{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-label-studio","slug":"pypi-label-studio","name":"label-studio","type":"repo","url":"https://pypi.org/project/label-studio/","page_url":"https://unfragile.ai/pypi-label-studio","categories":["model-training"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-label-studio__cap_0","uri":"capability://data.processing.analysis.multi.modal.data.annotation.with.configurable.labeling.interfaces","name":"multi-modal data annotation with configurable labeling interfaces","description":"Provides a declarative XML-based labeling interface system that dynamically generates annotation UIs for images, text, audio, video, and time-series data without code changes. The frontend architecture uses React components that parse Label Studio's custom XML schema to render task-specific controls (bounding boxes, classifications, relations, etc.), enabling teams to define complex annotation workflows through configuration rather than custom development.","intents":["I need to set up annotation tasks for multiple data types without building custom UIs","I want to reuse labeling templates across different projects with minimal configuration","I need to support domain-specific annotation patterns like medical image markup or NER tagging"],"best_for":["ML teams building labeled datasets across heterogeneous data types","annotation service providers needing white-label flexibility","enterprises standardizing annotation workflows across departments"],"limitations":["Complex custom annotation logic requires extending React components; XML schema has limited expressiveness for non-standard tasks","Performance degrades with >10,000 tasks per project in single-page view due to DOM rendering","No built-in support for 3D point clouds or volumetric medical imaging without custom plugins"],"requires":["Python 3.8+","Node.js 14+ for frontend development","PostgreSQL 10+ or SQLite for task storage","Modern browser with ES6 support"],"input_types":["image (JPEG, PNG, WebP, TIFF)","text (plain text, HTML, markdown)","audio (WAV, MP3, OGG)","video (MP4, WebM)","time-series (CSV, JSON)","PDF documents"],"output_types":["JSON annotations with task metadata","XML annotation format","COCO format for computer vision","IOB/BIO format for NER","VTT format for video subtitles"],"categories":["data-processing-analysis","annotation-labeling"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-label-studio__cap_1","uri":"capability://planning.reasoning.intelligent.task.sequencing.with.next.task.algorithm","name":"intelligent task sequencing with next-task algorithm","description":"Implements a pluggable next-task selection algorithm (documented in label_studio/projects/functions/next_task.py) that determines which task to present to annotators based on project configuration, annotation progress, and optional ML model predictions. The system supports sequential ordering, random sampling, and active learning strategies that prioritize uncertain predictions from integrated ML models, reducing annotation effort for model-in-the-loop workflows.","intents":["I want to prioritize annotation of ambiguous samples identified by my ML model","I need to balance annotation load across multiple annotators fairly","I want to implement active learning to minimize labeling cost while maximizing model performance"],"best_for":["teams implementing active learning pipelines with iterative model retraining","large-scale annotation projects where task ordering significantly impacts efficiency","projects with heterogeneous data difficulty requiring intelligent prioritization"],"limitations":["Algorithm selection is project-level only; cannot dynamically switch strategies per-annotator","Active learning strategy requires pre-trained ML model predictions; cold-start projects default to sequential ordering","No built-in support for multi-objective optimization (e.g., balancing uncertainty with data diversity)"],"requires":["Python 3.8+","PostgreSQL 10+ for efficient task querying","Optional: ML model API endpoint for prediction-based strategies"],"input_types":["task metadata (created_at, updated_at, agreement_score)","ML model predictions (confidence scores, uncertainty estimates)","annotator assignment data"],"output_types":["ordered task ID for next annotation","task metadata with priority score"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-label-studio__cap_10","uri":"capability://automation.workflow.background.job.processing.for.async.operations","name":"background job processing for async operations","description":"Uses Celery task queue (documented in Advanced Topics: Background Jobs and Tasks) to handle long-running operations asynchronously, including batch exports, model predictions, and data syncs. Jobs are queued with status tracking, allowing users to monitor progress and retrieve results without blocking the web interface. Supports job retry logic and failure notifications.","intents":["I