Customer-facing chatbots
embed a Flowise-built bot on your marketing site in minutes
// official site: flowiseai.com ↗
AI / LLM · PRO TIER
Flowisepro
Flowise is the low-code drag-and-drop builder for LLM applications — chatbots, agents, RAG pipelines, document-Q&A — built on top of LangChain. Visual canvas where you wire nodes (LLM, vector store, retriever, tool, memory) into a runnable flow, then expose it as a REST API or embeddable chat widget.
// What it is
A closer look.
Flowise is the low-code drag-and-drop builder for LLM applications — chatbots, agents, RAG pipelines, document-Q&A — built on top of LangChain. Visual canvas where you wire nodes (LLM, vector store, retriever, tool, memory) into a runnable flow, then expose it as a REST API or embeddable chat widget.
If LangChain is the framework, Flowise is the IDE.
// Use cases
What it's for.
Concrete scenarios where teams pick Flowise over the SaaS alternative.
Internal RAG over docs
drag a PDF loader → embedder → Qdrant retriever → LLM, deploy in an afternoon
AI agent prototyping
visually wire tool-using agents (web search, calculator, custom HTTP)
API-as-a-product
turn a flow into a versioned /predict endpoint with API-key auth
Education & demos
teach LangChain concepts visually before going to code
// Who it's for
Built for these teams.
If your team profile matches one of these, Flowise is a strong fit out of the box.
ML engineers
prototyping LangChain pipelines visually before refactoring to code
Solo developers
shipping AI-powered features without writing the chain orchestration themselves
Education / training providers
teaching RAG + LLM concepts to engineers via visual flows
Agencies
building customer-facing chatbots for clients quickly with embeddable widgets
Product teams
exposing AI logic as an API endpoint without standing up a Python backend
// Differentiators
Why teams pick Flowise.
When evaluating self-hosted options for this category, here are the dimensions on which Flowise consistently lands above the alternatives.
- ✓LangChain-native — every node maps 1:1 to LangChain primitives, no abstraction tax
- ✓Multi-tenant marketplace flows — share/import flows as JSON
- ✓Embeddable widget — <script> tag for marketing-site bots
- ✓API-key auth + rate limiting — built in
- ✓30+ vector store integrations — (Qdrant, Chroma, Weaviate, Milvus, pgvector, …)
- ✓MIT license, Apache 2.0 dependencies — clean for commercial use
// Integrations
Connects to.
The stack you'll plug Flowise into — services, protocols, and adjacent apps in the BluixApps catalog.
◇
Vector stores
Qdrant, Chroma, Weaviate, Milvus, pgvector, Pinecone, MongoDB, Redis
◈
LLM providers
OpenAI, Anthropic, Cohere, Hugging Face, Mistral, Ollama, custom OpenAI-compatible
◆
Document loaders
PDF, DOCX, web scrapers, GitHub, Notion, S3, Confluence, Airtable
▣
Embeddings
OpenAI, Cohere, Hugging Face, local sentence-transformers, FastEmbed
▦
Agent tools
web search (SerpAPI, Brave), calculator, code interpreter, custom HTTP, MCP servers
▩
Embeddable widget
<script> tag with token-gated access for marketing sites
▼
Webhook + REST API
every flow becomes an authenticated /predict endpoint
// Adoption & deployment
Notable users & community
- 35k+ GitHub stars
- Featured in "production LLM stack" articles on Towards Data Science, Medium AI publications
- Active Discord, weekly releases, strong RAG-tutorial pipeline
- Common pairing with Qdrant + Ollama in self-hosted AI tutorials
- MIT-licensed core gives clean commercial use for agencies + product teams
What we ship
- Docker compose: Flowise + Postgres for flow persistence
- Pinned
flowiseai/flowise:2.2.4, monthly upstream tracking - Admin user with random password on first boot, surfaced in install report
- Pre-configured to detect Ollama/Open WebUI on same VPS for zero-config LLM hookup
- Vector store nodes pre-installed for Qdrant / Chroma when those apps also deployed
- SSL via Let's Encrypt; embeddable widget URL points to HTTPS hostname
- Backup hook covers Postgres + flow JSON exports
// Tips & operations
Run it properly.
Operational guidance from running this in production — what to lock down, what surprises people.
// PERFORMANCE
Put Flowise behind a CDN
when you embed widgets — public traffic hits your origin every page-load otherwise
// SECURITY
Switch to Postgres for multi-instance
DATABASE_TYPE=postgres; default SQLite breaks under concurrent writes
// OPERATIONS
Rate-limit API keys
a public widget without per-key limits can run up your OpenAI bill in hours
// RELIABILITY
Rotate keys regularly
generate distinct API keys per consumer (one per embed location)
// DEPLOYMENT
Test in dev with cheap LLM
wire Ollama local for prototyping, swap to GPT-4 only at production deploy
// SCALING
Persist /root/.flowise
flows + credentials live here; mount on a volume from day one
1024
// min ram (MB)
5
// min disk (GB)
3001
// access port
http
// protocol
pro
// bluixapps tier
// Alternatives in AI / LLM
Compare with
Project resources
Official siteflowiseai.com ↗