Flowise
A free, open-source drag-and-drop builder for wiring together chatbots and AI agents out of LLM building blocks, without writing the underlying code.
🔗 Visit Flowise
Description
Building an AI chatbot that actually retrieves answers from your own documents usually means stitching together a language model, a database, and some retrieval logic — normally a real coding project. Flowise turns that into something closer to connecting boxes on a whiteboard: drag a model here, a document loader there, draw a line between them, and you have a working AI pipeline.
Flowise is an open-source, drag-and-drop visual builder for constructing LLM-powered applications, AI agents, and retrieval-augmented generation (RAG) pipelines without writing code. It offers two workflow types: Chatflow, for linear, sequential pipelines good for Q&A bots and simple retrieval, and Agentflow, for autonomous agent loops where the model decides which tools to call, suited to research and multi-step tasks. It integrates with over 100 tools including LangChain and LlamaIndex, and — notably — it's free and self-hostable since the underlying project is fully open-source. In August 2025, Workday acquired Flowise, folding the visual agent-building technology into its enterprise HR and finance products, while the open-source project continues independently.
💬 Our review
The short version: if you want to build an LLM app or agent visually without becoming a LangChain expert, and you're comfortable self-hosting or paying a modest cloud fee, Flowise gives you a real head start for free.
Its biggest strength against paid no-code competitors like Stack AI or n8n's AI nodes is that the core product is open-source — you can self-host it for zero licensing cost and full control over your data, which matters if you're processing anything sensitive. The Workday acquisition (August 2025) is a double-edged signal: it validates the technology and should mean better long-term maintenance, but it also raises the usual open-source-acquired-by-enterprise question of whether the free, self-hosted version stays a first-class citizen over time, or whether new features increasingly favor the paid cloud plans. Total cost in practice tends to be higher than the sticker price once you add LLM API tokens and a vector database for RAG use cases — budget for that beyond the $35-65/month cloud tiers. Best fit: developers and technical teams who want visual LLM app building with the option to self-host. Weaker fit: non-technical users who want a fully managed, zero-maintenance experience — Stack AI or Dust are a more turnkey choice.
💰 Pricing
📊 Global score
🤖 AI-enriched data
Gratuit et auto-hébergeable (open-source). Cloud : Starter ~35 $/mois, Pro ~65 $/mois, Enterprise sur devis. Palier gratuit cloud : 100 prédictions/mois, 5 Mo, 2 flows max.
Pros
Open-source et auto-hébergeable gratuitement — contrôle total des données
Deux modes de workflow : Chatflow (pipelines linéaires) et Agentflow (agents autonomes)
100+ intégrations dont LangChain et LlamaIndex
Racheté par Workday (août 2025) — signal de maturité et de maintenance long terme
Cons
Coût réel souvent plus élevé que l'abonnement affiché une fois les tokens LLM et la base vectorielle ajoutés
Rachat par une grande entreprise = incertitude sur la priorité donnée à la version gratuite auto-hébergée
Palier gratuit cloud très limité (100 prédictions/mois, 2 flows)
Nécessite des bases techniques (LLM, RAG) pour en tirer pleinement parti