convilyn-edge 0.1.0b21

Convilyn Edge AI Workflow SDK — the 7-primitive Device Data Plane SPI for building auditable, offline-capable edge AI workflows

convilyn-edge

The Convilyn Edge AI Workflow SDK — the Device Data Plane + Edge Runtime SPI for building auditable, offline-capable edge/IoT AI workflows.

Public mirror of Convilyn's primary repository (the source of truth). Contributions are welcome and land in the shipped package — see CONTRIBUTING.md (fork → PR → upstreamed, authorship preserved).

Alpha (0.1.0b series). v0.1 ships the seven typed SPI Protocols + the Event Envelope + a Result type — with zero runtime dependencies — plus, landed across the 0.1 beta series: the client_compute on-device model keystone (convilyn_edge.clientcompute), the durable offline queue + emitter (convilyn_edge.offline), the device simulator + convilyn-edge CLI (convilyn_edge.simulator / .cli), and a capability probe. Vertical scenario logic ships as removable Solution Packs built on this SPI — never inside this package. Each section below documents the module as it exists in this release.

What this is (and is not)

Convilyn splits an edge AI product into three planes:

Plane	Home
AI Workflow Plane — SOP lookup, explain, re-ground, HITL, escalate, gated tools + the 7 server-enforced safety checks	the Convilyn cloud service
Device Data Plane + Edge Runtime + adapter/provider SPI	this package (convilyn-edge)
Vertical logic — scenario rules, device state, workflows	a removable Solution Pack (its own package, built on this SPI)

Convilyn ships the SPI + a simulator + reference adapters only — never hardware drivers or action connectors. Real OPOS/.NET, Zebra/Kotlin, serial / MQTT / camera adapters and any device actuation beyond R0/R1 are integrator / community work. That boundary — what the SDK ships vs. integrator work — is the anti-divergence guarantee.

Build once, run anywhere. Workflows are authored in Convilyn's chat-driven Builder — a shared, client-agnostic capability in the AI Workflow Plane, not part of this SDK. Every client (web, desktop, or device) then runs that same compiled workflow (uw_…); the Edge SDK consumes workflows via its ModelOperator (cloud placement wraps the consumer SDK's client.goals.run; edge placement runs a local model), it never builds one.

The 7 primitives (convilyn_edge.spi)

Each is one narrow Protocol — depend on the interface, not a runtime (DIP/ISP).

#	Primitive	Essence
1	EventSource	events enter the SDK → AsyncIterator[EventEnvelope]
2	Normalizer[Raw, Canonical]	raw vendor payload → canonical event (Result, sync)
3	StateProvider[T]	environment state at event time (async)
4	DeterministicOperator[In, Out]	pure, no-LLM rules (Result, sync)
5	ModelOperator[In, Out]	typed inference — edge/cloud/auto (keystone)
6	HumanReview	structured human-in-the-loop → typed ReviewOutcome
7	ActionSink[In, Out]	gated side effects, risk R0–R3

Everything crosses the SDK inside one EventEnvelope (uniform id / schema version / correlation / ordering — the basis for dedup, replay, and audit).

from convilyn_edge import new_envelope, EventSourceRef, Ok, Err

env = new_envelope(
    event_type="device.barcode.scan.received",
    event_schema="convilyn://schemas/barcode-scan/v1",
    source=EventSourceRef("scanner-8f-03", "opos-scanner", "0.3.1"),
    data={"scanData": "4711234567890", "symbology": "EAN13"},
)
wire = env.to_wire()                    # camelCase JSON object
assert EventEnvelope.from_wire(wire) == env

Client-compute — the on-device keystone (convilyn_edge.clientcompute)

When a cloud workflow routes the extractor role to the device, it pauses with a client_compute interrupt and hands the device a content-free delegation request (files by reference only). The device runs a local model over its own copy of the file and returns grounded anchors; the server re-grounds them before trusting them. convilyn-edge confirms-and-consumes that frozen contract:

import os
from convilyn import AsyncConvilyn
from convilyn_edge.clientcompute import (
    ClientComputeBridge, EdgeModelOperator, HttpLocalExtractor,
)

# A local inference server (Ollama / any OpenAI-compatible endpoint), chosen by env.
operator = EdgeModelOperator(HttpLocalExtractor.from_env(os.environ))
# `resolver.resolve(file_id) -> local text` — the device reads its OWN file copy.
bridge = ClientComputeBridge(operator, resolver)

async with AsyncConvilyn() as client:
    job = await client.goals.wait(job_id)
    # If the cloud delegated an extract step, fulfil it locally and resume:
    updated = await bridge.handle_if_present(client.goals, job)

The consumer SDK is injected (a narrow GoalClientPort Protocol), never imported — so convilyn-edge itself stays dependency-free. Values that aren't a verbatim substring of the local source degrade to "Not specified" on the device, exactly as the server would degrade them — an ungrounded (possibly injected) string never crosses the boundary.

