Langflow低代码框架，视觉化构建和部署 LLM 功能应用和代理

4.2 (6)

审阅者 Daniel Nikulshyn·更新 2026年5月

Open Source Low-Code Visual Builder LLM RAG Agents API Python

概览

Langflow 是一款开源的视觉化开发环境，用于设计基于大型语言模型的应用。通过拖放界面，用户可以将提示、模型、向量存储、内存、工具和自定义逻辑连接起来，创建聊天机器人、RAG 管道和自主代理，且无需编写大量 boilerplate 代码。每个流程可以在编辑器中直接测试，并以 API endpoint 形式导出，使其适合快速原型和生产部署。Langflow 支持广泛的供应商和集成，包括主要 LLM 供应商、嵌入式模型和数据库，以及通过自定义 Python 组件扩展功能时提供的更多控制。

主要功能

drag-and-drop 流程设计器
集成主要 LLM 供应商
嵌入式 RAG 和向量数据库连接器
代理和工具协调
API 输出以部署
自定义 Python 组件

价格

模型: Freemium
分类: AI Agents
评分: 4.2 / 5 (6)

使用场景

视觉化构建 LLM 聊天机器人

通过将提示、模型和内存组件拖放到视觉画布中，快速设计和测试聊天机器人流程，无需编写大量 boilerplate 代码。

构建 RAG 管道

将向量数据库、嵌入式模型和 LLM 连接起来，创建能回答自定义知识库上的问题的检索增强生成工作流。

部署流程为生产 API

导出完成的流程为 API 端点，使团队能够在应用程序和生产系统中整合 LLM 功能。

协调自主代理

将工具、模型和自定义 Python 组件连接起来，创建能推理、调用外部服务和执行多步任务的代理。

优点 & 缺点

优点

开源且具有活跃的社区
直观的视觉界面可大大加快原型
与 LLMs、向量存储和工具的广泛集成
流程可以公开为 API 以供生产使用
可扩展自定义 Python 组件

缺点

复杂的流程可能在视觉上变得难以管理
新用 LLM 概念的用户可能存在学习曲线
自主部署需要某些技术设置

评测

4.2

6 个评分的平均值。

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Leila Hassan

Mar 8, 2026

Solid for our team

We rolled this out across the team last quarter and open-source with active community. Built-in support for major LLM providers fits neatly into how we already work, and aPI export for deployment removed a step we used to do by hand. Learning curve for users new to LLM concepts, which is the main caveat, but it has held up under daily use.

Pierre Dubois

Jan 10, 2026

Does the job

Pretty happy overall. API export for deployment just works and extensible with custom Python components. Learning curve for users new to LLM concepts can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

Ingrid Bauer

Dec 29, 2025

Compared a few options

Evaluated this against two competitors. Where it wins: custom component creation in Python and broad integrations with LLMs, vector stores, and tools. On balance the feature set — especially integrated RAG and vector database connectors — justifies the 5 stars for our use case.

Tariq Aziz

Dec 22, 2025

Compared a few options

Evaluated this against two competitors. Where it wins: agent and tool orchestration and flows can be exposed as APIs for production use. Where it lags: self-hosting requires some technical setup. On balance the feature set — especially built-in support for major LLM providers — justifies the 4 stars for our use case.

Grace Okafor

Nov 20, 2025

Compared a few options

Evaluated this against two competitors. Where it wins: drag-and-drop flow builder and open-source with active community. Where it lags: complex flows can become difficult to manage visually. On balance the feature set — especially agent and tool orchestration — justifies the 4 stars for our use case.

Liam O’Connor

Jul 15, 2025

Compared a few options

Evaluated this against two competitors. Where it wins: built-in support for major LLM providers and open-source with active community. Where it lags: complex flows can become difficult to manage visually. On balance the feature set — especially custom component creation in Python — justifies the 4 stars for our use case.