browser-use--browser-use/browser_use/tokens/service.py

"""
Token cost service that tracks LLM token usage and costs.

Fetches pricing data from LiteLLM repository and caches it for 1 day.
Automatically tracks token usage when LLMs are registered and invoked.
"""

import logging
import os
from datetime import datetime, timedelta
from pathlib import Path
from typing import Any

import anyio
import httpx
from dotenv import load_dotenv

from browser_use.llm.base import BaseChatModel
from browser_use.llm.views import ChatInvokeUsage
from browser_use.tokens.custom_pricing import CUSTOM_MODEL_PRICING
from browser_use.tokens.mappings import MODEL_TO_LITELLM
from browser_use.tokens.openrouter_pricing import get_openrouter_model_pricing, is_openrouter_pricing_model
from browser_use.tokens.views import (
	CachedPricingData,
	ModelPricing,
	ModelUsageStats,
	ModelUsageTokens,
	TokenCostCalculated,
	TokenUsageEntry,
	UsageSummary,
)
from browser_use.utils import create_task_with_error_handling

load_dotenv()

from browser_use.config import CONFIG

logger = logging.getLogger(__name__)
cost_logger = logging.getLogger('cost')


def xdg_cache_home() -> Path:
	default = Path.home() / '.cache'
	if CONFIG.XDG_CACHE_HOME and (path := Path(CONFIG.XDG_CACHE_HOME)).is_absolute():
		return path
	return default


class TokenCost:
	"""Service for tracking token usage and calculating costs"""

	CACHE_DIR_NAME = 'browser_use/token_cost'
	CACHE_DURATION = timedelta(days=1)
	DEFAULT_PRICING_URL = 'https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json'

	def __init__(self, include_cost: bool = False, pricing_url: str | None = None):
		self.include_cost = include_cost or os.getenv('BROWSER_USE_CALCULATE_COST', 'false').lower() == 'true'
		self.pricing_url = pricing_url or CONFIG.BROWSER_USE_MODEL_PRICING_URL or self.DEFAULT_PRICING_URL

		self.usage_history: list[TokenUsageEntry] = []
		self.registered_llms: dict[str, BaseChatModel] = {}
		self._pricing_model_names: dict[str, str] = {}
		self._pricing_data: dict[str, Any] | None = None
		self._initialized = False
		self._cache_dir = xdg_cache_home() / self.CACHE_DIR_NAME

	async def initialize(self) -> None:
		"""Initialize the service by loading pricing data"""
		if not self._initialized:
			if self.include_cost:
				await self._load_pricing_data()
			self._initialized = True

	async def _load_pricing_data(self) -> None:
		"""Load pricing data from cache or fetch from GitHub"""
		# Try to find a valid cache file
		cache_file = await self._find_valid_cache()

		if cache_file:
			await self._load_from_cache(cache_file)
		else:
			await self._fetch_and_cache_pricing_data()

	async def _find_valid_cache(self) -> Path | None:
		"""Find the most recent valid cache file"""
		try:
			# Ensure cache directory exists
			self._cache_dir.mkdir(parents=True, exist_ok=True)

			# List all JSON files in the cache directory
			cache_files = list(self._cache_dir.glob('*.json'))

			if not cache_files:
				return None

			# Sort by modification time (most recent first)
			cache_files.sort(key=lambda f: f.stat().st_mtime, reverse=True)

			# Check each file until we find a valid one
			for cache_file in cache_files:
				is_valid, should_delete = await self._get_cache_status(cache_file)
				if is_valid:
					return cache_file
				if should_delete:
					# Clean up old cache files
					try:
						os.remove(cache_file)
					except Exception:
						pass

			return None
		except Exception:
			return None

	async def _get_cache_status(self, cache_file: Path) -> tuple[bool, bool]:
		"""Return whether a cache file is usable and whether it should be deleted."""
		try:
			if not cache_file.exists():
				return False, False

