{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-chatboxai--chatbox","slug":"chatboxai--chatbox","name":"chatbox","type":"product","url":"https://chatboxai.app?utm_medium=github","page_url":"https://unfragile.ai/chatboxai--chatbox","categories":["chatbots-assistants"],"tags":["assistant","chatbot","chatgpt","claude","copilot","deepseek","gemini","gpt","gpt-5","ollama","openai"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-chatboxai--chatbox__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.unified.api","name":"multi-provider llm abstraction with unified api","description":"Chatbox implements a provider abstraction layer that normalizes API calls across 10+ LLM providers (OpenAI, Anthropic, Google Gemini, DeepSeek, Ollama, etc.) through a unified interface. The system uses a provider implementation pattern where each provider has its own adapter class that handles authentication, request formatting, streaming response parsing, and error handling specific to that provider's API contract. All providers are accessed through a single message-sending interface regardless of backend, enabling users to switch models without changing application logic.","intents":["I want to build a multi-model AI client that supports switching between OpenAI, Claude, and local Ollama without rewriting integration code","I need to abstract away provider-specific API differences so my UI layer doesn't know about OpenAI vs Anthropic implementation details","I want to add support for a new LLM provider without touching the core application logic"],"best_for":["developers building cross-provider AI applications","teams wanting to avoid vendor lock-in to a single LLM provider","open-source projects needing extensible model support"],"limitations":["Provider-specific features (e.g., vision models, function calling schemas) require adapter-level implementation — not all features are uniformly available across providers","Streaming response handling varies by provider; some providers have latency differences in token delivery","Error handling must be implemented per-provider as error codes and messages differ significantly"],"requires":["API keys for desired providers (OpenAI, Anthropic, Google, etc.) or local Ollama instance","TypeScript/JavaScript runtime environment","Network connectivity for cloud providers"],"input_types":["text prompts","conversation history (message arrays)","model configuration objects (temperature, max_tokens, etc.)"],"output_types":["streamed text tokens","complete text responses","structured metadata (usage stats, finish_reason)"],"categories":["tool-use-integration","multi-provider-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chatboxai--chatbox__cap_1","uri":"capability://text.generation.language.streaming.response.processing.with.token.level.control","name":"streaming response processing with token-level control","description":"Chatbox implements real-time streaming of LLM responses at the token level, parsing provider-specific streaming formats (Server-Sent Events for OpenAI, different chunking for Anthropic, etc.) and emitting individual tokens to the UI as they arrive. The system handles backpressure, error recovery mid-stream, and graceful degradation if a stream is interrupted. Streaming is abstracted through the provider layer so the UI receives a consistent token stream regardless of backend provider.","intents":["I want to display LLM responses token-by-token as they arrive for a more responsive user experience","I need to handle streaming interruptions gracefully without losing partial responses","I want to implement real-time token counting or cost estimation as tokens arrive"],"best_for":["interactive chat applications requiring low-latency response display","applications with token-based billing needing real-time cost tracking","developers building responsive AI UIs where perceived latency matters"],"limitations":["Streaming adds complexity to error handling — errors mid-stream may result in partial responses displayed to users","Token-level processing introduces ~10-50ms per-token overhead depending on UI rendering performance","Some providers (e.g., local Ollama) may have inconsistent streaming behavior or buffering that delays token delivery"],"requires":["Provider support for streaming APIs (most modern LLM providers support this)","UI framework capable of handling rapid state updates (React, Vue, etc.)","Network connection with reasonable latency (streaming over high-latency connections degrades UX)"],"input_types":["streaming HTTP response body (Server-Sent Events or chunked encoding)","provider-specific JSON payloads containing token data"],"output_types":["individual token strings","token metadata (finish_reason, usage stats)","complete accumulated response text"],"categories":["text-generation-language","streaming-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chatboxai--chatbox__cap_10","uri":"capability://image.visual.image.generation.with.provider.integration","name":"image generation with provider integration","description":"Chatbox integrates with image generation providers (DALL-E, Midjourney, Stable Diffusion, etc.) allowing users to generate images directly within conversations. Users can describe an image in text, and the system invokes the appropriate image generation provider, retrieves the generated image, and displays it in the conversation. Image generation can be triggered manually or as part of an LLM-driven workflow where the LLM decides to generate images.","intents":["I want to generate images from text descriptions without leaving the chat interface","I need the AI to generate images as part of a creative workflow (e.g., brainstorming visual designs)","I want to compare images from different generation providers"],"best_for":["creative professionals using AI for design and ideation","content creators needing quick image generation","teams building multi-modal AI workflows"],"limitations":["Image generation adds significant latency (10-60 seconds per image depending on provider)","Quality and style vary significantly between providers; no unified quality guarantee","Image generation costs are typically higher than text generation; usage can become expensive","Generated images may have copyright or licensing restrictions depending on provider","No built-in image editing or refinement — users must regenerate for variations"],"requires":["Image generation API key (DALL-E, Midjourney, Stable Diffusion, etc.)","Sufficient API credits/quota for image generation","Network connectivity to image generation provider"],"input_types":["text prompts describing desired image","image generation parameters (size, style, quality, etc.)"],"output_types":["generated image URLs or base64-encoded images","image metadata (size, generation time, provider)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chatboxai--chatbox__cap_11","uri":"capability://automation.workflow.cross.platform.build.system.with.electron.and.capacitor","name":"cross-platform build system with electron and capacitor","description":"Chatbox uses a unified TypeScript codebase compiled to multiple platforms: Electron for desktop (Windows, macOS, Linux), Capacitor for mobile (iOS, Android), and web browsers. The build system uses a shared renderer codebase with platform-specific main process implementations. This enables feature parity across platforms while allowing platform-specific optimizations (e.g., native file dialogs on desktop, native camera access on mobile). The build pipeline handles code signing, app store distribution, and auto-updates.","intents":["I want to build an AI client that works on desktop, mobile, and web from a single codebase","I need to maintain feature parity across platforms without duplicating code","I want to distribute my app on app stores (App Store, Google Play, Windows Store) with proper signing and updates"],"best_for":["teams building cross-platform applications with limited resources","developers wanting to share code between web and native platforms","organizations needing app store distribution with auto-updates"],"limitations":["Platform-specific features (native APIs, hardware access) require platform-specific code in addition to shared code","Build complexity increases with each platform; debugging platform-specific issues requires platform-specific tools","App store distribution requires platform-specific certificates, provisioning profiles, and review processes","Performance on mobile may be lower than native apps due to Electron/Capacitor overhead","Some native APIs (e.g., advanced camera controls) may not be fully accessible through Capacitor"],"requires":["Node.js and npm/pnpm for build tooling","Electron for desktop builds","Capacitor for mobile builds","Platform-specific SDKs (Xcode for iOS, Android Studio for Android)","Code signing certificates for app store distribution"],"input_types":["TypeScript source code","platform-specific configuration files","build scripts and configuration"],"output_types":["desktop executables (EXE, DMG, AppImage)","mobile apps (IPA, APK)","web bundle (HTML, CSS, JS)"],"categories":["automation-workflow","build-system"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chatboxai--chatbox__cap_12","uri":"capability://text.generation.language.internationalization.i18n.with.multi.language.support","name":"internationalization (i18n) with multi-language support","description":"Chatbox implements comprehensive internationalization supporting 10+ languages (English, Chinese, Spanish, French, etc.). The system uses a translation file structure where UI strings are defined in a base language and translated to other languages. Language selection is persisted in user settings and applied globally. The i18n system handles pluralization, date/time formatting, and right-to-left language support. Developers can add new languages by providing translation files.","intents":["I want to use Chatbox in my native language (not English)","I need to add support for a new language to Chatbox","I want to ensure my AI client is accessible to users worldwide"],"best_for":["global applications serving users in multiple countries","open-source projects wanting community translations","organizations