Griptape

In the previous post, I broke down the basic concepts of the Griptape AI framework, and now it’s time to put them into practice. We’ll try to use them to develop a small application that helps run a link-blog on Telegram. The application will receive a URL, download its content, run it through an LLM to generate a summary, translate that summary into a couple of other languages, combine everything, and publish it to Telegram via a bot. The general flow can be seen in the diagram below: ...

Today we will look at Griptape, a framework for building AI applications, which offers a clean Pythonic API for those tired of LangChain’s abstraction layers. It provides primitives for building assistants, RAG systems, and integrating with external tools. Honestly, in my experience, most people tired of LangChain switch to custom-written wrappers around lower-level libraries like OpenAI or LiteLLM. But who knows, maybe they’re missing out. Let’s dive in. A Bit of History Personally, I’ve been hearing about Griptape for about a year and a half. As far as I remember, It started as a sort of LangChain competitor with quite similar primitives, but their paths gradually diverged. As of the time of the writing, it has 2.3k stars on GitHub, which is somewhat less than LangChain’s 109k, but still enough to consider the project quite mature. Besides the open-source framework, it has also developed its own cloud where you can run your applications, ETLs, and RAGs, and a visual builder, Griptape Nodes, allowing non-professionals to click together applications in minutes. 1 ...