en.Wedoany.com Reported - The Linux Foundation, in collaboration with data analytics giant Databricks, has launched the OpenSharing project, aiming to build a vendor-neutral open protocol for organizations to share AI assets and data. The project will leverage the experience of the Delta Sharing protocol to create an open method for exchanging agent skills, AI models, and unstructured data volumes.

Enterprises are actively joining the agent boom, but lack standardized means for exchanging information. The Linux Foundation notes that this often forces companies to rely on point-to-point integrations or proprietary marketplaces. OpenSharing enables secure cross-organizational sharing through a single open protocol, allowing organizations to publish AI assets and data that any user can access regardless of platform or hosting environment.

"OpenSharing addresses the critical need for a universal, vendor-neutral framework that enables organizations to securely and interoperably exchange AI assets across platforms and ecosystems," said Jim Zemlin, CEO of the Linux Foundation. "By bringing this technology to the Linux Foundation, we can foster open collaboration, broad industry participation, and the shared governance needed to accelerate large-scale AI innovation."

While the existing Delta Sharing protocol already supports connectors for a range of platforms, the non-profit organization stated that the upcoming OpenSharing initiative will expand cross-platform interoperability for Apache Iceberg receivers.

"Delta Sharing has proven that the industry will choose openness over lock-in," noted Matei Zaharia, CTO of Databricks. "OpenSharing extends this principle to the full AI stack, while expanding the cross-platform ecosystem to Iceberg receivers and on-premises providers. The agent era deserves an open foundation, and OpenSharing delivers on that."

OpenSharing is the second Linux-backed project launched in the past week, following the Tokenomics Foundation, which aims to open industry standards and benchmarks for the economics of AI infrastructure.

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