Offline-first (convilyn_edge.offline)

The device keeps working when the cloud is unreachable — structured events buffer durably and flush exactly once on reconnect:

from pathlib import Path
from convilyn_edge.offline import DurableQueue, EventEmitter, event_key

queue = DurableQueue(Path("edge-events.jsonl"), key_of=event_key)
emitter = EventEmitter(sink, queue)          # sink: EventSink (your HTTP/MQTT transport)

await emitter.emit(envelope)                 # delivered, or durably buffered if offline
report = await emitter.flush()               # drain on reconnect; report.clean == True

Enqueue is idempotent (keyed by the envelope's unique event_id), and derive_idempotency_key reproduces the server's content-addressed reconcile key byte-for-byte — so a retried flush is a no-op, never a duplicate.

The manufactured contract (convilyn_edge.authored)

A workflow authored on the Convilyn platform compiles its decision-critical AI node into a grounded contract: the prompt, the typed output fields, and a deterministic grounding rule per field. The artifact ships to the device inside a uw_* bundle; on-device you mount it in one line:

from convilyn_edge.authored import ContractModelOperator

operator = ContractModelOperator.for_contract("installed/pet_cat_locate.uw.json")
result = await operator.infer({"scene": scene_text}, schema={})

for_contract loads the artifact, builds the reference HTTP-local extractor from the environment (EDGE_LLM_URL → OpenAI-compatible, else Ollama), resolves the local model tag from the contract's model_binding, and wires the closed_set steering below — all overridable (extractor=, model=, env=). The multi-step assembly (load_contract + your own runner) remains for full control. A supplied extractor= still composes with steering: a guidance-empty HttpLocalExtractor gets the contract's guidance injected automatically, an extractor that can't carry guidance triggers a loud UserWarning, and steering="caller" declares steering caller-managed (silent, as-is).

Environment contract (from_env)

These are the canonical variables the reference extractor reads — packs should map their own configuration onto these names rather than invent parallel ones:

Variable	Meaning	Default
EDGE_LLM_URL	OpenAI-compatible base URL; setting it selects the openai-compat backend	unset (→ Ollama)
EDGE_LLM_API_KEY	Bearer token for the OpenAI-compatible server	unset
EDGE_LLM_MODEL	Model tag (overridden by an explicit model= / contract binding)	qwen3:4b
OLLAMA_BASE	Ollama base URL when EDGE_LLM_URL is unset	http://localhost:11434

Generation params & honest degradation (reasoning models)

Reasoning models can silently spend their whole token budget thinking — producing an empty answer that a naive integration reads as "server down". Both are first-class now:

operator = ContractModelOperator.for_contract(
    "installed/pet_cat_locate.uw.json",
    max_tokens=4096,        # openai-compat max_tokens / ollama num_predict
    reasoning=False,        # ollama think:false; openai-compat chat_template_kwargs
    extra_body={"reasoning_effort": "low"},  # vendor passthrough, merged last
)

result = await operator.infer({"scene": scene_text}, schema={})
if result.status == "unavailable":
    result.degrade_reason   # "server_unreachable" | "deadline_exceeded"
                            # | "output_unparseable" | "error"

degrade_reason is the difference between "the server is offline" and "the model ran and produced nothing parseable" — assert on it in your load-bearing test tier (below) so a green fallback can't hide a dead model.

The two per-field weapons: closed_set and field_guidance

Every contract field carries one of two deterministic grounding modes:

• verbatim — the value must appear (whitespace-collapsed) in the device's own source text. For extraction: quotes, names, readings. Anything else degrades to the missing sentinel — blank over fabrication.
• closed_set — the value must normalise into the field's authored allowed_values, and the grounded output is always the authored canonical label, never the model's raw string. For classification and derived answers ("is the cat present" → {"true","false"}) — values that legitimately never appear verbatim in the source.

closed_set fields need one more thing: an unguided model is steered by the blanket "answer verbatim from the source" rule, which a closed-set answer can never satisfy. guidance_from_contract(contract) renders each closed_set field's authored labels as a per-field answer rule; pass it as the extractor's field_guidance and each such field is steered toward its own label set (for_contract does this automatically):

from convilyn_edge.authored import guidance_from_contract, load_contract
from convilyn_edge.clientcompute.engine import HttpLocalExtractor

contract = load_contract("installed/pet_cat_locate.uw.json")
extractor = HttpLocalExtractor(
    model="qwen3:4b", field_guidance=guidance_from_contract(contract)
)

Steering is advisory; grounding is enforced regardless. A model that answers off-set is degraded to the sentinel — never trusted.

