{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"kestra","slug":"kestra","name":"Kestra","type":"repo","url":"https://github.com/kestra-io/kestra","page_url":"https://unfragile.ai/kestra","categories":["data-pipelines"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"kestra__cap_0","uri":"capability://automation.workflow.declarative.yaml.workflow.definition.with.pebble.templating","name":"declarative yaml workflow definition with pebble templating","description":"Kestra enables workflow definition through declarative YAML syntax that gets parsed and validated against a Flow model schema. The system uses Pebble templating engine (integrated via PebbleExpressionService in core/runners) to enable dynamic variable interpolation, conditional logic, and expression evaluation within workflow definitions. YAML is deserialized into strongly-typed Flow objects with built-in validation, allowing developers to define complex orchestration logic without imperative code while maintaining type safety and IDE support through schema validation.","intents":["Define multi-step data pipelines using configuration-as-code without writing imperative orchestration logic","Use dynamic expressions and variables to parameterize workflows across different environments and execution contexts","Version control workflow definitions alongside application code with full Git integration","Enable non-technical stakeholders to understand and modify workflow logic through readable YAML syntax"],"best_for":["Data engineers building reusable ETL pipelines","DevOps teams managing infrastructure automation workflows","Organizations standardizing on infrastructure-as-code practices"],"limitations":["Complex conditional logic becomes verbose in YAML; deeply nested conditionals reduce readability","Pebble templating has limited expression complexity compared to full programming languages","No built-in IDE schema validation without VS Code extension or external tooling"],"requires":["YAML syntax knowledge","Understanding of Pebble template syntax for variable interpolation","Kestra server running (local or cloud-hosted)"],"input_types":["YAML text","JSON configuration objects","Environment variables"],"output_types":["Parsed Flow objects","Validated workflow definitions","Execution-ready workflow metadata"],"categories":["automation-workflow","configuration-as-code"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kestra__cap_1","uri":"capability://tool.use.integration.plugin.based.task.execution.with.500.pre.built.integrations","name":"plugin-based task execution with 500+ pre-built integrations","description":"Kestra implements a modular plugin system where tasks are loaded dynamically from a registry of 500+ pre-built plugins covering databases, cloud platforms, messaging systems, and data tools. Each plugin is a self-contained module with its own build.gradle configuration that implements task interfaces and registers handlers with the core execution engine. The plugin system includes automatic documentation generation and schema validation, allowing developers to extend Kestra with custom tasks by implementing standard interfaces without modifying core code.","intents":["Execute tasks against external systems (databases, APIs, cloud services) without writing custom integration code","Reduce boilerplate by using pre-built connectors for common tools like Snowflake, BigQuery, Kafka, and AWS services","Extend Kestra with custom tasks by implementing plugin interfaces and registering with the plugin registry","Maintain consistent task behavior and error handling across diverse integrations through standardized plugin contracts"],"best_for":["Teams using diverse technology stacks requiring multi-system orchestration","Organizations wanting to avoid custom integration development","Developers building domain-specific extensions on top of Kestra"],"limitations":["Plugin quality and maintenance varies; community-contributed plugins may lack production-grade error handling","Adding new plugins requires Java/Gradle knowledge and rebuilding the application","Plugin dependencies can create version conflicts if not carefully managed in the build system","No hot-reload for plugins; changes require application restart"],"requires":["Java 11+ for plugin development","Gradle build system knowledge for custom plugins","Credentials/API keys for external systems being integrated"],"input_types":["Task configuration YAML","External system credentials","Data from previous task outputs"],"output_types":["Task execution results","Structured data from external systems","Error logs and execution metrics"],"categories":["tool-use-integration","plugin-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kestra__cap_10","uri":"capability://code.generation.editing.script.task.execution.with.multiple.language.support.python.bash.node.js.etc","name":"script task execution with multiple language support (python, bash, node.js, etc.)","description":"Kestra provides script task types that execute arbitrary code in multiple languages (Python, Bash, Node.js, PowerShell, etc.) within containerized environments. The Script Tasks system (core/runners) handles language detection, dependency