{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"airbyte","slug":"airbyte","name":"Airbyte","type":"repo","url":"https://github.com/airbytehq/airbyte","page_url":"https://unfragile.ai/airbyte","categories":["data-pipelines"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"airbyte__cap_0","uri":"capability://code.generation.editing.declarative.manifest.based.connector.generation","name":"declarative-manifest-based-connector-generation","description":"Generates source connectors from YAML manifest files without writing custom code, using the Declarative Manifest Framework to define API endpoints, pagination, authentication, and stream transformations. The framework parses manifest definitions and auto-generates connector logic for REST APIs, eliminating boilerplate while supporting complex patterns like nested pagination, cursor-based iteration, and request/response transformations through declarative syntax.","intents":["I need to build a connector for a REST API without writing Python code","I want to quickly add support for a new SaaS tool with minimal development effort","I need to handle pagination, authentication, and schema evolution automatically"],"best_for":["teams building connectors for REST APIs with standard pagination patterns","organizations wanting to reduce connector development time from weeks to days","non-expert developers contributing connectors to the Airbyte ecosystem"],"limitations":["Limited to REST APIs — cannot handle binary protocols or custom socket connections","Complex business logic requiring stateful transformations may require Python CDK fallback","Manifest validation happens at runtime, not compile-time — errors surface during sync execution","No built-in support for GraphQL APIs — requires custom Python components"],"requires":["YAML manifest file following Airbyte's declarative schema","Understanding of the target API's authentication method (OAuth2, API key, Basic Auth)","Airbyte 0.40.0 or later with Declarative Framework support"],"input_types":["YAML manifest definition","API endpoint URLs","Authentication credentials"],"output_types":["Python connector code (auto-generated)","Airbyte protocol messages (AirbyteMessage format)"],"categories":["code-generation-editing","connector-development"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"airbyte__cap_1","uri":"capability://data.processing.analysis.bulk.cdk.kotlin.framework.for.high.throughput.extraction","name":"bulk-cdk-kotlin-framework-for-high-throughput-extraction","description":"Provides a Kotlin-based Connector Development Kit (Bulk CDK) optimized for high-throughput data extraction using Apache Beam for distributed processing. The framework abstracts source connector logic into Extract and Load phases, with built-in support for Change Data Capture (CDC) via Debezium, partition-based parallelization, and type-safe schema evolution through TableSchemaFactory and TableSchemaEvolutionClient components.","intents":["I need to extract millions of rows from a database efficiently using parallel processing","I want to implement CDC-based incremental syncs with automatic schema change detection","I need to build a connector that scales horizontally across multiple workers"],"best_for":["teams building connectors for large-scale databases (PostgreSQL, MySQL, Oracle)","organizations requiring sub-second latency for incremental syncs via CDC","enterprises needing distributed extraction across Kubernetes clusters"],"limitations":["Kotlin/JVM overhead adds ~500ms startup time compared to Python CDK","Requires understanding of Apache Beam concepts (PTransforms, PCollections) — steeper learning curve than Python CDK","CDC support limited to databases with Debezium connectors (PostgreSQL, MySQL, Oracle, SQL Server)","No built-in support for non-relational sources (APIs, file systems) — use Python CDK instead"],"requires":["Java 11 or later","Kotlin 1.8+","Apache Beam 2.40+ (bundled in Bulk CDK)","Gradle 7.0+ for building connectors","For CDC: Debezium-compatible database with logical replication enabled"],"input_types":["Database connection strings","SQL queries or table definitions","CDC configuration (log position, snapshot mode)"],"output_types":["Airbyte protocol messages (AirbyteMessage format)","Parquet or JSON records in staging storage"],"categories":["data-processing-analysis","connector-development"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"airbyte__cap_10","uri":"capability://tool.use.integration.airbyte.protocol.abstraction.for.connector.interoperability","name":"airbyte-protocol-abstraction-for-connector-interoperability","description":"Defines a standardized protocol (AirbyteMessage format) for communication between connectors and the core platform, enabling any connector to work with any destination without custom integration code. The protocol abstracts source/destination specifics (SQL dialects, API formats) into a common message format (JSON with schema, state, logs), allowing connectors to be developed independently and composed flexibly.","intents":["I want to build a connector that works with any destination without custom code","I need to decouple connector development from platform updates","I want to reuse connectors across different Airbyte deployments (self-hosted, Cloud, third-party)"],"best_for":["connector developers building reusable, platform-agnostic connectors","organizations running multiple Airbyte instances needing connector portability","teams building custom platforms on top of Airbyte's protocol"],"limitations":["Protocol overhead adds ~5-10% latency per message