Teams that want a ChatGPT-style interface without sending every prompt to a single vendor can run a self-hosted AI chat interface. These open-source applications give you one chat front end that can talk to local models, cloud APIs, or both, while you keep control of where the software runs and how conversations are stored.
The problem
Most teams start with a hosted chat product. It works well for individuals, but it creates friction once several people, several models and sensitive internal data are involved.
- Conversations and uploaded documents leave your infrastructure and sit with one provider.
- Switching models means switching tools, so every team ends up with a different workflow.
- Access control, usage visibility and shared prompts are hard to standardize across accounts.
- Teams that want to use local models for private work have no common interface for them.
The goal is a single interface that your team can use every day, that connects to the models you choose, and that runs on infrastructure you control.
Solutions in the catalog
Browser-based chat platforms for teams
Open WebUI is a self-hosted, extensible AI chat interface. It connects Ollama and any OpenAI-compatible API in one place, so the same interface serves local and cloud models. It is the most widely starred project in this group, and it is a natural starting point for a shared team chat.
LibreChat is an open-source, self-hosted AI chat platform, released under the MIT license. It unifies models from OpenAI, Anthropic, Google and others, and adds agents, MCP tools and Code Interpreter. Choose it if you want a multi-provider platform with tool use built in.
Desktop apps for individuals
If a team member needs a private assistant on their own machine, desktop apps avoid a server altogether.
locally-uncensored is a local AI studio that combines chat, image and video generation and a coding agent in one free, open-source app, licensed under AGPL-3.0. It runs on Windows and Linux without Docker, a terminal or a cloud connection.
Atomic-Chat is a local AI app and inference engine for agents. It runs open-weight LLMs on your computer and is designed to work fully offline.
Workspaces and automation
Some teams need more than a chat window. LobeHub describes itself as an open-source Chief Agent Operator, a workspace that organizes AI agents into continuous operations by hiring, scheduling and reporting on a team of agents. FLUJO, an MIT-licensed project, provides a multi-agent and automation harness with graph-based workflows, MCP support and self-improving agents. These suit teams building agent pipelines rather than only chatting.
clawpanel is a management panel for OpenClaw and Hermes Agent engines, built with Tauri v2 for cross-platform desktop use and offering an AI assistant with tool calling and image recognition.
How to start
- Define the models you need. List the local models you want to run and the cloud providers you already pay for. This determines which interface supports your setup.
- Pick one deployment pattern. For a shared team server, start with Open WebUI or LibreChat. For individual privacy, use Atomic-Chat or locally-uncensored.
- Run a pilot. Deploy the chosen tool on a single machine, connect one local model and one cloud API, and have a small group use it for real tasks for a week.
- Check data handling. Confirm where conversations and uploads are stored, who can access them, and how logs are kept before you widen access.
- Add tools carefully. Enable MCP servers or agent features only after the basic chat works. The MCP servers catalog lists options, and the developer tools section covers supporting utilities.
You can browse more options in the AI Tools and Desktop Apps categories, and in self-hosted web apps if you want a server deployment.
Recommendation
For a team that needs one shared interface across local and cloud models, start with Open WebUI because it connects Ollama and any OpenAI-compatible API in one place. If you also need multi-provider agents and MCP tools, evaluate LibreChat alongside it. For individuals who need fully local, private use on their own computer, Atomic-Chat or locally-uncensored are practical choices. Run a short pilot with one local and one cloud model before committing, and decide based on where your data is allowed to go.