Directed Acyclic Graph Python Packages

bnlearn

Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.

paradag

A robust DAG implementation for parallel execution

scitex-clew

Verifiable knowledge graph for scientific experiments

ontodag

Associative memory and categories based on a directed acyclic graph data structure

znflow

A general purpose framework for building and running computational graphs.

dawgz

Unleash the true power of scheduling

django-postgresql-dag

Directed Acyclic Graphs with a variety of methods for both Nodes and Edges, and multiple exports (NetworkX, Pandas, etc). This project is the foundation for a commercial product, so expect regular improvements. PR's and other contributions are welcomed.

synapse-orch-ai

Build AI agents that actually do things. Synapse is an open-source platform for creating, connecting, and orchestrating AI agents powered by any LLM — local, cloud or CLIs.

railway-framework

This is a Python framework for Railway Oriented Programming.

dagabaaz

DAG orchestration library for Python. Barrier sync, fan-out/fan-in, skip cascades, scatter-gather correlation, edge filtering, and retry -- without adopting a platform. Bring your own database and queue.

jupyterlab-dagitty

JupyterLab renderer of dagitty causal diagrams

audiodag

A lazy-DAG component approach to signal generation. Designed for psychophysics stimuli.

dagsim

A framework and specification language for simulating data based on graphical models

ancestree-track

Experiment tracking without the infrastructure. No server, no dependencies. Just Python, your filesystem, and one call to visualise the whole pipeline. Works for any workflow.

tknodesystem

Create visual node-based UI with Tkinter!

pypeworks

Easy and lightweight parallelized dataflows in Python

bnclassify

bnlearn

daggre

A convenience layer on top of dagre-d3/dagre-d3-es, for use in ipydagred3

py-bnlearn

Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.