installation, and execution isolation, allowing developers to embed custom logic directly in workflows without creating separate plugins. Scripts can access the execution context through environment variables and stdin, and return results through stdout or files, enabling flexible integration of custom code with the orchestration platform.","intents":["Execute custom Python, Bash, or Node.js scripts within workflows without creating plugins","Implement data transformations and business logic directly in workflows","Leverage existing scripts and tools by wrapping them in Kestra script tasks","Prototype workflow logic quickly without the overhead of plugin development"],"best_for":["Data scientists embedding Python scripts in workflows","DevOps engineers running Bash scripts as part of orchestration","Teams needing quick prototyping without plugin development overhead"],"limitations":["Script execution is slower than native plugins due to container startup overhead (~1-5 seconds per script)","Dependency management is manual; scripts must install dependencies at runtime or use pre-built images","Debugging script execution is harder than native code; requires examining logs and stdout","Script isolation is container-based; no fine-grained resource limits per script","Large scripts or high-volume script execution can impact cluster performance"],"requires":["Docker or container runtime for script execution","Language runtime installed in container image (Python 3.x, Node.js, Bash, etc.)","Script code provided inline or from external files"],"input_types":["Script code (Python, Bash, Node.js, PowerShell, etc.)","Environment variables from execution context","Input files and data"],"output_types":["Script stdout/stderr output","Exit codes and error messages","Output files and structured results"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kestra__cap_11","uri":"capability://text.generation.language.built.in.ai.task.integration.for.llm.powered.workflow.steps","name":"built-in ai task integration for llm-powered workflow steps","description":"Kestra includes native AI task types that integrate with LLM providers (OpenAI, Anthropic, etc.) to enable AI-powered workflow steps. These tasks accept prompts, context, and configuration parameters, send requests to LLM APIs, and return structured results that can be used in downstream tasks. The AI integration is implemented as standard tasks within the plugin system, allowing workflows to incorporate AI-powered decision-making, content generation, and data analysis without external orchestration.","intents":["Use LLMs to generate content, summarize data, or make decisions within workflows","Implement AI-powered data processing steps (classification, extraction, transformation)","Build intelligent workflows that adapt behavior based on LLM outputs","Integrate LLM capabilities without building custom API clients or orchestration logic"],"best_for":["Teams building AI-powered data pipelines","Organizations automating content generation or analysis workflows","Developers prototyping AI-integrated workflows quickly"],"limitations":["LLM API costs accumulate with workflow execution; no built-in cost tracking or budgeting","LLM response latency (typically 1-30 seconds) can slow down workflows significantly","No built-in retry logic for LLM API failures; requires manual error handling","LLM outputs are non-deterministic; same workflow may produce different results on repeated execution","Prompt engineering is required for reliable results; poor prompts lead to unpredictable behavior"],"requires":["LLM provider API key (OpenAI, Anthropic, etc.)","Network access to LLM provider APIs","Prompt engineering knowledge for reliable results"],"input_types":["Prompts and context","LLM configuration (model, temperature, max tokens)","Input data for processing"],"output_types":["LLM-generated text responses","Structured extraction results","Classification or decision outputs"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kestra__cap_12","uri":"capability://automation.workflow.flow.versioning.and.git.based.workflow.management","name":"flow versioning and git-based workflow management","description":"Kestra enables workflows to be stored in Git repositories and synced with the Kestra server, providing version control, change tracking, and collaborative workflow development. Workflows are defined as YAML files that can be committed to Git, enabling teams to use standard Git workflows (branches, pull requests, code review) for workflow changes. The system supports bidirectional sync between Git and Kestra, allowing workflows to be edited in the UI or in Git and synchronized automatically.","intents":["Version control workflow definitions alongside application code using Git","Implement code review processes for workflow changes through pull requests","Track workflow changes and rollback to previous versions using Git history","Enable collaborative workflow development with multiple team members"],"best_for":["Teams practicing infrastructure-as-code and version control","Organizations requiring change tracking and audit trails for workflows","Enterprises implementing code review