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`createSpan`, `Span`, `SpanData`, `SpanEvent`, `SpanKind`, `SpanStatus`, `SpanHandler` | Low-level span creation with nested context propagation |\n| **Cost Engine** | `createCostEngine`, `registerModelTier`, `CostEngine`, `CostEngineConfig`, `BudgetConfig`, `LoopDetectionConfig`, `CostAlert`, `CostDimension`, `CostIntelligenceReport`, `ModelSuggestion`, `ModelTierEntry` | Budget enforcement, cost projections, loop detection, and model optimization suggestions |\n| **Tracer** | `observe`, `Tracer`, `TracerConfig`, `TracerOutput`, `TracerExporter`, `CostReport` | High-level tracing with sampling, console output, and custom exporters |\n| **Metrics** | `createMetrics`, `MetricsCollector`, `MetricEntry` | Counters, gauges, and histograms for application-level metrics |\n| **Audit Trail** | `createAuditTrail`, `auditMiddleware`, `auditStreamMiddleware`, `AuditEventType`, `AuditEvent`, `AuditStorageAdapter`, `AuditQueryFilter`, `AuditIntegrityResult`, `AuditTrailConfig`, `AuditTrail` | SHA-256 hash-chained audit events with tamper detection, middleware for both completion and streaming calls |\n| **Audit Sinks** | `createSinkManager`, `createWebhookSink`, `createSplunkSink`, `createDatadogSink`, `AuditSink`, `AuditSinkRetryConfig`, `SinkManagerConfig`, `SinkManager`, `WebhookSinkConfig`, `SplunkSinkConfig`, `DatadogSinkConfig` | Export audit events to external systems (webhooks, Splunk, Datadog) with batching, retry, per-sink filtering, and dead letter queue |\n| **Provenance** | `createProvenanceTracker`, `ProvenanceRecord`, `ProvenanceTracker` | Full lineage tracking per output: prompt, model, config, input, output |\n| **Verifiable Agent Execution** | `createProofRecorder`, `verifyProof`, `compareProofs`, `ProofRecorder`, `ProofSession`, `ExecutionProof`, `ProofEvent`, `VerifyProofResult`, `ReplayDiff`, `ReplayStrategy` | Signed per-run execution proofs with hash-chained events. Build on `@elsium-ai/core` Ed25519 + `WriteOnceStore`. Third parties verify proofs offline with just the public key, and `compareProofs(a, b, { strategy })` diffs two proofs for reproducibility. CLI: `elsium verify` and `elsium replay`. |\n| **AI-BOM** | `generateAiBom`, `verifyAiBom`, `diffAiBom`, `passesGate`, `serializeAiBom`, `AiBom`, `AiBomInput`, `AiBomDiff`, `ComponentDrift`, `DriftSeverity`, `VerifyAiBomResult` | Ed25519-signed manifest of what an agent is made of — models, prompts, tools (with sandbox capabilities), MCP servers, eval datasets, policies, thresholds. Deterministic and order-independent; `diffAiBom` ranks composition drift by whether the blast radius grew. Where a proof records one run, a BOM records the composition every run inherits. CLI: `elsium bom verify` and `elsium bom diff`. |\n| **Studio Exporter** | `createStudioExporter`, `StudioExporter`, `StudioExporterConfig` | Write traces, X-Ray, and costs to `.elsium/` for the `elsium studio` dashboard |\n| **OpenTelemetry** | `toOTelSpan`, `toOTelExportRequest`, `toTraceparent`, `parseTraceparent`, `injectTraceContext`, `extractTraceContext`, `createOTLPExporter`, `OTelSpan`, `OTelSpanKind`, `OTelStatusCode`, `OTelAttribute`, `OTelAttributeValue`, `OTelEvent`, `OTelResource`, `OTelExportRequest`, `TraceContext`, `OTLPExporterConfig` | W3C Trace Context propagation, OTel span conversion, and OTLP JSON export |\n\n---\n\n## Spans\n\nLow-level building blocks for distributed tracing. Each span records a named operation with timing, metadata, events, and parent-child relationships.\n\n### `SpanKind`\n\n```ts\ntype SpanKind = 'llm' | 'tool' | 'agent' | 'workflow' | 'custom'\n```\n\n### `SpanStatus`\n\n```ts\ntype SpanStatus = 'running' | 'ok' | 'error'\n```\n\n### `SpanEvent`\n\n```ts\ninterface SpanEvent {\n name: string\n timestamp: number\n data?: Record\n}\n```\n\n### `SpanData`\n\nThe serializable representation of a span, returned by `span.toJSON()`.\n\n```ts\ninterface SpanData {\n id: string\n traceId: string\n parentId?: string\n name: string\n kind: SpanKind\n status: SpanStatus\n startTime: number\n endTime?: number\n durationMs?: number\n metadata: Record\n events: SpanEvent[]\n}\n```\n\n### `Span`\n\nThe live span interface used during operation execution.\n\n```ts\ninterface Span {\n readonly id: string\n readonly traceId: string\n readonly name: string\n readonly kind: SpanKind\n\n addEvent(name: string, data?: Record): void\n setMetadata(key: string, value: unknown): void\n end(result?: { status?: SpanStatus; metadata?: Record }): void\n child(name: string, kind?: SpanKind): Span\n toJSON(): SpanData\n}\n```\n\n### `SpanHandler`\n\nCallback invoked when a span ends.\n\n```ts\ntype SpanHandler = (span: SpanData) => void\n```\n\n### `createSpan()`\n\nCreates a new span for tracing an operation.\n\n```ts\nfunction createSpan(\n name: string,\n options?: {\n traceId?: string\n parentId?: string\n kind?: SpanKind\n onEnd?: SpanHandler\n },\n): Span\n```\n\n| Parameter | Type | Default | Description |\n|---|---|---|---|\n| `name` | `string` | -- | Human-readable name for the operation |\n| `options.traceId` | `string` | auto-generated | Trace ID to group related spans |\n| `options.parentId` | `string` | `undefined` | Parent span ID for nesting |\n| `options.kind` | `SpanKind` | `'custom'` | Category of work being performed |\n| `options.onEnd` | `SpanHandler` | `undefined` | Callback fired when `span.end()` is called |\n\n**Returns:** `Span`\n\n```ts\nimport { createSpan } from '@elsium-ai/observe'\n\nconst span = createSpan('generate-summary', { kind: 'llm' })\nspan.setMetadata('model', 'gpt-4o')\nspan.addEvent('prompt-sent', { tokens: 120 })\n\n// Create a child span for a sub-operation\nconst child = span.child('embed-context', 'tool')\nchild.end()\n\nspan.end({ status: 'ok', metadata: { outputTokens: 350 } })\n\nconsole.log(span.toJSON())\n```\n\n---\n\n## Cost Engine\n\nBudget enforcement and cost intelligence for LLM usage. Tracks spend across models, agents, users, and features. Detects runaway loops and suggests cheaper model alternatives.