{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-szczyglis-dev--py-gpt","slug":"szczyglis-dev--py-gpt","name":"py-gpt","type":"app","url":"https://pygpt.net","page_url":"https://unfragile.ai/szczyglis-dev--py-gpt","categories":["chatbots-assistants","rag-knowledge"],"tags":["ai","ai-assistant","artificial-intelligence","autonomous-agent","chatbot","claude","deepseek","desktop-app","gemini","gpt-4","gpt-5","grok","llama-index","llm","mcp","o1","ollama","openai","perplexity","sora2"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-szczyglis-dev--py-gpt__cap_0","uri":"capability://text.generation.language.multi.provider.llm.abstraction.with.unified.chat.interface","name":"multi-provider llm abstraction with unified chat interface","description":"Abstracts 10+ AI providers (OpenAI, Anthropic, Google, Ollama, DeepSeek, Perplexity, Grok, Bielik) through a unified Chat mode interface that normalizes request/response formats across different SDK implementations. Uses a provider-agnostic message routing layer that maps provider-specific APIs (openai.ChatCompletion, anthropic.Anthropic, etc.) to a common internal message schema, enabling seamless model switching without code changes.","intents":["Switch between GPT-4, Claude, Gemini, and local Ollama models in the same conversation without re-architecting","Build a desktop app that doesn't lock users into a single AI provider","Test the same prompt across multiple models to compare outputs"],"best_for":["Desktop app developers building provider-agnostic AI assistants","Teams evaluating multiple LLM providers before committing to one","Researchers comparing model outputs across vendors"],"limitations":["Provider-specific features (e.g., OpenAI vision, Anthropic computer use) require mode-specific handling; no automatic feature parity across providers","Rate limiting and quota management per provider must be configured separately","Response latency varies by provider; no built-in load balancing or failover between providers"],"requires":["Python 3.9+","API keys for desired providers (OpenAI, Anthropic, Google, etc.) or local Ollama instance","PySide6 for GUI rendering","Individual provider SDKs (openai, anthropic, google-generativeai, etc.)"],"input_types":["text prompts","conversation history (multi-turn)","system messages/presets"],"output_types":["text responses","streaming token chunks","structured metadata (model name, tokens used, latency)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-szczyglis-dev--py-gpt__cap_1","uri":"capability://memory.knowledge.rag.enabled.document.chat.with.llamaindex.vector.indexing","name":"rag-enabled document chat with llamaindex vector indexing","description":"Implements a 'Chat with Files' mode that uses LlamaIndex to parse, chunk, and embed documents (PDF, DOCX, TXT, etc.) into a vector store, then retrieves relevant context for each user query before passing to the LLM. Uses a retrieval-augmented generation pipeline where document embeddings are indexed locally or in a vector database, and a retriever component fetches top-k similar chunks based on semantic similarity to the user query.","intents":["Ask questions about uploaded documents and get answers grounded in the document content","Index a knowledge base of internal documents and chat with it using natural language","Reduce hallucination by constraining LLM responses to retrieved document context"],"best_for":["Knowledge workers processing large document sets (contracts, research papers, manuals)","Teams building internal knowledge bases without external RAG services","Users wanting local document processing without cloud uploads"],"limitations":["Chunking strategy is fixed; no built-in support for hierarchical or semantic chunking strategies","Vector store is ephemeral per session unless explicitly persisted; no automatic index versioning or incremental updates","Embedding model is provider-dependent (OpenAI embeddings, local models); switching embedding models requires re-indexing","Large document sets (>1GB) may cause memory pressure on desktop systems; no streaming or lazy-loading of indices"],"requires":["Python 3.9+","LlamaIndex library (llama-index)","Document parser libraries (pypdf, python-docx, etc.)","Embedding model (OpenAI API key or local embedding model)","Vector store backend (in-memory, Chroma, Pinecone, etc.)"],"input_types":["PDF files","DOCX documents","TXT files","text queries"],"output_types":["text responses with source citations","retrieved document chunks","relevance scores"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-szczyglis-dev--py-gpt__cap_10","uri":"capability://automation.workflow.preset.and.assistant.configuration.management.with.persistent.state","name":"preset and assistant configuration management with persistent state","description":"Implements a preset system that allows users to save and load configurations for prompts, system messages, model parameters, and mode-specific settings. Presets are stored as JSON files in the application's config directory and can be quickly switched to apply a consistent set of parameters across conversations. Assistants are a specialized preset type that include additional metadata (name, description, avatar) and can be shared or exported. The system handles preset versioning, import/export, and conflict resolution when loading presets.","intents":["Save a set of prompts and parameters as a reusable preset for quick access","Create and share custom assistants