Latest Articles

LightRAG combines knowledge graphs, vector embeddings, and multiple query modes in one RAG workflow. This practical guide covers its architecture, document pipeline, deployment security, and cost trade-offs.

Google ADK 2.0 separates Agent reasoning and tool use from Workflow execution order and state. This article examines its workflow runtime, Task API, multi-agent composition, MCP tools, evaluation, deployment, and the practical checks required before production adoption.

Chrome DevTools MCP connects live Chrome interaction, Console, network, performance, and memory analysis to MCP, giving AI coding agents a verifiable debugging loop between source code and browser state. This article explains the tool surface, adoption path, and security boundaries.

Microsoft VibeVoice is an open-source voice AI family for long-form speech recognition and synthesis. This article explains VibeVoice-ASR setup, structured transcription, Realtime TTS, and responsible deployment limits.

LocalAI turns diverse inference backends, model formats, and hardware accelerators into a self-hosted service layer. With OpenAI-compatible APIs, on-demand backends, built-in agents, and distributed capabilities, it lowers the cost of connecting local models to real products. This article examines its architecture, deployment path, governance boundaries, and the teams it fits best.

Microsoft Agent Framework is worth watching not because it wraps another chat API, but because it places agents, workflows, state, human approval, observability, and deployment within one engineering model. This article follows the official documentation and a runnable Python quickstart to explain where it fits, how to begin, and why teams should not start by piling on multiple agents.