AnythingLLM vs LlamaGPT
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
A self-hosted chatbot that runs Llama 2 models locally, so your chats 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 LlamaGPT if you need…
- People who want a private chatbot and don't want to send prompts to a cloud provider
- Users with a home server running umbrelOS who want a one-click install
- Developers who want a local, OpenAI-style API for testing
- Mac users with Docker who want to try local models
Side-by-side
| AnythingLLM | LlamaGPT | |
|---|---|---|
| Type | App | App |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 67.8k | 10.9k |
| Setup difficulty | Beginner | Intermediate |
| How you get it | Download the app | Clone and build from source |
| Main language | JavaScript | TypeScript |
| Last code update | 6 days ago | 3 years 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.
LlamaGPT
LlamaGPT is a ChatGPT-style chat interface that runs open Llama 2 and Code Llama models on your own hardware instead of a cloud service. The project says no data leaves your device. It is packaged with Docker, so you start it with a script and then use it in a browser at localhost:3000.
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
LlamaGPT
- People who want a private chatbot and don't want to send prompts to a cloud provider
- Users with a home server running umbrelOS who want a one-click install
- Developers who want a local, OpenAI-style API for testing
- Mac users with Docker who want to try local models
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
LlamaGPT
- Runs locally with no data sent out, as the README states
- Supports several model sizes, from 7B to 70B, plus Code Llama variants
- Offers an OpenAI-compatible API and one-command Docker setup
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
LlamaGPT
- People with limited RAM or no capable CPU or GPU, since the larger models need 6GB to over 40GB of memory per the README table and run slowly on small devices
- Anyone who needs the latest hosted models or top-quality answers, because these are smaller open models and the README does not compare their quality to GPT-4
- Users who need to load their own custom models today, since the README lists that feature as not yet done
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
LlamaGPT
- Speed varies a lot by hardware; the README benchmarks range from 0.9 to 54 tokens per second
- Large models need a lot of memory and disk space
- Custom models and switching models in the UI are still on the roadmap
Getting started
LlamaGPT
Clone the repository
git clone https://github.com/getumbrel/llama-gpt.gitEnter the project folder
cd llama-gptFrequently asked questions
What is the difference between AnythingLLM and LlamaGPT?
AnythingLLM: Private AI chat app for your documents, with agents, runs locally or with cloud models. LlamaGPT: A self-hosted chatbot that runs Llama 2 models locally, so your chats stay on your machine. AnythingLLM suits people who want a private chatgpt-style assistant over their own pdfs and text files, while LlamaGPT suits people who want a private chatbot and don't want to send prompts to a cloud provider.
Are AnythingLLM and LlamaGPT free?
AnythingLLM: Free and open source. LlamaGPT: Free and open source.
Which is easier to set up, AnythingLLM or LlamaGPT?
AnythingLLM is easier to get started with (rated beginner); LlamaGPT is rated intermediate and usually needs more setup.
Is AnythingLLM or LlamaGPT more popular?
AnythingLLM has more GitHub stars (67.8k) than LlamaGPT (10.9k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to AnythingLLM and LlamaGPT?
Other options people compare them with include Vane, serviette, Paperclip, Octop, Hermes WebUI.
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.