adriane-ai 1.4.3

Python SDK for the Adriane agentic graph runtime — one Rust engine, thin per-language SDKs.

Adriane — Python SDK

A thin, Pythonic SDK over the Adriane Rust engine, exposed through a pyo3 native extension module.

Install adriane-ai, import adriane_ai. The distribution name on PyPI is adriane-ai (hyphen, matching the @adriane-ai npm scope); the import package is adriane_ai (underscore, per PEP 8 — Python module names can't contain a hyphen). This is the standard pip↔import convention.

pip install adriane-ai

import adriane_ai

adriane_ai.engine_version()   # -> the bound Rust engine version, e.g. "0.1.0"

One engine, two SDKs — what to install where

The graph model, validator, and DSL compiler live once in Rust (under crates/). Each language SDK is a thin shim over that single engine, not a re-implementation — so a graph that validates one way in TypeScript validates exactly the same way in Python. There is no second source of truth to drift.

	TypeScript	Python (this package)
Install	npm i @adriane-ai/graph-sdk	pip install adriane-ai
Import	import { createGraph } from "@adriane-ai/graph-sdk"	import adriane_ai
Rust engine	optional — @adriane-ai/napi activates it; falls back to the in-bundle TS engine when absent	built in — the wheel ships the compiled pyo3 extension
Bridge	napi-rs (crates/bindings)	pyo3 (crates/py-bindings)
Surface	full builder + custom handlers + streaming	JSON-in / JSON-out: validate, compile, model policy, catalogs, run paths

Both bindings expose the identical JSON-in / JSON-out core (graph validation, DSL compilation, the model policy, the component/prebuilt catalogs, and the fully-Rust run paths). The TypeScript SDK adds a builder and custom node handlers on top; the Python SDK is the thin JSON surface.

API

Graph model & DSL

import adriane_ai

adriane_ai.engine_version()            # -> the bound Rust engine version string

adriane_ai.validate_graph({            # -> list[dict] of validation errors ([] if sound)
    "id": "g", "version": "0.0.0", "name": "g", "channels": {},
    "nodes": [{"id": "a", "type": "action", "label": "a"}],
    "edges": [{"id": "e1", "from": "a", "to": "ghost", "type": "default"}],
    "entryNodeId": "a",
})
# [{'code': 'INVALID_EDGE_REFERENCE', 'message': "Edge 'e1' references unknown node 'ghost'.", 'path': ['e1']}]

adriane_ai.compile_graph_yaml("""    # -> dict (a compiled GraphDefinition)
id: g
version: 0.0.0
name: g
entryNodeId: a
nodes:
  - id: a
    type: action
    label: A
edges: []
channels: {}
""")

validate_graph returns the full list of structural errors (it does not raise on an invalid-but-parseable graph). compile_graph_yaml raises ValueError (adriane_ai.GraphCompileError) when the DSL fails to parse, compile, or validate.

Model policy

adriane_ai.available_providers()       # -> list[str], from process env credentials
# e.g. ["mistral"] when MISTRAL_API_KEY is set; [] when none are.

adriane_ai.resolve_model("fast", available=["mistral"])
# -> {'provider': 'mistral', 'model': 'mistral-small-latest', 'recommended': True}

# Tiers: "frontier" | "balanced" | "fast" | "creative".
# Omit `available` to derive it from the env. A provider/model override wins
# over the policy choice and flags `recommended = False`:
adriane_ai.resolve_model("frontier", available=["anthropic"], provider="mistral", model="mistral-tiny")
# -> {'provider': 'mistral', 'model': 'mistral-tiny', 'recommended': False}

Catalogs

adriane_ai.list_components()   # -> list[str] of the component kinds, e.g. "promptBuilder"
adriane_ai.list_prebuilt()     # -> list[dict] of the 16 prebuilt micro-agents
# each: {'name', 'description', 'tier', 'systemPrompt', 'toolNames',
#        'suspendForApproval', 'outputChannel'}  (camelCase, from the Rust engine)

Run paths (fully on Rust)

Both runs execute end-to-end in Rust — no Python callbacks. When no provider credentials are present in the env, run_prebuilt falls back to a deterministic mock gateway, so a run still completes offline.

adriane_ai.run_component(              # -> dict, the component's channel-update map
    "promptBuilder",
    {"template": "Hello {{name}}!", "into": "prompt"},
    {"name": "Ada"},
)
# {'prompt': 'Hello Ada!'}

adriane_ai.run_prebuilt("summarizer", "please summarise this long text")
# -> {'status': 'completed',
#     'channels': {'input': ..., 'summary': {...}},
#     'resolvedModel': {'provider': 'mock', 'model': 'mock-model'}}

# Ergonomic accessor: each attribute is bound to that agent name.
adriane_ai.prebuilt.summarizer("please summarise this long text")   # same as run_prebuilt("summarizer", ...)
adriane_ai.prebuilt.classifier("is this spam?", provider="mistral") # override forwarded through

run_component and run_prebuilt raise ValueError (adriane_ai.RunError) on an unknown kind/agent, invalid input, or an engine/runtime failure.

Install

A single cp39-abi3 wheel covers CPython 3.9+ (the extension targets the stable ABI), so nothing compiles on the user's machine:

pip install adriane-ai

import adriane_ai
print(adriane_ai.engine_version())

From source (dev)

The package is built with maturin over the Rust workspace crate crates/py-bindings, driven by python/pyproject.toml:

. "$HOME/.cargo/env"
python3 -m venv .venv && source .venv/bin/activate
pip install maturin

cd python
maturin develop            # build the extension + install into the active venv
# …or build a distributable wheel:
maturin build --release    # -> target/wheels/adriane_ai-<version>-cp39-abi3-*.whl

python -c "import adriane_ai; print(adriane_ai.engine_version())"

maturin compiles the pyo3 cdylib and places it as the adriane_ai.adriane submodule — the leaf import name adriane resolves the PyInit_adriane symbol emitted by #[pymodule] fn adriane in crates/py-bindings/src/lib.rs.

Tests

cd python
maturin develop                 # build + install the extension into the venv
python -m pytest tests -q       # if pytest is installed
python tests/test_adriane.py    # plain-assert fallback when pytest is absent