operating in multiple language regions"],"limitations":["Maintaining translation quality across languages requires native speakers or professional translators","Adding new languages requires translating all UI strings; incomplete translations result in mixed-language UIs","Some languages have complex grammar rules (pluralization, gender) that require special handling","Right-to-left languages (Arabic, Hebrew) require additional CSS and layout adjustments","Translation updates require rebuilding and redeploying the application"],"requires":["Translation files for each supported language","i18n library (e.g., i18next, react-i18next)","Native speakers or translators for quality assurance"],"input_types":["translation key-value pairs","language selection (user preference)"],"output_types":["localized UI strings","formatted dates and numbers","language-specific layouts"],"categories":["text-generation-language","localization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chatboxai--chatbox__cap_13","uri":"capability://automation.workflow.theme.system.with.light.dark.mode.and.customization","name":"theme system with light/dark mode and customization","description":"Chatbox includes a theming system that supports light and dark modes with customizable colors, fonts, and layout options. The theme is persisted in user settings and applied globally across the application. The system uses CSS variables for theme values, enabling runtime theme switching without page reload. Users can select from preset themes or customize individual theme properties. The theme system respects system preferences (OS dark mode) and allows manual override.","intents":["I want to use Chatbox in dark mode to reduce eye strain","I need to customize the UI colors to match my brand or preferences","I want the app to automatically switch to dark mode at night based on system settings"],"best_for":["users with accessibility needs (dark mode for low-light environments)","organizations wanting to brand the application with custom colors","developers building customizable UI frameworks"],"limitations":["Custom theme colors must be carefully chosen to maintain contrast and accessibility","Theme switching adds complexity to CSS and component styling","Some third-party components may not respect theme settings if they use hardcoded colors","System preference detection may not work on all platforms or browsers"],"requires":["CSS variable support in the browser/platform","Theme configuration storage (local settings)"],"input_types":["theme selection (light, dark, auto)","custom color values (hex, RGB, etc.)"],"output_types":["applied theme CSS variables","themed UI components"],"categories":["automation-workflow","ui-customization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chatboxai--chatbox__cap_14","uri":"capability://automation.workflow.keyboard.shortcuts.and.navigation.system","name":"keyboard shortcuts and navigation system","description":"Chatbox implements a comprehensive keyboard shortcut system for common actions (send message, new conversation, search, etc.) with customizable keybindings. The system displays available shortcuts in the UI and allows users to rebind shortcuts to their preferences. Keyboard navigation is fully supported for accessibility, enabling users to navigate the entire application without a mouse. The shortcut system is platform-aware, using platform conventions (Cmd on macOS, Ctrl on Windows/Linux).","intents":["I want to use keyboard shortcuts to work faster without reaching for the mouse","I need to customize keyboard shortcuts to match my muscle memory from other applications","I want to ensure the application is accessible to keyboard-only users"],"best_for":["power users and developers who prefer keyboard navigation","accessibility-conscious applications","teams with specific keyboard shortcut conventions"],"limitations":["Keyboard shortcut conflicts with OS or browser shortcuts may prevent some shortcuts from working","Customizing shortcuts requires careful UX to avoid user confusion","Some shortcuts may not work in all contexts (e.g., shortcuts disabled in text input fields)","Platform differences (macOS vs Windows) require different shortcut conventions"],"requires":["Keyboard event handling in the UI framework","Shortcut configuration storage (local settings)"],"input_types":["keyboard events (key presses)","shortcut configuration (key combinations, actions)"],"output_types":["triggered actions (send message, new conversation, etc.)","shortcut help UI"],"categories":["automation-workflow","accessibility"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chatboxai--chatbox__cap_15","uri":"capability://text.generation.language.message.rendering.and.markdown.support","name":"message rendering and markdown support","description":"Chatbox renders messages with full markdown support, including code blocks with syntax highlighting, tables, lists, and formatted text. The system uses a markdown parser to convert markdown to HTML, then renders the HTML with sanitization to prevent XSS attacks. Code blocks are highlighted using a