			# Read the cached data
			cached = CachedPricingData.model_validate_json(await anyio.Path(cache_file).read_text())

			# Check if cache is still valid
			if datetime.now() - cached.timestamp >= self.CACHE_DURATION:
				return False, True

			# Keep caches from other sources so different pricing URLs don't delete each other.
			return self._cache_source_matches(cached), False
		except Exception:
			return False, True

	def _cache_source_matches(self, cached: CachedPricingData) -> bool:
		"""Only use cached pricing files from the same source URL."""
		if cached.source_url is None:
			return self.pricing_url == self.DEFAULT_PRICING_URL

		return cached.source_url == self.pricing_url

	async def _load_from_cache(self, cache_file: Path) -> None:
		"""Load pricing data from a specific cache file"""
		try:
			content = await anyio.Path(cache_file).read_text()
			cached = CachedPricingData.model_validate_json(content)
			self._pricing_data = cached.data
		except Exception as e:
			logger.debug(f'Error loading cached pricing data from {cache_file}: {e}')
			# Fall back to fetching
			await self._fetch_and_cache_pricing_data()

	async def _fetch_and_cache_pricing_data(self) -> None:
		"""Fetch pricing data from LiteLLM GitHub and cache it with timestamp"""
		try:
			async with httpx.AsyncClient() as client:
				response = await client.get(self.pricing_url, timeout=30)
				response.raise_for_status()

				self._pricing_data = response.json()

			# Create cache object with timestamp
			cached = CachedPricingData(timestamp=datetime.now(), source_url=self.pricing_url, data=self._pricing_data or {})

			# Ensure cache directory exists
			self._cache_dir.mkdir(parents=True, exist_ok=True)

			# Create cache file with timestamp in filename
			timestamp_str = datetime.now().strftime('%Y%m%d_%H%M%S')
			cache_file = self._cache_dir / f'pricing_{timestamp_str}.json'

			await anyio.Path(cache_file).write_text(cached.model_dump_json(indent=2))
		except Exception as e:
			logger.debug(f'Error fetching pricing data: {e}')
			# Fall back to empty pricing data
			self._pricing_data = {}

	async def get_model_pricing(self, model_name: str) -> ModelPricing | None:
		"""Get pricing information for a specific model"""
		# Check custom pricing first
		if model_name in CUSTOM_MODEL_PRICING:
			data = CUSTOM_MODEL_PRICING[model_name]
			return ModelPricing(
				model=model_name,
				input_cost_per_token=data.get('input_cost_per_token'),
				output_cost_per_token=data.get('output_cost_per_token'),
				max_tokens=data.get('max_tokens'),
				max_input_tokens=data.get('max_input_tokens'),
				max_output_tokens=data.get('max_output_tokens'),
				cache_read_input_token_cost=data.get('cache_read_input_token_cost'),
				cache_creation_input_token_cost=data.get('cache_creation_input_token_cost'),
				cache_creation_1h_input_token_cost=data.get('cache_creation_1h_input_token_cost'),
			)

		# Ensure we're initialized before checking remote LiteLLM pricing.
		if not self._initialized:
			await self.initialize()

		if is_openrouter_pricing_model(model_name):
			openrouter_pricing = await get_openrouter_model_pricing(model_name)
			if openrouter_pricing is not None:
				return openrouter_pricing

		# Map model name to LiteLLM model name if needed
		litellm_model_name = MODEL_TO_LITELLM.get(model_name, model_name)

		if self._pricing_data and litellm_model_name in self._pricing_data:
			data = self._pricing_data[litellm_model_name]
			return ModelPricing(
				model=model_name,
				input_cost_per_token=data.get('input_cost_per_token'),
				output_cost_per_token=data.get('output_cost_per_token'),
				max_tokens=data.get('max_tokens'),
				max_input_tokens=data.get('max_input_tokens'),
				max_output_tokens=data.get('max_output_tokens'),
				cache_read_input_token_cost=data.get('cache_read_input_token_cost'),
				cache_creation_input_token_cost=data.get('cache_creation_input_token_cost'),
				cache_creation_1h_input_token_cost=data.get('cache_creation_1h_input_token_cost'),
			)

