{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-mylxsw--aidea","slug":"mylxsw--aidea","name":"aidea","type":"app","url":"https://ai.aicode.cc","page_url":"https://unfragile.ai/mylxsw--aidea","categories":["app-builders"],"tags":["ai","chatbot","flutter","gpt","gpt-4","stable-diffusion","tongyiqianwen","wenxinyiyan"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-mylxsw--aidea__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.chat.with.unified.interface","name":"multi-provider llm chat with unified interface","description":"Integrates OpenAI, Anthropic, and Chinese LLM providers (Tongyi Qianwen, Wenxin Yiyan) through a provider-agnostic abstraction layer that normalizes API schemas and handles authentication tokens. Uses BLoC pattern for state management to decouple chat logic from UI, enabling seamless provider switching within conversations without losing context or message history.","intents":["I want to chat with multiple AI models without switching apps or managing separate API keys","I need to compare responses from different LLM providers in the same conversation thread","I want to use Chinese LLMs alongside OpenAI without complex integration work"],"best_for":["developers building cross-provider AI applications","users in China needing access to local LLMs alongside Western models","teams evaluating multiple LLM providers for production use"],"limitations":["No built-in request batching across providers — each provider call is sequential, adding latency for multi-model comparisons","Provider-specific features (vision, function calling) require manual capability detection and fallback logic","Token counting differs per provider; no unified token budget management across models"],"requires":["API keys for at least one provider (OpenAI, Anthropic, Aliyun, Baidu, etc.)","Flutter 3.0+ for BLoC state management","Network connectivity for real-time API calls"],"input_types":["text","voice (transcribed to text)"],"output_types":["text","structured chat messages with metadata"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mylxsw--aidea__cap_1","uri":"capability://memory.knowledge.conversation.context.management.with.message.history.persistence","name":"conversation context management with message history persistence","description":"Maintains chat room state with full message history, user/assistant role tracking, and context window optimization using local SQLite storage. The BLoC pattern manages conversation state transitions (loading, success, error) while the APIServer handles pagination and lazy-loading of historical messages to prevent memory bloat on mobile devices.","intents":["I need to resume conversations across app sessions without losing context","I want to manage multiple independent chat rooms with different AI models","I need to efficiently load large conversation histories without freezing the UI"],"best_for":["mobile app developers building persistent chat interfaces","users conducting long-form research or creative projects requiring conversation continuity","teams needing local-first chat storage for privacy"],"limitations":["SQLite storage is single-device only — no built-in cloud sync or multi-device conversation sharing","Context window optimization is manual; no automatic summarization of old messages to fit token limits","Pagination adds complexity; loading very old messages (100+ turns) may cause UI lag on low-end devices"],"requires":["Flutter 3.0+","SQLite plugin for local persistence","Minimum 50MB free storage for conversation history"],"input_types":["text messages","voice input (transcribed)"],"output_types":["structured message objects with timestamps, roles, and metadata"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mylxsw--aidea__cap_10","uri":"capability://tool.use.integration.apiserver.abstraction.layer.for.provider.agnostic.api.integration","name":"apiserver abstraction layer for provider-agnostic api integration","description":"Centralizes all external API communication through a single APIServer component that abstracts provider-specific details (authentication, request/response formats, error handling). Each provider (OpenAI, Anthropic, Aliyun, Baidu) has a dedicated adapter that translates between the provider's API schema and AIdea's internal message format, enabling seamless provider switching and fallback logic without touching business logic layers.","intents":["I want to add support for a new AI provider without rewriting chat/image generation logic","I need to handle provider-specific errors and rate limits transparently","I want to switch providers at runtime based on availability or cost"],"best_for":["developers building multi-provider AI applications","teams needing provider redundancy or cost optimization","architects designing extensible AI client architectures"],"limitations":["Adding a new provider requires implementing a full adapter class — no code generation or schema inference","Provider-specific features (vision, function calling, streaming) require adapter-level implementation","Adapter mismatch bugs are hard to debug because errors are abstracted away","Performance overhead of abstraction layer adds ~50-100ms per request"],"requires":["Dart 2.17+","HTTP client library (dio or http)","Understanding of each provider's API schema"],"input_types":["provider name","API