Flowise AI

Open-source low-code builder for LLM apps and AI agents

4.7 (6)

Évalué par Daniel Nikulshyn·Mis à jour mai 2026

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

Aperçu

Flowise AI is an open-source platform that lets developers and teams design AI agents and LLM-powered applications through a visual drag-and-drop interface. Users connect nodes representing models, prompts, vector stores, tools, and memory to assemble chatbots, retrieval pipelines, and multi-step agents without writing extensive boilerplate code. It integrates with popular frameworks like LangChain and LlamaIndex and supports a wide range of LLM providers, embedding models, and data sources. Built flows can be exported as APIs, embedded into websites, or self-hosted, making Flowise suitable for prototyping as well as production deployments. Because it is open source, teams can self-host for full data control, extend it with custom components, and adapt it to internal infrastructure or compliance requirements.

Fonctionnalités clés

Drag-and-drop flow builder for LLM pipelines
Prebuilt nodes for chains, agents, and memory
Integrations with OpenAI, Hugging Face, and local models
Vector store and RAG support
API endpoints and chat widget embedding
Self-hosted or cloud deployment options

Cas d’usage

Prototype LLM Chatbots Visually

Drag and drop nodes to assemble chatbots with prompts, memory, and tools, letting teams quickly iterate on conversational AI without writing extensive boilerplate code.

Build RAG Retrieval Pipelines

Connect vector stores, embedding models, and LLMs to create retrieval-augmented generation pipelines that answer questions from custom knowledge bases.

Deploy Flows as APIs

Export built flows as API endpoints or embed them as chat widgets on websites, enabling production deployment of LLM applications with minimal engineering overhead.

Self-Host Multi-Step AI Agents

Use prebuilt agent and chain nodes with LangChain or LlamaIndex integrations to design multi-step agents and self-host them for data privacy and control.

Pour & contre

Pour

Free and open source with self-hosting option
Visual interface lowers the barrier to building LLM apps
Broad integrations with models, tools, and vector databases
Flows exportable as APIs for easy deployment
Active community and extensible component system

Contre

Requires technical setup for self-hosting
Complex agents can become hard to debug visually
Documentation can lag behind rapid feature changes
Some advanced use cases still need custom code

Avis

4.7

Moyenne sur 6 avis.

Connecte-toi pour laisser un avis.

Tomáš Novák

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

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

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

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

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

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.