AgentForge lets non‑technical users and developers create, debug, and deploy fully functional AI agents in minutes by converting a one‑line requirement into a complete agent with built‑in memory, tool integration, scheduling, and enterprise‑grade deployment pipelines.
This article reviews five popular open‑source low‑code AI application platforms—Langflow, Flowise, Dify, SuperAGI and Bee Agent—detailing their core features, supported tech stacks, typical users, and providing a side‑by‑side comparison to help developers choose the right tool for their workflow.
By mid‑2026 enterprises will shift from experimental AI agents to fully integrated, low‑code, industry‑specific agents, making speed of adoption, multi‑agent orchestration, and measurable KPI impact the decisive factors for competitive advantage.
The article introduces the SKILL framework, explains its file‑based structure and LLM‑driven entry point, compares it with traditional API‑centric backends, outlines its suitable use cases and limitations, and argues that mastering SKILL will become a core productivity skill for developers.
The article argues that instead of crafting ever‑more complex AI agents, developers should treat agents as generic containers and equip them with modular “Skills”—simple folder‑based packages of scripts and documentation—that provide domain expertise, reduce context overload, and democratize AI development for non‑technical users.
This guide walks through Coze's large‑model configuration—including model selection, generation diversity, input/output parameters, and persona templates—demonstrates a complete e‑commerce chatbot setup, and details two plugin integration methods with three concise best‑practice rules for effective agent development.
This article provides a comprehensive, hands‑on tutorial for installing Ollama, Docker, and the open‑source Coze Studio on a local machine, configuring various LLM services such as Qwen 3, DeepSeek‑V3, and OpenRouter, and running the platform via Docker Compose to create and test AI agents.
This article provides a step‑by‑step guide to creating a breakfast‑shop reception AI agent on Baidu's Wenxin Intelligent Agent platform, highlighting its core features, model selection with DeepSeek R1, and practical tips for configuring personas, knowledge bases, and plugins.
This article examines the challenges of extracting data from complex financial reports and presents an AI‑driven solution that combines advanced layout analysis, table recognition, OCR, and large‑language‑model integration using Baidu’s PaddlePaddle low‑code platform, detailing model selection, training, performance tuning, and deployment.
