- name
- memory-cache
- description
- High-performance temporary storage system using Redis. Supports namespaced keys (mema:*), TTL management, and session context caching. Use for: (1) Saving agent state, (2) Caching API results, (3) Sharing data between sub-agents.
- metadata
- {"openclaw":{"requires":{"bins":["python3"],"env":["REDIS_URL"]},"install":[{"id":"pip-dependencies","kind":"exec","command":"pip install -r requirements.txt"}]}}
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.