{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-star-the-repo","slug":"star-the-repo","name":"star the repo","type":"repo","url":"https://github.com/Shubhamsaboo/awesome-llm-apps","page_url":"https://unfragile.ai/star-the-repo","categories":["automation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-star-the-repo__cap_0","uri":"capability://planning.reasoning.curated.llm.application.template.library","name":"curated-llm-application-template-library","description":"Provides a hierarchically-organized collection of 30+ production-ready and educational LLM application templates spanning seven architectural categories (starter agents, advanced single agents, multi-agent systems, RAG tutorials, MCP agents, voice agents, and memory-augmented apps). Templates are organized by complexity level (beginner to expert) and include complete working implementations with dependencies, configuration examples, and framework-specific patterns, enabling developers to clone, customize, and deploy reference architectures without building from scratch.","intents":["I need a working example of how to build a multi-agent system for a specific domain","I want to understand different RAG patterns and see production-ready implementations","I'm learning LLM integration and need educational examples progressing from simple to complex","I need to quickly prototype an AI agent for investment analysis, travel planning, or web scraping","I want to see how to integrate MCP (Model Context Protocol) with LLM agents"],"best_for":["developers building LLM agents for the first time seeking reference implementations","teams evaluating different agent frameworks (Agno, LangChain, LangGraph) before committing","researchers studying multi-agent coordination patterns and RAG architectures","startup founders prototyping AI-powered products with limited ML expertise"],"limitations":["Templates are snapshots — may lag behind latest framework versions or API changes","No built-in CI/CD or automated testing across all templates; maintenance burden on community","Requires manual adaptation for production use; templates assume specific API keys and environment configurations","Documentation depth varies by template; some advanced patterns lack detailed explanation"],"requires":["Python 3.9+","Git for cloning repository","API keys for at least one LLM provider (OpenAI, Anthropic, Google Gemini, Cohere, or Ollama for local)","Node.js 18+ for Streamlit UI templates","Docker (optional, for containerized deployments)"],"input_types":["Python source code","requirements.txt dependency files","README documentation","Configuration files (.env templates)"],"output_types":["Executable Python applications","Streamlit web UIs","Agent response outputs","RAG retrieval results with citations"],"categories":["planning-reasoning","template-library","reference-implementations"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-star-the-repo__cap_1","uri":"capability://planning.reasoning.framework.agnostic.agent.pattern.reference","name":"framework-agnostic-agent-pattern-reference","description":"Demonstrates implementation patterns across three major agent frameworks (Agno, LangChain/LangGraph, and MCP) with explicit code examples showing how the same architectural goal (e.g., multi-agent coordination, RAG integration) is achieved differently in each framework. Includes pattern documentation for tool calling, state management, context passing, and agent composition, allowing developers to understand framework trade-offs and migrate between ecosystems.","intents":["I need to understand how Agno's high-level abstractions differ from LangGraph's explicit state management","I want to migrate an existing LangChain agent to use MCP for better tool integration","I'm evaluating which framework to use for a production system and need concrete comparison","I need to understand how different frameworks handle multi-agent communication and coordination"],"best_for":["architects choosing between agent frameworks for new projects","teams migrating from one framework to another","developers building framework-agnostic agent abstractions","researchers comparing agent framework design decisions"],"limitations":["Pattern documentation is implicit in code examples rather than explicit comparison tables","Framework versions may diverge; templates may not reflect latest API changes across all three ecosystems simultaneously","No automated testing to ensure pattern equivalence across frameworks","Learning curve still requires reading source code; not a high-level abstraction layer"],"requires":["Python 3.9+","Agno framework (pip install agno)","LangChain and LangGraph (pip install langchain langgraph)","MCP SDK (pip install mcp)","Understanding of async/await patterns in Python"],"input_types":["Python source code","Framework configuration objects","Tool/function definitions"],"output_types":["Agent execution traces","Tool call sequences","State transitions"],"categories":["planning-reasoning","tool-use-integration","framework-comparison"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-star-the-repo__cap_10","uri":"capability://planning.reasoning.research.agent.with.gemini.interactions.api","name":"research-agent-with-gemini-interactions-api","description":"Demonstrates a production-ready research agent using Google Gemini's Interactions API for advanced reasoning and multi-turn interactions. Shows how to structure