Deploy AnythingLLM
Chat with your documents using any LLM, in your own private AI workspace.
Chat with your documents using any LLM, in your own private AI workspace.
AnythingLLM turns your documents into a private AI workspace. Upload PDFs, notes, or web pages and chat with them using the model you choose, including OpenAI, Anthropic, Google, and Ollama. This template builds the official repository, so you always get the current version.
Deploy AnythingLLM to NEXUS AI · AnythingLLM on GitHub · Tested 2026-09-25 · MIT license
Security
A fresh AnythingLLM has no password. Anyone with the URL can use it until you turn on password protection in Settings, Security.
What gets created
- The AnythingLLM web app and document processor, built from the official repository
- A public HTTPS URL on port 3001
- Storage for its database, documents, and vectors inside the container
Before you deploy
- An API key for a model provider such as OpenAI or Anthropic, or an Ollama server you can reach
- Runs on the Free plan: about 380 MB of memory when idle
- First build takes about 12 minutes
How to deploy
- Press Deploy. Sign in or create a NEXUS AI account and you return to the same page.
- Choose a name for your URL, review the settings, and press Deploy again.
- Wait for the build, then open your URL when the status is Running.
After it deploys
- Open your URL, then go to Settings, Security, and turn on password protection before anything else
- Choose your LLM provider and paste its API key
- For large document libraries, pick an API-based embedding provider to keep memory use low
- Create a workspace, upload documents, and start asking questions
Your data
Data lives inside the container. Stopping and starting keeps it; rebuilding the deployment starts with empty storage.
AnythingLLM is an independent open-source project. NEXUS AI is not affiliated with it, and the name belongs to its owners.
About NEXUS AI
NEXUS AI is an agentic AI app builder and full-stack deployment platform. Explore the AI App Builder, learn more on the About page, read the documentation, or contact the team through nexusai.run/contact.
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