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automation

WFGY: Simplify AI Workflows

Streamline AI reasoning and workflow automation with WFGY, a tool for founders, backed by 1.8k+ GitHub stars.
advanced⏱ 1-2 hours💵 Free + self-hosted (if desired)
1,774 stars162 forksJupyter NotebookQuality 8/10Updated 7/26/2026100% free · open source
What it is

Simplify your AI project's workflows with WFGY, by letting you create and manage complex automated tasks.

What you can make with it

Automations like: 'Send a daily sales report to my team, using data from Stripe and Excel' automatically, without manual work.

How it helps

Streamlines AI project development by allowing you to easily manage complex workflows, saving time and energy.

Real use case example

"A founder wants to set up a new user onboarding process, so they create an automation in WFGY that sends a welcome email via Mailchimp when a new user signs up on a website. They connect Stripe and Mailchimp to WFGY in a few clicks, and now the process is automated. Every new user gets a personalized welcome email."

If you're new

Beginners can start using WFGY once they've got a basic understanding of what automation and AI workflows do.

If you're senior

Senior engineers and professionals will reach for WFGY when they need to create complex workflows, and want a powerful, open-source solution.

Common confusion cleared up

Some users might think WFGY only automates simple tasks, but it's actually designed to handle complex, AI-driven workflows.

Pairs with
Your own Python code, for advanced custom automationsOther open-source tools within this marketplace, for maximum flexibilityStripe webhook, for automated sales updatesNotion database, for centralized data storage
Why we list it on WorkflowStacks: WFGY is open-source and included in this marketplace, providing a best-in-class, free solution for advanced workflow automation.
What it does

Provides an open-source toolkit for building advanced AI agents with enhanced reasoning and debugging capabilities for complex workflow automation.

When to use it
  • •When creating intelligent systems that require multi-step problem solving
  • •For startups needing advanced RAG (Retrieval-Augmented Generation) workflows
  • •When you need sophisticated debugging and tracing for AI agent development
Quick start
  1. 1Clone the GitHub repository: git clone https://github.com/onestardao/WFGY
  2. 2Install dependencies using pip or conda
  3. 3Review the Problem Map documentation for initial workflow design
  4. 4Experiment with the Global Debug Card to trace AI reasoning paths
Ready-to-paste prompt
python wfgy_agent.py --mode='reasoning' --workflow='startup_problem_solver'
Saves to your device

Topics

ai-agents
alignment
debugging
evaluation
graphrag
hallucination
information-retrieval
knowledge-graph
llm
rag
reasoning
retrieval-augmented-generation
What's inside — free to inspect
No purchase needed

Read the entire source before you build — unlike paid marketplaces that hide it behind a buy button.

18
top-level files
20
folders
324.7M
repo size
Other
license
Key files
README.md
File tree
.github/
adapters/
archive/
automation/
Avatar/
citation/
core/
docs/
EasterEggs/
I_am_not_lizardman/
legacy/
OS/
Polaris/
ProblemMap/
recognition/
reference/
SemanticBlueprint/
stargazers/
StarterVillage/
TensionUniverse/
.gitignore
ADOPTERS.md
AI_GUIDE.md
CASE_EVIDENCE.md
Quick Actions
Details
Creator
onestardao
Language
Jupyter Notebook
Category
automation
Published
6/4/2025

Are you the creator of this tool? Claim your listing → and earn 85% of every sale.

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