Local AI Infrastructure
Distributed AI workflow platform using Python, Ollama, Docker, Playwright and Open WebUI for autonomous orchestration and local LLM execution across multiple systems.
Overview
A distributed local AI ecosystem built using Ollama, Docker, Playwright, and Open WebUI to enable privacy-first AI workflows capable of browser automation, intelligent reasoning, and engineering task automation without relying on cloud-hosted language models.
Fully self-hosted AI infrastructure for autonomous workflow orchestration and browser automation.
The Problem
Cloud AI systems introduce latency, recurring API costs, privacy concerns, and limited customization. Workflows depending on external APIs are fragile — rate limits, outages, and pricing changes directly impact productivity.
Why I Built It
To build a modular AI platform capable of orchestrating multiple local language models and intelligent automation workflows while maintaining complete ownership of data and infrastructure.
Objectives
- Run LLMs entirely on local hardware with no cloud dependency
- Enable browser automation driven by AI reasoning
- Build reusable workflow orchestration primitives
- Manage infrastructure remotely via SSH and Tailscale
- Keep all data private and infrastructure owned end-to-end
My Role
Designed the overall architecture, configured all Dockerized services, integrated Ollama-hosted models, built browser automation pipelines using Playwright, developed workflow orchestration logic, and managed remote infrastructure access.
Architecture
Docker Compose manages all services as isolated containers. Ollama serves local LLMs via API. Open WebUI provides a browser-based interface. Playwright handles browser automation triggered by AI outputs. Tailscale provides secure remote access across devices. Python orchestration layer ties everything together.
Tech Stack
AI & Models
Infrastructure
Automation
Engineering Challenges
Hardware Resource Management
Running multiple LLMs simultaneously requires careful memory allocation and model swapping strategies to avoid OOM errors.
Cross-device Networking
Establishing secure, low-latency connections across multiple machines required a robust overlay network solution — Tailscale solved this cleanly.
AI Output Reliability
LLM outputs are non-deterministic. Building reliable automation on top of them required output validation, retry logic, and structured prompting.
Development Process
- 01Researched local LLM options and selected Ollama for its API compatibility
- 02Set up Docker Compose infrastructure with service isolation
- 03Configured Open WebUI for model interaction
- 04Built initial Playwright automation scripts
- 05Integrated AI reasoning layer to drive browser automation
- 06Set up Tailscale for secure remote access
- 07Developed reusable workflow templates
Key Features
Local LLM Execution
Multiple models (Llama, Mistral, etc.) running via Ollama with API-compatible endpoints.
Browser Automation
Playwright-driven workflows that navigate, extract, and interact with web applications.
Workflow Orchestration
Python-based orchestration layer chaining AI reasoning with automated actions.
Remote Access
Tailscale overlay network enables secure management from any device.
Implementation Details
All services run inside Docker containers orchestrated via Compose. The Python automation layer communicates with Ollama's local API to generate decisions, then passes those decisions to Playwright for execution. Structured output formats ensure predictable automation behavior.
Future Improvements
- Add a web dashboard for workflow monitoring and scheduling
- Expand to multi-node distributed inference
- Build a visual workflow builder
- Integrate vector database for long-term AI memory
Lessons Learned
Local infrastructure gives a level of control and privacy that cloud solutions simply can't match
Structured prompting is as important as model selection for reliable automation
Docker Compose is underrated for personal infrastructure projects