syntax highlighter (e.g., Prism.js or Highlight.js) with support for 100+ programming languages. Messages can include embedded media (images, videos) and interactive elements (buttons, links).","intents":["I want code snippets in conversations to be syntax-highlighted for readability","I need to render complex formatted content (tables, lists, formatted text) from AI responses","I want to display images and media inline in conversations"],"best_for":["developers sharing and discussing code","technical documentation and tutorials","applications displaying rich formatted content"],"limitations":["Markdown rendering adds complexity and potential security vulnerabilities if not properly sanitized","Very large messages with complex markdown may have slow rendering performance","Some markdown extensions (tables, strikethrough) are not part of standard markdown and may not render consistently","Code block syntax highlighting requires language detection; misdetected languages may highlight incorrectly"],"requires":["Markdown parser library (e.g., marked, remark)","HTML sanitization library (e.g., DOMPurify) to prevent XSS","Syntax highlighter library (e.g., Prism.js, Highlight.js)"],"input_types":["markdown text","code blocks with language specification","embedded media URLs"],"output_types":["rendered HTML","syntax-highlighted code","formatted text and media"],"categories":["text-generation-language","rendering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chatboxai--chatbox__cap_2","uri":"capability://data.processing.analysis.local.first.data.persistence.with.libsql.sqlite","name":"local-first data persistence with libsql/sqlite","description":"Chatbox stores all conversation history, user settings, and session data locally using libsql (a fork of SQLite) accessed via IPC from the Electron main process. This ensures user data never leaves the device and provides offline-first functionality. The system implements data migrations for schema changes and provides a structured query interface for accessing conversations, messages, and metadata. All data is persisted to disk immediately, enabling recovery from crashes.","intents":["I want to build an AI client where user conversations stay on their device and are never sent to servers","I need to query conversation history efficiently (search, filter by date, etc.) without external databases","I want my application to work offline and sync conversations when connectivity returns"],"best_for":["privacy-conscious users and organizations with data residency requirements","developers building desktop/mobile apps where local storage is feasible","teams wanting to avoid cloud database costs and complexity"],"limitations":["Local storage limits are device-dependent; very large conversation histories (100k+ messages) may impact query performance","No built-in cloud sync — requires custom implementation for multi-device access","Database schema migrations must be carefully managed; breaking changes require data transformation logic","IPC communication between Electron renderer and main process adds ~5-20ms latency per database operation"],"requires":["Electron runtime (for IPC-based database access)","libsql/SQLite support in the platform","Sufficient local disk space for conversation storage"],"input_types":["conversation messages (text, metadata)","user settings and preferences","session state"],"output_types":["structured conversation data (messages, timestamps, metadata)","query results (filtered/paginated conversations)","settings objects"],"categories":["data-processing-analysis","local-storage"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chatboxai--chatbox__cap_3","uri":"capability://automation.workflow.state.management.with.jotai.atomic.atoms","name":"state management with jotai atomic atoms","description":"Chatbox uses Jotai, a primitive atoms-based state management library, to manage application state (current conversation, message list, UI settings, provider configuration, etc.). Each piece of state is represented as an atom, and components subscribe to atoms they need, enabling fine-grained reactivity and minimal re-renders. Atoms can be composed and derived, allowing complex state logic to be built from simple primitives. This approach avoids the boilerplate of Redux while maintaining predictable state updates.","intents":["I want to manage complex application state (conversations, messages, settings) without Redux boilerplate","I need fine-grained reactivity so only components using specific state atoms re-render when that state changes","I want to derive state from other atoms (e.g., filtered message list based on search atom) without manual memoization"],"best_for":["React-based applications needing lightweight state management","teams wanting to avoid Redux complexity for moderate state complexity","developers building real-time chat UIs where render performance matters"],"limitations":["Jotai's atom-based model can become complex with deeply nested derived atoms — debugging state flow requires understanding atom dependency graphs","No built-in time-travel debugging or Redux DevTools integration (though Jotai has basic DevTools support)","Async atoms and side effects require careful handling to avoid race conditions","Learning curve for developers familiar only with Redux or Context API"],"requires":["React 16.8+ (hooks support)","Jotai library installed","Understanding of atomic state management patterns"],"input_types":["atom definitions (primitive and derived)","component subscriptions to atoms"],"output_types":["reactive state values","state update functions","derived computed values"],"categories":["automation-workflow","state-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chatboxai--chatbox__cap_4","uri":"capability://tool.use.integration.mcp.server.integration.for.extensible.tool.use","name":"mcp server integration for extensible tool use","description":"Chatbox integrates with Model Context Protocol (MCP) servers, allowing users to extend the application with custom tools and data sources. The system can connect to MCP servers (local or remote), discover available tools/resources, and invoke them within conversations. Tool results are passed back to the LLM for further processing. This enables integration with external systems (databases, APIs, file systems) without modifying Chatbox core code.","intents":["I want to extend Chatbox with custom tools (e.g., database queries, API calls) without forking the codebase","I need to connect Chatbox to external data sources (knowledge bases, file systems, APIs) via MCP","I want to build a plugin ecosystem where users can add tools by running MCP servers"],"best_for":["teams building extensible AI applications with custom tool requirements","developers wanting to integrate Chatbox with proprietary systems via MCP","organizations needing a plugin architecture without maintaining a plugin marketplace"],"limitations":["MCP server discovery and lifecycle management adds operational complexity — requires users to run and maintain separate MCP server processes","Tool invocation latency depends on MCP server performance; slow servers degrade conversation responsiveness","Error handling for tool failures must be implemented in both MCP server and Chatbox; failures can interrupt conversation flow","No built-in authentication/authorization for MCP servers — security depends on network isolation"],"requires":["MCP server implementation (local or remote)","Network connectivity to MCP server","LLM provider support for tool calling (most modern providers support this)"],"input_types":["MCP server endpoint (stdio or HTTP)","tool schemas (JSON schema format)","tool invocation requests from LLM"],"output_types":["tool results (text, structured data)","tool metadata and schemas","execution status and errors"],"categories":["tool-use-integration","plugin-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chatboxai--chatbox__cap_5","uri":"capability://memory.knowledge.knowledge.base.system.with.semantic.search","name":"knowledge base system with semantic search","description":"Chatbox includes a knowledge base feature that allows users to upload documents, which are indexed and made available for semantic search within conversations. Documents are processed, chunked, and embedded (using provider-specific embeddings), then stored in a vector index. When users reference the knowledge base in conversations, relevant document chunks are retrieved and injected into the LLM context. This enables RAG-style augmented generation without requiring external vector databases.","intents":["I want to upload company documents and have the AI reference them in conversations","I need semantic search over my documents so the AI finds relevant context even with paraphrased queries","I want to build a document-aware AI assistant without setting up a separate vector database"],"best_for":["organizations with internal knowledge bases (documentation, policies, FAQs)","teams building domain-specific AI assistants","users wanting RAG without managing external infrastructure"],"limitations":["Embedding quality depends on the embedding model used; poor embeddings result in irrelevant document retrieval","Large knowledge bases (10k+ documents) may have slow indexing and retrieval times","No built-in document update/versioning — replacing documents requires re-indexing","Vector storage is local; no built-in multi-device sync for knowledge bases","Chunking strategy is fixed; users cannot customize chunk size or overlap"],"requires":["Document files (PDF, TXT, DOCX, etc.) to upload","Embedding model access (provider-specific or local)","Sufficient disk space for vector index storage"],"input_types":["document files (various formats)","user queries (text)","embedding model configuration"],"output_types":["indexed document chunks","retrieved relevant chunks","embedding vectors"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chatboxai--chatbox__cap_6","uri":"capability://search.retrieval.web.search.and.browsing.integration","name":"web search and browsing integration","description":"Chatbox can perform web searches and retrieve web content to augment LLM responses. When enabled, the system can search the web for current information, fetch and parse web pages, and inject the retrieved content into the LLM context. This enables the AI to provide up-to-date information beyond its training data cutoff. Web search is integrated as an optional capability that can be toggled per conversation.","intents":["I want the AI to search the web for current information (news, prices, weather) that's beyond its training data","I need to verify facts by having the AI fetch and analyze web pages","I want to build a conversational search engine that combines web results with LLM reasoning"],"best_for":["applications requiring current information (news, stock prices, weather)","fact-checking and research assistants","users wanting to augment LLM knowledge with real-time web data"],"limitations":["Web search adds latency to responses (typically 2-5 seconds per search)","Search quality depends on the search provider (Bing, Google, etc.); results may be noisy","Web page parsing is fragile — complex layouts, JavaScript-rendered content, or paywalls may cause retrieval failures","No built-in caching of search results; repeated searches for the same query hit the API again","Search API costs may apply depending on provider"],"requires":["Web search API key (Bing, Google, or other provider)","Network connectivity for web searches","LLM provider support for tool calling (to invoke search tools)"],"input_types":["user queries (text)","search parameters (number of results, language, etc.)"],"output_types":["search results (title, URL, snippet)","fetched web page content","augmented LLM responses with web context"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chatboxai--chatbox__cap_7","uri":"capability://image.visual.file.and.media.handling.with.multi.format.support","name":"file and media handling with multi-format support","description":"Chatbox supports uploading and processing various file types (images, PDFs, documents, code files, etc.) within conversations. Files are processed according to their type — images are analyzed by vision-capable models, PDFs are extracted and chunked, code files are syntax-highlighted and analyzed. The system handles file encoding, format conversion, and context injection so the LLM can reason about file contents. File handling is abstracted to support different file types without duplicating logic.","intents":["I want to upload an image and ask the AI to analyze it or describe what's in it","I need to share code files with the AI for debugging or code review","I want to upload a PDF and have the AI summarize or answer questions about its contents"],"best_for":["developers sharing code for analysis or debugging","users wanting to analyze images, documents, or PDFs","teams collaborating on file-based content with AI assistance"],"limitations":["File size limits depend on provider and model; large files may be rejected or truncated","Vision model quality varies; not all models support image analysis","PDF extraction is fragile for complex layouts, scanned documents, or non-standard formats","File processing adds latency; large files may take several seconds to process","No built-in file storage or version control; files are processed per-conversation"],"requires":["File upload capability in UI","Vision-capable LLM model for image analysis (e.g., GPT-4V, Claude 3 Vision)","File parsing libraries for supported formats (PDF, DOCX, etc.)"],"input_types":["image files (PNG, JPG, GIF, WebP, etc.)","document files (PDF, DOCX, TXT, etc.)","code files (any programming language)","media files (audio, video metadata)"],"output_types":["extracted text content","image analysis and descriptions","code analysis and suggestions","document summaries"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chatboxai--chatbox__cap_8","uri":"capability://planning.reasoning.ai.copilots.system.with.custom.assistant.creation","name":"ai copilots system with custom assistant creation","description":"Chatbox includes an AI copilots system that allows users to create custom assistants with predefined system prompts, knowledge bases, and tool configurations. Copilots are templates that encapsulate a specific use case (e.g., 'Code Reviewer', 'Content Writer', 'Research Assistant') with customized instructions and capabilities. Users can create, save, and share copilots, and switch between them within the application. Each copilot maintains its own conversation history and configuration.","intents":["I want to create a custom AI assistant with specific instructions for code review without modifying the base application","I need to save and reuse assistant configurations (system prompt, tools, knowledge base) across conversations","I want to share my custom assistant configuration with team members"],"best_for":["teams with specialized use cases (code review, content creation, research)","organizations wanting to standardize AI assistant behavior across users","power users wanting to create and share custom assistant templates"],"limitations":["Copilot sharing requires manual export/import or a centralized registry — no built-in marketplace","Copilot