want to export 1M annotations without blocking the UI","I need to run batch predictions on all tasks in the background","I want to sync data from cloud storage periodically without manual intervention"],"best_for":["large-scale projects where operations take >30 seconds","teams needing scheduled tasks (e.g., nightly exports, periodic syncs)","workflows requiring reliable job execution with retry logic"],"limitations":["Requires Redis or RabbitMQ broker; adds operational complexity","Job results are stored in broker; no persistent job history after completion","No built-in job scheduling UI; requires Celery Beat configuration","Failed jobs require manual retry; no automatic exponential backoff"],"requires":["Python 3.8+","Redis 5.0+ or RabbitMQ 3.8+ as message broker","Celery 5.0+ (included in Label Studio)","Worker process running separately from web server"],"input_types":["job parameters (export format, filter criteria, task IDs)","job type (export, predict, sync)","retry configuration"],"output_types":["job status (queued, running, completed, failed)","job result (file path, summary statistics)","error messages for failed jobs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-label-studio__cap_11","uri":"capability://automation.workflow.feature.flag.system.for.gradual.rollout.and.a.b.testing","name":"feature flag system for gradual rollout and a/b testing","description":"Implements feature flag system (documented in Advanced Topics: Managing Feature Flags) allowing teams to enable/disable features per-organization or per-user without code deployment. Flags are stored in database and evaluated at runtime, supporting gradual rollouts, A/B testing, and quick rollback if issues are detected. Integrates with frontend and backend to control feature visibility.","intents":["I want to roll out a new annotation feature to 10% of users first","I need to A/B test two different UI layouts for the same feature","I want to quickly disable a buggy feature without redeploying"],"best_for":["teams running Label Studio in production with continuous deployment","organizations testing new features with subset of users","rapid iteration workflows where rollback speed is critical"],"limitations":["Feature flags are evaluated at runtime; no compile-time safety","Flag changes take effect immediately; no grace period for in-flight requests","No built-in analytics for flag usage; requires external monitoring","Flags are per-organization or per-user; no fine-grained targeting (e.g., by project type)"],"requires":["Python 3.8+","PostgreSQL 10+ or SQLite for flag storage","Frontend code to check flags before rendering features"],"input_types":["flag name and description","flag value (boolean, percentage, user list)","scope (organization, user, global)"],"output_types":["flag evaluation result (enabled/disabled)","flag metadata for UI rendering"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-label-studio__cap_12","uri":"capability://tool.use.integration.rest.api.for.programmatic.access.and.automation","name":"rest api for programmatic access and automation","description":"Exposes comprehensive REST API (documented in API Reference section) covering Projects, Tasks, Annotations, Users, Organizations, Storage, and Data Manager endpoints. API uses standard HTTP methods (GET, POST, PATCH, DELETE) with JSON request/response bodies, supporting filtering, pagination, and bulk operations. Authentication via API tokens enables external tools and scripts to automate Label Studio workflows.","intents":["I want to create projects and import tasks programmatically from my data pipeline","I need to export annotations via API for integration with my ML training system","I want to automate user provisioning and project assignment"],"best_for":["teams integrating Label Studio into larger ML pipelines","automation scripts that need to interact with Label Studio without UI","third-party tools building on top of Label Studio"],"limitations":["API is REST-only; no GraphQL or gRPC alternatives","Pagination is cursor-based; no offset-based pagination for large result sets","Bulk operations have limits (e.g., max 1000 tasks per import request)","API rate limiting is per-user; no per-application rate limits"],"requires":["Python 3.8+ (or any HTTP client in any language)","API token (generated in user settings)","Network access to Label Studio server"],"input_types":["JSON request bodies with project, task, annotation data","URL query parameters for filtering and pagination","API token in Authorization header"],"output_types":["JSON response bodies with resource data","HTTP status codes (200, 201, 400, 404, etc.)","Pagination metadata (next, previous, count)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-label-studio__cap_13","uri":"capability://automation.workflow.docker.and.kubernetes.deployment.with.configuration.management","name":"docker and kubernetes deployment with configuration