> ## Documentation Index > Fetch the complete documentation index at: https://docs.flows.super.ai/llms.txt > Use this file to discover all available pages before exploring further. # Analyze flow definition and extract schemas > Analyze a flow definition to extract task output schemas, dynamic data, and validation errors. **Overview** This endpoint validates a flow definition and returns metadata needed for flow building, execution planning, and UI generation. It processes all tasks in the flow definition to extract their output schemas (for type validation), dynamic configuration data (for UI dropdowns and conditional fields), and any structural or semantic validation errors. **Use Cases** - **Flow Builder UI**: Get task output schemas for downstream task input validation - **Dynamic Configuration**: Retrieve runtime-dependent options (e.g., available spreadsheets, databases) - **Flow Validation**: Check for errors before saving or executing a flow - **Schema Discovery**: Understand what data each task will produce - **Custom Integrations**: Programmatically validate and analyze flow definitions **How It Works** 1. Parses and validates the flow definition structure (basic validation) 2. For each task in the flow: - Loads the task executor implementation - Calls the executor's `get_output_model()` to get the output schema - Calls the executor's dynamic data methods (if available) to get runtime options - Collects any errors encountered during schema/data extraction 3. If `full_validation=true`, performs additional semantic validation (checks task parameter values, validates connections between tasks, etc.) 4. Returns all schemas, dynamic data, and validation errors in a single response **Dynamic Data Explained** Dynamic data represents configuration options that depend on external state or user credentials. For example: - Google Sheets task: List of spreadsheets accessible with user's credentials - Database task: List of available tables/columns - API task: Available endpoints or data models from connected service **Validation Levels** - **Basic (default)**: Structure validation only (required fields, data types) - **Full (`full_validation=true`)**: Includes semantic validation (valid references, parameter constraints, external resource availability) **Performance Notes** - May make external API calls to fetch dynamic data (e.g., Google Sheets API) - Response time varies based on number of tasks and external service latency - Typical response time: 100ms-2s depending on task executors used **Related Endpoints** - `POST /flows` - Create flow (uses this endpoint's validation internally) - `PUT /flows/{flow_id}` - Update flow (uses this endpoint's validation internally) - `GET /task-executors` - List available task executors ## OpenAPI ````yaml /openapi/superai-flows-openapi.json post /api/task-data openapi: 3.1.0 info: description: >- SuperAI Flows is a workflow orchestration platform that enables you to design, deploy, and monitor automated workflows at scale. Build complex workflows using our declarative YAML DSL, execute them reliably, and integrate seamlessly with AI models, cloud storage, and enterprise systems. **Key Capabilities:** - **Workflow Management**: Define workflows as code with version control and automated execution - **Task Orchestration**: Chain together API calls, data processing, and AI operations - **Real-time Monitoring**: Track execution progress with WebSocket notifications and comprehensive logging - **Multi-tenant Architecture**: Organization-scoped resources with role-based access control **API Versioning and Breaking Changes:** We follow a strict compatibility policy to ensure your integrations remain stable: - **Non-breaking changes** (safe, no action required): - Adding new API endpoints - Adding new optional query parameters to existing endpoints - Adding new fields to API responses - Adding new values to existing enums - **Breaking changes** (requires client updates): - Removing or renaming API endpoints - Removing query parameters or request fields - Removing response fields - Changing field types or validation rules - Removing values from existing enums **Client Implementation Requirements:** Your API clients MUST be designed to gracefully handle additional fields in responses. We may add new fields to any response object without considering this a breaking change. Ensure your JSON parsers ignore unknown fields rather than raising errors. **Backward Compatibility Guarantee:** We commit to maintaining backward compatibility for all non-breaking changes. Breaking changes will be: - Announced at least 15 days in advance - Documented in our changelog with migration guide For the latest API updates and migration guides, see our changelog. title: SuperAI Flow Platform version: 0.1.0 servers: - description: Production server url: https://flows.super.ai security: [] tags: - description: >- Flow management operations for defining and organizing workflows. Flows represent workflow definitions with tasks, dependencies, and execution logic. They serve as reusable templates that can be executed multiple times with different inputs. **What is a flow?