{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"argilla","slug":"argilla","name":"Argilla","type":"repo","url":"https://github.com/argilla-io/argilla","page_url":"https://unfragile.ai/argilla","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"argilla__cap_0","uri":"capability://data.processing.analysis.schema.driven.dataset.configuration.with.multi.question.types","name":"schema-driven dataset configuration with multi-question types","description":"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.","intents":["Define custom annotation schemas without writing code","Create datasets with mixed question types (ratings + entity spans + text fields)","Enforce validation rules and field constraints during annotation","Reuse annotation templates across multiple datasets"],"best_for":["ML teams building RLHF datasets with heterogeneous feedback types","Domain experts designing annotation workflows without backend knowledge","Organizations needing audit trails of schema evolution"],"limitations":["Schema changes on populated datasets require migration logic not exposed in UI","No built-in branching logic for conditional questions based on prior responses","Custom field types require frontend Vue component development"],"requires":["Argilla Server 1.0+","Python 3.8+ for SDK schema definition","Vue.js 3.x for custom field extensions"],"input_types":["JSON schema definitions","Python dataclass/Pydantic models","YAML configuration files"],"output_types":["Structured annotation records with typed responses","Dataset metadata with schema versioning"],"categories":["data-processing-analysis","dataset-configuration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"argilla__cap_1","uri":"capability://automation.workflow.collaborative.annotation.workflow.with.role.based.access.control","name":"collaborative annotation workflow with role-based access control","description":"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.","intents":["Distribute annotation tasks across a team of annotators","Prevent concurrent edits and ensure data consistency","Track which annotator provided which feedback for quality analysis","Manage reviewer workflows for quality assurance gates"],"best_for":["Teams with 5+ annotators requiring task distribution","Organizations with compliance requirements for annotation audit trails","Projects needing reviewer approval workflows before dataset finalization"],"limitations":["No built-in inter-annotator agreement metrics (requires external calculation)","Record locking is pessimistic (blocks all users, not optimistic conflict resolution)","Reviewer workflows are sequential, not parallel (no multi-reviewer consensus)"],"requires":["Argilla Server with database backend (PostgreSQL recommended for production)","User authentication system (OIDC, LDAP, or local accounts)","Workspace isolation requires separate database schemas or row-level security"],"input_types":["User credentials and role assignments","Record batches for distribution"],"output_types":["Annotated records with response metadata","Audit logs with user attribution and timestamps"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"argilla__cap_10","uri":"capability://automation.workflow.docker.and.kubernetes.deployment.with.configuration.management","name":"docker and kubernetes deployment with configuration management","description":"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.","intents":["Deploy Argilla on-premises or in cloud environments","Scale Argilla horizontally using Kubernetes","Configure Argilla for different environments (dev, staging, production)","Deploy Argilla to Hugging Face Spaces without infrastructure setup"],"best_for":["DevOps teams managing Argilla deployments at scale","Organizations with on-premises requirements","Researchers prototyping with Hugging Face Spaces"],"limitations":["Kubernetes deployment requires manual manifest customization","Database migration is manual (no automatic schema updates)","Horizontal scaling requires external load balancer configuration"],"requires":["Docker 20.10+ for container deployment","Kubernetes 1.20+ for orchestration","PostgreSQL 12+ for production deployments","Hugging Face account for Spaces deployment"],"input_types":["Docker Compose files","Kubernetes manifests","Environment variable configuration"],"output_types":["Running Argilla containers","Kubernetes deployments and services","Hugging Face Spaces instances"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"argilla__cap_11","uri":"capability://tool.use.integration.rest.api.with.openapi.documentation","name":"rest api with openapi documentation","description":"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.","intents":["Integrate Argilla with external data pipelines and tools","Build custom UIs or dashboards on top of Argilla","Automate dataset management through scripts","Enable third-party integrations and plugins"],"best_for":["Teams building custom integrations with Argilla","Organizations with