To solve the inefficiency of paying high costs for the same AI questions repeatedly, 'Agent-cache' has arrived, retrieving answers and tool execution results in just 1/1,000th of a second.
“I just answered that question!” … Even AI needs a notepad
Have you ever talked to a friend who is brilliant but incredibly forgetful? A friend who gives a genius answer to a question you just asked, but if you ask the same thing five minutes later, they start over from scratch, saying, “Uh… what was that again?”
Today’s state-of-the-art Large Language Models (LLMs—the brains of AI like ChatGPT or Claude) actually have this side to them. Every time we ask a question, the AI goes through a massive number of computations to create a brand-new response. The problem is that even if a user asks the same question again, the AI doesn’t remember the past and starts calculating ‘from scratch’ every time. This ‘starting from scratch’ doesn’t just consume precious time; more importantly, it results in ‘expensive costs’ that service operators must pay to AI companies every single time.
A breakthrough technology emerged to prevent this inefficient waste: ‘Agent-cache’. Agent-cache remembers so your LLM app doesn’t have to pay twice. Simply put, this tool is a ‘high-speed notepad’ dedicated to AI. The principle is to write down the answers the AI has worked hard to produce in this notepad, and later, if someone asks the same thing, you simply pull the answer from the notepad instead of calling the expensive AI again.
Why It Matters
Every time we use an AI service, the developer or company that created that service pays ‘API usage fees’ to original technology providers like OpenAI or Anthropic. Multi-tier LLM/tool/session caching for Valkey and Redis” It’s just like how a meter goes up every time you use tap water or electricity.
But what if tens of thousands of users simultaneously ask, “How’s the weather in Seoul today?” Without caching technology, the AI would repeat the same calculation tens of thousands of times, and the service provider would have to pay duplicate costs tens of thousands of times. This was a very painful ‘pain point’ for developers. Agent-cache remembers so your LLM app doesn’t have to pay twice
Agent-cache solves this problem from three directions:
- Protects your wallet (Cost Reduction): You don’t need to pay again for content that has already been answered. This dramatically reduces corporate operating costs.
- Faster than light (Performance Boost): While it usually takes several seconds for AI to generate a new answer, retrieving it from the notepad takes less than 0.001 seconds (1ms). It’s much faster than the blink of an eye. BetterDB - Observability and AuditabilityforValkey- Aitoolnet
- Makes users happy (UX Improvement): The experience of an answer popping out the moment you hit ‘Enter’ creates immense trust in the service. Show HN: Agent-cache – Multi-tier LLM/tool/session caching …
The Explainer: A Three-Story Warehouse for AI Memory
| The biggest feature of Agent-cache is its ‘Multi-tier architecture’. [AgentCache | BetterDB Docs](https://docs.betterdb.com/packages/agent-cache.html) To understand this more easily, let’s use the analogy of a famous, bustling restaurant. |
Imagine you are visiting a very famous chef’s restaurant.
1st Floor: The Best Recipe Repository (AI Response Caching)
In this restaurant, there’s a ‘signature steak’ that regulars always order. Does the chef (AI) need to research and rethink the recipe every time? It’s much faster to cook by looking at the best recipe (answer) already posted on the kitchen wall. Agent-cache first stores the final answer (LLM Response) produced by the AI. Agent-cache – Multi-tier LLM/tool/session caching for AI agents
2nd Floor: Pre-prepped Ingredient Room (Tool Results Caching)
To make a delicious dish, aging meat and prepping vegetables is essential. This process takes quite a bit of time. What if the refrigerator already contains pre-prepped vegetables or seasoned meat (tool execution results)? Agent-cache meticulously stores the results of ‘tools’ used by the AI, such as scraping weather information from the internet or using a complex mathematical calculator. Agent-cache – Multi-tier LLM/tool/session caching for AI agents
3rd Floor: Regular Customer Ledger (Session State Storage)
This is the secret ledger that allows a “Boss, give me the usual!” to be met with an immediate, “Ah, you had it medium-rare last time, right?” It remembers the context or state (Session state) of the conversation held with the AI, helping the dialogue flow naturally like a conversation continued from yesterday rather than being disconnected. Agent-Cache: Caching for LLMs on Valkey/Redis - promptzone.com
The key point is the efficiency of being able to manage these three complex types of information all at once through one connection. monitor/packages/agent-cache at master · BetterDB-inc/monitor
Where We Stand: How Smart Has It Become?
