reme_memory

A file-based long-term memory system for an agent, managed through the ReMe command-line tool. It stores selected conversation records, daily notes, resources, and longer summaries in a local workspace.

In plain words
What is it for?
Checking whether ReMe is installed and running, starting or configuring it when requested, retrieving prior context, and saving or consolidating durable memories.
Why use it?
It lets the agent retrieve useful context from earlier work instead of relying only on the current conversation. It keeps stored memory in files owned by the user.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/agentscope-ai/reme/reme_memory
Any agent
npx skills add agentscope-ai/ReMe --skill reme_memory
Clone the repo
git clone --depth 1 https://github.com/agentscope-ai/ReMe

Made for: Claude Code, Codex.

Per session 62 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,928 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00062 $0.01928
Opus 5 $0.00031 $0.00964
Sonnet 5 $0.00012 $0.00386
Haiku 4.5 $0.00006 $0.00193

Measured yesterday against content hash 8d029a441861, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 yesterday.

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.

skills/reme_memory/SKILL.md · 224 lines

How it starts

The opening of the file, as written. The whole thing — 224 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, and auto_dream require an LLM configuration.

When those model-powered jobs are needed, have the user provide valid values through the environment or a .env file:

Read the full file on GitHub · 224 lines

Changes

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.

  1. yesterday First seen · 224 lines · 62 tokens per session scan A 8d029a441861

Subscribe to this mod's changes

reme_memory is a skill published in the GitHub repository agentscope-ai/ReMe (3,369 stars, last pushed 3d ago), licensed Apache-2.0. It adds 62 tokens to every session and 1,928 once invoked, about $0.0003 per session on Opus 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.

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