What you can build
Process documents at scale
Extract data from invoices, contracts, and forms with AI-powered workflows.
Chain API operations
Connect multiple services, handle retries, and manage complex business logic.
Automate business processes
Build durable workflows that handle failures gracefully and resume automatically.
Why developers choose SuperAI Flows
Built for reliability
- Automatic retries — Configure retry policies per task with exponential backoff
- Durable execution — Workflows survive service restarts and infrastructure failures
- Version control — Roll back to previous workflow versions without breaking running executions
- Real-time monitoring — Track execution progress with WebSocket notifications and comprehensive logging
Enterprise-ready
- Multi-tenant architecture — Organization-scoped resources with role-based access control
- Audit logging — Complete execution history for compliance and debugging
- Scalable infrastructure — Distributed architecture designed for high-throughput workloads
- Secure by default — JWT authentication, API key management, and webhook signature verification
Core concepts
Flows
A Flow is a workflow definition — a declarative YAML file that specifies tasks, dependencies, and execution logic. Flows are versioned, allowing you to iterate safely. Why developers love our YAML definitions:- Readable and maintainable — Define complex workflows in plain YAML without writing orchestration code
- Type-safe — Every task includes a complete JSON schema for inputs and outputs, ensuring data integrity
- Visual representation — YAML maps directly to the visual flow editor at flows.super.ai
- Reusable components — Leverage pre-built task executors or create custom ones in Python
{{task_name.output.field}}, and the platform handles dependencies, retries, and data flow automatically. See the quickstart for a complete YAML workflow example.
Flow executions
A Flow Execution is a single run of a flow with specific input data. Each execution has a unique ID and tracks status through its lifecycle:queued → running → completed or failed.
Tasks
Tasks are the building blocks of flows. Each task performs a single operation (API call, data transformation, AI inference) and can depend on outputs from previous tasks.Task executors
Task Executors are pre-built or custom Python functions that define how tasks run. SuperAI provides executors for common operations like HTTP requests, LLM calls, and data processing.Integration patterns
Synchronous (polling)
Create an execution, poll for completion, retrieve results:Asynchronous (webhooks)
Add aWebhook Notification task to receive real-time updates when referenced tasks produce output:
- Automatic dependency resolution — Webhooks trigger after referenced tasks produce output
- Customizable payload — Reference outputs from any previous task using
{{task_name.output.field}} - Multiple webhooks per flow — Send different data to different endpoints at different stages
API design philosophy
Predictable and consistent
- RESTful endpoints with intuitive resource naming
- Consistent response formats across all endpoints
- Standard HTTP status codes for error handling
- Comprehensive field descriptions in all responses
Built for forward compatibility
Your integrations won’t break. We follow strict versioning rules. Non-breaking changes (safe, no action required):- Adding new API endpoints
- Adding optional query parameters
- Adding new fields to responses
- Adding new enum values
- Removing endpoints or fields
- Changing field types or validation
- Removing enum values
All breaking changes are announced at least 15 days in advance with migration guides.
Client requirements
Your API clients must gracefully handle additional fields in responses. We may add new fields to any response object without advance notice — design your parsers to ignore unknown fields.Ready to build?
API Quickstart
Authenticate and run your first workflow in under 5 minutes.
API Reference
Explore every endpoint, with request and response schemas.