tedx-flow 0.1.2

A lightweight task flow orchestration library for Python - zero dependencies, thread-safe

tedx-flow

A lightweight task flow orchestration library for Python.

Features

• Zero Dependencies: Uses only Python standard library
• Thread-Safe: Built-in synchronization for parallel execution
• Simple API: Easy to define and chain tasks
• Streaming Support: Real-time output as tasks complete
• Dynamic Scheduling: Tasks can schedule next tasks at runtime
• Parallel Execution: Independent tasks run concurrently

Installation

pip install tedx-flow

Quick Start

from concurrent.futures import ThreadPoolExecutor
from tedx_flow import Flow, Context, NextTask, TaskOutput

# Define tasks
def fetch_data(ctx: Context) -> TaskOutput:
    data = {"items": [1, 2, 3, 4, 5]}
    return TaskOutput(output=data, next_tasks=[NextTask("process")])

def process(ctx: Context) -> TaskOutput:
    data = ctx.get("fetch_data")
    result = sum(data["items"])
    return TaskOutput(output=result)

# Create and run flow
with ThreadPoolExecutor(max_workers=4) as executor:
    flow = Flow(executor)
    flow.add_task("fetch_data", fetch_data)
    flow.add_task("process", process)
    
    results = flow.run("fetch_data")
    print(results)  # {"process": 15}

Core Concepts

Task

A task is the basic unit of a flow. Each task:

1. Receives a Context object (optionally with inputs)
2. Returns a TaskOutput containing output value and optional next tasks

def my_task(ctx: Context, inputs: dict = None) -> TaskOutput:
    # Access previous task outputs
    previous_result = ctx.get("previous_task")
    
    # Access inputs passed via NextTask
    batch_size = inputs.get("batch_size", 10) if inputs else 10
    
    # Execute business logic
    result = do_something(previous_result, batch_size)
    
    # Return output and schedule next tasks
    return TaskOutput(
        output=result,
        next_tasks=[
            NextTask("next_task", inputs={"processed": True}),
            NextTask("parallel_task")  # Runs in parallel
        ]
    )

Context

Context is a shared data container between tasks:

• ctx.set(key, value) - Store a value
• ctx.get(key) - Get a value (blocks until available)
• ctx.to_dict() - Export as dictionary

TaskOutput

# Terminal task (no next tasks)
TaskOutput(output="done")

# Chain to next task
TaskOutput(output=result, next_tasks=[NextTask("next_step")])

# Fan out to multiple tasks (parallel)
TaskOutput(output=result, next_tasks=[
    NextTask("branch_a"),
    NextTask("branch_b")
])

NextTask

# Simple scheduling
NextTask("task_name")

# With input parameters
NextTask("task_name", inputs={"key": "value"})

# Allow parallel instances of same task
NextTask("task_name", spawn_another=True)

Streaming

Get real-time results as tasks complete:

with ThreadPoolExecutor(max_workers=4) as executor:
    flow = Flow(executor)
    flow.add_task("task_a", task_a)
    flow.add_task("task_b", task_b)
    
    for chunk in flow.stream("task_a"):
        print(f"Task {chunk.task_id} completed: {chunk.value}")

Parallel Task Instances

Run multiple instances of the same task:

def fan_out(ctx: Context) -> TaskOutput:
    return TaskOutput(
        output="started",
        next_tasks=[
            NextTask("worker", inputs={"id": 1}, spawn_another=True),
            NextTask("worker", inputs={"id": 2}, spawn_another=True),
            NextTask("worker", inputs={"id": 3}, spawn_another=True),
        ]
    )

Error Handling

Exceptions in tasks are automatically propagated:

try:
    results = flow.run("start_task")
except Exception as e:
    print(f"Flow execution failed: {e}")

API Reference

Classes

Class	Description
Flow	Main orchestration engine
Context	Shared state between tasks
TaskOutput	Task return type
NextTask	Specifies next task to run
StreamChunk	Streaming output container
State	Thread-safe value container

Flow Methods

Method	Description
add_task(name, action)	Register a task
run(start_task_id, inputs)	Execute flow synchronously
stream(start_task_id, inputs)	Execute with streaming output
get_context()	Get flow context

Use Cases

• Data Processing Pipelines: ETL, data cleaning, transformation
• AI Workflows: Multi-step LLM calls, RAG pipelines
• Batch Processing: Parallel sub-task processing
• Business Processes: Order processing, approval workflows

License

MIT License