research tasks (planning, execution, synthesis), integrate web search and document retrieval, and use Gemini's reasoning capabilities for complex analysis. Enables developers to build sophisticated research and analysis agents that can decompose complex questions into research subtasks.","intents":["I want to build a research agent that can plan and execute complex research tasks","I need to integrate Google Gemini's Interactions API with my agent framework","I'm building a system that needs to synthesize information from multiple sources for analysis","I want to see how to structure multi-turn interactions with an LLM for iterative research"],"best_for":["developers building research and analysis agents","teams implementing complex question-answering systems","builders creating competitive intelligence or market research tools","researchers studying multi-turn reasoning and information synthesis"],"limitations":["Gemini Interactions API is relatively new; documentation and community support may be limited","Research task decomposition requires careful prompt engineering; no automated task planning","Multi-turn interactions increase latency and API costs; no optimization guidance provided","Research quality depends on source quality and retrieval relevance; templates don't include source evaluation"],"requires":["Python 3.9+","Google Cloud account with Gemini API access","Gemini API key","Web search API (Google Custom Search or similar)","Document retrieval system (RAG or knowledge base)"],"input_types":["Research questions or topics","Document sources","Web search results"],"output_types":["Research plan (decomposed subtasks)","Gathered information from sources","Synthesized research report or analysis"],"categories":["planning-reasoning","search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-star-the-repo__cap_11","uri":"capability://planning.reasoning.investment.and.finance.agent.patterns","name":"investment-and-finance-agent-patterns","description":"Provides production-ready implementations of AI agents for investment analysis and financial decision-making. Shows how to integrate financial data APIs (stock prices, company fundamentals, market data), implement financial reasoning patterns, and generate investment recommendations. Demonstrates domain-specific prompting for finance, risk assessment, and portfolio analysis. Enables developers to build financial advisory agents with real-time market data integration.","intents":["I want to build an investment analysis agent that can evaluate stocks and companies","I need to integrate real-time financial data (stock prices, fundamentals) with my LLM agent","I'm building a portfolio analysis tool and need to see how to structure financial reasoning","I want to create an agent that can generate investment recommendations based on market data"],"best_for":["fintech teams building AI-powered investment tools","financial advisors creating AI assistants for client analysis","traders building decision-support systems","researchers studying AI applications in finance and investment"],"limitations":["Financial data APIs require subscriptions (Bloomberg, Reuters, etc.); free alternatives have limited coverage","Investment recommendations require domain expertise; LLM-generated recommendations may lack nuance or miss important factors","Market conditions change rapidly; agent recommendations may become stale without real-time updates","Regulatory compliance (investment advice licensing) may apply; templates don't address legal considerations"],"requires":["Python 3.9+","Financial data API (Alpha Vantage, IEX Cloud, Polygon.io, or similar)","LLM API key (OpenAI, Anthropic, or similar)","Understanding of financial concepts (stocks, fundamentals, portfolio theory)","Optional: Real-time market data feed for live analysis"],"input_types":["Stock symbols or company names","Investment criteria (risk tolerance, sector preferences)","Portfolio holdings"],"output_types":["Financial metrics and analysis","Investment recommendations","Risk assessments","Portfolio analysis reports"],"categories":["planning-reasoning","data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-star-the-repo__cap_12","uri":"capability://automation.workflow.web.scraping.agent.with.browser.automation","name":"web-scraping-agent-with-browser-automation","description":"Demonstrates web scraping agents that combine LLM reasoning with browser automation (Selenium, Playwright) to extract and analyze information from websites. Shows how agents can navigate complex websites, extract structured data, handle dynamic content, and synthesize information across multiple pages. Enables developers to build agents that can autonomously gather information from the web for analysis or monitoring.","intents":["I want to build an agent that can scrape and analyze information from websites","I need to extract structured data from complex, JavaScript-heavy websites","I'm building a competitive intelligence tool and need to see how agents can gather web data","I want to create an agent that can navigate multi-page websites and synthesize information"],"best_for":["developers building web scraping and data extraction agents","teams implementing competitive intelligence or market monitoring systems","builders creating data aggregation tools","researchers studying web automation and information extraction"],"limitations":["Web scraping is fragile; website