Flowise: Visual LLM Workflows for Practical AI Applications

Building applications with large language models often starts simple and quickly becomes complex. Prompt chains grow, tools need to be connected, memory has to be managed, and experimentation turns into maintenance work. Flowise addresses this problem by providing a visual way to design, test, and run LLM-powered workflows without losing technical flexibility.

What Is Flowise?

Flowise is an open-source visual builder for creating applications based on large language models. Built on top of LangChain, it exposes chains, agents, tools, and memory through a node-based interface.

Why Visual LLM Workflows Matter

Flowise makes orchestration explicit: prompts, context, tools, and outputs are visible and easier to reason about, reducing complexity and speeding up iteration.

Open Source and Extensible

Being open source, Flowise offers transparency, extensibility, and freedom from vendor lock-in while benefiting from the LangChain ecosystem.

Running Flowise with Docker

Using Docker, Flowise can be deployed reproducibly alongside LLM backends, vector databases, and other services using Docker Compose.

Flowise and Local Models

Flowise integrates well with local inference runtimes like Ollama, enabling privacy-friendly, cost-predictable AI workflows.

Typical Use Cases

• Chatbots with memory and tools
• RAG pipelines
• AI agent prototyping
• Prompt and model experimentation

See real examples in these Flowise workflows.

Final Thoughts

Flowise complements code-based approaches by adding clarity and speed during experimentation and early production stages.