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
Flagsmith is an open-source platform for managing feature flags and remote configuration across web, mobile, and server applications. It provides a comprehensive REST API for programmatic management of flags, segments, and identities, and can be deployed on private infrastructure for secure, compliant feature flag management with full operational control. The platform distinguishes itself through a server-side flag evaluation engine that resolves segments, traits, and percentage rollouts per request, alongside a multivariate flag variant system supporting multiple typed values with configurab
GrowthBook is a feature flagging and experimentation platform that utilizes a warehouse-native approach to data analysis. It serves as a system for managing feature rollouts and conducting A/B tests by executing SQL queries directly against existing data warehouses to calculate experiment results. The platform is distinguished by its integration of a Model Context Protocol server, which allows AI coding assistants and IDEs to manage flags and query analytics using natural language. It also provides specialized capabilities for AI model optimization, enabling the testing of prompts and models
mcp-context-forge is a Model Context Protocol federation gateway that unifies diverse AI tool servers and APIs into a single consistent interface for discovery and execution. It acts as a centralized proxy that aggregates multiple servers and APIs, allowing AI agents to access and invoke a unified set of tools, prompts, and resources. The project distinguishes itself through a multi-protocol translation bridge that converts communication between standard I/O, SSE, gRPC, and REST to enable interoperability between disparate tool servers. It includes a comprehensive LLM evaluation framework for
Flipt is a Git-native feature management platform and enterprise feature toggle server. It uses Git repositories as the primary source of truth for feature flag configurations, implementing a GitOps workflow where application functionality is controlled through version control.
The main features of markphelps/flipt are: Application Functionality Toggles, Feature Toggle Servers, Feature Flag Management, User-Specific Targeting Rules, Git-Native Feature Management, Declarative Schemas, Evaluation Engines, GitOps Workflows.
Open-source alternatives to markphelps/flipt include: maiot-io/zenml — ZenML is an extensible machine learning orchestration framework designed to manage the end-to-end lifecycle of data… flagsmith/flagsmith — Flagsmith is an open-source platform for managing feature flags and remote configuration across web, mobile, and… growthbook/growthbook — GrowthBook is a feature flagging and experimentation platform that utilizes a warehouse-native approach to data… ibm/mcp-context-forge — mcp-context-forge is a Model Context Protocol federation gateway that unifies diverse AI tool servers and APIs into a… inngest/inngest — Inngest is a durable execution framework and event-driven automation engine designed to orchestrate background… finsys/dockhand — Dockhand is a multi-host Docker manager and container management interface used to control the lifecycle of…
