{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-airflow","slug":"pypi-airflow","name":"airflow","type":"framework","url":"https://github.com/apache/incubator-airflow","page_url":"https://unfragile.ai/pypi-airflow","categories":["data-pipelines"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-airflow__cap_0","uri":"capability://automation.workflow.dag.based.workflow.orchestration.with.dynamic.task.dependency.resolution","name":"dag-based workflow orchestration with dynamic task dependency resolution","description":"Airflow represents workflows as Directed Acyclic Graphs (DAGs) where tasks are nodes and dependencies are edges. The scheduler parses Python DAG definitions, builds the dependency graph at runtime, and executes tasks in topologically-sorted order with support for conditional branching, dynamic task generation, and cross-DAG dependencies. This approach enables declarative workflow definition in code rather than configuration files, allowing programmatic task generation and complex dependency patterns.","intents":["Define complex multi-step data pipelines with explicit task dependencies and conditional logic","Dynamically generate tasks at runtime based on external data or configuration","Manage workflows that span multiple days, weeks, or months with backfill capabilities","Express fan-out/fan-in patterns and complex branching without manual orchestration code"],"best_for":["Data engineering teams building ETL/ELT pipelines at scale","Organizations needing to manage hundreds of interdependent batch jobs","Teams requiring auditability and version control of workflow definitions"],"limitations":["DAG parsing happens on every scheduler heartbeat — large DAGs (1000+ tasks) can cause scheduler bottlenecks","No native support for real-time streaming workflows; designed for batch/scheduled execution","Dynamic task generation requires careful memory management to avoid scheduler overload","Circular dependency detection is post-hoc; complex dynamic DAGs can create cycles at runtime"],"requires":["Python 2.7+ (early versions) or Python 3.6+ (modern versions)","Metadata database (PostgreSQL, MySQL, or SQLite for development)","Message broker for task queue (Celery, Kubernetes, or local executor)","Airflow installation via pip or Docker"],"input_types":["Python code (DAG definitions)","Configuration files (airflow.cfg)","External data sources (for dynamic task generation)"],"output_types":["Task execution logs","Task state transitions (queued, running, success, failed)","Workflow execution history and metrics"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-airflow__cap_1","uri":"capability://automation.workflow.distributed.task.execution.with.pluggable.executor.backends","name":"distributed task execution with pluggable executor backends","description":"Airflow decouples task scheduling from execution through an executor abstraction layer supporting multiple backends: SequentialExecutor (single-process), LocalExecutor (multiprocessing), CeleryExecutor (distributed message queue), KubernetesExecutor (containerized tasks), and custom executors. Tasks are serialized, pushed to a message broker or queue, and executed by worker processes that pull and execute them, with results persisted back to the metadata database. This architecture enables horizontal scaling and heterogeneous task execution environments.","intents":["Scale task execution from single machine to distributed cluster without code changes","Run tasks in isolated environments (containers, separate processes) for dependency isolation","Execute long-running tasks without blocking the scheduler or other tasks","Support different compute resources for different task types (CPU-intensive vs I/O-bound)"],"best_for":["Teams running pipelines with 100+ concurrent tasks requiring distributed execution","Organizations with heterogeneous infrastructure (on-prem, cloud, hybrid)","Data teams needing task isolation and resource limits per task"],"limitations":["CeleryExecutor requires Redis/RabbitMQ setup and monitoring — adds operational complexity","KubernetesExecutor has high per-task overhead (pod creation latency ~5-30s) unsuitable for sub-second tasks","Task serialization/deserialization adds latency; complex Python objects may not serialize cleanly","No built-in task prioritization or preemption — all tasks treated equally in queue"],"requires":["Python 3.6+","Metadata database (PostgreSQL recommended for production)","Message broker for distributed execution (Redis/RabbitMQ for Celery, Kubernetes API for K8s executor)","Worker processes/pods configured to pull from task queue"],"input_types":["Task definitions (Python callables or operators)","Task context (execution date, run_id, parameters)","Resource specifications (CPU, memory limits)"],"output_types":["Task execution status (success, failure, retry)","Task logs and stdout/stderr","Task return values (XCom)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-airflow__cap_10","uri":"capability://automation.workflow.scheduler.with.configurable.execution.intervals.and.cron.based.scheduling","name":"scheduler with configurable execution intervals and cron-based scheduling","description":"Airflow's scheduler is a long-running process that periodically