{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_energeticai","slug":"energeticai","name":"EnergeticAI","type":"framework","url":"https://energeticai.org","page_url":"https://unfragile.ai/energeticai","categories":["automation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_energeticai__cap_0","uri":"capability://data.processing.analysis.cold.start.optimized.text.embeddings.with.bundled.model.loading","name":"cold-start optimized text embeddings with bundled model loading","description":"Generates sentence-level embeddings for English text using pre-trained TensorFlow models optimized for Node.js serverless environments. The implementation bundles models directly into the application package to eliminate network latency during cold starts, achieving 67x faster initialization (3.7s vs 250s) compared to vanilla TensorFlow.js by pre-compiling and caching model weights. Warm-start inference completes in ~55ms, enabling semantic operations like similarity search and recommendation ranking within latency budgets typical of API handlers.","intents":["I need to embed user queries and documents for semantic search without waiting 250+ seconds on cold start","I want to build a recommendation engine that compares text similarity in serverless functions without DevOps overhead","I need to generate vector representations for clustering or nearest-neighbor operations in a Node.js app"],"best_for":["Indie developers building Node.js applications on AWS Lambda, Google Cloud Functions, or Vercel","Small teams prototyping semantic search features without ML infrastructure expertise","Startups needing rapid embedding capability without expensive GPU provisioning"],"limitations":["English language only — no multilingual embedding models documented or supported","Cold-start latency of 3.7 seconds still significant for sub-100ms SLA requirements despite 67x improvement","Model architecture and training data unknown — cannot assess bias, domain specificity, or suitability for specialized vocabularies","Bundled model approach increases application package size by ~2.3MB, impacting deployment time and memory footprint in constrained environments","No documented support for custom embedding dimensions or model fine-tuning — locked to pre-trained model outputs"],"requires":["Node.js 18.0.0 or higher","NPM or Yarn package manager","~2.3MB additional disk space for bundled model weights","Basic JavaScript/async-await familiarity"],"input_types":["plain text strings","multi-sentence paragraphs","user queries"],"output_types":["floating-point vector arrays (dimension unknown)","structured embedding objects with metadata"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_energeticai__cap_1","uri":"capability://data.processing.analysis.few.shot.text.classification.with.minimal.training.examples","name":"few-shot text classification with minimal training examples","description":"Classifies English text into custom categories using a few-shot learning approach that requires only a handful of training examples per class. The implementation leverages pre-trained embeddings combined with lightweight classifiers (likely k-NN or logistic regression on embedding space) to avoid full model retraining, enabling rapid category definition without ML expertise. Training mechanism details are undocumented, but the pattern suggests embedding-space classification where new categories are defined by example rather than parameter updates.","intents":["I need to categorize user feedback or support tickets into custom buckets without labeling thousands of examples","I want to build a content moderation system that learns new violation categories from a few examples","I need to classify product reviews as positive/negative/neutral with minimal training data"],"best_for":["Product teams iterating on classification rules without data science resources","Support teams automating ticket routing with domain-specific categories","Startups building content moderation without labeled datasets"],"limitations":["Training API and example format completely undocumented — cannot assess how many examples constitute 'few' or how to structure training data","No documented support for multi-label classification, confidence scores, or prediction explanations","English language only — no multilingual classifier variants available","Unknown whether trained classifiers persist across application restarts or require retraining on each invocation","No documented handling of class imbalance, overlapping categories, or out-of-distribution inputs","Performance characteristics unknown — latency per classification, throughput limits, and accuracy metrics not published"],"requires":["Node.js 18.0.0 or higher","Training examples in undocumented format (likely JSON or array structure)","Basic understanding of classification task definition and category semantics"],"input_types":["plain text strings","user-provided training examples (format unknown)","category labels as strings"],"output_types":["predicted category label","confidence score (if supported — undocumented)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_energeticai__cap_2","uri":"capability://automation.workflow.rapid.model.deployment.pipeline.for.node.js.serverless.environments","name":"rapid model deployment pipeline for node.js serverless environments","description":"Provides a streamlined deployment workflow that packages pre-trained models and inference code into Node.js applications