Redis

What This Skill Does

The Redis skill provides an interface for the OpenClaw AI agent to manage high-performance data storage, caching, and message brokering using Redis. It enables the agent to interact with Redis clusters and instances to handle transient data, distributed locks, rate limiting, and reliable message queuing through stream processing. This skill is optimized for developers needing to implement efficient state management and performance-critical operations without manually managing low-level connection protocols.

Installation

Install the skill via the command line interface: clawhub install openclaw/skills/skills/ivangdavila/redis-store

Use Cases

• Efficient Caching: Implement robust cache-aside patterns to reduce database load, ensuring memory stays optimized through proper TTL usage.
• Rate Limiting: Protect APIs by creating sliding-window rate limiters using Sorted Sets, or simple fixed-window limits via atomic increments.
• Distributed Task Queues: Utilize Redis Streams to build reliable, persistent, and acknowledged message queues that outperform standard Pub/Sub patterns.
• Distributed Locking: Manage synchronization across multiple AI agent instances using atomic SET NX EX commands to prevent race conditions.
• High-Performance Analytics: Use HyperLogLog for memory-efficient unique visitor tracking or large-scale metric aggregation.

Example Prompts

1. "Implement a cache-aside pattern for the user profile service, setting a 3600-second expiration to prevent memory leaks."
2. "Create a distributed lock for the process_orders task with a 30-second timeout and a unique token for safe release."
3. "Set up a Redis stream for my notification service using XADD and ensure the agent knows how to acknowledge messages with XACK."

Tips & Limitations

• Memory Management: Always define a maxmemory policy. Prefer allkeys-lru for pure caches and ensure keys have expiration times to prevent memory growth beyond the cluster limit.
• Atomicity: Avoid 'GET then SET' logic; instead, use Lua scripts or native atomic operators like INCR or SETNX to ensure data integrity during concurrent operations.
• Clustering: When using Redis Clusters, utilize hash tags like {user:1} to group related keys within the same slot, enabling atomic multi-key operations that would otherwise fail across nodes.
• Pub/Sub vs Streams: Avoid Pub/Sub for critical data delivery as it lacks persistence; prioritize Redis Streams for reliable, at-least-once message processing.
• Persistence: Balance your durability needs by selecting between RDB snapshots for fast recovery or AOF logs for minimal data loss; configure appendfsync everysec for a safe middle ground.