Author of the rpcx microservice framework, original book author, and chair of Baidu's Go CMC committee.
The article analyzes how Manus, a general‑purpose AI agent, leverages E2B's Firecracker micro‑VM sandbox and self‑hosting deployment to achieve fast startup, full OS capabilities, session persistence, multi‑tenant isolation, and future cross‑OS support, illustrated with real‑world use cases and trade‑off assessments.
The article analyzes the context‑window bottleneck of large language models, compares two architectural strategies—strengthening the model (Gemini Deep Research) and parallel agent decomposition (Manus Wide Research)—and details a wind‑power investment case study, technical implementation, and future directions.
This article explains how the ManusAgent, built on LangGraphGo, combines a persistent three‑file Markdown workflow with a graph execution engine to solve AI context window constraints, detailing its design, implementation steps, core features, usage scenarios, and a side‑by‑side comparison with a simpler planning agent.
The report analyzes the rapid emergence of Claude Skills and OpenAI's Agentic Commerce Protocol, detailing their technical architectures, benchmark performance, cross‑platform compatibility, enterprise adoption metrics, security challenges, and strategic implications for businesses entering the agentic AI era.
BettaFish is a Go‑based, AI‑driven multi‑agent opinion analysis platform that tackles information silos, overload, and bias by aggregating data from diverse sources, iteratively refining results through reflection loops, and delivering visualized, actionable reports for scientific decision‑making.
The article introduces the Plan‑Execute‑Verify (PEV) agent pattern added to langgraphgo, explains its three‑stage workflow, core features, configuration, concrete Go examples, implementation details, comparisons with ReAct and Reflection, and discusses best practices, limitations, and trade‑offs for high‑risk automation.
This article walks through the open‑source DeerFlow framework—its multi‑agent architecture, core features, and a step‑by‑step implementation using the Go‑based LangGraphGo library, covering planner, researcher, reporter and podcast nodes, state‑graph design, CLI/web modes, and deployment instructions.
The first week of the LangGraphGo project saw five version releases culminating in v0.5.0, the completion of seven full‑scale showcase replications, the launch of a groundbreaking Programmatic Tool Calling (PTC) package with up to ten‑fold performance gains, over 21,000 new lines of code, extensive bilingual documentation, and a fully deployed website and knowledge base, all backed by detailed metrics and community contributions.
This article walks through the design and implementation of a visually striking, real‑time front‑end for an AI agent, explaining why Server‑Sent Events were chosen, showing Go and JavaScript code for streaming, and detailing a session‑replay feature that lets users review every agent decision step.
This article explains how the agent-web framework builds a powerful AI agent orchestration system in Go without external libraries, detailing the reasons for choosing Go, the use of go:embed for single‑binary deployment, core data structures, planning and execution logic, and the interaction handling that enables both CLI and web interfaces.
