AnythingLLM vs localGPT
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Private AI chat app for your documents, with agents, runs locally or with cloud models.
Open source · MIT
Chat with your own documents using a local AI model; files stay on your machine.
The short answer
Choose AnythingLLM if you need…
- People who want a private ChatGPT-style assistant over their own PDFs and text files
- Teams that need several users with separate access on one instance (Docker version)
- Developers who want to try many LLM providers from one interface
- Anyone who wants to run local models on their own CPU or GPU without much setup
Choose localGPT if you need…
- People who want to query private documents without sending them to a cloud service
- Developers who want a local RAG backend with a REST API to build on
- Users comfortable running Docker or Python and installing Ollama
- Anyone who wants to inspect and tweak retrieval settings through a config file
Side-by-side
| AnythingLLM | localGPT | |
|---|---|---|
| Type | App | App |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 67.8k | 22.2k |
| Setup difficulty | Beginner | Intermediate |
| How you get it | Download the app | Run with Docker |
| Main language | JavaScript | Python |
| Last code update | 7 days ago | 2 months ago |
What each one is
AnythingLLM
AnythingLLM is an all-in-one AI application that lets you chat with your own documents and use AI agents. You can connect a local model that runs on your own computer or a cloud model from a provider such as OpenAI or Anthropic. The README says it includes built-in agents, multi-user support, vector databases, and document pipelines, and is available as a desktop app for Mac, Windows, and Linux.
localGPT
LocalGPT is a document question-answering app. You load PDFs, Word files, HTML, Markdown or text files, then ask questions in plain English and get answers grounded in those files. It runs models through Ollama on your own computer, so the README says no data leaves your device.
Best for
AnythingLLM
- People who want a private ChatGPT-style assistant over their own PDFs and text files
- Teams that need several users with separate access on one instance (Docker version)
- Developers who want to try many LLM providers from one interface
- Anyone who wants to run local models on their own CPU or GPU without much setup
localGPT
- People who want to query private documents without sending them to a cloud service
- Developers who want a local RAG backend with a REST API to build on
- Users comfortable running Docker or Python and installing Ollama
- Anyone who wants to inspect and tweak retrieval settings through a config file
Strengths
AnythingLLM
- Works with many LLM providers, both local and cloud, as listed in the README
- Desktop installers are provided for Mac, Windows, and Linux
- Released under the MIT license with a large community on GitHub
localGPT
- Privacy-focused design: the README states documents and queries stay on the local device
- Shows source chunks with answers, which helps check results
- Open MIT license and an active project with a large star count
Not ideal for
AnythingLLM
- People who only want a simple hosted chatbot and do not want to install or manage anything; a hosted instance exists but is a paid option
- Users needing multi-user permissions or the embeddable website widget, which the README marks as Docker version only
- Anyone expecting a guaranteed result from local models, since output quality depends on the model you choose
localGPT
- People with limited hardware: the README recommends 8GB+ RAM and 16GB+ for comfort
- Non-technical users who want a one-click install, since setup involves Ollama, model pulls and a terminal
- Anyone needing hosted, multi-user deployment; the README describes a local-device tool
Trade-offs
AnythingLLM
- Several features, including multi-user support and the website widget, are limited to the Docker version
- The README is long and lists many options, so setup choices take some reading
- Local model performance depends on your hardware and the model you pick
localGPT
- Requires local hardware and model downloads; the README lists 8GB+ RAM minimum
- Several advanced features are off by default and need config changes to enable
- The README is long and layered, so first-time setup takes some reading
Getting started
localGPT
Clone the repository
git clone https://github.com/PromtEngineer/localGPT.gitStart Ollama and pull the models (Docker path)
ollama pull qwen3.5:9bFrequently asked questions
What is the difference between AnythingLLM and localGPT?
AnythingLLM: Private AI chat app for your documents, with agents, runs locally or with cloud models. localGPT: Chat with your own documents using a local AI model; files stay on your machine. AnythingLLM suits people who want a private chatgpt-style assistant over their own pdfs and text files, while localGPT suits people who want to query private documents without sending them to a cloud service.
Are AnythingLLM and localGPT free?
AnythingLLM: Free and open source. localGPT: Free and open source.
Which is easier to set up, AnythingLLM or localGPT?
AnythingLLM is easier to get started with (rated beginner); localGPT is rated intermediate and usually needs more setup.
Is AnythingLLM or localGPT more popular?
AnythingLLM has more GitHub stars (67.8k) than localGPT (22.2k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to AnythingLLM and localGPT?
Other options people compare them with include Vane, LlamaGPT, serviette, Paperclip, Octop.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.