** A flow consists of: - **Tasks**: Individual units of work that perform specific actions - **Dependencies**: Relationships between tasks that define execution order - **Configuration**: Settings and parameters that control flow behavior **Use these endpoints to:** - Create new flow definitions from scratch or import from YAML - List and search existing flows in your organization - Retrieve detailed flow information including task definitions - Update flow configurations and task parameters - Delete flows that are no longer needed Flows are the foundation of your workflow automation—once defined, they can be executed via the flow-executions endpoints. name: flows x-displayName: Flows - description: >- Flow execution operations for running and monitoring workflows. Flow executions represent runtime instances of flows. When you execute a flow, a new flow execution is created with its own unique ID, status, and execution context. Each execution is independent and maintains its own state, input data, and results. **Key concepts:** - **Execution Status**: Tracks the lifecycle (running, completed, failed, cancelled) - **Run Number**: Sequential identifier for executions of the same flow - **Execution Context**: Contains input parameters, user information, and metadata - **Task Results**: Stores outputs from each task within the execution **Use these endpoints to:** - Start new flow executions with custom input parameters - Monitor execution progress and status in real-time - Retrieve execution history and detailed results - Filter and search executions by status, date, or flow - Cancel running executions when needed - Access task-level execution details and outputs Flow executions transform flow definitions into actual work and deliverable results. name: flow-executions x-displayName: Flow Executions - description: >- Task execution operations for tracking individual task runs within flow executions. Task executions represent individual task runs within a flow execution. Each task in a flow execution has its own task execution record that tracks its lifecycle, inputs, outputs, and status. **What is tracked:** - **Status**: Current state (pending, running, completed, failed, skipped) - **Input/Output**: Data passed to and produced by the task - **Timing**: Start time, end time, and duration - **Errors**: Failure details and error messages - **Tags**: Metadata and categorization **Use these endpoints to:** - Query task execution status for debugging - Retrieve task outputs for analysis - Debug task failures with detailed error information - Track task performance and timing - Monitor task execution progress within flows Task executions provide granular visibility into workflow execution, essential for debugging and optimization. name: task-executions x-displayName: Task Executions - description: >- Task executor operations for discovering available task types and their capabilities. Task executors define the available task types that can be used in flow definitions. Each executor specifies its input schema, execution behavior, and capabilities. This endpoint enables programmatic discovery of task types for building flows dynamically. **What task executors provide:** - **Input Schema**: JSON Schema defining required and optional parameters - **Output Schema**: Expected output structure and data types - **Description**: Human-readable explanation of functionality - **Agent Prompts**: Instructions for AI-powered flow generation **Use these endpoints to:** - Discover available task types for flow building - View task input/output schemas for validation - Understand task capabilities and requirements - Build flow builder UIs with dynamic task lists - Enable AI agents to select appropriate tasks Task executors enable dynamic, flexible workflow creation by providing a registry of available operations. name: task-executors x-displayName: Task Executors - description: >- Task output operations for storing and retrieving task execution results. Task outputs store the results produced by task executions in a structured, queryable format. While task executions track the full lifecycle (input, output, error, status), task outputs focus specifically on the final output data formatted for display, analysis, and downstream consumption. **Key characteristics:** - **Summary Field**: Structured output data ready for consumption - **Historical Record**: Permanent storage of task results - **Queryable**: Filter and search outputs by task, execution, or flow - **Linked**: Connected to parent flow execution and task execution **Use these endpoints to:** - Store task results after successful execution - Retrieve outputs for specific tasks or entire executions - Query historical outputs for analysis and reporting - Access formatted results for display in dashboards - Feed outputs to downstream tasks or external systems