JFrog Fly Accelerates AI-Driven Software Development with Agentic Repository and Seamless GitHub Integration
JFrog Fly is a zero-configuration agentic repository that automates artifact orchestration for AI-driven software development. It integrates with GitHub and AI coding assistants to streamline release management.

JFrog Fly: Simplifying AI-Driven Software Development
JFrog has launched JFrog Fly, a zero-configuration “agentic repository” designed to speed up AI-driven software development. Released on September 9, this new tool supports agentic workflows, allowing development teams to automate artifact orchestration throughout the software lifecycle. The goal is to let developers focus on delivering code to production efficiently and at scale.
Developers interested in trying JFrog Fly can join the beta waitlist.
Built for Small Teams with AI Integration
JFrog Fly is built on top of the JFrog Software Supply Chain Platform and aims to provide small development teams with an AI-driven experience. It integrates tightly with GitHub and popular AI coding assistants like Cursor, Claude Code, and GitHub Copilot. The platform also supports integration with other AI repositories and platforms that follow the Model Context Protocol, ensuring consistent, context-aware decisions by AI agents across different tools.
Key Features Accelerating Software Delivery
- Centralized management: JFrog Fly manages all agentic artifacts and containers in one place. This simplifies sharing, storing, and distributing release versions, helping teams move at the pace of modern coding practices.
- Semantic release metadata: The platform provides clear, meaningful release information. This gives developers better insight into changes, enabling informed decisions about upgrades and integrations.
- Seamless environment integration: JFrog Fly works smoothly with agentic development environments, package managers, and GitHub repositories. It can detect and set up tech stacks automatically within a few clicks, reducing setup time.
JFrog Fly offers a practical approach to managing AI-driven development workflows, making artifact handling and release management more transparent and efficient.
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