OpenRouter Caching Strategy

Overview

OpenRouter charges per token, so caching identical or similar requests can dramatically cut costs. Deterministic requests (temperature=0) with the same model and messages produce identical outputs -- these are safe to cache. This skill covers in-memory caching, persistent caching with TTL, and Anthropic prompt caching via OpenRouter.

Prerequisites

• An OpenRouter API key (sk-or-v1-...) exported as OPENROUTER_API_KEY — see the openrouter-install-auth skill for setup
• Python 3.8+ with the OpenAI SDK, plus the redis client package for the persistent cache; Node.js 18+ with the OpenAI SDK for the TypeScript variant in the references
• A Redis server reachable at localhost:6379 for Persistent Cache with Redis (the in-memory LLMCache needs no infrastructure)
• Deterministic request settings — caching is only safe at temperature=0

Instructions

1. Confirm the requests you want to cache are deterministic (temperature=0); non-zero temperatures produce different outputs each call and must never be cached.
2. Start with the In-Memory Cache: LLMCache plus cached_completion() gives you TTL expiry and hit/miss counters in a single process.
3. For multi-instance deployments, switch to Persistent Cache with Redis — redis_cached_completion() stores results under or:<sha256> keys with r.setex TTL expiry and falls through to a direct API call on a miss.
4. Build keys per Cache Key Design: include the model ID (with variants like :floor), messages, temperature, max_tokens, and top_p; exclude stream and the HTTP-Referer/X-Title headers.
5. For large static system prompts (RAG context), add cache_control: {"type": "ephemeral"} per Anthropic Prompt Caching via OpenRouter — cache reads bill at 0.1x the input rate.
6. Wire the Cache Invalidation table: flush per-model keys on model version updates, flush everything on system prompt changes, and let TTL handle the rest.

In-Memory Cache

import os, hashlib, json, time
from typing import Optional
from openai import OpenAI

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key=os.environ["OPENROUTER_API_KEY"],
    default_headers={"HTTP-Referer": "https://my-app.com", "X-Title": "my-app"},
)

class LLMCache:
    def __init__(self, ttl_seconds: int = 3600):
        self._cache: dict[str, tuple[dict, float]] = {}
        self._ttl = ttl_seconds
        self.hits = 0
        self.misses = 0

    def _key(self, model: str, messages: list, **kwargs) -> str:
        blob = json.dumps({"model": model, "messages": messages, **kwargs}, sort_keys=True)
        return hashlib.sha256(blob.encode()).hexdigest()

    def get(self, model: str, messages: list, **kwargs) -> Optional[dict]:
        k = self._key(model, messages, **kwargs)
        if k in self._cache:
            data, ts = self._cache[k]
            if time.time() - ts < self._ttl:
                self.hits += 1
                return data
            del self._cache[k]
        self.misses += 1
        return None

    def set(self, model: str, messages: list, response: dict, **kwargs):
        k = self._key(model, messages, **kwargs)
        self._cache[k] = (response, time.time())

cache = LLMCache(ttl_seconds=1800)

def cached_completion(messages, model="anthropic/claude-3.5-sonnet", **kwargs):
    """Only cache deterministic requests (temperature=0)."""
    kwargs.setdefault("temperature", 0)
    kwargs.setdefault("max_tokens", 1024)

    cached = cache.get(model, messages, **kwargs)
    if cached:
        return cached

    response = client.chat.completions.create(model=model, messages=messages, **kwargs)
    result = {
        "content": response.choices[0].message.content,
        "model": response.model,
        "usage": {"prompt": response.usage.prompt_tokens, "completion": response.usage.completion_tokens},
    }
    cache.set(model, messages, result, **kwargs)
    return result

Persistent Cache with Redis

import redis, json, hashlib

r = redis.Redis(host="localhost", port=6379, db=0)

def redis_cached_completion(messages, model="openai/gpt-4o-mini", ttl=3600, **kwargs):
    """Cache in Redis with automatic TTL expiry."""
    kwargs["temperature"] = 0  # Must be deterministic
    key = f"or:{hashlib.sha256(json.dumps({'m': model, 'msgs': messages, **kwargs}, sort_keys=True).encode()).hexdigest()}"

