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getmem.ai Memory

v1.0.2

Persistent memory for AI agents via getmem.ai. Call mem.get() before each LLM call to inject context, and mem.ingest() after each turn to save the conversation.

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Install

OpenClaw Prompt Flow

Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for nimblev2023/getmem.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "getmem.ai Memory" (nimblev2023/getmem) from ClawHub.
Skill page: https://clawhub.ai/nimblev2023/getmem
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

Bare skill slug

openclaw skills install getmem

ClawHub CLI

Package manager switcher

npx clawhub@latest install getmem
Security Scan
Capability signals
Requires sensitive credentials
These labels describe what authority the skill may exercise. They are separate from suspicious or malicious moderation verdicts.
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Benign
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high confidence
Purpose & Capability
The SKILL.md and install metadata describe installing the getmem-ai package and calling mem.get()/mem.ingest(), which is coherent with a persistent-memory skill. However, the registry metadata claims no required env vars or install spec while the SKILL.md explicitly requires GETMEM_API_KEY and includes a pip install entry — a clear metadata/instruction mismatch.
!
Instruction Scope
Runtime instructions tell the agent to send user and assistant messages to getmem.ai (mem.ingest) and to fetch contextual memory (mem.get). This is expected for a memory service, but it means full conversation content is transmitted to an external service and stored indefinitely (no TTL/purge). The instructions also reference os.environ['GETMEM_API_KEY'] even though the registry metadata doesn't declare it. Transmitting potentially sensitive data off-host with long retention is a material privacy/security risk.
Install Mechanism
The SKILL.md includes a pip install (getmem-ai on PyPI), which is a common and expected distribution method (moderate risk). The registry metadata earlier reported no install spec — another inconsistency between registry and SKILL.md. No downloads from arbitrary URLs are used, which reduces installation risk.
!
Credentials
The skill requires an API key (GETMEM_API_KEY) according to SKILL.md, but the registry metadata lists no required env vars or primary credential. The missing declaration of a secret-level environment variable is an incoherence and increases the chance a user won't realize they're providing a long-lived secret that allows an external service to store conversation data.
Persistence & Privilege
The skill does not request always:true and does not claim elevated system privileges. Autonomous invocation is allowed by default (normal). Note: combining autonomous invocation with remote indefinite storage increases blast radius for data exfiltration, but that is a behavioral/privacy concern rather than an OS-privilege escalation.
What to consider before installing
This skill appears to be a legitimate getmem.ai integration, but there are important mismatches and privacy implications you should consider before installing: - The SKILL.md requires a GETMEM_API_KEY and includes a pip install for getmem-ai, but the registry metadata does not declare that secret or the install step. Ask the publisher to correct the metadata so the required secret is explicit. - Using the skill will send user and assistant messages to getmem.ai and the documentation says memory persists indefinitely. Do not enable this skill for agents that will handle sensitive, regulated, or confidential data unless you confirm retention, encryption, access controls, and deletion policies with getmem.ai. - Verify the PyPI package (getmem-ai) is the official SDK (check maintainer, code repository, and recent releases) before installing. Prefer installing in an isolated/sandboxed environment first. - If you need memory but cannot risk sending data externally, consider a local or self-hosted memory solution instead. If you want to proceed, request that the publisher update the registry metadata to declare GETMEM_API_KEY as a required secret and to provide a homepage/source repository and clear privacy/retention documentation.

Like a lobster shell, security has layers — review code before you run it.

Runtime requirements

🧠 Clawdis
latestvk970pa2w6bzedpc7c8dgba513h854xmw
81downloads
0stars
3versions
Updated 1w ago
v1.0.2
MIT-0

getmem.ai Memory Skill

Persistent memory for your AI agent via getmem.ai.

Setup

Set your API key in the environment:

export GETMEM_API_KEY=gm_live_YOUR_KEY_HERE

Get your key at https://platform.getmem.ai$20 free credit on signup.

Usage

import getmem_ai as getmem, os

mem = getmem.init(os.environ["GETMEM_API_KEY"])

# Before each LLM call — get relevant memory context
result = mem.get(user_id, query=user_message)
context = result["context"]  # inject into system prompt

# After each turn — save both user + assistant messages
mem.ingest(user_id, messages=[
    {"role": "user", "content": user_message},
    {"role": "assistant", "content": reply},
])

How it works

  1. mem.get() fetches only the relevant memories for the current query (semantic search)
  2. Context is injected into your system prompt — typically 200-800 tokens
  3. mem.ingest() saves the full conversation exchange asynchronously
  4. Memory persists indefinitely — no TTL, no purge

Token savings

Standard approach: full conversation history every turn = 10,000-40,000 tokens With getmem: only relevant context = 200-800 tokens. Save up to 95% on context tokens.

Links

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