SAN FRANCISCO — February 2, 2026 — LF Edge, an umbrella organization within the Linux Foundation that has created an open, interoperable framework for edge computing independent of hardware, cloud, or operating system, today announced that its EdgeLake project has advanced from Stage 1 (“At-Large”) to Stage 2 (“Growth”) within LF Edge. The project’s progression reflects increasing adoption, growing contributor momentum, and readiness for broader production use across edge and industrial environments.
As part of this momentum, EdgeLake has introduced an implementation of the Model Context Protocol (MCP), enabling AI agents and Large Language Models (LLMs) to access and reason over live edge data directly, without centralizing data or relying on traditional analytics stacks.
Move to Stage 2 (Growth) Signals Maturity and Industry Momentum
LF Edge Stage 2 projects demonstrate sustained community growth, mature governance, and expanding real-world adoption. EdgeLake’s promotion reflects continued progress across key areas, including:
“EdgeLake’s advancement to Stage 2 reflects the project’s growing adoption and strong alignment with LF Edge’s mission to enable scalable, open, and interoperable edge solutions,” said Arpit Joshipura, general manager, Networking, Edge and IoT at the Linux Foundation. “Capabilities like MCP demonstrate how open source edge platforms can enable real-time AI and data intelligence where it matters most: at the edge.”
“Stage 2 validates EdgeLake as a foundational data layer for agentic AI at the edge,” said Moshe Shadmon, Founder and CEO of AnyLog. “With MCP and a unified namespace, we’re removing the need for centralized analytics stacks and specialized intermediaries, allowing AI agents to reason directly over live operational data, securely and in real time.”
MCP Enables Direct, Self-Service AI Access to Live Edge Data
EdgeLake’s MCP implementation allows AI agents and applications to query and interact with distributed edge data using natural language, SQL, and Unified Namespace (UNS) hierarchies. This approach reduces the need for centralized BI platforms, custom dashboards, and specialized data science workflows traditionally required to make operational data usable for AI.
Unlike traditional environments that depend on cloud ingestion, ETL processes, and centralized intermediaries, EdgeLake’s MCP capabilities enable:
By exposing edge data as AI-ready context, MCP helps transform environments from factories and transportation systems to smart cities, energy grids, and defense infrastructure into intelligent, queryable systems accessible by both humans and autonomous agents.
Learn more about EdgeLake, join the LF Edge community, and follow on LinkedIn for real-time project updates.
LF Edge Advancing Cross-Project Implementations
LF Edge continues to accelerate adoption of open, interoperable edge infrastructure through cross-project collaboration across Linux Foundation communities. A recent LF Edge case study highlights an exploratory Proof of Concept (PoC) integrating LF Edge’s InstantX with Automotive Grade Linux (AGL) to enable Vehicle-to-Cloud (V2C) communication using open source technologies, including real-time vehicle telemetry streaming via lightweight messaging and a modular architecture that supports future expansion. View the case study here.
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Media Contact:
Sunny Schatz
scai@linuxfoundation.org
The Linux Foundation