A high-performance semantic caching skill that stores LLM responses to slash API costs and accelerate agent reaction times.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install openclaw-langcache
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install openclaw-langcache using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Redis LangCache Semantic Caching?
Redis LangCache is a professional-grade skill designed to integrate fully-managed semantic caching into your AI workflows. By leveraging vector similarity, this Openclaw Skills integration allows agents to recognize and reuse previous LLM outputs for conceptually similar queries, rather than just exact matches. This effectively bridges the gap between raw LLM processing and efficient data retrieval, ensuring your agents remain fast and cost-effective.
This skill provides a robust framework for managing the lifecycle of AI responses, including intelligent search strategies and strict privacy controls. By implementing LangCache, developers can significantly enhance the scalability of their Openclaw Skills projects while maintaining high response quality through customizable similarity thresholds.
Redis LangCache Semantic Caching Use Cases
- Reducing repetitive LLM API costs by caching common factual queries and documentation explanations.
- Speeding up agent response times for frequently used templates and style transformations.
- Implementing semantic search across historical agent interactions to improve context retrieval.
- Orchestrating multi-model environments by partitioning cached data using metadata attributes.
How Redis LangCache Semantic Caching Works
- The agent intercepts a user prompt and performs a semantic search via LangCache to find similar past interactions.
- If a match is found exceeding the defined similarity threshold (e.g., 0.90), the cached response is returned instantly.
- In the event of a cache miss, the agent forwards the request to the primary LLM.
- Once the LLM responds, the agent stores the new prompt-response pair in the cache with relevant metadata attributes.
- Advanced regex patterns automatically scan and block sensitive data like credentials or PII from being cached.
Redis LangCache Semantic Caching Setup
To enable this skill within the Openclaw Skills framework, configure the following environment variables in your ~/.openclaw/secrets.env file:
LANGCACHE_HOST=<your-langcache-host>
LANGCACHE_CACHE_ID=<your-cache-id>
LANGCACHE_API_KEY=<your-api-key>
Redis LangCache Semantic Caching Data Schema & Taxonomy
LangCache organizes data using a combination of vector embeddings for meaning and metadata attributes for filtering.
| Attribute | Description | Example |
|---|---|---|
model |
The specific LLM used for the generation | gpt-4o |
category |
The type of content stored | factual, command, style |
threshold |
The strictness of the semantic match | 0.95 |
strategy |
The lookup logic employed | semantic, exact |
attributes |
Custom key-value pairs for partitioning | user_id=123 |
Redis LangCache Semantic Caching Advanced Features
- Hybrid search strategies that support combining semantic vector matching with case-insensitive exact matches.
- Granular attribute-based cache management for targeted deletions and organizational partitioning.
- Automated safety enforcement that hard-blocks the caching of temporal data, credentials, and PII.
- Performance observability through similarity score logging and hit/miss ratio tracking.
SKILL.md
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