\n\n### `BudgetConfig`\n\n```ts\ninterface BudgetConfig {\n totalBudget?: number\n dailyBudget?: number\n perUser?: number\n perFeature?: number\n perAgent?: number\n}\n```\n\n### `LoopDetectionConfig`\n\n```ts\ninterface LoopDetectionConfig {\n maxCallsPerMinute?: number\n maxCostPerMinute?: number\n}\n```\n\n### `CostAlert`\n\n```ts\ninterface CostAlert {\n type: 'threshold' | 'loop_detected' | 'budget_exceeded' | 'projection_warning'\n dimension: string\n currentValue: number\n limit: number\n message: string\n timestamp: number\n}\n```\n\n### `CostDimension`\n\nAggregated cost data for a single dimension (model, agent, user, or feature).\n\n```ts\ninterface CostDimension {\n totalCost: number\n totalTokens: number\n callCount: number\n firstCallAt: number\n lastCallAt: number\n}\n```\n\n### `CostIntelligenceReport`\n\nFull cost intelligence report with projections and recommendations.\n\n```ts\ninterface CostIntelligenceReport {\n totalSpend: number\n totalTokens: number\n totalCalls: number\n projectedDailySpend: number\n projectedMonthlySpend: number\n byModel: Record\n byAgent: Record\n byUser: Record\n byFeature: Record\n recommendations: string[]\n alerts: CostAlert[]\n}\n```\n\n### `ModelSuggestion`\n\nA recommendation to switch to a cheaper model.\n\n```ts\ninterface ModelSuggestion {\n currentModel: string\n suggestedModel: string\n estimatedSavings: number\n reason: string\n}\n```\n\n### `ModelTierEntry`\n\nDefines the pricing tier for a model.\n\n```ts\ninterface ModelTierEntry {\n tier: 'low' | 'mid' | 'high'\n costPerMToken: number\n}\n```\n\n### `CostEngineConfig`\n\n```ts\ninterface CostEngineConfig {\n totalBudget?: number\n dailyBudget?: number\n perUser?: number\n perFeature?: number\n perAgent?: number\n loopDetection?: LoopDetectionConfig\n onAlert?: (alert: CostAlert) => void\n alertThresholds?: number[]\n}\n```\n\n### `CostEngine`\n\n```ts\ninterface CostEngine {\n middleware(): Middleware\n getReport(): CostIntelligenceReport\n suggestModel(currentModel: string, inputTokens: number): ModelSuggestion | null\n trackCall(\n response: LLMResponse,\n dimensions?: { agent?: string; user?: string; feature?: string },\n ): void\n reset(): void\n}\n```\n\n### `createCostEngine()`\n\nCreates a cost engine with budget enforcement, alerting, and cost intelligence.\n\n```ts\nfunction createCostEngine(config?: CostEngineConfig): CostEngine\n```\n\n| Parameter | Type | Default | Description |\n|---|---|---|---|\n| `config.totalBudget` | `number` | `undefined` | Maximum total spend before calls are rejected |\n| `config.dailyBudget` | `number` | `undefined` | Maximum daily spend rate before alerts fire |\n| `config.perUser` | `number` | `undefined` | Per-user budget limit |\n| `config.perFeature` | `number` | `undefined` | Per-feature budget limit |\n| `config.perAgent` | `number` | `undefined` | Per-agent budget limit |\n| `config.loopDetection` | `LoopDetectionConfig` | `undefined` | Thresholds for detecting runaway loops |\n| `config.onAlert` | `(alert: CostAlert) => void` | `undefined` | Callback for every alert |\n| `config.alertThresholds` | `number[]` | `undefined` | Budget percentage thresholds that trigger alerts (e.g. `[0.5, 0.8, 0.95]`) |\n\n**Returns:** `CostEngine`\n\n```ts\nimport { createCostEngine } from '@elsium-ai/observe'\n\nconst engine = createCostEngine({\n totalBudget: 50.0,\n dailyBudget: 5.0,\n perAgent: 10.0,\n loopDetection: { maxCallsPerMinute: 60, maxCostPerMinute: 1.0 },\n alertThresholds: [0.5, 0.8, 0.95],\n onAlert: (alert) => console.warn(alert.message),\n})\n\n// Use as middleware in an ElsiumAI gateway\n// gateway.use(engine.middleware())\n\n// Or track calls manually\n// engine.trackCall(response, { agent: 'summarizer', user: 'user-123' })\n\n// Get a full intelligence report\nconst report = engine.getReport()\nconsole.log('Projected monthly spend:', report.projectedMonthlySpend)\nconsole.log('Recommendations:', report.recommendations)\n\n// Get model optimization suggestions\nconst suggestion = engine.suggestModel('claude-opus-4-6', 200)\nif (suggestion) {\n console.log(`Switch to ${suggestion.suggestedModel} for ${suggestion.estimatedSavings.toFixed(0)}% savings`)\n}\n```\n\n### `registerModelTier()`\n\nRegisters a custom model with its pricing tier so the cost engine can track it.\n\n```ts\nfunction registerModelTier(model: string, entry: ModelTierEntry): void\n```\n\n| Parameter | Type | Description |\n|---|---|---|\n| `model` | `string` | The model identifier |\n| `entry` | `ModelTierEntry` | The tier classification and cost per million tokens |\n\n```ts\nimport { registerModelTier } from '@elsium-ai/observe'\n\nregisterModelTier('my-custom-model', { tier: 'mid', costPerMToken: 1.5 })\n```\n\nThe cost engine ships with built-in tiers for common models including GPT-4o, GPT-4.1, GPT-5, Claude Sonnet 4.6, Claude Opus 4.6, Claude Haiku 4.5, Gemini 2.0 Flash, Gemini 2.5 Pro, o1, o3, o4-mini, and more.\n\n---\n\n## Tracer\n\nHigh-level tracing API that wraps spans with sampling, console output, cost tracking, and pluggable exporters.\n\n### `TracerOutput`\n\n```ts\ntype TracerOutput = 'console' | 'json-file' | TracerExporter\n```\n\n### `TracerExporter`\n\nInterface for custom span exporters.\n\n```ts\ninterface TracerExporter {\n name: string\n export(spans: SpanData[]): void | Promise\n}\n```\n\n### `TracerConfig`\n\n```ts\ninterface TracerConfig {\n output?: TracerOutput[]\n costTracking?: boolean\n samplingRate?: number\n maxSpans?: number\n}\n```\n\n### `CostReport`\n\n```ts\ninterface CostReport {\n totalCost: number\n totalTokens: number\n totalInputTokens: number\n totalOutputTokens: number\n callCount: number\n byModel: Record<\n string,\n {\n cost: number\n tokens: number\n calls: number\n }\n >\n}\n```\n\n### `Tracer`\n\n```ts\ninterface Tracer {\n startSpan(name: string, kind?: SpanKind): Span\n getSpans(): SpanData[]\n getCostReport(): CostReport\n trackLLMCall(data: {\n model: string\n inputTokens: number\n outputTokens: number\n cost: number\n latencyMs: number\n }): void\n reset(): void\n flush(): Promise\n}\n```\n\n### `observe()`\n\nCreates a tracer instance for recording spans and LLM call costs.