TL;DR: i built a visual drag-and-drop tool for building AI automation workflows. connect nodes on a canvas, wire them together, run them. no boilerplate. the whole thing is open source.

what it is

it is a pipeline builder for AI workflows.

you drag nodes onto a canvas, connect them, and run the pipeline with one click.

nodes can be things like:

  • an LLM call (OpenAI, Anthropic, Gemini, OpenRouter)
  • a web search
  • a document or PDF reader
  • a RAG retrieval from a knowledge base
  • a text splitter
  • an API call
  • a sub-workflow
  • an input and an output

wire them together, pass variables between nodes using {{ nodeId.field }} syntax, and you have a working AI system.

why i built it

i have been building AI products for a while.

and every time i prototype something new, there is the same setup cost:

  • set up the project,
  • connect the providers,
  • write the prompt plumbing,
  • wire up retrieval,
  • hook up the output.

it is not hard, but it is slow. and it does not need to be.

i wanted something that lets me test an idea in minutes, not hours.

and i wanted something where non-engineers could inspect and adjust the logic without touching code.

so i built it.

what it looks like in practice

you open the app, drag an Input node, a Web Search node, an LLM node, and an Output node onto the canvas.

you connect them.

you set the LLM prompt to something like:

using this context: {{ webSearch.results }}, answer: {{ input.query }}

you click run.

the pipeline executes node by node, streaming the LLM response in real time, and shows you the output.

that is it.

what it can actually do

  • multi-provider LLM calls with streaming
  • web search with Exa, Tavily, or Brave
  • RAG pipelines with knowledge base management
  • file ingestion — PDF, CSV, plain text
  • YouTube transcript extraction
  • API calls to external services
  • sub-workflow execution (pipelines inside pipelines)
  • workflow save, load, and run history

where to find it

it is open source and on GitHub.

the backend is FastAPI + Python. the frontend is React + React Flow.

you can run it locally with one LLM provider key and no other setup.

final thought

i did not build this to replace LangChain or n8n.

i built it because i wanted something simple, visual, and mine.

something i can extend, break, and rebuild.

if you are building AI systems and want a faster way to prototype and inspect them, this might be useful for you too.

check it out. contributions welcome.