AnythingLLM vs DocsGPT
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
Self-hosted platform for AI agents that answer from your own documents, with any model.
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 DocsGPT if you need…
- Teams that want an internal assistant over their own documents and wikis
- Developers who want to embed a documentation chatbot on a product site
- People who need to keep data inside their own infrastructure
- Users who want to run local models through Ollama or vLLM
Side-by-side
| AnythingLLM | DocsGPT | |
|---|---|---|
| Type | App | App |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 67.8k | 18.4k |
| Setup difficulty | Beginner | Intermediate |
| How you get it | Download the app | See the README |
| Main language | JavaScript | Python |
| Last code update | 6 days ago | 4 days 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.
DocsGPT
DocsGPT turns your documents, websites and connected apps into AI agents that answer questions and cite their sources. You can build agents and visual workflows, give them tools, and embed them in a website, an OpenAI-compatible API, or an MCP server. The README says it can run entirely in your own environment with cloud or local models.
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
DocsGPT
- Teams that want an internal assistant over their own documents and wikis
- Developers who want to embed a documentation chatbot on a product site
- People who need to keep data inside their own infrastructure
- Users who want to run local models through Ollama or vLLM
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
DocsGPT
- Self-hosting and the MIT license let you keep data in your environment
- Supports many model providers, including local models
- Answers include citations to the source documents
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
DocsGPT
- People who only want a quick hosted chatbot and no setup, since the cloud option is a separate service and the self-hosted route needs Docker and configuration
- Non-technical users without someone to run the install and manage upgrades, backups and security settings
- Teams that need guarantees about model quality, as the README does not make claims about answer accuracy beyond citing sources
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
DocsGPT
- Self-hosting involves several services (API, worker, Postgres, Redis, vector store), so it takes real setup effort
- The README is truncated here, so detailed requirements and limits are not fully visible
- Features like guardrails and deep research depend on configuration the README does not fully detail
Getting started
DocsGPT
Install on macOS or Linux (one command, from README)
curl -fsSL https://docs.ac/install | bashInstall on Windows PowerShell (from README)
irm https://docs.ac/install.ps1 | iexFrequently asked questions
What is the difference between AnythingLLM and DocsGPT?
AnythingLLM: Private AI chat app for your documents, with agents, runs locally or with cloud models. DocsGPT: Self-hosted platform for AI agents that answer from your own documents, with any model. AnythingLLM suits people who want a private chatgpt-style assistant over their own pdfs and text files, while DocsGPT suits teams that want an internal assistant over their own documents and wikis.
Are AnythingLLM and DocsGPT free?
AnythingLLM: Free and open source. DocsGPT: Free and open source.
Which is easier to set up, AnythingLLM or DocsGPT?
AnythingLLM is easier to get started with (rated beginner); DocsGPT is rated intermediate and usually needs more setup.
Is AnythingLLM or DocsGPT more popular?
AnythingLLM has more GitHub stars (67.8k) than DocsGPT (18.4k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to AnythingLLM and DocsGPT?
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.