\n\n```ts\nfunction observe(config?: TracerConfig): Tracer\n```\n\n| Parameter | Type | Default | Description |\n|---|---|---|---|\n| `config.output` | `TracerOutput[]` | `['console']` | Where to send completed spans |\n| `config.costTracking` | `boolean` | `true` | Whether `trackLLMCall` records data |\n| `config.samplingRate` | `number` | `1.0` | Fraction of spans to sample (0.0 to 1.0) |\n| `config.maxSpans` | `number` | `10000` | Maximum spans held in memory before oldest are evicted |\n\n**Returns:** `Tracer`\n\n```ts\nimport { observe } from '@elsium-ai/observe'\n\nconst tracer = observe({\n output: ['console'],\n samplingRate: 1.0,\n costTracking: true,\n})\n\n// Start a span\nconst span = tracer.startSpan('chat-completion', 'llm')\nspan.setMetadata('model', 'gpt-4o')\nspan.end()\n\n// Track an LLM call for cost reporting\ntracer.trackLLMCall({\n model: 'gpt-4o',\n inputTokens: 500,\n outputTokens: 200,\n cost: 0.0035,\n latencyMs: 1200,\n})\n\n// Get cost report\nconst report = tracer.getCostReport()\nconsole.log('Total cost:', report.totalCost)\n\n// Flush spans to all exporters\nawait tracer.flush()\n```\n\nYou can provide a custom exporter:\n\n```ts\nimport { observe } from '@elsium-ai/observe'\nimport type { TracerExporter } from '@elsium-ai/observe'\n\nconst myExporter: TracerExporter = {\n name: 'my-backend',\n async export(spans) {\n await fetch('https://my-telemetry.example.com/spans', {\n method: 'POST',\n body: JSON.stringify(spans),\n })\n },\n}\n\nconst tracer = observe({ output: [myExporter] })\n```\n\n---\n\n## Metrics\n\nGeneral-purpose metrics collection with counters, gauges, and histograms.\n\n### `MetricEntry`\n\n```ts\ninterface MetricEntry {\n name: string\n type: 'counter' | 'gauge' | 'histogram'\n value: number\n tags: Record\n timestamp: number\n}\n```\n\n### `MetricsCollector`\n\n```ts\ninterface MetricsCollector {\n increment(name: string, value?: number, tags?: Record): void\n gauge(name: string, value: number, tags?: Record): void\n histogram(name: string, value: number, tags?: Record): void\n getMetrics(): MetricEntry[]\n reset(): void\n}\n```\n\n### `createMetrics()`\n\nCreates a metrics collector for counters, gauges, and histograms.\n\n```ts\nfunction createMetrics(options?: { maxEntries?: number }): MetricsCollector\n```\n\n| Parameter | Type | Default | Description |\n|---|---|---|---|\n| `options.maxEntries` | `number` | `50000` | Maximum metric entries held in memory |\n\n**Returns:** `MetricsCollector`\n\n```ts\nimport { createMetrics } from '@elsium-ai/observe'\n\nconst metrics = createMetrics()\n\n// Increment a counter (default increment is 1)\nmetrics.increment('llm.calls', 1, { model: 'gpt-4o' })\n\n// Set a gauge to a current value\nmetrics.gauge('queue.depth', 42, { queue: 'embeddings' })\n\n// Record a histogram observation\nmetrics.histogram('llm.latency_ms', 1200, { model: 'gpt-4o' })\n\n// Retrieve all recorded entries\nconst entries = metrics.getMetrics()\nconsole.log(`Recorded ${entries.length} metric entries`)\n\n// Reset all state\nmetrics.reset()\n```\n\n---\n\n## Audit Trail\n\nTamper-evident audit logging with SHA-256 hash-chaining. Every event is linked to the previous one via its hash, enabling integrity verification of the full event history.\n\n### `AuditEventType`\n\n```ts\ntype AuditEventType =\n | 'llm_call'\n | 'tool_execution'\n | 'security_violation'\n | 'budget_alert'\n | 'policy_violation'\n | 'auth_event'\n | 'approval_request'\n | 'approval_decision'\n | 'config_change'\n | 'provider_failover'\n | 'circuit_breaker_state_change'\n```\n\n### `AuditEvent`\n\n```ts\ninterface AuditEvent {\n id: string\n sequenceId: number\n type: AuditEventType\n timestamp: number\n actor?: string\n traceId?: string\n data: Record\n hash: string\n previousHash: string\n}\n```\n\n### `AuditStorageAdapter`\n\nInterface for custom audit storage backends (database, file system, etc.).\n\n```ts\ninterface AuditStorageAdapter {\n append(event: AuditEvent): void | Promise\n query(filter: AuditQueryFilter): AuditEvent[] | Promise\n count(): number | Promise\n verifyIntegrity(): AuditIntegrityResult | Promise\n getLastHash?(): string | Promise\n}\n```\n\n### `AuditQueryFilter`\n\n```ts\ninterface AuditQueryFilter {\n type?: AuditEventType | AuditEventType[]\n actor?: string\n traceId?: string\n fromTimestamp?: number\n toTimestamp?: number\n limit?: number\n offset?: number\n}\n```\n\n### `AuditIntegrityResult`\n\n```ts\ninterface AuditIntegrityResult {\n valid: boolean\n totalEvents: number\n brokenAt?: number\n chainComplete?: boolean\n}\n```\n\n### `AuditBatchConfig`\n\n```ts\ninterface AuditBatchConfig {\n size?: number\n intervalMs?: number\n}\n```\n\n| Field | Type | Default | Description |\n|---|---|---|---|\n| `size` | `number` | `100` | Flush the buffer when it reaches this many events. |\n| `intervalMs` | `number` | `50` | Flush the buffer on this interval (ms), regardless of size. |\n\n### `AuditTrailConfig`\n\n```ts\ninterface AuditTrailConfig {\n storage?: AuditStorageAdapter | 'memory'\n hashChain?: boolean\n maxEvents?: number\n batch?: AuditBatchConfig\n sinks?: AuditSink[] | SinkManagerConfig\n context?: Record\n onError?: (error: unknown) => void\n}\n```\n\n### `AuditTrail`\n\n```ts\ninterface AuditTrail {\n log(\n type: AuditEventType,\n data: Record,\n options?: { actor?: string; traceId?: string },\n ): void\n ready(): Promise\n query(filter: AuditQueryFilter): Promise\n verifyIntegrity(): Promise\n flush(): Promise\n dispose(): Promise\n readonly count: number\n readonly pending: number\n}\n```\n\n| Member | Description |\n|---|---|\n| `log()` | Append an event. In batched mode, this buffers the event without hashing — zero CPU on the hot path. |\n| `ready()` | Resolves once async initialization (e.g. `getLastHash`) has completed. |\n| `query()` | Query events by type, actor, traceId, or timestamp range. Auto-flushes pending events in batched mode. |\n| `verifyIntegrity()` | Verify the hash chain has not been tampered with. Auto-flushes pending events in batched mode. |\n| `flush()` | Drain all pending events — computes hashes and writes to storage. No-op when not in batched mode. |\n| `dispose()` | Stop the flush timer, drain remaining events, await pending storage writes, and shut down sinks. Returns a promise — await it on shutdown. |\n| `count` | Total events (stored + pending). |\n| `pending` | Number of buffered events not yet flushed (0 when not in batched mode). |\n\n### `createAuditTrail()`\n\nCreates a hash-chained audit trail for tamper-evident event logging.