with specific personalities or expertise","Switch between different conversation styles (e.g., technical vs. creative) without manual reconfiguration"],"best_for":["Power users managing multiple conversation styles or personas","Teams sharing assistant configurations across users","Developers building custom assistants on top of py-gpt"],"limitations":["Presets are not versioned; overwriting a preset loses the previous configuration","No conflict detection when importing presets with the same name","Preset sharing requires manual file export/import; no built-in sharing mechanism","Presets are user-specific; no team-level preset management","No preset templates or marketplace; users must create presets from scratch"],"requires":["Python 3.9+","Write access to py-gpt config directory","JSON editing capability (for manual preset creation)"],"input_types":["preset configuration (JSON)","assistant metadata (name, description, avatar)"],"output_types":["loaded preset (applied to current session)","exported preset file (for sharing)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-szczyglis-dev--py-gpt__cap_11","uri":"capability://text.generation.language.multi.language.localization.with.dynamic.ui.translation","name":"multi-language localization with dynamic ui translation","description":"Implements a localization system that translates the entire UI (menus, buttons, dialogs, help text) into multiple languages using JSON-based translation files. The system detects the user's system language and loads the appropriate translation file at startup; users can manually override the language in settings. Translations are applied dynamically to all UI elements without requiring application restart. Supports pluralization, context-specific translations, and fallback to English if a translation is missing.","intents":["Use py-gpt in your native language without language barriers","Deploy py-gpt to international teams with localized interfaces","Contribute translations for new languages"],"best_for":["International teams using py-gpt","Non-English speakers wanting a native language interface","Organizations deploying py-gpt globally"],"limitations":["Translation quality depends on community contributions; some languages may have incomplete translations","LLM responses are not translated; only the UI is localized","Adding new UI strings requires manual translation into all supported languages","No built-in translation management tool; translations are manually edited JSON files","Right-to-left (RTL) languages are not fully supported; UI layout may not adapt correctly"],"requires":["Python 3.9+","Translation files (JSON) for desired languages"],"input_types":["system language setting","translation files (JSON)"],"output_types":["localized UI text"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-szczyglis-dev--py-gpt__cap_12","uri":"capability://memory.knowledge.conversation.history.management.with.context.window.optimization","name":"conversation history management with context window optimization","description":"Manages conversation history by storing messages in a structured format and intelligently selecting which messages to include in the LLM context window. Uses a sliding window approach (keep recent N messages) or summarization-based approach (summarize old messages and include summary) to stay within provider token limits. Handles message serialization, persistence to disk, and retrieval for multi-turn conversations. Supports conversation export (JSON, Markdown) and import for backup/sharing.","intents":["Maintain long conversations without hitting token limits","Export conversations for documentation or sharing","Resume conversations across application restarts"],"best_for":["Users having long, multi-turn conversations","Teams documenting conversations for compliance or knowledge sharing","Developers building conversation-aware AI systems"],"limitations":["Sliding window approach loses context from older messages; summarization approach may lose nuance","No automatic conversation segmentation; users must manually manage conversation length","Conversation storage is local; no cloud backup or sync across devices","No built-in conversation search; finding specific messages requires manual scrolling","Conversation export formats are basic (JSON, Markdown); no rich formatting options"],"requires":["Python 3.9+","Local storage for conversation history (JSON files or database)"],"input_types":["user messages","LLM responses","metadata (timestamp, model, tokens)"],"output_types":["conversation history (JSON, Markdown)","context window (selected messages for LLM)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-szczyglis-dev--py-gpt__cap_13","uri":"capability://automation.workflow.theme.and.ui.customization.with.pyside6.styling","name":"theme and ui customization with pyside6 styling","description":"Provides a theming system that allows users to customize the application's appearance through CSS-like stylesheets (QSS - Qt Style Sheets). Includes built-in light and dark themes, and users can create custom themes by editing QSS files. The system handles theme persistence, dynamic theme switching without restart, and font/color customization. Uses PySide6's native styling engine for consistent cross-platform appearance.","intents":["Customize the application appearance to match personal preferences","Create dark mode for low-light environments","Build branded versions of py-gpt with custom colors and fonts"],"best_for":["Users wanting customized UI appearance","Organizations deploying branded versions of py-gpt","Accessibility users needing high-contrast or large-font themes"],"limitations":["Theme customization requires QSS knowledge; no visual theme editor","Some UI elements may not respond to theme changes (third-party widgets)","Theme changes require application restart for some components","No theme marketplace or community themes; users must create themes from scratch","Accessibility features (high contrast, large fonts) are not built-in; must be manually configured"],"requires":["Python 3.9+","PySide6","QSS knowledge for custom themes (optional)"],"input_types":["QSS stylesheet files","theme configuration (colors, fonts)"],"output_types":["styled UI"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-szczyglis-dev--py-gpt__cap_14","uri":"capability://tool.use.integration.model.configuration.and.provider.credential.management","name":"model configuration and provider credential management","description":"Manages model configurations and API credentials through a centralized settings system. Stores provider API keys securely (encrypted at rest if possible), allows users to configure model parameters (temperature, max_tokens, top_p, etc.) per provider, and maintains a registry of available models per provider. Supports model discovery (fetching available models from provider APIs) and validation of credentials before use. Configuration is stored in JSON files with sensitive data optionally encrypted.","intents":["Configure API keys for multiple providers without hardcoding them","Adjust model parameters (temperature, max tokens) for different use cases","Discover and select from available models without manual configuration"],"best_for":["Users managing credentials for multiple AI providers","Teams deploying py-gpt with shared configurations","Developers building provider-agnostic AI applications"],"limitations":["Credential storage is not encrypted by default; API keys are stored in plaintext JSON","No built-in credential rotation or expiration management","Model discovery requires API calls; not all providers support model listing","Parameter validation is minimal; invalid parameters may cause API errors","No audit logging for credential access or model configuration changes"],"requires":["Python 3.9+","API keys for desired providers","Write access to config directory"],"input_types":["API keys","model parameters (temperature, max_tokens, etc.)","provider configuration"],"output_types":["validated credentials","available models list","model parameters"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-szczyglis-dev--py-gpt__cap_2","uri":"capability://planning.reasoning.12.mode.operational.system.with.mode.specific.llm.workflows","name":"12-mode operational system with mode-specific llm workflows","description":"Implements a modular mode system where each operational mode (Chat, Chat with Files, Audio, Research, Completion, Image Generation, Assistants, Agents, Experts, Computer Use) encapsulates a distinct LLM workflow pattern. Each mode is a separate class (pygpt_net.core.modes.*) that defines its own message handling, context management, and provider integration, allowing users to switch between fundamentally different interaction patterns (e.g., from chat to agentic reasoning to image generation) within the same application.","intents":["Use different LLM interaction patterns for different tasks without switching applications","Build a single desktop app that supports chat, agents, image generation, and code completion","Extend the application with custom modes that follow the existing mode architecture"],"best_for":["Power users who need multiple LLM interaction patterns in one interface","Developers building extensible AI desktop applications","Teams wanting a unified AI assistant that handles diverse tasks (chat, coding, image gen, research)"],"limitations":["Mode switching is manual; no automatic mode recommendation based on user intent","Context is not shared between modes; switching modes loses conversation history","Some modes require specific providers (e.g., Computer Use requires Anthropic, Research requires Perplexity); mode availability depends on configured providers","Mode-specific features (e.g., streaming in Chat vs. batch in Completion) are not unified; each mode has its own latency and throughput characteristics"],"requires":["Python 3.9+","PySide6 for mode UI rendering","Provider SDKs for modes being used (OpenAI for Assistants, Anthropic for Computer Use, etc.)","Mode-specific dependencies (e.g., audio libraries for Realtime+Audio mode)"],"input_types":["text prompts","audio streams (Realtime+Audio)","images (Image Generation)","code snippets (Completion)","documents (Chat with Files)"],"output_types":["text responses","images (Image Generation)","audio (Realtime+Audio)","agent action sequences (Agent modes)","code completions (Completion)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-szczyglis-dev--py-gpt__cap_3","uri":"capability://text.generation.language.openai.assistants.api.integration.with.persistent.thread.management","name":"openai assistants api integration with persistent thread management","description":"Wraps the OpenAI Assistants API (pygpt_net.core.modes.assistant.Assistant) to enable stateful, multi-turn conversations with persistent thread management. Handles assistant creation, thread lifecycle (create, retrieve, update), message