5.1 The Rise of Platform-Based Construction

As technology matures, we see more and more capabilities being "platformized." Just as website development has evolved from hand-writing HTML/CSS/JS to using website building platforms like WordPress and Wix, agent construction has also ushered in a wave of platformization. This chapter will focus on how to use graphical, modular low-code platforms to quickly and intuitively build, debug, and deploy agent applications, shifting our focus from "implementation details" to "business logic."

5.1.1 Why Low-Code Platforms Are Needed

"Reinventing the wheel" is crucial for deep learning, but in practical work pursuing engineering efficiency and innovation, we often need to stand on the shoulders of giants. Although we encapsulated reusable classes like ReActAgent and PlanAndSolveAgent in Chapter 4, when business logic becomes complex, the maintenance cost and development cycle of pure code will rise sharply. The emergence of low-code platforms is precisely to solve these pain points.

Their core value is mainly reflected in the following aspects:

1. Lowering Technical Barriers: Low-code platforms encapsulate complex technical details (such as API calls, state management, concurrency control) into easy-to-understand "nodes" or "modules." Users don't need to be proficient in programming; they only need to drag and connect these nodes to build powerful workflows. This enables non-technical personnel such as product managers, designers, and business experts to participate in the design and creation of agents, greatly expanding the boundaries of innovation.
2. Improving Development Efficiency: For professional developers, platforms can also bring huge efficiency improvements. In the early stages of a project, when an idea needs to be quickly validated or a prototype needs to be built, using a low-code platform can complete work that would originally take days of coding in hours or even minutes. Developers can invest more energy in business logic organization and prompt engineering optimization rather than low-level engineering implementation.
3. Providing Better Visualization and Observability: Compared to printing logs in the terminal, graphical platforms naturally provide end-to-end visualization of agent running trajectories. You can clearly see how data flows between each node, which link takes the longest time, and which tool call fails. This intuitive debugging experience is incomparable to pure code development.
4. Standardization and Best Practice Accumulation: Excellent low-code platforms usually have many industry best practices built in. For example, they provide preset ReAct templates, optimized knowledge base retrieval engines, standardized tool integration specifications, etc. This not only prevents developers from "stepping on landmines" but also makes team collaboration smoother because everyone develops based on the same set of standards and components.

In short, low-code platforms are not meant to replace code but provide a higher level of abstraction. They allow us to free ourselves from tedious low-level implementation and focus more on the logic of agent "thinking" and "action" itself, thereby turning ideas into reality faster and better.

5.1.2 Choosing a Low-Code Platform

Currently, the low-code platform market for agents and LLM applications presents a flourishing situation, with each platform having its unique positioning and advantages. Which platform to choose often depends on your core needs, technical background, and the ultimate goal of the project. In the subsequent content of this chapter, we will focus on introducing and practicing several representative platforms: Coze, Dify, FastGPT, and n8n. Before that, let's give them a brief introduction.

Coze

• Core Positioning: Launched by ByteDance, Coze^[1] focuses on zero-code/low-code Agent building experience, allowing users without programming backgrounds to easily create.
• Feature Analysis: Coze has an extremely friendly visual interface. Users can create agents by dragging and dropping plugins, configuring knowledge bases, and setting workflows, just like building LEGO blocks. It has a very rich plugin library built in and supports one-click publishing to mainstream platforms such as Douyin, Feishu, and WeChat Official Accounts, greatly simplifying the distribution process.
• Target Audience: Entry-level users of AI applications, product managers, operations personnel, and individual creators who want to quickly turn ideas into interactive products.

Dify

• Core Positioning: Dify is an open-source, full-featured LLM application development and operation platform^[2], aiming to provide developers with a one-stop solution from prototype construction to production deployment.
• Feature Analysis: It integrates the concepts of backend services and model operations, supporting multiple capabilities such as Agent workflows, RAG Pipeline, data annotation, and fine-tuning. For enterprise-level applications pursuing professionalism, stability, and scalability, Dify provides a solid foundation.
• Target Audience: Developers with some technical background, teams that need to build scalable enterprise-level AI applications.

FastGPT

• Core Positioning: FastGPT is an open-source, LLM-based knowledge base Q&A platform and Agent building tool^[3], focusing on providing easy-to-use RAG (Retrieval-Augmented Generation) solutions and visual workflow orchestration capabilities.
• Feature Analysis: FastGPT's core advantage lies in its extreme optimization for knowledge base Q&A scenarios. It provides a complete RAG pipeline from data import, automatic text chunking, vectorized storage to intelligent retrieval, and supports orchestrating complex conversation flows and Agent workflows through an intuitive visual interface (Flow module). The platform adopts a model-neutral design, flexibly connecting to various mainstream domestic and international large models such as OpenAI, Claude, and Tongyi Qianwen, while providing comprehensive API interfaces and a plugin market for quick integration with existing systems like WeChat Work, DingTalk, and Feishu.
• Target Audience: Developers and small-to-medium enterprise teams who want to quickly build intelligent customer service, internal knowledge assistants, or document Q&A robots based on private knowledge bases, as well as technology enthusiasts interested in RAG who wish to lower the implementation barrier.

n8n

• Core Positioning: n8n is essentially an open-source workflow automation tool^[4], not a pure LLM platform. In recent years, it has actively integrated AI capabilities.

• Feature Analysis: n8n's strength lies in "connection." It has hundreds of preset nodes that can easily connect various SaaS services, databases, and APIs into complex automated business processes. You can embed LLM nodes in this process, making it part of the entire automation chain. Although it is not as specialized in LLM functionality as the first two, its general automation capability is unique. However, its learning curve is also relatively steep.

• Target Audience: Developers and enterprises that need to deeply integrate AI capabilities into existing business processes and achieve highly customized automation.

In the following subsections, we will get hands-on experience with these platforms one by one, and more intuitively feel their respective charms through actual operations.