\n\n```ts\nfunction createAuditTrail(config?: AuditTrailConfig): AuditTrail\n```\n\n| Parameter | Type | Default | Description |\n|---|---|---|---|\n| `config.storage` | `AuditStorageAdapter \\| 'memory'` | `'memory'` | Storage backend for audit events |\n| `config.hashChain` | `boolean` | `true` | Enable SHA-256 hash chaining for tamper detection |\n| `config.maxEvents` | `number` | `10000` | Maximum events retained (ring buffer — O(1) eviction) |\n| `config.batch` | `AuditBatchConfig` | `undefined` | Enable batched mode for high-volume scenarios |\n| `config.sinks` | `AuditSink[] \\| SinkManagerConfig` | `undefined` | Export events to external systems (webhooks, SIEM, etc.) |\n| `config.context` | `Record` | `undefined` | Global fields merged into every event's `data` (e.g. environment, service name, version) |\n| `config.onError` | `(error: unknown) => void` | `undefined` | Error handler for async storage failures |\n\n**Returns:** `AuditTrail`\n\n```ts\nimport { createAuditTrail } from '@elsium-ai/observe'\n\nconst audit = createAuditTrail({ hashChain: true })\n\n// Log events\naudit.log('llm_call', {\n model: 'gpt-4o',\n inputTokens: 500,\n outputTokens: 200,\n cost: 0.0035,\n}, { actor: 'user-123', traceId: 'trc_abc' })\n\naudit.log('tool_execution', {\n tool: 'web-search',\n query: 'latest news',\n resultCount: 10,\n})\n\n// Query events\nconst llmEvents = await audit.query({ type: 'llm_call', limit: 50 })\nconsole.log(`Found ${llmEvents.length} LLM call events`)\n\n// Verify the hash chain has not been tampered with\nconst integrity = await audit.verifyIntegrity()\nconsole.log('Audit chain valid:', integrity.valid)\nconsole.log('Total events:', integrity.totalEvents)\n```\n\n#### Batched mode (high-volume)\n\nIn high-volume scenarios, `log()` computes a SHA-256 hash on every call — blocking the hot path. Batched mode moves hashing off the critical path: `log()` buffers raw event data, and hashing + storage writes happen asynchronously on flush.\n\n```ts\nimport { createAuditTrail } from '@elsium-ai/observe'\n\nconst audit = createAuditTrail({\n batch: {\n size: 500, // Flush after 500 events\n intervalMs: 100, // Or every 100ms, whichever comes first\n },\n maxEvents: 100_000,\n})\n\n// log() is now near-zero cost — just pushes to an internal buffer\naudit.log('llm_call', { model: 'gpt-4o', tokens: 100 })\n\n// Force-flush before reading (query/verifyIntegrity auto-flush)\nawait audit.flush()\n\n// Clean up on shutdown (awaits pending writes and sink delivery)\nprocess.on('SIGTERM', async () => await audit.dispose())\n```\n\nHash chain integrity is fully preserved — events are hashed sequentially during flush, not during `log()`.\n\n#### Context enrichment\n\nAdd global fields to every event without repeating them at each call site:\n\n```ts\nconst audit = createAuditTrail({\n context: {\n env: 'production',\n service: 'my-ai-service',\n version: '1.2.0',\n region: 'us-east-1',\n },\n})\n\naudit.log('llm_call', { model: 'gpt-4o', tokens: 100 })\n// event.data → { env: 'production', service: 'my-ai-service', version: '1.2.0', region: 'us-east-1', model: 'gpt-4o', tokens: 100 }\n```\n\nEvent-specific data always takes precedence over global context fields.\n\n### `auditMiddleware()`\n\nCreates an ElsiumAI middleware that automatically logs every LLM call (success or failure) to the given audit trail.\n\n```ts\nfunction auditMiddleware(auditTrail: AuditTrail): Middleware\n```\n\n| Parameter | Type | Description |\n|---|---|---|\n| `auditTrail` | `AuditTrail` | The audit trail instance to log events to |\n\n**Returns:** `Middleware` (from `@elsium-ai/core`)\n\n```ts\nimport { createAuditTrail, auditMiddleware } from '@elsium-ai/observe'\n\nconst audit = createAuditTrail({ hashChain: true })\nconst middleware = auditMiddleware(audit)\n\n// Use with an ElsiumAI gateway\n// gateway.use(middleware)\n```\n\nThe middleware automatically records `llm_call` events containing `provider`, `model`, `inputTokens`, `outputTokens`, `totalTokens`, `cost`, `latencyMs`, and `stopReason`. On errors, it records the error message and `success: false`.\n\n### `auditStreamMiddleware()`\n\nCreates a `StreamMiddleware` that audits streaming LLM calls. Intercepts `message_end` events to capture token usage and latency, and `error` events to capture failures — all without buffering or blocking the stream.\n\n```ts\nfunction auditStreamMiddleware(auditTrail: AuditTrail): StreamMiddleware\n```\n\n| Parameter | Type | Description |\n|---|---|---|\n| `auditTrail` | `AuditTrail` | The audit trail instance to log events to |\n\n**Returns:** `StreamMiddleware` (from `@elsium-ai/core`)\n\n```ts\nimport { createAuditTrail, auditStreamMiddleware } from '@elsium-ai/observe'\n\nconst audit = createAuditTrail({ hashChain: true })\nconst streamMw = auditStreamMiddleware(audit)\n\n// Use with an ElsiumAI gateway\n// gateway.use({ streamMiddleware: [streamMw] })\n```\n\nThe middleware records `llm_call` events with `provider`, `model`, `inputTokens`, `outputTokens`, `totalTokens`, `latencyMs`, `stopReason`, and `streaming: true`. On errors, it records the error message and `success: false`.\n\n---\n\n## Audit Sinks\n\nExport audit events to external observability and governance systems. Sinks receive finalized `AuditEvent` objects after they are hashed and stored — a sink failure never blocks the audit trail or other sinks.