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Flowcraft docs

Visual workflow tooling for Python automation and data flows.

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Flowcraft

Visual workflow platform for Python automation and data flows. Model scripts as flowcharts, execute them step by step, and review performance dashboards.

View on GitHub
pip install git+https://github.com/oscarjbyles/flowcraft.git@before-refactor
pip install --force-reinstall --no-cache-dir git+https://github.com/oscarjbyles/flowcraft.git@before-refactor

Summary

Flowcraft is a visual workflow platform for Python automation and data flows. It lets users model scripts as flowcharts, execute them step by step, monitor live output, and review historical performance in dashboards.

Value Proposition

  • Reduce friction when orchestrating ad hoc automation.
  • Provide a no-code facade for developer-authored scripts.
  • Centralize run history, timing, and diagnostics.
  • Improve dataflow visibility and debugging clarity.

Primary Use Cases

Build Flows

Drag nodes, bind them to scripts, and connect execution order on a canvas.

Run and Monitor

Execute flows, stream live output, and stop runs early when needed.

Analyze and Iterate

Review run history, inspect variables, and iterate on failed steps.

Manage Scripts

Browse, create, move, and delete Python scripts in the node directory.

Product Walkthrough

  1. Launch the CLI to serve the UI and API locally.
  2. Build a flowchart with Python nodes and conditions.
  3. Run flows, watch streaming output, and save results.
  4. Inspect dashboards for run history and timing.
  5. Manage scripts inside the file explorer.
  6. Inspect variable exchange between nodes.

Architecture Overview

Frontend

Builder UI and dashboards built in a single-page app.

Backend

Flask API endpoints for flowcharts, execution, and file management.

Execution

Python subprocess orchestration with validation utilities.

Storage

Flow definitions and run history stored as JSON artifacts.

API Surface

EndpointDescription
GET /api/flowchartsList available flows.
POST /api/flowchartPersist flowchart updates with run summaries.
POST /api/runExecute linked scripts sequentially.
POST /api/execute-nodeInvoke a single node with mock inputs.
POST /api/analyze-connectionInspect variable exchange between nodes.

Installation

  1. Confirm Python 3.9 or later is installed.
  2. Create a project folder and open a terminal in it.
  3. Set up and activate a virtual environment.
  4. Install Flowcraft from the repository.
  5. Run the flowcraft command to launch the app.

Future Enhancements

  • Support pluggable node types beyond Python scripts.
  • Enable API node templates to call external services.
  • Ship AI nodes for automation and analysis.
  • Implement multi-queued runs for long workflows.

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