{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_scoopika","slug":"scoopika","name":"Scoopika","type":"product","url":"https://www.scoopika.com","page_url":"https://unfragile.ai/scoopika","categories":["app-builders"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_scoopika__cap_0","uri":"capability://tool.use.integration.multimodal.agent.orchestration.with.unified.input.handling","name":"multimodal agent orchestration with unified input handling","description":"Scoopika provides an Agent abstraction that accepts parallel multimodal inputs (text, images, audio, URLs) in a single execution context, routing each input type to appropriate processors (vision-capable LLMs for images, speech-to-text for audio, web scrapers for URLs) before passing unified context to the LLM. The Agent class encapsulates LLM provider connections, tool bindings, memory management, and output validation, abstracting away the complexity of coordinating multiple input modalities.","intents":["Build conversational AI that accepts voice, text, and image inputs simultaneously without managing separate pipelines","Create applications where users can describe tasks using mixed media (e.g., 'analyze this screenshot and schedule a call')","Reduce boilerplate for multimodal input preprocessing and normalization across different LLM providers"],"best_for":["Full-stack developers building consumer-facing AI applications who want to ship multimodal features without infrastructure complexity","Startups prototyping AI-powered products that require voice, text, and vision in a single interaction loop"],"limitations":["Audio format support and codecs are undocumented — unclear which file types (WAV, MP3, OGG) are accepted","Image format support not specified — must infer from vision-capable LLM constraints (likely JPEG, PNG, WebP)","No built-in audio preprocessing (noise reduction, normalization) — relies on LLM provider's audio handling","Parallel input processing latency not documented — unclear if inputs are processed sequentially or concurrently","Free tier provides no persistent memory, limiting multimodal conversation continuity across sessions"],"requires":["Node.js environment (version not specified, likely 16+)","Active LLM provider account (OpenAI, Anthropic, Google, etc.) with API credentials","Scoopika account (free tier available with limitations)","@scoopika/scoopika npm package installed"],"input_types":["text (string messages)","audio (file paths or buffers, format undocumented)","images (file paths or buffers, format undocumented)","URLs (web pages for scraping/context)"],"output_types":["streaming text tokens","validated JSON (schema-based)","audio/voice responses","tool invocation metadata"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scoopika__cap_1","uri":"capability://text.generation.language.real.time.streaming.response.generation.with.token.level.hooks","name":"real-time streaming response generation with token-level hooks","description":"Scoopika streams LLM responses token-by-token to the client via onToken hooks, enabling real-time UI updates and low-latency user feedback. The streaming architecture bypasses batch processing, allowing developers to render partial responses as they arrive rather than waiting for complete generation. This is particularly critical for voice applications where <300ms latency is claimed for voice response generation.","intents":["Build responsive chat interfaces that show AI thinking in real-time as tokens arrive","Create voice-based AI applications where users expect immediate audio feedback (<300ms latency)","Implement progressive rendering of long-form content (articles, code) as it generates"],"best_for":["Developers building consumer-facing conversational AI where perceived latency directly impacts UX","Voice-first application builders who need sub-300ms response times for natural interaction"],"limitations":["Streaming latency SLAs only documented for voice (<300ms); text streaming latency not specified","No built-in buffering or batching strategies — developers must handle token arrival rates in UI","Hook callback signatures and available events not fully documented — unclear what metadata is passed with each token","No backpressure mechanism documented — unclear how to throttle token consumption if client is slower than generation"],"requires":["WebSocket or HTTP streaming support in client environment","Callback-based event handling (onToken hook implementation)","Real-time UI framework capable of incremental rendering (React, Vue, Svelte, etc.)"],"input_types":["agent execution context (text, images, audio, URLs)"],"output_types":["streaming text tokens (individual characters or subword units)","token metadata (position, confidence, etc. — undocumented)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scoopika__cap_10","uri":"capability://tool.use.integration.llm.provider.abstraction.with.multi.provider.support","name":"llm provider abstraction with multi-provider support","description":"Scoopika abstracts LLM provider differences through a unified Agent interface, allowing developers to switch between