Building an Agentic AI Workforce with Librechat

I've spoken a lot in the past about open-source chatbots like OpenWebUI, with their rich feature set and powerful capabilities like prompt libraries, multi-user support, artifacts, and many more incredible features one can only dream of compared to ChatGPT or Claude.ai.

However, another big player that has recently caught my attention even more is LibreChat. While it is a more recent project and may have fewer features than OpenWebUI at first glance, it has recently implemented two killer features that make it for me indispensable as a chatbot or, as I would call it, an agent factory: full  MCP and multi-agent support.

With these features, you can build solutions for almost any everyday task: reading and drafting emails, manipulating Excel sheets, controlling your browser, and thousands of other scenarios. And the beauty of it is that you don't have to build any workflows (e.g. with N8n or Make); you just connect the data source to the model and craft the prompt for what you want to accomplish.

And with multi-agent support, the quality of the outputs is immensely better than single-agent solutions.

Sounds promising? In this and upcoming blog posts, I'll explain the core concepts and key features that can help you achieve more with little to no coding skills.

First, let's start with setting up the basics.

1 - Install it

Getting started with LibreChat is straightforward using Docker.

Alternatively, local installation via npm is possible, though it requires setting up a MongoDB instance (e.g., using MongoDB Atlas), as detailed here:

https://www.librechat.ai/docs/local/npm

Other installation options, including deployment on cloud providers like Railway, are available in the official documentation.

For this guide, we will use the simplest and most cost-effective method: Docker.