path: root/packages/core/src/services/loopDetectionService.ts

/**
 * @license
 * Copyright 2025 Google LLC
 * SPDX-License-Identifier: Apache-2.0
 */

import { createHash } from 'crypto';
import { GeminiEventType, ServerGeminiStreamEvent } from '../core/turn.js';
import { logLoopDetected } from '../telemetry/loggers.js';
import { LoopDetectedEvent, LoopType } from '../telemetry/types.js';
import { Config, DEFAULT_GEMINI_FLASH_MODEL } from '../config/config.js';
import { SchemaUnion, Type } from '@google/genai';

const TOOL_CALL_LOOP_THRESHOLD = 5;
const CONTENT_LOOP_THRESHOLD = 10;

/**
 * The number of recent conversation turns to include in the history when asking the LLM to check for a loop.
 */
const LLM_LOOP_CHECK_HISTORY_COUNT = 20;

/**
 * The number of turns that must pass in a single prompt before the LLM-based loop check is activated.
 */
const LLM_CHECK_AFTER_TURNS = 30;

/**
 * The default interval, in number of turns, at which the LLM-based loop check is performed.
 * This value is adjusted dynamically based on the LLM's confidence.
 */
const DEFAULT_LLM_CHECK_INTERVAL = 3;

/**
 * The minimum interval for LLM-based loop checks.
 * This is used when the confidence of a loop is high, to check more frequently.
 */
const MIN_LLM_CHECK_INTERVAL = 5;

/**
 * The maximum interval for LLM-based loop checks.
 * This is used when the confidence of a loop is low, to check less frequently.
 */
const MAX_LLM_CHECK_INTERVAL = 15;

const SENTENCE_ENDING_PUNCTUATION_REGEX = /[.!?]+(?=\s|$)/;

/**
 * Service for detecting and preventing infinite loops in AI responses.
 * Monitors tool call repetitions and content sentence repetitions.
 */
export class LoopDetectionService {
  private readonly config: Config;

  // Tool call tracking
  private lastToolCallKey: string | null = null;
  private toolCallRepetitionCount: number = 0;

  // Content streaming tracking
  private lastRepeatedSentence: string = '';
  private sentenceRepetitionCount: number = 0;
  private partialContent: string = '';

  // LLM loop track tracking
  private turnsInCurrentPrompt = 0;
  private llmCheckInterval = DEFAULT_LLM_CHECK_INTERVAL;
  private lastCheckTurn = 0;

  constructor(config: Config) {
    this.config = config;
  }

  private getToolCallKey(toolCall: { name: string; args: object }): string {
    const argsString = JSON.stringify(toolCall.args);
    const keyString = `${toolCall.name}:${argsString}`;
    return createHash('sha256').update(keyString).digest('hex');
  }

  /**
   * Processes a stream event and checks for loop conditions.
   * @param event - The stream event to process
   * @returns true if a loop is detected, false otherwise
   */
  addAndCheck(event: ServerGeminiStreamEvent): boolean {
    switch (event.type) {
      case GeminiEventType.ToolCallRequest:
        // content chanting only happens in one single stream, reset if there
        // is a tool call in between
        this.resetSentenceCount();
        return this.checkToolCallLoop(event.value);
      case GeminiEventType.Content:
        return this.checkContentLoop(event.value);
      default:
        return false;
    }
  }

  /**
   * Signals the start of a new turn in the conversation.
   *
   * This method increments the turn counter and, if specific conditions are met,
   * triggers an LLM-based check to detect potential conversation loops. The check
   * is performed periodically based on the `llmCheckInterval`.
   *
   * @param signal - An AbortSignal to allow for cancellation of the asynchronous LLM check.
   * @returns A promise that resolves to `true` if a loop is detected, and `false` otherwise.
   */
  async turnStarted(signal: AbortSignal) {
    this.turnsInCurrentPrompt++;

    if (
      this.turnsInCurrentPrompt >= LLM_CHECK_AFTER_TURNS &&
      this.turnsInCurrentPrompt - this.lastCheckTurn >= this.llmCheckInterval
    ) {
      this.lastCheckTurn = this.turnsInCurrentPrompt;
      return await this.checkForLoopWithLLM(signal);
    }

    return false;
  }

  private checkToolCallLoop(toolCall: { name: string; args: object }): boolean {
    const key = this.getToolCallKey(toolCall);
    if (this.lastToolCallKey === key) {
      this.toolCallRepetitionCount++;
    } else {
      this.lastToolCallKey = key;
      this.toolCallRepetitionCount = 1;
    }
    if (this.toolCallRepetitionCount >= TOOL_CALL_LOOP_THRESHOLD) {
      logLoopDetected(
        this.config,
        new LoopDetectedEvent(LoopType.CONSECUTIVE_IDENTICAL_TOOL_CALLS),
      );
      return true;
    }
    return false;
  }

  private checkContentLoop(content: string): boolean {
    this.partialContent += content;