OpenAI, Anthropic, Google, and other providers by changing configuration without modifying agent code. The platform claims to never share LLM credentials with Scoopika servers (credentials remain on developer's infrastructure), though the technical mechanism for this is undocumented. This enables provider flexibility and reduces vendor lock-in at the LLM layer.","intents":["Switch between LLM providers (OpenAI to Anthropic, etc.) without rewriting agent logic","Reduce LLM costs by comparing providers and selecting the most cost-effective option for each use case","Maintain control over LLM credentials and ensure they are not shared with third-party platforms"],"best_for":["Developers building LLM-agnostic applications who want flexibility to switch providers","Cost-conscious teams comparing LLM providers and wanting to optimize spend","Security-focused organizations requiring full control over LLM credentials"],"limitations":["Supported LLM providers not fully documented — unclear which providers are officially supported (OpenAI, Anthropic, Google, Cohere, etc.)","Provider-specific features not documented — unclear if all providers support vision, function calling, structured output equally","Credential handling mechanism not documented — unclear how 'never share credentials with servers' is technically enforced (client-side execution? API proxying?)","Provider fallback or load balancing not mentioned — unclear if developers can specify multiple providers for redundancy","Cost tracking per provider not documented — unclear if Scoopika provides visibility into per-provider costs","Provider rate limiting and quota management not documented — unclear how Scoopika handles provider-specific rate limits"],"requires":["API credentials for at least one LLM provider (OpenAI, Anthropic, Google, etc.)","Understanding of provider-specific capabilities and limitations (vision support, function calling, etc.)","Secure credential storage (environment variables, secrets manager, etc.)"],"input_types":["LLM provider configuration (API key, model name, parameters)","Agent execution context (text, images, audio, URLs)"],"output_types":["LLM responses (text, JSON, audio, etc.)","Provider-specific metadata (token counts, model version, etc.)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scoopika__cap_11","uri":"capability://automation.workflow.tiered.pricing.with.quota.based.resource.limits","name":"tiered pricing with quota-based resource limits","description":"Scoopika uses a freemium model with three tiers (Hobby free, Pro $25/mo, Scale $70/mo) that enforce quota limits on memory operations, voice processing, knowledge store queries, and audio processing. Each tier provides different monthly quotas (e.g., Pro: 1M memory reads, 500K writes; Scale: 4M reads, 2M writes), and exceeding quotas results in service degradation or blocking. This enables cost control and prevents runaway bills while allowing free experimentation on the Hobby tier.","intents":["Experiment with Scoopika features on a free tier before committing to paid plans","Understand pricing and quota requirements before scaling applications to production","Control costs by selecting a tier that matches expected usage patterns"],"best_for":["Indie developers and startups prototyping AI features with limited budgets","Teams evaluating Scoopika before committing to production deployment"],"limitations":["Free tier provides no persistent memory, no voice output, no knowledge stores — severely limits feature testing","Voice character quota (50 chars/month on Hobby) is impractical for any real-world voice application","Quota enforcement behavior not documented — unclear if exceeding limits results in hard cutoff, throttling, or overage charges","No documented overage pricing — unclear if exceeding monthly quotas incurs additional charges or service blocking","Quota reset timing not specified — unclear if quotas reset on calendar month or subscription anniversary","No documented quota monitoring or alerting — unclear if developers are notified when approaching limits","Pro tier (middle tier) lacks region replication for memory stores — unclear if this is a hard limitation or can be added for additional cost","Custom plan ($750 for 10 clients) is vague — unclear what features or quotas are included"],"requires":["Scoopika account (free tier available)","Payment method for Pro ($25/mo) or Scale ($70/mo) tiers","Understanding of expected usage patterns to select appropriate tier"],"input_types":["tier selection (Hobby, Pro, Scale, or Custom)","usage metrics (memory reads/writes, voice characters, knowledge queries, audio minutes)"],"output_types":["quota limits (per tier)","current usage tracking (if available)","billing information"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scoopika__cap_12","uri":"capability://memory.knowledge.edge.served.knowledge.and.memory.infrastructure.with.global.region.distribution","name":"edge-served knowledge and memory infrastructure with global region distribution","description":"Scoopika serves Knowledge Stores and Memory Stores from 26 global edge regions, reducing latency