processes for workflow changes"],"limitations":["Git sync requires manual configuration and polling; no real-time bidirectional sync","Merge conflicts in YAML workflows require manual resolution; no built-in conflict resolution","Git-based workflows are read-only in the UI if sync is configured; changes must be made in Git","Large workflow repositories can slow down Git operations and sync performance"],"requires":["Git repository (GitHub, GitLab, Gitea, etc.)","Git credentials configured in Kestra","Webhook configuration for push-based sync (optional)"],"input_types":["YAML workflow files","Git commit history","Branch and tag information"],"output_types":["Synced workflow definitions","Git commit references","Version history and change tracking"],"categories":["automation-workflow","version-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kestra__cap_13","uri":"capability://safety.moderation.secrets.management.with.encrypted.storage.and.namespace.isolation","name":"secrets management with encrypted storage and namespace isolation","description":"Kestra provides a secrets management system that stores sensitive credentials (API keys, database passwords, etc.) in encrypted form within the persistent data layer. Secrets are scoped to namespaces and can be referenced in workflow definitions using a special syntax (e.g., `{{ secret.api_key }}`), which are resolved at execution time. The system supports multiple secret backends (encrypted database storage, external vaults) and provides audit logging for secret access.","intents":["Store API keys, database credentials, and other secrets securely without exposing them in workflow definitions","Reference secrets in workflows using a safe syntax that doesn't expose values in logs or UI","Rotate secrets without modifying workflow definitions","Audit access to secrets for compliance and security purposes"],"best_for":["Organizations with security requirements for credential management","Teams needing to share workflows without exposing sensitive credentials","Enterprises implementing compliance and audit requirements"],"limitations":["Secrets are encrypted at rest but decrypted in memory during execution; no end-to-end encryption","No built-in secret rotation; manual updates required or external vault integration needed","Secret values are visible in execution logs if tasks print them; requires careful task design","No fine-grained access control per secret; namespace-level isolation only"],"requires":["Encryption key configured for secret storage","Persistent database for encrypted secret storage","Namespace assignment for secret scoping"],"input_types":["Secret names and values","Secret metadata and expiration"],"output_types":["Decrypted secret values at execution time","Secret access audit logs"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kestra__cap_14","uri":"capability://automation.workflow.flow.versioning.and.git.integration.for.workflow.management","name":"flow versioning and git integration for workflow management","description":"Enables version control of workflows through Git integration, allowing workflows to be stored in Git repositories and synced with Kestra. Each workflow version is tracked with commit history, enabling rollback to previous versions. The system supports multiple deployment strategies (manual sync, automatic CI/CD, polling). Workflows can be deployed from Git branches, enabling environment-specific configurations (dev, staging, prod) without duplicating workflow definitions.","intents":["Version control workflows in Git with full commit history","Implement GitOps practices for workflow deployment","Rollback to previous workflow versions if issues arise","Deploy different workflow versions to different environments from Git branches"],"best_for":["Teams adopting GitOps practices for infrastructure and workflow management","Organizations requiring audit trails and change tracking for workflows","Multi-environment deployments with environment-specific workflow versions"],"limitations":["Git integration requires manual setup; no automatic Git repository creation","Conflict resolution for concurrent workflow edits is manual; no merge conflict handling","Polling-based sync adds latency; webhook-based sync requires Git platform integration","No built-in workflow diff visualization; requires external Git tools"],"requires":["Git repository with workflow files","Git credentials (SSH key or token) for authentication","Webhook or polling configuration for sync"],"input_types":["Git repository URL","Branch name","Workflow file path"],"output_types":["Workflow synced from Git","Version history with commit metadata","Deployment status (success, conflict, error)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kestra__cap_2","uri":"capability://automation.workflow.distributed.execution.with.controller.worker.architecture","name":"distributed execution with controller-worker architecture","description":"Kestra implements a distributed execution model with a Controller component that manages workflow scheduling and state, and Worker components that execute individual tasks in isolation. The architecture uses a message queue (Kafka or in-memory) for