28 related articles

Acrux Core is an open-source LLM observability platform supporting self-hosted deployment with prompt management, dynamic tool binding, user feedback loops, and full-chain tracing—a free alternative to LangSmith and Langfuse.

Agent DevTools is an open-source local debugger for AI Agents that lets you inspect prompts, memory, retrieval, and tool calls, with good vs. bad run comparison to quickly find root causes.

Deep dive into AI Agent observability tools for production debugging and hallucination governance, covering full-chain tracing, semantic evaluation, and continuous improvement strategies.

Agent DevTools is an open-source AI Agent debugging tool inspired by Browser DevTools, offering execution visualization, tool call tracing, and breakpoint analysis to help developers diagnose Agent failures.

TraceLLM is an open-source observability platform for production AI apps, built on OpenTelemetry, offering Prompt tracing, Token monitoring, latency analysis, and full distributed tracing.

Build high-quality AI projects on a budget. Learn how to use Ollama, Groq, Chroma, and other free open-source tools to build RAG systems and multi-Agent workflows from scratch.

Build high-quality AI projects on a budget. Learn how to use Ollama, Groq, Chroma, and other free open-source tools to build RAG systems and multi-Agent workflows from scratch.

Deep dive into Wattage, an AI Agent token consumption profiling and cost regression protection tool, exploring its core features, industry context, and value for developers.

Cosmonapse is an open-source multi-agent framework that replaces central orchestrators with peer nodes, using typed signals and an event bus to fully decouple tool calls, memory, and HITL. Supports Python and TypeScript under Apache 2.0.

Learn LangChain 1.3 core concepts including LLM model abstraction, RAG retrieval-augmented generation, and Agent orchestration. Build a Deep Agent with planners, tools, and reflection modules.

Programmers transitioning to AI engineering aren't starting from scratch. Learn the 6 core skills — LLM APIs, RAG, prompt engineering, LLMOps — needed to make the leap.

Model capabilities are converging, making inference cost and scalability the new focus of AI competition. A deep analysis of AI infrastructure's core layers.

A deep dive into AI Agent development: real architecture, entry barriers, and learning paths. From ReAct to multi-agent systems and LangChain — cut through the hype.

ManagedAgents.sh is a model-agnostic managed agent platform from OpenComputer, supporting Claude, Pi, and Codex runtimes with Slack and GitHub integration.

An exclusive look at the AI Engineer Summit dress rehearsals, decoding the paradigm shift from research to production. A deep dive into AI Engineer challenges, RAG, agent systems, and AI engineering as a distinct discipline.

Want to become an Agent engineer? This article systematically covers three core skill tracks—LLM fundamentals, LangChain architecture development, and enterprise deployment—to help you avoid detours.

Most Agent projects lack competitiveness in interviews due to missing business value and engineering depth. This article breaks down the 6 core standards of high-value Agent projects.

Frontend hiring now treats AI capabilities as a core assessment, covering RAG knowledge bases, AI Agent development, and LangChain.js engineering. Learn how LangChain.js + Nuxt.js helps frontend developers build memory- and retrieval-capable AI full-stack apps.

A hands-on guide to building an enterprise-grade AI Agent workflow orchestration app with Electron Forge and LangGraph, covering local LLM deployment (Qwen3-0.6B), node-based visual canvas design, and full Function Calling integration.

A complete guide to building RAG systems: covering data preprocessing, vector databases, embedding models, hybrid search, re-ranking, and advanced topics like Graph RAG and multimodal RAG.