{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_hive","slug":"hive","name":"Hive","type":"product","url":"https://thehive.ai","page_url":"https://unfragile.ai/hive","categories":["model-training"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_hive__cap_0","uri":"capability://safety.moderation.multi.model.content.moderation.via.unified.api","name":"multi-model content moderation via unified api","description":"Hive provides a single REST API endpoint that routes content moderation requests to multiple pre-trained neural network models (trained on proprietary datasets for explicit content, violence, hate speech, etc.). The platform abstracts model selection and versioning, allowing developers to call a single endpoint and receive moderation scores across multiple violation categories without managing individual model deployments or version control.","intents":["I need to moderate user-generated content (text, images, video) at scale without building my own classifiers","I want to detect explicit, violent, or hateful content across multiple content types with a single API call","I need to get confidence scores for different violation categories to make nuanced moderation decisions"],"best_for":["startups building social platforms or marketplaces with UGC","teams without ML expertise who need production-grade moderation immediately","companies wanting to avoid the cost and complexity of training custom classifiers"],"limitations":["No fine-tuning or custom model training available — locked into Hive's pre-trained models","Moderation categories and thresholds are fixed; no per-customer customization of violation definitions","Latency depends on Hive's cloud infrastructure; no on-premise or edge deployment options","No real-time streaming moderation — batch or request-response only"],"requires":["API key from Hive dashboard","HTTPS client library (REST, Python SDK, Node.js SDK, or raw HTTP)","Network connectivity to Hive's cloud endpoints"],"input_types":["image (JPEG, PNG, WebP, GIF)","text (UTF-8 strings, up to documented length limit)","video (via frame extraction or URL reference)"],"output_types":["JSON with moderation scores per category","confidence percentages (0-100 or 0-1 scale)","boolean flags for policy violations"],"categories":["safety-moderation","content-classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hive__cap_1","uri":"capability://image.visual.image.classification.and.object.detection.via.pre.trained.vision.models","name":"image classification and object detection via pre-trained vision models","description":"Hive exposes pre-trained computer vision models that perform image classification (labeling objects, scenes, attributes) and object detection (bounding boxes with confidence scores) through a REST API. Models are trained on large-scale datasets and support multiple image formats; the platform handles image preprocessing, model inference, and result serialization without requiring developers to manage PyTorch/TensorFlow stacks.","intents":["I need to automatically tag or categorize images in my application without training a custom vision model","I want to detect specific objects in images and get their locations (bounding boxes) for downstream processing","I need to extract visual attributes (color, style, composition) from images for search or recommendation features"],"best_for":["e-commerce platforms building product image understanding","content platforms needing automatic image tagging and search","teams prototyping vision features without ML infrastructure"],"limitations":["Pre-trained models have fixed label sets; custom object detection requires external fine-tuning","No real-time video processing — image-by-image only","Accuracy depends on model training data; domain-specific images may have lower performance","No explainability features (e.g., attention maps, feature visualization)"],"requires":["API key from Hive","Image file or URL accessible to Hive's servers","HTTP client for REST API calls"],"input_types":["image (JPEG, PNG, WebP, GIF, BMP)","image URL (publicly accessible)","base64-encoded image data"],"output_types":["JSON with classification labels and confidence scores","bounding box coordinates (x, y, width, height)","attribute key-value pairs (e.g., color: 'blue', style: 'modern')"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hive__cap_10","uri":"capability://data.processing.analysis.confidence.scoring.and.multi.category.classification.results","name":"confidence scoring and multi-category classification results","description":"Hive's classification models return structured results with confidence scores for each category, enabling developers to make nuanced decisions based on model certainty. Results include per-category confidence percentages (0-100 or 0-1 scale), allowing applications to filter low-confidence predictions or implement custom thresholds. This pattern is consistent across moderation, vision, and NLP models.","intents":["I want to know how confident the model is in its predictions, not just the top result","I need to set custom confidence thresholds for different use cases (e.g., stricter for moderation)","I want to implement fallback logic when model confidence is below a threshold"],"best_for":["applications requiring nuanced decision-making based on model certainty","platforms with variable risk tolerance across different content types","teams implementing human-in-the-loop workflows with model confidence as a signal"],"limitations":["Confidence scores are model-specific; thresholds that work for one model may not work for another","No calibration information provided; confidence scores may not reflect true probability of correctness","No uncertainty quantification beyond point estimates; no confidence intervals or Bayesian estimates","Developers must implement their own threshold logic; no built-in confidence-based filtering"],"requires":["understanding of confidence score interpretation for specific models","logic to parse and threshold confidence scores in application code"],"input_types":["same as underlying models"],"output_types":["JSON with category labels and confidence scores","optional metadata about model version or inference