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cherry studio

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Basic Information

Cherry Studio is a cross-platform desktop client that provides a unified interface to multiple large language model providers and local models. It is designed for users who want to run AI assistants and manage conversational workloads on Windows, macOS, and Linux without manual environment setup. The project bundles pre-configured assistants and lets users create custom assistants, run simultaneous multi-model conversations, and process documents including text, images, Office files and PDFs. It includes file management and backup via WebDAV, a Model Context Protocol (MCP) server component, ready-made themes and UI options, and an Enterprise Edition option for private deployments with centralized model, knowledge and access management. The repository supports community contributions, developer co-creation incentives, and a public roadmap for features such as memory, OCR, TTS, plugins and mobile clients.

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31549
App URL
https://github.com/CherryHQ/cherry-studio
Github Repository
https://github.com/CherryHQ/cherry-studio/blob/main/README.md

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App Details

Features
Cherry Studio aggregates diverse LLM provider support including cloud services and web integrations named in the README such as OpenAI, Gemini and Anthropic, plus AI web services like Claude, Peplexity and Poe. It supports local model runtimes like Ollama and LM Studio. The app ships with 300+ pre-configured AI assistants and allows custom assistant creation and multi-model concurrent conversations. Document and data processing features handle text, images, office formats and PDFs with markdown rendering, code syntax highlighting and Mermaid chart visualization. Usability features include global search, topic management, AI translation, drag-and-drop sorting, mini program support, theme gallery and transparent window options. Developer-facing components include an MCP server, plugin roadmap items and cross-platform packaging for ready-to-use desktop installs.
Use Cases
For individual users Cherry Studio simplifies access to multiple LLMs and local models through a single desktop client, enabling quick AI-assisted conversations, content creation and document processing without configuring providers individually. The many pre-configured assistants and multi-model conversations help users experiment and compare models. For teams and enterprises the project offers an Enterprise Edition that centralizes model management, knowledge bases, role-based access control, private deployment and data backup to meet compliance needs. The MCP server, roadmap for memory and knowledge features, and planned plugin and mobile support make it useful as a platform for building AI workflows. The repository also welcomes contributions and provides a contributor rewards program to foster ongoing improvement.

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