task distribution and state synchronization across workers. Workers pull tasks from the queue, execute them in containerized environments (Docker or native), and report results back to the Controller, enabling horizontal scaling and fault isolation without requiring shared state between workers.","intents":["Scale workflow execution across multiple machines to handle high task throughput","Isolate task failures so that one worker crash doesn't affect other running workflows","Distribute long-running tasks across a cluster to reduce individual machine load","Enable blue-green deployments and rolling updates without stopping active workflows"],"best_for":["Organizations running high-volume workflow orchestration requiring horizontal scaling","Teams needing fault isolation and resilience across distributed infrastructure","Enterprises with strict resource isolation requirements between workflow executions"],"limitations":["Distributed architecture adds operational complexity; requires message queue infrastructure (Kafka) for production","Network latency between Controller and Workers introduces overhead (~50-200ms per task dispatch)","Debugging distributed execution is harder than single-machine deployments; requires centralized logging","State consistency requires careful handling; eventual consistency model may not suit all use cases"],"requires":["Kubernetes or Docker for containerized task execution","Message queue (Kafka recommended for production; in-memory for development)","Persistent storage for workflow state and execution logs","Network connectivity between Controller and Worker nodes"],"input_types":["Task definitions from Controller","Execution context and variables","Input files and data from previous tasks"],"output_types":["Task execution results","Execution logs and metrics","State updates sent back to Controller"],"categories":["automation-workflow","distributed-systems"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kestra__cap_3","uri":"capability://automation.workflow.real.time.event.driven.workflow.triggering.with.webhook.support","name":"real-time event-driven workflow triggering with webhook support","description":"Kestra implements a trigger system that enables workflows to be initiated by external events through HTTP webhooks, message queue events (Kafka, RabbitMQ), or scheduled cron expressions. The Execution API exposes webhook endpoints that accept event payloads, validate them against trigger schemas, and immediately queue workflow executions with the event data as input. The trigger system integrates with the Scheduler component to handle both time-based and event-based activation patterns, supporting complex trigger conditions and event filtering without requiring polling.","intents":["Trigger data pipelines immediately when events occur in external systems (file uploads, API calls, database changes)","React to real-time events from message queues without polling or continuous monitoring","Build event-driven architectures where workflows respond to application events in near real-time","Integrate Kestra workflows into existing event-driven systems and microservice architectures"],"best_for":["Real-time data processing teams building event-driven pipelines","Organizations integrating Kestra into event-driven microservice architectures","Teams needing to react to external system events without polling overhead"],"limitations":["Webhook endpoints must be publicly accessible or require VPN/firewall configuration for external triggers","Event payload size is limited by HTTP request limits (typically 10-100MB depending on configuration)","No built-in event deduplication; duplicate events may trigger multiple workflow executions","Event ordering is not guaranteed across distributed workers; events may be processed out of sequence"],"requires":["Network access to Kestra webhook endpoints from event sources","Event source configuration to send webhooks to Kestra (requires URL and authentication)","Optional: Kafka/RabbitMQ for message queue-based triggers"],"input_types":["HTTP POST payloads (JSON, form data)","Message queue events","Cron schedule expressions"],"output_types":["Workflow execution with event data as inputs","Execution ID and status","Trigger validation results"],"categories":["automation-workflow","event-driven-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kestra__cap_4","uri":"capability://automation.workflow.input.validation.and.dynamic.form.generation.from.workflow.schemas","name":"input validation and dynamic form generation from workflow schemas","description":"Kestra automatically generates input forms and validation rules from workflow YAML schemas using a JSON Schema-based approach. The Input Resolution system validates user-provided inputs against defined input types (string, integer, select, file, etc.) before workflow execution, with support for conditional inputs, default values, and dynamic field visibility. The frontend (ui/src/components/InputForms) renders these schemas as interactive forms, enabling non-technical users to provide workflow inputs through a UI rather than manually constructing YAML or API calls.","intents":["Generate user-friendly