graph-based-task-orchestrator 0.1.1

A simple yet powerful task orchestration library based on graph data structure

Graph-Based Task Orchestrator

A simple yet powerful task orchestration library for Python based on graph data structures.

Features

• Linear Graph Execution: Create simple pipelines where each node passes data to the next
• Parallel Paths: Split execution into multiple parallel paths and merge results
• Map-Reduce Pattern: Dynamically create parallel execution paths for iterable data
• Graph Simplification: At each execution step, the graph is simplified by moving the START node forward
• Execution History: Full execution history for debugging, retrying, and replaying
• Data Validation: Type hints and Pydantic model validation for inputs and outputs

Installation

Using pip:

pip install -e .

Using uv (recommended):

uv pip install -e .

This will install the package in editable mode and make the graph-validate command available globally.

Quick Start

1. Linear Graph

from graph_orchestrator import Graph, START, END, Node

class MultiplierByTwo(Node):
    def execute(self, input_data):
        return input_data * 2

# Create graph
start = START(s=10)
end = END()
g = Graph(start=start)

# Build pipeline
multiplier = MultiplierByTwo()
g.add_edge(start, multiplier)
g.add_edge(multiplier, end)

# Run
result = g.run()  # Returns 20

2. Parallel Paths

# Create parallel paths that merge
g.add_edge(start, node1)
g.add_edge(node1, node2)  # Path 1
g.add_edge(node1, node3)  # Path 2
g.add_edge(node2, merger)
g.add_edge(node3, merger)
g.add_edge(merger, end)

3. Map-Reduce

# Map-reduce pattern
g.add_edge(start, list_generator)
g.add_map_reduce(list_generator, mapper, reducer)
g.add_edge(reducer, end)

Creating Custom Nodes

Simply inherit from the Node class and implement the execute method:

from graph_orchestrator import Node

class MyCustomNode(Node):
    def execute(self, input_data):
        # Your logic here
        return processed_data

With Type Validation

Use type hints and Pydantic models for automatic validation:

from typing import List
from pydantic import BaseModel
from graph_orchestrator import Node

class InputModel(BaseModel):
    name: str
    age: int

class OutputModel(BaseModel):
    message: str
    
class ValidatedNode(Node):
    def execute(self, input_data: InputModel) -> OutputModel:
        return OutputModel(
            message=f"Hello {input_data.name}, you are {input_data.age} years old"
        )

Static Validation

The library includes a powerful static validation tool that can analyze your graph structures before runtime, checking for:

• Type compatibility between connected nodes
• Graph cycles
• Unreachable nodes
• Pydantic model field compatibility
• Map-reduce pattern correctness

Using the Validation Tool

Method 1: Using the installed command (after uv pip install -e .):

# Basic validation
graph-validate validate examples/validation_example.py

# Enhanced validation with deep type checking
graph-validate validate --enhanced examples/validation_example.py

# Validate directory
graph-validate validate --directory examples/

# Verbose output
graph-validate validate -v examples/map_reduce_graph.py

Method 2: Using the convenience script:

# Basic validation
./scripts/validate.sh basic examples/validation_example.py

# Enhanced validation
./scripts/validate.sh enhanced examples/

# Validate all examples
./scripts/validate.sh examples

# Run validation demo
./scripts/validate.sh demo

Method 3: Direct Python scripts:

# Simple validation script
python validate.py examples/validation_example.py

# With enhanced type checking
python validate.py examples/validation_example.py --enhanced

# Using the module directly
python -m graph_orchestrator.cli validate examples/validation_example.py

Validation Features

1. AST Analysis: Parses Python files to extract graph construction patterns
2. Type Checking: Validates type compatibility between node connections
3. Structural Validation: Checks for cycles, unreachable nodes, and missing connections
4. Pydantic Support: Deep inspection of Pydantic model compatibility
5. Map-Reduce Validation: Ensures correct types in map-reduce patterns

Example Validation Output

============================================================
File: examples/invalid_graph.py
============================================================

❌ Errors (2):
  • Type incompatibility: node_a (NodeA) -> node_b (NodeB): Type mismatch: OutputModel is not compatible with int
  • Graph 'cyclic_graph' contains a cycle

⚠️  Warnings (1):
  • Graph 'g' has unreachable nodes: orphan_node

❌ Validation failed!

Examples

See the examples/ directory for complete working examples:

• linear_graph.py - Simple linear pipeline
• parallel_graph.py - Parallel execution paths with merging
• map_reduce_graph.py - Map-reduce pattern
• validation_example.py - Data validation with Pydantic
• debug_example.py - Execution history and debugging

Run all examples:

python test_all_features.py

Run validation examples:

python validate_graphs.py

Quick Validation Commands

Using Make (recommended for development):

make validate-examples        # Validate all examples
make validate FILE=myfile.py  # Validate specific file
make validate-demo           # Run validation demo

Using the shell script:

./scripts/validate.sh examples  # Validate all examples
./scripts/validate.sh help      # Show all available commands

Using the installed command:

graph-validate validate --help  # Show validation options