Open WebUI vs AnythingLLM vs LibreChat: Best Self-Hosted AI Chat in 2026

You're running Ollama or LM Studio locally. You've got models downloaded. Now you need something better than a terminal window to actually talk to them.

Three open-source projects dominate the self-hosted AI chat space in 2026: Open WebUI (124K+ GitHub stars), LibreChat (22K+ stars), and AnythingLLM (54K+ stars). All three connect to local models. All three offer RAG. All three run in Docker. But they're built for different users with different priorities.

We've run all three side-by-side — same hardware, same models, same workflows — to give you an honest comparison. No "it depends" hedging. Clear recommendations at the end.

The Quick Comparison

Open WebUI

• GitHub stars: 124K+
• Install: Docker (single container)
• Primary use case: Ollama frontend, team chat
• RAG: Built-in (ChromaDB)
• Multi-model: Ollama + OpenAI-compatible APIs
• Auth/multi-user: RBAC with admin/user roles, SSO (OIDC)
• Agents: Pipelines system, tool/function calling
• Desktop app: No (web only)
• License: MIT

AnythingLLM

• GitHub stars: 54K+
• Install: Docker or native desktop app (Mac/Win/Linux)
• Primary use case: Document Q&A, workspace-based RAG
• RAG: Core feature (LanceDB default, multiple vector DBs)
• Multi-model: Ollama, OpenAI, Anthropic, Azure, LM Studio, many more
• Auth/multi-user: Multi-user with permissions (Docker only)
• Agents: Built-in agent framework with tool use
• Desktop app: Yes (Electron — zero-config)
• License: MIT

LibreChat

• GitHub stars: 22K+
• Install: Docker Compose (multi-container)
• Primary use case: Multi-provider unified chat
• RAG: Yes (via RAG API service)
• Multi-model: OpenAI, Anthropic, Google, Azure, Ollama, custom endpoints
• Auth/multi-user: Full auth system, LDAP, social login, token usage tracking
• Agents: Yes, with MCP tool servers
• Desktop app: No (web only)
• License: MIT

Installation and Setup

Open WebUI — Fastest to Running

docker run -d -p 3000:8080 \
  --add-host=host.docker.internal:host-gateway \
  -v open-webui:/app/backend/data \
  --name open-webui \
  ghcr.io/open-webui/open-webui:main

That's it. One container, one command. If Ollama is running on your host, Open WebUI auto-detects it. First user to sign up becomes admin. You're chatting with your local models in under two minutes.

The --add-host=host.docker.internal:host-gateway flag is the only gotcha on Linux — it lets the container reach Ollama on your host machine. On Mac and Windows Docker Desktop, it works without it.

Verdict: The gold standard for quick setup. If you just want a web UI for Ollama, stop reading here and install Open WebUI.

AnythingLLM — Desktop App Changes the Game

AnythingLLM offers something the others don't: a native desktop app. Download, install, open. No Docker, no terminal, no port mapping. It embeds its own LanceDB for vector storage and connects to Ollama or any OpenAI-compatible endpoint.

# Or Docker if you prefer
docker pull mintplexlabs/anythingllm
docker run -d -p 3001:3001 \
  -v anythingllm:/app/server/storage \
  mintplexlabs/anythingllm

The desktop app is genuinely zero-config for non-technical users. Point it at your Ollama instance, select a model, and start chatting. The Docker version adds multi-user support but otherwise works the same.

Verdict: Best option for non-technical users or anyone who doesn't want to touch Docker. The desktop app is a real differentiator.

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> 📖 Full review: LibreChat 2026 Review: Open-Source ChatGPT Alternative — installation guide, pros/cons, and honest comparison with paid options.

LibreChat — More Setup, More Power

LibreChat requires Docker Compose with multiple services — the app, MongoDB, a RAG API service (Meilisearch), and optionally a vector database for RAG.

git clone https://github.com/danny-avila/LibreChat.git
cd LibreChat
cp .env.example .env
# Edit .env with your API keys and config
docker compose up -d

You'll also want to configure librechat.yaml for your model endpoints — this is where LibreChat's power lives but also where the complexity is. Setting up Ollama as a custom endpoint, configuring API keys for cloud providers, tuning model parameters — it's all configurable but it's not instant.

Verdict: 15-30 minutes to a working setup vs. 2 minutes for Open WebUI. The trade-off is that LibreChat's configuration system is the most flexible of the three once you've set it up.

UI and User Experience

Open WebUI — ChatGPT Clone Done Right

Open WebUI's interface is an obvious ChatGPT homage — and that's a compliment. Left sidebar for conversations, model selector at the top, clean message bubbles, markdown rendering, code blocks with syntax highlighting and copy buttons. If you've used ChatGPT, you know how to use Open WebUI.

What elevates it beyond a clone:

• Model switching mid-conversation. Start with a fast 8B model for brainstorming, switch to a 70B for the final answer. No new chat required.
• Artifacts. Renders HTML/code outputs in a side panel — similar to Claude's artifacts. Useful for prototyping.
• Image generation. Integrates with AUTOMATIC1111, ComfyUI, and OpenAI DALL-E. Generate images inline in chat.
• Voice input/output. Speech-to-text and TTS built in. Supports local Whisper for privacy.

