Ollama Client — Shishir Chaurasiya

Ollama Client – Local LLM Chat in Your Browser

Ollama Client is a privacy-focused browser extension for interacting with locally hosted AI models. Connect to supported local LLM servers and chat directly inside your browser without relying on cloud-based inference.

Supported Providers
• Ollama
• LM Studio
• llama.cpp compatible servers

Features
• Connect and manage multiple local AI providers
• Switch models and monitor provider status
• Streaming chat responses with stop and regenerate controls
• Reasoning and thinking traces from models that support them
• Tool calling, so models can run actions and return results
• Web search through your own provider (SearXNG, Brave, or Tavily), off by default and configurable
• Session history and chat management
• Local file attachments and optional webpage context
• Selected-text actions from the right-click context menu
• Saved knowledge and memory for reusable context
• Custom prompt templates and model parameter controls
• Side panel and popup access
• Multi-language interface (English, German, Spanish, French, Hindi, Italian, Japanese, Russian, and Simplified Chinese)
• Responsive interface optimised for desktop workflows

Privacy
• No cloud inference
• No external data transfer required
• Data stays on your device and local network
• Web search is optional and routes only through the provider you choose

Who It's For
• Developers working with local AI models
• Researchers testing self-hosted LLMs
• Students learning offline AI workflows
• Privacy-conscious users

Setup
1. Install the extension
2. Run a supported local LLM server
3. Connect using localhost or a LAN IP
4. Start chatting

Important Notes
• This extension is a frontend client and does not include AI models
• Performance depends on your hardware and backend server configuration

When you ask the model to use the current page, the extension first tries to talk to its content script on that tab. On tabs where the content script was not already loaded (for example, a tab that was open before the extension was installed or updated), there is no receiver to answer. In that case the extension uses scripting to inject its content script on demand into that single tab, so it can extract the page text and hand it to the model.

This injection is:
• On demand only — it runs in response to your action, never in the background or on a schedule
• Scoped to the active tab you are using, not all tabs
• Limited to reading page content for the request you made

Useful Links

Chrome Web Store:
https://chromewebstore.google.com/detail/ollama-client/bfaoaaogfcgomkjfbmfepbiijmciinjl

Setup Guide:
https://www.ollamaclient.in/guides/provider-setup

Website:
https://www.ollamaclient.in

GitHub:
https://github.com/Shishir435/ollama-client