existing API-based infrastructure","Developers building tools on top of Argilla"],"limitations":["API rate limiting may throttle bulk operations","No GraphQL support (REST-only)","Pagination is cursor-based (no offset-based pagination)"],"requires":["Argilla Server with REST API enabled","API key for authentication","HTTP client library (curl, requests, etc.)"],"input_types":["JSON request bodies","URL path and query parameters"],"output_types":["JSON response bodies","OpenAPI/Swagger documentation"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"argilla__cap_12","uri":"capability://automation.workflow.huggingface.spaces.deployment","name":"huggingface-spaces-deployment","description":"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.","intents":["Deploy Argilla quickly for prototyping without infrastructure setup","Share annotation projects with collaborators via Hugging Face Spaces URL","Build public annotation workflows for community datasets","Integrate Argilla into Hugging Face ecosystem for seamless dataset publishing"],"best_for":["researchers and hobbyists prototyping annotation workflows","teams building community datasets on Hugging Face","organizations wanting quick Argilla evaluation without infrastructure investment"],"limitations":["Spaces have resource limits (2 CPU cores, 16GB RAM) — not suitable for large-scale annotation","Persistent storage is limited — may not support datasets >10GB","Spaces are public by default — requires manual access control configuration","No automatic backups — data loss risk if Space is deleted"],"requires":["Hugging Face account","Spaces quota available","Internet connection for Space access"],"input_types":["Space configuration (name, description, privacy)","Docker Compose template"],"output_types":["running Argilla instance on Hugging Face Spaces","public URL for annotation access"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"argilla__cap_2","uri":"capability://search.retrieval.semantic.search.and.filtering.across.annotated.datasets","name":"semantic search and filtering across annotated datasets","description":"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.","intents":["Find records with specific annotation patterns (e.g., low confidence responses)","Identify outliers or edge cases in annotated data","Filter datasets by metadata (source, date range, model version)","Retrieve records for quality review based on complex criteria"],"best_for":["Data scientists analyzing annotation quality and coverage","Teams debugging model failures by finding similar annotated examples","Researchers studying annotation disagreement patterns"],"limitations":["Semantic search requires embedding computation (adds latency on first query)","Query DSL is Python-only, no GraphQL or REST query language","Filtering on nested response structures requires manual query construction"],"requires":["Elasticsearch or similar search backend (optional, falls back to SQL queries)","Python 3.8+ for SDK query DSL","Embedding model for semantic search (Sentence Transformers integration provided)"],"input_types":["Query DSL expressions (Python)","Metadata filter objects","Embedding vectors for similarity search"],"output_types":["Paginated record lists with relevance scores","Aggregation results (counts, distributions)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"argilla__cap_3","uri":"capability://tool.use.integration.bidirectional.sdk.to.server.synchronization.with.conflict.resolution","name":"bidirectional sdk-to-server synchronization with conflict resolution","description":"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.","intents":["Programmatically create and populate datasets from Python scripts","Integrate Argilla into ML pipelines for automated data curation","Sync annotations back to external systems (Hugging Face Hub, databases)","Handle concurrent updates from multiple SDK clients without data loss"],"best_for":["ML engineers building end-to-end annotation pipelines","Teams integrating Argilla with existing Python-based data infrastructure","Researchers automating dataset creation for multiple experiments"],"limitations":["Conflict resolution is last-write-wins only (no custom merge strategies)","Batch operations have size limits (typically 1000 records per request)","SDK caching adds memory overhead for large datasets (no streaming mode)"],"requires":["Python 3.8+","Argilla Server with REST API enabled","API key for authentication"],"input_types":["Python dictionaries or Pydantic models for records","Pandas DataFrames for bulk ingestion","JSON files for import"],"output_types":["Python Record objects with response data","Pandas DataFrames for export","JSON/CSV exports"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"argilla__cap_4","uri":"capability://automation.workflow.dataset.versioning.and.snapshot.management","name":"dataset versioning and snapshot management","description":"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.","intents":["Maintain audit trail