Open WebUI vs LibreChat vs AnythingLLM: Self-Hosted Chat Interface Shootout

Self-hosted chat interfaces have become essential tools for organizations and individuals who want ChatGPT-like experiences while keeping their data private and their model choices flexible. Open WebUI, LibreChat, and AnythingLLM are the three most popular open-source options in 2026, each with distinct strengths that make them ideal for different scenarios. This comparison covers their RAG capabilities, multi-user features, extensibility, deployment experience, and community health so you can pick the right one for your self-hosted AI setup.

Quick Comparison

Feature	Open WebUI	LibreChat	AnythingLLM
Primary focus	Ollama-native chat UI	Multi-provider chat platform	RAG-first workspace tool
Local model support	Ollama, OpenAI-compatible	Ollama, custom endpoints	Ollama, LM Studio, llama.cpp
Cloud providers	OpenAI-compatible APIs	OpenAI, Anthropic, Google, Azure, AWS	OpenAI, Anthropic, Azure, others
RAG/documents	Document upload, web search	Basic file attachments	Full workspace RAG system
Vector database	Built-in (ChromaDB)	N/A (minimal)	ChromaDB, Pinecone, Qdrant, Weaviate, pgvector
Multi-user	Yes (RBAC, admin panel)	Yes (RBAC, admin panel)	Yes (roles, workspaces)
Authentication	Local, LDAP, OAuth	Local, LDAP, OAuth, social	Local accounts
Plugins/tools	Tools, functions, pipelines	Plugins system	Agent skills, tools
Mobile responsive	Yes	Yes	Desktop app available
Deployment	Docker (primary)	Docker (primary)	Docker, desktop app, cloud
Tech stack	SvelteKit + Python	React + Node.js	React + Node.js
GitHub stars	80K+	25K+	35K+
License	MIT	MIT	MIT
Image generation	Via connected providers	DALL-E, Stable Diffusion endpoints	Via connected providers
Conversation export	Yes (JSON, Markdown)	Yes (multiple formats)	Yes
Web search	Built-in	Via plugins	Built-in

RAG Capability

AnythingLLM

AnythingLLM was designed around RAG from the beginning, and it shows. The workspace model means each workspace can have its own set of documents, its own vector database configuration, and its own retrieval parameters. You upload documents (PDF, DOCX, TXT, web pages), they are chunked, embedded, and stored in your chosen vector database. When you chat in that workspace, relevant document chunks are automatically retrieved and included in the context.

AnythingLLM supports multiple vector database backends — ChromaDB (default, embedded), Pinecone, Qdrant, Weaviate, Milvus, and pgvector. This flexibility lets you start with the built-in ChromaDB for simplicity and migrate to a production vector database as your document collection grows.

Document management is visual and intuitive. You can see which documents are in each workspace, preview chunks, and adjust embedding settings. The retrieval quality is configurable with similarity thresholds, chunk counts, and reranking options.

Open WebUI

Open WebUI has strong RAG capabilities that have improved significantly over the past year. You can upload documents directly into conversations, and Open WebUI processes them using built-in embedding models and a ChromaDB vector store. The knowledge base feature allows you to create persistent document collections that can be attached to specific models or conversations.

Open WebUI also integrates web search (via SearXNG, Google, Brave, or other providers), which functions as a form of real-time RAG — pulling relevant web content into the model’s context. The combination of document RAG and web search gives Open WebUI a versatile retrieval system.

The RAG implementation is less configurable than AnythingLLM’s — you have fewer options for vector database backends and chunking strategies — but for most users, the defaults work well.

LibreChat

LibreChat’s approach to document handling is lighter than the other two. It supports file attachments in conversations, and with the right configuration, can process documents for context. However, LibreChat’s core strength is multi-provider chat, not document retrieval. If RAG is a primary requirement, LibreChat is the weakest of the three.

LibreChat does integrate with retrieval tools through its plugin system, but this requires additional configuration and is not as seamless as the built-in RAG systems in Open WebUI and AnythingLLM.

Multi-User Support

Open WebUI

Open WebUI has the most mature multi-user implementation. The admin panel provides user management with role-based access control (admin, user, pending). Admins can control which models users can access, set default models, and manage system-wide settings. Authentication supports local accounts, LDAP/Active Directory, and OAuth providers (Google, GitHub, Microsoft).

Shared conversations, model presets, and community tools make Open WebUI feel like a platform rather than a personal tool. For organizations, the admin controls are sufficient for most deployment scenarios without needing enterprise features.

