{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"label-studio","slug":"label-studio","name":"Label Studio","type":"repo","url":"https://github.com/HumanSignal/label-studio","page_url":"https://unfragile.ai/label-studio","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"label-studio__cap_0","uri":"capability://data.processing.analysis.multi.modal.annotation.interface.with.configurable.labeling.templates","name":"multi-modal annotation interface with configurable labeling templates","description":"Provides 40+ pre-built annotation templates (classification, NER, bounding box, polygon, keypoint, relation extraction, etc.) that can be composed via XML-based label configuration. The frontend uses React with canvas-based rendering for spatial annotations and dynamically loads template schemas that map to backend task models, enabling users to define custom labeling interfaces without code.","intents":["Define custom annotation schemas for image bounding boxes, text entity tagging, and video frame labeling without writing code","Reuse pre-built templates across multiple projects to standardize labeling interfaces","Configure multi-step annotation workflows with conditional logic based on previous labels"],"best_for":["ML teams building computer vision datasets (object detection, segmentation, keypoint)","NLP teams creating NER and relation extraction training data","Non-technical project managers designing labeling workflows"],"limitations":["Template composition is XML-based, requiring familiarity with Label Studio's DSL for advanced customization","Canvas rendering performance degrades with >1000 objects per image due to DOM-based event handling","Custom template logic limited to predefined control types; arbitrary JavaScript execution not supported"],"requires":["Label Studio 1.0+","Modern browser with HTML5 Canvas support","Backend running Django REST Framework for template validation"],"input_types":["image (JPEG, PNG, WebP, TIFF)","video (MP4, WebM, MOV)","text (plain text, HTML, PDF)","audio (WAV, MP3, OGG)","time series (CSV with timestamp columns)"],"output_types":["JSON annotation objects with bounding boxes, polygons, keypoints, text spans","XML/JSON serialized label configurations","Prediction objects for ML-assisted labeling"],"categories":["data-processing-analysis","annotation-interface"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"label-studio__cap_1","uri":"capability://planning.reasoning.task.sampling.and.active.learning.queue.management","name":"task sampling and active learning queue management","description":"Implements a pluggable next-task algorithm (in label_studio/projects/functions/next_task.py) that ranks unlabeled tasks based on sampling strategies (random, sequential, uncertainty sampling from ML predictions, consensus-based disagreement). The Data Manager API filters and sorts tasks using database queries with optional ML model predictions, enabling prioritization of high-value samples for labeling efficiency.","intents":["Automatically surface the most uncertain or disagreed-upon samples to labelers to maximize annotation value","Implement active learning loops where model predictions guide which tasks to label next","Distribute labeling work fairly across team members with load-balancing strategies"],"best_for":["Teams with large unlabeled datasets who want to minimize labeling cost via uncertainty sampling","ML engineers building active learning pipelines with iterative model retraining","Projects with multiple annotators requiring fair task distribution"],"limitations":["Uncertainty sampling requires pre-computed model predictions; no built-in model training, only prediction ingestion","Next-task algorithm runs synchronously on each request, causing latency spikes with >100k tasks without database indexing","Consensus-based disagreement requires multiple annotations per task, increasing labeling overhead"],"requires":["Label Studio backend with PostgreSQL or MySQL for task filtering","ML model predictions uploaded via Predictions API (external model required)","Python 3.9+ for backend task ranking logic"],"input_types":["Task metadata (status, created_at, updated_at)","Model predictions (confidence scores, class probabilities)","Annotation history (per-annotator labels for consensus calculation)"],"output_types":["Ranked task queue (ordered list of task IDs)","Sampling metadata (uncertainty scores, disagreement metrics)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"label-studio__cap_10","uri":"capability://automation.workflow.finite.state.machine.fsm.based.task.state.management","name":"finite state machine (fsm) based task state management","description":"Implements FSM-based state transitions for tasks (label_studio/tasks/models.py or similar) where tasks move through defined states (unlabeled → in-progress → completed or skipped). State transitions are validated to prevent invalid state changes (e.g., cannot go from completed back to unlabeled). FSM is configurable per project, allowing custom state workflows.","intents":["Enforce valid task state transitions to prevent invalid annotation workflows","Define custom task state workflows per project (e.g., unlabeled → in-progress → review → approved → completed)","Track task lifecycle and prevent concurrent state changes"],"best_for":["Teams with complex annotation workflows