The Agent Communication Protocol (ACP) is an open standard designed to enable seamless communication between AI agents, applications, and humans. As AI systems are often developed using diverse frameworks and infrastructures, they can end up isolated and incompatible, limiting their ability to collaborate. ACP addresses this fragmentation by offering a unified RESTful API that facilitates:…
Introduction: What is Context Engineering? Context engineering refers to the discipline of designing, organizing, and manipulating the context that is fed into large language models (LLMs) to optimize their performance. Rather than fine-tuning the model weights or architectures, context engineering focuses on the input—the prompts, system instructions, retrieved knowledge, formatting, and even the ordering of…
In this tutorial, we implement the BioCypher AI Agent, a powerful tool designed for building, querying, and analyzing biomedical knowledge graphs using the BioCypher framework. By combining the strengths of BioCypher, a high-performance, schema-based interface for biological data integration, with the flexibility of NetworkX, this tutorial empowers users to simulate complex biological relationships such as…
Together AI has released DeepSWE, a state-of-the-art, fully open-sourced software engineering agent that is trained entirely through reinforcement learning (RL). Built on top of the Qwen3-32B language model, DeepSWE achieves 59% accuracy on the SWEBench-Verified benchmark and 42.2% Pass@1, topping the leaderboard among open-weight models. This launch represents a significant shift for Together AI, from…
In this tutorial, we walk you through the seamless integration of AutoGen and Semantic Kernel with Google’s Gemini Flash model. We begin by setting up our GeminiWrapper and SemanticKernelGeminiPlugin classes to bridge the generative power of Gemini with AutoGen’s multi-agent orchestration. From there, we configure specialist agents, ranging from code reviewers to creative analysts, demonstrating…
Rare diseases impact some 400 million people worldwide, accounting for over 7,000 individual disorders, and most of these, about 80%, have a genetic cause. Notwithstanding their incidence, diagnosing rare diseases is notoriously difficult. Patients already suffer through lengthy diagnostic processes that average more than five years, often resulting in sequential misdiagnoses and invasive procedures. All…
The ability to build custom tools is critical for building customizable AI Agents. In this tutorial, we demonstrate how to create a powerful and intelligent data analysis tool using Python that can be integrated into AI agents powered by LangChain. By defining a structured schema for user inputs and implementing key functionalities like correlation analysis,…
Tencent’s Hunyuan team has introduced Hunyuan-A13B, a new open-source large language model built on a sparse Mixture-of-Experts (MoE) architecture. While the model consists of 80 billion total parameters, only 13 billion are active during inference, offering a highly efficient balance between performance and computational cost. It supports Grouped Query Attention (GQA), 256K context length, and…
Google recently released the Gemini CLI, a powerful command-line tool designed to supercharge developer workflows with AI. Whether you’re working across massive codebases, automating tedious tasks, or generating new apps from sketches and PDFs, Gemini CLI brings multimodal intelligence right to your terminal. With Gemini CLI, you can: Query and edit large codebases—even beyond the…
In this tutorial, we introduce an advanced AI agent built using Nebius’ robust ecosystem, particularly the ChatNebius, NebiusEmbeddings, and NebiusRetriever components. The agent utilizes the Llama-3.3-70B-Instruct-fast model to generate high-quality responses, incorporating external functionalities such as Wikipedia search, contextual document retrieval, and safe mathematical computation. By combining structured prompt design with LangChain’s modular framework, this…