion, open a new issue with background, expected behavior, and impact scope.
• Code contributions: Before making changes, read the contribution guide and code framework, and follow the CLI / Service / Application / Job / Step / Component layering.
• Documentation contributions: For user-visible installation, configuration, invocation, or behavior changes, update docs/en/, docs/zh/, or the README files accordingly.
• Commit convention: Conventional Commits are recommended, for example feat(search): add link expansion option or docs(zh): update quick start.
• Pre-submit checks: Before submitting a PR, try to run pre-commit run --all-files and pytest. If tests that depend on LLMs, embeddings, or external services cannot run, explain that in the PR.
• Get help: Use GitHub Issues for bugs and feature requests. Project documentation is available at https://reme.agentscope.io/.

Contributors

Thanks to everyone who has contributed to ReMe:

📄 Citation

@software{ReMe2026,
  title = {Remember me, Refine me: Memory Management Kit for Agents},
  author = {ReMe Team},
  url = {https://reme.agentscope.io},
  year = {2026}
}

⚖️ License

This project is open source under the Apache License 2.0. See LICENSE for details.

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