due to JSON serialization","Protocol versioning is complex — breaking changes require careful migration (e.g., schema changes)","Destination-specific optimizations are harder to implement — protocol abstracts away database-specific features (e.g., Snowflake's VARIANT type)","Debugging protocol issues requires understanding JSON schema and Airbyte's message format"],"requires":["Understanding of Airbyte's protocol specification (documented in GitHub)","JSON schema validation library (for message validation)","Connector framework (Python CDK, Bulk CDK, or custom implementation)"],"input_types":["AirbyteMessage objects (JSON)","Connector configuration"],"output_types":["AirbyteMessage objects (JSON) with records, state, logs"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"airbyte__cap_11","uri":"capability://tool.use.integration.api.and.cli.for.programmatic.sync.orchestration","name":"api-and-cli-for-programmatic-sync-orchestration","description":"Exposes REST API and CLI tools for programmatic control of syncs, enabling integration with external orchestration platforms (Airflow, Dagster, dbt Cloud). The API supports triggering syncs, querying status, retrieving logs, and managing connections, allowing users to embed Airbyte into larger data pipelines without relying on Airbyte's built-in scheduler.","intents":["I want to trigger Airbyte syncs from my Airflow DAG based on upstream task completion","I need to query sync status and logs programmatically for monitoring and alerting","I want to manage Airbyte connections and syncs via Infrastructure-as-Code (Terraform, Pulumi)"],"best_for":["teams using external orchestration platforms (Airflow, Dagster, Prefect)","organizations building custom data platforms on top of Airbyte","enterprises needing Infrastructure-as-Code for sync management"],"limitations":["API is REST-only — no GraphQL or gRPC support for complex queries","Rate limiting is enforced (varies by deployment) — high-frequency polling may hit limits","API authentication requires API tokens — no OAuth2 support for third-party integrations","API versioning is not always backward-compatible — breaking changes may require code updates"],"requires":["Airbyte API token (generated in web UI)","HTTP client library (curl, requests, axios, etc.)","Network access to Airbyte API (port 8001 for self-hosted)"],"input_types":["API requests (JSON body)","Query parameters (sync ID, connection ID, etc.)"],"output_types":["JSON responses with sync status, logs, connection details"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"airbyte__cap_12","uri":"capability://tool.use.integration.data.quality.monitoring.with.dbt.integration","name":"data-quality-monitoring-with-dbt-integration","description":"Integrates with dbt (data build tool) to enable data quality checks and transformations post-sync, allowing users to define dbt models that validate data freshness, completeness, and accuracy. Airbyte can trigger dbt runs after syncs complete, with built-in support for dbt Cloud and dbt Core, enabling end-to-end data pipeline observability.","intents":["I want to run dbt tests after syncs to validate data quality","I need to trigger dbt model refreshes automatically after Airbyte syncs","I want to monitor data freshness and completeness across my data warehouse"],"best_for":["teams using dbt for transformation and data quality","organizations with mature data warehousing practices","enterprises needing end-to-end pipeline observability"],"limitations":["dbt integration requires dbt Cloud or self-hosted dbt Core — adds operational complexity","dbt tests are SQL-based — complex business logic validation may require custom Python tests","Integration is one-way (Airbyte → dbt) — no feedback loop if dbt tests fail","dbt run time adds latency to sync completion — syncs must wait for dbt to finish before marking as complete"],"requires":["dbt Cloud account or dbt Core installation","dbt project with models and tests defined","Airbyte 0.40.0+ with dbt integration support"],"input_types":["dbt project configuration","dbt Cloud API token or dbt Core connection details"],"output_types":["dbt test results (passed/failed)","dbt model refresh logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"airbyte__cap_2","uri":"capability://data.processing.analysis.schema.evolution.and.automatic.type.coercion","name":"schema-evolution-and-automatic-type-coercion","description":"Automatically detects schema changes in source data and applies type coercion rules to handle mismatches between source and destination schemas. 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The framework stores the last extracted cursor value and resumes from that point on the next sync, avoiding full table scans and enabling efficient daily/hourly incremental updates without re-processing historical data.","intents":["I want to sync only new or changed records since the last sync, not the entire table","I need to resume interrupted syncs from the last checkpoint without data loss","I want to reduce API rate limit consumption by fetching only deltas"],"best_for":["teams syncing large tables (>1M rows) where full refreshes are prohibitively slow","organizations with strict API rate limits requiring delta-only extraction","data pipelines running frequent incremental syncs (hourly, daily)"],"limitations":["Requires source to have a reliable cursor field (timestamp or monotonic ID) — not all APIs provide this","Deleted records are not detected — only new/updated records; requires separate deletion tracking logic","Cursor precision matters — if two records