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 agentmods add skills/agentscope-ai/reme/reme-memorynpx 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.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00037 | $0.00718 |
| Opus 5 | $0.00018 | $0.00359 |
| Sonnet 5 | $0.00007 | $0.00144 |
| Haiku 4.5 | $0.00004 | $0.00072 |
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 5d 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ReMe Memory
ReMe is the persistent, file-native memory layer for this agent. It stores conversations and
resources as Markdown files with frontmatter and [[wikilinks]], and consolidates them into
long-term digest knowledge. Your job with this plugin is recall.
The recall tools come from the reme MCP server (surfaced as mcp__reme__…): search, traverse,
daily_list, frontmatter_read, read. They are only available when the user has the server
running:
reme start service.backend=http
If the tools are missing, that server is not running — tell the user the command above instead of guessing answers.
Recall (read long-term memory)
Before answering questions about previous conversations, user preferences, project history,
decisions, or long-term context, recall from ReMe first. ReMe answers three independent kinds of
question — pick the mode the request needs; don't merge them into one call. Durable knowledge
lives under digest/, daily notes under daily/, external materials under resource/.
- Semantic (default — "what do we know about X?"):
searchwithquery="<question/keywords>",limit=5(optionalmin_score). Hybrid vector + BM25 with one-hop wikilink expansion. - Topological ("what links to this node?"):
traversewithpath="<node>",depth=1(raise to 2 only when needed),direction=bothto walk the[[wikilink]]graph. - State ("what exists / what was recorded on ?"):
daily_listwithdate="YYYY-MM-DD"(empty = today) to list a day's notes, orfrontmatter_readwith apathto inspect one file's frontmatter — structural lookup, no semantic matching.
Then read the relevant hits by path (optionally start_line/end_line; prefer digest/ paths
for durable knowledge) to pull the content behind a hit. Cite the workspace-relative paths you used.
If nothing useful comes back, say so plainly rather than guessing.
Server status
To check ReMe is up: call version and health_check, then summarize the version and the health
snapshot (components, workspace). If the mcp__reme__… tools are not available at all, the server
is not running — tell the user to start it with the command above. The plugin connects at
http://127.0.0.1:2333/mcp; a different host/port must match the url in the bundled .mcp.json.
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.
- 5d ago First seen · 59 lines · 37 tokens per session scan A 2f41c3301090
reme-memory is a skill published in the GitHub repository agentscope-ai/ReMe (3,383 stars, last pushed 3d ago), licensed Apache-2.0. It adds 37 tokens to every session and 718 once invoked, about $0.0002 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.
Other skills, from other repositories
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Use when spawning a Claude Code, Codex, Gemini, Aider, or other CLI task agent whose work needs parent Eliza runtime context. Covers the read-only loopback bridge for character, room, memory, and active workspace state.
aatmf-t04-memory-manipulation
AATMF T4 — Multi-Turn & Memory Manipulation. Persistent memory injection, conversation-state poisoning, cross-session contamination, ghost-context leak.
browserwing-admin
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kb-builder
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