Search Results for "dag graph"

LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application...

Tributary is a library for constructing dataflow graphs in Python. Unlike many other DAG libraries in Python (airflow, luigi, prefect, dagster, dask, kedro, etc), tributary is not designed with data/etl pipelines or scheduling in mind. Instead, tributary is more similar to libraries like mdf, loman, pyungo, streamz, or pyfunctional, in that it is designed to be used as the implementation for a data model. One such example is the greeks library, which leverages tributary to build data models...

Argo Workflows is an open source container-native workflow engine for orchestrating parallel jobs on Kubernetes. Argo Workflows is implemented as a Kubernetes CRD (Custom Resource Definition). Define workflows where each step in the workflow is a container. Model multi-step workflows as a sequence of tasks or capture the dependencies between tasks using a directed acyclic graph (DAG). Easily run compute intensive jobs for machine learning or data processing in a fraction of the time using Argo...

Provides easy-to-use node customization capabilities and out-of-the-box interactive components, allowing us to quickly build flowcharts, DAG diagrams, ER diagrams and other graph applications. Extremely easy to customize: support custom node styles and interactions using SVG/HTML/React/Vue/Angular. Out of the box: built-in 10+ image editing supporting extensions, such as frame selection, alignment line, small map, etc. A complete event system that can listen to any event that occurs...

Couler is a system designed for unified machine learning workflow optimization in the cloud. Couler endeavors to provide a unified interface for constructing and optimizing workflows across various workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow. Couler enhances workflow efficiency through features like Autonomous Workflow Construction, Automatic Artifact Caching Mechanisms, Big Workflow Auto Parallelism Optimization, and Automatic Hyperparameters Tuning.

Cyclone is a powerful workflow engine and end-to-end pipeline solution implemented with native Kubernetes resources, with no extra dependencies. It can run anywhere Kubernetes is deployed: public cloud, on-prem or hybrid cloud. Cyclone is architectured with a low-level workflow engine that is application agnostic, offering capabilities like workflow DAG scheduling, resource lifecycle management and most importantly, a pluggable and extensible framework for extending the core APIs. Above which...

Fast, insightful and highly customizable Git history analysis. Hercules is an amazingly fast and highly customizable Git repository analysis engine written in Go. Batteries are included. Powered by go-git. There are two command-line tools: hercules and labours. The first is a program written in Go that takes a Git repository and executes a Directed Acyclic Graph (DAG) of analysis tasks over the full commit history. The second is a Python script that shows some predefined plots over...

... platform Easy Machine Learning presents a general-purpose dataflow-based system for easing the process of applying machine learning algorithms to real-world tasks. In the system, a learning task is formulated as a directed acyclic graph (DAG) in which each node represents an operation (e.g. a machine learning algorithm), and each edge represents the flow of the data from one node to its descendants.

Workflow is a Java application to manage the creation, execution and monitoring of a directed acyclic graph (DAG) of commands. Workflow supports local execution as well as execution on Sun Grid Engine, Condor and other grid types.

Dagger evaluates file dependencies in a directed-acyclic-graph (DAG) like GNU make, but timestamps or hashes can be used on a per-file basis when evaluating which file nodes are up to date or stale. Use fast timestamp comparisons on large files when hashing is too slow, and hashing on small files. When hashing is used, it's stored in either a text file or sqlite database (in-memory at runtime is optional). Files can be forced as stale or uptodate. Dagger can be used as a building block...

A C++ database library stores a taxonomy that is represented with DAG(Directed Acyclic Graph). It structure like normal taxonomy, but each node can have more than one direct parent node.