Flowise AI开源低代码构建器，适用于 LLM 应用和 AI 代理

4.7 (6)

审阅者 Daniel Nikulshyn·更新 2026年7月

Open Source Low-Code LLM AI Agents RAG Self-Hosted Visual Builder API

概览

Flowise AI 是一个开源平台，允许开发者和团队通过可视化的拖拽界面设计 AI 代理和基于 LLM 的应用。用户可以连接代表模型、提示、向量存储、工具和记忆的节点，组装聊天机器人、检索流水线和多步骤代理，而无需编写大量样板代码。它集成了 LangChain、LlamaIndex 等流行框架，并支持多种 LLM 提供商、嵌入模型和数据源。构建好的流程可以导出为 API，嵌入网站，或自行托管，使 Flowise 既适用于原型开发，也适用于生产部署。由于是开源的，团队可以自行托管以实现完整的数据控制，使用自定义组件进行扩展，并根据内部基础设施或合规要求进行适配。

主要功能

拖拽式流程构建器，用于 LLM 流水线
预构建节点，支持链、代理和记忆
与 OpenAI、Hugging Face 以及本地模型的集成
向量存储和 RAG 支持
API 端点和聊天小部件嵌入
支持自行托管或云部署选项

价格

模型: Free
分类: AI Agents Frameworks
评分: 4.7 / 5 (6)

使用场景

可视化原型 LLM 聊天机器人

通过拖拽节点组装包含提示、记忆和工具的聊天机器人，让团队能够在不编写大量样板代码的情况下快速迭代对话式 AI。

构建 RAG 检索流水线

连接向量存储、嵌入模型和 LLM，创建检索增强生成（RAG）流水线，以从自定义知识库回答问题。

将流程部署为 API

将构建好的流程导出为 API 端点或嵌入为网站的聊天小部件，以最小的工程投入实现 LLM 应用的生产部署。

自行托管多步骤 AI 代理

使用预构建的代理和链节点，结合 LangChain 或 LlamaIndex 集成，设计多步骤代理并自行托管，以确保数据隐私和控制。

优点 & 缺点

优点

免费且开源，提供自行托管选项
可视化界面降低了构建 LLM 应用的门槛
与模型、工具和向量数据库的广泛集成
流程可导出为 API，便于部署
活跃的社区和可扩展的组件系统

缺点

需要技术配置才能自行托管
复杂的代理在可视化上可能难以调试
文档可能跟不上快速的功能迭代
某些高级用例仍需自定义代码

评测

4.7

6 个评分的平均值。

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Tomáš Novák

Mar 5, 2026

Does the job

Pretty happy overall. Integrations with OpenAI, Hugging Face, and local models just works and active community and extensible component system. Documentation can lag behind rapid feature changes can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

Ahmed Saleh

Jan 25, 2026

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on drag-and-drop flow builder for LLM pipelines, and free and open source with self-hosting option caught me off guard. Complex agents can become hard to debug visually is why this isn't a perfect score, still, I'd recommend giving it a real trial.

Joanna Kowalski

Jan 8, 2026

Solid for our team

We rolled this out across the team last quarter and broad integrations with models, tools, and vector databases. Vector store and RAG support fits neatly into how we already work, and self-hosted or cloud deployment options removed a step we used to do by hand. Some advanced use cases still need custom code, which is the main caveat, but it has held up under daily use.

Tariq Aziz

Sep 12, 2025

Does the job

Pretty happy overall. Drag-and-drop flow builder for LLM pipelines just works and broad integrations with models, tools, and vector databases. Documentation can lag behind rapid feature changes can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

Marcus Bell

Jun 8, 2025

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on prebuilt nodes for chains, agents, and memory, and visual interface lowers the barrier to building LLM apps caught me off guard. still, I'd recommend giving it a real trial.

Sofia Lindqvist

Jun 3, 2025

Does the job

Pretty happy overall. Self-hosted or cloud deployment options just works and active community and extensible component system. Some advanced use cases still need custom code can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.