request (chat, image generation, etc.)"],"output_types":["normalized response object","error with provider context"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mylxsw--aidea__cap_11","uri":"capability://text.generation.language.real.time.streaming.response.rendering.with.progressive.display","name":"real-time streaming response rendering with progressive display","description":"Streams API responses token-by-token from providers supporting streaming (OpenAI, Anthropic, Stable Diffusion) and renders them progressively in the UI using Dart streams and Flutter's StreamBuilder widget. The chat interface updates in real-time as tokens arrive, creating a typewriter effect that improves perceived responsiveness compared to waiting for full response completion.","intents":["I want to see AI responses appear in real-time instead of waiting for the full response","I need to cancel long-running requests mid-stream if the response is going off-track","I want to measure time-to-first-token and streaming latency for performance analysis"],"best_for":["users with slow network connections who benefit from early feedback","developers building responsive chat UIs","teams optimizing perceived performance through progressive rendering"],"limitations":["Streaming adds complexity to error handling — partial responses may be incomplete if connection drops","Not all providers support streaming; fallback to non-streaming responses requires UI adaptation","Token-by-token rendering can cause UI jank on low-end devices if not throttled","Cancellation mid-stream may still consume API credits depending on provider billing model"],"requires":["Provider support for streaming (OpenAI, Anthropic, Ollama, etc.)","Stable network connection (streaming is sensitive to packet loss)","Flutter 2.0+ with StreamBuilder support"],"input_types":["streaming HTTP response"],"output_types":["progressive text updates","completion metadata"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mylxsw--aidea__cap_12","uri":"capability://automation.workflow.offline.aware.message.composition.with.deferred.sending","name":"offline-aware message composition with deferred sending","description":"Detects network connectivity using the connectivity plugin and allows users to compose messages while offline, storing them in a local queue (SQLite) with 'pending' status. When connectivity is restored, the app automatically retries sending queued messages in order, updating message status from 'pending' to 'sent' or 'failed' based on API response.","intents":["I want to compose chat messages while offline and send them when connectivity returns","I need visibility into which messages failed to send and why","I want automatic retry of failed messages without manual intervention"],"best_for":["mobile users in areas with intermittent connectivity","developers building resilient chat applications","teams prioritizing user experience over strict consistency"],"limitations":["No conflict resolution if user edits a pending message — last edit wins without merge logic","Retry logic is simple exponential backoff; no smart retry strategies based on error type","Pending messages are not encrypted locally — sensitive data is at risk if device is compromised","No UI indication of message ordering if multiple messages are pending and connectivity is slow"],"requires":["connectivity plugin for network state detection","SQLite for local message queue storage","Background task scheduling (WorkManager on Android, BackgroundTasks on iOS)"],"input_types":["text message","network state change"],"output_types":["message with status (pending, sent, failed)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mylxsw--aidea__cap_13","uri":"capability://data.processing.analysis.model.capability.detection.and.feature.gating","name":"model capability detection and feature gating","description":"Queries each AI provider's API to detect supported capabilities (vision, function calling, streaming, image generation) and gates UI features accordingly. For example, if a model doesn't support vision, the image upload button is hidden; if it doesn't support streaming, responses are fetched as complete blocks. Capability metadata is cached locally to avoid repeated API calls.","intents":["I want the UI to show only features supported by the selected model","I need to know which models support specific capabilities before selecting them","I want to gracefully degrade features when switching to models with fewer capabilities"],"best_for":["developers building model-agnostic AI applications","teams supporting diverse model portfolios with varying capabilities","users who need transparency about model limitations"],"limitations":["Capability detection requires API calls or hardcoded metadata — no standard capability schema across providers","Cached capabilities become stale if providers update models; no automatic refresh mechanism","Feature gating adds UI complexity — conditional rendering for every capability increases code maintenance burden","Some capabilities (e.g., 'reasoning quality') are subjective and can't be detected programmatically"],"requires":["Model metadata API or hardcoded capability matrix","Local cache (SQLite or SharedPreferences) for capability metadata","UI framework supporting conditional rendering (Flutter)"],"input_types":["model identifier"],"output_types":["capability flags (vision, streaming, function_calling, etc.)