parses DAGs, creates task instances for scheduled execution dates, and submits them to executors. Scheduling is defined via schedule_interval (cron expression or timedelta) on each DAG. The scheduler maintains a heartbeat loop that checks for DAGs to schedule, monitors task progress, and enforces SLAs. Scheduling is time-based (not event-based), with configurable minimum scheduling interval (default 1 minute). The scheduler is single-threaded in early versions, becoming a bottleneck for large deployments.","intents":["Schedule DAGs to run at regular intervals (hourly, daily, weekly) using cron expressions","Automatically create task instances for scheduled execution dates","Monitor task progress and enforce SLA thresholds","Trigger DAG runs based on external events (via sensors or custom triggers)"],"best_for":["Batch data pipelines with regular execution schedules (hourly, daily ETL)","Organizations with SLA requirements and need for automated scheduling","Teams wanting centralized scheduling without external cron or job schedulers"],"limitations":["Single-threaded scheduler becomes bottleneck with 1000+ DAGs; scheduling latency increases linearly","Cron-based scheduling has minimum 1-minute granularity; sub-minute scheduling requires workarounds","No built-in support for event-driven scheduling (webhooks, Kafka); requires custom sensors","Scheduler is single point of failure; high availability requires external orchestration (Kubernetes, etc.)"],"requires":["Python 3.6+","Airflow core installation","Metadata database","Scheduler process running continuously (airflow scheduler command)"],"input_types":["DAG schedule_interval (cron expression or timedelta)","DAG start_date and end_date","Scheduler configuration (min_file_process_interval, dag_dir_list_interval)"],"output_types":["Task instances created for scheduled dates","Scheduler logs and metrics","DAG run history"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-airflow__cap_11","uri":"capability://memory.knowledge.variable.and.parameter.management.with.templating.support","name":"variable and parameter management with templating support","description":"Airflow provides Variables for storing configuration values (strings, JSON) in the metadata database, accessible to tasks via the Variable API. DAG and task parameters support Jinja2 templating, enabling dynamic value substitution at task execution time. Template variables include execution_date, run_id, task_id, and custom variables. This enables parameterized DAGs that adapt to execution context without code changes, supporting multi-environment deployments and dynamic configuration.","intents":["Store configuration values (API endpoints, thresholds, feature flags) without hardcoding","Parameterize DAGs for multi-environment deployments (dev, staging, prod)","Generate task parameters dynamically based on execution date or upstream task outputs","Implement feature flags and A/B testing logic in pipelines"],"best_for":["Multi-environment deployments requiring environment-specific configuration","Teams needing to change pipeline behavior without code deployment","Workflows with dynamic parameters based on execution context"],"limitations":["Variables are stored in metadata database; no versioning or audit trail of changes","Jinja2 templating is evaluated at task execution time; complex templates can be hard to debug","No built-in support for variable validation or type checking","Variable access is not restricted; all tasks can read all variables (security risk)"],"requires":["Python 3.6+","Airflow core installation","Metadata database for variable storage","Jinja2 for template rendering"],"input_types":["Variable names and values (strings, JSON)","Jinja2 template expressions in task parameters","Execution context (execution_date, run_id, task_id)"],"output_types":["Rendered task parameters with variable substitution","Variable values retrieved at task execution time","Variable history and audit logs"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-airflow__cap_12","uri":"capability://automation.workflow.logging.with.pluggable.log.handlers.and.remote.log.storage","name":"logging with pluggable log handlers and remote log storage","description":"Airflow implements a pluggable logging system where task logs are written to local files by default but can be stored in remote backends (S3, GCS, Azure Blob Storage) via custom log handlers. Logs are streamed to the web UI from the configured log backend. The logging system captures task stdout/stderr, Airflow framework logs, and custom application logs. Log retention is configurable; old logs can be automatically deleted. This enables centralized log management and audit trails without requiring external logging infrastructure.","intents":["Capture task execution logs for debugging and auditing","Store logs in cloud storage for long-term retention and compliance","Stream logs to web UI for real-time monitoring","Implement log retention policies for cost management"],"best_for":["Production deployments requiring centralized log management","Organizations with compliance requirements for audit trails","Teams needing long-term log retention without local storage"],"limitations":["Remote log retrieval adds latency (100-500ms per log fetch from S3/GCS); not suitable for real-time log streaming","Log handler configuration is global; no per-task log routing","No built-in log aggregation or search (requires external tools like ELK, Splunk)","Log cleanup is asynchronous; old logs may persist longer than configured retention"],"requires":["Python 3.6+","Airflow core installation","Remote log storage (S3, GCS, Azure Blob Storage) for production","Cloud credentials (AWS keys, GCS service account, Azure credentials)"],"input_types":["Task execution logs (stdout, stderr, application logs)","Log handler configuration (S3, GCS, local filesystem)","Log retention policy (days to keep logs)"],"output_types":["Logs stored in configured backend (local files or cloud storage)","Logs streamed to web UI","Log cleanup and retention metrics"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-airflow__cap_2","uri":"capability://automation.workflow.sensor.based.task.triggering.with.polling.and.event.driven.patterns","name":"sensor-based task triggering with polling and event-driven patterns","description":"Airflow provides Sensor operators that poll external systems (S3, databases, HTTP endpoints, file systems) at configurable intervals until a condition is met, then trigger downstream tasks. Sensors implement exponential backoff, timeout handling, and poke modes (synchronous polling vs asynchronous deferral). This enables event-driven workflows where task execution depends on external state changes without requiring external event systems, though it trades efficiency for simplicity.","intents":["Wait for external data arrival (S3 files, database records) before processing","Implement SLA-aware task triggering with timeout and retry logic","Build workflows that react to external system state changes without webhooks","Coordinate multi-system pipelines where one system's output triggers another's input"],"best_for":["Teams with external data dependencies (third-party APIs, partner data feeds)","Organizations without event infrastructure (Kafka, SNS) or unable to modify upstream systems","Workflows with variable data arrival times requiring flexible wait logic"],"limitations":["Polling-based sensors create database/API load proportional to check frequency; not suitable for sub-minute latency requirements","Sensor tasks occupy worker slots while waiting — can exhaust worker capacity if many sensors run concurrently","No native support for event-driven triggering (webhooks, Kafka); requires custom sensor implementations","Timeout handling is task-level, not system-level — long-running sensors can cause scheduler memory bloat"],"requires":["Python 3.6+","Airflow core installation","Credentials/access to external systems being polled (S3, databases, APIs)","Appropriate sensor operators installed (boto3 for S3Sensor, psycopg2 for SQL sensors, etc.)"],"input_types":["External system credentials (AWS keys, database connection strings)","Polling parameters (interval, timeout, exponential backoff config)","Condition definitions (file patterns, SQL queries, HTTP response codes)"],"output_types":["Boolean success/failure (task passes when condition met)","Execution logs showing polling attempts and results","Sensor state (poke_count, last_check_time)"],"categories":["automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-airflow__cap_3","uri":"capability://automation.workflow.task.retry.and.failure.handling.with.exponential.backoff.and.sla.enforcement","name":"task retry and failure handling with exponential backoff and sla enforcement","description":"Airflow provides configurable retry logic at task level with exponential backoff, jitter, and max retry counts. Failed tasks can trigger alert callbacks, email notifications, or custom handlers. SLA (Service Level Agreement) monitoring tracks task execution time and triggers alerts if tasks exceed defined thresholds. Retry logic is implemented in the task execution loop, allowing tasks to be re-queued with exponential delay between attempts, while SLA checks run asynchronously in the scheduler.","intents":["Automatically retry transient failures (network timeouts, temporary service unavailability) without manual intervention","Alert on-call teams when tasks breach SLA thresholds (e.g., ETL must complete by 6am)","Implement circuit-breaker patterns where repeated failures trigger escalation or workflow cancellation","Distinguish between retryable (transient) and non-retryable (permanent) failures"],"best_for":["Production pipelines with external dependencies prone to transient failures","Teams with SLA requirements and on-call rotation","Workflows requiring sophisticated failure handling beyond simple retry"],"limitations":["Retry logic is task-level only; no built-in DAG-level rollback or compensation","SLA monitoring is best-effort; clock skew or scheduler delays can cause false positives","Exponential backoff is hardcoded formula; no support for custom backoff strategies without code modification","Email/callback notifications require SMTP configuration and can fail silently if misconfigured"],"requires":["Python 3.6+","Airflow core installation","SMTP server configuration for email alerts (optional but recommended)","Task definitions with retry_delay and sla