ChatPanel: A Privacy-First AI Agent Side Panel

ChatPanel, an open-source browser side panel, lets users orchestrate multiple LLM agents locally with privacy features like local PII redaction and voice-to-text processing. The tool supports model-agnostic switching between Codex, Claude Code, or local models, and includes a built-in MCP hub for extensibility.

ChatPanel: A Privacy-First AI Agent Side Panel Privacy-centric AI tools usually sacrifice functionality for security, but ChatPanel manages to bridge the gap by acting as a local orchestrator for various LLM agents. Instead of being locked into a single provider, this browser side panel lets you plug in your own agents—whether that's For anyone building a custom AI workflow, the open-source nature of the project is the biggest win. You can actually verify the privacy claims in the code rather than trusting a marketing page. 4. Add your preferred MCP servers to the hub to extend the agent's capabilities. Claude /en/tags/claude/ Code, local models, or remote APIs—while keeping the data flow under your control.The core value here is the flexibility of the "bridge." You install the extension and a few npm packages to connect your local AI environment to the browser. Key Technical Capabilities Model Agnostic: You can toggle between Codex, Claude Code, or local in-browser models on a per-message basis. Local Processing: Voice-to-text and PII Personally Identifiable Information redaction happen locally, meaning sensitive data is scrubbed before it ever hits a remote server. Context Integration: It functions as a local note-taking app and meeting assistant, allowing you to search across your own history and notes. Extensibility: It includes a built-in MCP Model Context Protocol hub and supports CLI agents like OpenCode. Browser Interaction: The "act mode" allows the agent to interact with tools like Excalidraw directly in the browser. For anyone building a custom AI workflow, the open-source nature of the project is the biggest win. You can actually verify the privacy claims in the code rather than trusting a marketing page. Setup Workflow To get this running, you generally follow these steps: 1. Install the browser extension. 2. Install the required bridge packages via npm: npm install -g chatpanel-bridge 3. Configure your API keys or point the extension to your local LLM endpoint e.g., Ollama .4. Add your preferred MCP servers to the hub to extend the agent's capabilities. This is a solid choice for developers who want a "command center" for their AI agents without shipping every single keystroke to a third-party cloud. Next Mapbox GL JS: Turning Cities into 3D Cutscenes → /en/threads/2448/ All Replies （3） J D Does this support custom system prompts for the different agents, or is that handled externally? 0 A Been using a similar setup for work docs; makes a huge difference for peace of mind. 0