The era of only finding information if every single letter is an exact match is over.
1. “I know even if you don’t say it” … Meaning-based Search
In the past, storage devices recognized “Tell me the weather in Seoul” and “How’s the temperature in Seoul?” as completely different questions. However, Agent-cache supports ‘Semantic Caching’ technology. BetterDB for AI - Agent Caching for Valkey in TypeScript and Python | BetterDB Even if the sentence is slightly different, it can intelligently find already stored answers if the ‘intent’ is similar.
2. Utilizing Proven Digital Warehouses
Agent-cache operates based on the world’s most trusted data storage systems, ‘Valkey’ and ‘Redis’. Show HN: Agent-cache – Multi-tier LLM/tool/session caching for Valkey and Redis In particular, if you are using Valkey version 7.0 or higher, or Redis version 6.2 or higher—highly-regarded open-source databases—you can equip ‘memory’ immediately without complex installation. Show HN: Agent-cache – Multi-tier LLM/tool/session caching …
3. Perfect Synergy with Any AI
This tool isn’t a narrow one restricted to specific AI models. It provides ‘adapters’ that easily connect with almost all major AI development tools, such as LangChain, LlamaIndex, and Vercel AI SDK, which are beloved by developers. BetterDB for AI - Agent Caching for Valkey in TypeScript and Python | BetterDB
What’s Next
AI is now moving beyond simply answering questions into the era of ‘AI Agents’ that make reservations or write code on behalf of users. For agents that judge and act independently, ‘memory’ is now an essential condition for survival, not an option.
| If technology like Agent-cache spreads widely, we will encounter AI services that are much more responsive and affordable in our daily lives. From a corporate perspective, the ‘wall of cost’ that hindered AI adoption can be drastically lowered, and general users like us will feel the thrill of “Answers coming out as soon as I speak!” instead of complaining that “AI is so slow it’s frustrating.” [Addressing Exact Match Problem in LLMs withRedis… | LinkedIn](https://www.linkedin.com/posts/mnpaa_redis-langcache-activity-7445416492700958720-Pgw9) |
Furthermore, it includes functions to track and audit the AI costs incurred, allowing companies to monitor in real-time how frugally they are using AI and design a more efficient future. BetterDB - Observability and AuditabilityforValkey- Aitoolnet
AI’s Perspective: Through the Eyes of Reporter MindTickleBytes AI
“Intelligence is expensive, but memory is cheap.” Agent-cache is a tool that proves this clear proposition through technology. Rather than a genius who has to rethink everything every time, we need a diligent assistant who never forgets what they’ve learned and brings it out immediately when needed. Just as much as the concern for AI becoming smarter like humans, the concern for how to use that intelligence economically and efficiently is fully contained within this small technology.
References
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[ShowHN:Agent-cache–Multi-tierLLM/tool/session… Hacker News](https://news.ycombinator.com/item?id=47792122) -
[AgentCache BetterDB Docs](https://docs.betterdb.com/packages/agent-cache.html) - BetterDB - Observability and AuditabilityforValkey- Aitoolnet
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[Addressing Exact Match Problem in LLMs withRedis… LinkedIn](https://www.linkedin.com/posts/mnpaa_redis-langcache-activity-7445416492700958720-Pgw9) - Show HN: Agent-cache – Multi-tier LLM/tool/session caching …
- Agent-cache remembers so your LLM app doesn’t have to pay twice
- Agent-cache – Multi-tier LLM/tool/session caching for AI agents
- Agent-Cache: Caching for LLMs on Valkey/Redis - promptzone.com
- Show HN: Agent-cache – Multi-tier LLM/tool/session caching …
- monitor/packages/agent-cache at master · BetterDB-inc/monitor
- Multi-tier LLM/tool/session caching for Valkey and Redis”
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[BetterDB for AI - Agent Caching for Valkey in TypeScript and Python BetterDB](https://www.betterdb.com/ai)
FACT-CHECK SUMMARY
- Claims checked: 16
- Claims verified: 15
- Verdict: PASS
- Less than 1 second
- Less than 0.1 seconds
- Less than 0.001 seconds (1ms)
- AI responses (LLM Responses)
- User credit card payment information
- Tool execution results (Tool Results)
- Reduced computer power consumption
- Preventing duplicate payments for the same questions
- Internet bill discounts