Using Conversational Search in IDEs
Pieces connects to popular IDEs, editors, and productivity tools—most often through Model Context Protocol (MCP)—so you can use the same workflow in the environment you prefer.
Cross-environment access
Editor integrations today run primarily through MCP (see the Integrations overview). Legacy editor plugins are retired; use the MCP guides for current setup.
In supported environments you get PiecesOS and Long-Term Memory context alongside Conversational Search. Legacy Pieces Drive remains available where documented; new workflows should use LTM and Timeline.
MCP and editor guides
| Environment | Documentation |
|---|---|
| JetBrains IDEs | Setup for JetBrains via MCP |
| Visual Studio Code | VS Code via MCP |
| GitHub Copilot in Visual Studio | Visual Studio via MCP |
| Other editors (MCP hub) | Raycast, notebooks, and additional MCP hosts |
| Browser | Web Extension |
| Neovim | Neovim plugin (external) |
Additionally:
Integration with other applications
In IDEs that use the Pieces Applet or MCP, you get a familiar chat experience with context-aware suggestions tied to your project. LTM preferences and model choices stay aligned with the Desktop App where supported.
Shared conversation threads
Conversational Search supports cross-threading: chat history and context can move between the Desktop App and other connected environments so you can continue the same thread.
The Pieces user experience
Core behavior—chat history, context controls, and LLM configuration—is designed to feel consistent across environments.
Flutter-supported environments
On platforms such as JetBrains IDEs, Visual Studio Code, and the Pieces Web Extension, a Flutter-based Applet delivers a standardized experience for Conversational Search and related tools.
Custom UI environments
Where a Flutter applet is not available (for example some editors or the CLI), the UI is tailored to the host while keeping the same capabilities.
Next Steps
Return to the Desktop-focused guides when you configure memory, context, and models in the app:
