/**
 * Literacy TraceAdapter (D0088 slice 2).
 *
 * Literacy runs our own Anthropic/OpenAI tool loop (`agentic-provider.ts`).
 * Unlike MCP — which exposes a single `metadata.toolCalls` provenance — the
 * literacy trace fuses two views into one ordered `toolCalls` array:
 *
 *   - the model's `tool_use` transcript (`metadata.toolCalls`,
 *     `LiteracyToolCall[]`) → `loop-transcript` calls. These ARE the request
 *     sequence: what the model decided to do, in turn order.
 *   - the agent-observer HTTP log (`metadata.agentBehavior`) → `http-observer`
 *     calls synthesized as `WebSearch` (one per `searchQueries[]`) and
 *     `WebFetch` (one per `docPageVisits[]`). These carry the authoritative
 *     visited doc URLs/slugs and search terms, which the collector's
 *     access-pattern extraction turns into `searchTerms` / `urlsVisited`.
 *
 * Per-turn LLM latency (`metadata.llmTimings`) is forwarded so the collector
 * builds `llm-turn` timeline spans. v1 captures tool calls + timing + HTTP
 * only — no extended-thinking content (D0088).
 *
 * The agent-observer session's real wall-clock bounds
 * (`AgentBehaviorLog.startedAt`/`endedAt`) are forwarded as the trace's
 * `startedAt`/`completedAt` so the persisted window reflects execution time,
 * not emission time (W-trace-real-timestamps).
 *
 * @see packages/eval/src/agent-observer/agentic-provider.ts
 * @see packages/eval/src/agent-observer/types.ts (AgentBehaviorLog, LiteracyToolCall)
 */
import type { TraceAdapter } from "../trace-adapter.js";
import type { TraceNativeResult } from "./native-result.js";
export declare const literacyTraceAdapter: TraceAdapter<TraceNativeResult>;
