This article was originally published on aifoss.dev
---
title: 'Open WebUI vs AnythingLLM vs PrivateGPT: 2026 Comparison'
description: 'Open WebUI, AnythingLLM, and PrivateGPT — three takes on local AI chat. Which wins in 2026? RAG depth, setup friction, hardware needs, and a clear verdict.'
pubDate: 'May 19 2026'
tags: ["openwebui", "ai", "selfhosted", "llm", "docker"]
Three tools, one overlapping use case: replace the cloud chat UI with something that runs on your hardware. Open WebUI, AnythingLLM, and PrivateGPT each solve this differently — and picking the wrong one costs you hours of reconfiguration.
Versions tested: Open WebUI v0.9.5, AnythingLLM v1.12.1, and PrivateGPT v0.6.2.
Quick Verdict
Open WebUI wins on breadth. It's the most feature-complete local chat interface available, with active development, multi-model conversations, web search, and an enterprise-ready auth story. It's the right pick if you want a team-ready, multi-modal workspace that goes well beyond "chat with documents."
AnythingLLM wins on document ingestion. If "chat with my PDFs and notes" is 90% of your use case, nothing sets it up faster or keeps workspaces cleaner. The desktop app embeds Ollama — there's nothing else to install.
PrivateGPT is the odd one out. It's less a UI product and more a RAG API framework. Its Gradio interface works fine for testing, but if you want a daily driver, you're using PrivateGPT as a backend, not a frontend.
What Each Tool Actually Is
Open WebUI (v0.9.5, custom BSD-based license, 138k GitHub stars) started as an Ollama frontend and has since grown into a full platform: multi-model conversations, web search across 15+ providers, voice and video support, image generation via ComfyUI and AUTOMATIC1111, enterprise authentication (LDAP, SCIM 2.0, OAuth, SSO), and a built-in RAG pipeline with nine vector database options. It connects to Ollama for local inference, or any OpenAI-compatible API for cloud and hybrid setups. As of v0.9.0 there's also a desktop app and scheduled automations — this has become a full local AI operating environment.
AnythingLLM (v1.12.1, MIT license, 60.3k GitHub stars) is purpose-built for RAG. Workspaces act as isolated knowledge bases — documents added to Workspace A stay there, invisible to Workspace B. The desktop version embeds Ollama directly, so first-time users get local LLMs without touching a terminal. It supports 60+ LLM providers, multiple vector databases (LanceDB by default, with Pinecone, Chroma, Qdrant, Weaviate, Milvus, and PGVector as options), and a no-code agent builder with MCP compatibility. Version 1.12.0 added automatic mode for tool calling, meaning capable models now handle tool selection without the @agent prefix.
PrivateGPT (v0.6.2, Apache 2.0 license, 57k+ GitHub stars) is the most developer-oriented of the three. The core offering is a FastAPI server with a full RAG pipeline you query programmatically. A Gradio UI is included for testing, but it's not a polished app — it's scaffolding. PrivateGPT follows the OpenAI API spec, supports streaming responses, and works with LlamaCPP and Ollama for local inference. Its last formal release was August 2024; active commits continued through early 2026, but the release cadence has slowed as the team focuses on the commercial Zylon enterprise product.
Comparison Table
| Open WebUI | AnythingLLM | PrivateGPT | |
|---|---|---|---|
| Latest version | v0.9.5 (May 2026) | v1.12.1 (Apr 2026) | v0.6.2 (Aug 2024) |
| License | Custom BSD (branding restrictions >50 users) | MIT | Apache 2.0 |
| GitHub stars | 138k | 60.3k | 57k+ |
| Primary interface | Web app + PWA + desktop | Web app + desktop (Ollama embedded) | Gradio (testing-focused) |
| RAG support | Yes (9 vector DB options) | Yes (RAG-first design) | Yes (API-first) |
| Local LLMs | Ollama or OpenAI-compat API | Ollama embedded + 60+ providers | LlamaCPP or Ollama |
| Multi-user | Yes (LDAP/OAuth/SSO) | Yes (workspace permissioning) | Minimal |
| Workspace isolation | Folders and channels | Dedicated workspaces | Collections |
| Agent/tool use | Pipelines + function calling | No-code agent builder + MCP | Limited |
| Desktop app | Yes (v0.0.20) | Yes (Ollama bundled) | No |
| Web search in RAG | Yes (15+ providers) | No | No |
| Active development | Very active | Very active | Slower cadence |
Install and Setup
Open WebUI
The fastest start is Docker:
docker run -d -p 3000:80 \
--add-host=host.docker.internal:host-gateway \
-v open-webui:/app/backend/data \
--name open-webui \
--restart always \
ghcr.io/open-webui/open-webui:main
That assumes Ollama is running on the host at port 11434. The --add-host flag lets the container reach your host network. Browse to http://localhost:3000, create your admin account, and point it at your Ollama instance or paste in an OpenAI-compatible API key. Configuration lives in the UI, and first-run setup takes under 10 minutes.
Open WebUI itself is lightweight — 300–500 MB RAM for the service. VRAM requirements are entirely model-driven.
AnythingLLM
Docker:
docker pull mintplexlabs/anythingllm
docker run -d -p 3001:3001 \
--cap-add SYS_ADMIN \
-v ${PWD}/anythingllm:/app/server/storage \
-e STORAGE_DIR="/app/server/storage" \
mintplexlabs/anythingllm
Or skip Docker entirely: download the desktop app, open it, and Ollama is bundled. That's genuinely zero-setup for newcomers — models download straight from the AnythingLLM interface. For teams who don't want to run a server, this is the fastest path to a working local RAG setup.
AnythingLLM itself requires about 2 GB RAM and a 2-core CPU. Inference requirements are the same model-based math as everything else.
PrivateGPT
git clone https://github.com/zylon-ai/private-gpt
cd private-gpt
pip install poetry
poetry install --extras "ui llms-ollama embeddings-ollama vector-stores-qdrant"
PGPT_PROFILES=ollama make run
Docker Compose is also available, with profiles (cpu, cuda, ollama) that ship in the repo. The friction here is choosing your components upfront — LLM backend, embedding model, vector store — before you see the UI. Developers comfortable with Python environments and service composition will handle this in 20 minutes. Everyone else should budget more time and read the docs before starting.
RAG: Where the Real Differences Show
RAG is where these three tools diverge most sharply.
AnythingLLM has the most thoughtful RAG UX. Each workspace is a separate knowledge base. Documents added to "Client Project A" workspace never bleed into "Personal Notes." This isn't just a folder — it's an isolated embedding space. The document manager shows what's indexed, lets you remove files, and displays citation sources inline with responses. As of v1.12.1, embedding progress streams in real time rather than leaving you guessing whether the upload finished.
Open WebUI's RAG is comprehensive but higher-configuration. Documents can live in the global Knowledge base or be scoped to specific chats or folders. Nine vector database options give flexibility, but the config surface is larger. The real differentiator is web search integration — 15+ providers mean you can blend document context with live web results in a single query. AnythingLLM can't do this natively. For teams that need internal documents and current external information in the same chat, that combination is significant.
PrivateGPT's RAG is an API, not a product. You ingest documents via POST request, query via GET, and receive structured JSON with citations. The Gradio UI wraps this for manual testing. If you're building a custom application — a Slack bot, a customer support tool, an internal search system — PrivateGPT's modular LlamaIndex-based architecture is a clean starting point. If you want to chat with documents without writing cod
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