management","description":"Provides Docker image and Kubernetes manifests (documented in Build and Deployment section) for containerized deployment with environment-based configuration. Supports PostgreSQL backend, Redis for caching, and Celery workers, with Helm charts for simplified Kubernetes deployment. Configuration is managed via environment variables, enabling teams to deploy Label Studio across development, staging, and production environments with minimal code changes.","intents":["I want to deploy Label Studio to Kubernetes with high availability","I need to configure Label Studio for my cloud provider (AWS, GCP, Azure)","I want to scale annotation workers independently from the web server"],"best_for":["teams deploying Label Studio in production with uptime requirements","organizations using Kubernetes for container orchestration","enterprises requiring multi-region or multi-cloud deployments"],"limitations":["Docker image is large (>1GB); may be slow to pull in bandwidth-constrained environments","Kubernetes manifests are basic examples; production deployments require customization (ingress, TLS, resource limits)","No built-in backup/restore for PostgreSQL; requires external backup strategy","Scaling Celery workers requires manual configuration; no auto-scaling based on queue depth"],"requires":["Docker 20.10+ or Kubernetes 1.20+","PostgreSQL 10+ (external or in-cluster)","Redis 5.0+ (external or in-cluster)","Sufficient compute resources (2+ CPU cores, 4GB+ RAM)"],"input_types":["environment variables (DATABASE_URL, REDIS_URL, SECRET_KEY)","Kubernetes ConfigMaps and Secrets","Docker build arguments"],"output_types":["Docker image with Label Studio and dependencies","Kubernetes Deployment, Service, and StatefulSet manifests","Helm chart for simplified deployment"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-label-studio__cap_2","uri":"capability://tool.use.integration.cloud.storage.integration.with.multi.provider.sync","name":"cloud storage integration with multi-provider sync","description":"Provides abstraction layer (label_studio/io_storages/) supporting S3, Google Cloud Storage, Azure Blob Storage, and local filesystem for bidirectional data sync. Tasks are imported from cloud buckets on-demand, and completed annotations are exported back to configured storage with automatic format conversion, enabling seamless integration with ML training pipelines without manual file transfers.","intents":["I want to label data directly from my S3 bucket without downloading files locally","I need to automatically export annotations back to cloud storage in my training pipeline's expected format","I want to support multiple storage backends without rewriting integration code"],"best_for":["teams with data already in cloud storage (AWS, GCP, Azure)","organizations requiring HIPAA/SOC2 compliance with data residency constraints","large-scale projects where local storage is impractical (>100GB datasets)"],"limitations":["Sync is pull-based (Label Studio polls storage); no push notifications for new files, causing latency up to polling interval","Large file downloads (>500MB) may timeout; requires manual chunking or pre-processing","No built-in deduplication; duplicate files in storage create duplicate tasks","Credentials stored in database; no integration with cloud IAM roles or temporary credentials"],"requires":["Python 3.8+","Cloud provider SDK (boto3 for S3, google-cloud-storage for GCS, azure-storage-blob for Azure)","IAM credentials with read/write permissions on target buckets","Network access to cloud storage endpoints"],"input_types":["S3 bucket paths (s3://bucket/prefix/)","GCS bucket paths (gs://bucket/prefix/)","Azure container paths (azure://container/prefix/)","Local filesystem paths"],"output_types":["JSON annotations synced to cloud storage","COCO/Pascal VOC/YOLO format exports","CSV/Parquet for structured data","Manifest files for ML training pipelines"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-label-studio__cap_3","uri":"capability://safety.moderation.role.based.access.control.with.multi.tenant.organization.support","name":"role-based access control with multi-tenant organization support","description":"Implements organization and user management (label_studio/organizations/, label_studio/users/) with role-based access control (RBAC) supporting Admin, Manager, Annotator, and Reviewer roles at both organization and project levels. Uses Django's permission system with custom mixins to enforce access policies, enabling teams to isolate projects by department, control who can export data, and audit annotation activity across organizational boundaries.","intents":["I need to isolate annotation projects by team/department with separate access controls","I want to prevent annotators from exporting raw data while allowing reviewers to approve annotations","I need audit logs showing which user made which annotations for compliance"],"best_for":["enterprises with