The UI is responsive, fast, and polished. It feels like a product, not a side project.

AnythingLLM — Workspace-First Design

AnythingLLM organizes everything around workspaces — isolated environments each with their own documents, model config, and conversation history. Think of workspaces as project folders for AI chat.

This workspace model is opinionated but powerful:

• Upload documents to a workspace → they're automatically embedded and available for RAG
• Each workspace can use a different model and different system prompt
• Conversations within a workspace have access to that workspace's documents
• Drag-and-drop document upload — PDF, DOCX, TXT, code files, even web URLs

The UI is clean but simpler than Open WebUI. Less visual polish, more functional clarity. The document management panel is the star — you see exactly what's embedded, how many vectors were created, and can delete/re-embed individual documents.

The trade-off: the workspace model can feel rigid. If you want a quick one-off chat without setting up a workspace, there's friction. Open WebUI and LibreChat treat chat as the primary interaction; AnythingLLM treats documents-plus-chat as the primary interaction.

LibreChat — The Multi-Provider Dashboard

LibreChat's UI strength is switching between AI providers seamlessly. A dropdown lets you swap between GPT-4o, Claude, Gemini, and your local Ollama models — all in the same interface, same conversation history format, same settings panel.

Standout UI features:

• Preset system. Save model + system prompt + temperature combinations as presets. "Creative Writing (Claude Opus)" or "Code Review (Local Llama 3)" — one click to switch contexts.
• Fork conversations. Branch a conversation at any message to explore different paths. Surprisingly useful for comparing how different models handle the same prompt.
• Token usage tracking. See exactly how many tokens each message consumed and what it cost. Critical if you're mixing free API tiers with paid ones.
• Plugin system. Extend LibreChat with tools — web search, DALL-E, code interpreter. The MCP (Model Context Protocol) integration means compatible tool servers just work.

The interface is clean but denser than Open WebUI. More buttons, more options, more panels. Power users love it. People who want simplicity might find it overwhelming.

RAG: Document Q&A Compared

RAG is where these tools diverge most. All three support it, but the implementations reflect different philosophies.

Open WebUI RAG

Open WebUI added RAG as a feature on top of its chat-first design. Upload documents via the chat interface (drag-and-drop or click), and they're embedded using nomic-embed-text by default (pulled via Ollama). The vector store is ChromaDB, embedded in the same container.

Strengths: Zero config. Pull the embedding model (ollama pull nomic-embed-text), upload a document, ask questions. It works immediately. You can attach documents to specific conversations or make them available globally.

Weaknesses: Limited control over chunking strategy, embedding models, and retrieval parameters. No per-collection metadata filtering. For basic document Q&A it's excellent; for production RAG pipelines with advanced retrieval, you'll hit limits fast.

AnythingLLM RAG

RAG is AnythingLLM's core identity, not a bolted-on feature. Documents live in workspaces, and every workspace is a self-contained RAG environment.

Strengths: Best document management of the three. See embedding status per document. Re-embed with different settings. Support for multiple vector databases — built-in LanceDB (zero config), or connect to Pinecone, Chroma, Weaviate, Qdrant, Milvus. Web scraping built in — paste a URL and AnythingLLM crawls and embeds it. Workspace isolation means different projects don't pollute each other's vector indices.

Weaknesses: The workspace-centric model means you can't easily do cross-workspace RAG queries. Chunking controls are good but not as granular as a dedicated RAG framework. The agent's RAG retrieval can be chatty — it sometimes retrieves and injects too many chunks, inflating context and slowing responses.

LibreChat RAG

LibreChat's RAG runs as a separate service (the RAG API) using Meilisearch for full-text search alongside vector embeddings. This hybrid approach — semantic search plus keyword matching — gives better retrieval quality out of the box than pure vector search.

Strengths: Hybrid search is genuinely better for most use cases. The separate RAG service means it scales independently. File management UI shows embedded files with metadata. Supports per-conversation and global document scopes.

Weaknesses: More moving parts. The RAG API is another container to manage, another service to monitor. Setup requires configuring embedding endpoints and the vector store. If you want simple document Q&A without infrastructure overhead, AnythingLLM or Open WebUI are faster to get running.

Multi-User and Authentication

If you're deploying for a team — even a small one — auth matters.

Open WebUI has the most mature multi-user system. Role-based access control with admin and user roles. SSO via OIDC (connect to Google, GitHub, Okta, etc.). Admins can manage models, presets, and documents globally. User-level conversation isolation. Usage quotas per user. For small-to-medium teams, it's production-ready.

LibreChat offers comprehensive auth: local accounts, LDAP, social login (Google, GitHub, Discord, OpenID). Token usage tracking per user — you can see exactly how much each person is consuming across providers. User balance/credit system for paid API usage. Strong for organizations that need accountability.

AnythingLLM supports multi-user in Docker mode only (not the desktop app). Permissions are workspace-scoped — admins control who can access which workspaces. It's functional but simpler than Open WebUI or LibreChat. The desktop app is inherently single-user.