requiring state validation","Projects with review/approval steps before annotation export","ML engineers building custom task lifecycle management"],"limitations":["FSM is project-level only; no per-task custom state workflows","State transitions are synchronous; no async state change notifications","No built-in state change history; only current state is tracked","FSM configuration is not validated until state transition is attempted"],"requires":["Label Studio backend with task model and FSM logic","Database for storing task state"],"input_types":["Task state (unlabeled, in-progress, completed, skipped)","State transition requests (e.g., mark as completed)"],"output_types":["Updated task state","State transition validation results (allowed/denied)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"label-studio__cap_11","uri":"capability://automation.workflow.background.job.queue.for.asynchronous.task.processing","name":"background job queue for asynchronous task processing","description":"Integrates a background job queue (likely Celery with Redis or similar) for asynchronous processing of long-running tasks (bulk import, export, ML prediction requests, annotation processing). Jobs are queued, executed by worker processes, and results are stored in the database or cache. Job status can be tracked via API.","intents":["Process bulk imports and exports asynchronously without blocking the UI","Queue ML prediction requests and process them in the background","Track job progress and handle failures gracefully"],"best_for":["Teams with large datasets requiring bulk import/export without UI blocking","ML pipelines requiring asynchronous prediction processing","Projects with long-running annotation processing tasks"],"limitations":["Job queue adds operational complexity; requires Redis or similar message broker","Job failures are not automatically retried; requires manual intervention or custom retry logic","Job results are not persisted by default; requires external storage (database, cache)","Job monitoring requires additional tools (Flower, custom dashboards)"],"requires":["Message broker (Redis, RabbitMQ, etc.)","Celery or similar job queue framework","Worker processes running to execute jobs","Database or cache for storing job results"],"input_types":["Job definitions (task type, parameters)","Job data (files, task IDs, etc.)"],"output_types":["Job status (queued, running, completed, failed)","Job results (import/export results, prediction outputs)","Job progress (percentage complete)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"label-studio__cap_12","uri":"capability://automation.workflow.database.schema.versioning.and.migration.management","name":"database schema versioning and migration management","description":"Uses Django migrations (label_studio/migrations/) to version database schema changes and manage schema evolution. Migrations are applied sequentially during deployment, enabling rollback if needed. Supports both forward and backward migrations for schema compatibility.","intents":["Version database schema changes and apply them safely during deployment","Rollback schema changes if deployment fails or issues are discovered","Manage schema evolution across multiple environments (dev, staging, production)"],"best_for":["Teams deploying Label Studio to production with multiple environments","Projects requiring schema versioning and rollback capability","ML engineers managing database schema changes alongside code changes"],"limitations":["Migrations are applied sequentially; large migrations may cause downtime","Rollback requires manual intervention; no automatic rollback on deployment failure","Complex migrations (e.g., data transformation) may require custom SQL","Migration conflicts can occur if multiple developers modify schema simultaneously"],"requires":["Django ORM and migration framework","Database (PostgreSQL, MySQL, SQLite)","Python 3.9+"],"input_types":["Schema changes (model definitions, field additions/removals)","Migration files (auto-generated or custom)"],"output_types":["Updated database schema","Migration history (applied migrations)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"label-studio__cap_13","uri":"capability://tool.use.integration.restful.api.for.programmatic.access.to.all.platform.features","name":"restful api for programmatic access to all platform features","description":"Exposes comprehensive REST APIs (label_studio/projects/api.py, label_studio/tasks/api.py, label_studio/organizations/api.py, etc.) for all platform features (project management, task CRUD, annotation CRUD, user management, storage configuration, ML integration, import/export). APIs use Django REST Framework with token-based authentication and support filtering, pagination, and sorting. API documentation is auto-generated from code.","intents":["Programmatically create projects, import tasks, and export annotations without UI","Integrate Label Studio with external ML pipelines and data management systems","Build custom applications on top of Label Studio's data and annotation capabilities"],"best_for":["ML engineers building custom annotation workflows and integrations","Teams automating project setup and data import via scripts","Developers building applications on top of Label Studio's API"],"limitations":["API rate limiting is not enforced by default; requires