history, and run execution with automatic polling for completion. Supports file uploads, code interpreter, and retrieval augmentation through the Assistants API native features.","intents":["Use OpenAI Assistants with persistent conversation threads without managing thread IDs manually","Build a desktop interface to OpenAI Assistants with file upload and code execution","Maintain conversation state across application restarts using thread persistence"],"best_for":["Teams using OpenAI Assistants API and wanting a desktop client","Developers building stateful AI assistants with persistent memory","Users wanting code execution and file analysis through Assistants"],"limitations":["Assistants API has higher latency than Chat Completions (run polling adds 1-5s overhead per message)","File uploads are limited by OpenAI's API constraints (20MB per file, 10GB per assistant)","Thread persistence requires external storage (database, file system); no built-in thread versioning or rollback","Code interpreter and retrieval features are OpenAI-only; no equivalent for other providers"],"requires":["Python 3.9+","OpenAI API key with Assistants API access","openai SDK (v1.0+)","Storage backend for thread IDs and metadata (local JSON, database, etc.)"],"input_types":["text messages","file uploads (PDF, DOCX, images, code, etc.)","system instructions/assistant configuration"],"output_types":["text responses","code execution results","file references","thread metadata"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-szczyglis-dev--py-gpt__cap_4","uri":"capability://planning.reasoning.llamaindex.agent.orchestration.with.expert.multi.agent.coordination","name":"llamaindex agent orchestration with expert multi-agent coordination","description":"Implements two agent modes: Agent (LlamaIndex) uses LlamaIndex's agent framework to decompose tasks into tool-calling steps with automatic planning and execution, while Experts mode coordinates multiple specialized agents (each with different system prompts and tool sets) to solve complex problems through expert consensus or delegation. Both modes use LlamaIndex's ReActAgent or similar patterns to generate reasoning chains and tool calls, with support for custom tool registration and execution.","intents":["Decompose complex tasks into subtasks and execute them using tool calls (Agent mode)","Route queries to specialized expert agents based on task type (Experts mode)","Build autonomous workflows that reason about which tools to use and in what order"],"best_for":["Developers building autonomous AI agents with tool use","Teams needing multi-agent systems where different experts handle different domains","Researchers experimenting with agent reasoning patterns and tool orchestration"],"limitations":["Agent reasoning is sequential; no built-in parallelization of tool calls or expert queries","Tool execution errors are not automatically recovered; agents may get stuck in loops if tool calls fail","Expert routing is manual (user selects expert) or rule-based; no learned routing based on task success","Context window limitations mean complex task decompositions may exceed token limits","No built-in observability for agent decision-making; debugging agent behavior requires log inspection"],"requires":["Python 3.9+","LlamaIndex library (llama-index)","LLM provider (OpenAI, Anthropic, etc.) for agent reasoning","Tool definitions (custom functions or integrations)","Vector store for agent memory (optional)"],"input_types":["natural language task descriptions","tool definitions (function signatures, descriptions)","expert system prompts (Experts mode)"],"output_types":["final task result","reasoning chain (thought, action, observation steps)","tool call logs","expert selection/routing decisions"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-szczyglis-dev--py-gpt__cap_5","uri":"capability://text.generation.language.real.time.audio.conversation.with.streaming.speech.recognition.and.synthesis","name":"real-time audio conversation with streaming speech recognition and synthesis","description":"Implements a Realtime+Audio mode that handles bidirectional audio streaming using OpenAI's Realtime API or Google's speech services. Captures audio input via system microphone, streams to the provider's speech-to-text engine, passes transcribed text to the LLM, and streams the response back through text-to-speech synthesis with audio playback. Uses asynchronous I/O to manage concurrent audio capture, transcription, LLM inference, and synthesis without blocking the UI.","intents":["Have hands-free voice conversations with an AI assistant","Use the AI assistant while driving or multitasking (audio-only interaction)","Build a voice-first interface to LLM capabilities"],"best_for":["Users wanting voice-first AI interaction on desktop","Accessibility use cases (users who prefer audio over text)","Hands-free scenarios (driving, cooking, etc.)"],"limitations":["Latency is higher than text chat due to audio encoding/decoding and streaming overhead (typically 2-5s round-trip)","Speech recognition accuracy depends on audio quality and background noise; no built-in noise cancellation","Text-to-speech voice selection is limited to provider offerings; no custom voice training","Audio streaming requires stable internet connection; no offline fallback","Concurrent audio streams (multiple conversations) are not supported; single audio session per mode instance"],"requires":["Python 3.9+","OpenAI