6 रिपॉजिटरी
Runs arbitrary API requests from the command line to inspect or interact with the system without a separate HTTP client.
Distinct from Arbitrary: Distinct from Arbitrary: focuses on executing API requests from the CLI, not running arbitrary system commands.
Explore 6 awesome GitHub repositories matching development tools & productivity · API Request Execution from CLI. Refine with filters or upvote what's useful.
Genkit is an open-source framework for building AI-powered applications. It provides a unified interface for connecting to hundreds of generative AI models from multiple providers, enabling text, image, audio, and video generation through a single API. The framework structures multi-step AI interactions—including chat, retrieval-augmented generation, tool use, and agentic workflows—as composable, traceable flows with built-in streaming and state management. The framework distinguishes itself through a comprehensive developer toolkit that includes a command-line interface and a local developer
Executes flows directly from the command line for testing or ad-hoc tasks.
LXD is a unified platform for managing both system containers and virtual machines through a single REST API and command-line interface. It provides a programmatic HTTP interface for controlling the full lifecycle of instances, enabling automation and integration with external tools. The system runs unprivileged containers with per-instance UID/GID mappings, seccomp filters, and AppArmor profiles for kernel-level isolation, while supporting multiple storage backends including directory, Btrfs, LVM, ZFS, Ceph, LINSTOR, and TrueNAS through a unified driver interface. The platform distinguishes
Runs arbitrary API requests from the command line to inspect or interact with the system.
ZenML is an extensible machine learning orchestration framework designed to manage the end-to-end lifecycle of data pipelines and AI agent workflows. It functions as a durable orchestrator that executes machine learning tasks as directed acyclic graphs, ensuring that every step is containerized for consistent performance across local, cloud, and hybrid infrastructure. By decoupling pipeline code from underlying compute and storage backends, the platform allows developers to define infrastructure-agnostic stacks that remain portable across diverse environments. The project distinguishes itself
Triggers execution of a specific flow version or tagged route via CLI, SDK, or HTTP requests to initiate remote processing tasks.
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
Provides CLI commands to trigger specific versions or tagged routes of deployed machine learning flows.
Evans is a gRPC client and API explorer designed for testing, debugging, and automating remote procedure calls. It functions as an interactive client and a stateless command line utility for executing gRPC methods and inspecting remote server API surfaces. The tool provides specialized support for the gRPC-Web protocol to facilitate communication with web-based implementations. It enables the discovery of services and message structures through server reflection or definition files, and supports the management of unary, client-side, server-side, and bidirectional streaming communication. The
Provides a command line interface for running requests that accept input from files and output results as JSON.
Thunder Client is a REST API client extension for VS Code that functions as an HTTP request manager, testing tool, and mocking workspace. It allows users to send requests, organize them into collections, and manage API configurations directly within the editor. The project distinguishes itself through a command-line interface for executing automated test suites in CI/CD pipelines and a Git-based synchronization system for sharing request collections and environment configurations across teams. It also incorporates artificial intelligence to automate the conversion of API scripts during migrat
Allows automated execution of entire API request collections via a CLI for CI/CD integration.