Task outputs provide the data foundation for workflow analytics, debugging, and result visualization. name: task-outputs x-displayName: Task Outputs - description: >- Task data operations for flow validation, schema discovery, and dynamic configuration. The task-data endpoint analyzes flow definitions to extract task output schemas, dynamic configuration options, and validation errors. This is essential for building flow configuration UIs, validating flows before execution, and understanding task data structures. **Primary Use Cases:** - **Flow Builder UI**: Get task output schemas for validating downstream task inputs - **Schema Discovery**: Understand the structure of data each task produces - **Dynamic Configuration**: Retrieve runtime-dependent options (e.g., available spreadsheets, database tables) - **Flow Validation**: Validate flow definitions programmatically before saving or executing - **Custom Integrations**: Build tools that work with SuperAI Flows programmatically **What You Get:** - **Output Schemas**: JSON Schema definitions for each task's output structure - **Dynamic Data**: Runtime-dependent configuration options (dropdown choices, conditional fields) - **Validation Errors**: Detailed error messages for invalid flow definitions **How It Works:** 1. Submit a flow definition in SuperAI Flows DSL format 2. The endpoint validates the structure and loads each task executor 3. For each task, it extracts the output schema and any dynamic configuration data 4. Returns all schemas, dynamic data, and any validation errors encountered **Key Concepts:** - **Output Schema**: JSON Schema describing the structure of data a task produces - **Dynamic Data**: Configuration options that depend on user credentials or external state (e.g., list of Google Sheets accessible with your credentials) - **Validation Levels**: Basic (structure only) vs Full (includes semantic validation) **Integration Notes:** - The flow builder frontend uses this endpoint to power the task configuration UI - Flows API uses this validation internally when creating/updating flows - Response includes validation errors even on HTTP 200 - always check `validation_errors` field - May make external API calls to fetch dynamic data, so response time varies (100ms-2s typical) **Related Endpoints:** - `GET /task-executors`: List available task executor types and their schemas - `POST /flows`: Create flow (uses task-data validation internally) - `PUT /flows/{flow_id}`: Update flow (uses task-data validation internally) name: task-data x-displayName: Task Data - description: >- Task tag operations for categorizing and organizing tasks with metadata. Task tags provide metadata and categorization for tasks within flow executions. Tags enable flexible organization, filtering, and grouping of tasks beyond their structural relationships. **What tags enable:** - **Categorization**: Group related tasks across different flows - **Filtering**: Query executions by tag criteria - **Metadata**: Attach custom key-value pairs to tasks - **Organization**: Create logical groupings for reporting **Use these endpoints to:** - Manage task labels and categories - Add organizational metadata to tasks - Query tasks by tag criteria - Build custom views and dashboards Task tags provide flexible organization capabilities that enhance workflow management and analysis. name: task-tags x-displayName: Task Tags - description: >- Integration operations for connecting flows to external services and platforms. Integrations enable your workflows to interact with third-party services, databases, storage systems, and communication platforms. They provide a unified interface for managing credentials, testing connections, and configuring service-specific features. **Supported integration types:** - **Databases**: PostgreSQL, MySQL, and other SQL databases - **Storage**: SFTP, cloud storage, and file systems - **Communication**: Email (SMTP, SES), webhooks, notifications - **Collaboration**: SharePoint, Google Drive, document management - **Custom**: Plugin architecture for extending functionality **Key concepts:** - **Credentials**: Securely stored authentication information per integration - **Plugins**: Modular integration implementations with discovery and registration - **OAuth Flows**: Automated authorization for third-party services - **Webhooks**: Inbound event handling from external platforms - **Connection Testing**: Validate credentials and connectivity before use **Use these endpoints to:** - Configure and manage integration credentials - Test connections to external services - Handle OAuth authorization flows for cloud services - Set up and manage webhooks for inbound events - Discover available integration capabilities - Access integration-specific features (folders, schemas, etc.) Integrations bridge your workflows with the external world, enabling powerful automation across your entire technology stack. name: integrations x-displayName: Integrations - description: >- Authentication operations