have identical timestamps, one may be skipped on resume","Checkpoint storage adds latency (~50ms per sync) and requires external state persistence (Postgres, S3)"],"requires":["Source with cursor field (updated_at, modified_date, or auto-increment ID)","State storage backend (Airbyte's internal Postgres or external S3/GCS)","Connector support for cursor-based filtering (most REST APIs support ?updated_since parameter)"],"input_types":["Cursor field name (e.g., 'updated_at')","Last cursor value from previous sync","Sync state (JSON blob)"],"output_types":["Incremental records (only new/changed rows)","Updated cursor value for next sync","Sync state checkpoint"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"airbyte__cap_4","uri":"capability://automation.workflow.multi.destination.loading.with.staging.optimization","name":"multi-destination-loading-with-staging-optimization","description":"Loads extracted data into multiple destination types (data warehouses, databases, data lakes) using a staging layer that optimizes for batch writes and minimizes network round-trips. The DestinationLifecycle component orchestrates the load phase, writing records to intermediate storage (S3, GCS, or local disk) before bulk-inserting into the destination, supporting transactions and rollback on failure.","intents":["I want to load data into Snowflake, BigQuery, and Postgres simultaneously from a single sync","I need to optimize write performance by batching records instead of row-by-row inserts","I want automatic rollback if the load fails mid-transaction"],"best_for":["teams syncing to multiple data warehouses (Snowflake, BigQuery, Redshift, Postgres)","organizations needing high-throughput loads (>100K records/sec)","enterprises requiring ACID guarantees and transaction rollback"],"limitations":["Staging storage adds latency (~2-5 seconds per sync) and requires external storage (S3, GCS, or local disk)","Destination-specific SQL dialects require custom load logic per destination — not all destinations support the same bulk-load syntax","Transaction support varies by destination — some data lakes (S3-based) don't support ACID transactions","Network bandwidth between staging and destination can be a bottleneck for very large syncs (>10GB)"],"requires":["Staging storage backend (S3, GCS, Azure Blob, or local disk with sufficient space)","Destination connector with bulk-load support (most modern data warehouses have this)","Network connectivity from Airbyte worker to both staging and destination"],"input_types":["Extracted records (Airbyte protocol format)","Destination connection credentials","Staging storage path"],"output_types":["Loaded records in destination table","Load statistics (rows inserted, duration, errors)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"airbyte__cap_5","uri":"capability://code.generation.editing.python.cdk.for.custom.connector.development","name":"python-cdk-for-custom-connector-development","description":"Provides a Python SDK for building custom source and destination connectors with full control over extraction logic, authentication, and data transformation. The Python CDK abstracts Airbyte's protocol layer (AirbyteMessage serialization, state management, logging) while allowing developers to write connector-specific logic in Python, supporting decorators for stream definition, incremental sync, and error handling.","intents":["I need to build a connector for a proprietary API or internal data source not in the Airbyte catalog","I want to implement custom business logic (filtering, enrichment, deduplication) during extraction","I need to handle complex authentication (OAuth2 with refresh tokens, mTLS, custom headers)"],"best_for":["developers building connectors for proprietary or internal APIs","teams needing custom transformation logic beyond declarative manifests","organizations with complex authentication requirements (OAuth2, mTLS, SAML)"],"limitations":["Python CDK has lower throughput than Bulk CDK — no built-in parallelization, single-threaded extraction","Developers must handle pagination, rate limiting, and error retry logic manually","Testing requires mocking external APIs — no built-in fixtures for common patterns","Deployment requires Python environment setup (pip, virtual env) — more operational overhead than declarative manifests"],"requires":["Python 3.9 or later","airbyte-cdk Python package (pip install airbyte-cdk)","Understanding of Airbyte's protocol (AirbyteMessage, state management)","Poetry or pip for dependency management"],"input_types":["Python source code","API credentials and configuration","Stream definitions (schema, cursor fields)"],"output_types":["Airbyte protocol messages (AirbyteMessage format)","Connector Docker image"],"categories":["code-generation-editing","connector-development"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"airbyte__cap_6","uri":"capability://tool.use.integration.300.plus.pre.built.connector.catalog.with.versioning","name":"300-plus-pre-built-connector-catalog-with-versioning","description":"Maintains a curated catalog of 300+ pre-built source and destination connectors (HubSpot, Google Ads, Salesforce, Snowflake, BigQuery, etc.) with semantic versioning, automated testing, and release management. Each connector is independently versioned and tested, allowing users to pin specific connector versions and receive updates without breaking changes, with metadata (supported sync modes, schema handling, rate limits) published in the connector registry.","intents":["I want to sync from Salesforce/HubSpot/Google Ads without