EdgeAI Forge: Building a Local-First Physical AI Architecture for Autonomous Industrial Pipelines

Developer Afrid Thenabanda has released EdgeAI Forge, an early-stage local-first agentic AI platform for physical AI, machine vision, robotics, ROS 2, PLC integration, and edge deployment. The platform routes frequent and private workflows to local models while using cloud models selectively for complex reasoning, and is open-sourced under the BSD 3-Clause License.

AI is moving beyond chat. In industrial automation, the useful question is not only whether a model can answer an engineering question, but whether an AI system can help plan, generate, test, benchmark, review, deploy, and improve software that operates close to machines. That is the idea behind EdgeAI Forge , an early-stage local-first agentic AI platform for Physical AI, machine vision, robotics, ROS 2, PLC integration, and edge deployment. The target workflow is: php Requirement - Plan - Generate - Test - Benchmark - Optimize - Review - Deploy - Monitor - Learn Industrial workloads have constraints that general cloud-first assistants do not always address well: Agentic workflows are also token-intensive. A single engineering request may require planning, code generation, validation, retrying, documentation, and testing. Sending every intermediate step to a cloud model can become expensive and can expose data that should remain on premises. EdgeAI Forge therefore follows a simple principle: Run frequent and private workflows locally. Use cloud models selectively for complex reasoning or large-context tasks. Instead of asking one model to do everything, the target design assigns focused responsibilities to specialized agents: The current proof of concept implements Planner, Vision, and ROS agents through an asynchronous Ollama client. The broader repository includes a FastAPI gateway, a Next.js command-center prototype, Docker infrastructure, observability configuration, benchmark utilities, and design scaffolds for the remaining modules. The target architecture separates coordination from model execution: Those layers can connect to Jetson devices, cameras, sensors, robots, PLCs, and HMI/SCADA systems. Cloud endpoints remain optional extensions rather than the default path. Consider this request: Build a ROS 2 pipeline that reads USB-camera frames, detects objects, publishes detections, measures FPS, and prepares deployment to Jetson Orin. The long-term goal is for the platform to turn that requirement into reviewed engineering artifacts such as: Physical AI must be human-governed. EdgeAI Forge is not intended to bypass engineering review, machine interlocks, or functional-safety systems. Real robot motion, PLC logic, production deployment, and machine-control changes should require explicit approval. Simulation, dry runs, tests, logs, and rollback plans should be first-class workflow stages. This is an early prototype, not a production platform. Some components are runnable, while others are interfaces, placeholders, or roadmap items. I am sharing it now because architecture feedback is most valuable before the design hardens. The project is developed in public under the BSD 3-Clause License. GitHub: https://github.com/afridali123/EdgeAI Forge https://github.com/afridali123/EdgeAI Forge Long-form background: https://www.linkedin.com/pulse/edgeai-forge-my-journey-toward-local-agentic-ai-afrid-thenebanda-lpdhc/ https://www.linkedin.com/pulse/edgeai-forge-my-journey-toward-local-agentic-ai-afrid-thenebanda-lpdhc/ I would especially value feedback on local model routing, ROS 2 artifact generation, simulation-first safety gates, Jetson deployment, observability, and meaningful evaluation of engineering agents.