VibeBlocks

AI-First Orchestration for Python.

VibeBlocks evolves the concept of task orchestration into an AI-ready framework. It maintains the "Zero-Gravity" architecture (no external dependencies) while introducing a Semantic Layer for LLM integration and dynamic flow generation.

Key Concepts

1. Block: The atomic unit of execution.
2. Chain: A linear sequence of Blocks.
3. Flow: High-level orchestrator with failure strategies.

Installation

pip install vibeblocks

Quick Start

1. Classic Usage

from vibeblocks import Flow, ExecutionContext, block, execute_flow

# 1. Define your blocks with @block decorator
@block(description="Extracts data from source")
def extract(ctx: ExecutionContext):
    print("Extracting data...")
    ctx.data["raw"] = [1, 2, 3, 4, 5]
    return ctx.data["raw"]

@block(description="Doubles the input values")
def transform(ctx: ExecutionContext):
    print("Transforming data...")
    data = ctx.data["raw"]
    ctx.data['processed'] = [x * 2 for x in data]
    return ctx.data['processed']

@block(description="Loads data to destination")
def load(ctx: ExecutionContext):
    print(f"Loading data: {ctx.data['processed']}")
    return True

# 2. Create the Flow
pipeline = Flow("ETL_Flow", [extract, transform, load])

# 3. Execute
result = execute_flow(pipeline, data={})

if result.status == "SUCCESS":
    print("Flow completed successfully!")
else:
    print(f"Flow failed: {result.errors}")

2. AI-Driven Dynamic Flows

VibeBlocks allows LLMs to generate flows on the fly using JSON schemas.

from vibeblocks.vibeblocks import VibeBlocks

# JSON definition (could come from an LLM)
flow_request = {
    "name": "DynamicETL",
    "blocks": ["extract", "transform", "load"],
    "strategy": "ABORT"
}

# Available blocks registry
blocks_registry = {
    "extract": extract,
    "transform": transform,
    "load": load
}

# Execute dynamically
result = VibeBlocks.run_from_json(flow_request, initial_data={}, available_blocks=blocks_registry)

License

MIT License. See LICENSE for details.