Langcache Semantic Caching for OpenClaw

This skill should be used when the user asks to "enable semantic caching", "cache LLM responses", "reduce API costs", "speed up AI responses", "configure LangCache", "search the semantic cache", "store responses in cache", or mentions Redis LangCache, semantic similarity caching, or LLM response caching. Provides integration with Redis LangCache managed service for semantic caching of prompts and responses.

What to check before installing/use: 1) Fix metadata mismatch: confirm the registry metadata lists LANGCACHE_HOST, LANGCACHE_CACHE_ID, LANGCACHE_API_KEY as required so permission reviews are accurate. 2) Audit the --force path: the CLI allows bypassing the 'NEVER CACHE' rules — remove or restrict --force unless you explicitly need it and understand the risk of storing PII/credentials. 3) Be careful with ~/.openclaw/secrets.env: the scripts source that file wholesale. Only store the required LANGCACHE_* variables there and tighten its file permissions (chmod 600) — avoid dumping unrelated secrets (OpenAI keys, cloud creds) into it. 4) If you plan to use the Python example, note it expects OPENAI_API_KEY (or another LLM API key); the SKILL.md should declare that. 5) Verify LANGCACHE_HOST endpoint ownership and trust the provider (the skill will send prompts/responses to that host). 6) If you need stricter guarantees, run the scripts in an isolated environment, review/modify the code to remove the --force bypass, and/or hard-code allowed env reads so only the necessary variables are loaded. 7) Overall: functionality appears legitimate for caching, but the policy bypass and metadata/env inconsistencies warrant manual review before deployment.

Type: OpenClaw Skill Name: openclaw-langcache Version: 1.0.0 The skill is designed for semantic caching and includes robust, explicit security measures to prevent the caching and retrieval of sensitive data. Both `scripts/langcache.sh` and `examples/agent-integration.py` implement extensive regex patterns to hard-block temporal information, credentials (e.g., API keys, passwords), identifiers (e.g., emails, phone numbers), and personal context from being stored or retrieved from the cache. While the skill uses high-privilege tools like `Bash` and `WebFetch`, their usage is strictly confined to interacting with the specified LangCache API, and there is no evidence of data exfiltration to unauthorized endpoints, malicious execution, or prompt injection attempts in `SKILL.md`.