Performance and Resource Usage

All three run comfortably on modest hardware when connected to a local Ollama instance. The UI itself isn't the bottleneck — your GPU and model size are.

• Open WebUI: ~300-500 MB RAM for the container. Lightweight. The Pipelines system can add overhead if you're running multiple processing pipelines.
• AnythingLLM (Desktop): ~200-400 MB RAM. The Electron app is surprisingly light. The embedded LanceDB adds minimal overhead.
• AnythingLLM (Docker): ~400-600 MB RAM. Similar to Open WebUI.
• LibreChat: ~500-800 MB RAM across all containers (app + MongoDB + RAG API). The multi-container architecture uses more resources but each service is individually lightweight.

For the LLM inference itself — which dominates actual resource usage — check our guide on running Ollama in production. An RTX 4090 with 24 GB VRAM handles most 7B-13B models at full speed and can run quantized 70B models at usable inference rates. All three UIs stream tokens as they're generated, so perceived speed is good even with larger models.

Agent and Tool Capabilities

Agents — LLMs that can use tools, browse the web, execute code, and take actions — are the fastest-evolving feature across all three platforms.

Open WebUI Pipelines are a plugin system for extending the chat pipeline. You can write Python functions that run before or after model inference — add web search, filter outputs, inject RAG context, call external APIs. The Pipelines architecture is powerful but requires Python knowledge to extend.

AnythingLLM Agents have a built-in agent framework. Create agents that use tools (web browsing, code execution, RAG retrieval) within workspaces. The agent builder UI lets non-technical users configure tools without code. It's less flexible than Open WebUI's Pipelines but more accessible.

LibreChat integrates with MCP (Model Context Protocol) tool servers, giving it access to a growing ecosystem of standardized tools. It also supports OpenAI-style plugins and has built-in code interpreter functionality. For teams building coding agents or multi-agent workflows, LibreChat's MCP support is the most future-proof approach.

Who Should Choose What

Choose Open WebUI if:

• You want the best Ollama frontend with minimal setup
• You're deploying for a small-to-medium team with RBAC/SSO needs
• You want a ChatGPT-like experience with local models
• Image generation integration matters
• You value the largest community and fastest development pace (124K stars means rapid feature development and bug fixes)

Choose AnythingLLM if:

• Document Q&A and RAG are your primary use case
• You want a desktop app without Docker (great for non-technical users)
• You organize work into projects/workspaces with isolated document sets
• You need to switch between multiple vector database backends
• You want the simplest path from "I have documents" to "I'm asking questions about them"

Choose LibreChat if:

• You use multiple AI providers (OpenAI + Anthropic + local) and want one unified interface
• Token usage tracking and cost management matter (mixing paid and free APIs)
• You want conversation forking and advanced preset management
• MCP tool integration is important for your workflow
• You need the most flexible endpoint configuration (custom APIs, Azure, Bedrock, Vertex)

Our Recommendation

For most self-hosted local LLM users: Open WebUI.

It's the fastest to set up, has the largest community, and covers 90% of what you need for local AI chat. The RAG is good enough for personal and small-team use. The multi-user system is production-ready. It's where most Ollama users end up, and for good reason.

If documents are your thing: AnythingLLM. The workspace-centric RAG design and native desktop app make it the best choice for people whose primary workflow is "ask questions about my documents." The zero-config desktop experience is unmatched.

If you're a power user juggling providers: LibreChat. The multi-provider architecture, token tracking, and MCP integration make it the most capable platform for advanced users who want one interface for everything — local and cloud models combined.

All three are MIT-licensed, actively maintained, and genuinely good. The self-hosted AI chat space is mature enough that there's no wrong answer — just different right answers for different workflows.

*Disclosure: Links above are affiliate links. ToolHalla may earn a commission at no extra cost to you. We only recommend hardware we'd actually use.*

*Running local models? See our Ollama production config guide for optimizing inference speed, and our RAG vs long context comparison for choosing the right retrieval strategy. Want cloud GPUs instead of local hardware? We compared the top platforms.*

FAQ

What is Open WebUI and is it free?

Open WebUI is a free, open-source ChatGPT-like interface for Ollama and OpenAI-compatible APIs. It runs via Docker, supports multi-user access, conversation history, RAG, and tools. Completely free to self-host.

What is AnythingLLM used for?

AnythingLLM is a self-hosted AI workspace combining chat, document RAG, and agent capabilities. Best for teams wanting a private internal knowledge base powered by local LLMs.

How does LibreChat differ from Open WebUI?

LibreChat focuses on multi-model conversations — switching between GPT-4, Claude, Gemini, and local models. Open WebUI is more Ollama-centric with stronger tool/function calling. LibreChat excels at API gateway routing; Open WebUI at local LLM management.

Can I use these tools for a team?

All three support multi-user deployments. Open WebUI has RBAC (admin/user roles). LibreChat has full user management. AnythingLLM has workspace-level permissions. All run behind a reverse proxy for secure team access.

What are the system requirements?

Open WebUI: 4GB RAM, any GPU. AnythingLLM: 4GB minimum, 8GB recommended. LibreChat: 2GB RAM, Node.js required. All run on Docker for straightforward deployment.