external rate limiting (API gateway, middleware)","API pagination is cursor-based; no offset-based pagination for large result sets","API responses are not versioned; breaking changes require API version management","No built-in API key rotation; requires manual key management"],"requires":["Label Studio backend running","API authentication (token-based or OAuth2)","HTTP client library (requests, curl, etc.)"],"input_types":["HTTP requests with JSON payloads","Query parameters for filtering, pagination, sorting","Authentication tokens"],"output_types":["JSON responses with resource objects","HTTP status codes and error messages","Pagination metadata (next/previous links, total count)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"label-studio__cap_2","uri":"capability://tool.use.integration.ml.model.integration.for.pre.annotation.and.prediction.ingestion","name":"ml model integration for pre-annotation and prediction ingestion","description":"Provides an ML API (label_studio/ml/api.py) that accepts predictions from external models via REST endpoints, stores predictions in the database, and displays them as pre-filled annotations in the labeling interface. Supports both synchronous prediction requests (send task data to model, receive predictions) and asynchronous batch prediction uploads. Predictions are versioned and can be compared against ground-truth annotations for model evaluation.","intents":["Pre-fill annotations with model predictions to reduce annotator effort for high-confidence predictions","Ingest batch predictions from external ML pipelines (e.g., Hugging Face models, custom PyTorch models) without modifying Label Studio code","Track model performance by comparing predictions against final annotations for active learning feedback loops"],"best_for":["Teams with pre-trained models who want to accelerate labeling via pre-annotation","ML engineers building active learning loops that require prediction ingestion","Projects requiring model evaluation metrics (precision, recall) computed from annotations"],"limitations":["No built-in model training or fine-tuning; Label Studio only ingests predictions from external models","Synchronous prediction requests require model to respond within HTTP timeout (default 30s), limiting use with slow models","Predictions are not automatically updated when tasks are re-labeled; requires manual re-prediction or external orchestration"],"requires":["External ML model with REST API or batch prediction capability","Label Studio ML backend running (optional, for synchronous predictions)","API key for authentication if using Label Studio Cloud"],"input_types":["JSON prediction objects with class labels, confidence scores, bounding boxes, or text spans","Batch CSV/JSON files with predictions for multiple tasks","Raw task data (images, text) sent to external model for synchronous prediction"],"output_types":["Prediction objects stored in database (versioned, comparable to annotations)","Pre-filled annotation UI with model predictions as default values","Evaluation metrics (precision, recall, F1) computed from prediction-annotation pairs"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"label-studio__cap_3","uri":"capability://tool.use.integration.cloud.storage.integration.with.s3.gcs.and.azure.blob.storage","name":"cloud storage integration with s3, gcs, and azure blob storage","description":"Implements pluggable storage backends (label_studio/io_storages/) that connect to cloud providers via their native SDKs (boto3 for S3, google-cloud-storage for GCS, azure-storage-blob for Azure). Tasks can be imported directly from cloud buckets, and annotations can be exported back to cloud storage. Storage configuration is managed per-project with credentials stored encrypted in the database, enabling multi-cloud deployments without code changes.","intents":["Import large datasets directly from S3/GCS/Azure without downloading to local disk","Export completed annotations back to cloud storage for downstream ML pipelines","Manage cloud credentials securely per-project without exposing keys in configuration files"],"best_for":["Teams with datasets already in cloud storage (S3, GCS, Azure) who want to avoid data transfer costs","ML pipelines that require annotations exported to cloud for model training","Multi-tenant deployments requiring per-project cloud credential isolation"],"limitations":["Cloud storage import is synchronous; importing >10k files causes UI blocking without pagination","No built-in data deduplication; importing the same bucket twice creates duplicate tasks","Credentials stored in database; requires encrypted database and secure key management for production use","Export to cloud is asynchronous but not resumable; large exports may fail without retry logic"],"requires":["AWS S3, Google Cloud Storage, or Azure Blob Storage account with appropriate IAM permissions","Cloud provider credentials (access key/secret for S3, service account JSON for GCS, connection string for Azure)","Label Studio backend with encrypted database for credential storage"],"input_types":["Cloud storage paths (s3://bucket/prefix, gs://bucket/prefix, azure://container/prefix)","Cloud provider credentials (AWS access keys, GCS service account, Azure connection string)","File formats supported by cloud storage (images, videos, text, audio, time