of dataset changes for compliance","Rollback to previous dataset state if annotation errors are discovered","Compare annotation changes between versions to identify quality issues","Create reproducible dataset versions for model training"],"best_for":["Regulated industries requiring immutable audit trails","Teams iterating on annotation schemas and needing to track changes","Researchers publishing datasets and needing version reproducibility"],"limitations":["Snapshots are read-only (cannot branch from historical versions)","Delta encoding adds complexity to version comparison queries","Storage overhead grows linearly with number of versions (no garbage collection)"],"requires":["Argilla Server 1.0+","Database with sufficient storage for delta-encoded snapshots","Python 3.8+ for SDK version management"],"input_types":["Dataset state at snapshot time","Version metadata (author, message)"],"output_types":["Snapshot metadata with timestamps and authors","Diff reports showing changes between versions","Restored dataset state from historical snapshot"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"argilla__cap_5","uri":"capability://tool.use.integration.hugging.face.hub.integration.for.dataset.publishing.and.model.suggestions","name":"hugging face hub integration for dataset publishing and model suggestions","description":"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.","intents":["Publish curated datasets to Hugging Face Hub for community use","Use pre-trained models to generate initial annotations for human review","Sync annotation progress with Hub-hosted dataset versions","Generate dataset cards with metadata and license information"],"best_for":["Open-source projects sharing datasets with the community","Teams using Hugging Face models for active learning workflows","Researchers publishing benchmarks with annotation metadata"],"limitations":["Hub integration requires Hugging Face account and API token","Model suggestions are limited to models available on Hub (no custom model serving)","Dataset card generation requires manual metadata input (not fully automated)"],"requires":["Hugging Face Hub account","Hugging Face API token with write permissions","Sentence Transformers or compatible model for embeddings"],"input_types":["Annotated Argilla datasets","Hugging Face model identifiers","Dataset metadata (license, description)"],"output_types":["Hugging Face Hub dataset repository","Model-generated suggestions for records","Dataset cards with metadata"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"argilla__cap_6","uri":"capability://image.visual.custom.field.rendering.with.vue.js.components","name":"custom field rendering with vue.js components","description":"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.","intents":["Annotate specialized data types (3D models, medical images, structured tables)","Create domain-specific annotation UIs without forking the codebase","Build interactive visualizations for complex data exploration","Extend Argilla for vertical-specific use cases"],"best_for":["Teams with specialized data types requiring custom visualization","Organizations building white-label annotation solutions","Researchers prototyping novel annotation interfaces"],"limitations":["Custom components must be written in Vue.js (no framework agnostic approach)","Component development requires frontend build toolchain knowledge","No hot-reload for custom components (requires server restart)"],"requires":["Vue.js 3.x knowledge","Node.js 16+ for frontend build","Argilla frontend source code access"],"input_types":["Record data in any format","Response state objects"],"output_types":["Annotation responses in custom format","Rendered Vue components in annotation UI"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"argilla__cap_7","uri":"capability://data.processing.analysis.rlhf.specific.feedback.collection.with.ranking.and.preference.annotations","name":"rlhf-specific feedback collection with ranking and preference annotations","description":"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.","intents":["Collect pairwise preference judgments between model outputs","Generate ranking annotations for RLHF reward model training","Annotate LLM outputs directly from LangChain chains","Compute preference matrices for Bradley-Terry model fitting"],"best_for":["Teams training reward models for RLHF fine-tuning","LLM researchers collecting human preference data","Organizations building preference-based ranking systems"],"limitations":["Preference annotations require careful UI design to avoid annotator bias","No built-in Bradley-Terry model fitting (requires external library)","LangChain integration is one-way (annotation results don't feed back to chain)"],"requires":["Argilla Server 1.0+","LangChain 0.1+ for chain integration (optional)","Python 3.8+ for preference data processing"],"input_types":["Pairs of model outputs for comparison","Ranked lists of candidates","LangChain chain outputs"],"output_types":["Preference