memory-cache
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.
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- RabbitAI-Lab/rabbit-plugins-upstream
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- July 26, 2026 at 20:50
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- 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.
# Memory Cache
Standardized Redis-backed caching system for OpenClaw agents.
## Prerequisites
- **Binary**: `python3` must be available on the host.
- **Credentials**: `REDIS_URL` environment variable (e.g., `redis://localhost:6379/0`).
## Setup
1. Copy `env.example.txt` to `.env`.
2. Configure your connection in `.env`.
3. 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.
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