Capability · Framework — orchestration

Flowise

Flowise provides a node-based visual builder on top of LangChain.js, letting non-coders wire up RAG pipelines, agents, and chatbots. Every flow is exposed as a REST API, with built-in credentials, embeddings, and vector-store nodes for most popular backends.

Framework facts

Category: orchestration
Language: TypeScript / Node.js
License: Apache-2.0
Repository: https://github.com/FlowiseAI/Flowise

Install

npm install -g flowise
npx flowise start
# or Docker
docker run -d -p 3000:3000 flowiseai/flowise

Quickstart

# After npx flowise start, open http://localhost:3000
# Drag a ChatModel node + a Retriever + a Memory into the canvas,
# connect them, hit Save, and copy the generated cURL:
curl -X POST http://localhost:3000/api/v1/prediction/<chatflow-id> \
  -H 'Content-Type: application/json' \
  -d '{"question":"explain attention"}'

Alternatives

Langflow — Python-based visual builder
Dify — product-flavoured no-code platform
n8n — general automation with LLM nodes
Rivet — desktop-first visual prompt IDE

Frequently asked questions

Is Flowise production-ready?

Flowise is used in production by smaller teams, but the single-Node process is not built for heavy concurrency. For enterprise traffic you'll want to run it behind a queue or move the proven flow into native LangChain.js code.

Flowise vs Langflow?

Flowise is TypeScript / LangChain.js; Langflow is Python / LangChain-py. Choose based on the ecosystem you deploy into. Flowise is generally easier to embed into JS stacks; Langflow has more Python-native components.

Sources

Flowise — docs — accessed 2026-04-20
Flowise GitHub — accessed 2026-04-20