    if (!SENTENCE_ENDING_PUNCTUATION_REGEX.test(this.partialContent)) {
      return false;
    }

    const completeSentences =
      this.partialContent.match(/[^.!?]+[.!?]+(?=\s|$)/g) || [];
    if (completeSentences.length === 0) {
      return false;
    }

    const lastSentence = completeSentences[completeSentences.length - 1];
    const lastCompleteIndex = this.partialContent.lastIndexOf(lastSentence);
    const endOfLastSentence = lastCompleteIndex + lastSentence.length;
    this.partialContent = this.partialContent.slice(endOfLastSentence);

    for (const sentence of completeSentences) {
      const trimmedSentence = sentence.trim();
      if (trimmedSentence === '') {
        continue;
      }

      if (this.lastRepeatedSentence === trimmedSentence) {
        this.sentenceRepetitionCount++;
      } else {
        this.lastRepeatedSentence = trimmedSentence;
        this.sentenceRepetitionCount = 1;
      }

      if (this.sentenceRepetitionCount >= CONTENT_LOOP_THRESHOLD) {
        logLoopDetected(
          this.config,
          new LoopDetectedEvent(LoopType.CHANTING_IDENTICAL_SENTENCES),
        );
        return true;
      }
    }
    return false;
  }

  private async checkForLoopWithLLM(signal: AbortSignal) {
    const recentHistory = this.config
      .getGeminiClient()
      .getHistory()
      .slice(-LLM_LOOP_CHECK_HISTORY_COUNT);

    const prompt = `You are a sophisticated AI diagnostic agent specializing in identifying when a conversational AI is stuck in an unproductive state. Your task is to analyze the provided conversation history and determine if the assistant has ceased to make meaningful progress.

An unproductive state is characterized by one or more of the following patterns over the last 5 or more assistant turns:

Repetitive Actions: The assistant repeats the same tool calls or conversational responses a decent number of times. This includes simple loops (e.g., tool_A, tool_A, tool_A) and alternating patterns (e.g., tool_A, tool_B, tool_A, tool_B, ...).

Cognitive Loop: The assistant seems unable to determine the next logical step. It might express confusion, repeatedly ask the same questions, or generate responses that don't logically follow from the previous turns, indicating it's stuck and not advancing the task.

Crucially, differentiate between a true unproductive state and legitimate, incremental progress.
For example, a series of 'tool_A' or 'tool_B' tool calls that make small, distinct changes to the same file (like adding docstrings to functions one by one) is considered forward progress and is NOT a loop. A loop would be repeatedly replacing the same text with the same content, or cycling between a small set of files with no net change.

Please analyze the conversation history to determine the possibility that the conversation is stuck in a repetitive, non-productive state.`;
    const contents = [
      ...recentHistory,
      { role: 'user', parts: [{ text: prompt }] },
    ];
    const schema: SchemaUnion = {
      type: Type.OBJECT,
      properties: {
        reasoning: {
          type: Type.STRING,
          description:
            'Your reasoning on if the conversation is looping without forward progress.',
        },
        confidence: {
          type: Type.NUMBER,
          description:
            'A number between 0.0 and 1.0 representing your confidence that the conversation is in an unproductive state.',
        },
      },
      required: ['reasoning', 'confidence'],
    };
    let result;
    try {
      result = await this.config
        .getGeminiClient()
        .generateJson(contents, schema, signal, DEFAULT_GEMINI_FLASH_MODEL);
    } catch (e) {
      // Do nothing, treat it as a non-loop.
      this.config.getDebugMode() ? console.error(e) : console.debug(e);
      return false;
    }

    if (typeof result.confidence === 'number') {
      if (result.confidence > 0.9) {
        if (typeof result.reasoning === 'string' && result.reasoning) {
          console.warn(result.reasoning);
        }
        logLoopDetected(
          this.config,
          new LoopDetectedEvent(LoopType.LLM_DETECTED_LOOP),
        );
        return true;
      } else {
        this.llmCheckInterval = Math.round(
          MIN_LLM_CHECK_INTERVAL +
            (MAX_LLM_CHECK_INTERVAL - MIN_LLM_CHECK_INTERVAL) *
              (1 - result.confidence),
        );
      }
    }
    return false;
  }

  /**
   * Resets all loop detection state.
   */
  reset(): void {
    this.resetToolCallCount();
    this.resetSentenceCount();
    this.resetLlmCheckTracking();
  }

  private resetToolCallCount(): void {
    this.lastToolCallKey = null;
    this.toolCallRepetitionCount = 0;
  }

  private resetSentenceCount(): void {
    this.lastRepeatedSentence = '';
    this.sentenceRepetitionCount = 0;
    this.partialContent = '';
  }

  private resetLlmCheckTracking(): void {
    this.turnsInCurrentPrompt = 0;
    this.llmCheckInterval = DEFAULT_LLM_CHECK_INTERVAL;
    this.lastCheckTurn = 0;
  }
}