import type { RLMConfig, RLMResult } from '../types/index.js';
interface Message {
    role: 'system' | 'user' | 'assistant';
    content: string;
}
/**
 * Callback type for LLM completions.
 * The RLM engine is LLM-agnostic — the caller provides the completion function.
 * This works with any provider: OpenAI, Anthropic, Ollama, LiteLLM, etc.
 */
export type LLMCompletionFn = (messages: Message[]) => Promise<string>;
export declare class RLMEngine {
    private config;
    private repl;
    private llmComplete;
    constructor(llmComplete: LLMCompletionFn, config?: Partial<RLMConfig>);
    /**
     * Analyze a document with a query using the RLM pattern.
     *
     * The full document text is stored as a Python variable — it is NEVER
     * sent to the LLM directly. The LLM interacts with it through code execution.
     *
     * @param query - The user's question about the document
     * @param context - The full document text (can be millions of characters)
     * @returns RLMResult with the answer, iteration count, and code execution count
     */
    analyze(query: string, context: string): Promise<RLMResult>;
    private runAnalysisLoop;
    /**
     * Handle recursive LLM calls from within the REPL.
     * The sub-context IS sent directly in the prompt (it should be small enough).
     */
    private callRecursiveLLM;
    /**
     * Parse FINAL(answer) or FINAL_VAR(var_name) from LLM output.
     */
    private parseFinalAnswer;
    /**
     * Extract Python code blocks from LLM response.
     * Supports ```python ... ``` and ``` ... ``` formats.
     */
    private extractCodeBlocks;
}
export {};
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