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What is Langflow? Build AI Workflows with Python, Gen AI, & MCP Tools

ref：: YouTube Channel IBM Technology

在这个人工智能时代，作为一名应用程序开发人员，你可以采取许多不同的方法将人工智能引入你的应用程序中。你可能会使用多种语言中的一种采取代码驱动的方法，或者是界面驱动的方法，也可能两者兼而有之。也许你的公司只允许特定的供应商，或者你希望走一条更开放的道路。我们需要考虑诸如可观测性工具或MCP等因素。

As an application developer in this era of AI, there are a bunch of different approaches you can take to bring AI into your applications. You could possibly take a code-driven approach with one of many various languages, or maybe a UI-driven one, and possibly a bit of both. Maybe your company only allows a particular vendor, or you'd like to take a more open path. Considerations like observability tools or possibly MCP need to be taken into account.

如果你希望在应用程序上快速构建和迭代人工智能，这正是Langflow发挥作用的地方。Langflow是一个面向开发人员的开源可视化工作室，允许你通过拖放、低代码/无代码的方式构建代理RAG（检索增强生成）和生成式AI工作流。可视化工作流构建器确实有助于简化将AI组件链接在一起的过程，而无需大量代码。

If you are looking to quickly build and iterate on AI on your apps, this is where Langflow really comes into play. Langflow is an open-source visual studio for developers that allows you to build agentic RAG in gen AI workflows in a drag-and-drop, low code/no code way. Visual workflow builders can really help simplify the process of chaining AI components together without a lot of code.

它完全不依赖于特定的模型和向量存储，支持数十种模型和向量数据库提供商。如果你想使用本地模型或坚持使用特定的供应商，这些选项都是可用的。由于其底层完全是Python，你可以编辑组件或使用Python代码创建自定义组件。因为它是开源的，你可以检查和修改任何组件，或将你自己的组件贡献回社区。

It is completely model and vector store-agnostic, with support for many dozens of model and vector database providers. If you want to use local models or stick with a particular model vendor, these are all options that you have available. It is all Python under the hood, so if you want to make custom components, you can absolutely add those in with some Python code. Because it's open source, you can inspect and modify any component or contribute your own back to the community.

Langflow是一个可视化工作室，使用拖放组件来构建AI工作流。它使用基于节点的架构，节点带有表示其类型的颜色。点击节点可以提供有关其类型的信息，并过滤系统中的其他组件以显示哪些是兼容的。在代理组件中，你可以选择模型和提供商，但你也可以换入本地模型或不同的基础提供商。

Langflow is a visual studio that uses drag-and-drop components to build out AI workflows. Langflow uses a node-based architecture, and the nodes have colors that denote the type. If you click on a node, it will tell you what kind of type it is and filter on the other components in the system to show which ones are compatible. In the agent itself, you choose a model and provider, but you can use local models or bring a component over to change any of the base providers.

一旦基本的代理就位，你可以使用顶角的操场（playground）直接开始测试代理，并在连接应用程序之前理顺基本流程。如果真的想让代理执行任务，你需要连接工具。Langflow中的大多数组件都有一个可以开启的工具模式，允许你将诸如URL组件之类的东西直接挂接到代理中。一旦连接好，代理将自动发现该工具的用途以及何时使用它。

Once a basic agent is in place, you can use the playground in the top corner to start testing the agent directly and work out the basic flow well before hooking up an application. If you really want an agent to do something, you need to hook up tools. Most components in Langflow have a tool mode that you can switch on, allowing you to hook things like a URL component right into an agent. Once hooked up, the agent will auto discover what that tool is for, when to use it, and start using it right away.

Langflow对MCP拥有原生支持，既允许将MCP工具添加到工作流中，也允许将工作流本身作为MCP工具公开。这提供了访问数千种工具的可能性，并允许你创建带有特定代理指令的精选MCP工具，以便在下游代理（如AI代码伴侣）中使用。

Langflow also has native support for MCP, both in being able to add MCP tools to your flows and expose your flows as MCP tools themselves. This gives you access to possibly thousands of tools and allows you to easily create MCP tools, curated with your own agent instructions and capabilities that you can use in downstream agents like an AI code companion.

为了便于集成到应用程序中，Langflow中的每个工作流都有自己的API访问权限，允许执行工作流、传递数据、流式传输数据和接收响应。你可以调整组件中的任何参数，从而获得对工作流的完全程序化控制。Langflow不仅限于基本工作流，还可以处理使用数据库访问、条件逻辑和结构化输出的复杂示例，以确保代理以确定性的方式符合要求。

To facilitate integration into applications, every flow in Langflow has its own API access that allows you to execute flows, pass in data, stream data, and receive responses. You can tweak any of the parameters in components, giving you full programmatic control over your flows. Langflow is not limited to basic flows but can handle complex examples that use database access, agents, conditional logic, and structured output to ensure agents conform exactly to application requirements in a more deterministic way.

#Langflow #AI #Python #GenAI #OpenSource #RAG #LowCode #MCP #IBMTechnology

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9 个通用 Prompt 框架，几乎适用于任何 AI（建议收藏）

如何使用AI学习，五种干货提示词范式

Prompt Engineering指南：来自AI巨头的最佳实践

Prompt Engineering 101：让AI效果翻10倍的简单步骤

Google Gemini 全方位使用指南 (中文版 Cheat Sheet)

5个经过实战检验的Prompt技巧

收藏级：Prompt Engineering完全指南（原理+技巧+进阶）

如何用 AI 打造一个「高转化」的 LinkedIn Banner（封面图）

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