DocsGPT 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
Self-hosted platform for AI agents that answer from your own documents, with any model.
Open source · MIT
A self-hosted chatbot that runs Llama 2 models locally, so your chats stay on your machine.
The short answer
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
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
| DocsGPT | LlamaGPT | |
|---|---|---|
| Type | App | App |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 18.4k | 10.9k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | See the README | Clone and build from source |
| Main language | Python | TypeScript |
| Last code update | 4 days ago | 3 years ago |
What each one is
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.
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
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
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
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
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
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
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
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
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
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 | iexLlamaGPT
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 DocsGPT and LlamaGPT?
DocsGPT: Self-hosted platform for AI agents that answer from your own documents, with any model. LlamaGPT: A self-hosted chatbot that runs Llama 2 models locally, so your chats stay on your machine. DocsGPT suits teams that want an internal assistant over their own documents and wikis, while LlamaGPT suits people who want a private chatbot and don't want to send prompts to a cloud provider.
Are DocsGPT and LlamaGPT free?
DocsGPT: Free and open source. LlamaGPT: Free and open source.
Is DocsGPT or LlamaGPT more popular?
DocsGPT has more GitHub stars (18.4k) than LlamaGPT (10.9k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to DocsGPT and LlamaGPT?
Other options people compare them with include AnythingLLM, Vane, Open SaaS, doccano, DB-GPT.
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.