for knowledge retrieval and memory operations by serving requests from geographically close infrastructure. This edge-serving architecture is transparent to developers — they upload knowledge or create agents, and the platform automatically distributes and serves from the nearest region. Memory store region replication is available on the Scale tier ($70/mo) for additional redundancy.","intents":["Reduce latency for knowledge retrieval and memory operations by serving from geographically close infrastructure","Ensure low-latency agent execution for users distributed globally without manual CDN configuration","Improve reliability through region replication on higher tiers"],"best_for":["Global applications where latency is critical (real-time voice, chat, etc.)","Teams serving users across multiple continents who need consistent low-latency performance"],"limitations":["Developer cannot select specific regions — platform auto-selects based on request origin","Region replication only available on Scale tier ($70/mo) — Pro tier has no documented replication","Edge serving latency not documented — unclear what latency improvements are achieved vs. centralized infrastructure","Cache invalidation strategy not documented — unclear when edge caches are refreshed after knowledge updates","No documented support for data residency requirements (GDPR, CCPA) — unclear if developers can constrain data to specific regions","Failover behavior not documented — unclear what happens if an edge region becomes unavailable"],"requires":["Scoopika paid account (Pro tier minimum for knowledge stores; Scale tier for memory replication)","Global user base or latency-sensitive application"],"input_types":["knowledge sources (files, PDFs, websites)","conversation data (for memory stores)"],"output_types":["edge-served knowledge retrieval results","edge-served memory reads/writes","latency metrics (if available)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scoopika__cap_2","uri":"capability://tool.use.integration.tool.and.api.invocation.with.context.aware.function.binding","name":"tool and api invocation with context-aware function binding","description":"Scoopika enables agents to invoke custom developer-defined functions, generic HTTP APIs, and built-in tools (Google Search) based on LLM reasoning about task requirements. The platform provides a tool registry mechanism where developers bind functions to the agent, and the LLM decides when and how to invoke them based on conversation context. Tool invocation is surfaced via onToolCall hooks, allowing developers to observe and potentially intercept function calls before execution.","intents":["Enable AI agents to take actions (schedule calls, fetch data, trigger workflows) by invoking external APIs or custom functions","Build autonomous agents that reason about which tools to use based on user intent without explicit routing logic","Integrate third-party services (Slack, Calendly, databases) into conversational workflows"],"best_for":["Developers building automation-focused AI agents that need to interact with external systems","Teams integrating AI into existing application stacks with custom business logic"],"limitations":["Tool/function interface specification not documented — unclear how to define function signatures, parameters, and return types","No built-in error recovery for failed tool invocations — 'built-in errors recovery' claim is vague and unverified","Generic API connector capabilities not detailed — unclear what HTTP methods, authentication schemes, or response formats are supported","No tool result validation or schema enforcement documented — unclear if tool outputs are validated before being fed back to LLM","onToolCall hook signature and available metadata not specified — unclear what information is passed about pending tool invocations"],"requires":["Custom function definitions in JavaScript/TypeScript","API credentials for external services (if using generic API connector)","Understanding of LLM function-calling conventions (schema-based, likely OpenAI or Anthropic format)"],"input_types":["function definitions (JavaScript/TypeScript callables)","API endpoint specifications (URL, method, auth)","tool metadata (name, description, parameters)"],"output_types":["tool invocation metadata (tool name, parameters, timestamp)","tool execution results (return values, errors)","LLM-generated tool calls (structured function invocations)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scoopika__cap_3","uri":"capability://memory.knowledge.serverless.encrypted.conversation.memory.with.region.replicated.persistence","name":"serverless encrypted conversation memory with region-replicated persistence","description":"Scoopika provides a managed Memory Store abstraction that persists conversation history across sessions with encryption at rest and optional region replication on higher tiers. Developers do not manage database infrastructure; the platform handles storage, encryption, and retrieval. Memory is tied to agent execution context and is automatically updated after each agent.run() call, enabling multi-turn