for user authentication, authorization, and session management. Authentication endpoints handle user identity verification, token generation, and access control throughout the platform. The system uses JWT (JSON Web Token) based authentication managed through core.flows.super.ai. **Authentication flow:** 1. Obtain credentials from your account at core.flows.super.ai 2. Authenticate to receive JWT access token and refresh token 3. Include access token in API requests via Authorization header 4. API validates tokens and extracts user identity and organization context 5. Tokens expire after 1 hour and must be refreshed **Key concepts:** - **Bearer Tokens**: Short-lived JWT tokens (1 hour) used for API requests - **Anon-Key**: Long-lived API key used to authenticate to the authentication API. - **Refresh Tokens**: Long-lived tokens used to obtain new access tokens - **Protected Routes**: Most endpoints require valid authentication - **Public Endpoints**: Limited whitelisted paths accessible without auth **Getting started with authentication:** 1. **Obtain your Anonymous Key** (public endpoint - no authentication required): ```bash curl https://flows.super.ai/api/auth/anon-key ``` 2. **Authenticate** to receive your tokens: ```bash curl -X POST 'https://core.flows.super.ai/auth/v1/token?grant_type=password' \ -H 'Content-Type: application/json' \ -H 'apikey: ANON_KEY' \ -d '{"email": "you@example.com", "password": "your-password"}' ``` This will return a JWT access token and refresh token. 3. **Use the access token** in your requests: ```bash curl -H "Authorization: Bearer YOUR_ACCESS_TOKEN" \ https://flows.super.ai/api/flows ``` **Token management:** - Access tokens expire after 1 hour - Use refresh tokens to obtain new access tokens without re-authenticating - Store tokens securely and never commit them to version control All API endpoints (except whitelisted public paths) require authentication. Include your bearer token in the Authorization header: `Bearer ` name: auth x-displayName: Authentication - description: >- User profile operations for viewing and updating account information. Profile endpoints allow authenticated users to view and update their own account information and preferences. These endpoints operate on the currently authenticated user (determined from the JWT token) and do not require specifying a user ID. **Account information includes:** - **Personal Details**: First name, last name, email - **Organization**: Associated organization and role - **Status**: Account status and permissions - **Metadata**: Created/updated timestamps **Use these endpoints to:** - Retrieve current user information for display - Update personal details (name, preferences) - View organization membership and role - Access account metadata Profile endpoints provide self-service account management capabilities, reducing the need for administrative intervention for routine updates. **Note:** All profile endpoints are protected and automatically scope to the authenticated user. Email and organization changes require admin privileges. name: profile x-displayName: Profile - description: >- Model operations for querying available AI models and their configurations. Model endpoints provide information about the AI models available through the platform. These endpoints enable discovery of models, their capabilities, and regional availability. **Key features:** - **Model Discovery**: List all available AI models - **Regional Filtering**: Query models by geographic region (EU, Global) - **Model Metadata**: Access model information including provider, location, and capabilities **Use these endpoints to:** - Discover available AI models for your workflows - Filter models by region for compliance or latency requirements - Retrieve model metadata for configuration and selection - Build dynamic model selection UIs **Regional filtering:** - `eu`: Models deployed in European regions (West Europe, France Central, Germany, Sweden) - `global`: Models deployed in US and other global regions Model endpoints enable intelligent model selection and routing based on your requirements for compliance, performance, and cost. name: models x-displayName: Models - description: >- File download operations for retrieving files from gs:// storage URIs. When Super.AI Flows processes documents, task outputs often include file references as `gs://` URIs pointing to Google Cloud Storage. These endpoints let you download those files without needing to understand GCS internals or parse storage URLs. **The problem these endpoints solve:** Previously, downloading a file required: 1. Recognizing the URL as a GCS reference 2. Parsing out the file key from the URI 3. Calling a separate endpoint to get a signed URL 4. Downloading using that signed URL **Now it's simple:** Pass the `gs://` URI exactly as received and get your file. **Two ways to download:** 1. **Direct download** (`GET /files/download`): Returns a redirect to the file. Perfect for curl with `-L` flag. ``` curl -L -H "X-API-Key: saf_xxx" "https://flows.super.ai/api/files/download?uri=gs://..." -o file.pdf ``` 2. **Resolve first** (`POST /files/resolve`): Returns a JSON response with the download URL. Better for programmatic use and sensitive files (keeps URI out of logs). ``` curl -X POST "https://flows.super.ai/api/files/resolve" \ -H "X-API-Key: saf_xxx" \ -H "Content-Type: application/json" \ -d '{"uri": "gs://..."