splime

Reuse Python functions across projects without rewriting or redeploying them.

splime turns trusted Python functions into versioned, portable nodes that can be reused across projects and executed locally or remotely. You publish a function to a private library once, then any project can call it by name, run it where the data or the hardware lives, and read back the result and artifacts — without copying code or redeploying.

• Python-first. Plain Python functions and pipelines, no DSL to learn.
• Reuse-first. Publish once, call by name from anywhere.
• Private-team-first. Your own libraries and workers, with explicit ownership and scoped access.
• Local or remote. The same call runs on your machine during development, or on a private worker that has the data, the GPU, or the credentials.

splime is a private node registry and execution layer — not a workflow orchestrator, a scheduler, or a public marketplace. It does not replace Airflow, Prefect, or Temporal.

Requirements

• Python 3.13+

Install

pip install splime

The distribution is named splime; the Python import package is spl.

Quickstart

1. Start the local daemon (it stores your objects and runs workers):

spl-daemon serve            # listens on http://127.0.0.1:8765 by default

2. Publish a function and call it — a plain SPLClient() is fully local and never contacts a server:

from spl import SPLClient

def daily_total(date: str) -> float:
    prices = {"2026-06-08": [11.0, 6.5, 24.5]}
    return sum(prices.get(date, []))

client = SPLClient()                       # local-first; no server contact
client.publish(daily_total, name="daily_total")

result = client.call("daily_total", kwargs={"date": "2026-06-08"})
print(result.mode)     # "local"
print(result.output)   # 42.0  (unwrapped value; result.value keeps the raw port dict)

That is the whole loop: define a function, publish it as a versioned node, then call it by name and get back the value (plus logs and any artifacts).

Run it where the data lives

The same call becomes a remote run when you point it at a library, an owner, or a target machine. This requires a connected splime server and a private worker; the local daemon builds an isolated environment on the worker before executing.

client = SPLClient(user_token="…", machine_token="…")   # connect the daemon to your server

result = client.call(
    "daily_total",
    kwargs={"date": "2026-06-08"},
    target_machine="gpu-box",        # hand the run to a private worker
)
print(result.mode)    # "server"

SPLClient() without tokens stays entirely local — connecting to a server is always optional.

Libraries

Libraries group versioned objects and control who can see and run them. Creating and curating libraries uses a server-connected client:

client.library.create("risk", display_name="Risk", visibility="private")
client.publish(risk_score, name="risk_score", library="risk")

# Grant scoped access to a teammate
client.library.grant("risk", "analyst1", scopes=["metadata:read", "objects:read", "execute"])

A library can also reference a live object from another library (add_reference, follows latest) or take an owned snapshot with provenance (copy_object).

Security & trust

splime runs code that you publish on purpose, on machines you control. It is built around:

• explicit ownership of every published object,
• scoped access grants per library (read metadata, read objects, execute),
• private worker boundaries — the server coordinates, your own workers execute,
• isolated environments built by the daemon before a run,
• an auditable run history.

How it fits together

Development

git clone https://github.com/yastrebovks/splime
cd splime
pip install -e '.[test]'
pytest

Project status

Alpha (0.1.0). The API may change between releases. Feedback and issues are welcome at the issue tracker.

Links

• Website: https://splime.io
• Source: https://github.com/yastrebovks/splime

License

Licensed under the Apache License 2.0.