conversations with full context retention without explicit state management code.","intents":["Build multi-turn conversational agents that remember context across sessions without managing databases","Enable long-running conversations where the AI references previous interactions without explicit context passing","Ensure conversation privacy through encrypted storage without implementing encryption infrastructure"],"best_for":["Indie developers and startups who want persistent memory without database administration overhead","Applications requiring GDPR/privacy compliance where encrypted storage is non-negotiable"],"limitations":["Free tier provides zero memory reads/writes — persistent memory requires paid subscription (Pro: 1M reads/500K writes/month; Scale: 4M reads/2M writes/month)","Memory encryption algorithm and key management strategy not documented — unclear if encryption is per-conversation, per-user, or global","Region replication only available on Scale tier ($70/mo) — Pro tier (middle tier) has no documented replication SLA","No documented API for manual memory management (clear, export, audit) — unclear if developers can inspect or delete conversation history","Memory quota enforcement not detailed — unclear what happens when monthly limits are exceeded (hard cutoff? throttling?)","Latency impact of memory reads/writes not documented — unclear if memory operations add latency to agent execution"],"requires":["Scoopika paid account (Pro tier minimum for any memory functionality)","Agent execution context (conversation ID, user ID — format undocumented)","Compliance with Scoopika's data retention and encryption policies"],"input_types":["conversation messages (text, images, audio metadata)","agent execution results","user/session identifiers"],"output_types":["conversation history (full or filtered)","memory metadata (timestamps, token counts)","context for LLM prompt augmentation"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scoopika__cap_4","uri":"capability://memory.knowledge.serverless.knowledge.base.with.rag.powered.context.augmentation","name":"serverless knowledge base with rag-powered context augmentation","description":"Scoopika provides a Knowledge Store abstraction that ingests files (PDFs, documents), websites, and raw text, converts them to vector embeddings, and serves them from 26 global edge regions. During agent execution, the platform automatically retrieves relevant knowledge snippets based on query similarity and augments the LLM prompt with retrieved context (Retrieval-Augmented Generation). Developers upload knowledge sources once and the platform handles embedding, indexing, caching, and retrieval without requiring vector database management.","intents":["Build AI agents that answer questions about company documentation, product manuals, or knowledge bases without fine-tuning","Enable agents to cite sources and provide grounded answers by augmenting prompts with relevant retrieved documents","Reduce hallucination by constraining agent responses to factual knowledge from uploaded sources"],"best_for":["Customer support teams building AI agents that need to reference company documentation","Product teams building AI features that require grounding in proprietary knowledge (internal wikis, product specs)"],"limitations":["Underlying vector database technology not documented — unclear if using Pinecone, Weaviate, or proprietary solution","Knowledge ingestion formats partially documented (files, PDFs, websites) — unclear what file types are supported beyond PDFs (DOCX? PPTX? CSV?)","Embedding model not specified — unclear if using OpenAI embeddings, open-source models, or proprietary embeddings","Retrieval strategy not documented — unclear how many snippets are retrieved, ranking algorithm, or relevance threshold","Knowledge store quotas enforced by tier (Hobby: none; Pro: 300K requests/month; Scale: 1.5M requests/month) — unclear what constitutes a 'request' (per query? per snippet?)","No documented API for knowledge management (update, delete, search) — unclear if developers can inspect or modify indexed knowledge","Cached query results mentioned but cache invalidation strategy not documented — unclear when cached results are refreshed"],"requires":["Scoopika paid account (Pro tier minimum for knowledge store access)","Knowledge sources in supported formats (files, PDFs, websites)","Understanding of RAG concepts and relevance-based retrieval"],"input_types":["PDF files","text documents","website URLs","raw text snippets","user queries (during agent execution)"],"output_types":["vector embeddings (internal representation)","retrieved knowledge snippets (text passages)","relevance scores (undocumented)","augmented LLM prompts (with retrieved context)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scoopika__cap_5","uri":"capability://data.processing.analysis.json.schema.based.output.validation.and.structured.data.extraction","name":"json schema-based output validation and structured data extraction","description":"Scoopika enables agents to generate validated JSON outputs by specifying a schema that the LLM