6 مستودعات
Patterns for combining task-based execution graphs into hierarchical and reusable workflow structures.
Distinct from Modular Program Composition: Focuses on the composition of parallel execution graphs specifically, rather than general program units or test flows.
Explore 6 awesome GitHub repositories matching software engineering & architecture · Execution Graph Compositions. Refine with filters or upvote what's useful.
Cpp-taskflow is a C++ task-parallelism framework and task graph scheduler designed to manage and execute complex dependency graphs of parallel tasks across CPU and GPU hardware. It provides a parallel algorithm library for high-performance implementations of reductions, sorts, pipelines, and iterations. The framework distinguishes itself through its ability to offload heavy computational workloads from a task graph to graphics processors for acceleration. It also includes a task profiling tool and a performance analysis interface for visualizing task execution flow and dependency structures t
Implements modular graph compositions that allow pre-defined task blocks to be combined into larger, reusable workflows.
Eino is an AI agent development kit and LLM application framework designed for building autonomous agents and orchestrating complex language model workflows. It serves as a multi-agent orchestration engine and workflow orchestrator, providing a graph-based execution model to route data between models, tools, and retrievers. The framework distinguishes itself through a robust set of multi-agent coordination patterns, including supervisor-led management, sequential flows, and autonomous reasoning loops like ReAct. It features advanced agent execution controls such as active turn preemption, che
Composes business logic into reusable execution graphs or chains to organize AI sequences.
Caffe2 is a high-performance deep learning framework and C++ machine learning library. It serves as a modular system for designing, training, and executing scalable neural networks. The project functions as an inference engine and a scalable neural network engine designed to run models across distributed systems and diverse hardware. Its architecture allows for the construction of custom neural network components that can be scaled from research to production environments. The framework covers the full lifecycle of deep learning development, including modular network architecture design, mod
Represents neural networks as modular directed graphs of independent operators to enable scalable execution.
Flynn is an open-source Platform as a Service (PaaS) that automates the full lifecycle of containerized applications across any infrastructure. It functions as a container orchestration platform, scheduling and managing application containers with isolated filesystem and network stacks, while also providing a health-checked service discovery router that directs traffic only to healthy container instances. The platform is built around a Git-push deployment model, where pushing code to a configured repository triggers an automated build-test-deploy cycle. It supports declarative scaling, allowi
Builds applications by composing multiple containerised services that communicate through defined network layers.
Elsa Core is a workflow engine framework designed for defining, executing, and managing long-running business processes. It functions as a distributed workflow orchestrator and event-driven trigger system, capable of operating as a multi-tenant platform with secure data isolation. The project distinguishes itself through a flexible approach to workflow definitions, supporting a visual drag-and-drop designer, programmatic C# definitions, and portable JSON specifications. It provides a highly extensible architecture allowing for the development of custom activities and the use of a dynamic expr
Orchestrates complex logic by grouping tasks into sequences, flowcharts, or parallel execution graphs.
ZIO is a functional effect system for the JVM that models asynchronous and concurrent programs as pure, composable values with typed error handling and dependency injection. Its core identity is built on fiber-based concurrency, where lightweight, non-blocking fibers execute millions of concurrent tasks with structured lifecycle management, and a dual-channel error model that separates expected business failures from unexpected system defects at compile time. The system provides effect-typed dependency injection through a layer-based dependency graph, pull-based reactive stream processing with
Provides a type-safe, composable layer system for wiring service dependencies.