import type { Logger } from "@onenomad/przm-cortex-core";
import type { LLMRouter } from "@onenomad/przm-cortex-llm-core";
import type { MemoryConfig } from "../config.js";
import type { EngramClient } from "./engram.js";
export interface MemoryBootResult {
    /** The backend Cortex will use for ingest/search/health from this point. */
    client: EngramClient;
    /** Which backend was actually selected. Always 'pgvector' since
     *  Cortex 0.3 — the engram backend was dropped to make Cortex
     *  standalone-deployable (no @onenomad/engram-memory runtime dep).
     *  Old yamls with primary='engram' get auto-translated to pgvector
     *  embedded with a warn-level log so operators notice the change. */
    selected: "pgvector";
    /** Always true now (single backend, no fallback). Kept on the
     *  result shape for back-compat with callers reading it. */
    primaryHealthy: boolean;
}
/**
 * Boot the memory backend.
 *
 * Cortex 0.3+ uses pgvector exclusively. Two modes:
 *   - embedded: PGlite in-process (zero deps, default for personal installs)
 *   - external: connection string to your own Postgres + pgvector
 *
 * Embeddings come from a Cortex-internal Xenova model (MiniLM-L6-v2,
 * 384-dim) by default; an LLM router takes over when one is wired
 * (provider-specific embedding model for higher quality on cloud
 * deploys with credentials).
 *
 * Migration: yamls that still say `memory.primary: engram` are
 * auto-translated to pgvector embedded (warn-logged so the operator
 * sees the rewrite). The engram backend was Pyre's per-user memory
 * concern; Cortex is the multi-tenant knowledge engine and is now
 * entirely separate.
 */
export declare function createMemoryClient(args: {
    memory: MemoryConfig;
    /** Optional. When wired, embeddings come from the configured
     *  provider's embed task. When omitted, Cortex's local Xenova
     *  embedder (Cortex-internal) handles embeddings. */
    llmRouter?: LLMRouter;
    logger: Logger;
}): Promise<MemoryBootResult>;
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