series)"],"output_types":["Annotation files exported to cloud storage (JSON, XML, CSV formats)","Task metadata exported to cloud (task IDs, annotator names, timestamps)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"label-studio__cap_4","uri":"capability://automation.workflow.multi.user.collaboration.with.role.based.access.control.and.annotation.history","name":"multi-user collaboration with role-based access control and annotation history","description":"Implements Django-based user and organization management (label_studio/organizations/, label_studio/users/) with role-based access control (RBAC) at project and organization levels. Tracks annotation history per task, enabling review of who labeled what and when. Supports team workspaces with per-project role assignments (annotator, reviewer, manager) and audit logging for compliance.","intents":["Manage team access to projects with granular role-based permissions (annotator, reviewer, manager)","Track annotation history and audit trail for compliance and quality assurance","Assign tasks to specific annotators and review their work before export"],"best_for":["Teams with multiple annotators requiring task assignment and review workflows","Enterprises requiring audit trails and compliance logging for regulated datasets","Organizations with hierarchical access control (admins, managers, annotators)"],"limitations":["RBAC is project-level only; no fine-grained field-level permissions (e.g., hide sensitive data from annotators)","Annotation history is stored in database but not versioned; only latest annotation per task is queryable","No built-in approval workflow; reviewers must manually compare annotations and approve/reject","Audit logging is basic; no integration with external SIEM systems"],"requires":["Label Studio backend with user authentication (local, LDAP, OAuth2, SAML)","Database for storing user roles and audit logs","Django user model and permission framework"],"input_types":["User credentials (username/password, LDAP, OAuth2 tokens)","Role assignments (annotator, reviewer, manager)","Project membership and access control lists"],"output_types":["Annotation history with annotator names and timestamps","Audit logs with user actions (login, annotation, export)","Access control decisions (allowed/denied per user-project pair)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"label-studio__cap_5","uri":"capability://data.processing.analysis.data.import.with.format.detection.and.task.creation","name":"data import with format detection and task creation","description":"Implements a data import pipeline (label_studio/data_manager/api.py, label_studio/io_storages/) that accepts multiple file formats (JSON, CSV, XML, images, videos, audio, time series) and automatically detects format based on file extension or MIME type. Imported data is parsed and converted into Task objects in the database, with support for bulk import via ZIP files or cloud storage. Import progress is tracked asynchronously via background jobs.","intents":["Bulk import datasets from CSV, JSON, or image folders without manual task creation","Automatically detect file formats and parse data into task objects","Track import progress and handle errors gracefully without blocking the UI"],"best_for":["Teams with existing datasets in standard formats (CSV, JSON, images) who want to quickly set up labeling projects","ML engineers building data pipelines that require bulk task creation","Projects requiring import from multiple sources (local files, cloud storage, URLs)"],"limitations":["Format detection is based on file extension; ambiguous formats (e.g., .txt could be plain text or CSV) may be misdetected","Bulk import is asynchronous but not resumable; failed imports require re-uploading the entire file","No built-in data validation; invalid data (e.g., missing required fields) is silently skipped","Import performance degrades with >100k files due to sequential task creation without batch optimization"],"requires":["Label Studio backend with background job queue (Celery or similar)","Database for storing imported tasks","File storage (local disk or cloud storage) for uploaded files"],"input_types":["CSV files with columns mapped to task data","JSON files with task objects (one per line or array)","XML files with task elements","Image files (JPEG, PNG, WebP, TIFF)","Video files (MP4, WebM, MOV)","Audio files (WAV, MP3, OGG)","Time series data (CSV with timestamp columns)","ZIP archives containing multiple files"],"output_types":["Task objects created in database","Import progress reports (number of tasks created, errors)","Task IDs for downstream processing"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"label-studio__cap_6","uri":"capability://data.processing.analysis.annotation.export.with.format.conversion.and.filtering","name":"annotation export with format conversion and filtering","description":"Implements an export pipeline (label_studio/tasks/api.py, label_studio/io_storages/) that converts annotations from internal JSON format to multiple output formats (JSON, XML, CSV, COCO, Pascal VOC, YOLO, Hugging Face datasets). Export can be filtered by annotation status (completed, in-progress, skipped), annotator, or date range. Exports are generated asynchronously and can be downloaded or pushed to cloud storage.","intents":["Export completed annotations in ML