CherryHQ/cherry-studio

AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access to frontier LLMs

View on GitHub ↗Jump to charts ↓Open shareable report →

Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 28 minutes ago
Added to GitGenius on August 31st, 2026
Created on May 24th, 2024
Open Issues & Pull Requests: 1,740 (+1)
GitHub issues: Enabled
Number of forks: 5,046
Total Stargazers: 52,461 (+0)
Total Subscribers: 208 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 2.2 hours
Mean response time: 3.7 days
90th percentile: 6.4 days
Tracked items: 9,775

How this project is maintained

Roughly one issue in three opened in the past year never receives a reply. 51% of open issues come from outside the core team, a mix of external reports and the maintainers' own roadmap. 18% of tracked open issues have had no activity in three months. 78% of issues opened in the past year have been closed, leaving a working backlog. Three people close 52% of everything that gets resolved.

Charts & Analytics

Fetching additional details & charts...

Issue Activity (beta)

Issue analytics are temporarily unavailable.

Detailed Description

Cherry Studio is an AI productivity application that provides unified access to multiple large language models through a smart chat interface with autonomous agent capabilities.

The tool addresses the fragmentation of working with different AI models by consolidating access to frontier language models in a single interface. It enables users to interact with various LLMs, deploy autonomous agents, and leverage a library of pre-built assistants without switching between separate platforms. The agent system supports skills and code generation, allowing automation of complex workflows.

Developers and teams working with multiple AI models should consider Cherry Studio if they want to avoid context-switching between different AI services. The tool suits projects requiring flexible model selection, agent-based automation, and integration of specialized assistants. It works across different model providers, making it appropriate for workflows that benefit from comparing outputs across models or routing tasks to the most suitable LLM for each job.

The project receives issue reports from both core maintainers and external users, indicating adoption beyond the immediate team without creating an unsustainable support burden. Maintainers typically respond to new issues and pull requests within hours. Work tracked in the issue system centers on bug fixes, feature development, and inactive items.