ReMe is a local-first memory management system that turns conversations and other resources into searchable, editable Markdown knowledge shared across AI agents. It is for users and agent runtimes that need durable, interconnected knowledge they can inspect and maintain. The catalogue add-ons connect coding agents and other agents to ReMe through skills, hooks, plugins, MCP, and related interfaces.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add agentscope-ai/ReMe --skill reme_memorygit clone --depth 1 https://github.com/agentscope-ai/ReMeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/agentscope-ai/reme/reme_memory)<a href="https://agentmods.dev/skills/agentscope-ai/reme/reme_memory"><img src="https://agentmods.dev/badge/skills/agentscope-ai/reme/reme_memory/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agentscope-ai/reme/reme_memory"><img src="https://agentmods.dev/badge/skills/agentscope-ai/reme/reme_memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00062 | $0.02111 |
| Opus 5.5 | $0.00025 | $0.00844 |
| Sonnet 5.5 | $0.00012 | $0.00422 |
| Haiku 4.5 | $0.00006 | $0.00211 |
Grade A, and why
reme_memory scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ReMe Memory
Use ReMe as the persistent memory layer for this Agent. ReMe stores filtered conversation source records, daily notes,
resources, and long-term digest memories in a user-owned local workspace. auto_memory omits recalled tool results and
base64 data when it persists a source record so retrieved or binary content does not become conversation source
material.
Bootstrap ReMe
Run this workflow before first use and whenever a ReMe command cannot reach the service. Distinguish a missing CLI from an installed but stopped service.
1. Check whether ReMe is installed
Run:
command -v reme
If this prints an executable path, treat ReMe as installed and continue to service discovery. Do not reinstall or upgrade an existing installation unless the user requests it.
If the command is missing, check Python before installing:
python3 -c 'import sys; print(sys.version); raise SystemExit(0 if sys.version_info >= (3, 11) else 1)'
ReMe requires Python 3.11 or newer. If the user has requested setup or installation, install the recommended package in the active Python environment:
python3 -m pip install "reme-ai[core]"
When working from a ReMe source checkout and the user explicitly wants an editable source installation, run this from the repository root instead:
python3 -m pip install -e ".[core]"
Do not silently install into or modify a Python environment when the user only asked to use memory. Explain that ReMe is
missing and ask before installing. After installation, run command -v reme again. If it is still missing, check that
the active environment's executable directory is on PATH; do not repeatedly reinstall.
2. Configure optional model credentials
Basic file operations, BM25 search, wikilink traversal, and reading existing proactive topics work without model
credentials. auto_memory, auto_resource, auto_dream, and proactive refresh require an LLM configuration.
When those model-powered jobs are needed, have the user provide valid values through the environment or a .env file:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago Changed · +5 lines 6a059f72c116
- 6d ago Changed · +2 lines dc2f23aec3c1
- 1mo ago Changed · +6 lines 75f22d95222b
- 1mo ago First seen · 224 lines · 62 tokens per session scan A 8d029a441861
reme_memory is a skill published in the GitHub repository agentscope-ai/ReMe (3,646 stars, last pushed 3d ago), licensed Apache-2.0. It adds 62 tokens to every session and 2,111 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
knowledge-org
A guide to organising large bodies of knowledge so an AI system can navigate relationships, summaries, and layers instead of searching only flat text chunks. It covers tree indexes, knowledge graphs, file-like structures, incremental updates, and long-term memory layouts.
browserwing-executor
Control browser automation through HTTP API. Supports page navigation, element interaction (click, type, select), data extraction, accessibility snapshot analysis, screenshot, JavaScript execution, and batch operations.
dev-browser
Browser automation with persistent page state. Use when users ask to navigate websites, fill forms, take screenshots, extract web data, test web apps, or automate browser workflows. Trigger phrases include "go to [url]", "click on", "fill out the form", "take a screenshot", "scrape", "automate", "test the website"…
ask-user-question
Ask users questions via the UI. Use when you need clarification, user preferences, or confirmation before proceeding. The user CANNOT see CLI output - this tool is the ONLY way to communicate with them.
pixelbrowse
Screenshot and visually read any web page or document using pixelshot. Use instead of fetching raw HTML when you need to see what a page looks like, read visual content (charts, diagrams, infographics), check layouts, or verify UI. Triggers: "look at this page", "screenshot", "what does this site look like", "check…
memos-memory-guide
Use the MemOS Local memory system to search and use the user's past conversations. Use this skill whenever the user refers to past chats, their own preferences or history, or when you need to answer from prior context. When auto-recall returns nothing (long or unclear user query), generate your own short search query…