framework-specific formats (COCO, YOLO, Pascal VOC) for model training","Filter annotations by status, annotator, or date range before export","Push annotations to cloud storage for downstream ML pipelines without manual download"],"best_for":["ML engineers building training pipelines that require annotations in specific formats (COCO, YOLO, etc.)","Teams exporting annotations to multiple downstream systems with different format requirements","Projects requiring filtered exports (e.g., only completed annotations from specific annotators)"],"limitations":["Format conversion is lossy; some annotation types (e.g., relations) may not be fully supported in all output formats","Export filtering is done in-memory; exporting >100k annotations causes memory spikes without pagination","No built-in data validation; invalid annotations (e.g., missing required fields) are silently skipped","Export to cloud is asynchronous but not resumable; large exports may fail without retry logic"],"requires":["Label Studio backend with background job queue (Celery or similar)","Database for querying annotations","File storage (local disk or cloud storage) for export files"],"input_types":["Annotation filter criteria (status, annotator, date range, project)","Output format specification (JSON, XML, CSV, COCO, YOLO, etc.)","Cloud storage destination (optional)"],"output_types":["Annotation files in requested format (JSON, XML, CSV, COCO, YOLO, etc.)","Export metadata (number of annotations, format version)","Export status reports (success/failure, error messages)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"label-studio__cap_7","uri":"capability://data.processing.analysis.project.configuration.and.labeling.interface.customization","name":"project configuration and labeling interface customization","description":"Provides a project settings API (label_studio/projects/api.py, label_studio/projects/models.py) that allows users to configure project metadata (name, description, label configuration), sampling strategy, and annotation guidelines. The label configuration is stored as XML (LSF format) and defines the annotation interface (controls, predictions display, etc.). Projects can be cloned to reuse configurations across similar datasets.","intents":["Create and configure labeling projects with custom annotation interfaces without code","Define project-level settings (sampling strategy, annotation guidelines, label configuration)","Clone projects to reuse configurations across similar datasets"],"best_for":["Non-technical project managers setting up labeling workflows","Teams with multiple similar projects who want to reuse configurations","ML engineers defining complex annotation interfaces with conditional logic"],"limitations":["Label configuration is XML-based, requiring familiarity with LSF format for advanced customization","Project cloning copies configuration but not tasks or annotations; requires separate data import","No version control for label configurations; changes overwrite previous versions","Project settings are not validated until annotation interface is rendered; invalid configurations may cause UI errors"],"requires":["Label Studio backend with project model and API","Database for storing project metadata and configurations","User authentication for project access control"],"input_types":["Project metadata (name, description)","Label configuration (XML LSF format)","Sampling strategy (random, sequential, uncertainty, etc.)","Annotation guidelines (text or HTML)"],"output_types":["Project objects with configuration","Rendered annotation interface (HTML/React components)","Cloned project with copied configuration"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"label-studio__cap_8","uri":"capability://data.processing.analysis.task.annotation.workflow.with.concurrent.multi.annotator.support","name":"task annotation workflow with concurrent multi-annotator support","description":"Implements a task annotation system (label_studio/tasks/models.py, label_studio/tasks/api.py) where multiple annotators can label the same task concurrently. Each annotation is stored separately with annotator metadata (user ID, timestamp). Tasks track annotation status (unlabeled, in-progress, completed, skipped) and support agreement metrics (inter-annotator agreement, Kappa) for quality assurance. Annotations can be reviewed and approved before export.","intents":["Enable multiple annotators to label the same task for quality assurance and agreement metrics","Track annotation status and progress per task","Calculate inter-annotator agreement (Kappa, Fleiss' Kappa) for quality assessment"],"best_for":["Teams requiring multiple annotations per task for quality assurance","Projects with inter-annotator agreement requirements (e.g., medical imaging)","ML engineers building active learning loops that use disagreement as a signal"],"limitations":["Agreement metrics are calculated post-hoc; no real-time agreement feedback during annotation","No built-in conflict resolution; reviewers must manually choose between conflicting annotations","Concurrent annotation locking is optimistic; simultaneous edits by multiple annotators may cause conflicts","Agreement calculation requires >2 annotations per task, increasing labeling overhead"],"requires":["Label Studio backend with task and annotation models","Database for storing