Determinism-by-design ≠ model correctness (tiered testing)

The SDK's guarantees are structural, and it's important to test at the right tier:

• T0 — structure & schema (the SDK guarantees this). Every infer returns a total, schema-shaped field dict; closed_set answers are always authored labels; verbatim answers always appear in your sources; failures degrade to the sentinel instead of fabricating. You don't need to test any of that — it's pinned by the SDK's own suite.
• T1 — model output quality (you must test this). Whether your local model on your hardware answers your scenes correctly is not something the SDK can promise. Build a small fixed eval set and assert per-case:

CASES = [  # (scene text, expected grounded fields) — grow this from real traffic
    ("Cat curled on the sofa by the window.", {"present": "true", "zone": "sofa"}),
    ("Empty room, feeder untouched.", {"present": "false"}),
]

async def evaluate(operator) -> float:
    passed = 0
    for scene, expected in CASES:
        result = await operator.infer({"scene": scene}, schema={})
        got = result.output or {}
        passed += all(got.get(k) == v for k, v in expected.items())
    return passed / len(CASES)  # gate your rollout on a floor, e.g. >= 0.9

Run it against every model/quantisation/prompt change and compare to the last score before swapping anything in production — a candidate model that scores below your floor never ships, no matter how good one demo answer looked.

CLI — simulate with no hardware (convilyn-edge)

A developer shouldn't need a real scanner to test a workflow. Replay a JSON scenario through the built-in simulator:

convilyn-edge simulate scenario.json --no-delay    # prints one wire-JSON envelope per event
convilyn-edge init adapter my-sensor               # scaffold a device adapter
convilyn-edge init workflow my-workflow            # scaffold a workflow (workflow.py Pipeline skeleton)
python -m convilyn_edge.cli --help                 # same CLI on vendored/no-pip installs

A scenario declares a device and an ordered list of events (each with an optional delay_ms / repeat); SimulatedSource — the first concrete EventSource — replays it as an EventEnvelope stream. (dev run / trace replay land in v0.2 with the workflow executor; simulate --no-delay is the deterministic replay.)

Device-RAM fit guard (convilyn_edge.runners)

Declare a model's minimum device RAM on its RunnerConfig and the selector warns before the model loads — never an OOM diagnosed after:

from convilyn_edge.runners import RunnerConfig, select_runner

config = RunnerConfig(model="qwen3:4b", min_ram_mb=4096)
runner = select_runner("llama_cpp", config)            # logs a warning if short
runner = select_runner("llama_cpp", config, strict_fit=True)  # raises RamFitError instead

By default a shortfall only warns (you may know your device better than the probe); strict_fit=True turns it into a hard RamFitError. check_ram_fit() returns the underlying RamFitReport (both numbers + message) for your own UX, and available_ram_mb=... checks fit against another device's manifest instead of the local host. Omit min_ram_mb and nothing changes — the check is opt-in.

Health vs warmup — cold start ≠ offline (convilyn_edge.warmup)

health() answers "is the local inference server reachable?" — it says nothing about whether the model weights are loaded. The first inference after boot can take tens of seconds on an edge device; without a warmup probe that latency is indistinguishable from an outage. warmup() is the assertable three-state answer:

from convilyn_edge.runners import RunnerConfig, select_runner, warmup_runner

runner = select_runner("ollama", RunnerConfig(model="qwen3:4b"))
report = runner.warmup(deadline_ms=30_000)   # pay the cold start before opening
if report.state == "unreachable":
    show_offline_banner(report.detail)       # a different fix than "loading…"
elif report.state == "cold_started":
    log_startup_latency(report.latency_ms)   # the next request will be fast
# "warm" → the model was already loaded; nothing to do

unreachable is never faked as a slow cold start (reachability is checked first), and a probe that exceeds its deadline while the server stays up reports cold_started — the model is loading, not offline. Runners without a warmup hook are handled generically: warmup_runner(runner) returns already-warm for them, so the call is always safe. ContractModelOperator forwards both hooks (operator.warmup(...) / operator.health()) — no second extractor needed just to warm the manufactured-contract path.

One more doctor check: health() proves the server is up, not that it can serve your model. model_available() answers the binding question:

report = operator.model_available()   # also on HttpLocalExtractor / OpenAICompatRunner
report.state   # "available" | "missing" | "unreachable" | "unknown"

missing means the server listed its models and the bound tag isn't there (keep your banner honest); an empty or unrecognizable listing is unknown, never a false missing. Doctor-surface data — never gate execution on it.

Design principles (enforced in code, not just docs)

• The device is never a second source of truth. The server holds the 7 server-enforced safety checks and re-grounds every device value; the edge SPI inherits that contract.
• No LLM in DeterministicOperator — a sync signature makes "no I/O, no model" a type-level guarantee. Scenario rules live in a removable pack.
• One envelope, one Result, one observability convention. No parallel transports; no if provider == ....

The removability check

Delete an entire vertical Solution Pack. Does the remaining SDK still let you build another IoT AI workflow?

If yes, this is a general SDK — not a vertical wearing an SDK costume. That question is a committed CI lint in the package.

Install

uv add --prerelease=allow convilyn-edge   # or: pip install --pre convilyn-edge

Python ≥ 3.10. Zero runtime dependencies. Runnable examples live in examples/ — start with examples/drive_pipeline.py. The examples (including the pet_monitoring reference Solution Pack) travel in the source distribution and the public mirror, not the wheel — grab them with pip download --no-binary :all: convilyn-edge or from the repository.

License

Apache-2.0.