API key (for Realtime API) or Google Cloud credentials (for Speech API)","Audio input device (microphone)","Audio output device (speakers)","pyaudio or similar library for audio I/O","Stable internet connection (for streaming)"],"input_types":["audio stream (PCM, 16-bit, 24kHz or provider-specific format)"],"output_types":["transcribed text","LLM response text","synthesized audio stream"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-szczyglis-dev--py-gpt__cap_6","uri":"capability://search.retrieval.web.search.integration.for.research.enhanced.conversations","name":"web search integration for research-enhanced conversations","description":"Implements a Research mode that augments LLM responses with real-time web search results from Perplexity API or OpenAI's web search capability. Before generating a response, the mode queries a search provider for current information, retrieves top results, and passes them as context to the LLM, enabling responses grounded in recent web data. Handles search query formulation, result ranking, and context injection into the LLM prompt.","intents":["Ask questions about current events, recent news, or real-time data without hallucination","Get LLM responses backed by web sources with citations","Research topics using AI-powered web search without leaving the application"],"best_for":["Researchers and journalists needing current information","Users wanting factual, up-to-date responses without hallucination","Teams building research assistants with web-grounded reasoning"],"limitations":["Web search latency adds 2-5s per query; slower than pure LLM responses","Search result quality depends on search provider; no automatic result validation or fact-checking","Search queries are auto-formulated by the LLM; may not match user intent for ambiguous queries","Citation accuracy depends on search provider's metadata; no built-in citation verification","Search result freshness depends on provider's crawl frequency; real-time data may lag by hours"],"requires":["Python 3.9+","Perplexity API key or OpenAI API key with web search enabled","LLM provider for response generation","Stable internet connection for web search"],"input_types":["natural language queries","search parameters (number of results, time range, etc.)"],"output_types":["search results (title, URL, snippet)","LLM response with web context","source citations"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-szczyglis-dev--py-gpt__cap_7","uri":"capability://image.visual.image.and.video.generation.with.provider.specific.model.support","name":"image and video generation with provider-specific model support","description":"Implements an Image Generation mode that supports multiple generative providers (OpenAI DALL-E, Google Imagen, Sora for video) through a unified interface. Accepts text prompts and optional image parameters (size, quality, style), routes requests to the selected provider's API, and returns generated images or videos. Handles image encoding, caching, and local storage of generated assets.","intents":["Generate images from text descriptions without switching to separate tools","Create multiple image variations and compare outputs from different models","Generate videos using Sora or similar models integrated into the assistant"],"best_for":["Designers and content creators wanting AI image generation in their workflow","Teams building creative AI assistants with image/video generation","Users wanting a unified interface to multiple image generation models"],"limitations":["Image generation latency is high (30-60s per image); not suitable for real-time interaction","Generated images are subject to provider content policies; some requests may be rejected","Image quality and style vary significantly between providers; no automatic quality normalization","Video generation (Sora) is limited to OpenAI and has strict usage quotas","Generated images are stored locally; no built-in cloud backup or sharing"],"requires":["Python 3.9+","API keys for image generation providers (OpenAI, Google, etc.)","Sufficient API quota for image generation (can be expensive)","Local storage for generated images"],"input_types":["text prompts","image parameters (size, quality, style, count)","reference images (optional, for some models)"],"output_types":["generated images (PNG, JPEG)","generated videos (MP4 for Sora)","image metadata (prompt, model, generation time)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-szczyglis-dev--py-gpt__cap_8","uri":"capability://automation.workflow.anthropic.computer.use.mode.for.autonomous.desktop.control","name":"anthropic computer use mode for autonomous desktop control","description":"Implements a Computer Use mode that leverages Anthropic's computer use capability to enable the AI to autonomously control the desktop (take screenshots, click, type, scroll). The mode captures the current screen state, passes it to Claude with computer use instructions, receives action sequences (click coordinates, text input, etc.), and executes them on the user's desktop. Maintains a loop of perception (screenshot) → reasoning (Claude) → action (execute) until the task is complete.","intents":["Let an AI assistant autonomously complete desktop tasks (fill forms, navigate websites, etc.)","Automate repetitive GUI-based workflows without writing scripts","Build a general-purpose desktop automation agent"],"best_for":["Users wanting GUI automation without learning scripting","Teams