must conform to. The platform enforces schema validation on LLM responses, ensuring structured data extraction (e.g., extracting contact information, scheduling details, or form fields) returns well-formed JSON matching the developer-defined schema. This prevents malformed or hallucinated JSON from being returned to the application.","intents":["Extract structured data from unstructured inputs (e.g., parse a voice description into calendar event fields)","Ensure API contracts are respected by validating LLM outputs against expected JSON schemas before returning to client code","Build form-filling or data-entry automation where AI must populate specific fields with validated types"],"best_for":["Developers building data extraction pipelines where output format is critical","Teams integrating AI into structured data workflows (CRM, database inserts, API calls)"],"limitations":["Schema definition format not documented — unclear if using JSON Schema, TypeScript interfaces, or custom DSL","Validation error handling not specified — unclear what happens when LLM output fails schema validation (retry? fallback? error thrown?)","No documented support for conditional schemas or schema composition — unclear if complex validation rules are supported","Schema size limits not documented — unclear if there are constraints on schema complexity or field count","No examples provided of schema definition syntax — developers must infer from undocumented API"],"requires":["Schema definition in undocumented format (likely JSON Schema or TypeScript types)","LLM provider capable of structured output (GPT-4, Claude 3, etc.)","Understanding of JSON schema validation concepts"],"input_types":["schema definitions (format undocumented)","agent execution context (text, images, audio, URLs)"],"output_types":["validated JSON objects matching schema","validation error messages (if schema validation fails)","type-safe structured data"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scoopika__cap_6","uri":"capability://text.generation.language.real.time.voice.input.output.with.sub.300ms.latency","name":"real-time voice input/output with sub-300ms latency","description":"Scoopika provides native voice I/O capabilities where audio input is transcribed to text, processed by the agent, and responses are synthesized back to audio with claimed <300ms latency for voice response generation. Audio processing is offered in two modes: 'fast' (2s average latency, quota-limited) and 'slow' (unlimited, ~10s latency). Voice responses are streamed in real-time, enabling natural voice-based conversational experiences without round-trip delays.","intents":["Build voice-first AI applications (voice assistants, voice-controlled automation) where users expect immediate audio feedback","Create hands-free interfaces where text input is impractical (driving, accessibility, field work)","Enable natural conversation flow by minimizing latency between user speech and AI response"],"best_for":["Developers building voice assistant applications or voice-controlled smart home integrations","Accessibility-focused teams building voice interfaces for users who cannot use text input"],"limitations":["Audio format support not documented — unclear which codecs (MP3, WAV, OGG, FLAC) and sample rates are accepted","Voice synthesis model not specified — unclear if using natural-sounding TTS or basic synthesis","Fast mode audio processing quota enforced by tier (Hobby: 0 mins; Pro: 500 mins/month; Scale: 1000 mins/month) — slow mode unlimited but ~10s latency is impractical for real-time interaction","Voice character quota separate from audio processing quota (Hobby: 50 chars/month; Pro: 100K chars/month; Scale: 1.2M chars/month) — unclear how these quotas interact","<300ms latency claim is for voice response generation only — total round-trip latency (transcription + agent execution + synthesis) not documented","Audio preprocessing (noise reduction, normalization) not mentioned — unclear if raw audio is processed or requires preprocessing","No documented support for speaker identification or voice biometrics — unclear if multi-speaker scenarios are handled"],"requires":["Audio input device (microphone) or audio file in undocumented format","Scoopika paid account (Pro tier minimum for any voice functionality; Hobby tier has 50 char/month quota only)","Real-time audio streaming capability in client environment (WebRTC, Web Audio API, etc.)","Browser or Node.js environment with audio support"],"input_types":["audio files (format undocumented)","audio streams (real-time microphone input)","audio buffers"],"output_types":["transcribed text (from speech-to-text)","synthesized audio (from text-to-speech)","audio streams (real-time voice responses)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scoopika__cap_7","uri":"capability://image.visual.image.input.processing.with.vision.capable.llm.integration","name":"image input processing with vision-capable llm integration","description":"Scoopika accepts images as agent inputs and automatically routes them to vision-capable LLM providers (GPT-4V, Claude Vision, etc.) for visual