mylxsw/aidea

An APP that integrates mainstream large language models and image generation models, built with Flutter, with fully open-source code.

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Summary Information

Updated 30 minutes ago
Added to GitGenius on September 9th, 2026
Created on August 30th, 2023
Open Issues & Pull Requests: 23 (+0)
GitHub issues: Enabled
Number of forks: 1,044
Total Stargazers: 6,931 (+0)
Total Subscribers: 44 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 3.2 days
Mean response time: 78.8 days
90th percentile: 527.2 days
Tracked items: 9

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Issue Activity (beta)

Open issues: 9
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 675 days
Stale 30+ days: 9
Stale 90+ days: 9

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • feature (1)

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Detailed Description

AIdea is a mobile application that integrates mainstream large language models and image generation models, built with Flutter and released as fully open-source code.

The project addresses the need for a unified interface to multiple AI services by bundling support for several large language models and image generation capabilities into a single cross-platform application. Rather than requiring users to maintain separate accounts and interfaces for different AI providers, AIdea consolidates access to these services through a Flutter-based mobile app. The open-source nature of the codebase means users can inspect the implementation, modify it for their needs, or self-host components if desired.

Developers should choose this tool if they need a mobile-first interface to multiple AI models or want to build on top of an existing Flutter application that already handles integration with various language models and image generation services. The project suits teams building consumer-facing AI applications who want to avoid vendor lock-in and prefer working with open-source foundations. It is particularly relevant for those targeting iOS and Android simultaneously through a single codebase, since Flutter enables that deployment pattern.

The project shows active development with regular commits addressing bug fixes and feature additions. Work spans multiple areas including core functionality improvements, dependency updates, and platform-specific refinements. The codebase receives consistent attention to maintaining compatibility across supported platforms and integrating new model providers as they become available.