neuralosd — deterministic-first agentic runtime

One pip install. Two sandbox backends. Any data source. Offline. On CPU.

neuralosd is the agentic runtime for neuralOS — the 121M-parameter on-device tool-calling model. It turns any data source into a private, offline question-answering service inside hardware-isolated microVMs.

pip install neuralosd[all]

Single-file binaries (no Python required)

Every release ships standalone Nuitka binaries in three variants — the Python runtime, the framework, the docs and the skills are all embedded in one file:

Each variant carries exactly its own backend — check with neuralosd backends:

Sandbox backends:
  boxlite  [available]
  msb      [not installed]

— = no upstream wheel for that platform (boxlite: no Windows/Intel-macOS; microsandbox: no Intel-macOS).

# base
curl -L -o neuralosd https://github.com/DrOlu/neuralosd/releases/latest/download/neuralosd-linux-x64
chmod +x neuralosd
./neuralosd init --source data.csv --name mydata
./neuralosd ask --instance-dir ./mydata "how many rows"

# with a sandbox backend
curl -L -o neuralosd-msb https://github.com/DrOlu/neuralosd/releases/latest/download/neuralosd-msb-macos-arm64
chmod +x neuralosd-msb
./neuralosd-msb backends            # msb [available]
./neuralosd-msb deploy --instance-dir ./mydata --name box --backend msb

The first run extracts the payload once (~10 s); later runs are cached and start in ~0.25 s. Built with Nuitka; every binary is verified by a 12-check CLI smoke suite plus a strict backend check in CI.

What it does

YOUR DATA (CSV, DB, API, logs)     THE RUNTIME                THE RESULT
──────────────────────           ──────────────            ──────────────
profile → model → generate  →   @probe + router    →    ask in English,
                         →   chains + guardrails     get verified
                         →   serve + monitor         answers, offline

Two sandbox backends

Both run the same probe contract. Switch by configuration, not code.

Quick start

# 1. Install
pip install neuralosd[all]

# 2. Build an instance from your data
neuralosd init --source your_data.csv --name my-analyst

# 3. Deploy into a sandbox
neuralosd deploy --name my-analyst --backend boxlite

# 4. Ask
neuralosd ask --instance my-analyst "how many records"

The @probe framework

from neuralosd import probe, Instance

@probe(
    description="Customers ranked by lifetime spend",
    triggers=["top customers", "best customers"],
    args={"limit": {"type": "integer", "min": 1, "max": 25, "default": 10}},
    pii=["email"],
)
def top_customers(limit: int = 10):
    return db.query("SELECT ... LIMIT %s", (limit,))

inst = Instance(name="my-analyst", probes=[top_customers])
env = inst.ask("top customers")
print(env["results"])

Chains (multi-step answers)

from neuralosd import chain, ChainRunner

@chain(name="genre_deep_dive", steps=[
    {"probe": "top_genres"},
    {"probe": "tracks_by_genre", "args": {"genre": "{{step_0.genre}}"}},
])
def genre_deep_dive(ctx): ...

runner = ChainRunner(probes_by_name)
runner.register("genre_deep_dive", genre_deep_dive._chain_steps)
result = runner.run("genre_deep_dive")

Meta-selector (multi-instance routing)

from neuralosd import MetaSelector

selector = MetaSelector({
    "finance": {"description": "revenue, invoices, customers"},
    "ops":     {"description": "incidents, uptime, alerts"},
})
instance = selector.route("how many open incidents")  # → "ops"

HITL guardrails

from neuralosd import ConfirmStore
store = ConfirmStore()
pending = store.create(probe_fn, args, reason="low confidence")
# ... human confirms ...
result = store.confirm(pending["confirm_token"])

MCP server (expose probes to any AI agent)

python3 -m neuralosd.mcp /path/to/instance
# or add to Claude Desktop / Cursor MCP config

License

MIT