details"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hive__cap_11","uri":"capability://automation.workflow.rate.limiting.and.quota.management.with.usage.tracking","name":"rate limiting and quota management with usage tracking","description":"Hive enforces rate limits and API quotas at the account level, tracking usage across all API calls and returning rate limit headers in responses. Developers can monitor usage via the Hive dashboard and implement client-side rate limiting or backoff strategies. The platform provides usage metrics and quota information to help teams plan capacity and optimize costs.","intents":["I need to understand my API usage and costs to budget for Hive","I want to implement rate limiting in my application to stay within quota","I need to monitor API usage and get alerts when approaching quota limits"],"best_for":["teams with variable or unpredictable API usage patterns","applications needing to implement client-side rate limiting","organizations tracking API costs and usage across teams"],"limitations":["Rate limits are account-level; no per-endpoint or per-model granularity","No built-in quota alerts; developers must implement their own monitoring","Rate limit headers are returned in responses; no proactive rate limit information before hitting limits","No ability to request higher quotas programmatically; requires manual account upgrade"],"requires":["API key from Hive","monitoring of HTTP response headers for rate limit information","optional: webhook or monitoring service for quota alerts"],"input_types":["HTTP request headers (rate limit information returned in responses)"],"output_types":["rate limit headers in HTTP responses","usage metrics in Hive dashboard","quota information in account settings"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hive__cap_2","uri":"capability://text.generation.language.intent.classification.and.semantic.understanding.for.nlp.tasks","name":"intent classification and semantic understanding for nlp tasks","description":"Hive provides pre-trained NLP models that classify text into intents (e.g., customer support tickets into 'billing', 'technical', 'complaint'), extract entities (names, dates, locations), and perform sentiment analysis. Models are accessed via REST API and return structured JSON with classification confidence scores and extracted entities, enabling developers to build NLP features without training custom transformers.","intents":["I need to automatically route customer support tickets or messages to the right team based on intent","I want to extract structured data (entities like names, dates, amounts) from unstructured text","I need to analyze sentiment or emotion in user feedback or social media content"],"best_for":["customer support platforms automating ticket triage","chatbot builders needing intent recognition without training NLU models","teams analyzing customer feedback or reviews at scale"],"limitations":["Intent categories are pre-defined; custom intent types require external model training","No multi-language support documented; primarily English-focused","Context window is limited; long documents may need chunking before analysis","No fine-tuning API — models are frozen and cannot be adapted to domain-specific language"],"requires":["API key from Hive","Text input (UTF-8 encoded)","HTTP client for REST API"],"input_types":["plain text (UTF-8 strings)","structured text (JSON with text fields)"],"output_types":["JSON with intent labels and confidence scores","extracted entities with types and positions","sentiment scores (positive/negative/neutral)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hive__cap_3","uri":"capability://data.processing.analysis.batch.processing.for.high.volume.content.analysis","name":"batch processing for high-volume content analysis","description":"Hive supports batch API endpoints that accept multiple items (images, text, videos) in a single request and return results asynchronously. The platform queues batch jobs, processes them in parallel across its infrastructure, and provides webhooks or polling endpoints for result retrieval. This pattern reduces per-request overhead and enables cost-effective analysis of large content libraries.","intents":["I need to analyze thousands of images or documents without making individual API calls","I want to process my entire content library through moderation or classification models efficiently","I need to reduce API costs by batching requests instead of making individual calls per item"],"best_for":["platforms with large content libraries needing periodic re-analysis","teams with non-real-time processing requirements (e.g., overnight batch jobs)","cost-conscious teams wanting to minimize per-request API charges"],"limitations":["Batch processing is asynchronous — not suitable for real-time use cases","No guaranteed SLA on batch completion time; processing may take hours depending on queue depth","Batch size limits may apply (e.g., max 1000 items per batch)","No streaming results — must wait for entire batch to complete before retrieving results"],"requires":["API key from Hive","Batch API endpoint documentation","Webhook endpoint or polling mechanism for result retrieval","Storage for batch job IDs and status tracking"],"input_types":["JSON array of image URLs or base64-encoded images","JSON array of text strings","CSV or JSONL files with content references"],"output_types":["batch job ID for status tracking","JSON results file (downloadable or via webhook)","status updates (pending, processing, completed, failed)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hive__cap_4","uri":"capability://tool.use.integration.multi.provider.model.orchestration.with.provider.abstraction","name":"multi-provider model orchestration with provider abstraction","description":"Hive abstracts away differences between underlying AI model providers (e.g., different vision models, NLP engines) by exposing a unified API layer. Developers specify a task (e.g., 'classify image') without choosing which provider's model to use; Hive routes requests to the optimal model based on performance, cost, or availability. This enables transparent model swapping and A/B testing