parameters"],"input_types":["Task retry configuration (max_tries, retry_delay, retry_exponential_backoff)","SLA definition (execution_timeout, sla_duration)","Callback functions (on_failure_callback, on_retry_callback)"],"output_types":["Task state transitions (failed → queued for retry → success/failed)","Alert notifications (email, Slack via custom callback)","SLA violation logs and metrics"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-airflow__cap_4","uri":"capability://memory.knowledge.xcom.cross.communication.for.inter.task.data.passing.with.serialization","name":"xcom (cross-communication) for inter-task data passing with serialization","description":"Airflow provides XCom (cross-communication) as a key-value store for passing data between tasks. Tasks push values to XCom (serialized to JSON or pickle), and downstream tasks pull values by task_id and key. XCom is backed by the metadata database, enabling data persistence across task executions and worker processes. This decouples task execution from direct inter-process communication, but introduces serialization overhead and database I/O for every data exchange.","intents":["Pass task outputs (file paths, record counts, configuration) to downstream tasks without shared filesystems","Implement dynamic task parameters based on upstream task results","Share state across distributed workers without direct process communication","Build conditional branching logic based on upstream task outputs"],"best_for":["Workflows with small-to-medium data exchanges between tasks (< 100MB per message)","Distributed execution environments where tasks run on different machines","Teams needing auditability of inter-task communication"],"limitations":["XCom values are serialized to JSON/pickle and stored in database — large payloads (>100MB) cause performance degradation","No built-in compression; large XCom values bloat metadata database","Serialization/deserialization adds latency (~10-100ms per exchange depending on payload size)","XCom is pull-based; downstream tasks must explicitly pull values, creating tight coupling"],"requires":["Python 3.6+","Airflow core installation","Metadata database with sufficient storage for XCom values","Serializable task return values (JSON-compatible or pickle-able objects)"],"input_types":["Task return values (any Python object that can be serialized)","XCom keys (string identifiers for values)","Task IDs (to identify source of XCom value)"],"output_types":["Serialized values in metadata database (JSON or pickle format)","Retrieved values in downstream task context","XCom history and audit trail"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-airflow__cap_5","uri":"capability://tool.use.integration.operator.abstraction.layer.with.built.in.operators.for.common.integrations","name":"operator abstraction layer with built-in operators for common integrations","description":"Airflow provides an Operator base class that encapsulates task logic and execution. Built-in operators handle common patterns: BashOperator (shell commands), PythonOperator (Python functions), SQLOperator (database queries), S3Operator (S3 operations), EmailOperator (email sending), and 100+ community operators for external systems (Spark, Kubernetes, Salesforce, etc.). Operators define task behavior, retry logic, and resource requirements declaratively, abstracting away execution details and enabling reusable task templates.","intents":["Execute shell commands, Python functions, or SQL queries as tasks without boilerplate code","Integrate with external systems (cloud storage, databases, APIs) using pre-built operators","Define task behavior (timeouts, retries, resource limits) declaratively in DAG code","Build custom operators for domain-specific logic while inheriting Airflow's execution framework"],"best_for":["Teams building workflows with common patterns (ETL, data validation, notifications)","Organizations with diverse technology stacks requiring multiple integrations","Developers wanting to avoid writing boilerplate execution and error handling code"],"limitations":["Operator abstraction adds overhead; simple tasks have 10-50ms execution overhead from Airflow framework","Community operators have varying quality and maintenance status; not all are production-ready","Custom operators require understanding Airflow's task lifecycle and context model","Operator parameters are static at DAG parse time; dynamic operator configuration requires workarounds"],"requires":["Python 3.6+","Airflow core installation","Provider packages for specific operators (airflow-providers-amazon for S3Operator, etc.)","Credentials/access to external systems (AWS keys, database connections, API tokens)"],"input_types":["Operator class instantiation with parameters (bash_command, python_callable, sql, etc.)","Task context (execution_date, run_id, task_instance)","External system credentials (passed via Airflow Connections)"],"output_types":["Task execution status (success, failure, skipped)","Task logs and stdout/stderr","Operator-specific outputs (query results, file paths, API responses)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-airflow__cap_6","uri":"capability://automation.workflow.backfill.and.historical.data.reprocessing.with.time.based.task.scheduling","name":"backfill