building a custom connector","I need to know which connector versions are stable and which have known issues","I want to pin a specific connector version to avoid unexpected breaking changes"],"best_for":["teams syncing from popular SaaS platforms (Salesforce, HubSpot, Stripe, etc.)","organizations wanting zero-code integration without custom development","enterprises needing stable, tested connectors with vendor support"],"limitations":["Connector quality varies — some are community-maintained with slower bug fixes","API changes in source systems require connector updates — Airbyte may lag behind API deprecations","Connector features are limited to what maintainers implement — custom transformations may require additional logic","Connector versioning is semantic but not always backward-compatible — major version upgrades may require configuration changes"],"requires":["Airbyte 0.35.0 or later with connector registry support","Valid credentials for the source/destination system","Network connectivity to the source API"],"input_types":["Connector name and version","Source/destination credentials","Configuration (streams to sync, sync frequency)"],"output_types":["Synced records in destination","Connector logs and error messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"airbyte__cap_7","uri":"capability://automation.workflow.kubernetes.native.deployment.with.horizontal.scaling","name":"kubernetes-native-deployment-with-horizontal-scaling","description":"Deploys Airbyte on Kubernetes using Helm charts and custom operators, enabling horizontal scaling of sync workers, automatic resource management, and multi-tenancy. The platform orchestrates connector pods as Kubernetes Jobs, manages state persistence via ConfigMaps/Secrets, and scales worker replicas based on sync queue depth, supporting both self-hosted and managed cloud deployments.","intents":["I want to deploy Airbyte on our Kubernetes cluster with auto-scaling for peak sync loads","I need to isolate syncs across multiple teams/customers using Kubernetes namespaces","I want to monitor and log syncs using Kubernetes-native observability (Prometheus, ELK)"],"best_for":["enterprises running Kubernetes clusters (EKS, GKE, AKS, self-managed)","organizations needing multi-tenancy and resource isolation per team","teams with existing Kubernetes observability stacks (Prometheus, Grafana, ELK)"],"limitations":["Kubernetes adds operational complexity — requires understanding of Helm, RBAC, networking, storage classes","State persistence requires external database (Postgres) — no embedded SQLite for production","Horizontal scaling is limited by source API rate limits — scaling workers doesn't help if the API throttles","Debugging failed syncs in Kubernetes is harder than Docker Compose — requires kubectl logs, port-forwarding, etc."],"requires":["Kubernetes 1.20+ cluster","Helm 3.0+","PostgreSQL 12+ for state storage (external or in-cluster)","Persistent storage (EBS, GCP Persistent Disk, or NFS) for staging data","Container registry access (Docker Hub, ECR, GCR) for connector images"],"input_types":["Helm values.yaml with cluster configuration","Kubernetes secrets for credentials","Connector Docker images"],"output_types":["Kubernetes Deployments, StatefulSets, Jobs","Synced data in destination","Logs in stdout (captured by Kubernetes logging)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"airbyte__cap_8","uri":"capability://automation.workflow.managed.cloud.service.with.zero.ops.deployment","name":"managed-cloud-service-with-zero-ops-deployment","description":"Offers a fully managed Airbyte Cloud service (airbyte.com) that eliminates infrastructure management — users configure connectors via web UI, and Airbyte handles scaling, monitoring, upgrades, and disaster recovery. The service runs on Airbyte's multi-tenant Kubernetes infrastructure, with automatic connector updates, built-in observability, and SLA guarantees.","intents":["I want to use Airbyte without managing infrastructure or Kubernetes clusters","I need automatic connector updates and security patches without manual intervention","I want SLA guarantees and vendor support for production syncs"],"best_for":["small teams and startups without DevOps resources","organizations prioritizing time-to-value over cost","enterprises needing vendor support and SLA guarantees"],"limitations":["Managed service pricing is higher than self-hosted (per-sync-run or per-GB-synced model)","Limited customization — cannot modify connector code or deploy custom connectors easily","Data residency constraints — managed service may not support all regions or compliance requirements (HIPAA, SOC2)","Vendor lock-in — migrating away requires exporting configuration and rebuilding on self-hosted"],"requires":["Airbyte Cloud account (free tier available with limits)","Valid payment method for usage-based pricing","Network connectivity from Airbyte Cloud to source/destination (may require IP whitelisting)"],"input_types":["Web UI configuration (connector selection, credentials, sync schedule)","Source/destination credentials"],"output_types":["Synced data in destination","Sync logs and monitoring dashboard"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"airbyte__cap_9","uri":"capability://tool.use.integration.web.ui.for.sync.configuration.and.monitoring","name":"web-ui-for-sync-configuration-and-monitoring","description":"Provides a web-based dashboard for configuring data syncs, monitoring sync history, viewing logs, and managing connections without writing code. 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