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mylxsw--aidea__cap_2","uri":"capability://text.generation.language.group.chat.with.simultaneous.multi.model.responses","name":"group chat with simultaneous multi-model responses","description":"Enables users to send a single prompt to multiple AI models in parallel and display responses side-by-side, coordinating concurrent API calls through async/await patterns in Dart. The UI layer renders responses as they arrive using StreamBuilder widgets, allowing partial responses to display before all models complete, while the BLoC layer manages request/response lifecycle and error handling per model.","intents":["I want to compare how different AI models approach the same problem in real-time","I need to get multiple perspectives on a question without manually switching between models","I want to benchmark model quality and speed on the same prompt"],"best_for":["researchers comparing LLM outputs for academic or production evaluation","content creators seeking diverse creative perspectives","developers prototyping multi-model AI features"],"limitations":["Parallel API calls multiply token costs and rate-limit exposure — no built-in request throttling or cost estimation","UI rendering 3+ concurrent streaming responses can cause frame drops on devices with <4GB RAM","No automatic response deduplication or similarity detection — users must manually compare outputs"],"requires":["API keys for 2+ providers","Flutter 3.0+ with StreamBuilder support","Network bandwidth sufficient for parallel streaming (recommend 5+ Mbps)"],"input_types":["text prompt"],"output_types":["multiple text responses with model metadata and latency metrics"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mylxsw--aidea__cap_3","uri":"capability://tool.use.integration.voice.input.transcription.and.audio.processing","name":"voice input transcription and audio processing","description":"Captures audio input from device microphone, sends it to a speech-to-text provider (integrated via APIServer abstraction), and converts transcribed text into chat messages. Uses platform-specific audio recording APIs (iOS AVAudioEngine, Android AudioRecord) wrapped in Flutter plugins, with automatic audio format normalization (WAV/MP3) before transmission to ensure provider compatibility.","intents":["I want to chat with AI using voice instead of typing on mobile","I need hands-free AI interaction while driving or multitasking","I want to transcribe voice notes into structured AI conversations"],"best_for":["mobile-first users with accessibility needs","professionals in field work (research, journalism) needing voice-based AI interaction","non-English speakers using speech-to-text for their native language"],"limitations":["Speech-to-text accuracy varies by language and provider; no built-in confidence scoring or correction UI","Audio processing adds 2-5 second latency before text reaches the LLM","Requires microphone permissions; no graceful degradation if permission denied"],"requires":["iOS 14.0+ or Android 8.0+","Microphone permission granted","API key for speech-to-text provider (e.g., OpenAI Whisper, Baidu Speech)","Network connectivity for audio transmission"],"input_types":["audio (WAV, MP3, M4A)"],"output_types":["transcribed text","confidence metadata"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mylxsw--aidea__cap_4","uri":"capability://image.visual.ai.powered.image.generation.with.multiple.model.support","name":"ai-powered image generation with multiple model support","description":"Integrates image generation models (DALL-E, Stable Diffusion, and Chinese alternatives) through the provider abstraction layer, accepting text prompts and returning generated images. The Creative Island feature manages image generation workflows, storing generated images locally with metadata (prompt, model, parameters) and enabling batch generation of multiple variations through sequential or parallel API calls.","intents":["I want to generate images from text descriptions using different AI models","I need to create multiple image variations of the same prompt for comparison","I want to store and organize generated images with their prompts for future reference"],"best_for":["designers and artists exploring AI-assisted creative workflows","content creators generating social media assets","teams evaluating image generation model quality"],"limitations":["Image generation is slow (10-60 seconds per image) — no real-time preview or progressive rendering","Generated images consume significant local storage; no automatic cleanup or cloud backup","No built-in image editing post-generation — requires external tools for refinement","Model-specific parameters (resolution, style, guidance scale) require manual UI for each provider"],"requires":["API key for image generation provider (OpenAI, Stability AI, Aliyun, etc.)","Minimum 500MB free storage for image cache","Network connectivity with sufficient bandwidth for image download (1-5 MB per image)"],"input_types":["text prompt","optional image parameters (size, style, quality)"],"output_types":["PNG/JPEG images","metadata JSON with generation