nodina 1.0.0

Ultra fast agents for nodnod.

Ultra fast agents for nodnod
We make a nodnod family 🧑‍🧑‍🧒‍🧒

Getting started

import asyncio

from nodina import AsyncNodinaAgent, Scope, scalar_node


@scalar_node
class Hello:
    @classmethod
    async def __compose__(cls) -> str:
        return "Hello,"


@scalar_node
class World:
    @classmethod
    async def __compose__(cls) -> str:
        return "World!"


async def main() -> None:
    agent = AsyncNodinaAgent.build({Hello, World})
    mapped_scopes = {}

    async with Scope(detail="local") as local_scope:
        await agent.run(local_scope, mapped_scopes)
        print(local_scope[Hello], local_scope[World])


asyncio.run(main())

How it works

nodina is a small DAG scheduler for nodnod graphs, built as a Cython extension over a tiny native pthread work pool. It ships two agents:

• AsyncNodinaAgent resolves the dependency graph on the running asyncio event loop — one task per node, so independent async nodes (and any node that awaits real I/O) run concurrently.
• NodinaAgent resolves it synchronously, dispatching independent nodes onto a shared pthread pool (nodina/core/nodina_pool.c). The pool's queueing and waiting run without the GIL — the GIL is held only inside each __compose__ call. A lone ready node with nothing else in flight composes inline to skip a thread hand-off.

Both pick the first successful SequentialEither / ConcurrentEither candidate in declared order; ConcurrentEither composes all of its candidates concurrently, SequentialEither composes them lazily. Neither cancels losing candidates.

Performance & the GIL, honestly

backend_name() returns "cython". The thread pool overlaps nodes that release the GIL (blocking I/O, time.sleep, C extensions). On a normal CPython build, CPU-bound __compose__ work still serializes on the GIL — that is a property of the interpreter, not of nodina.

On a free-threaded build (python3.14t, PEP 703) the GIL is gone and the pool runs CPU-bound nodes truly in parallel. The extension declares freethreading_compatible, so importing it does not re-enable the GIL:

# 8 independent CPU nodes, 8-node graph, 11-core machine
python3.14   (GIL):  8 nodes = 206 ms   speedup vs serial 1.0x
python3.14t  (no GIL): 8 nodes =  40 ms   speedup vs serial 5.1x

python -m benchmarks.bench               # vs a pure-Python reference
python3.14t -m benchmarks.freethreading_demo    # CPU parallelism with the GIL off

The benchmark suite in benchmarks/ measures nodina against a ~150-line pure-Python reference scheduler. nodina ties or beats it on every case (deep chains and small graphs 3-4x faster); see benchmarks/RESULTS.md and benchmarks/BASELINE.md.