3 dépôts
Extensibility points that allow developers to execute custom code at specific stages of a request lifecycle.
Distinct from Custom Logic Filtering Hooks: Distinct from Custom Logic Filtering Hooks which are specifically for filesystem traversal; these are for general API request lifecycles.
Explore 3 awesome GitHub repositories matching software engineering & architecture · Execution Lifecycle Hooks. Refine with filters or upvote what's useful.
ZenML is an orchestration platform designed for building, deploying, and monitoring reproducible machine learning pipelines and agentic workflows. It provides a unified framework that manages the entire lifecycle of machine learning assets, from data processing and model training to the deployment of persistent inference services. By decoupling pipeline logic from underlying compute and storage, the platform enables teams to transition workflows seamlessly from local development environments to production-grade cloud infrastructure. The platform distinguishes itself through a service-oriented
Triggers user-defined code at specific stages of a pipeline or step execution to automate notifications and external workflows.
WPGraphQL is a GraphQL interface for WordPress that transforms a WordPress installation into a headless content source. It functions as a GraphQL schema provider that maps database structures and relational data into a standardized schema, exposing posts, pages, and custom data types through a single flexible endpoint. The project includes an integrated API query builder and schema explorer, allowing for the visual composition of queries and real-time validation of responses. It provides a system for extending the schema with custom fields and relationships to expose specific business data.
Provides a hook system to execute custom code at specific lifecycle points or modify data during execution.
This project is an autonomous workflow engine and orchestration platform designed to coordinate specialized AI agents. It functions as a development framework that manages the end-to-end lifecycle of complex, multi-step tasks, including persona definition, persistent memory management, and the execution of automated coding workflows. By acting as a Model Context Protocol server, it enables standardized communication between development tools and external AI models. The platform distinguishes itself through an event-driven architecture that routes typed messages between agent personas, allowin
Triggers external scripts during specific orchestration phases to automate environment setup or state mutations.