Tools4AI VS Deeplearning4j

Tools4AI offers versatile applications like the examples provided earlier. It can work with Google Gemini, OpenAI and Anthropic and Since the entire codebase is written in Java, it can be executed on any Java-enabled device, including Android phones. Furthermore, as an open-source project, developers have the opportunity to enhance, contribute to, or share the codebase, fostering collaborative innovation in AI-driven solutions for various domains.

In my previous post, I showcased how Tools4AI seamlessly integrates with Selenium for UI testing and transforms prompts into actionable insights. Additionally, it excels in triggering actions based on images or converting images in Java Pojo, It can process scripts written in plain English as well. Now, let's explore more use cases where Tools4AI shines

Tools4AI is a Java-based LLM (Large Language Model) Agent platform.

Using Tools4AI function calling (tools integration) with OpenAI is simple and efficient. When a user provides a prompt, Tools4AI dynamically determines which group the prompt belongs to, using AI. Then, it calls the action associated with that group. In under 10 minutes, you can kickstart your journey to developing an autonomous agent in Java by leveraging the power of OpenAI. All you need is your OpenAI key and, of course, a refreshing cup of coffee (or tea) . Let's illustrate this with examples :

Function calling or building autonomous agents is a crucial aspect of AI application development. While most articles and research in this area have been conducted in Python, this article explores these aspects using Java. Additionally, I will share my experience in building LLM Agents with both Gemini and OpenAI using Java. This article does not aim to compare features between the platforms, as both excel in their own capacity. Instead, it reflects my experiences and learnings from contributing to the open-source project Tools4AI

Tools4AI with Selenium can revolutionize UI validation by automating the process of verifying UI elements and ensuring consistency with design specifications. This approach goes beyond traditional UI validation methods by validating entire UI sections at once instead of examining individual elements. As a result, Tools4AI integration with Selenium can significantly streamline the testing process, allowing you to automate a comprehensive verification of web-based applications. With this integration, you can leverage a combination of natural language and Java code to create Selenium test scripts in a more human-readable format. This simplifies UI testing and increases efficiency by allowing non-programmers to write test scenarios in plain English.

Let's create an example where Tools4AI, processes the image of a library management system's UI to generate a Java object representation of the latest books and member management sections.

Automated UI testing has long been a staple in ensuring that web applications meet design and functionality standards. Traditional methods, such as using Selenium, involve interacting with the UI through specific code tied to the structure of the webpage. However, a new AI-driven method using Tools4AI is changing the game by allowing us to translate visual elements directly into code objects, simplifying the testing process considerably.

Tools4AI is an open-source project that showcases a 100% Java implementation of a Large Action Model (LAM). It functions as a Language Model (LLM) agent and is designed to integrate AI with enterprise Java applications. The project facilitates converting natural language prompts into actionable behaviors, which can be particularly useful in automating workflows and enhancing interaction with AI systems.

Tools4AI, an open-source stands out for integrating AI into Java enterprise applications seamlessly. At its core, it’s a Java-built project that introduces a Large Action Model (LAM) or an LLM agent. This design is perfect for developers looking to add AI features into their Java applications efficiently.

Have you considered migrating off of Python to just using JVM ML libraries then? I hear good things about Deeplearning4j, but there's quite a few.

I've gone to the linux workflow as directed in the docs and reconstructed the maven command line:

DL4J

Hey folks, just letting you know we see this thread and I appreciate you guys running these benchmarks. I'm not seeing any of your posts on our forums. I think I saw a notification from our examples but we do not actually monitor that. Please use: https://community.konduit.ai/ or at least the main repo dl4j issues: https://github.com/eclipse/deeplearning4j/issues and you'll get a lot more visibility. Thanks!

Also, the website is now redirected to: https://deeplearning4j.konduit.ai/

On top of this several popular projects have been built. This includes tensorflow-java and our project eclipse deeplearning4j: https://github.com/eclipse/deeplearning4j

Nd4j is actively developed. The latest commit was 6 hours ago. Nd4j is part of deeplearning4j which is now owned by eclipse (but the main contributors are from a company) https://github.com/eclipse/deeplearning4j/tree/master/nd4j