Memanto is a companion memory agent that manages what other AI agents remember, including durable facts, preferences, conflicts, stale information, and which agents should receive updates. It is for teams running multiple agents that need coordinated memory across conversations, platforms, and storage systems.
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.
git clone --depth 1 https://github.com/moorcheh-ai/memantoWrote 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/plugins/moorcheh-ai/memanto/lifecycle-hooks)<a href="https://agentmods.dev/plugins/moorcheh-ai/memanto/lifecycle-hooks"><img src="https://agentmods.dev/badge/plugins/moorcheh-ai/memanto/lifecycle-hooks/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/plugins/moorcheh-ai/memanto/lifecycle-hooks"><img src="https://agentmods.dev/badge/plugins/moorcheh-ai/memanto/lifecycle-hooks.svg" alt="Reviewed on agentmods" width="80" height="20"></a>Grade A, and why
memanto-skills 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 1mo 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.
What it actually says
{
"$schema": "https://code.claude.com/schemas/plugin-manifest.json",
"name": "memanto-skills",
"displayName": "Memanto Engineering Memory",
"description": "Cross-session engineering memory for Claude Code skills, powered by Memanto. Recalls past architectural decisions before a skill runs and distills new ones after — automatically, via lifecycle hooks.",
"version": "0.1.0",
"author": {
"name": "Moorcheh AI",
"url": "https://memanto.ai"
},
"homepage": "https://memanto.ai",
"repository": "https://github.com/moorcheh-ai/memanto",
"license": "MIT",
"keywords": ["memanto", "memory", "skills", "hooks", "claude-code", "mattpocock"],
"skills": [
"./skills/memanto-companion"
]
}
What it installs
The manifest is a name and a version. 1 skill travel with it, and installing the plugin installs all of them — 72 tokens a session between them. Each is measured on its own page, and each can be installed alone.
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.
- 1mo ago First seen · 19 lines scan A 6a8d35b6ab2d
memanto-skills is a plugin published in the GitHub repository moorcheh-ai/memanto (2,342 stars, last pushed today), licensed MIT. Its token cost is not measured: this kind of file is read by the harness, not the model. 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 plugins, from other repositories
hippo-memory
Make your agent's memory work like a brain. Hippo is long-term memory for coding agents.
goodmem
GoodMem memory infrastructure for AI agents. Use Python SDK skills to write code that manages embedders, spaces, and memories, or use MCP tools to perform GoodMem operations directly via natural language.
pseudolife-memory
Persistent long-term memory for Claude Code — wiring for the Pseudolife-MCP daemon (session briefing + identity hooks + memory commands; MCP transport via the installer's stdio shim or claude mcp add). Requires the daemon stack: https://github.com/Pseudogiant-xr/Pseudolife-MCP#quickstart.
runtime-memory
Persistent memory with outcome-based learning for AI coding agents. Stores knowledge from coding sessions, learns which memories actually help, and gets smarter over time.
memanto-mcp
Typed semantic memory for Claude — wraps memanto/Moorcheh as an MCP server with remember/recall/answer tools.
memX
memX: local-first semantic memory for coding agents. Native Claude Code lifecycle hooks.