structure changes break extraction logic; no automated adaptation","Browser automation is slow (seconds per page); not suitable for scraping large numbers of pages","Legal and ethical considerations; many websites prohibit scraping; templates don't address compliance","Dynamic content handling requires JavaScript execution; increases latency and resource usage"],"requires":["Python 3.9+","Browser automation library (Selenium or Playwright)","Web driver (ChromeDriver for Selenium, or Playwright's built-in browsers)","LLM API key for reasoning about extracted data","Understanding of HTML/CSS for element selection"],"input_types":["Website URLs","Extraction instructions (what data to gather)","Navigation instructions (how to interact with site)"],"output_types":["Extracted structured data","Analyzed information","Monitoring reports or alerts"],"categories":["automation-workflow","data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-star-the-repo__cap_2","uri":"capability://memory.knowledge.rag.architecture.pattern.catalog","name":"rag-architecture-pattern-catalog","description":"Provides implementations of seven distinct RAG patterns (Gemini Agentic RAG, Database Routing RAG, Deepseek Local RAG, Corrective RAG, Hybrid RAG, Cohere RAG Agent, Autonomous RAG with Reasoning) with complete code examples showing retrieval strategy, vector database integration, prompt engineering, and response generation. Each pattern includes architectural diagrams and trade-off analysis, enabling developers to select and implement the RAG approach best suited to their data characteristics and latency requirements.","intents":["I need to implement RAG but don't know which retrieval strategy (corrective, hybrid, agentic) fits my use case","I want to see how to integrate vector databases (Pinecone, Weaviate, Chroma) with LLM agents","I need to understand the difference between local RAG (Deepseek) and cloud-based RAG (Gemini)","I'm building a system that needs to reason about retrieved documents and want to see how autonomous RAG works"],"best_for":["developers implementing semantic search over proprietary documents or knowledge bases","teams building domain-specific chatbots (customer support, internal knowledge systems)","researchers studying retrieval quality and ranking strategies for LLM augmentation","builders needing to choose between local vs. cloud RAG based on latency/privacy constraints"],"limitations":["Vector database setup and embedding model selection are not fully automated; requires manual configuration","Retrieval quality depends heavily on document chunking strategy and embedding model; templates don't provide optimization guidance","Corrective and Hybrid RAG patterns add latency (multiple retrieval passes); trade-offs not quantified in templates","Local RAG (Deepseek) requires significant compute resources; cloud-based patterns require API keys and incur per-token costs"],"requires":["Python 3.9+","Vector database (Pinecone, Weaviate, Chroma, or local Faiss)","Embedding model (OpenAI, Cohere, or local SentenceTransformers)","LLM API key (OpenAI, Anthropic, Google Gemini, Cohere, or local Ollama)","Document corpus in text or PDF format"],"input_types":["Text documents or PDFs","Structured data (for database routing RAG)","User queries (natural language)"],"output_types":["Retrieved document chunks with relevance scores","LLM-generated responses with source citations","Retrieval quality metrics (precision, recall)"],"categories":["memory-knowledge","search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-star-the-repo__cap_3","uri":"capability://planning.reasoning.multi.agent.system.coordination.patterns","name":"multi-agent-system-coordination-patterns","description":"Demonstrates multi-agent architectures through two production examples: SEO Audit Team (specialized agents for technical SEO, content analysis, backlink analysis coordinating results) and Home Renovation Agent (agents for budgeting, design, contractor coordination). Implementations show agent communication patterns (message passing, shared state, hierarchical coordination), task decomposition, and result aggregation using frameworks like Agno and LangGraph, enabling developers to build team-based AI systems where agents specialize in subtasks.","intents":["I need to build a system where multiple AI agents collaborate on a complex task","I want to understand how to decompose a problem into agent subtasks and coordinate results","I'm building an SEO analysis tool and need to see how specialized agents can work together","I need to implement hierarchical agent coordination where one agent delegates to others"],"best_for":["teams building domain-specific multi-agent systems (SEO, finance, project management)","developers implementing agent orchestration for complex workflows","researchers studying multi-agent communication and coordination patterns","builders needing to parallelize agent work for latency reduction"],"limitations":["Coordination overhead increases with agent count; no guidance on optimal team size or communication topology","Shared state management becomes complex at scale; templates don't address distributed state or persistence","Agent specialization requires careful prompt engineering; no automated agent role definition","Debugging multi-agent systems is harder than single-agent; templates lack observability/tracing infrastructure"],"requires":["Python 3.9+","Agno