without code changes.","intents":["I want to use the best model for my task without being locked into a single provider","I need to compare different models' performance on my data without rewriting integration code","I want Hive to automatically switch models if one provider has downtime or performance issues"],"best_for":["teams wanting to avoid vendor lock-in with a single AI provider","organizations comparing multiple models' performance on their specific data","platforms needing high availability and automatic failover between models"],"limitations":["Model selection logic is opaque — developers cannot directly control which provider's model is used","No per-request model specification; routing is determined by Hive's backend logic","Consistency across model versions may vary; model updates could change results without warning","No SLA guarantees on which model will be used or when model swaps occur"],"requires":["API key from Hive","Understanding of Hive's supported models and their capabilities","No direct integration with individual provider SDKs (Hive abstracts them)"],"input_types":["same as underlying models (images, text, etc.)"],"output_types":["standardized JSON format across all providers","optional metadata indicating which model/provider was used"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hive__cap_5","uri":"capability://safety.moderation.explicit.content.and.nsfw.detection.for.images.and.video","name":"explicit content and nsfw detection for images and video","description":"Hive provides specialized pre-trained models that detect explicit sexual content, nudity, and adult material in images and video frames. The models return confidence scores for different explicit content categories (e.g., 'nudity', 'sexual activity', 'suggestive') and can be used to filter or flag content before it reaches users. Detection is performed server-side via REST API without requiring local image processing.","intents":["I need to automatically filter or flag explicit content in user-generated images before publishing","I want to protect minors from NSFW content in my platform","I need to comply with content policies by detecting and removing explicit material at scale"],"best_for":["social platforms and marketplaces with UGC moderation requirements","dating apps and adult-oriented platforms needing nuanced explicit content detection","platforms with child safety obligations (COPPA, etc.)"],"limitations":["Model accuracy varies with image quality, lighting, and artistic content (e.g., may flag art or medical imagery)","No ability to customize sensitivity thresholds per customer","False positives on non-explicit content (e.g., swimwear, medical images, art) require manual review","No video frame sampling strategy exposed — developers cannot control which frames are analyzed"],"requires":["API key from Hive","Image or video file accessible to Hive's servers","HTTP client for REST API"],"input_types":["image (JPEG, PNG, WebP, GIF)","video (via URL or frame extraction)"],"output_types":["JSON with explicit content category scores","confidence percentages per category","boolean flag indicating if content exceeds policy threshold"],"categories":["safety-moderation","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hive__cap_6","uri":"capability://tool.use.integration.api.first.integration.with.language.specific.sdks","name":"api-first integration with language-specific sdks","description":"Hive provides REST APIs as the primary integration mechanism, with official SDKs for Python, Node.js, and other languages that wrap HTTP calls and handle authentication, serialization, and error handling. SDKs provide type hints, async/await support, and convenience methods that reduce boilerplate compared to raw HTTP clients. All models are accessed through the same SDK interface regardless of underlying model type.","intents":["I want to integrate Hive models into my application with minimal boilerplate code","I need type-safe API calls with IDE autocomplete and error handling","I want to use async/await patterns for non-blocking API calls in my application"],"best_for":["developers building applications in Python, Node.js, or other supported languages","teams wanting quick integration without learning REST API details","applications requiring async/await patterns for high-concurrency scenarios"],"limitations":["SDKs only available for Python and Node.js; other languages require raw HTTP clients","SDK versions may lag behind API updates; feature parity not guaranteed","No offline mode — all requests require network connectivity to Hive's cloud","Rate limiting and quota management are enforced at the API level; SDKs don't provide local caching"],"requires":["Python 3.7+ or Node.js 14+","Hive API key","pip install hive-ai (Python) or npm install hive-ai (Node.js)","Network connectivity to Hive's cloud endpoints"],"input_types":["native Python/JavaScript objects (dicts, objects)","file paths or URLs","base64-encoded data"],"output_types":["native Python/JavaScript objects with type hints","structured data matching API response schema"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hive__cap_7","uri":"capability://safety.moderation.hate.speech.and.toxic.language.detection","name":"hate speech and toxic language detection","description":"Hive provides pre-trained NLP models that detect hate speech, toxic language, harassment, and abusive content in text. Models return confidence scores for different violation categories (e.g., 'hate speech', 'harassment', 'profanity') and can be used to flag or filter harmful user-generated content. Detection is performed via REST API without requiring local NLP infrastructure.","intents":["I need to automatically detect and flag hate speech or toxic language in user comments","I want to protect my community from harassment and abusive content","I need to comply with platform policies by filtering harmful language at scale"],"best_for":["social platforms and forums with community moderation requirements","gaming platforms protecting players from toxic behavior","platforms with hate speech or harassment policies"],"limitations":["Model accuracy varies with context, slang, and cultural