}' ``` **Security:** - Files are organization-scoped: you can only download files from flows your organization owns - URIs are validated to prevent path traversal attacks - Download URLs expire after 1 hour **Common use cases:** - Download processed documents from completed flow executions - Retrieve extracted data files from task outputs - Integrate file downloads into automated pipelines name: files x-displayName: Files - description: >- Human review task operations for creating and managing human-in-the-loop review workflows. name: human-review-tasks x-displayName: Human Review Tasks - description: >- Service account operations for managing programmatic API access credentials. name: service-accounts x-displayName: Service Accounts - description: >- Organization operations for viewing credit balance and organization-scoped information. name: organizations x-displayName: Organizations - description: Single Sign-On operations for configuring SAML-based SSO authentication. name: sso x-displayName: SSO - description: >- Plugin operations for managing integration plugin instances, OAuth flows, and webhooks. name: plugins x-displayName: Plugins paths: /api/task-data: post: tags: - task-data summary: Analyze flow definition and extract schemas description: >- Analyze a flow definition to extract task output schemas, dynamic data, and validation errors. **Overview** This endpoint validates a flow definition and returns metadata needed for flow building, execution planning, and UI generation. It processes all tasks in the flow definition to extract their output schemas (for type validation), dynamic configuration data (for UI dropdowns and conditional fields), and any structural or semantic validation errors. **Use Cases** - **Flow Builder UI**: Get task output schemas for downstream task input validation - **Dynamic Configuration**: Retrieve runtime-dependent options (e.g., available spreadsheets, databases) - **Flow Validation**: Check for errors before saving or executing a flow - **Schema Discovery**: Understand what data each task will produce - **Custom Integrations**: Programmatically validate and analyze flow definitions **How It Works** 1. Parses and validates the flow definition structure (basic validation) 2. For each task in the flow: - Loads the task executor implementation - Calls the executor's `get_output_model()` to get the output schema - Calls the executor's dynamic data methods (if available) to get runtime options - Collects any errors encountered during schema/data extraction 3. If `full_validation=true`, performs additional semantic validation (checks task parameter values, validates connections between tasks, etc.) 4. Returns all schemas, dynamic data, and validation errors in a single response **Dynamic Data Explained** Dynamic data represents configuration options that depend on external state or user credentials. For example: - Google Sheets task: List of spreadsheets accessible with user's credentials - Database task: List of available tables/columns - API task: Available endpoints or data models from connected service **Validation Levels** - **Basic (default)**: Structure validation only (required fields, data types) - **Full (`full_validation=true`)**: Includes semantic validation (valid references, parameter constraints, external resource availability) **Performance Notes** - May make external API calls to fetch dynamic data (e.g., Google Sheets API) - Response time varies based on number of tasks and external service latency - Typical response time: 100ms-2s depending on task executors used **Related Endpoints** - `POST /flows` - Create flow (uses this endpoint's validation internally) - `PUT /flows/{flow_id}` - Update flow (uses this endpoint's validation internally) - `GET /task-executors` - List available task executors operationId: get_task_dynamic_data_api_task_data_post parameters: - description: >- Enable comprehensive semantic validation in addition to basic structural validation. When false (default): Only validates flow structure (required fields, data types, task executor existence). When true: Performs additional validation including: - Task parameter value validation (correct types, valid enums, etc.). - Task connection validation (input/output type compatibility). - Circular dependency detection. - External resource validation (e.g., checking if referenced files exist). Full validation may take longer and make external API calls. Use during flow save/publish operations. Use false (default) for faster validation during editing. in: query name: full_validation required: false schema: default: false