getmem.ai Memory

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.

NimbleV2023@nimblev2023

Install

openclaw skills install @nimblev2023/getmem

getmem.ai Memory Skill

Persistent memory for your AI agent via getmem.ai.

Setup

Set your API key in the environment:

bash

export GETMEM_API_KEY=gm_live_YOUR_KEY_HERE

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

Usage

python

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

mem.get() fetches only the relevant memories for the current query (semantic search)
Context is injected into your system prompt — typically 200-800 tokens
mem.ingest() saves the full conversation exchange asynchronously
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.