optimized for serverless platforms (AWS Lambda, Google Cloud Functions, Vercel). The pipeline handles model bundling, weight optimization, and cold-start tuning automatically, abstracting away TensorFlow.js configuration complexity. Developers install via NPM and invoke model inference through a simple JavaScript API without managing model files, dependencies, or runtime configuration.","intents":["I want to deploy an ML model to production without learning TensorFlow.js, Docker, or ML DevOps","I need to ship an AI feature in a Node.js app within days, not weeks of infrastructure setup","I want to avoid managing separate model servers or GPU infrastructure for small-scale inference"],"best_for":["Solo developers and small teams without dedicated ML/DevOps engineers","Rapid prototyping and MVP validation where time-to-market outweighs optimization","Node.js-first organizations (Next.js, Express, Fastify) wanting to embed AI without polyglot infrastructure"],"limitations":["Deployment targets limited to Node.js environments — no Python, Go, or other language support documented","Free tier scalability constraints unknown — no documentation on concurrent inference limits, request throttling, or upgrade path to production pricing","Model selection is pre-curated and fixed — no ability to bring custom TensorFlow models or swap model architectures","Unclear whether deployment integrates with standard CI/CD pipelines (GitHub Actions, GitLab CI) or requires proprietary tooling","No documented rollback mechanism, A/B testing support, or canary deployment patterns","Pricing transparency absent for production workloads — free tier boundaries and paid tier costs not published"],"requires":["Node.js 18.0.0 or higher","NPM or Yarn package manager","Serverless platform account (AWS, Google Cloud, Vercel, etc.) — optional but recommended for deployment","Basic JavaScript and async/await knowledge"],"input_types":["text strings","structured data objects"],"output_types":["embeddings","classification results","structured inference outputs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_energeticai__cap_3","uri":"capability://data.processing.analysis.pre.trained.model.abstraction.with.zero.configuration.inference","name":"pre-trained model abstraction with zero-configuration inference","description":"Exposes pre-trained embeddings and classification models through a high-level JavaScript API that requires no model loading, weight management, or TensorFlow configuration. Models are pre-bundled and automatically initialized on first use, with inference callable through simple function signatures (e.g., `embed(text)` or `classify(text, categories)`). This abstraction hides TensorFlow.js complexity and model serialization details, enabling developers unfamiliar with ML frameworks to invoke inference with single-line function calls.","intents":["I want to use an ML model without understanding TensorFlow, model formats, or weight loading","I need to call an embedding or classification function as easily as any other JavaScript library","I want to avoid debugging model initialization, dependency conflicts, or TensorFlow.js version mismatches"],"best_for":["Full-stack JavaScript developers without ML background","Teams prioritizing developer velocity over model customization","Prototyping and MVP phases where abstraction simplicity outweighs flexibility"],"limitations":["No access to underlying TensorFlow.js models or weights — cannot inspect, fine-tune, or replace models","Model selection is fixed to EnergeticAI's curated set — no ability to load custom or third-party models","API surface completely undocumented — specific function signatures, parameter names, and error handling unknown","No documented support for batch inference, streaming, or asynchronous model loading","Unknown whether models are loaded synchronously (blocking) or asynchronously (non-blocking) on first invocation","No introspection API to query model metadata, input/output shapes, or supported languages"],"requires":["Node.js 18.0.0 or higher","NPM or Yarn package manager","No ML framework knowledge required"],"input_types":["text strings"],"output_types":["embeddings (vector arrays)","classification results (category labels and scores)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_energeticai__cap_4","uri":"capability://search.retrieval.planned.semantic.search.and.question.answering.capability","name":"planned semantic search and question-answering capability","description":"Upcoming feature (not yet released) intended to enable question-answering and semantic search over document collections using embeddings and retrieval-augmented generation (RAG) patterns. The planned implementation will likely combine text embeddings with vector similarity search to retrieve relevant documents, then pass retrieved context to a language model for answer generation. Current status is 'Planned' with no timeline, API specification, or implementation details published.","intents":["I want to build a chatbot that answers questions by searching a knowledge base","I need to implement semantic search over product documentation or FAQ databases","I want to enable users to ask natural language questions and receive contextual answers"],"best_for":["Teams planning future semantic search features but requiring current embedding/classification capabilities","Organizations evaluating EnergeticAI for long-term RAG use cases"],"limitations":["Feature is