\n\n### `AuditSink`\n\n```ts\ninterface AuditSink {\n name: string\n filter?: (event: AuditEvent) => boolean\n send(events: AuditEvent[]): Promise\n shutdown?(): Promise\n}\n```\n\nThe optional `filter` function controls which events a sink receives. When set, only events for which the filter returns `true` are sent to that sink. Sinks without a filter receive all events.\n\n### `SinkManagerConfig`\n\n```ts\ninterface SinkManagerConfig {\n sinks: AuditSink[]\n batch?: {\n size?: number // default: 50\n intervalMs?: number // default: 5000\n }\n retry?: AuditSinkRetryConfig\n maxBufferSize?: number // default: 10000\n deadLetterSink?: AuditSink\n onError?: (sinkName: string, error: unknown) => void\n}\n```\n\nThe `deadLetterSink` receives events that a sink failed to deliver after all retry attempts. This prevents data loss for compliance-critical audit events.\n\n### `AuditSinkRetryConfig`\n\n```ts\ninterface AuditSinkRetryConfig {\n maxRetries?: number // default: 3\n baseDelayMs?: number // default: 1000\n maxDelayMs?: number // default: 30000\n}\n```\n\n### Quick start\n\nPass sinks directly to `createAuditTrail()`:\n\n```ts\nimport { createAuditTrail, createWebhookSink, createSplunkSink } from '@elsium-ai/observe'\n\nconst audit = createAuditTrail({\n sinks: [\n createWebhookSink({ url: 'https://hooks.example.com/audit' }),\n createSplunkSink({\n url: 'https://splunk:8088/services/collector',\n token: 'your-hec-token',\n }),\n ],\n})\n\naudit.log('security_violation', { threat: 'prompt_injection', score: 0.95 })\n```\n\nFor advanced configuration, pass a `SinkManagerConfig`:\n\n```ts\nconst audit = createAuditTrail({\n sinks: {\n sinks: [createWebhookSink({ url: 'https://hooks.example.com/audit' })],\n batch: { size: 100, intervalMs: 10_000 },\n retry: { maxRetries: 5, baseDelayMs: 2000 },\n maxBufferSize: 50_000,\n onError: (sinkName, error) => {\n console.error(`Sink ${sinkName} failed:`, error)\n },\n },\n})\n```\n\n#### Per-sink filtering\n\nRoute different event types to different destinations:\n\n```ts\nimport { createAuditTrail, createSplunkSink, createDatadogSink } from '@elsium-ai/observe'\n\nconst audit = createAuditTrail({\n sinks: [\n {\n ...createSplunkSink({ url: 'https://splunk:8088/services/collector', token: 'tok' }),\n filter: (event) => event.type === 'security_violation' || event.type === 'auth_event',\n },\n createDatadogSink({ apiKey: process.env.DD_API_KEY! }),\n ],\n})\n```\n\nThe Splunk sink only receives security and auth events; the Datadog sink receives everything.\n\n#### Dead letter queue\n\nPrevent data loss when sinks fail after all retry attempts:\n\n```ts\nconst audit = createAuditTrail({\n sinks: {\n sinks: [createWebhookSink({ url: 'https://hooks.example.com/audit' })],\n deadLetterSink: createWebhookSink({ url: 'https://dlq.example.com/audit-dlq' }),\n retry: { maxRetries: 3 },\n },\n})\n```\n\nEvents that cannot be delivered after exhausting retries are sent to the dead letter sink for later replay.\n\n### `createWebhookSink()`\n\nGeneric HTTP webhook sink. Sends `{ events: AuditEvent[] }` as JSON.\n\n```ts\nfunction createWebhookSink(config: WebhookSinkConfig): AuditSink\n```\n\n```ts\ninterface WebhookSinkConfig {\n url: string\n headers?: Record\n method?: 'POST' | 'PUT'\n timeoutMs?: number // default: 10000\n}\n```\n\n```ts\ncreateWebhookSink({\n url: 'https://hooks.example.com/audit',\n headers: { Authorization: 'Bearer token123' },\n})\n```\n\n### `createSplunkSink()`\n\nSends events to Splunk HTTP Event Collector (HEC) as newline-delimited JSON.\n\n```ts\nfunction createSplunkSink(config: SplunkSinkConfig): AuditSink\n```\n\n```ts\ninterface SplunkSinkConfig {\n url: string\n token: string\n index?: string\n source?: string // default: 'elsium-ai'\n sourcetype?: string // default: 'elsium:audit'\n timeoutMs?: number // default: 10000\n}\n```\n\n```ts\ncreateSplunkSink({\n url: 'https://splunk:8088/services/collector',\n token: 'your-hec-token',\n index: 'ai_audit',\n})\n```\n\n### `createDatadogSink()`\n\nSends events to the Datadog Log Intake API (v2).\n\n```ts\nfunction createDatadogSink(config: DatadogSinkConfig): AuditSink\n```\n\n```ts\ninterface DatadogSinkConfig {\n apiKey: string\n site?: string // default: 'datadoghq.com'\n service?: string // default: 'elsium-ai'\n source?: string // default: 'elsium-ai-audit'\n tags?: Record\n timeoutMs?: number // default: 10000\n}\n```\n\n```ts\ncreateDatadogSink({\n apiKey: process.env.DD_API_KEY!,\n site: 'datadoghq.eu',\n tags: { env: 'production', team: 'platform' },\n})\n```\n\n### Custom sinks\n\nImplement the `AuditSink` interface to send events anywhere:\n\n```ts\nimport type { AuditSink, AuditEvent } from '@elsium-ai/observe'\n\nconst kafkaSink: AuditSink = {\n name: 'kafka',\n async send(events: AuditEvent[]) {\n await producer.send({\n topic: 'audit-events',\n messages: events.map(e => ({ value: JSON.stringify(e) })),\n })\n },\n async shutdown() {\n await producer.disconnect()\n },\n}\n```\n\n---\n\n## Provenance\n\nTracks the full lineage of every AI-generated output by hashing the prompt, model, config, input, and output. Enables reproducibility audits and output-to-source tracing.\n\n### `ProvenanceRecord`\n\n```ts\ninterface ProvenanceRecord {\n id: string\n outputHash: string\n promptVersion: string\n modelVersion: string\n configHash: string\n inputHash: string\n timestamp: number\n traceId?: string\n metadata?: Record\n}\n```\n\nAll hash fields (`outputHash`, `promptVersion`, `modelVersion`, `configHash`, `inputHash`) are SHA-256 hex digests of their respective inputs.\n\n### `ProvenanceTracker`\n\n```ts\ninterface ProvenanceTracker {\n record(data: {\n prompt: string\n model: string\n config: Record\n input: string\n output: string\n traceId?: string\n metadata?: Record\n }): ProvenanceRecord\n query(filter: {\n outputHash?: string\n promptVersion?: string\n modelVersion?: string\n traceId?: string\n }): ProvenanceRecord[]\n getLineage(outputHash: string): ProvenanceRecord[]\n readonly count: number\n clear(): void\n}\n```\n\n### `createProvenanceTracker()`\n\nCreates a provenance tracker for recording and querying output lineage.