redis-semantic-cache
Redis LangCache guidance for semantic caching of LLM responses on Redis Cloud — calling search/set via the SDK or REST API, tuning the similarity threshold, separating caches per task type, and filtering with custom attributes. Use when caching LLM completions or RAG answers to cut API cost and latency, building a cache-aside layer in front of OpenAI / Anthropic / etc., tuning hit rate vs precision, or splitting one app's LLM workloads into multiple LangCache caches.
Install
With your agent
Install OMGSkills skill "redis/agent-skills:skills/redis-semantic-cache" into this agent's normal skills directory: call the OMGSkills MCP get_install_plan tool for your agent and run the script it returns (it installs only the pinned commit and verifies SKILL.md), and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.
Manual command
git clone https://github.com/redis/agent-skills /tmp/agent-skills && ln -s /tmp/agent-skills/skills/redis-semantic-cache ~/.claude/skills/redis-semantic-cacheFrom README
Redis Semantic Cache Semantic caching for LLM responses with Redis Cloud's LangCache service. Stores prompts as embeddings; subsequent semantically-similar prompts return the cached response without re-calling the model. LangCache is currently in preview on Redis Cloud. Features and behavior may change. When to apply Wrapping an LLM call (OpenAI, Anthropic, etc.) with a cache layer to cut cost and latency. Caching RAG answers, classification outputs, or any deterministic LLM workload. Tuning the precision/hit-rate trade-off for a semantic cache. Splitting one application's LLM workloads across multiple cache instances. The cache-aside flow LangCache fits in front of any LLM call as a standard cache-aside pattern: Send the user's prompt to LangCache's search. Cache hit — return the stored response directly. Cache miss — call the LLM, then set the response so future similar prompts hit.
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