multiple teams sharing a single Label Studio instance","regulated industries (healthcare, finance) requiring strict access control and audit trails","managed annotation services offering white-label solutions to multiple clients"],"limitations":["RBAC is coarse-grained (role-level); no fine-grained attribute-based access control (ABAC) for conditional policies","Audit logs stored in database; no integration with external SIEM systems","No built-in SSO/SAML; requires manual user provisioning or custom authentication backend","Role changes take effect immediately; no grace period for in-flight annotation sessions"],"requires":["Python 3.8+","PostgreSQL 10+ for multi-tenant data isolation","Django 3.2+ (included in Label Studio)","Optional: LDAP/Active Directory for user sync"],"input_types":["user credentials (email, password or SSO token)","role assignments (Admin, Manager, Annotator, Reviewer)","organization membership data"],"output_types":["access tokens (JWT or session cookies)","audit log entries with timestamp, user, action, resource","permission check results (allowed/denied)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-label-studio__cap_4","uri":"capability://tool.use.integration.ml.model.integration.for.pre.annotation.and.active.learning","name":"ml model integration for pre-annotation and active learning","description":"Provides ML Integration subsystem (label_studio/ml/) that accepts predictions from external models via REST API, stores them as pre-annotations, and feeds uncertainty scores back to the next-task algorithm for active learning. Supports both batch prediction (pre-annotate entire project) and online prediction (score tasks as they're created), with automatic format conversion between Label Studio's internal representation and model-specific output formats.","intents":["I want to pre-annotate my dataset with my trained model to reduce manual labeling effort","I need to integrate my model's confidence scores into the active learning loop","I want to compare multiple model predictions on the same task to identify disagreements"],"best_for":["teams with existing trained models looking to accelerate annotation","active learning workflows where model uncertainty drives task prioritization","model evaluation pipelines comparing predictions across model versions"],"limitations":["ML model must expose REST API; no native support for local model inference (requires wrapper service)","Pre-annotations are static; no automatic retraining or prediction updates as new annotations arrive","Format conversion is one-way (model output → Label Studio); no reverse conversion for exporting to model-specific formats","No built-in handling of model failures; timeout or error responses cause task to be skipped"],"requires":["Python 3.8+","External ML model service with REST API","Network connectivity from Label Studio to model endpoint","Model output must be serializable to JSON"],"input_types":["task data (image, text, audio, etc.)","model predictions (JSON with confidence scores, class labels, bounding boxes)","model metadata (name, version, threshold)"],"output_types":["pre-annotations in Label Studio format","uncertainty scores for active learning","model comparison reports"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-label-studio__cap_5","uri":"capability://data.processing.analysis.flexible.annotation.export.with.format.conversion","name":"flexible annotation export with format conversion","description":"Exports completed annotations in multiple formats (JSON, COCO, Pascal VOC, YOLO, IOB/BIO, VTT, CSV) via configurable export pipelines (label_studio/tasks/serializers.py). Each format has a dedicated serializer that transforms Label Studio's internal annotation representation into domain-specific schemas, with support for filtering by annotator, agreement score, or annotation status before export.","intents":["I need to export annotations in COCO format for my object detection training pipeline","I want to export only high-confidence annotations (filtered by inter-annotator agreement)","I need to support multiple downstream tools (different teams use different formats)"],"best_for":["teams with heterogeneous ML pipelines requiring multiple annotation formats","quality assurance workflows filtering annotations by agreement metrics","organizations integrating Label Studio with existing ML infrastructure"],"limitations":["Export is batch-only; no streaming export for large datasets (>1GB)","Format conversion is lossy for some types (e.g., hierarchical relations → flat COCO format)","Custom formats require implementing new serializer classes; no declarative format definition","No built-in versioning; exports don't track which Label Studio version created them"],"requires":["Python 3.8+","Completed annotations in Label Studio database","Sufficient disk space for export file"],"input_types":["annotation data from Label Studio database","filter criteria (annotator, agreement threshold, status)","format specification (COCO, Pascal VOC, YOLO, etc.)"],"output_types":["JSON files (COCO, Label Studio native format)","XML files (Pascal VOC)","Text files (YOLO, IOB/BIO)","CSV/Parquet for structured data","VTT for video subtitles"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-label-studio__cap_6","uri":"capability://data.processing.analysis.inter.annotator.agreement.measurement.and.quality.control","name":"inter-annotator agreement measurement and quality control","description":"Calculates inter-annotator agreement metrics (Kappa, F1, Precision/Recall) when multiple annotators label the same task, storing agreement scores in the database for filtering and quality assessment. The Data Manager subsystem (label_studio/data_manager/) provides UI for visualizing agreement distributions and identifying low-agreement tasks for review or re-annotation.","intents":["I want to measure annotation quality by comparing multiple annotators on the same task","I need to identify ambiguous tasks where annotators disagree for manual review","I want to filter exports to include only high-agreement annotations"],"best_for":["quality assurance workflows requiring objective agreement metrics","projects with multiple annotators where consistency is critical","research datasets where inter-rater reliability is a publication requirement"],"limitations":["Agreement metrics require at least 2 annotations per task; single-annotator projects cannot measure agreement","Metrics are task-level only; no corpus-level agreement statistics (e.g., Fleiss' Kappa)","No automatic handling of partial overlaps (e.g., bounding boxes with IoU threshold)","Agreement calculation is synchronous; large projects (>100k tasks) may experience UI lag"],"requires":["Python 3.8+","Multiple annotators assigned to same tasks","Completed annotations from at least 2 annotators per task"],"input_types":["annotations from multiple annotators","annotation type (classification, NER, bounding box, etc.)","task metadata"],"output_types":["agreement scores (Kappa, F1, Precision/Recall)","agreement distribution visualizations","filtered task lists (low-agreement tasks)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-label-studio__cap_7","uri":"capability://search.retrieval.data.manager.with.advanced.filtering.and.search","name":"data manager with advanced filtering and search","description":"Provides Data Manager subsystem (label_studio/data_manager/api.py) with SQL-based filtering, full-text search, and faceted navigation across tasks and annotations. Supports complex queries combining multiple filters (annotator, agreement score, prediction confidence, task metadata) with efficient database indexing, enabling teams to quickly locate specific subsets of data for review or re-annotation.","intents":["I need to find all tasks annotated by a specific person that have low agreement scores","I want to search for tasks containing specific text or metadata values","I need to filter tasks by prediction confidence to identify model uncertainty"],"best_for":["large projects (>10k tasks) where manual browsing is impractical","quality assurance workflows requiring targeted task selection","research teams analyzing annotation patterns across subsets"],"limitations":["Full-text search requires PostgreSQL; SQLite backend has limited search capabilities","Complex queries with many filters may timeout on projects >1M tasks","Search is case-sensitive for text fields; no fuzzy matching","Faceted navigation is read-only; cannot modify tasks in bulk from search results"],"requires":["Python 3.8+","PostgreSQL 10+ for full-text search (SQLite has limited support)","Indexed database columns for performance"],"input_types":["filter criteria (annotator, agreement threshold, prediction confidence, metadata)","search queries (text, regex patterns)","sort order (created_at, agreement_score, etc.)"],"output_types":["filtered task lists with metadata","faceted navigation results","search result rankings"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-label-studio__cap_8","uri":"capability://automation.workflow.project.configuration.and.labeling.template.management","name":"project configuration and labeling template management","description":"Allows teams to define project-level settings including label taxonomy, annotation interface (via XML schema), task sampling strategy, and quality control rules. Projects are stored as database records with serialized configuration (label_studio/projects/serializers.py), enabling teams to create reusable templates and clone projects with identical settings, reducing setup time for similar annotation tasks.","intents":["I want to create a reusable annotation template for similar projects","I need to configure which labels are available for a specific project","I want to enforce quality rules like minimum agreement score before export"],"best_for":["organizations running multiple