framework or LangGraph for agent orchestration","Multiple LLM API keys (or local models) for parallel agent execution","Understanding of async/await for concurrent agent execution","Message queue or state store for agent communication (optional but recommended)"],"input_types":["High-level task description","Domain-specific context (e.g., website URL for SEO audit, project scope for renovation)"],"output_types":["Structured results from each agent","Aggregated analysis or recommendations","Agent execution traces showing coordination"],"categories":["planning-reasoning","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-star-the-repo__cap_4","uri":"capability://tool.use.integration.mcp.protocol.agent.integration","name":"mcp-protocol-agent-integration","description":"Demonstrates Model Context Protocol (MCP) integration patterns through three implementations: Travel Planner and GitHub Agents (using MCP servers for external tool access), Notion and Multi-MCP Agents (coordinating multiple MCP servers), and Browser Automation Agent (MCP for browser control). Shows how MCP's server-client architecture enables agents to access external tools and data sources through standardized protocol bindings rather than direct API calls, improving modularity and enabling tool composition.","intents":["I want to integrate my LLM agent with external tools (GitHub, Notion, browser) using MCP","I need to understand how MCP servers expose tools to agents and how agents call them","I'm building a system that needs to coordinate multiple external services and want to use MCP","I want to see how browser automation integrates with LLM agents via MCP"],"best_for":["developers building agents that need access to external APIs and services","teams standardizing tool integration across multiple agents using MCP","builders creating MCP servers to expose internal tools to LLM agents","researchers studying protocol-based agent-tool interaction patterns"],"limitations":["MCP server setup requires understanding of server-client architecture; not as simple as direct API calls","Tool availability depends on MCP server implementations; not all APIs have MCP servers yet","Debugging MCP communication requires understanding protocol details and message formats","Performance depends on MCP server latency; no built-in caching or optimization for repeated tool calls"],"requires":["Python 3.9+","MCP SDK (pip install mcp)","MCP servers for desired tools (GitHub, Notion, browser, etc.)","Understanding of async/await and protocol-based communication","LLM framework with MCP support (Agno, LangChain with MCP integration)"],"input_types":["Tool definitions (MCP server schemas)","Agent prompts requesting tool use","External service credentials (GitHub tokens, Notion API keys)"],"output_types":["Tool call results from MCP servers","Agent responses incorporating external data","Tool execution traces"],"categories":["tool-use-integration","automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-star-the-repo__cap_5","uri":"capability://text.generation.language.voice.agent.speech.integration","name":"voice-agent-speech-integration","description":"Provides implementations of voice-enabled agents combining speech-to-text (STT) and text-to-speech (TTS) with LLM reasoning. Templates show integration patterns with speech APIs (Google Cloud Speech, OpenAI Whisper, ElevenLabs TTS), real-time audio streaming, and voice interaction loops. Enables developers to build conversational agents that accept spoken input, process it through LLM reasoning, and respond with synthesized speech.","intents":["I want to build a voice-enabled chatbot that understands spoken input and responds with speech","I need to integrate speech-to-text and text-to-speech with my LLM agent","I'm building a hands-free AI assistant and need to see how to handle real-time audio streaming","I want to understand latency trade-offs between different STT/TTS providers"],"best_for":["developers building voice-first AI assistants and chatbots","teams implementing accessibility features (voice input/output) in LLM applications","builders creating smart speaker or IoT device integrations","researchers studying conversational AI with speech modalities"],"limitations":["Real-time speech processing adds latency (STT processing time + LLM inference + TTS generation); no optimization guidance provided","Speech quality depends on audio input hardware and ambient noise; templates don't include noise filtering","STT accuracy varies by language and accent; templates assume English-language input","TTS voice selection and naturalness vary by provider; no comparison of voice quality across providers"],"requires":["Python 3.9+","Speech API credentials (Google Cloud Speech, OpenAI Whisper, or local Vosk)","TTS API credentials (ElevenLabs, Google Cloud TTS, or local TTS models)","Audio input device (microphone) and output device (speaker)","Audio processing library (librosa, PyAudio, or similar)"],"input_types":["Audio streams (WAV, MP3, or raw PCM)","Spoken language input"],"output_types":["Transcribed text from speech","LLM-generated responses","Synthesized speech output"],"categories":["text-generation-language","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-star-the-repo__cap_6","uri":"capability://memory.knowledge.memory.augmented.llm.application.patterns","name":"memory-augmented-llm-application-patterns","description":"Demonstrates