references; context-dependent toxicity may be missed","No multi-language support documented; primarily English-focused","False positives on reclaimed slurs or sarcasm require manual review","No ability to customize violation categories or severity thresholds per customer"],"requires":["API key from Hive","Text input (UTF-8 encoded)","HTTP client for REST API"],"input_types":["plain text (UTF-8 strings)","user comments or messages"],"output_types":["JSON with toxicity category scores","confidence percentages per category","boolean flag indicating if content violates policy"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hive__cap_8","uri":"capability://automation.workflow.webhook.based.asynchronous.result.delivery","name":"webhook-based asynchronous result delivery","description":"Hive supports webhook callbacks for asynchronous operations (batch processing, long-running analysis). When a job completes, Hive sends an HTTP POST request to a developer-specified webhook URL with the results. This pattern enables non-blocking integration where applications don't need to poll for results; instead, Hive pushes results to the application when ready.","intents":["I want to process content asynchronously without blocking my application","I need to receive results from Hive batch jobs via push notifications instead of polling","I want to decouple my application from Hive's processing latency"],"best_for":["applications with non-real-time processing requirements","platforms processing large batches of content overnight","teams wanting to avoid polling overhead and reduce API calls"],"limitations":["Webhook delivery is not guaranteed; failed deliveries may not be retried indefinitely","No built-in webhook signature verification; developers must implement security checks","Webhook endpoint must be publicly accessible and handle concurrent requests","No guaranteed delivery order if multiple webhooks are sent in rapid succession"],"requires":["publicly accessible webhook endpoint (HTTPS)","ability to receive and parse HTTP POST requests","webhook URL registered in Hive dashboard","mechanism to correlate webhook results with original requests (e.g., job ID)"],"input_types":["HTTP POST request with JSON payload"],"output_types":["JSON with batch results, job status, and metadata"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hive__cap_9","uri":"capability://image.visual.image.url.and.base64.input.handling.with.automatic.preprocessing","name":"image url and base64 input handling with automatic preprocessing","description":"Hive's image APIs accept multiple input formats: publicly accessible image URLs, base64-encoded image data, and direct file uploads. The platform automatically handles image preprocessing (resizing, format conversion, EXIF rotation) before feeding images to vision models. This abstraction eliminates the need for developers to manage image preprocessing pipelines.","intents":["I want to analyze images from URLs without downloading them locally","I need to send images to Hive without managing preprocessing or format conversion","I want to support multiple image input formats (URL, base64, file upload) in my application"],"best_for":["web applications analyzing images from user uploads or external URLs","platforms needing flexible image input handling","teams without image processing expertise"],"limitations":["URL-based images must be publicly accessible; private/authenticated URLs may fail","Base64 encoding increases payload size by ~33%; large images may hit request size limits","Automatic preprocessing may alter image content (e.g., lossy compression, EXIF rotation)","No control over preprocessing parameters; developers cannot customize resize dimensions or quality"],"requires":["API key from Hive","image in one of supported formats (JPEG, PNG, WebP, GIF, BMP)","for URLs: publicly accessible image endpoint","for base64: base64-encoded image data"],"input_types":["image URL (HTTP/HTTPS)","base64-encoded image string","multipart file upload"],"output_types":["same as underlying vision model (classification labels, bounding boxes, etc.)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"low","permissions":["API key from Hive dashboard","HTTPS client library (REST, Python SDK, Node.js SDK, or raw HTTP)","Network connectivity to Hive's cloud endpoints","API key from Hive","Image file or URL accessible to Hive's servers","HTTP client for REST API calls","understanding of confidence score interpretation for specific models","logic to parse and threshold confidence scores in application code","monitoring of HTTP response headers for rate limit information","optional: webhook or monitoring service for quota alerts"],"failure_modes":["No fine-tuning or custom model training available — locked into Hive's pre-trained models","Moderation categories and thresholds are fixed; no per-customer customization of violation definitions","Latency depends on Hive's cloud infrastructure; no on-premise or edge deployment options","No real-time streaming moderation — batch or request-response only","Pre-trained models have fixed label sets; custom object detection requires external fine-tuning","No real-time video processing — image-by-image only","Accuracy depends on model training data; domain-specific images may have lower performance","No explainability features (e.g., attention maps, feature visualization)","Confidence scores are model-specific; thresholds that work for one model may not work for another","No calibration information provided; confidence scores may not reflect true probability of correctness","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":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:30.893Z","last_scraped_at":"2026-04-05T13:23:42.552Z","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=hive","compare_url":"https://unfragile.ai/compare?artifact=hive"}},"signature":"bK7EnQ71k+4IWT87D/mMOQJYjWhO/T2SBrIJMq83qsMr3RVT8n843351U7EQW8ITrLDiHP20Ee700BvVEpt3Aw==","signedAt":"2026-06-21T03:08:57.236Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/hive","artifact":"https://unfragile.ai/hive","verify":"https://unfragile.ai/api/v1/verify?slug=hive","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"}}