understanding. Images can be passed alongside text, audio, and URLs in a single agent execution, enabling multimodal reasoning where the LLM analyzes visual content in context with other input modalities. Image format support and preprocessing are handled transparently by the platform.","intents":["Build AI agents that analyze screenshots, photos, or diagrams to understand visual context (e.g., 'analyze this screenshot and suggest improvements')","Enable document processing where images of receipts, invoices, or forms are analyzed for data extraction","Create visual search or image understanding features without managing vision model infrastructure"],"best_for":["Developers building visual AI features (document analysis, screenshot understanding, image-based automation)","Teams integrating computer vision into conversational workflows without dedicated ML infrastructure"],"limitations":["Image format support not documented — unclear which formats (JPEG, PNG, WebP, GIF) are accepted","Image size limits not specified — unclear if there are constraints on resolution or file size","Vision model selection not documented — unclear if developers can choose between GPT-4V, Claude Vision, or if platform auto-selects","Image preprocessing not mentioned — unclear if images are resized, compressed, or normalized before being sent to vision models","No documented support for batch image processing — unclear if multiple images in single execution are processed in parallel or sequentially","Vision model latency not documented — unclear how image analysis impacts overall agent execution time","No documented support for image-specific features (OCR, object detection, segmentation) — unclear what vision capabilities are exposed"],"requires":["Image files in undocumented format (likely JPEG, PNG, WebP)","Vision-capable LLM provider (OpenAI GPT-4V, Anthropic Claude Vision, Google Gemini Vision, etc.)","LLM provider account with vision model access"],"input_types":["image files (format undocumented)","image buffers or base64-encoded images","image URLs"],"output_types":["visual understanding text (LLM interpretation of image content)","structured data extracted from images (if schema validation is used)","multimodal reasoning combining image and text analysis"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scoopika__cap_8","uri":"capability://search.retrieval.built.in.google.search.tool.for.real.time.information.retrieval","name":"built-in google search tool for real-time information retrieval","description":"Scoopika provides a built-in Google Search tool that agents can invoke to retrieve current information from the web during execution. The agent decides when to search based on task context (e.g., 'what's the current weather?' triggers a search), and search results are automatically integrated into the LLM prompt for grounded responses. This enables agents to answer questions about real-time information without relying solely on training data.","intents":["Build agents that answer questions about current events, weather, stock prices, or other real-time information","Enable agents to verify facts or find recent information without hallucinating outdated answers","Create research assistants that can search the web and synthesize findings into coherent responses"],"best_for":["Developers building general-purpose AI assistants that need access to current information","Customer support agents that need to look up real-time data (order status, service availability, etc.)"],"limitations":["Search result ranking and filtering not documented — unclear how many results are retrieved or how relevance is determined","Search result format not specified — unclear if results include snippets, full pages, or metadata only","No documented control over search scope (domain restrictions, language, region) — unclear if developers can constrain searches","Search latency not documented — unclear how search delays impact overall agent execution time","No documented caching of search results — unclear if repeated searches for same query are cached","Search quota not mentioned in pricing tiers — unclear if searches are unlimited or quota-limited","No documented fallback behavior if search fails — unclear if agent continues without search results or errors"],"requires":["Internet connectivity for web search","LLM provider capable of reasoning about search results (all major providers support this)","No explicit API key required (built-in to Scoopika platform)"],"input_types":["agent execution context (text, images, audio, URLs)","implicit search queries generated by LLM reasoning"],"output_types":["search results (snippets, URLs, metadata)","augmented LLM responses with real-time information","citations or source attribution (if supported)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_scoopika__cap_9","uri":"capability://automation.workflow.framework.agnostic.deployment.with.serverless.compatibility","name":"framework-agnostic deployment with serverless compatibility","description":"Scoopika is distributed as an npm package (@scoopika/scoopika) that can be integrated into any