persistent conversation memory patterns enabling LLM applications to maintain context across multiple interactions. Implementations show memory storage strategies (in-memory, database, vector store), context window management, and conversation history retrieval. Enables developers to build stateful LLM applications where agents remember previous interactions, user preferences, and learned information across sessions.","intents":["I need my LLM agent to remember previous conversations and user context","I want to implement persistent memory for a chatbot that learns user preferences","I need to manage conversation history efficiently without exceeding LLM context limits","I'm building a system where agents need to reference past interactions to make decisions"],"best_for":["developers building stateful chatbots and conversational agents","teams implementing user personalization in LLM applications","builders creating long-running agents that need to learn from interactions","researchers studying memory mechanisms in LLM systems"],"limitations":["Memory storage adds latency (database queries, vector search); no guidance on optimal memory retrieval strategies","Context window limits require selective memory retrieval; templates don't include automatic summarization","Memory management at scale (millions of conversations) requires careful database design; templates assume small-scale use","Privacy and data retention policies complicate memory storage; templates don't address GDPR or data deletion"],"requires":["Python 3.9+","Database for memory storage (PostgreSQL, MongoDB, or SQLite)","Vector database for semantic memory retrieval (optional but recommended)","LLM framework with memory support (Agno, LangChain)","Understanding of conversation state management"],"input_types":["User messages","Conversation history","User metadata (preferences, context)"],"output_types":["Retrieved memory context","Updated conversation history","Agent responses informed by memory"],"categories":["memory-knowledge","data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-star-the-repo__cap_7","uri":"capability://search.retrieval.domain.specific.chat.application.templates","name":"domain-specific-chat-application-templates","description":"Provides specialized chat application templates for interacting with specific data sources: GitHub repositories (code search, issue analysis), PDF documents (document Q&A), and YouTube videos (transcript analysis, content summarization). Each template shows data ingestion, indexing, retrieval, and LLM-based response generation specific to that data type. Enables developers to quickly build domain-specific chatbots without implementing data source integration from scratch.","intents":["I want to build a chatbot that answers questions about my GitHub repositories","I need a system to answer questions about PDF documents in my knowledge base","I want to create a chatbot that summarizes and answers questions about YouTube video content","I'm building a customer support system and need to see how to integrate domain-specific data sources"],"best_for":["developers building domain-specific chatbots for internal knowledge systems","teams implementing customer support bots with proprietary data sources","builders creating content analysis tools (GitHub code search, document Q&A)","researchers studying domain-specific information retrieval and LLM augmentation"],"limitations":["Data source integration is specific to each type; no unified abstraction for adding new sources","Indexing and retrieval strategies are optimized for each data type; may not generalize","Data freshness requires periodic re-indexing; templates don't include automated update mechanisms","Query performance depends on data volume; templates don't include optimization for large repositories"],"requires":["Python 3.9+","Data source credentials (GitHub token for repo access, PDF files, YouTube API key)","Vector database for semantic search (Pinecone, Weaviate, or Chroma)","LLM API key (OpenAI, Anthropic, or local model)","Data extraction libraries (PyPDF2 for PDFs, youtube-transcript-api for YouTube)"],"input_types":["GitHub repository URLs or local code","PDF files","YouTube video URLs"],"output_types":["Indexed documents or code snippets","Semantic search results","LLM-generated answers with source citations"],"categories":["search-retrieval","memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-star-the-repo__cap_8","uri":"capability://automation.workflow.streamlit.ui.development.patterns","name":"streamlit-ui-development-patterns","description":"Demonstrates Streamlit-based UI development patterns for LLM applications, showing how to build interactive web interfaces for agents, RAG systems, and multi-agent teams. Templates include chat interfaces, real-time streaming responses, file upload handling, and agent execution visualization. Enables developers to quickly create web UIs for LLM applications without frontend expertise.","intents":["I need to build a web UI for my LLM agent without learning React or JavaScript","I want to show real-time agent execution and streaming responses in a web interface","I need to handle file uploads (PDFs, documents) in my LLM application UI","I'm building a demo or MVP and need a quick way to create a professional-looking interface"],"best_for":["developers building LLM application prototypes and MVPs","teams creating internal