# llm-caching

Adds multi-layer LLM request caching across Redis, GPTCache, and provider APIs. Reduces API costs by 30–70% when queries repeat often.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 317 installs · ★ 1177
- Author: devops-skills
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/BagelHole/DevOps-Security-Agent-Skills/tree/HEAD/devops/ai/llm-caching/SKILL.md
- Updated: 2026-05-22
- HTML: https://affpapa.org/en/nevibecode/skills/llm-caching

## What it does

This skill implements a three-tier architecture for model requests: exact matching via Redis, semantic similarity checks, and provider-side prompt caching. The exact cache returns answers instantly. It deliberately stores responses only when `temperature` is 0.0 to prevent caching non-deterministic completions.

The semantic layer relies on a Qdrant vector backend alongside GPTCache or the `BAAI/bge-small-en-v1.5` sentence transformer model. It applies similarity thresholds such as 0.80 or 0.88, with vector entries expiring after 86400 seconds. For large system prompts, the skill integrates Anthropic ephemeral cache blocks to cut token costs.

Because the local layers depend directly on running Redis and Qdrant instances, you must have both services running before testing the caching code.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add BagelHole/DevOps-Security-Agent-Skills --skill llm-caching -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/llm-caching/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/llm-caching/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add BagelHole/DevOps-Security-Agent-Skills --skill llm-caching -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/llm-caching/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add BagelHole/DevOps-Security-Agent-Skills --skill llm-caching -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/llm-caching/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install BagelHole/DevOps-Security-Agent-Skills llm-caching
```

2. **Manually: put the files in place**
- File: `.github/skills/llm-caching/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add BagelHole/DevOps-Security-Agent-Skills --skill llm-caching -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/BagelHole/DevOps-Security-Agent-Skills --path devops/ai/llm-caching
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/llm-caching/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add BagelHole/DevOps-Security-Agent-Skills --skill llm-caching -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/BagelHole/DevOps-Security-Agent-Skills/tree/HEAD/devops/ai/llm-caching/SKILL.md

## When to use it
- cut API costs on recurring queries in support bots
- implement semantic response caching using Qdrant vector search
- add ephemeral prompt caching for large context in Anthropic API
- reduce response latency for deterministic model calls

## Example prompts
- Implement multi-layer LLM caching with Redis and Qdrant
- Configure Anthropic prompt caching for our long system prompt
- Set up a custom semantic cache using sentence-transformers and Qdrant

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 4e85182cd0c55a85.

BEGIN THIRD-PARTY FILE 4e85182cd0c55a85
````text
---
name: llm-caching
description: Implement multi-layer LLM caching with exact match, semantic similarity, and provider-side prompt caching. Reduce API costs by 30–70%, cut latency, and improve throughput using Redis, GPTCache, and provider caching APIs.
license: MIT
metadata:
  author: devops-skills
  version: "1.0"
---

# LLM Caching

Cut LLM costs and latency with exact match, semantic, and provider-side caching layers.

## When to Use This Skill

Use this skill when:
- The same or similar queries are asked repeatedly (FAQ bots, support tools)
- LLM API costs are growing and you need immediate savings
- Serving high request volumes where repeated queries cause bottlenecks
- Implementing prompt caching for long system prompts (Anthropic/OpenAI)
- Building offline-capable AI features that need response persistence

## Caching Layers

```
Request → Exact Cache → Semantic Cache → Provider Cache → LLM API
             ↓ hit            ↓ hit             ↓ hit
           instant          ~5ms           50-80% cheaper
```

## Layer 1: Exact Match Cache (Redis)

```python
import hashlib
import json
import redis
from openai import OpenAI

r = redis.Redis(host="localhost", port=6379, decode_responses=True)
client = OpenAI()

def build_cache_key(model: str, messages: list, temperature: float) -> str:
    """Deterministic key from request parameters."""
    payload = json.dumps({
        "model": model,
        "messages": messages,
        "temperature": temperature,
    }, sort_keys=True)
    return f"llm:exact:{hashlib.sha256(payload.encode()).hexdigest()}"

def cached_completion(model: str, messages: list, temperature: float = 0.0,
                      ttl: int = 3600) -> dict:
    key = build_cache_key(model, messages, temperature)

    # Check cache
    if cached := r.get(key):
        return json.loads(cached)

    # Call API
    response = client.chat.completions.create(
        model=model, messages=messages, temperature=temperature
    )
    result = response.model_dump()

    # Cache result (only cache deterministic responses)
    if temperature == 0.0:
        r.setex(key, ttl, json.dumps(result))

    return result
```

## Layer 2: Semantic Cache (GPTCache)

```python
from gptcache import cache, Config
from gptcache.adapter import openai
from gptcache.embedding import Onnx
from gptcache.manager import CacheBase, VectorBase, get_data_manager
from gptcache.similarity_evaluation.distance import SearchDistanceEvaluation

# Configure GPTCache with Qdrant backend
def init_gptcache(cache_obj, llm: str):
    onnx = Onnx()                              # local embedding model
    data_manager = get_data_manager(
        CacheBase("redis"),                    # metadata store
        VectorBase("qdrant",
                   host="localhost",
                   port=6333,
                   collection_name=f"llm-cache-{llm}",
````
END THIRD-PARTY FILE 4e85182cd0c55a85

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