Argilla

Enables creation of structured annotation datasets through a declarative schema system supporting diverse question types (text, rating, span labeling, multi-select) with validation rules. The frontend DatasetConfigurationForm component orchestrates question creation across EntityLabelSelection, RatingConfiguration, and SpanConfiguration sub-components, while the backend enforces schema constraints via the Questions and Fields data model. This approach decouples annotation schema definition from data ingestion, allowing reusable templates across multiple datasets.

Manages multi-user annotation campaigns through workspace-level isolation, user role assignment (admin, annotator, reviewer), and record distribution strategies. The User and Workspace Management system controls access to datasets and annotation tasks, while the Annotation Workflows component distributes records to annotators and tracks response provenance. Records are locked during annotation to prevent concurrent edits, and responses are stored with user attribution for quality auditing.

Provides containerized deployment through Docker images and Kubernetes manifests, with environment-based configuration for database connections, authentication, and feature flags. The deployment system supports multiple database backends (SQLite for development, PostgreSQL for production) and integrates with Hugging Face Spaces for zero-infrastructure deployment. Configuration is managed through environment variables and YAML files, enabling GitOps workflows.

Exposes all platform functionality through a REST API with OpenAPI/Swagger documentation, enabling integration with external systems and custom tooling. The API follows RESTful conventions with JSON request/response bodies, pagination support, and standard HTTP status codes. Authentication uses API keys or OAuth2, and rate limiting is enforced per user.

Provides pre-configured Hugging Face Spaces template that deploys Argilla with single-click setup, handling container orchestration, environment configuration, and persistent storage automatically. The template includes Docker Compose configuration optimized for Spaces' resource constraints and pre-configured authentication using Hugging Face credentials, enabling users to launch Argilla without DevOps knowledge.

Enables querying datasets using semantic similarity, metadata filters, and response-based criteria through the Search and Querying Data subsystem. The Python SDK exposes a query DSL that translates to Elasticsearch or similar backend queries, supporting filters on record metadata, annotation responses, and computed fields. Search results are ranked by relevance and can be paginated for large datasets, enabling efficient exploration of annotation progress and quality issues.

Provides a Python SDK that enables programmatic dataset creation, record ingestion, and response retrieval with automatic conflict resolution for concurrent updates. The Argilla SDK uses a client-side cache with version tracking to detect conflicts when records are modified both locally and on the server, implementing a last-write-wins strategy with optional merge callbacks. Batch operations are optimized for throughput, supporting bulk record insertion and response updates with transaction-like semantics.

Tracks dataset evolution through immutable snapshots that capture record state, annotation responses, and schema at specific points in time. The platform stores version metadata including creation timestamp, author, and change summary, enabling rollback to previous states and comparison of annotation changes across versions. Snapshots are stored efficiently using delta encoding, reducing storage overhead for large datasets with incremental changes.

Enables direct publishing of annotated datasets to Hugging Face Hub with automatic format conversion and metadata generation. The integration also supports fetching pre-trained models from Hub for generating model-based suggestions on records, creating a feedback loop where annotators can review and correct model predictions. The platform handles authentication, dataset card generation, and version synchronization with Hub.

Allows extension of annotation UI through custom Vue.js components for specialized data types (3D objects, multi-column layouts, metadata tables). The frontend architecture exposes a component registry where developers can register custom field types that render alongside standard fields. Custom components receive record data and response state as props, enabling rich interactive annotations for domain-specific data.

Supports collection of human preferences and rankings for RLHF workflows through specialized question types that capture pairwise comparisons and ranked orderings. The platform stores preference data in a normalized format enabling efficient computation of preference matrices and Bradley-Terry model fitting. Integration with LangChain enables direct annotation of LLM outputs within Argilla workflows.

Provides UI localization across multiple languages (English, Spanish, German, and extensible) through a translation file system. The frontend uses a translation object loaded from JSON files, enabling community contributions of new languages without code changes. Language selection is stored per-user and persists across sessions.

Distributes annotation records to annotators using configurable strategies (round-robin, random, or custom) and tracks completion progress at dataset and annotator levels. The Distribution subsystem maintains task queues per annotator, preventing duplicate assignments and enabling fair workload distribution. Progress metrics include completion percentage, response counts, and estimated time to completion.