input forms automatically from workflow definitions without manual UI development","Validate user inputs before execution to catch configuration errors early","Enable non-technical users to trigger workflows by filling out forms rather than writing YAML","Support conditional inputs where field visibility depends on other field values"],"best_for":["Organizations building self-service workflow platforms for non-technical users","Teams wanting to reduce user errors through input validation before execution","Enterprises needing to expose workflows through user-friendly interfaces"],"limitations":["Complex input validation logic is limited to JSON Schema constraints; custom validation requires task-level checks","Form generation is automatic but styling customization is limited without modifying frontend code","No support for multi-step forms or wizard-style input flows","File inputs are limited by browser upload constraints (typically 2GB max)"],"requires":["Workflow inputs defined in YAML with type specifications","Frontend access to Kestra UI for form rendering","JSON Schema knowledge for advanced input validation"],"input_types":["Workflow input schema definitions","User form submissions","JSON Schema validation rules"],"output_types":["Validated input objects","Rendered HTML forms","Input validation error messages"],"categories":["automation-workflow","user-interface"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kestra__cap_5","uri":"capability://automation.workflow.execution.context.and.variable.interpolation.with.pebble.expressions","name":"execution context and variable interpolation with pebble expressions","description":"Kestra's RunContext provides a unified execution context that tracks workflow state, task outputs, and variables throughout execution. The PebbleExpressionService evaluates Pebble template expressions within this context, enabling dynamic variable interpolation, conditional logic, and access to execution metadata (task outputs, execution ID, timestamps). Variables are resolved at task execution time, allowing downstream tasks to reference upstream task outputs using expressions like `{{ outputs.taskName.result }}`, with support for complex object navigation and filtering.","intents":["Reference outputs from previous tasks in downstream task configurations without manual data passing","Use execution metadata (timestamps, execution ID, trigger data) in task parameters dynamically","Implement conditional task execution based on upstream task results or input values","Transform and filter data between tasks using Pebble expressions without intermediate transformation tasks"],"best_for":["Data pipeline developers building multi-step workflows with data dependencies","Teams needing dynamic workflow behavior based on runtime conditions","Organizations implementing complex data transformations across multiple tasks"],"limitations":["Pebble expression evaluation adds latency (~10-50ms per expression depending on complexity)","Complex expressions become difficult to debug; error messages don't always pinpoint the exact issue","No support for custom Pebble filters without modifying core code","Expression evaluation happens at task execution time; syntax errors are caught late in the workflow"],"requires":["Pebble template syntax knowledge","Understanding of RunContext structure and available variables","Access to upstream task outputs (requires tasks to complete successfully)"],"input_types":["Pebble template expressions","RunContext variables and task outputs","Execution metadata"],"output_types":["Interpolated string values","Resolved task parameters","Conditional execution decisions"],"categories":["automation-workflow","templating"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kestra__cap_6","uri":"capability://data.processing.analysis.persistent.execution.history.and.audit.logging.with.queryable.storage","name":"persistent execution history and audit logging with queryable storage","description":"Kestra maintains a complete execution history in a persistent data layer (supporting PostgreSQL, MySQL, H2) that stores execution records, task results, logs, and audit trails. The Data Persistence Layer uses JDBC drivers (jdbc-postgres, jdbc-mysql, jdbc-h2) to abstract database interactions, enabling queries across execution history for debugging, compliance, and analytics. Execution logs are stored alongside execution metadata, allowing developers to retrieve full execution traces including task outputs, error messages, and timing information for any past execution.","intents":["Debug failed workflows by reviewing complete execution logs and task outputs from past runs","Audit workflow executions for compliance and security purposes with immutable execution records","Analyze workflow performance and identify bottlenecks by querying execution history and timing data","Implement retry logic and error recovery by examining previous execution failures"],"best_for":["Organizations with compliance requirements needing immutable audit trails","Data teams debugging complex multi-step pipelines with historical context","Enterprises analyzing workflow performance and optimizing execution