\n\n```ts\nfunction createProvenanceTracker(options?: {\n maxRecords?: number\n}): ProvenanceTracker\n```\n\n| Parameter | Type | Default | Description |\n|---|---|---|---|\n| `options.maxRecords` | `number` | `10000` | Maximum records held in memory |\n\n**Returns:** `ProvenanceTracker`\n\n```ts\nimport { createProvenanceTracker } from '@elsium-ai/observe'\n\nconst provenance = createProvenanceTracker()\n\n// Record a generation\nconst record = provenance.record({\n prompt: 'Summarize the following article...',\n model: 'gpt-4o',\n config: { temperature: 0.7, maxTokens: 500 },\n input: 'The article text here...',\n output: 'A concise summary of the article.',\n traceId: 'trc_abc',\n})\n\nconsole.log('Output hash:', record.outputHash)\n\n// Query records by output hash\nconst matches = provenance.query({ outputHash: record.outputHash })\n\n// Get the full lineage for a given output (all records sharing the same traceId)\nconst lineage = provenance.getLineage(record.outputHash)\nconsole.log(`Lineage has ${lineage.length} steps`)\n\n// Check count and clear\nconsole.log('Total records:', provenance.count)\nprovenance.clear()\n```\n\n---\n\n## OpenTelemetry\n\nCompatibility layer for converting ElsiumAI spans to the OpenTelemetry format, propagating W3C Trace Context headers, and exporting via OTLP JSON to any OTel-compatible backend (Jaeger, Grafana Tempo, Datadog, Honeycomb, etc.).\n\n### `OTelSpanKind`\n\n```ts\ntype OTelSpanKind = 0 | 1 | 2 | 3 | 4 | 5\n// 0 = UNSPECIFIED, 1 = INTERNAL, 2 = SERVER, 3 = CLIENT, 4 = PRODUCER, 5 = CONSUMER\n```\n\n### `OTelStatusCode`\n\n```ts\ntype OTelStatusCode = 0 | 1 | 2\n// 0 = UNSET, 1 = OK, 2 = ERROR\n```\n\n### `OTelAttributeValue`\n\n```ts\ninterface OTelAttributeValue {\n stringValue?: string\n intValue?: number\n doubleValue?: number\n boolValue?: boolean\n arrayValue?: { values: OTelAttributeValue[] }\n}\n```\n\n### `OTelAttribute`\n\n```ts\ninterface OTelAttribute {\n key: string\n value: OTelAttributeValue\n}\n```\n\n### `OTelEvent`\n\n```ts\ninterface OTelEvent {\n name: string\n timeUnixNano: string\n attributes: OTelAttribute[]\n}\n```\n\n### `OTelSpan`\n\nThe OpenTelemetry-compatible span representation.\n\n```ts\ninterface OTelSpan {\n traceId: string\n spanId: string\n parentSpanId?: string\n name: string\n kind: OTelSpanKind\n startTimeUnixNano: string\n endTimeUnixNano: string\n attributes: OTelAttribute[]\n events: OTelEvent[]\n status: {\n code: OTelStatusCode\n message?: string\n }\n}\n```\n\n### `OTelResource`\n\n```ts\ninterface OTelResource {\n attributes: OTelAttribute[]\n}\n```\n\n### `OTelExportRequest`\n\nThe full OTLP JSON export payload structure.\n\n```ts\ninterface OTelExportRequest {\n resourceSpans: Array<{\n resource: OTelResource\n scopeSpans: Array<{\n scope: {\n name: string\n version: string\n }\n spans: OTelSpan[]\n }>\n }>\n}\n```\n\n### `TraceContext`\n\nParsed W3C Trace Context.\n\n```ts\ninterface TraceContext {\n traceId: string\n spanId: string\n traceFlags: number\n traceState?: string\n}\n```\n\n### `OTLPExporterConfig`\n\n```ts\ninterface OTLPExporterConfig {\n /** OTLP endpoint URL (e.g. http://localhost:4318/v1/traces) */\n endpoint: string\n /** Optional headers (e.g. for auth) */\n headers?: Record\n /** Service name for resource attributes */\n serviceName?: string\n /** Service version */\n serviceVersion?: string\n /** Batch size before sending */\n batchSize?: number\n /** Flush interval in ms */\n flushIntervalMs?: number\n}\n```\n\n### `toOTelSpan()`\n\nConverts an ElsiumAI `SpanData` to an OpenTelemetry-compatible span.\n\n```ts\nfunction toOTelSpan(span: SpanData): OTelSpan\n```\n\n| Parameter | Type | Description |\n|---|---|---|\n| `span` | `SpanData` | The ElsiumAI span to convert |\n\n**Returns:** `OTelSpan`\n\n```ts\nimport { createSpan, toOTelSpan } from '@elsium-ai/observe'\n\nconst span = createSpan('my-operation', { kind: 'llm' })\nspan.end()\n\nconst otelSpan = toOTelSpan(span.toJSON())\nconsole.log(otelSpan.traceId, otelSpan.spanId)\n```\n\nElsiumAI span kinds are mapped as follows: `llm` to CLIENT (3), `tool`/`agent`/`workflow` to INTERNAL (1), `custom` to UNSPECIFIED (0).\n\n### `toOTelExportRequest()`\n\nBuilds a full OTLP JSON export request from a batch of spans.\n\n```ts\nfunction toOTelExportRequest(\n spans: SpanData[],\n options?: {\n serviceName?: string\n serviceVersion?: string\n },\n): OTelExportRequest\n```\n\n| Parameter | Type | Default | Description |\n|---|---|---|---|\n| `spans` | `SpanData[]` | -- | Batch of spans to export |\n| `options.serviceName` | `string` | `'elsium-ai'` | Service name in resource attributes |\n| `options.serviceVersion` | `string` | `'0.1.0'` | Service version in resource attributes |\n\n**Returns:** `OTelExportRequest`\n\n```ts\nimport { observe, toOTelExportRequest } from '@elsium-ai/observe'\n\nconst tracer = observe({ output: [] })\nconst span = tracer.startSpan('process-request', 'agent')\nspan.end()\n\nconst payload = toOTelExportRequest(tracer.getSpans(), {\n serviceName: 'my-ai-service',\n serviceVersion: '1.0.0',\n})\n\n// Send to any OTLP-compatible endpoint\nawait fetch('http://localhost:4318/v1/traces', {\n method: 'POST',\n headers: { 'Content-Type': 'application/json' },\n body: JSON.stringify(payload),\n})\n```\n\n### `toTraceparent()`\n\nCreates a W3C `traceparent` header value from a span.\n\n```ts\nfunction toTraceparent(span: SpanData): string\n```\n\n| Parameter | Type | Description |\n|---|---|---|\n| `span` | `SpanData` | The span to derive the traceparent from |\n\n**Returns:** `string` -- Format: `00-{traceId}-{spanId}-01`\n\n```ts\nimport { createSpan, toTraceparent } from '@elsium-ai/observe'\n\nconst span = createSpan('outgoing-call', { kind: 'llm' })\nspan.end()\n\nconst header = toTraceparent(span.toJSON())\n// e.g. \"00-4bf92f3577b34da6a3ce929d0e0e4736-00f067aa0ba902b7-01\"\n```\n\n### `parseTraceparent()`\n\nParses a W3C `traceparent` header string into a `TraceContext`.