Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
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.
skill openclawclawhub Free
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.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, scripts/langcache.sh, examples/basic-caching.sh, examples/agent-integration.py, references/best-practices.md, references/api-reference.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- manvinder01
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Redis LangCache Semantic Caching
This skill integrates Redis LangCache, a fully-managed semantic caching service, into OpenClaw workflows. LangCache stores LLM prompts and responses, returning cached results for semantically similar queries to reduce costs and latency.
Prerequisites
Before using LangCache, ensure the following environment variables are configured: LANGCACHE_HOST=<your-langcache-host> LANGCACHE_CACHE_ID=<your-cache-id> LANGCACHE_API_KEY=<your-api-key> Store these in ~/.openclaw/secrets.env or configure them in the OpenClaw settings.
Search for Cached Response
Before calling an LLM, check if a semantically similar response exists: ./scripts/langcache.sh search "What is semantic caching?" With similarity threshold (0.0-1.0, higher = stricter match): ./scripts/langcache.sh search "What is semantic caching?" --threshold 0.95 With attribute filtering: ./scripts/langcache.sh search "What is semantic caching?" --attr "model=gpt-5"
Store New Response
After receiving an LLM response, cache it for future use: ./scripts/langcache.sh store "What is semantic caching?" "Semantic caching stores responses based on meaning similarity..." With attributes for filtering/organization: ./scripts/langcache.sh store "prompt" "response" --attr "model=gpt-5" --attr "user_id=123"
Delete Cached Entries
By entry ID: ./scripts/langcache.sh delete --id "<entry-id>" By attributes: ./scripts/langcache.sh delete --attr "user_id=123"
Flush Cache
Clear all entries (use with caution): ./scripts/langcache.sh flush
Integration Pattern
The recommended pattern for integrating LangCache into agent workflows: 1. Receive user prompt 2. Search LangCache for similar cached response 3. If cache hit (similarity >= threshold): - Return cached response immediately - Log cache hit for observability 4. If cache miss: - Call LLM API - Store prompt + response in LangCache - Return LLM response
Default Caching Policy
This policy is enforced automatically. All cache operations MUST respect these rules.
CACHEABLE (white-list)
CategoryExamplesThresholdFactual Q&A"What is X?", "How does Y work?"0.90Definitions / docs / help textAPI docs, command help, explanations0.90Command explanations"What does git rebase do?"0.92Reusable reply templates"polite no", "follow-up", "scheduling", "intro"0.88Style transforms"make this warmer/shorter/firmer"0.85Generic communication scriptsnegotiation templates, professional responses0.88
NEVER CACHE (hard blocks)
These patterns are blocked at the code level - cache operations will refuse to store them. CategoryPatterns to DetectReasonTemporal infotoday, tomorrow, this week, deadline, ETA, "in X minutes", appointments, schedulesStale immediatelyCredentialsAPI keys, tokens, passwords, OTP, 2FA codes, secretsSecurity riskIdentifiersphone numbers, emails, addresses, account IDs, order numbers, message IDs, chat IDs, JIDsPrivacy / PIIPersonal contextnames + relationships, private history, "who said what", specific conversationsPrivacy / context-dependent
Detection Patterns
The following regex patterns trigger a hard block: # Temporal \b(today|tomorrow|tonight|yesterday)\b \b(this|next|last)\s+(week|month|year|monday|tuesday|...)\b \b(in\s+\d+\s+(minutes?|hours?|days?))\b \b(deadline|eta|appointment|schedule[d]?)\b # Credentials \b(api[_-]?key|token|password|secret|otp|2fa)\b \b(bearer|auth[orization]*)\s+\S+ # Identifiers \b\d{10,}\b # phone numbers, long IDs \b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+ # emails \b(order|account|message|chat)[_-]?id\b # Personal context \b(my\s+(wife|husband|partner|friend|boss|mom|dad|brother|sister))\b \b(said\s+to\s+me|told\s+me|between\s+us)\b
Attribute Strategies
Use attributes to partition the cache: model: LLM model used (useful when switching models) category: factual, template, style, command skill: Which skill generated the response version: API or prompt version
Search Strategies
LangCache supports two search strategies: semantic (default): Vector similarity matching exact: Case-insensitive exact match Combine both for hybrid search: ./scripts/langcache.sh search "prompt" --strategy "exact,semantic"
Observability
Monitor cache performance: Track hit/miss ratios Log similarity scores for hits Alert on high miss rates (may indicate threshold too high) Review stored entries periodically for relevance
References
API Reference - Complete REST API documentation Best Practices - Optimization techniques
Examples
examples/basic-caching.sh - Simple cache workflow examples/agent-integration.py - Python integration pattern
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Docs3 Scripts
- SKILL.md
- references/api-reference.md
- references/best-practices.md
- examples/agent-integration.py
- examples/basic-caching.sh
- scripts/langcache.sh