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Memory Cache
Standardized Redis-backed caching system for OpenClaw agents.
Prerequisites
- Binary:
python3must be available on the host. - Credentials:
REDIS_URLenvironment variable (e.g.,redis://localhost:6379/0).
Setup
- Copy
env.example.txtto.env. - Configure your connection in
.env. - Dependencies are listed in
requirements.txt.
Core Workflows
1. Store and Retrieve
- Store:
python3 $WORKSPACE/skills/memory-cache/scripts/cache_manager.py set mema:cache:<name> <value> [--ttl 3600] - Fetch:
python3 $WORKSPACE/skills/memory-cache/scripts/cache_manager.py get mema:cache:<name>
2. Search & Maintenance
- Scan:
python3 $WORKSPACE/skills/memory-cache/scripts/cache_manager.py scan [pattern] - Ping:
python3 $WORKSPACE/skills/memory-cache/scripts/cache_manager.py ping
Key Naming Convention
Strictly enforce the mema: prefix:
mema:context:*– Session state.mema:cache:*– Volatile data.mema:state:*– Persistent state.
Created by
@1999azzarPersistent memory
Give your OpenClaw agent a memory layer
Mem0 remembers users and context across sessions so you send fewer tokens and get better answers.
Try Mem0Mem0 + OpenClaw guide