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
- Block: The atomic unit of execution.
- Chain: A linear sequence of Blocks.
- 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.
1 maintainer
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