Skip to main content

Compose Python logic into data-flow pipelines — sync or async, run anywhere

Project description

lythonic

PyPI version Documentation

Compose Python logic into data-flow pipelines — sync or async, run anywhere.

Write plain Python functions. Wire them with >>. Data flows visibly between nodes — you can see what went in, what came out, what failed. Unlike task schedulers where jobs are opaque units, lythonic tracks the data itself.

Quick Start

uv add lythonic
from lythonic.compose.namespace import Dag

def fetch(url: str) -> dict:
    return {"source": url, "values": [1, 2, 3]}

def double(data: dict) -> dict:
    return {**data, "values": [v * 2 for v in data["values"]]}

dag = Dag()
dag.node(fetch) >> dag.node(double)

# Run it — sync or async, doesn't matter
import asyncio
result = asyncio.run(dag(url="https://example.com"))
print(result.outputs)  # {"double": {"source": "...", "values": [2, 4, 6]}}

Why lythonic?

Data flow, not task flow. Each node receives typed data from upstream and passes results downstream. The DAG runner wires inputs to outputs by type — fan-out, fan-in, and map-reduce built in. Provenance tracking records what data flowed through each edge.

Compose freely. DAGs nest inside DAGs. dag.node(sub_dag) runs a sub-DAG as a single step. dag.map(sub_dag) runs it on each element of a collection, concurrently. Build small, reuse everywhere.

Run transparently. await dag() for a quick test. DagRunner with provenance for production. lyth start for a long-running engine with cron-triggered pipelines. Same code, different execution context.

Sync and async — mixed freely. Write sync functions, async functions, or both in the same DAG. Sync nodes run in a thread executor automatically.

Features

  • DAG composition>> wiring, callable DAGs, MapNode, CallNode
  • @dag_factory — define reusable DAG templates as decorated functions
  • Triggers — cron-scheduled or push-triggered execution via TriggerManager
  • Provenance — SQLite-backed tracking of runs, node executions, edge traversals
  • Caching — per-callable SQLite cache with probabilistic TTL refresh
  • lyth CLIstart, stop, run, fire, status commands
  • State — Pydantic-based SQLite ORM with schema management and multi-tenant support

Documentation

Full documentation at walnutgeek.github.io/lythonic.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

lythonic-0.0.14.tar.gz (196.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

lythonic-0.0.14-py3-none-any.whl (74.9 kB view details)

Uploaded Python 3

File details

Details for the file lythonic-0.0.14.tar.gz.

File metadata

  • Download URL: lythonic-0.0.14.tar.gz
  • Upload date:
  • Size: 196.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: uv/0.8.13

File hashes

Hashes for lythonic-0.0.14.tar.gz
Algorithm Hash digest
SHA256 541f67e51640b171e826ab5aa46b2efeeb7365ca06646bda16f987a1670ded64
MD5 a4518523834686986649470e15a0be0a
BLAKE2b-256 681d26a1d72617a45768a72f5c41ab360a3b4ffdd374a517a21a63d120cf0fe1

See more details on using hashes here.

File details

Details for the file lythonic-0.0.14-py3-none-any.whl.

File metadata

  • Download URL: lythonic-0.0.14-py3-none-any.whl
  • Upload date:
  • Size: 74.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: uv/0.8.13

File hashes

Hashes for lythonic-0.0.14-py3-none-any.whl
Algorithm Hash digest
SHA256 387dc70f4f93ee03b79ed254ee090652ab15e06980a77c75b4ea9be591b86c04
MD5 863d36a8ff177b370b4bf64f165829fc
BLAKE2b-256 513b721e367d02683361449d1a85f446d28901ba1c3f119f7ef3c899fbbfd8cb

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page