@openclaw/langcache

Semantic caching skill for OpenClaw using Redis LangCache.

Reduce LLM costs and latency by caching responses for semantically similar queries, with built-in privacy and security guardrails.

Features

• Semantic similarity matching - Cache hits for similar (not just identical) queries
• Hard block enforcement - Automatically blocks caching of sensitive data:
  • Temporal info (today, tomorrow, deadlines, appointments)
  • Credentials (API keys, passwords, tokens, OTP)
  • Identifiers (emails, phone numbers, account IDs)
  • Personal context (relationships, private conversations)
• Category-aware thresholds - Different similarity thresholds for factual Q&A vs style transforms
• CLI and Python integration - Use from shell scripts or embed in Python agents

Installation

Via npm (Recommended)

npm install openclaw-langcache

The skill will be automatically installed to your OpenClaw workspace.

Via Git

git clone https://github.com/openclaw/langcache.git ~/.openclaw/workspace/skills/langcache

Manual

Download and extract to ~/.openclaw/workspace/skills/langcache/

Configuration

Add your Redis LangCache credentials to ~/.openclaw/secrets.env:

LANGCACHE_HOST=your-instance.redis.cloud
LANGCACHE_CACHE_ID=your-cache-id
LANGCACHE_API_KEY=your-api-key

Get these from Redis Cloud Console after creating a LangCache instance.

Usage

Automatic (via OpenClaw agent)

The skill triggers automatically when you mention:

• "cache LLM responses"
• "semantic caching"
• "reduce API costs"
• "configure LangCache"

CLI

# Search for cached response
langcache.sh search "What is Redis?"

# With similarity threshold
langcache.sh search "What is Redis?" --threshold 0.9

# Store a response
langcache.sh store "What is Redis?" "Redis is an in-memory data store..."

# Check if content would be blocked
langcache.sh check "What's on my calendar today?"
# Output: BLOCKED: temporal_info

# Delete entries
langcache.sh delete --id <entry-id>
langcache.sh delete --attr model=gpt-4

Python Integration

from examples.agent_integration import CachedAgent, CacheConfig

agent = CachedAgent(config=CacheConfig(
    enabled=True,
    model_id="gpt-5",
))

# Automatically uses cache with policy enforcement
response = await agent.complete("What is semantic caching?")

Caching Policy

Cacheable (white-list)

| Category | Examples | Threshold | |----------|----------|-----------| | Factual Q&A | "What is X?", "How does Y work?" | 0.90 | | Definitions / docs | API docs, command help | 0.90 | | Command explanations | "What does git rebase do?" | 0.92 | | Reply templates | "polite no", "follow-up", "intro" | 0.88 | | Style transforms | "make this warmer/shorter" | 0.85 |

Never Cached (hard blocks)

| Category | Examples | |----------|----------| | Temporal | today, tomorrow, deadline, ETA, "in 20 minutes" | | Credentials | API keys, passwords, tokens, OTP/2FA | | Identifiers | emails, phone numbers, account IDs, UUIDs | | Personal | "my wife said", private conversations, relationships |

File Structure

skills/langcache/
├── SKILL.md              # Skill definition and instructions
├── scripts/
│   └── langcache.sh      # CLI wrapper with policy enforcement
├── references/
│   ├── api-reference.md  # Complete REST API documentation
│   └── best-practices.md # Optimization techniques
└── examples/
    ├── basic-caching.sh      # Simple cache workflow
    └── agent-integration.py  # Python integration pattern

API Reference

See references/api-reference.md for complete REST API documentation.

Key Endpoints

| Endpoint | Method | Description | |----------|--------|-------------| | /v1/caches/{id}/entries/search | POST | Search for cached response | | /v1/caches/{id}/entries | POST | Store new entry | | /v1/caches/{id}/entries/{entryId} | DELETE | Delete by ID | | /v1/caches/{id}/flush | POST | Clear all entries |

Requirements

• OpenClaw 2024.1.0+
• Redis Cloud account with LangCache enabled
• Node.js 18+ (for npm installation)
• jq and curl (for CLI usage)

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

MIT License - see LICENSE for details.

Resources

• Redis LangCache Documentation
• OpenClaw Documentation
• Semantic Caching Guide