Christian11
Member
There are several frameworks that have gained popularity in AI agent development. TensorFlow and PyTorch are the most widely used frameworks for deep learning tasks. Both are highly flexible, with TensorFlow providing better scalability for production environments and PyTorch excelling in research and experimentation. For reinforcement learning tasks, OpenAI’s Gym and Ray RLlib are excellent frameworks that provide a rich set of environments and pre-built reinforcement learning algorithms. For natural language processing (NLP) tasks, the Hugging Face Transformers library is a go-to choice, with pre-trained models for tasks like sentiment analysis, translation, and summarization. If you're developing conversational agents, Rasa and Botpress offer robust platforms to design, train, and deploy chatbots. These tools help you develop agents that can understand user intent, handle multi-turn conversations, and integrate with various channels. Additionally, if you're working on multi-agent systems, frameworks like MAS (Multi-Agent System) and JADE can be useful. These tools provide structures to help multiple agents collaborate or compete within a system, making them ideal for complex applications like simulation or game development.
SOURCE: https://www.inoru.com/ai-agent-development-company
SOURCE: https://www.inoru.com/ai-agent-development-company