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

    response = client.chat.completions.create(model=model, messages=messages, **kwargs)
    result = {
        "content": response.choices[0].message.content,
        "model": response.model,
        "tokens": response.usage.prompt_tokens + response.usage.completion_tokens,
    }
    r.setex(key, ttl, json.dumps(result))
    return result

Anthropic Prompt Caching via OpenRouter

Anthropic models on OpenRouter support prompt caching -- large system prompts are cached server-side, reducing input cost by 90% on cache hits.

# Mark large static content blocks with cache_control
response = client.chat.completions.create(
    model="anthropic/claude-3.5-sonnet",
    messages=[
        {
            "role": "system",
            "content": [
                {
                    "type": "text",
                    "text": "You are an expert. Here is the full source:\n" + large_context,
                    "cache_control": {"type": "ephemeral"},  # Cache this block
                }
            ],
        },
        {"role": "user", "content": "What does the main() function do?"},
    ],
    max_tokens=1024,
)
# First call: cache_creation_input_tokens charged at 1.25x
# Subsequent: cache_read_input_tokens charged at 0.1x (90% savings)

Cache Key Design

def cache_key(model: str, messages: list, **params) -> str:
    """Deterministic cache key. Include everything that affects output.

    Include: model ID (with variant like :floor), messages, temperature,
    max_tokens, top_p, transforms, provider routing.
    Exclude: stream (doesn't affect content), HTTP-Referer, X-Title.
    """
    canonical = json.dumps({
        "model": model, "messages": messages,
        "temperature": params.get("temperature", 0),
        "max_tokens": params.get("max_tokens"),
        "top_p": params.get("top_p"),
    }, sort_keys=True)
    return hashlib.sha256(canonical.encode()).hexdigest()

Cache Invalidation

Trigger	Action	Why
Model version update	Flush keys for that model	New version may give different outputs
System prompt change	Flush all keys	Output semantics changed
TTL expiry	Automatic eviction	Prevents stale data
Manual purge	r.delete(key) or clear by prefix	Debugging or policy change

Output

• Cached completion payloads returned without an API round-trip: {"content", "model", "usage"} from the in-memory cache or {"content", "model", "tokens"} from Redis
• Redis keys of the form or:<sha256-of-canonical-request> that expire automatically via TTL
• Hit/miss counters and a hit_rate figure you can use to justify the caching infrastructure
• On Anthropic models, cache_creation_input_tokens billed at 1.25x on the first call and cache_read_input_tokens at 0.1x (90% savings) on subsequent hits

Examples

Two identical deterministic calls through the ResponseCache from the references — the second returns instantly from cache:

result1 = cached_completion("What is Python?")   # [Cache MISS] key=3f8a92c1... (stored)
result2 = cached_completion("What is Python?")   # [Cache HIT] key=3f8a92c1...
print(f"Hit rate: {cache.hit_rate:.0%}")         # Hit rate: 50%

More worked examples, including a TypeScript Redis-style cache: references/examples.md.

Error Handling

Error	Cause	Fix
Stale cache response	TTL too long	Reduce TTL or version cache keys
Cache miss storm	Cold start or invalidation	Warm cache with common queries at deploy
Redis connection error	Redis down	Fall through to direct API call
Non-deterministic cache	temperature > 0 cached	Only cache when temperature=0

Enterprise Considerations

• Only cache deterministic requests (temperature=0) -- non-zero temperatures produce different outputs each time
• Use Anthropic prompt caching for large system prompts (RAG context) -- 90% cost reduction on cache hits
• Set TTL based on content freshness needs (30 min for dynamic, 24h for reference data)
• Track cache hit rate to justify caching infrastructure cost
• Use Redis or Memcached for multi-instance deployments; in-memory only works for single-process
• Version cache keys when updating system prompts or switching model versions

References

• Examples | Errors
• Prompt Caching | Models API