\n\n```ts\nfunction parseTraceparent(header: string): TraceContext | null\n```\n\n| Parameter | Type | Description |\n|---|---|---|\n| `header` | `string` | The traceparent header value |\n\n**Returns:** `TraceContext | null` -- Returns `null` if the header is malformed or uses an unsupported version.\n\n```ts\nimport { parseTraceparent } from '@elsium-ai/observe'\n\nconst ctx = parseTraceparent('00-4bf92f3577b34da6a3ce929d0e0e4736-00f067aa0ba902b7-01')\nif (ctx) {\n console.log('Trace ID:', ctx.traceId)\n console.log('Span ID:', ctx.spanId)\n console.log('Sampled:', ctx.traceFlags === 1)\n}\n```\n\n### `injectTraceContext()`\n\nInjects the `traceparent` header into an outgoing HTTP headers object for distributed trace propagation.\n\n```ts\nfunction injectTraceContext(\n span: SpanData,\n headers?: Record,\n): Record\n```\n\n| Parameter | Type | Default | Description |\n|---|---|---|---|\n| `span` | `SpanData` | -- | The span whose trace context to inject |\n| `headers` | `Record` | `{}` | Existing headers to merge with |\n\n**Returns:** `Record` -- A new headers object with `traceparent` set.\n\n```ts\nimport { createSpan, injectTraceContext } from '@elsium-ai/observe'\n\nconst span = createSpan('api-call', { kind: 'llm' })\nconst headers = injectTraceContext(span.toJSON(), {\n 'Content-Type': 'application/json',\n Authorization: 'Bearer token',\n})\n// headers now includes { traceparent: '00-...-...-01', ... }\n\nawait fetch('https://api.example.com/generate', { headers })\nspan.end()\n```\n\n### `extractTraceContext()`\n\nExtracts a `TraceContext` from incoming HTTP headers. Checks both `traceparent` and `Traceparent` keys.\n\n```ts\nfunction extractTraceContext(\n headers: Record,\n): TraceContext | null\n```\n\n| Parameter | Type | Description |\n|---|---|---|\n| `headers` | `Record` | Incoming HTTP headers |\n\n**Returns:** `TraceContext | null`\n\n```ts\nimport { extractTraceContext, createSpan } from '@elsium-ai/observe'\n\n// In an HTTP handler\nfunction handleRequest(req: { headers: Record }) {\n const parentCtx = extractTraceContext(req.headers)\n\n const span = createSpan('handle-request', {\n traceId: parentCtx?.traceId,\n parentId: parentCtx?.spanId,\n kind: 'agent',\n })\n\n // ... process request ...\n span.end()\n}\n```\n\n### `createOTLPExporter()`\n\nCreates an OTLP JSON exporter that sends spans to any OTel-compatible backend. Supports batching and automatic periodic flushing.\n\n```ts\nfunction createOTLPExporter(config: OTLPExporterConfig): TracerExporter\n```\n\n| Parameter | Type | Default | Description |\n|---|---|---|---|\n| `config.endpoint` | `string` | -- | OTLP endpoint URL (e.g. `http://localhost:4318/v1/traces`) |\n| `config.headers` | `Record` | `{}` | Optional HTTP headers (e.g. for authentication) |\n| `config.serviceName` | `string` | `undefined` | Service name for resource attributes |\n| `config.serviceVersion` | `string` | `undefined` | Service version for resource attributes |\n| `config.batchSize` | `number` | `100` | Number of spans to buffer before sending a batch |\n| `config.flushIntervalMs` | `number` | `5000` | Automatic flush interval in milliseconds |\n\n**Returns:** `TracerExporter`\n\n```ts\nimport { observe, createOTLPExporter } from '@elsium-ai/observe'\n\nconst exporter = createOTLPExporter({\n endpoint: 'http://localhost:4318/v1/traces',\n serviceName: 'my-ai-service',\n serviceVersion: '1.0.0',\n headers: { Authorization: 'Bearer my-token' },\n batchSize: 50,\n flushIntervalMs: 10000,\n})\n\nconst tracer = observe({ output: [exporter] })\n\nconst span = tracer.startSpan('generate', 'llm')\nspan.end()\n\n// Spans are batched and sent automatically, or flush manually:\nawait tracer.flush()\n```\n\n---\n\n## Experiments Persistence\n\n### `createFileExperimentStore`\n\nCreates a file-based storage adapter for saving and loading experiment results to disk. Experiment data is serialized as JSON files in the specified directory.\n\n```ts\nfunction createFileExperimentStore(dir: string): ExperimentStore\n```\n\n| Parameter | Type | Description |\n|---|---|---|\n| `dir` | `string` | Directory path where experiment result files will be stored |\n\n**Returns:** `ExperimentStore`\n\n```ts\ninterface ExperimentStore {\n save(experiment: ExperimentResults): Promise\n load(experimentId: string): Promise\n list(): Promise\n}\n```\n\n```ts\nimport { createExperiment, createFileExperimentStore } from '@elsium-ai/observe'\n\nconst store = createFileExperimentStore('./experiments')\n\nconst experiment = createExperiment({\n name: 'prompt-comparison',\n variants: [\n { name: 'concise', config: { system: 'Be brief.' } },\n { name: 'detailed', config: { system: 'Be thorough.' } },\n ],\n})\n\nconst results = await experiment.run(evaluator)\n\n// Persist results to disk\nawait store.save(results)\n\n// Load results later\nconst loaded = await store.load(results.id)\n```\n\n---\n\n## Studio Exporter\n\nBridges the observe system to the `.elsium/` directory so the `elsium studio` dashboard can display live traces, X-Ray data, and costs.\n\n### `createStudioExporter()`\n\n```ts\nfunction createStudioExporter(config?: StudioExporterConfig): StudioExporter\n```\n\n```ts\ninterface StudioExporterConfig {\n dir?: string // default: '.elsium'\n}\n\ninterface StudioExporter extends TracerExporter {\n writeXRayEntry(entry: Record): void\n writeCostReport(report: CostReport): void\n}\n```\n\n| Parameter | Type | Default | Description |\n|---|---|---|---|\n| `config.dir` | `string` | `'.elsium'` | Directory where trace, X-Ray, and cost files are written |\n\n**Returns:** `StudioExporter` (implements `TracerExporter` + X-Ray/cost writers)\n\n```ts\nimport { observe, createStudioExporter } from '@elsium-ai/observe'\n\nconst studio = createStudioExporter()\n\nconst tracer = observe({ output: [studio] })\n\nconst span = tracer.startSpan('my-operation', 'llm')\nspan.end()\nawait tracer.flush()\n\nstudio.writeXRayEntry({ traceId: 'trc_123', provider: 'anthropic', model: 'claude-sonnet-4-6' })\nstudio.writeCostReport(tracer.getCostReport())\n```\n\nThen run `elsium studio` in another terminal to see the data in the web dashboard.