Xkf

Xkf is a modular open-source framework designed to fuse heterogeneous knowledge sources for real-time artificial intelligence and decision making. It provides a dataflow engine, a plugin-based architecture for knowledge modules (KMs), and a policy layer that governs inference, execution order, versioning, and safety constraints. The framework emphasizes portability, cross-domain interoperability, and reproducibility, supporting streaming input, batch processing, and offline analysis.

It comprises a core engine that orchestrates task graphs, a set of knowledge modules (KMs) that implement domain-specific reasoning, a declarative configuration language for pipelines, and adapters that connect to external data stores and sensors. A lightweight runtime supports deployment on edge devices and in cloud environments. Data lineage and version control are integral to the design, enabling reproducible experiments.

History and status: Xkf was proposed in 2021 by researchers at the Xkf Consortium and has since seen multiple independent implementations. As of the mid-2020s, it remains a niche project with ongoing development and limited industrial adoption, mainly within academic laboratories and experimental pilots.

Applications: Common use cases include autonomous systems, industrial automation, robotics, and environmental monitoring, where diverse data sources must be fused to support real-time decision making. Typical deployments feature a central orchestrator that coordinates KMs providing sensor fusion, knowledge inference, and action selection.

Reception and outlook: Community feedback highlights modularity and support for data provenance and formal verification as strengths, while adoption is hindered by integration effort and the relatively small ecosystem of plugins. The project continues to evolve through academic collaborations and community contributions.