not yet available — no release date, API specification, or implementation details published","Unknown whether semantic search will support custom document collections or only pre-indexed datasets","No documentation on vector database integration, retrieval ranking, or answer generation approach","Unclear if feature will be available on free tier or require paid subscription","No published performance characteristics, latency targets, or scalability limits","Language support unknown — likely English-only based on current product, but not confirmed"],"requires":["Future release of EnergeticAI with semantic search feature","Document collection in undocumented format"],"input_types":["natural language questions (planned)","document collections (format unknown)"],"output_types":["answer text (planned)","retrieved document references (planned)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_energeticai__cap_5","uri":"capability://automation.workflow.serverless.optimized.model.initialization.with.lazy.loading","name":"serverless-optimized model initialization with lazy loading","description":"Implements lazy model loading strategy where pre-trained models are initialized on first inference request rather than at application startup, reducing cold-start latency for serverless functions that may not invoke ML capabilities. Models are cached in memory after first load, enabling subsequent inferences to complete in ~55ms. This pattern is particularly effective for serverless environments where function instances are ephemeral and initialization overhead directly impacts user-facing latency.","intents":["I want to minimize cold-start latency in AWS Lambda or Google Cloud Functions that use ML models","I need to deploy ML-enhanced APIs that respond quickly even after function warm-up","I want to avoid paying for model initialization time on every serverless function invocation"],"best_for":["Serverless-first organizations using AWS Lambda, Google Cloud Functions, or Vercel","APIs with variable traffic patterns where cold starts are unavoidable","Cost-conscious teams wanting to minimize compute time spent on model initialization"],"limitations":["First inference request still incurs 3.7-second latency — not suitable for sub-second SLA requirements","Lazy loading strategy only effective if function instances persist across multiple requests — ephemeral instances will re-initialize on each invocation","No documented control over initialization timing — cannot pre-warm models or customize lazy-loading behavior","Memory footprint of cached models unknown — may exceed serverless memory limits in constrained environments","No documented mechanism to unload models or manage memory in long-running function instances","Benchmark (3.7s cold-start, 55ms warm-start) is specific to M1 MacBook Pro — performance on actual serverless platforms (x86 AWS Lambda, etc.) unknown"],"requires":["Node.js 18.0.0 or higher","Serverless platform with persistent function instances (AWS Lambda with provisioned concurrency, Google Cloud Functions with min instances, etc.)","Sufficient memory allocation for bundled models (~2.3MB plus runtime overhead)"],"input_types":["text strings"],"output_types":["embeddings","classification results"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_energeticai__cap_6","uri":"capability://automation.workflow.free.tier.with.undocumented.production.scalability.boundaries","name":"free tier with undocumented production scalability boundaries","description":"Offers zero-cost entry point for Node.js developers to integrate embeddings and classification models without financial commitment. Free tier includes access to pre-trained English models and basic inference capabilities, with unclear boundaries on request volume, concurrent users, or production usage. Pricing model for production workloads is not published, creating uncertainty around upgrade path and cost scaling for successful applications.","intents":["I want to experiment with AI capabilities in Node.js without paying upfront","I need to prototype an MVP with ML features before committing to infrastructure costs","I want to evaluate EnergeticAI's capabilities before deciding on a long-term vendor"],"best_for":["Indie developers and bootstrapped startups with minimal budgets","Teams prototyping and validating product ideas before scaling","Educational use and learning projects"],"limitations":["Free tier scalability constraints completely undocumented — no published limits on requests/month, concurrent inference, or data volume","Upgrade path to production pricing unknown — no published pricing tiers, per-request costs, or feature gates between free and paid","No documented SLA, uptime guarantees, or support level on free tier","Unclear whether free tier includes rate limiting, throttling, or usage monitoring","No published terms of service regarding acceptable use, data retention, or model training on user data","Risk of service discontinuation or pricing changes without notice — no commitment to free tier availability"],"requires":["Node.js 18.0.0 or higher","NPM or Yarn package manager","No credit card or payment method required for free tier"],"input_types":["text strings"],"output_types":["embeddings","classification results"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_energeticai__cap_7","uri":"capability://data.processing.analysis.english.only.language.support.with.no.multilingual.capability","name":"english-only