GPT with LangGraph! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode The rapid advancement of large model technology is leading to an increasing application of agent technology across various fields and industries significantly transforming how people work and live. In complex and dynamic environments, multi-agent systems are able to tackle intricate tasks that would be challenging for a single agent, thanks to their collaborative and division-of-labor approaches. The following stack of research papers and hands on tutorials highlight the integrated use of GPT with LangGraph and CrewAI. LangGraph enhances information transmission efficiency through its graph-based structure, while CrewAI boosts team collaboration and system performance via intelligent task allocation and resource management. The key areas of this research include: The design of agent architectures based on LangGraph for precise control . The enhancement of agent capabilities through CrewAI to tackle a range of tasks. The goal of this study is to explore the combined potential of GPT and LangGraph and CrewAI in multi-agent systems, offering fresh insights for the ongoing evolution of agent technology and fostering innovation in the application of large model intelligent agents. References Duan, Z., & Wang, J. (2024, November 27). Exploration of LLM multi-agent application implementation based on LangGraph+CrewAI. arXiv. Retrieved March 9, 2025, from arxiv.org/abs/2411.18241 Horsey, J. (2025, March 9). Build a powerful Python chatbot in minutes with LangGraph. Geeky Gadgets. Retrieved March 9, 2025, from geeky-gadgets.com/build-a-po… Ong, R. (2024, July 10). GPT-4o and LangGraph tutorial: Build a TNT-LLM application. DataCamp. Retrieved March 9, 2025, from datacamp.com/tutorial/gpt-4o… Sivan, V. (2024). Building AI agent systems with LangGraph. Medium. Retrieved March 9, 2025, from medium.com/pythoneers/buildi… Wang, J., & Duan, Z. (2024, December 2). Intelligent Spark agents: A modular LangGraph framework for scalable, visualized, and enhanced big data machine learning workflows. arXiv. Retrieved March 9, 2025, from arxiv.org/abs/2412.01490
It is going to be like what happened in coding: as soon as models crossed a certain threshold (Opus 4.5, GPT-5.2, Gemini 3), suddenly Claude Code & Codex were viable. Before that, it was all about coding assistance, afterwards it was all about agents from relatively small gains.
RiboGen: RNA Sequence and Structure Co-Generation! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Mathematics #Programming #Coding #100DaysofCode #Genetics Media Lab. (n.d.). RiboGen: RNA sequence and structure co-generation. MIT Media Lab. Retrieved March 10, 2025, from media.mit.edu/projects/ribog… Nori, D., & Jin, W. (2024, May 1). RNAFlow: RNA structure & sequence co-design via inverse folding-based flow matching. In Proceedings of the GEM Workshop at ICLR 2024. Retrieved March 10, 2025, from openreview.net/pdf/31fa59255… Rubin, D., dos Santos Costa, A., Ponnapati, M., & Jacobson, J. (2025, March 5). RiboGen: RNA sequence and structure co-generation with equivariant MultiFlow. arXiv preprint arXiv:2503.02058v1. Retrieved March 10, 2025, from arxiv.org/html/2503.02058v1 Zhao, Q., Zhao, Z., Fan, X., & Yao, Y. (2020, August 15). RNA primary, secondary, and tertiary structures [Figure]. In Review of machine-learning methods for RNA secondary structure prediction. ResearchGate. Retrieved March 10, 2025, from researchgate.net/figure/RNA-…
AI just quietly took a MASSIVE step into cybersecurity. Anthropic’s new Project Glasswing uses its Claude Mythos Preview “superhacker” model to help big tech and critical infrastructure hunt bugs before attackers do – backed by $100M in AI credits and 40+ security partners.