AI Agent Caching Strategy 2026

Agents often repeatedly ask the same question. Every time, they skip the cache and call the API directly? That's how the money burns away. Caching can cut costs down to 1/5.

💡 What You Will Learn

Agents often repeatedly ask the same question. Every time, they skip the cache and call the API directly? That's how the money burns away. Caching can cut costs down to 1/5.

import hashlib, redis
r = redis.Redis()

def get_cached(query):
    key = hashlib.md5(query.encode()).hexdigest()
    cached = r.get(f"agent_cache:{key}")
    return json.loads(cached) if cached else None

def set_cache(query, response, ttl=3600):
    key = hashlib.md5(query.encode()).hexdigest()
    r.setex(f"agent_cache:{key}", ttl, json.dumps(response))

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Summary