similar annotation projects","teams standardizing annotation workflows across departments","service providers offering templated annotation solutions"],"limitations":["Configuration is project-level only; cannot override settings per-task or per-annotator","Template cloning copies all settings but not historical annotations; new project starts empty","No version control for configuration changes; no rollback to previous settings","Configuration changes apply immediately to in-progress annotations; no grace period"],"requires":["Python 3.8+","PostgreSQL 10+ or SQLite for project storage","Valid XML schema for labeling interface"],"input_types":["project metadata (name, description, owner)","label taxonomy (list of labels with colors/descriptions)","XML labeling interface schema","task sampling strategy (sequential, random, active learning)","quality control rules (minimum agreement, reviewer approval)"],"output_types":["project configuration JSON","cloned project with identical settings","project metadata for API responses"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-label-studio__cap_9","uri":"capability://data.processing.analysis.batch.task.import.with.format.detection.and.validation","name":"batch task import with format detection and validation","description":"Supports bulk import of tasks from multiple sources (CSV, JSON, cloud storage) with automatic format detection and validation against project schema. The import pipeline (label_studio/tasks/api.py) parses input files, validates data types, and creates task records in batch, with error reporting for malformed entries. Supports resumable imports for large datasets, allowing interrupted uploads to continue without re-processing.","intents":["I want to import 100k images from a CSV file with metadata","I need to validate that imported data matches my project's expected schema","I want to resume a failed import without re-processing already-imported tasks"],"best_for":["teams with large datasets requiring bulk import","projects with data in multiple formats (CSV, JSON, cloud storage)","workflows where data is updated incrementally and needs to be re-imported"],"limitations":["Import is synchronous; large files (>1GB) may timeout or consume excessive memory","Format detection is heuristic-based; ambiguous files may be misinterpreted","No deduplication; importing the same file twice creates duplicate tasks","Validation errors are reported in aggregate; no per-row error details for large imports"],"requires":["Python 3.8+","Input file in CSV, JSON, or cloud storage path","Sufficient disk space for temporary import staging","Valid project schema for validation"],"input_types":["CSV files with headers matching task fields","JSON files with array of task objects","Cloud storage paths (S3, GCS, Azure)","Multipart form uploads"],"output_types":["task records created in database","import summary (rows processed, errors, skipped)","error report with validation failures"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","Node.js 14+ for frontend development","PostgreSQL 10+ or SQLite for task storage","Modern browser with ES6 support","PostgreSQL 10+ for efficient task querying","Optional: ML model API endpoint for prediction-based strategies","Redis 5.0+ or RabbitMQ 3.8+ as message broker","Celery 5.0+ (included in Label Studio)","Worker process running separately from web server","PostgreSQL 10+ or SQLite for flag storage"],"failure_modes":["Complex custom annotation logic requires extending React components; XML schema has limited expressiveness for non-standard tasks","Performance degrades with >10,000 tasks per project in single-page view due to DOM rendering","No built-in support for 3D point clouds or volumetric medical imaging without custom plugins","Algorithm selection is project-level only; cannot dynamically switch strategies per-annotator","Active learning strategy requires pre-trained ML model predictions; cold-start projects default to sequential ordering","No built-in support for multi-objective optimization (e.g., balancing uncertainty with data diversity)","Requires Redis or RabbitMQ broker; adds operational complexity","Job results are stored in broker; no persistent job history after completion","No built-in job scheduling UI; requires Celery Beat configuration","Failed jobs require manual retry; no automatic exponential backoff","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:23.204Z","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=pypi-label-studio","compare_url":"https://unfragile.ai/compare?artifact=pypi-label-studio"}},"signature":"o0As9BkUa+CI6Yx/j0v7TW2GHMahY5hCVVfWeKM64EZBu3wn9+F3RiPsdOEWIEf+SjBNYg4z2/VWtrnZq9HUCQ==","signedAt":"2026-06-22T12:10:03.426Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-label-studio","artifact":"https://unfragile.ai/pypi-label-studio","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-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"}}