automating repetitive desktop workflows","Researchers experimenting with autonomous agent capabilities"],"limitations":["Computer use is slow (5-30s per action due to screenshot capture and Claude reasoning)","Action accuracy depends on screen layout; changes to UI may confuse the agent","No built-in error recovery; if an action fails, the agent may not adapt","Security risk: agent has full desktop control; requires careful task scoping and monitoring","Anthropic-only; no equivalent for other providers","No undo/rollback; destructive actions (delete, modify) are permanent"],"requires":["Python 3.9+","Anthropic API key with computer use capability enabled","Claude model with computer use support (Claude 3.5 Sonnet or later)","Screen capture capability (PIL, mss, or similar)","Input simulation library (pyautogui, pynput, or similar)"],"input_types":["natural language task descriptions","task constraints (allowed applications, restricted areas)"],"output_types":["action sequences (click, type, scroll)","task completion status","screenshots of intermediate states"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-szczyglis-dev--py-gpt__cap_9","uri":"capability://tool.use.integration.plugin.system.with.extensible.tool.and.mode.registration","name":"plugin system with extensible tool and mode registration","description":"Implements a plugin architecture (pygpt_net.core.plugins) that allows users to extend py-gpt with custom tools, modes, and integrations without modifying core code. Plugins are Python modules that register themselves with the plugin manager, exposing tool definitions (function signatures, descriptions) or custom modes (classes inheriting from Mode base class). The plugin system handles plugin discovery, loading, validation, and lifecycle management (enable/disable/uninstall).","intents":["Add custom tools to agents without modifying py-gpt source code","Build domain-specific modes (e.g., SQL query mode, code review mode) as plugins","Integrate third-party services (Slack, Jira, custom APIs) as agent tools"],"best_for":["Developers extending py-gpt with custom capabilities","Teams building domain-specific AI assistants on top of py-gpt","Organizations integrating py-gpt with internal tools and APIs"],"limitations":["Plugin API is not versioned; breaking changes to core plugin interfaces may break existing plugins","No built-in plugin marketplace or discovery; plugins must be manually installed","Plugin security is not enforced; malicious plugins can access full system resources","Plugin dependencies must be manually managed; no automatic dependency resolution","No plugin sandboxing; plugins run with the same permissions as py-gpt"],"requires":["Python 3.9+","Understanding of py-gpt plugin API (documented in docs/plugins)","Plugin development tools (Python IDE, testing framework)"],"input_types":["plugin module (Python file or package)","plugin configuration (JSON or YAML)"],"output_types":["registered tools (available to agents)","registered modes (available in mode selector)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API keys for desired providers (OpenAI, Anthropic, Google, etc.) or local Ollama instance","PySide6 for GUI rendering","Individual provider SDKs (openai, anthropic, google-generativeai, etc.)","LlamaIndex library (llama-index)","Document parser libraries (pypdf, python-docx, etc.)","Embedding model (OpenAI API key or local embedding model)","Vector store backend (in-memory, Chroma, Pinecone, etc.)","Write access to py-gpt config directory","JSON editing capability (for manual preset creation)"],"failure_modes":["Provider-specific features (e.g., OpenAI vision, Anthropic computer use) require mode-specific handling; no automatic feature parity across providers","Rate limiting and quota management per provider must be configured separately","Response latency varies by provider; no built-in load balancing or failover between providers","Chunking strategy is fixed; no built-in support for hierarchical or semantic chunking strategies","Vector store is ephemeral per session unless explicitly persisted; no automatic index versioning or incremental updates","Embedding model is provider-dependent (OpenAI embeddings, local models); switching embedding models requires re-indexing","Large document sets (>1GB) may cause memory pressure on desktop systems; no streaming or lazy-loading of indices","Presets are not versioned; overwriting a preset loses the previous configuration","No conflict detection when importing presets with the same name","Preset sharing requires manual file export/import; no built-in sharing mechanism","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.26101490128190136,"quality":0.5,"ecosystem":0.75,"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":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:57:09.057Z","last_commit":"2026-02-06T03:08:43Z"},"community":{"stars":1759,"forks":323,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=szczyglis-dev--py-gpt","compare_url":"https://unfragile.ai/compare?artifact=szczyglis-dev--py-gpt"}},"signature":"LC16dJJRgDuYJrmkwU2X9BqGYAVJG6E2WP0xt9djGs6Z/b0/eAEw3vbh6to2ndycpBy+VKHd1lK1g1NyEPIoAQ==","signedAt":"2026-06-22T22:05:03.534Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/szczyglis-dev--py-gpt","artifact":"https://unfragile.ai/szczyglis-dev--py-gpt","verify":"https://unfragile.ai/api/v1/verify?slug=szczyglis-dev--py-gpt","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"}}