multiple annotations per task","User authentication for tracking annotator identity"],"input_types":["Task data (images, text, video, audio, time series)","Annotation data (labels, bounding boxes, text spans, etc.)","Annotator identity (user ID, name)"],"output_types":["Annotation objects with annotator metadata","Task status (unlabeled, in-progress, completed, skipped)","Agreement metrics (Kappa, Fleiss' Kappa, percent agreement)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"label-studio__cap_9","uri":"capability://automation.workflow.feature.flag.system.for.gradual.feature.rollout.and.a.b.testing","name":"feature flag system for gradual feature rollout and a/b testing","description":"Implements a feature flag system (label_studio/core/feature_flags.py or similar) that allows toggling features on/off per user, organization, or globally. Feature flags are stored in the database and evaluated at runtime, enabling gradual rollout of new features without code deployment. Supports percentage-based rollout (e.g., enable feature for 10% of users) and user-based targeting.","intents":["Gradually roll out new features to a subset of users without full deployment","A/B test new annotation interfaces or algorithms with different user groups","Disable features in production without code changes if issues are discovered"],"best_for":["Teams deploying new features to production and wanting to minimize risk via gradual rollout","ML engineers A/B testing new active learning algorithms or annotation interfaces","Operations teams needing to disable features without code deployment"],"limitations":["Feature flag evaluation is done at runtime; no compile-time safety checks","Percentage-based rollout is probabilistic; exact percentage may vary due to randomness","No built-in analytics; feature flag usage must be tracked separately","Feature flag changes require database update; no caching without additional infrastructure"],"requires":["Label Studio backend with database for storing feature flags","Feature flag evaluation logic in frontend and backend code"],"input_types":["Feature flag definitions (name, description, enabled status)","User/organization targeting criteria","Percentage-based rollout configuration"],"output_types":["Feature flag status (enabled/disabled per user)","Feature flag evaluation results (boolean)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"label-studio__headline","uri":"capability://data.processing.analysis.open.source.data.labeling.platform","name":"open-source data labeling platform","description":"Label Studio is an open-source platform designed for data labeling and annotation, supporting various data types like text, images, audio, and video, making it ideal for creating high-quality training datasets for machine learning projects.","intents":["best data labeling platform","data annotation tool for machine learning","open-source annotation software","data labeling solution for multi-modal data","best tool for creating AI training datasets"],"best_for":["machine learning projects","multi-modal data annotation"],"limitations":[],"requires":[],"input_types":["text","image","audio","video","time series"],"output_types":["labeled datasets"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Label Studio 1.0+","Modern browser with HTML5 Canvas support","Backend running Django REST Framework for template validation","Label Studio backend with PostgreSQL or MySQL for task filtering","ML model predictions uploaded via Predictions API (external model required)","Python 3.9+ for backend task ranking logic","Label Studio backend with task model and FSM logic","Database for storing task state","Message broker (Redis, RabbitMQ, etc.)","Celery or similar job queue framework"],"failure_modes":["Template composition is XML-based, requiring familiarity with Label Studio's DSL for advanced customization","Canvas rendering performance degrades with >1000 objects per image due to DOM-based event handling","Custom template logic limited to predefined control types; arbitrary JavaScript execution not supported","Uncertainty sampling requires pre-computed model predictions; no built-in model training, only prediction ingestion","Next-task algorithm runs synchronously on each request, causing latency spikes with >100k tasks without database indexing","Consensus-based disagreement requires multiple annotations per task, increasing labeling overhead","FSM is project-level only; no per-task custom state workflows","State transitions are synchronous; no async state change notifications","No built-in state change history; only current state is tracked","FSM configuration is not validated until state transition is attempted","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"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":"2026-06-17T09:51:04.692Z","last_scraped_at":null,"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=label-studio","compare_url":"https://unfragile.ai/compare?artifact=label-studio"}},"signature":"mbuPTYJa/arug8c/o3CS/6kdt2wfqlLn5ZZHkFQHct7liQ4u4PIpAT6x59J5FkvO5WyUww37k+P3Uyu+5bAECw==","signedAt":"2026-06-22T17:24:04.374Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/label-studio","artifact":"https://unfragile.ai/label-studio","verify":"https://unfragile.ai/api/v1/verify?slug=label-studio","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"}}