and historical data reprocessing with time-based task scheduling","description":"Airflow's scheduler supports backfilling — reprocessing historical data by running DAGs for past execution dates. The backfill command generates task instances for a date range, respecting task dependencies and retry logic. This enables reprocessing data after bug fixes, schema changes, or missed runs. Backfill is implemented as a separate execution path that creates task instances for historical dates and executes them through the normal scheduler/executor pipeline.","intents":["Reprocess historical data after fixing bugs or schema changes in pipeline logic","Catch up on missed runs due to scheduler downtime or deployment","Run what-if scenarios with modified DAG logic on historical data","Validate data quality retroactively across entire historical dataset"],"best_for":["Data teams requiring data corrections or schema migrations","Production pipelines with SLA requirements and need for catch-up capability","Organizations with long-running historical datasets (years of data)"],"limitations":["Backfill can overwhelm scheduler and workers if date range is large (millions of task instances); requires careful rate limiting","Backfill doesn't handle idempotency automatically; tasks must be designed to handle re-execution safely","No built-in conflict detection if backfill overlaps with real-time execution; can cause duplicate data","Backfill performance degrades with large date ranges due to task instance creation overhead"],"requires":["Python 3.6+","Airflow CLI access (airflow backfill command)","Metadata database with sufficient capacity for task instances","Idempotent task logic (tasks must produce same results if run multiple times)"],"input_types":["DAG ID","Start and end dates for backfill range","Optional task subset (backfill specific tasks only)"],"output_types":["Task instances created for historical dates","Task execution logs and results","Backfill status and progress"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-airflow__cap_7","uri":"capability://automation.workflow.web.ui.for.workflow.monitoring.debugging.and.manual.intervention","name":"web ui for workflow monitoring, debugging, and manual intervention","description":"Airflow provides a Flask-based web UI displaying DAG structure, task status, execution history, logs, and metrics. The UI enables manual task triggering, task instance clearing (for re-execution), and DAG pause/unpause. Task logs are streamed from worker processes or log storage backends (S3, GCS). The UI is read-heavy but supports write operations (trigger, clear, pause) for operational control. This enables non-technical stakeholders to monitor pipelines and operators to debug failures without CLI access.","intents":["Monitor DAG execution status and task progress in real-time","Debug failed tasks by viewing logs and task context","Manually trigger DAGs or clear task instances for re-execution","Track workflow history and identify performance bottlenecks"],"best_for":["Operations teams needing visibility into pipeline health","Data engineers debugging pipeline failures","Non-technical stakeholders requiring workflow status dashboards"],"limitations":["Web UI can become slow with large DAGs (1000+ tasks) due to rendering overhead","Log retrieval from remote storage (S3, GCS) adds latency; not suitable for real-time log streaming","No built-in alerting dashboard; alerts are email/callback-based","UI permissions are coarse-grained (all-or-nothing access); no fine-grained DAG-level permissions in early versions"],"requires":["Python 3.6+","Airflow core installation","Flask and dependencies","Metadata database for task state and execution history","Optional: log storage backend (S3, GCS, local filesystem)"],"input_types":["DAG definitions and task instances from metadata database","Task logs from log storage backend","User actions (trigger, clear, pause)"],"output_types":["HTML UI displaying DAG structure and task status","Task logs and execution details","Workflow execution history and metrics"],"categories":["automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-airflow__cap_8","uri":"capability://safety.moderation.connection.and.credential.management.with.encrypted.storage","name":"connection and credential management with encrypted storage","description":"Airflow provides a Connections abstraction for storing credentials (database passwords, API keys, SSH keys) encrypted in the metadata database. Connections are referenced by name in tasks/operators, enabling credential rotation without DAG code changes. The Connections API supports multiple connection types (PostgreSQL, MySQL, S3, HTTP, SSH, etc.) with type-specific fields (host, port, login, password, extra JSON). Credentials are encrypted at rest using Fernet symmetric encryption with a configurable key.","intents":["Store and manage credentials securely without hardcoding in DAG code","Rotate credentials without modifying DAG definitions","Support multiple environments (dev, staging, prod) with different credentials","Audit credential access and usage"],"best_for":["Production environments requiring secure credential management","Teams with multiple