configuration is stored locally; no cloud sync across devices","System prompt injection vulnerabilities if users create copilots with untrusted instructions","No versioning or rollback for copilot configurations"],"requires":["Ability to define system prompts and instructions","Access to knowledge bases and tools to attach to copilots","Storage for copilot definitions (local database)"],"input_types":["copilot name and description","system prompt (text instructions)","attached knowledge bases","enabled tools and capabilities"],"output_types":["copilot configuration objects","conversation history per copilot","shareable copilot templates"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chatboxai--chatbox__cap_9","uri":"capability://automation.workflow.team.collaboration.and.conversation.sharing","name":"team collaboration and conversation sharing","description":"Chatbox provides team collaboration features that allow users to share conversations, copilots, and knowledge bases with team members. Shared conversations can be viewed, commented on, or used as templates for new conversations. The system manages access control and tracks who has access to shared resources. Sharing can be done via direct links or team-based access control. This enables teams to collaborate on AI-assisted tasks and share insights.","intents":["I want to share a conversation with my team so they can see the AI's analysis and provide feedback","I need to give team members access to a custom copilot or knowledge base","I want to track who has access to shared conversations and revoke access if needed"],"best_for":["teams collaborating on AI-assisted projects","organizations wanting to share AI insights across departments","teams needing audit trails for shared conversations"],"limitations":["Sharing mechanism is local-first; requires custom backend for multi-device sync","No built-in permission management beyond basic access control","Shared conversations are snapshots; changes to the original don't update shared copies","No real-time collaboration on conversations — users cannot edit simultaneously"],"requires":["Team infrastructure (shared storage, access control system)","User authentication and identity management","Network connectivity for sharing"],"input_types":["conversation IDs or copilot IDs to share","team member identities or email addresses","access control settings (view-only, edit, etc.)"],"output_types":["shareable links or access tokens","shared conversation data","access control lists"],"categories":["automation-workflow","collaboration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["API keys for desired providers (OpenAI, Anthropic, Google, etc.) or local Ollama instance","TypeScript/JavaScript runtime environment","Network connectivity for cloud providers","Provider support for streaming APIs (most modern LLM providers support this)","UI framework capable of handling rapid state updates (React, Vue, etc.)","Network connection with reasonable latency (streaming over high-latency connections degrades UX)","Image generation API key (DALL-E, Midjourney, Stable Diffusion, etc.)","Sufficient API credits/quota for image generation","Network connectivity to image generation provider","Node.js and npm/pnpm for build tooling"],"failure_modes":["Provider-specific features (e.g., vision models, function calling schemas) require adapter-level implementation — not all features are uniformly available across providers","Streaming response handling varies by provider; some providers have latency differences in token delivery","Error handling must be implemented per-provider as error codes and messages differ significantly","Streaming adds complexity to error handling — errors mid-stream may result in partial responses displayed to users","Token-level processing introduces ~10-50ms per-token overhead depending on UI rendering performance","Some providers (e.g., local Ollama) may have inconsistent streaming behavior or buffering that delays token delivery","Image generation adds significant latency (10-60 seconds per image depending on provider)","Quality and style vary significantly between providers; no unified quality guarantee","Image generation costs are typically higher than text generation; usage can become expensive","Generated images may have copyright or licensing restrictions depending on provider","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4184527420890831,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:58:39.623Z","last_commit":"2026-04-09T03:24:43Z"},"community":{"stars":39770,"forks":4031,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=chatboxai--chatbox","compare_url":"https://unfragile.ai/compare?artifact=chatboxai--chatbox"}},"signature":"ZkBRHzmeTEfyXU+oA1dKJxQWC0FPXOs/wY2Q9jmLojETdt/0Grne206abVpA4h51Q3pnG488uJLyCa+m7UXtCg==","signedAt":"2026-06-21T09:13:09.906Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/chatboxai--chatbox","artifact":"https://unfragile.ai/chatboxai--chatbox","verify":"https://unfragile.ai/api/v1/verify?slug=chatboxai--chatbox","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"}}