What is local-ai-packaged?

coleam00/local-ai-packaged — explained in plain English

Analysis updated 2026-07-03

★ 3,696PythonAudience · developerComplexity · 4/5Setup · hard

Mindmap

mindmap
  root((repo))
    What it does
      Run AI tools locally
      No cloud required
      One startup script
    Included services
      Ollama local models
      n8n agent automation
      Supabase database
    Storage options
      Qdrant vector DB
      Neo4j graph DB
      Postgres via Supabase
    Use cases
      Private AI chat
      Build AI agents
      Self-host AI stack
    Audience
      Developers
      Privacy-focused users

mindmap root((repo)) What it does Run AI tools locally No cloud required One startup script Included services Ollama local models n8n agent automation Supabase database Storage options Qdrant vector DB Neo4j graph DB Postgres via Supabase Use cases Private AI chat Build AI agents Self-host AI stack Audience Developers Privacy-focused users

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What do people build with it?

USE CASE 1

Run local language models privately on your own hardware with Ollama and chat through Open WebUI, without sending data to any cloud service.

USE CASE 2

Build AI agents in n8n or Flowise that search the web via SearXNG and store results in Qdrant for retrieval-augmented generation workflows.

USE CASE 3

Self-host a full AI app backend including PostgreSQL, auth, and file storage via Supabase, replacing cloud database services.

USE CASE 4

Monitor and debug AI agent behavior over time using Langfuse without sending telemetry to a third party.

What is it built with?

Docker ComposePythonOllaman8nSupabaseQdrantNeo4jFlowise

How does it compare?

	coleam00/local-ai-packaged	abhitronix/vidgear	generalmills/pytrends
Stars	3,696	3,697	3,695
Language	Python	Python	Python
Setup difficulty	hard	moderate	easy
Complexity	4/5	3/5	2/5
Audience	developer	developer	data

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · hard Time to first run · 1h+

Requires Docker with Compose, a compatible GPU or CPU-only profile selection, and filling in a configuration file with passwords and API keys before the first run.

So what is it?

Local AI Packaged is a Docker Compose setup that runs a collection of AI and automation tools together on your own machine or server, without sending data to external cloud services. You download the repository, fill in a configuration file with passwords and secret keys, run one Python script, and all the services start up together. The bundle includes Ollama, which downloads and runs large language models locally so you can chat with them privately. Open WebUI provides the chat interface for those models and also lets you connect to AI agent workflows. n8n is a visual workflow automation tool with over 400 pre-built integrations, used here to build the AI agents themselves. Flowise is a second, simpler tool for building AI agent flows with a drag-and-drop interface. Supabase provides a PostgreSQL database, authentication, and file storage as a self-hosted alternative to cloud database services. Qdrant and Neo4j offer two different ways to store and query AI-relevant data: Qdrant is a vector database for similarity search (used in retrieval-augmented generation workflows), and Neo4j is a graph database for storing relationships between pieces of information. SearXNG is a privacy-focused search engine that aggregates results from many sources, letting agents search the web without going through Google. Langfuse tracks what your AI agents are doing over time so you can observe and debug them. Caddy handles HTTPS certificates automatically if you want to expose the services on a public domain. Startup profiles select the GPU configuration: Nvidia, AMD, CPU-only, or a mode for Mac users who run Ollama outside of Docker since Apple Silicon cannot directly share the GPU with Docker containers. Pre-built n8n workflow files are included in the repository to give you working examples without starting from scratch. The README notes that recent Supabase updates have added new required environment variables, and it calls out the specific values to add if you have an existing installation from before mid-2025.

Copy-paste prompts

Prompt 1

I cloned local-ai-packaged and ran the setup script. How do I pull a new Ollama model and connect it to Open WebUI so I can chat with it locally?

Prompt 2

Walk me through building an n8n workflow in local-ai-packaged that takes a user question, searches the web with SearXNG, retrieves relevant chunks from Qdrant, and sends the answer to a local LLM.

Prompt 3

How do I expose local-ai-packaged services on a public domain with automatic HTTPS? Show me the Caddy and environment variable configuration.

Prompt 4

I have an AMD GPU. Which startup profile do I use for local-ai-packaged, and what configuration differs from the Nvidia GPU profile?

Prompt 5

My local-ai-packaged Supabase container won't start after updating. What new environment variables were added in mid-2025 and how do I add them to my existing install?

Frequently asked questions