patterns"],"limitations":["Execution history grows unbounded; requires periodic cleanup/archival to manage storage costs","Querying large execution histories (millions of records) can be slow without proper indexing","Log storage is not optimized for high-volume streaming logs; large task outputs can impact database performance","No built-in log retention policies; manual cleanup required or external archival needed"],"requires":["Persistent database (PostgreSQL recommended for production; H2 for development)","Sufficient storage capacity for execution logs and task outputs","Database credentials and network access to database server"],"input_types":["Execution records","Task outputs and logs","Audit events"],"output_types":["Execution history queries","Audit logs and compliance reports","Performance analytics and metrics"],"categories":["data-processing-analysis","monitoring-observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kestra__cap_7","uri":"capability://automation.workflow.scheduler.with.cron.based.and.interval.based.workflow.triggers","name":"scheduler with cron-based and interval-based workflow triggers","description":"Kestra's Scheduler component manages time-based workflow triggers using cron expressions and interval specifications. The Scheduler runs as a separate service (SchedulerCommand) that evaluates trigger conditions at regular intervals and queues workflow executions when conditions are met. It integrates with the distributed execution system to ensure that scheduled workflows are executed reliably even in clustered deployments, with built-in deduplication to prevent duplicate executions if multiple scheduler instances are running.","intents":["Schedule workflows to run at specific times or intervals (daily, hourly, every 5 minutes, etc.)","Implement recurring data pipeline executions without external cron jobs or schedulers","Combine scheduled triggers with event-based triggers for hybrid execution patterns","Manage workflow schedules through the UI without modifying system cron configuration"],"best_for":["Data teams running regular ETL pipelines on fixed schedules","Organizations consolidating workflow scheduling into a single platform","Teams needing to manage schedules through a UI rather than system cron"],"limitations":["Cron expression evaluation is limited to standard cron syntax; complex scheduling patterns require multiple workflows","Scheduler latency can cause execution delays if the scheduler is overloaded (typically <1 minute)","No built-in timezone support; all schedules are evaluated in server timezone","Backfill of missed executions is not automatic; requires manual intervention or custom logic"],"requires":["Scheduler service running (separate from Worker/Controller)","Cron expression syntax knowledge","Workflow definitions with trigger specifications"],"input_types":["Cron expressions","Interval specifications","Workflow trigger definitions"],"output_types":["Scheduled workflow executions","Execution queue entries","Trigger evaluation logs"],"categories":["automation-workflow","scheduling"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kestra__cap_8","uri":"capability://automation.workflow.real.time.execution.monitoring.and.status.tracking.via.websocket","name":"real-time execution monitoring and status tracking via websocket","description":"Kestra provides real-time execution monitoring through WebSocket connections that stream execution status updates, task progress, and log output to connected clients. The frontend (ui/src/components/ExecutionMonitoring) subscribes to execution updates and displays live progress, task status changes, and log streaming without requiring polling. The backend maintains execution state in memory and broadcasts updates to all connected clients, enabling multiple users to monitor the same execution simultaneously with sub-second latency.","intents":["Monitor workflow execution progress in real-time without refreshing the page","Stream task logs to the UI as they are generated, enabling live debugging","Track execution status changes (queued → running → completed) with immediate UI updates","Enable multiple users to monitor the same execution simultaneously"],"best_for":["Teams needing real-time visibility into running workflows","DevOps engineers debugging live executions and monitoring task progress","Organizations building dashboards that display live workflow status"],"limitations":["WebSocket connections require persistent network connectivity; disconnections lose real-time updates","High-volume log streaming can saturate WebSocket connections; large task outputs may cause UI lag","Memory usage increases with number of connected clients; scaling to thousands of concurrent monitors requires optimization","No built-in log buffering; if client disconnects, missed log lines are not automatically replayed"],"requires":["WebSocket support in browser and network infrastructure","Kestra server with WebSocket endpoints enabled","Network connectivity between client and Kestra server"],"input_types":["Execution status updates","Task progress events","Log output streams"],"output_types":["Real-time UI updates","Live log streams","Execution status