SynaFlow 🌊🧠

📖 Full documentation: humansoftware.github.io/synaflow

Write plain Python functions. SynaFlow builds the pipeline for you.

PyPI License Python

SynaFlow is a lightweight, pure-Python pipeline engine that uses Type Hints to automatically wire and execute Directed Acyclic Graphs (DAGs) with lockstep streaming and optional bounded handoff.

Why the name? Synapse + Flow. Just like synapses automatically wire neurons together, SynaFlow automatically wires your functions together based on their types. "Flow" represents the lazy, streaming nature of how data moves through those connections.

Quickstart

from collections.abc import Generator, Iterator
from typing import NamedTuple
from synaflow import pipeline, step, run

class Params(NamedTuple):
    count: int

def producer(count: int) -> Generator[int, None, None]:
    yield from range(count)

def transformer(producer: Iterator[int]) -> Generator[int, None, None]:
    for val in producer:
        yield val * 10

def consumer(transformer: Iterator[int]) -> None:
    for x in transformer:
        print(f"Consumed: {x}")

p = pipeline(
    name="example",
    params=Params,
    steps=[
        step("producer", fn=producer),
        step("transformer", fn=transformer),
        step("consumer", fn=consumer),
    ],
)

run(p, Params(count=5))

Three functions, three step() calls, zero manual wiring. SynaFlow reads the type hints and wires the DAG automatically.

What makes it different

Type-hint wiring

Parameter names match producer names — SynaFlow connects them automatically. Singular/plural/suffix synonyms work too (item → items, user_list → users).

Lazy streaming with bounded handoff

SynaFlow streams lazily by default. Multiple consumers can stay lockstep, one consumer can stay lazy while another materializes, and when you need a bounded window between stages you can set max_in_flight on the producing step.

This is especially useful for I/O-bound pipelines where one step starts work and the next resolves it, such as HTTP requests, RPC calls, or object-store reads.

Static validation at build time

Type errors, missing dependencies, circular graphs, mode conflicts — all caught when pipeline(...) is called. If it compiles, it's valid. No runtime surprises.

Build your own runner

The DAG compiles to a deterministic JSON contract. Write custom runners or auto-generate native DAGs for Airflow, Prefect, or Dagster.

How it compares

SynaFlow Hamilton Airflow / Prefect / Dagster
Auto wiring ✅ type hints + smart binding ✅ type hints (exact names) ❌ explicit A >> B
Lazy streaming ✅ lockstep + bounded handoff ❌ DataFrame-centric ❌ task-based
Smart binding ✅ singular/plural/suffix ❌ ❌
Scope In-process micro Feature engineering Cluster orchestration
DAG export ✅ JSON ✅ ✅
Sync/async parity ✅ identical ❌ ✅

Detailed comparisons: Hamilton · Java Streams · LINQ

Installation

pip install synaflow

Documentation

Start here: humansoftware.github.io/synaflow

Section Description
Tutorial 5-level step-by-step guide building a pipeline from scratch
Core Concepts How the DAG is wired, lockstep flow, max in flight, build vs run, event-based processing
Examples Every corpus pipeline with auto-generated diagrams and source code
Comparisons Detailed comparisons with Hamilton, Java Streams, and LINQ
Design Philosophy Architectural decisions, contracts, and design rationale

License

MIT License

Project links

Data verified by PyPI on Jun 19, 2026
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  • Released:
Newer release available (0.32.2)

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Credits

Author: mvallebr

GitHub Statistics

Data verified by PyPI on Jun 19, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT)

Requires

Python >=3.10

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