parameters"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mylxsw--aidea__cap_5","uri":"capability://image.visual.image.editing.and.manipulation.with.ai.assistance","name":"image editing and manipulation with ai assistance","description":"Provides image editing capabilities (inpainting, outpainting, style transfer) integrated with image generation models through the Creative Island module. Users select regions of existing images or upload new ones, specify editing instructions, and the system sends the image + mask + prompt to providers supporting image-to-image operations (Stable Diffusion, DALL-E variants).","intents":["I want to edit specific regions of generated or uploaded images using AI","I need to extend images beyond their original boundaries (outpainting)","I want to apply consistent style transformations across multiple images"],"best_for":["digital artists and designers using AI as a creative tool","content creators refining AI-generated assets","teams needing batch image transformations"],"limitations":["Mask creation UI is basic — no advanced selection tools (magic wand, feather, etc.)","Inpainting quality varies significantly by model and prompt specificity","Large image processing (>2048x2048) may timeout or consume excessive memory on mobile","No undo/redo history for edits — each operation is independent"],"requires":["API key for image-to-image capable provider (Stable Diffusion, DALL-E 3+, etc.)","Image file (PNG/JPEG) with minimum 256x256 resolution","Sufficient storage for edited image versions"],"input_types":["image file","mask (binary or grayscale)","text editing instruction"],"output_types":["edited image (PNG/JPEG)","generation metadata"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mylxsw--aidea__cap_6","uri":"capability://automation.workflow.credit.based.payment.and.usage.tracking.system","name":"credit-based payment and usage tracking system","description":"Implements a credit ledger system where users purchase credits that are consumed by API calls (different models/operations cost different amounts). The payment system integrates with platform-specific payment processors (Apple In-App Purchase, Google Play Billing) and tracks usage per user through a backend API, with local caching of credit balance to enable offline awareness of remaining quota.","intents":["I want to control my AI usage costs through a prepaid credit system","I need to see how many credits each operation costs before executing it","I want to purchase credits through my device's app store"],"best_for":["app developers monetizing AI features without per-API-call billing complexity","users preferring prepaid models over subscription or pay-as-you-go","teams needing usage analytics and cost attribution per user"],"limitations":["Credit pricing is fixed per operation — no dynamic pricing based on model cost fluctuations","No refund mechanism for failed API calls that consumed credits","Offline credit balance is cached locally; no real-time sync if user purchases credits on another device","Cross-platform credit sharing not supported — iOS and Android credits are separate"],"requires":["Backend API for credit ledger management","iOS 11.2+ (StoreKit) or Android 5.0+ (Google Play Billing Library)","User authentication system"],"input_types":["credit purchase amount","API operation type"],"output_types":["credit balance","transaction history","usage analytics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mylxsw--aidea__cap_7","uri":"capability://text.generation.language.multi.language.localization.with.dynamic.language.switching","name":"multi-language localization with dynamic language switching","description":"Uses Flutter's built-in localization framework (intl package) combined with custom implementations to support 10+ languages including English, Chinese (Simplified/Traditional), Japanese, and others. Language selection is persisted locally and applied dynamically without app restart using BLoC state management to trigger UI rebuilds across all screens when language changes.","intents":["I want to use the app in my native language without reinstalling","I need the app to respect my device's language settings automatically","I want to switch languages mid-session for testing or preference changes"],"best_for":["global app developers targeting non-English markets","teams supporting multilingual user bases","developers building accessible apps for diverse regions"],"limitations":["Right-to-left (RTL) language support is incomplete — Arabic and Hebrew UI layouts may have alignment issues","Translation strings are hardcoded in source; no runtime translation API integration (e.g., Google Translate)","Language-specific date/number formatting requires manual implementation per locale","Adding new languages requires code changes and app recompilation"],"requires":["Flutter 2.0+","intl package for localization","Translated string resources for each supported language"],"input_types":["language code (e.g., 'en', 'zh_CN')"],"output_types":["localized UI strings and formatting"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mylxsw--aidea__cap_8","uri":"capability://planning.reasoning.bloc.based.state.management.with.separation.of.concerns","name":"bloc-based state management with separation of concerns","description":"Implements the Business Logic Component (BLoC) pattern across the application to decouple