/**
 * Local-runner preflight: ensure the LLM model the pipeline is about to
 * call is actually loaded in LM Studio / pulled in Ollama before we spawn
 * the Python subprocess.
 *
 * Why this exists:
 *   - LM Studio's OpenAI-compat `/v1/chat/completions` will JIT-load a
 *     downloaded model on first request, but the request blocks for the
 *     entire load (60-120s for 8B-class quantised models). The pipeline
 *     subprocess sees no progress, the FE sees a stalled "Launching
 *     pipeline" spinner, the agent's first ChatResponse never arrives.
 *     Run d5ead8b8655b47fa hit this exactly: 1 `format openai` span, no
 *     `chat ...` span, no messages.
 *   - Ollama works similarly — first call to a model that isn't pulled
 *     will fail with "model not found"; downloads have to be explicit.
 *
 * What we do:
 *   1. Read the model name + provider from the pipeline's config.json.
 *   2. For lmstudio: GET /api/v0/models to see {id, state} for every
 *      model. If state="loaded" we're good. If state="not-loaded" we
 *      POST a tiny chat completion to force the JIT load while
 *      surfacing progress to the operator's stderr + emitting a
 *      socket event so the FE can show "Loading qwen/qwen3-vl-8b…".
 *      If model isn't in the list at all → fail fast with a clear
 *      "open LM Studio and download <name>" message.
 *   3. For ollama: GET /api/tags. If model not present → fail fast with
 *      `ollama pull <name>` instruction. If present, ollama auto-loads.
 */
export declare function classifyModel(id: string, lmstudioMeta?: LMStudioModelInfo): ModelProfile;
type LMStudioModelState = "loaded" | "not-loaded" | "loading" | string;
interface LMStudioModelInfo {
    id: string;
    state?: LMStudioModelState;
    type?: string;
    arch?: string;
    params?: string | number;
    max_context_length?: number;
}
export type ModelTier = "agentic-capable" | "agentic-marginal" | "text-only";
export interface ModelProfile {
    schemaVersion: number;
    id: string;
    tier: ModelTier;
    paramsBillion: number | null;
    family: string | null;
    supportsToolCalling: boolean;
    thinkingDefault: "on" | "off" | "n/a";
    recommendedMaxTokens: number;
    recommendedMaxIters: number;
    contextTokens: number | null;
    detectedAt: string;
    reason: string;
}
export interface ModelPreflightResult {
    ok: boolean;
    reason?: string;
    hint?: string;
    loaded?: boolean;
    profile?: ModelProfile;
}
export declare function preflightLmStudio(modelName: string, onProgress?: (msg: string) => void): Promise<ModelPreflightResult>;
export declare function preflightOllama(modelName: string, onProgress?: (msg: string) => void): Promise<ModelPreflightResult>;
/**
 * Top-level preflight — resolves the provider/model from the pipeline
 * config and dispatches to the right helper. Returns immediately for
 * non-local providers (cloud-spawn shouldn't reach the runner anyway).
 */
export declare function preflightModel(configJson: string, onProgress?: (msg: string) => void): Promise<ModelPreflightResult>;
export {};