flowhive 0.0.2

Task and Workflow orchestration.

FlowHive

FlowHive is a flexible Python library for building pipelines that seamlessly integrate sequential processing, parallel execution, AI agents, and distributed task orchestration. Whether you’re creating a simple chain of tasks or deploying multi-agent workflows on a cluster, FlowHive provides an intuitive API to build scalable and maintainable solutions.

Features

• Sequential Pipelines: Chain functions so that the output of one task is passed as the input to the next.
• Parallel Execution: Run multiple tasks concurrently on the same input to optimize performance.
• AI Agent Integration: Easily incorporate AI and multi-agent frameworks into your pipelines.
• Distributed Orchestration: Distribute tasks across a cluster for high-performance, scalable execution.
• Simple & Extensible API: Build your workflow with minimal configuration and customize it as your project grows.

Installation

Install FlowHive from PyPI using pip:

pip install flowhive

Quick Start

Sequential Pipeline Example

Create a simple pipeline where tasks are executed one after another:

from flowhive import Pipeline

def task1(data):
    print("Executing Task 1")
    return data + 1

def task2(data):
    print("Executing Task 2")
    return data * 2

# Create and run a sequential pipeline
pipeline = Pipeline([task1, task2])
result = pipeline.run(5)
print("Final Result:", result)  # Expected output: 12

Parallel Pipeline Example

Run multiple tasks concurrently on the same input:

from flowhive import ParallelPipeline

def task_a(data):
    return f"Task A processed {data * 2}"

def task_b(data):
    return f"Task B processed {data + 100}"

# Create and run a parallel pipeline
parallel_pipeline = ParallelPipeline([task_a, task_b])
results = parallel_pipeline.run(10)
print("Parallel Results:", results)

Distributed Task Orchestration

FlowHive also supports integration with distributed task queues and multi-agent orchestration frameworks. For complex workflows running across clusters, check out our detailed documentation for integration examples and best practices.

Documentation

For comprehensive details on usage, API reference, and advanced features, please visit our Documentation.

Contributing

We welcome contributions! If you'd like to help improve FlowHive, please check out our CONTRIBUTING.md for guidelines on how to get started.

License

FlowHive is distributed under the MIT License.

Contact

For support, questions, or business inquiries, please open an issue on GitHub or contact us at [e-mail].

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Happy piping with FlowHive!