OpenWebUI vs LibreChat: I ran both so you don’t have to

Development of everything related to AI is blazing fast nowadays and some things might not be true depending when the post is being read.

I spent the last month running both on my home server. Not switching back and forth. Just running them side by side, spending time in each one, trying to understand what each does well and where each falls short. If you are picking between these two, here is what I actually learned.

	OpenWebUI	LibreChat
🎯 Focus	Full platform. RAG, prompts, knowledge, tools	Multi-provider chat with agents, MCP
⚙️ Model setup	Auto-discover, click and go	YAML files, restart needed
👤 Best for	Document work, local models, shared prompts	Agents, web search, API-heavy setups
📜 License	BSD-3-Clause	MIT
⭐ GitHub stars	∼141k

🧠 Model adding

Both platforms let you connect multiple providers. But how they do it is fundamentally different.

OpenWebUI connects natively to Ollama and any OpenAI-compatible API. For those, you paste a URL and API key in the admin panel and models populate automatically. No files, no restarts.

Here is the catch. If you want to use multiple providers with different API formats, or use Anthropic, Google Gemini, Mistral, or any provider that does not speak the OpenAI format natively, you need an additional proxy layer. LiteLLM is the most common solution. You run it as a separate service, it translates between provider APIs, and OpenWebUI talks to it as a single OpenAI-compatible endpoint. That is another Docker container to manage, another service to keep running. Not complicated, but it is extra setup. ❌

LibreChat supports all major providers natively. OpenAI, Anthropic, Google, Azure, Mistral, Ollama, OpenRouter, and any custom endpoint. You define them in a YAML config file with their native API format. No proxy needed. No LiteLLM. No extra containers. It just works. ✅

The tradeoff is that LibreChat uses YAML configuration while OpenWebUI has a UI. If you are comfortable with YAML, LibreChat is cleaner. If you prefer UI forms, OpenWebUI is nicer for the providers it supports natively, but you will need LiteLLM once you go beyond OpenAI-compatible APIs.

Model icons. Small thing that drives me crazy in OpenWebUI. Every model in the dropdown shows the same generic logo. You cannot tell at a glance which model is from OpenAI, which is from Anthropic, which is local. The community has been asking for provider-specific icons but it is not there yet. ❌

LibreChat handles this properly. You set groupIcon in your model specs with a URL or a built-in key like “openAI”. Custom endpoints let you set iconURL to any image or a local file like /assets/my-icon.svg. Makes a real difference when you have ten models in the list. ✅

✅ Winner: LibreChat for provider support breadth. ✅ Winner: OpenWebUI for UI-based setup of the providers it supports natively.

📚 RAG

OpenWebUI has hybrid search built in. BM25 keyword search plus CrossEncoder re-ranking with configurable relevance thresholds. You can set a minimum score and the model only returns results above it. It handles PDFs, Word docs, Markdown, plain text, even YouTube videos (it transcribes them first). Also connects to Google Drive. ✅

LibreChat does RAG through an optional API built on LangChain with PostgreSQL and pgvector. You upload files, they get chunked and indexed, the model can reference them. It works for a handful of documents. ❌

✅ Winner: OpenWebUI. Not even close.

🤖 Agents

Here is where I lean hard toward LibreChat.

OpenWebUI has Tools. You write Python functions the model can call. Web search, image generation, whatever you build. There is a community marketplace where people share them. It works but it is DIY. 🤷

LibreChat has real agents. You create them from the UI. Give them instructions. Attach tools. Turn on code execution. The Code Interpreter runs Python in a sandbox and returns results inline, no setup needed. The agent handles multi-step tasks on its own. ✅

✅ Winner: LibreChat. I use agents constantly. OpenWebUI cannot touch it here.

🔌 MCP

Both support the Model Context Protocol now.

OpenWebUI added native MCP support and you configure servers through the admin panel. It works. But the documentation is thinner and when something breaks you kind of guess. 🤷

LibreChat has been at it longer. More guides for specific servers, a bridge that converts local MCP servers so they work over the network, clearer docs. When things break, and things do break, the LibreChat docs actually help. ✅

✅ Winner: LibreChat. Both work. LibreChat docs actually help when you get stuck.

🖼️ Images

Both let you upload images for analysis and generate new ones through DALL-E or Stable Diffusion.

LibreChat is smoother for iterating on generated images because the flow stays in the conversation. ✅

OpenWebUI works but the image tools feel bolted on, like they added them because everyone else had them. 🤷

✅ Winner: LibreChat. Neither is mind-blowing but LibreChat keeps the flow going.

💬 Prompts

I was wrong about this before. Both have proper prompt libraries with slash commands. Type /something in the chat and the prompt fires.

OpenWebUI prompts live in the Workspace panel. You assign a /command and add typed input variables: dropdowns, date pickers, number fields, checkboxes. Users get a clean form instead of editing raw text. There is a community marketplace with thousands of shared prompts. ✅

LibreChat added its Prompt Library a while back. You create prompts in the right side panel, assign a slash command like /summary, add variables with double braces, and pick a category. Admins can share prompts globally or restrict who can create their own. Simpler but clean. ✅

✅ Winner: Slight edge to OpenWebUI for the form fields. But both do the job.

🧠 Memory

OpenWebUI has Adaptive Memory. The model saves facts about you and recalls them across conversations. With tool calling it decides what to remember and when. It can be a bit too aggressive though. I caught it saving things I did not want saved and switched to manual mode. 🤷

LibreChat uses a key-value store with a dedicated memory agent that runs at the start of every request, reads what it knows about you, and writes back after responding. More boring. More predictable. ✅ None of those natively support proper agentic memory backends like Hindsight or Mem0

✅ Winner: LibreChat. I prefer boring and predictable.

🌐 Web search

Both have it.

OpenWebUI has agentic search where the model browses the web, follows links, and verifies facts on its own when you enable native function calling. It works. 🤷

LibreChat has built-in search with configurable providers and reranking through Jina or Cohere. Results come back formatted and the model uses them without flooding the context with junk. The search feels more deliberate, like someone designed it rather than just enabling it as a capability. ✅

✅ Winner: LibreChat. The search just feels better.

🏁 Which one fits which purpose

If I had to pick one based on what I actually do day to day, here is how I break it down.

Choose OpenWebUI if you mostly work with local models (Ollama), build knowledge bases from documents, or share prompts with other people. It is the Swiss Army knife. Everything is there in one place. The RAG is genuinely good, the prompt system is polished, and adding models takes no effort. If your workflow revolves around documents and local LLMs, this is your pick.

Choose LibreChat if you want agents that actually work, proper web search, or talk to multiple API providers with different settings. It does fewer things but the things it does feel finished. The agent framework is the big differentiator. If you need automation, code execution, or clean web search results, this is your pick.

Article written with the help of an AI