changes"],"categories":["automation-workflow","monitoring-observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kestra__cap_9","uri":"capability://automation.workflow.namespace.based.multi.tenancy.and.resource.isolation","name":"namespace-based multi-tenancy and resource isolation","description":"Kestra implements namespace-based isolation where workflows, executions, and secrets are scoped to namespaces, enabling multi-tenant deployments where different teams or customers have isolated workflow spaces. Namespaces are enforced at the API level and in the data persistence layer, ensuring that users can only access workflows and executions within their assigned namespaces. The Storage and KV Store systems also respect namespace boundaries, enabling per-namespace configuration and secrets management without cross-tenant data leakage.","intents":["Deploy Kestra as a multi-tenant platform where different teams have isolated workflow spaces","Implement role-based access control where users can only access workflows in their namespace","Manage secrets and configuration per namespace without exposing them to other teams","Build SaaS platforms on top of Kestra where each customer has an isolated namespace"],"best_for":["SaaS platforms building workflow orchestration as a service","Large enterprises with multiple teams needing isolated workflow spaces","Organizations implementing strict data isolation and compliance requirements"],"limitations":["Namespace isolation is logical, not physical; all namespaces share the same database and compute resources","No built-in resource quotas per namespace; one namespace can consume all available resources","Cross-namespace workflows are not supported; data sharing between namespaces requires external mechanisms","Namespace management requires API calls; no built-in UI for namespace administration"],"requires":["Namespace assignment for users (via authentication/authorization system)","API authentication and authorization configured","Persistent storage supporting namespace-scoped queries"],"input_types":["Namespace identifiers","User authentication tokens","Workflow and execution requests"],"output_types":["Namespace-scoped workflow lists","Isolated execution records","Namespace-specific secrets and configuration"],"categories":["automation-workflow","multi-tenancy"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kestra__headline","uri":"capability://automation.workflow.unified.orchestration.platform.for.workflows","name":"unified orchestration platform for workflows","description":"Kestra is a unified orchestration platform designed for managing both scheduled and event-driven workflows, featuring a declarative YAML interface and over 500 plugins for seamless integration.","intents":["best workflow orchestration platform","orchestration tool for data pipelines","event-driven workflow management solution","YAML-based workflow automation tool","best tools for managing workflows"],"best_for":["data engineers","DevOps teams"],"limitations":[],"requires":[],"input_types":["YAML configurations"],"output_types":["automated workflows"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["YAML syntax knowledge","Understanding of Pebble template syntax for variable interpolation","Kestra server running (local or cloud-hosted)","Java 11+ for plugin development","Gradle build system knowledge for custom plugins","Credentials/API keys for external systems being integrated","Docker or container runtime for script execution","Language runtime installed in container image (Python 3.x, Node.js, Bash, etc.)","Script code provided inline or from external files","LLM provider API key (OpenAI, Anthropic, etc.)"],"failure_modes":["Complex conditional logic becomes verbose in YAML; deeply nested conditionals reduce readability","Pebble templating has limited expression complexity compared to full programming languages","No built-in IDE schema validation without VS Code extension or external tooling","Plugin quality and maintenance varies; community-contributed plugins may lack production-grade error handling","Adding new plugins requires Java/Gradle knowledge and rebuilding the application","Plugin dependencies can create version conflicts if not carefully managed in the build system","No hot-reload for plugins; changes require application restart","Script execution is slower than native plugins due to container startup overhead (~1-5 seconds per script)","Dependency management is manual; scripts must install dependencies at runtime or use pre-built images","Debugging script execution is harder than native code; requires examining logs and stdout","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":"2026-06-17T09:51:04.692Z","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=kestra","compare_url":"https://unfragile.ai/compare?artifact=kestra"}},"signature":"LVvYdWdAGd22Wx4jqclUCCn/vNoxkoeVjb/OYSRofkPO4TzALqOGzjDK+ZsoxUGdowzSuBQ3qRl5y7i/3ut5Ag==","signedAt":"2026-06-22T09:20:45.297Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/kestra","artifact":"https://unfragile.ai/kestra","verify":"https://unfragile.ai/api/v1/verify?slug=kestra","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"}}