In this blog post, we'll explore how to optimize API calls made by AI agents in n8n by building a simple caching mechanism that can significantly enhance performance and reduce costs.
In the world of automation and integration, n8n (an open-source workflow automation tool) has gained traction for its flexibility and ease of use. One of the common challenges faced by developers is handling excessive API calls, which can lead to increased latency and incurred costs. This is particularly true when dealing with AI agents, which often rely on real-time data from various sources.
Why Caching?
Caching is a technique that stores frequently requested data in a temporary storage area so that future requests can be served faster. Instead of fetching the same data multiple times from the original source, we can leverage cached data. This not only reduces the load on the API but also improves the overall response time of the application.
Let's dive into how we can implement caching in n8n...
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