kernell-os-sdk 2.2.0b0

First public release of an agentic runtime for M2M coordination. Machines coordinate, verify, and settle value autonomously.

Kernell OS SDK

Policy-driven execution infrastructure for agent systems

Kernell OS SDK is not a simple LLM wrapper.
It is an execution control plane that optimizes inference cost, quality, and latency while collecting feedback to improve routing decisions over time.

What This SDK Is

• Policy-driven inference engine
• Multi-tier execution router (local, cheap API, premium API)
• Quality-aware runtime with verification and safe fallback
• Telemetry + labeling + training pipeline for continuous improvement
• Agent runtime foundation (security, sandboxing, economics, marketplace, governance)

What This SDK Is Not

• Prompt utility library
• Single-model client wrapper
• Static rule router without learning loop

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Core Value Proposition

Traditional flow:

input -> one LLM

Kernell OS flow:

input -> policy decision -> multi-layer execution -> verification -> telemetry -> retraining

Why this matters

• Optimize costs without blindly degrading quality
• Route dynamically per task/hardware/risk
• Learn from production behavior (data flywheel)
• Keep fallback safety under uncertainty

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Architecture Overview

Task Input
  -> Policy Model (Lite/Pro)
  -> (Optional) Task Decomposition
  -> Execution Layers:
       [Semantic Cache] -> [Local] -> [Cheap API] -> [Premium]
  -> Self Verification
  -> Re-route / Fallback (if needed)
  -> Telemetry
  -> Offline Labeling
  -> Dataset / Fine-tuning Pipeline

Routing Strategy

The router is policy-driven, not difficulty-only:

• route: local | cheap | premium | hybrid
• confidence
• risk
• expected_cost_usd
• expected_latency_s
• needs_decomposition

When confidence/risk/economic uncertainty is unsafe, it forces hybrid fallback path.

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Main Components (Real Modules)

Router and Policy

• kernell_os_sdk/router/intelligent_router.py
  Orchestrates execution across cache/local/cheap/premium, verification, telemetry.
• kernell_os_sdk/router/policy_lite.py
  Local policy model client with safety overrides.
• kernell_os_sdk/router/classifier_pro.py
  Cloud escalation client for higher-precision routing.
• kernell_os_sdk/router/types.py
  Canonical contracts (PolicyDecision, tiers, results).
• kernell_os_sdk/router/entrypoint.py
  Shadow/canary/full rollout entrypoint with safe fallback.

Quality, Cost, and Context

• kernell_os_sdk/router/verifier.py (SelfVerifier)
• kernell_os_sdk/router/estimator.py (CostEstimator)
• kernell_os_sdk/router/summarizer.py (RollingSummarizer)
• kernell_os_sdk/router/decomposer.py (TaskDecomposer)
• kernell_os_sdk/router/model_registry.py (ModelRegistry)

Telemetry and Learning Loop

• kernell_os_sdk/router/telemetry_collector.py
  Collects anonymized route/outcome/quality signals.
• kernell_os_sdk/router/offline_labeler.py
  Produces optimal-route labels from real outcomes (cost + quality aware).

Data Pipeline Scripts

• scripts/policy_build_dataset.py -> telemetry to labeled dataset
• scripts/policy_make_sft_jsonl.py -> labeled dataset to SFT JSONL
• scripts/policy_train_lora.py -> LoRA training scaffold
• scripts/policy_audit_dataset.py -> distribution and sampling audit

Runtime, Security, and Infra Domains

• kernell_os_sdk/runtime/
  Firecracker/Docker/Subprocess/hybrid runtime primitives
• kernell_os_sdk/security/
  policy, verifier, SSRF and capability controls
• kernell_os_sdk/cognitive/
  memory graph, execution graph, semantic modules
• kernell_os_sdk/marketplace/, governance/, cluster/, delegation/, escrow/
  economic coordination and distributed agent primitives

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Data Flywheel

Kernell OS improves routing through a closed learning loop:

1. Runtime emits telemetry from real executions
2. Offline labeler computes optimal route targets
3. Dataset is generated and audited
4. Model fine-tuning is prepared/executed
5. Updated policy models are deployed

This converts routing mistakes into training signal (underestimation, overestimation, misroute) and enables continuous optimization.

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Safety and Production Hardening

• Verification-aware routing to reduce low-quality escalations
• Risk-aware and budget-aware fallback to hybrid
• Optional Prometheus dependency (non-blocking no-op fallback in runtime metrics)
• Shadow/canary rollout strategy before full traffic cutover
• Rate limiting, sandbox controls, and capability policy modules available in SDK

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Installation

pip install kernell-os

Optional observability dependency:

pip install prometheus_client

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Minimal Example

from kernell_os_sdk.router import PolicyLiteClient, PolicyLiteConfig

# Integrate PolicyLiteClient into your IntelligentRouter wiring
# based on your local model backend and runtime configuration.

See kernell_os_sdk/router/ modules and tests for concrete integration patterns.

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Testing

Router and flywheel suites:

python -m pytest tests/test_router.py tests/test_data_flywheel.py tests/test_policy_lite.py -q

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Current Status

Implemented now

• Policy-driven router integration
• Telemetry v2 with policy signals
• Offline labeling pipeline with quality-aware guards
• Dataset audit tooling and sampling workflow
• Regression-tested router/flywheel suites

Planned / in progress

• Persistent semantic cache backend integration (distributed)
• Full training automation and model promotion gates
• Expanded online feedback correction and deploy gating benchmarks

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Open-Core Positioning

• SDK core: open source runtime and routing foundations
• Advanced policy intelligence and cloud services: source-available/commercial layers

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From static LLM calls to adaptive inference infrastructure.