self-hosted-ai-starter-kit README

The project's README from github.com/n8n-io/self-hosted-ai-starter-kit. As one markdown file: README.md.

Self-hosted AI starter kit

Self-hosted AI Starter Kit is an open-source Docker Compose template designed to swiftly initialize a comprehensive local AI and low-code development environment.

n8n.io - Screenshot

Curated by https://github.com/n8n-io, it combines the self-hosted n8n platform with a curated list of compatible AI products and components to quickly get started with building self-hosted AI workflows.

[!TIP] Read the announcement

What’s included

✅ Self-hosted n8n - Low-code platform with over 400 integrations and advanced AI components

✅ Ollama - Cross-platform LLM platform to install and run the latest local LLMs

✅ Qdrant - Open-source, high performance vector store with an comprehensive API

✅ PostgreSQL - Workhorse of the Data Engineering world, handles large amounts of data safely.

What you can build

⭐️ AI Agents for scheduling appointments

⭐️ Summarize Company PDFs securely without data leaks

⭐️ Smarter Slack Bots for enhanced company communications and IT operations

⭐️ Private Financial Document Analysis at minimal cost

Installation

Cloning the Repository

git clone https://github.com/n8n-io/self-hosted-ai-starter-kit.git
cd self-hosted-ai-starter-kit
cp .env.example .env # you should update secrets and passwords inside

Running n8n using Docker Compose

For Nvidia GPU users

git clone https://github.com/n8n-io/self-hosted-ai-starter-kit.git
cd self-hosted-ai-starter-kit
cp .env.example .env # you should update secrets and passwords inside
docker compose --profile gpu-nvidia up

[!NOTE] If you have not used your Nvidia GPU with Docker before, please follow the Ollama Docker instructions.

For AMD GPU users on Linux

git clone https://github.com/n8n-io/self-hosted-ai-starter-kit.git
cd self-hosted-ai-starter-kit
cp .env.example .env # you should update secrets and passwords inside
docker compose --profile gpu-amd up

For Mac / Apple Silicon users

If you’re using a Mac with an M1 or newer processor, you can't expose your GPU to the Docker instance, unfortunately. There are two options in this case:

  1. Run the starter kit fully on CPU, like in the section "For everyone else" below
  2. Run Ollama on your Mac for faster inference, and connect to that from the n8n instance

If you want to run Ollama on your mac, check the Ollama homepage for installation instructions, and run the starter kit as follows:

git clone https://github.com/n8n-io/self-hosted-ai-starter-kit.git
cd self-hosted-ai-starter-kit
cp .env.example .env # you should update secrets and passwords inside
docker compose up
For Mac users running OLLAMA locally

If you're running OLLAMA locally on your Mac (not in Docker), you need to modify the OLLAMA_HOST environment variable

  1. Set OLLAMA_HOST to host.docker.internal:11434 in your .env file.

  2. Additionally, after you see "Editor is now accessible via: http://localhost:5678/":

    1. Head to http://localhost:5678/home/credentials
    2. Click on "Local Ollama service"
    3. Change the base URL to "http://host.docker.internal:11434/"

For everyone else

git clone https://github.com/n8n-io/self-hosted-ai-starter-kit.git
cd self-hosted-ai-starter-kit
cp .env.example .env # you should update secrets and passwords inside
docker compose --profile cpu up

⚡️ Quick start and usage

The core of the Self-hosted AI Starter Kit is a Docker Compose file, pre-configured with network and storage settings, minimizing the need for additional installations. After completing the installation steps above, simply follow the steps below to get started.

  1. Open http://localhost:5678/ in your browser to set up n8n. You’ll only have to do this once.
  2. Open the included workflow: http://localhost:5678/workflow/srOnR8PAY3u4RSwb
  3. Click the Chat button at the bottom of the canvas, to start running the workflow.
  4. If this is the first time you’re running the workflow, you may need to wait until Ollama finishes downloading Llama3.2. You can inspect the docker console logs to check on the progress.

To open n8n at any time, visit http://localhost:5678/ in your browser.