environments and credential rotation requirements","Organizations with compliance requirements (SOC2, HIPAA) for credential handling"],"limitations":["Encryption key is stored in airflow.cfg; compromise of config file exposes all credentials","No built-in credential rotation; manual updates required","Connection passwords are visible in plain text in Airflow UI (security risk)","No integration with external secret managers (Vault, AWS Secrets Manager) in core; requires custom hooks"],"requires":["Python 3.6+","Airflow core installation","Metadata database","Fernet encryption key configured in airflow.cfg"],"input_types":["Connection type (PostgreSQL, MySQL, S3, HTTP, etc.)","Connection parameters (host, port, login, password, extra JSON)","Encryption key (Fernet key in airflow.cfg)"],"output_types":["Encrypted credentials in metadata database","Decrypted credentials available to tasks at runtime","Connection audit logs"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-airflow__cap_9","uri":"capability://safety.moderation.pluggable.authentication.and.authorization.with.role.based.access.control","name":"pluggable authentication and authorization with role-based access control","description":"Airflow supports multiple authentication backends (LDAP, Kerberos, OAuth, database) and role-based access control (RBAC) for the web UI. Authentication is pluggable via the auth_backend configuration; RBAC assigns roles (Admin, User, Viewer, Op) with granular permissions on DAGs, tasks, and UI features. Authorization is enforced at the web UI level, not at the task execution level. This enables multi-tenant deployments and compliance with organizational access policies.","intents":["Integrate Airflow authentication with corporate LDAP/Active Directory","Restrict DAG access to specific teams or roles","Audit user actions (trigger, clear, pause) in the web UI","Support multi-tenant deployments with isolated DAG access"],"best_for":["Enterprise deployments with corporate authentication infrastructure","Organizations with compliance requirements for access control","Multi-team environments requiring DAG-level access isolation"],"limitations":["RBAC is UI-level only; no enforcement at task execution level (all tasks run with same permissions)","Role-based permissions are coarse-grained; no fine-grained field-level permissions","Custom auth backends require implementing Airflow's auth interface; limited documentation","OAuth integration is basic; no support for advanced OAuth flows (PKCE, device flow)"],"requires":["Python 3.6+","Airflow core installation","Authentication backend (LDAP, Kerberos, OAuth provider, or database)","Metadata database for user and role storage"],"input_types":["User credentials (username/password, LDAP credentials, OAuth token)","Role definitions (Admin, User, Viewer, Op)","DAG-level permissions (which roles can view/trigger/edit)"],"output_types":["Authenticated user session","Role-based UI access (visible DAGs, available actions)","Audit logs of user actions"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 2.7+ (early versions) or Python 3.6+ (modern versions)","Metadata database (PostgreSQL, MySQL, or SQLite for development)","Message broker for task queue (Celery, Kubernetes, or local executor)","Airflow installation via pip or Docker","Python 3.6+","Metadata database (PostgreSQL recommended for production)","Message broker for distributed execution (Redis/RabbitMQ for Celery, Kubernetes API for K8s executor)","Worker processes/pods configured to pull from task queue","Airflow core installation","Metadata database"],"failure_modes":["DAG parsing happens on every scheduler heartbeat — large DAGs (1000+ tasks) can cause scheduler bottlenecks","No native support for real-time streaming workflows; designed for batch/scheduled execution","Dynamic task generation requires careful memory management to avoid scheduler overload","Circular dependency detection is post-hoc; complex dynamic DAGs can create cycles at runtime","CeleryExecutor requires Redis/RabbitMQ setup and monitoring — adds operational complexity","KubernetesExecutor has high per-task overhead (pod creation latency ~5-30s) unsuitable for sub-second tasks","Task serialization/deserialization adds latency; complex Python objects may not serialize cleanly","No built-in task prioritization or preemption — all tasks treated equally in queue","Single-threaded scheduler becomes bottleneck with 1000+ DAGs; scheduling latency increases linearly","Cron-based scheduling has minimum 1-minute granularity; sub-minute scheduling requires workarounds","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":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:05.295Z","last_scraped_at":"2026-05-03T15:20:22.334Z","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-airflow","compare_url":"https://unfragile.ai/compare?artifact=pypi-airflow"}},"signature":"ODFndWLon2PjzGHDk3rP5YOm12gg2NLU/AN2hLa1Kaj5E+l5tAzpCCWPIcynvVmNb+OZsIEnAG/3rpz6dqxYDg==","signedAt":"2026-06-23T13:31:04.420Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-airflow","artifact":"https://unfragile.ai/pypi-airflow","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-airflow","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"}}