language support with no multilingual capability","description":"All pre-trained models (embeddings and classifiers) are trained exclusively on English text and support only English language inputs. No multilingual models, language detection, or translation capabilities are documented or available. This design choice prioritizes model size and cold-start performance over language coverage, making EnergeticAI unsuitable for international applications or non-English content.","intents":["I need to embed or classify English text only","I'm building a product for English-speaking markets and don't need multilingual support"],"best_for":["English-only applications and markets","Teams with no international expansion plans"],"limitations":["No support for non-English languages — embeddings and classifiers will produce incorrect or meaningless results on non-English text","No language detection or automatic routing to language-specific models","No translation capabilities — users must translate content to English before processing","No documented roadmap for multilingual support — unclear if planned or permanently out of scope","Multilingual alternatives (Hugging Face Transformers, TensorFlow Hub) required for international applications"],"requires":["English language input text"],"input_types":["English text strings"],"output_types":["embeddings","classification results"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_energeticai__cap_8","uri":"capability://tool.use.integration.undocumented.api.surface.with.missing.code.examples.and.specifications","name":"undocumented api surface with missing code examples and specifications","description":"Core API for embeddings, classification, and model inference is not documented with function signatures, parameter specifications, error handling, or code examples. Developers must infer API structure from minimal product descriptions and trial-and-error experimentation. This documentation gap creates friction for adoption and makes it difficult to assess API design quality, error recovery patterns, or advanced features.","intents":["I want to understand how to call embedding and classification functions","I need to know what parameters are required and what outputs to expect","I want to see code examples of common use cases"],"best_for":["None — this is a limitation, not a capability that serves user intents"],"limitations":["No published API reference documentation — function signatures, parameter names, types, and defaults unknown","No code examples or tutorials — developers must reverse-engineer API from product marketing","No error handling documentation — unclear what exceptions are thrown and how to handle failures","No performance tuning guidance — no documentation on batch inference, caching, or optimization strategies","No troubleshooting guide — no known issues, common errors, or debugging strategies published","No architectural diagrams or design rationale — cannot understand why API is structured as it is"],"requires":["Significant trial-and-error experimentation"],"input_types":["unknown"],"output_types":["unknown"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Node.js 18.0.0 or higher","NPM or Yarn package manager","~2.3MB additional disk space for bundled model weights","Basic JavaScript/async-await familiarity","Training examples in undocumented format (likely JSON or array structure)","Basic understanding of classification task definition and category semantics","Serverless platform account (AWS, Google Cloud, Vercel, etc.) — optional but recommended for deployment","Basic JavaScript and async/await knowledge","No ML framework knowledge required","Future release of EnergeticAI with semantic search feature"],"failure_modes":["English language only — no multilingual embedding models documented or supported","Cold-start latency of 3.7 seconds still significant for sub-100ms SLA requirements despite 67x improvement","Model architecture and training data unknown — cannot assess bias, domain specificity, or suitability for specialized vocabularies","Bundled model approach increases application package size by ~2.3MB, impacting deployment time and memory footprint in constrained environments","No documented support for custom embedding dimensions or model fine-tuning — locked to pre-trained model outputs","Training API and example format completely undocumented — cannot assess how many examples constitute 'few' or how to structure training data","No documented support for multi-label classification, confidence scores, or prediction explanations","English language only — no multilingual classifier variants available","Unknown whether trained classifiers persist across application restarts or require retraining on each invocation","No documented handling of class imbalance, overlapping categories, or out-of-distribution inputs","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:30.284Z","last_scraped_at":"2026-04-05T13:23:42.561Z","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=energeticai","compare_url":"https://unfragile.ai/compare?artifact=energeticai"}},"signature":"lML6j5986dnsa+11rFpW2bLRuI8X/hS0rA3qOJ+JIGh9mIHFgRFredPy4lchyaHXxWuktAociHBfaWJ52lDkCw==","signedAt":"2026-06-21T20:04:59.697Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/energeticai","artifact":"https://unfragile.ai/energeticai","verify":"https://unfragile.ai/api/v1/verify?slug=energeticai","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"}}