With your n8n instance, you’ll have access to over 400 integrations and a suite of basic and advanced AI nodes such as AI Agent, Text classifier, and Information Extractor nodes. To keep everything local, just remember to use the Ollama node for your language model and Qdrant as your vector store.

[!NOTE] This starter kit is designed to help you get started with self-hosted AI workflows. While it’s not fully optimized for production environments, it combines robust components that work well together for proof-of-concept projects. You can customize it to meet your specific needs

Upgrading

docker compose --profile gpu-nvidia pull
docker compose create && docker compose --profile gpu-nvidia up

docker compose pull
docker compose create && docker compose up

docker compose --profile cpu pull
docker compose create && docker compose --profile cpu up

n8n is full of useful content for getting started quickly with its AI concepts and nodes. If you run into an issue, go to support.

🎥 Video walkthrough

🛍️ More AI templates

For more AI workflow ideas, visit the official n8n AI template gallery. From each workflow, select the Use workflow button to automatically import the workflow into your local n8n instance.

Learn AI key concepts

Local AI templates

Tips & tricks

Accessing local files

The self-hosted AI starter kit will create a shared folder (by default, located in the same directory) which is mounted to the n8n container and allows n8n to access files on disk. This folder within the n8n container is located at /data/shared -- this is the path you’ll need to use in nodes that interact with the local filesystem.

Nodes that interact with the local filesystem

📜 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

💬 Support

Join the conversation in the n8n Forum, where you can:

self-hosted-ai-starter-kit (n8n-io/self-hosted-ai-starter-kit)

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15.3K ·GitHub
Self-hosted-ai-starter-kit
What is this?A Docker Compose template that spins up a complete local AI and low-code development environment, bundling n8n, Ollama, Qdrant, and PostgreSQL so you can build self-hosted AI workflows without sending data to external services.
Is this a single app or a stack of tools?It is a Docker Compose stack combining four tools: n8n (low-code workflow automation), Ollama (local LLM runner), Qdrant (vector store), and PostgreSQL (database).
How do I get it running?Clone the repository and start it with Docker Compose; there are separate run commands depending on whether you have an Nvidia GPU, an AMD GPU on Linux, or no GPU at all.
Do I need Docker before trying this?Yes, Docker and Docker Compose are required since the whole stack is defined and launched as a compose file.
Can I use my Mac's GPU for faster inference?No. On Apple Silicon Macs you can't expose the GPU to the Docker instance, so you either run everything on CPU inside Docker or run Ollama natively on the Mac and point n8n to it.
Is this ready for production use?No, it's meant as a starting point for proof-of-concept projects, not a hardened production deployment.
What platforms can I run this on?It runs anywhere Docker Compose runs — Linux, macOS, and Windows — though GPU acceleration specifics differ per OS.
Does my data stay local?Yes, the whole point of the kit is to keep workflows and data self-hosted, for example summarizing company PDFs without data leaks.
What can I actually build with this kit?Examples include AI agents for scheduling appointments, secure company PDF summarization, smarter Slack bots, and private financial document analysis.
Can I access my own files from n8n workflows?Yes, the kit mounts a shared folder to the n8n container at /data/shared, usable with nodes like Read/Write Files from Disk, Local File Trigger, and Execute Command.
Are there pre-built workflow templates to start from?Yes, n8n's AI template gallery offers templates like a Tax Code Assistant, Financial Documents Assistant, and PDF chat workflows that can be imported directly.
What role does Qdrant play in the stack?Qdrant is the open-source vector store used for retrieval-augmented generation (RAG), storing embeddings for things like document search and chat-with-PDF workflows.
What are similar projects?sim, nocobase, m_flow, JeecgBoot, LongMemory, astron-agent, DB-GPT, open-webui
Which topics is it in?n8n, starter-kit, low-code, self-hosting, template, vector-database, workflow-automation, agent-orchestration, knowledge-base, ollama, rag, postgresql
Creator
n8n-io
Size
4 MB
Stars
15.3K
Updated
CommitJul 23
Age
2y
Commits · 90 Days