JavaScript/TypeScript web framework (Express, Next.js, Fastify, etc.) or deployed to serverless platforms (AWS Lambda, Vercel, Netlify, etc.). The library provides a client-side API (Scoopika class, Agent class) that developers use to instantiate and execute agents within their application code, with no framework-specific bindings or constraints. This enables flexible deployment patterns from monolithic servers to distributed serverless functions.","intents":["Integrate AI agents into existing web applications without rearchitecting for a specific framework","Deploy AI features to serverless platforms for cost-efficient, auto-scaling execution","Build full-stack applications where AI is one component among many (databases, APIs, authentication, etc.)"],"best_for":["Full-stack developers with existing JavaScript/TypeScript applications who want to add AI capabilities","Teams using diverse tech stacks (Next.js, Express, Fastify, etc.) who need a framework-agnostic AI layer","Startups deploying to serverless platforms (Vercel, AWS Lambda) for cost efficiency"],"limitations":["Node.js version requirements not specified — unclear if package supports Node 14, 16, 18, 20+","Framework compatibility not explicitly tested or documented — unclear which frameworks are officially supported vs. community-tested","Serverless cold start impact not documented — unclear if Scoopika initialization adds significant latency to Lambda/Vercel cold starts","No documented examples for popular frameworks (Next.js, Express, Fastify) — developers must infer integration patterns","Dependency footprint not documented — unclear if package has heavy dependencies that impact bundle size or cold start time","Environment variable handling for LLM credentials not documented — unclear how to securely pass API keys in serverless environments","No documented support for edge computing (Cloudflare Workers, Deno Deploy) — unclear if package works outside Node.js"],"requires":["Node.js 16+ (version not officially specified)","npm or yarn package manager","JavaScript/TypeScript web framework (Express, Next.js, Fastify, etc.) or serverless runtime","LLM provider credentials (stored securely in environment variables or secrets manager)"],"input_types":["npm package installation","Scoopika client initialization","Agent instantiation with configuration"],"output_types":["Agent execution results (text, JSON, audio, etc.)","HTTP responses (if deployed as API endpoint)","streaming responses (if using token-level hooks)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Node.js environment (version not specified, likely 16+)","Active LLM provider account (OpenAI, Anthropic, Google, etc.) with API credentials","Scoopika account (free tier available with limitations)","@scoopika/scoopika npm package installed","WebSocket or HTTP streaming support in client environment","Callback-based event handling (onToken hook implementation)","Real-time UI framework capable of incremental rendering (React, Vue, Svelte, etc.)","API credentials for at least one LLM provider (OpenAI, Anthropic, Google, etc.)","Understanding of provider-specific capabilities and limitations (vision support, function calling, etc.)","Secure credential storage (environment variables, secrets manager, etc.)"],"failure_modes":["Audio format support and codecs are undocumented — unclear which file types (WAV, MP3, OGG) are accepted","Image format support not specified — must infer from vision-capable LLM constraints (likely JPEG, PNG, WebP)","No built-in audio preprocessing (noise reduction, normalization) — relies on LLM provider's audio handling","Parallel input processing latency not documented — unclear if inputs are processed sequentially or concurrently","Free tier provides no persistent memory, limiting multimodal conversation continuity across sessions","Streaming latency SLAs only documented for voice (<300ms); text streaming latency not specified","No built-in buffering or batching strategies — developers must handle token arrival rates in UI","Hook callback signatures and available events not fully documented — unclear what metadata is passed with each token","No backpressure mechanism documented — unclear how to throttle token consumption if client is slower than generation","Supported LLM providers not fully documented — unclear which providers are officially supported (OpenAI, Anthropic, Google, Cohere, etc.)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.78,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.9,"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":"2026-05-24T12:16:33.095Z","last_scraped_at":"2026-04-05T13:23:42.551Z","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=scoopika","compare_url":"https://unfragile.ai/compare?artifact=scoopika"}},"signature":"SOpHvAOQ/WNc93NagOT2Amwn8jUgRTF2fxDLtj0f8GglqwVdZAWUTErOk1fCAizI4XIezwbbf0H6Rj0aOCg+BA==","signedAt":"2026-06-18T03:15:08.894Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/scoopika","artifact":"https://unfragile.ai/scoopika","verify":"https://unfragile.ai/api/v1/verify?slug=scoopika","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"}}