LibreChat

LibreChat also provides solid multi-user features. User registration, role-based access, and admin controls are built in. LibreChat’s multi-user strength is its per-user provider configuration — different users can connect to different AI providers with their own API keys. This is valuable in organizations where some teams use OpenAI, others use Anthropic, and some use local models.

Authentication supports local accounts, LDAP, OAuth, and social login (Google, GitHub, Discord). Token usage tracking per user helps organizations monitor costs.

AnythingLLM

AnythingLLM supports multi-user access with workspace-level permissions. Users can be assigned to specific workspaces with different roles. However, the multi-user features are less mature than Open WebUI or LibreChat — admin controls are simpler, and enterprise authentication options (LDAP, OAuth) are more limited.

AnythingLLM’s multi-user model works best when users are organized around specific document workspaces rather than needing broad, platform-wide access to all models and features.

Plugins and Extensibility

Open WebUI

Open WebUI’s extensibility comes through its tools, functions, and pipelines system. Tools extend the model’s capabilities (web search, code execution, image generation). Functions allow custom Python code to process inputs and outputs. Pipelines enable complex workflows that chain multiple steps.

The community has built a growing library of tools and functions that can be imported directly. This ecosystem makes Open WebUI highly extensible without requiring deep technical knowledge.

LibreChat

LibreChat has a plugin system that extends its capabilities. Built-in plugins include web search (Google, Bing), DALL-E image generation, code interpreter, and API tool calling. Custom plugins can be added through configuration files.

LibreChat’s plugin architecture is well-documented, and the project actively encourages community plugin development. The plugin system integrates with the chat interface cleanly — users can enable/disable plugins per conversation.

AnythingLLM

AnythingLLM extends functionality through agent skills and custom tools. The agent system can use tools like web browsing, code generation, and document analysis. Custom API tools can be defined through the workspace settings.

AnythingLLM’s extensibility is more focused on RAG-adjacent features — document processing, embedding customization, and retrieval tuning — than general-purpose plugin capabilities.

Deployment Ease

Open WebUI

Open WebUI deploys with a single Docker command:

docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway ghcr.io/open-webui/open-webui:main

It auto-detects Ollama on the host machine. Docker Compose files are available for more complex setups (with Ollama bundled, with HTTPS, etc.). The initial setup wizard handles admin account creation and basic configuration.

LibreChat

LibreChat deploys via Docker Compose with a configuration file (librechat.yaml) that defines providers, endpoints, and features. The setup requires more configuration than Open WebUI — you need to specify which providers to enable and provide API keys. Docker Compose is the standard deployment method.

The configuration file approach gives LibreChat more flexibility but adds initial setup time. Expect 15-30 minutes for a properly configured deployment versus 5 minutes for Open WebUI.

AnythingLLM

AnythingLLM offers the most deployment options: Docker, a desktop application (Electron-based), and a managed cloud version. The desktop app is the easiest path — download, install, and run. Docker deployment is straightforward and similar in complexity to Open WebUI.

For non-technical users, AnythingLLM’s desktop application is the simplest way to get a local chat UI with RAG capabilities running.

GitHub Activity and Community Health

Metric	Open WebUI	LibreChat	AnythingLLM
GitHub stars	80K+	25K+	35K+
Contributors	400+	200+	100+
Commit frequency	Daily	Daily	Several per week
Release cadence	Weekly	Bi-weekly	Weekly
Discord/community	Very active	Active	Active
Documentation	Comprehensive	Good	Good

Open WebUI has the largest and most active community, which translates to faster bug fixes, more third-party tools, and more deployment guides. LibreChat has a dedicated community with strong documentation. AnythingLLM has a growing community with particularly good documentation for RAG-specific workflows.

The Bottom Line

Choose Open WebUI if you want the most polished, most widely supported self-hosted chat UI with a strong community and good RAG capabilities. It is the safest default choice for individuals and teams.

Choose LibreChat if multi-provider support is your priority — you need seamless switching between OpenAI, Anthropic, Google, and local models with per-user configuration and API key management.

Choose AnythingLLM if RAG is your primary use case — you have documents that need to be searchable through natural language, and you want granular control over vector databases, embedding models, and retrieval settings.

All three are excellent, actively maintained projects. You cannot go wrong with any of them, but matching your primary use case to each project’s core strength will give you the best experience.