rlm-mem

Provides a policy-scoped memory runtime with layered JSONL storage for agents.

62|6|Updated Feb 12, 2026
One-click install
npx skills add https://github.com/zenchantlive/beadboard --skill rlm-mem
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: rlm-mem
Source: https://github.com/zenchantlive/beadboard/tree/main/.agents/skills/rlm-mem
Command: npx skills add https://github.com/zenchantlive/beadboard --skill rlm-mem

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

RLM-MEM provides a canonical, policy-scoped memory runtime for agents that persists memories with guardrails to ensure safe operation across sessions.

Core Features & Use Cases

  • Deterministic memory chunking and linking for provenance-rich recall.
  • Policy-scoped write/read separation with optional user-global controls.
  • Use cases include long-running agent workloads, cross-session task tracking, and memory-driven decision support.

Quick Start

Initialize a canonical RLM-MEM runtime at the project root and run the guard checks to verify integrity.

Frequently Asked Questions about rlm-mem

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I add persistent memory to an agent for cross-session continuity?

Persistent agent memory enables cross-session continuity by storing chunks in a layered JSONL memory store with deterministic linking. This runtime applies policy-scoped write/read separation to ensure safe recall with provenance and safeguards.

What is policy-scoped memory management for AI agents?

Policy-scoped memory management enforces read and write separation with optional user-global controls to securely persist agent memories. It uses mandatory guard scripts and deterministic chunking to maintain provenance-rich recall across long-running workloads.

How do I initialize a secure memory runtime at my project root?

To initialize a secure memory runtime, set up the canonical package path at your project root and execute the mandatory guard checks. This verifies memory store integrity and enforces the required security policies before runtime operation.

Can I use deterministic memory chunking for long-running agent workloads?

Deterministic memory chunking supports long-running agent workloads by consistently dividing and linking memory chunks for provenance-rich recall. The runtime applies safe recall policies to manage memory-driven decision support across sessions.

What are the limitations of relying on standard memory without policy-scoped guardrails?

Standard memory lacks policy-scoped write/read separation and mandatory guard scripts, risking unsafe recall and lost provenance across sessions. A canonical runtime with a layered JSONL store is required to enforce security and deterministic chunking.