\n\n---\n\n## Auto-Instrumentation\n\n### `instrumentComplete`\n\nWraps an LLM completion function with automatic span creation. Every call produces a span with model, token, cost, and latency metadata.\n\n```ts\nfunction instrumentComplete(\n complete: (request: CompletionRequest) => Promise,\n tracer: Tracer,\n): (request: CompletionRequest) => Promise\n```\n\n| Parameter | Type | Description |\n|---|---|---|\n| `complete` | `(request: CompletionRequest) => Promise` | The LLM completion function to instrument |\n| `tracer` | `Tracer` | The tracer instance to record spans to |\n\n**Returns:** A wrapped completion function with the same signature.\n\n```ts\nimport { observe, instrumentComplete } from '@elsium-ai/observe'\n\nconst tracer = observe()\n\nconst tracedComplete = instrumentComplete(\n (req) => llm.complete(req),\n tracer,\n)\n\n// Every call now creates an 'llm' span automatically\nconst response = await tracedComplete({ model: 'gpt-4o', messages })\n```\n\n### `instrumentAgent`\n\nWraps an agent's `run` method with automatic span creation. Produces an `agent` span that captures the agent name, input, output, token usage, and tool calls.\n\n```ts\nfunction instrumentAgent(\n agent: Agent,\n tracer: Tracer,\n): Agent\n```\n\n| Parameter | Type | Description |\n|---|---|---|\n| `agent` | `Agent` | The agent to instrument |\n| `tracer` | `Tracer` | The tracer instance to record spans to |\n\n**Returns:** A new `Agent` with the same interface, where `run` and `chat` are automatically traced.\n\n```ts\nimport { observe, instrumentAgent } from '@elsium-ai/observe'\nimport { defineAgent } from '@elsium-ai/agents'\n\nconst tracer = observe()\n\nconst agent = defineAgent(\n { name: 'assistant', system: 'You are helpful.' },\n { complete: (req) => llm.complete(req) },\n)\n\nconst tracedAgent = instrumentAgent(agent, tracer)\n\n// Every run/chat call now creates an 'agent' span automatically\nconst result = await tracedAgent.run('Hello')\n```\n\n---\n\n## Verifiable Agent Execution\n\n`createProofRecorder` produces signed `ExecutionProof` documents for each agent run. A third party with just the public key can verify offline that the recorded sequence of LLM calls, tool calls, RAG retrievals, and policy decisions actually occurred — and that no event was tampered with after the fact.\n\n### How it works\n\n- Every event is hashed (SHA-256 of canonical JSON) and chained to the previous event's hash.\n- On `finalize()`, the recorder signs the `chainHead` with Ed25519 and produces an `ExecutionProof`.\n- Optional persistence to a `WriteOnceStore` (in-memory or file-system with O_EXCL) makes the artifact tamper-evident at rest.\n- `recorder.verify(proof, keyRegistry)` recomputes the entire chain and validates the signature.\n\n### End-to-end example\n\n```ts\nimport {\n generateEd25519KeyPair,\n createEd25519Signer,\n createKeyRegistry,\n createFileWriteOnceStore,\n} from '@elsium-ai/core'\nimport { createProofRecorder, PROOF_SESSION_METADATA_KEY } from '@elsium-ai/observe'\nimport { gateway } from '@elsium-ai/gateway'\n\nconst pair = generateEd25519KeyPair()\nconst signer = createEd25519Signer({ privateKey: pair.privateKey, keyId: 'org-aperion-k7' })\nconst recorder = createProofRecorder({ signer })\n\nconst llm = gateway({\n provider: 'anthropic',\n apiKey: process.env.ANTHROPIC_API_KEY!,\n middleware: [recorder.middleware()],\n})\n\nconst session = recorder.startSession({\n agentId: 'invoice-extractor',\n agentVersion: '1.2.3',\n inputs: { messages: [{ role: 'user', content: 'Extract this invoice…' }] },\n})\n\nconst response = await llm.complete({\n messages: [{ role: 'user', content: 'Extract this invoice…' }],\n metadata: { [PROOF_SESSION_METADATA_KEY]: session.proofId },\n})\n\nsession.recordToolCall({ tool: 'parse_invoice', inputHash: 'h1', outputHash: 'h2' })\nsession.recordPolicyDecision({ rule: 'pii-allowed', result: 'allow' })\n\nconst store = createFileWriteOnceStore({ dir: './proofs' })\nconst proof = await session.finalize({ finalOutput: { invoiceTotal: 1234 }, store })\n// → ./proofs/proof_.json, signed with org-aperion-k7\n```\n\n### Verifying on another machine\n\nThe verifier needs only the public key and the proof file — no signer, no API keys:\n\n```ts\nimport { createKeyRegistry } from '@elsium-ai/core'\nimport { verifyProof } from '@elsium-ai/observe'\nimport { readFileSync } from 'node:fs'\n\nconst registry = createKeyRegistry({\n trustRoots: [{ keyId: 'org-aperion-k7', publicKey: ORG_PUBLIC_KEY_PEM }],\n})\n\nconst proof = JSON.parse(readFileSync('./proofs/proof_abc.json', 'utf8'))\nconst result = verifyProof(proof, registry)\n// result.valid === true if signature and full hash chain check out\n// result.chainBrokenAt when an event was mutated after signing\n```\n\n### When to record what\n\n| Method | Use for |\n|---|---|\n| `recordLLMCall(summary)` | Manually capture an LLM call. Auto-recorded by the middleware when `metadata.proofSessionId` is set. |\n| `recordToolCall(summary)` | Every tool invocation. Hash inputs/outputs to keep proof size bounded. |\n| `recordRagRetrieve(summary)` | Documents retrieved from a vector store. Captures `id` + score for each. |\n| `recordPolicyDecision(summary)` | Allow/deny outcome from a policy evaluator. |\n| `recordCustom(data)` | Anything domain-specific (e.g. external API call hashes). |\n\nThe proof shape is `version: 'elsium-proof/v1'` and the signing payload is `${version}\\n${proofId}\\n${chainHead}` — both pinned constants so verifiers can rely on stable framing.\n\n---\n\n## Part of ElsiumAI\n\nThis package is the observability layer of the [ElsiumAI](https://github.com/elsium-ai/elsium-ai) framework. See the [full documentation](https://github.com/elsium-ai/elsium-ai) for guides and examples.\n\n## License\n\n[MIT](https://github.com/elsium-ai/elsium-ai/blob/main/LICENSE)\n","readmeFilename":"README.md"}