description: >- Enable comprehensive semantic validation in addition to basic structural validation. When false (default): Only validates flow structure (required fields, data types, task executor existence). When true: Performs additional validation including: - Task parameter value validation (correct types, valid enums, etc.). - Task connection validation (input/output type compatibility). - Circular dependency detection. - External resource validation (e.g., checking if referenced files exist). Full validation may take longer and make external API calls. Use during flow save/publish operations. Use false (default) for faster validation during editing. title: Full Validation type: boolean - description: >- Include JSON Schema definitions for input task executors' input structure. Calls get_input_model() to generate dynamic input schemas for form building. Adds ~10-50ms latency for schema generation. in: query name: include_input_schema required: false schema: default: false description: >- Include JSON Schema definitions for input task executors' input structure. Calls get_input_model() to generate dynamic input schemas for form building. Adds ~10-50ms latency for schema generation. title: Include Input Schema type: boolean requestBody: content: application/json: schema: $ref: '#/components/schemas/TaskDynamicDataRequest' required: true responses: '200': content: application/json: schema: $ref: '#/components/schemas/TaskDynamicDataResponse' description: Flow definition successfully analyzed '400': content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' description: Bad Request - Invalid flow definition structure '422': content: application/json: schema: $ref: '#/components/schemas/HTTPValidationError' description: Validation Error '500': content: application/json: examples: internal_error: summary: Internal server error value: error: code: internal_error message: Internal server error request_id: 01K8KABR6S16YETA2SZPVBS9SP repository_error: summary: Database error value: error: code: repository_error message: Database operation failed request_id: 01K8KACP7D2XFGHJ9KLM4NPQR8 schema: $ref: '#/components/schemas/ErrorResponse' description: Internal Server Error - An unexpected error occurred security: - APIKeyAuth: [] BearerAuth: [] components: schemas: TaskDynamicDataRequest: description: |- Request model for task data analysis endpoint. Accepts a flow definition in the SuperAI Flows DSL format for validation and schema extraction. The flow definition should include all tasks, connections, and configurations needed to execute the flow. properties: flow_definition: additionalProperties: true description: >- Complete flow definition in SuperAI Flows DSL format. Must include: - 'name': Flow name (string, required). - 'tasks': List of task definitions with task_executor_name, parameters, etc. - 'connections': Optional list of task dependencies and data flows. - 'config': Optional flow-level configuration. The definition will be validated and analyzed to extract task output schemas and dynamic configuration data. Validation errors will be returned in the response if the definition is invalid. examples: - name: my-workflow tasks: - name: fetch_data parameters: method: GET url: https://api.example.com/data task_executor_name: http_request - name: process_data parameters: code: result = input_data['value'] * 2 task_executor_name: python_code title: Flow Definition type: object required: - flow_definition title: TaskDynamicDataRequest type: object TaskDynamicDataResponse: description: >- Response model containing task output schemas, dynamic data, and validation results. This response provides all metadata needed to build UIs for flow configuration, validate task connections, and understand what data each task will produce. Each field serves a specific purpose in the flow building and validation process. properties: dynamic_data: additionalProperties: true description: >- Runtime-dependent configuration options for tasks, keyed by task name. Contains dynamic values that depend on user credentials, external services, or other runtime state. Format: {task_name: {field_name: options}}. Common use cases: - Dropdown options (e.g., available Google Sheets, database tables). - Conditional field visibility based on other parameters. - Resource lists requiring authentication (e.g., Slack channels). - Default values computed from external state. The structure varies by task executor - check task executor documentation for specific formats. Empty dict if no tasks provide dynamic data or if fetching dynamic data failed (check validation_errors). examples: - read_sheet: spreadsheet_id: options: - label: Sales Data 2024 value: abc123 - label: Customer List value: def456 title: Dynamic Data type: object input_schemas: additionalProperties: true description: >- JSON schemas for input task executors' inputs, keyed by task name. Each schema describes the structure and types of data the task expects as input. Only included when include_input_schema=true