UI from business logic, using Dart streams and events to manage state transitions. Each feature (chat, image generation, settings) has its own BLoC that listens to events, processes them through business logic, and emits states that the UI consumes through StreamBuilder or BlocBuilder widgets.","intents":["I want to manage complex application state without tightly coupling UI and logic","I need to test business logic independently from Flutter widgets","I want to reuse the same business logic across multiple UI implementations"],"best_for":["Flutter developers building medium-to-large apps with complex state","teams prioritizing testability and code maintainability","developers migrating from other state management patterns (Provider, GetX)"],"limitations":["BLoC boilerplate is verbose — simple features require 200+ lines of event/state classes","Debugging state transitions requires understanding stream mechanics and event ordering","No built-in persistence — BLoCs lose state on app restart unless explicitly saved to storage","Learning curve is steep for developers unfamiliar with reactive programming"],"requires":["Flutter 2.0+","flutter_bloc package (^8.0.0 or later)","Understanding of Dart streams and async/await"],"input_types":["events (user actions, API responses)"],"output_types":["state objects (loading, success, error)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mylxsw--aidea__cap_9","uri":"capability://automation.workflow.cross.platform.build.and.deployment.ios.android.macos.windows","name":"cross-platform build and deployment (ios, android, macos, windows)","description":"Leverages Flutter's cross-platform capabilities to compile a single codebase to iOS, Android, macOS, and Windows with platform-specific configurations for signing, entitlements, and app store submission. Uses Xcode for iOS/macOS builds and Android Studio/Gradle for Android, with CI/CD integration for automated builds and deployment to app stores.","intents":["I want to deploy the same app to multiple platforms without maintaining separate codebases","I need to configure platform-specific features (push notifications, file access) per OS","I want to automate app store submissions and beta testing across platforms"],"best_for":["indie developers and small teams with limited resources","companies standardizing on Flutter for multi-platform development","teams needing rapid iteration across iOS, Android, and desktop"],"limitations":["Platform-specific bugs require separate debugging and testing per OS","iOS deployment requires Apple Developer account ($99/year) and Mac hardware","Android requires separate signing keys and Play Store account setup","Desktop builds (macOS, Windows) have smaller user bases and less app store discoverability"],"requires":["Flutter SDK 3.0+","Xcode 14+ (for iOS/macOS builds)","Android SDK and NDK (for Android builds)","Platform-specific developer accounts (Apple, Google)"],"input_types":["Dart source code","platform-specific configuration files"],"output_types":["iOS .ipa","Android .apk/.aab","macOS .app","Windows .exe"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"low","permissions":["API keys for at least one provider (OpenAI, Anthropic, Aliyun, Baidu, etc.)","Flutter 3.0+ for BLoC state management","Network connectivity for real-time API calls","Flutter 3.0+","SQLite plugin for local persistence","Minimum 50MB free storage for conversation history","Dart 2.17+","HTTP client library (dio or http)","Understanding of each provider's API schema","Provider support for streaming (OpenAI, Anthropic, Ollama, etc.)"],"failure_modes":["No built-in request batching across providers — each provider call is sequential, adding latency for multi-model comparisons","Provider-specific features (vision, function calling) require manual capability detection and fallback logic","Token counting differs per provider; no unified token budget management across models","SQLite storage is single-device only — no built-in cloud sync or multi-device conversation sharing","Context window optimization is manual; no automatic summarization of old messages to fit token limits","Pagination adds complexity; loading very old messages (100+ turns) may cause UI lag on low-end devices","Adding a new provider requires implementing a full adapter class — no code generation or schema inference","Provider-specific features (vision, function calling, streaming) require adapter-level implementation","Adapter mismatch bugs are hard to debug because errors are abstracted away","Performance overhead of abstraction layer adds ~50-100ms per request","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.33036241339159117,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-05-03T13:58:42.319Z","last_commit":"2026-03-04T15:28:00Z"},"community":{"stars":6947,"forks":1049,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mylxsw--aidea","compare_url":"https://unfragile.ai/compare?artifact=mylxsw--aidea"}},"signature":"im9u6YnzyjhmJCwIiTi7eThuATrZVlsRjNwU4po6FSiCV6+IIPfphJxV+3ZQQalo0dDtqSSA9uatg95b2HvjDA==","signedAt":"2026-06-20T08:27:02.005Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mylxsw--aidea","artifact":"https://unfragile.ai/mylxsw--aidea","verify":"https://unfragile.ai/api/v1/verify?slug=mylxsw--aidea","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}