AnythingLLM vs PrivateGPT

Last reviewed Aug 26, 2026 · 764 words

PrivateGPT is the one that proved it could be done; AnythingLLM is the one that turned it into a product. In May 2023 PrivateGPT was the repo everyone cloned to ask questions of a PDF on a laptop with no internet, and it still does exactly that. AnythingLLM took the same idea, RAG over your own files with a local model, and wrapped it in workspaces, user accounts, agents and a desktop installer. Three years on, the honest comparison is not feature-by-feature. It is "do you want an application or a building block."

What each one is, in one sentence

AnythingLLM (MIT, JavaScript, 65,203 stars) is an end-user application: a chat UI where each workspace holds its own documents, prompt and model settings, with an admin panel for users and an API on the side. PrivateGPT (Apache-2.0, Python, 57,466 stars) is a service: a FastAPI server exposing an OpenAI-shaped /v1/chat/completions plus ingestion endpoints, with a Gradio page bolted on for testing. Judge it by the API, not the Gradio page.

Set-up: an installer versus a Poetry project

AnythingLLM's desktop build for Windows, macOS and Linux installs like any other app and needs no Docker at all; the server edition is one container (mintplexlabs/anythingllm, port 3001) with a single volume to persist. Vector storage defaults to embedded LanceDB, so a working stack is that container plus Ollama. Catalogue rating: Medium, 4,096 MB minimum before you add a model.

PrivateGPT is a Python project you assemble with Poetry and "profiles" that pick the backend: llama.cpp in-process, Ollama, or a hosted API. The catalogue's "setup can be fiddly" is generous. Getting a CUDA-enabled llama-cpp-python to compile against the right Python version is where most PrivateGPT afternoons go, and its 8,192 MB floor and "GPU recommended" reflect that the default path runs the model inside the same process. The Docker images help but lag the source. Both are Medium on paper; in practice PrivateGPT is the harder install by a wide margin.

Where PrivateGPT still earns a place

Because it speaks the OpenAI API, PrivateGPT drops into anything already written against the openai client libraries: change the base URL and your existing app gets private retrieval with zero data leaving the host. For your own front-end, a team chat bot, or a batch pipeline over an internal document store, that is a cleaner foundation than scripting AnythingLLM's application-shaped API. The encrypted and offline-first tags on its catalogue entry are earned; it was designed from the first commit so that nothing needs to phone out.

The caveat I would not skip: commit activity on the PrivateGPT repository has thinned since 2024 as the maintainers' attention moved to their commercial product. It works, and the approach it pioneered is now standard, but do not expect quick fixes when a dependency breaks.

Where AnythingLLM pulls away

Multi-user workspaces with per-workspace documents and permissions, built-in agents that search the web, scrape pages, chart data and query SQL, pluggable vector databases (Qdrant, Milvus, Pinecone and others besides the LanceDB default), and a provider list covering every local runner and hosted API you are likely to name. Releases are frequent, and the desktop app means a non-technical colleague can trial it in an hour. Parsing quality is middling for scanned PDFs and dense tables; if that is your document pile, the AnythingLLM vs RAGFlow comparison covers the heavier option.

Decision table

What I'd do

Install AnythingLLM's desktop build, point it at Ollama, and drag in a folder of documents. For nine people in ten that is the whole project, and the server edition is waiting when a second user appears. Reach for PrivateGPT when you have code to write and want a private /v1 endpoint behind it, but pin your versions, and be ready to swap in a maintained OpenAI-compatible server if the repository stays quiet. What neither will fix is a weak model or poor embeddings; hardware for self-hosted LLMs is the other half of this decision.