is specified. Format: {task_name: json_schema_object}. JSON schemas follow JSON Schema Draft 7 specification and include: - 'type': Root type (usually 'object'). - 'properties': Object properties with types and descriptions. - 'required': List of required property names. - 'title': Human-readable schema name. Use these schemas to generate input forms for flow execution. Empty dict if include_input_schema=false or no input tasks found. examples: - custom_form_input: properties: document_url: description: URL to uploaded document type: string filename: description: Name of uploaded file type: string metadata: description: File metadata type: object mime_type: description: MIME type of uploaded file type: string required: - filename - mime_type - document_url title: CustomFormInput type: object title: Input Schemas type: object output_schemas: additionalProperties: true description: >- JSON schemas for task outputs, keyed by task name. Each schema describes the structure and types of data the task will produce. Format: {task_name: json_schema_object}. JSON schemas follow JSON Schema Draft 7 specification and include: - 'type': Root type (usually 'object'). - 'properties': Object properties with types and descriptions. - 'required': List of required property names. - 'title': Human-readable schema name. Use these schemas to validate downstream task inputs and generate UI forms. Empty dict if no tasks have output schemas or if all tasks failed to load. examples: - fetch_data: properties: body: description: Response body as JSON type: object status_code: description: HTTP response status type: integer required: - status_code - body title: HttpRequestOutput type: object title: Output Schemas type: object validation_errors: description: >- Human-readable validation error messages describing problems with the flow definition. Errors are returned even if the request succeeds (HTTP 200) - check this field to determine if the flow definition is valid. Empty list indicates a valid flow. Error categories: - Structural errors: Missing required fields, invalid task references. - Semantic errors (full_validation=true only): Invalid parameter values, unreachable tasks, circular dependencies. - Task executor errors: Unknown task executor, failed to load executor. - Schema extraction errors: Failed to determine output schema for task. Each error is a descriptive string suitable for display to users. Example: 'Task "process_data" references unknown task "fetch_data" in input mapping'. examples: - - >- Task 'invalid_task' uses unknown task executor 'nonexistent_executor' - [] items: type: string title: Validation Errors type: array title: TaskDynamicDataResponse type: object ErrorResponse: description: |- Standard API error response structure. All error responses from the API follow this format, ensuring consistent error handling for API consumers. Example: { "error": { "message": "Flow not found", "code": "not_found" }, "request_id": "01K8KABR6S16YETA2SZPVBS9SP" } examples: - error: code: not_found message: Resource not found request_id: 01K8KABR6S16YETA2SZPVBS9SP properties: error: $ref: '#/components/schemas/ErrorDetail' request_id: anyOf: - type: string - type: 'null' description: >- Unique request identifier in ULID format for debugging and support. Example: 01K8KABR6S16YETA2SZPVBS9SP title: Request Id required: - error title: ErrorResponse type: object HTTPValidationError: properties: detail: items: $ref: '#/components/schemas/ValidationError' title: Detail type: array title: HTTPValidationError type: object ErrorDetail: description: |- Standard error detail structure. This model matches the error format returned by the centralized exception handlers in app/api/errors/handlers.py. properties: code: anyOf: - type: string - type: 'null' description: Machine-readable error code for programmatic handling title: Code details: anyOf: - items: type: string type: array - items: additionalProperties: true type: object type: array - additionalProperties: true type: object - type: 'null' description: >- Additional error context, validation errors, or debugging information title: Details message: description: Human-readable error message title: Message type: string required: - message title: ErrorDetail type: object ValidationError: properties: loc: items: anyOf: - type: string - type: integer title: Location type: array msg: title: Message type: string type: title: Error Type type: string required: - loc - msg - type title: ValidationError type: object securitySchemes: APIKeyAuth: description: >- API key authentication. Include your API key in the X-API-Key header as: `X-API-Key YOUR_API_KEY` Example: ``` X-API-Key: saf_1234567890 ``` in: header name: X-API-Key type: apiKey BearerAuth: bearerFormat: JWT description: >- JWT Bearer token authentication. Include your access token in the Authorization header as: `Bearer YOUR_ACCESS_TOKEN` Example: ``` Authorization: Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9... ``` scheme: bearer type: http ````