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@ai-operations/ops-core
Advanced tools
Pipeline engine for AI Operations OS — intent classification, state machine, LLM integration, structured logging
Workflow engine, state machine, and intent classification for AI Operations OS.
Part of AI Operations OS — autonomous business workflow orchestration with safety enforcement.
npm install @ai-operations/ops-core
import { WorkflowEngine, IntentClassifier } from '@ai-operations/ops-core';
const classifier = new IntentClassifier();
const intent = classifier.classify('Please reply to John about the meeting');
// => 'reply'
const engine = new WorkflowEngine(connectorRegistry, safetyGate);
for await (const event of engine.execute(workflowRun)) {
console.log(event.type, event);
}
WorkflowEngineSequential step executor with safety gates. Drives a WorkflowRun through its steps, yielding typed events via an AsyncGenerator.
constructor(connectors: ConnectorRegistry, safetyGate: SafetyGate)
async *execute(run: WorkflowRun): AsyncGenerator<WorkflowEvent>
pause(): void
resume(): void
Event types: step_start | step_complete | step_blocked | step_failed | run_complete | run_failed
StateMachineEnforces valid workflow step state transitions with O(1) lookup.
const sm = new StateMachine();
sm.transition('pending', 'start'); // => 'running'
sm.canTransition('running', 'complete'); // => true
sm.validEvents('blocked'); // => ['approve']
Step events: start | complete | fail | block | approve | pause | resume
IntentClassifierKeyword-based heuristic intent classification. Fast, deterministic first pass.
const classifier = new IntentClassifier();
const result = classifier.classifyDetailed('Please reply to John');
// => { intent: 'reply', confidence: 'low', matchedKeywords: ['reply'] }
LLMIntentClassifierLLM-backed classification with heuristic fallback. Supports Anthropic, OpenAI, and Ollama providers via environment variables.
const llm = new LLMIntentClassifier();
const intent = await llm.classify('Can you handle the refund for order #123?');
// => 'refund' (via LLM when heuristic confidence is low)
Environment variables: OPS_LLM_PROVIDER, ANTHROPIC_API_KEY, OPENAI_API_KEY, OLLAMA_URL
TaskStoreIn-memory + JSON file task persistence with filtering.
Connector — { name: string; execute(operation, input): Promise<Record<string, unknown>> }ConnectorRegistry — { get(name): Connector | undefined }SafetyGate — (step, run) => Promise<SafetyGateResult>SafetyGateResult — { decision: CordDecision; score: number; reason: string }@ai-operations/shared-types — Task, WorkflowRun, and other core types@ai-operations/ops-connectors — Connector implementations (Gmail, Calendar, X, Shopify)@ai-operations/cord-adapter — CORD safety gate integration@ai-operations/ops-policy — Policy rules and autonomy managementMIT
FAQs
Pipeline engine for AI Operations OS — intent classification, state machine, LLM integration, structured logging
We found that @ai-operations/ops-core demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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Security News
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