
概览
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
- 王常服务
- 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
6 个评分的平均值。
登录以留下评测。
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.
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.
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.
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.
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.
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.
问答
Do I need to know how to code to use Langflow?
While Langflow has a visual interface that speeds up prototyping, users new to LLM concepts may still face a learning curve, but prior coding knowledge is not necessarily required.
Asked by Ola Eriksen · Oct 24, 2025
Can I customize Langflow with my own code?
Yes, Langflow allows developers to extend its functionality with custom Python components when more control is needed.
Asked by Fatima Zahra · Oct 22, 2025
What integrations does Langflow support?
Langflow supports a wide range of providers and integrations, including major LLMs, embedding models, databases, and hundreds of data sources, models, or vector stores.
Asked by Linda Petersen · Sep 27, 2025
Is Langflow free?
Langflow is open-source, which means it is free to use. Additionally, it offers a free enterprise-grade cloud to deploy apps.
Asked by Sanjay Gupta · Sep 21, 2025
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