A curated collection of production-ready workflow examples demonstrating various use cases for Render Workflows. Each example is self-contained, deployment-ready, and showcases different patterns and capabilities.
These examples demonstrate how to build robust, scalable workflows using Render's Python SDK. All examples follow best practices for production deployments and include comprehensive documentation.
Render Workflows support both Python and TypeScript. This repo contains Python examples using the render-sdk package, deployed as Workflow services on Render.
New to Workflows? Start with Hello World — it covers the core concepts with minimal complexity.
| Example | Use Case | Key Patterns | Extra Dependencies |
|---|---|---|---|
| Hello World | Learn workflow basics with simple number processing | Task definition, subtask calling with await, basic orchestration |
None |
| ETL Job | Process CSV data with validation and statistics | Subtasks, sequential processing, batch operations, data validation | None |
| OpenAI Agent | AI customer support agent with tool calling | Tool calling, nested subtasks (3 levels deep), stateful workflows, dynamic orchestration | openai |
| File Processing | Batch process multiple file formats in parallel | Parallel execution with asyncio.gather(), multi-format handling, aggregation |
None |
| Data Pipeline | Multi-source customer analytics pipeline | Parallel extraction, data enrichment, combining parallel + sequential patterns | httpx |
| File Analyzer | API service calling workflow tasks for file analysis | Client SDK + Task SDK, workflow slugs, service separation, FastAPI integration | fastapi, uvicorn |
The simplest possible workflow — learn the fundamentals through simple number processing.
- Ultra-simple task definitions
- Clear subtask calling examples
- Subtasks in loops demonstration
- Multi-step workflow orchestration
- Heavily commented code explaining every pattern
If you're new to Render Workflows, start here. No CSV files, no APIs, no databases — just pure workflow patterns.
Complete Extract, Transform, Load pipeline — process customer data from CSV files with validation, cleaning, and statistical analysis.
- CSV data extraction with retry logic
- Record validation and error tracking
- Batch processing with subtasks
- Statistical aggregation
- Comprehensive error handling
Intelligent conversational agent — a customer support bot that can answer questions, look up orders, and process refunds.
- Multi-turn conversations with context
- Dynamic tool/function calling
- Stateful workflow management
- Integration with OpenAI GPT-4
- Extensible tool framework
Parallel file processing — batch process files from storage, analyze content, and generate consolidated reports.
- Multi-format support (CSV, JSON, text)
- Parallel file processing
- Automatic content analysis
- Report generation
- Format-specific insights
View File Processing Example →
Multi-source data pipeline — build customer analytics by combining data from user service, transaction service, and analytics platform.
- Multi-source parallel extraction
- Data enrichment with external APIs
- Complex transformations
- User segmentation
- Aggregate insights generation
Complete Client SDK + Task SDK integration — a file analysis API with separate workflow and API services.
- Two-service architecture: Workflow service (Task SDK) + API service (Client SDK)
- Workflow slug pattern (
{service-slug}/{task-name}routing) - Client SDK usage for calling workflow tasks remotely
- File analysis pipeline: Parse → Statistics → Trends → Insights
- FastAPI integration with HTTP endpoints triggering workflow tasks
The only example that shows both Task SDK and Client SDK together — ideal for understanding how to build APIs that call workflow tasks.