tools and demos for stakeholders","researchers building interactive systems for user studies","builders needing rapid UI iteration without frontend development"],"limitations":["Streamlit is optimized for data apps, not production web applications; scaling to high traffic requires additional infrastructure","UI customization is limited compared to custom React/Vue applications; complex layouts may be difficult","State management is implicit in Streamlit's rerun model; complex multi-page applications become unwieldy","Performance degrades with large datasets or complex visualizations; no built-in optimization for real-time updates"],"requires":["Python 3.9+","Streamlit (pip install streamlit)","LLM framework (Agno, LangChain) for agent integration","Basic Python knowledge","Optional: Streamlit Cloud account for deployment"],"input_types":["User text input (chat messages)","File uploads (PDFs, documents, images)","Configuration parameters (sliders, dropdowns)"],"output_types":["Rendered web UI","Streaming text responses","Visualizations (charts, agent execution traces)","File downloads (results, reports)"],"categories":["automation-workflow","text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-star-the-repo__cap_9","uri":"capability://memory.knowledge.vector.database.integration.patterns","name":"vector-database-integration-patterns","description":"Demonstrates integration patterns for multiple vector databases (Pinecone, Weaviate, Chroma, Faiss) in RAG and memory systems. Shows data ingestion, embedding generation, semantic search, and result retrieval with different database backends. Enables developers to choose and integrate vector databases based on deployment constraints (cloud vs. local, managed vs. self-hosted, scale requirements).","intents":["I need to choose a vector database for my RAG system and understand the trade-offs","I want to see how to integrate Pinecone, Weaviate, or Chroma with my LLM agent","I need to implement semantic search over my document corpus","I'm building a system that needs to scale to millions of embeddings and need guidance on database selection"],"best_for":["developers implementing semantic search and RAG systems","teams evaluating vector database options for production systems","builders needing to migrate between vector database backends","researchers studying vector database performance and retrieval quality"],"limitations":["Vector database performance depends on embedding model quality; templates don't include embedding model selection guidance","Scaling to billions of vectors requires careful index tuning; templates assume moderate scale","Cost varies significantly by database (Pinecone SaaS vs. self-hosted Weaviate); no cost comparison provided","Retrieval quality depends on similarity metric and index parameters; templates don't include optimization for specific use cases"],"requires":["Python 3.9+","Vector database (Pinecone, Weaviate, Chroma, or Faiss)","Embedding model (OpenAI, Cohere, or local SentenceTransformers)","Document corpus in text format","Understanding of vector similarity and embedding concepts"],"input_types":["Documents or text chunks","Embedding vectors","Query vectors"],"output_types":["Indexed vectors in database","Semantic search results with similarity scores","Retrieved document chunks"],"categories":["memory-knowledge","search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Git for cloning repository","API keys for at least one LLM provider (OpenAI, Anthropic, Google Gemini, Cohere, or Ollama for local)","Node.js 18+ for Streamlit UI templates","Docker (optional, for containerized deployments)","Agno framework (pip install agno)","LangChain and LangGraph (pip install langchain langgraph)","MCP SDK (pip install mcp)","Understanding of async/await patterns in Python","Google Cloud account with Gemini API access"],"failure_modes":["Templates are snapshots — may lag behind latest framework versions or API changes","No built-in CI/CD or automated testing across all templates; maintenance burden on community","Requires manual adaptation for production use; templates assume specific API keys and environment configurations","Documentation depth varies by template; some advanced patterns lack detailed explanation","Pattern documentation is implicit in code examples rather than explicit comparison tables","Framework versions may diverge; templates may not reflect latest API changes across all three ecosystems simultaneously","No automated testing to ensure pattern equivalence across frameworks","Learning curve still requires reading source code; not a high-level abstraction layer","Gemini Interactions API is relatively new; documentation and community support may be limited","Research task decomposition requires careful prompt engineering; no automated task planning","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.049Z","last_scraped_at":"2026-05-03T14:00:07.640Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=star-the-repo","compare_url":"https://unfragile.ai/compare?artifact=star-the-repo"}},"signature":"pz+g7VDX8k7f7w5aVXDwu9R22HE/BaEE4HT54uVOSBIDnY3V7KLmX2h5jfYRa2UbmdFoHDbPh07631JZgoJqBg==","signedAt":"2026-06-20T12:29:03.297Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/star-the-repo","artifact":"https://unfragile.ai/star-the-repo","verify":"https://unfragile.ai/api/v1/verify?slug=star-the-repo","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"}}