annotations (A > B, A = B, A < B)","Ranking annotations with ordinal scores","Preference matrices for model training"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"argilla__cap_8","uri":"capability://text.generation.language.multi.language.support.with.extensible.translation.system","name":"multi-language support with extensible translation system","description":"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.","intents":["Deploy Argilla for international teams with language preferences","Contribute translations for new languages","Ensure consistent terminology across localized UIs"],"best_for":["Global teams requiring multi-language support","Open-source communities contributing translations","Organizations deploying Argilla in non-English regions"],"limitations":["Translation files are static (no runtime language switching without page reload)","Community translations may lag behind feature releases","No pluralization or context-aware translation rules"],"requires":["JSON translation files for target language","Frontend rebuild to add new language"],"input_types":["JSON translation files with key-value pairs"],"output_types":["Localized UI text in selected language"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"argilla__cap_9","uri":"capability://automation.workflow.record.distribution.and.task.assignment.with.progress.tracking","name":"record distribution and task assignment with progress tracking","description":"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.","intents":["Assign annotation tasks fairly across a team","Track annotation progress and identify bottlenecks","Prevent duplicate annotations of the same record","Estimate project completion time based on current velocity"],"best_for":["Teams managing large annotation projects with multiple annotators","Project managers needing visibility into annotation progress","Organizations with SLA requirements for annotation turnaround"],"limitations":["Distribution strategies are fixed (no dynamic rebalancing based on annotator speed)","Progress tracking is coarse-grained (no per-question metrics)","No built-in incentive mechanisms or gamification"],"requires":["Argilla Server with task queue backend","Database for tracking assignment state"],"input_types":["Record batches for distribution","Distribution strategy configuration"],"output_types":["Task assignments per annotator","Progress metrics and completion estimates","Workload distribution reports"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"argilla__headline","uri":"capability://data.processing.analysis.open.source.data.curation.platform.for.llm.fine.tuning","name":"open-source data curation platform for llm fine-tuning","description":"Argilla is an open-source platform that facilitates data curation for fine-tuning large language models, enabling seamless human feedback collection, data labeling, and dataset versioning.","intents":["best open-source data curation platform","data curation for LLM fine-tuning","open-source platform for RLHF workflows","best tools for dataset versioning","data labeling solutions for AI models"],"best_for":["AI engineers","data scientists"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Argilla Server 1.0+","Python 3.8+ for SDK schema definition","Vue.js 3.x for custom field extensions","Argilla Server with database backend (PostgreSQL recommended for production)","User authentication system (OIDC, LDAP, or local accounts)","Workspace isolation requires separate database schemas or row-level security","Docker 20.10+ for container deployment","Kubernetes 1.20+ for orchestration","PostgreSQL 12+ for production deployments","Hugging Face account for Spaces deployment"],"failure_modes":["Schema changes on populated datasets require migration logic not exposed in UI","No built-in branching logic for conditional questions based on prior responses","Custom field types require frontend Vue component development","No built-in inter-annotator agreement metrics (requires external calculation)","Record locking is pessimistic (blocks all users, not optimistic conflict resolution)","Reviewer workflows are sequential, not parallel (no multi-reviewer consensus)","Kubernetes deployment requires manual manifest customization","Database migration is manual (no automatic schema updates)","Horizontal scaling requires external load balancer configuration","API rate limiting may throttle bulk operations","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":"2026-06-17T09:51:02.370Z","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=argilla","compare_url":"https://unfragile.ai/compare?artifact=argilla"}},"signature":"Q/kZLmg7c0u43HZlDFGYem4ne18U/4lalIjdlxLQvWeZ2U1BhS+YchBlycBL8V5GC+TjSM3JvsSFpqDWre9jCA==","signedAt":"2026-06-21T07:46:04.213Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/argilla","artifact":"https://unfragile.ai/argilla","verify":"https://unfragile.ai/api/v1/verify?slug=argilla","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"}}