		return await get_openrouter_model_pricing(model_name)

	async def calculate_cost(self, model: str, usage: ChatInvokeUsage) -> TokenCostCalculated | None:
		if not self.include_cost:
			return None

		pricing_model = self._pricing_model_names.get(model, model)
		data = await self.get_model_pricing(pricing_model)
		if data is None:
			return None

		uncached_prompt_tokens = usage.prompt_tokens - (usage.prompt_cached_tokens or 0)
		pricing_multiplier = usage.pricing_multiplier or 1.0

		cache_creation_5m_tokens = usage.prompt_cache_creation_5m_tokens
		cache_creation_1h_tokens = usage.prompt_cache_creation_1h_tokens
		if cache_creation_5m_tokens is not None or cache_creation_1h_tokens is not None:
			prompt_cache_creation_cost = (cache_creation_5m_tokens or 0) * (data.cache_creation_input_token_cost or 0) + (
				cache_creation_1h_tokens or 0
			) * (data.cache_creation_1h_input_token_cost or data.cache_creation_input_token_cost or 0)
		else:
			prompt_cache_creation_cost = (
				usage.prompt_cache_creation_tokens * data.cache_creation_input_token_cost
				if data.cache_creation_input_token_cost and usage.prompt_cache_creation_tokens
				else None
			)

		return TokenCostCalculated(
			new_prompt_tokens=usage.prompt_tokens,
			new_prompt_cost=uncached_prompt_tokens * (data.input_cost_per_token or 0) * pricing_multiplier,
			# Cached tokens
			prompt_read_cached_tokens=usage.prompt_cached_tokens,
			prompt_read_cached_cost=usage.prompt_cached_tokens * data.cache_read_input_token_cost * pricing_multiplier
			if usage.prompt_cached_tokens and data.cache_read_input_token_cost
			else None,
			# Cache creation tokens
			prompt_cached_creation_tokens=usage.prompt_cache_creation_tokens,
			prompt_cache_creation_cost=prompt_cache_creation_cost * pricing_multiplier
			if prompt_cache_creation_cost is not None
			else None,
			# Completion tokens
			completion_tokens=usage.completion_tokens,
			completion_cost=usage.completion_tokens * float(data.output_cost_per_token or 0) * pricing_multiplier,
		)

	def add_usage(self, model: str, usage: ChatInvokeUsage) -> TokenUsageEntry:
		"""Add token usage entry to history (without calculating cost)"""
		entry = TokenUsageEntry(
			model=model,
			timestamp=datetime.now(),
			usage=usage,
		)

		self.usage_history.append(entry)

		return entry

	# async def _log_non_usage_llm(self, llm: BaseChatModel) -> None:
	# 	"""Log non-usage to the logger"""
	# 	C_CYAN = '\033[96m'
	# 	C_RESET = '\033[0m'

	# 	cost_logger.debug(f'🧠 llm : {C_CYAN}{llm.model}{C_RESET} (no usage found)')

	async def _log_usage(self, model: str, usage: TokenUsageEntry) -> None:
		"""Log usage to the logger"""
		if not self._initialized:
			await self.initialize()

		# ANSI color codes
		C_CYAN = '\033[96m'
		C_GREEN = '\033[92m'
		C_RESET = '\033[0m'

		# Always get cost breakdown for token details (even if not showing costs)
		cost = await self.calculate_cost(model, usage.usage)

		# Build input tokens breakdown
		input_part = self._build_input_tokens_display(usage.usage, cost)

		# Build output tokens display
		completion_tokens_fmt = self._format_tokens(usage.usage.completion_tokens)
		if self.include_cost and cost and cost.completion_cost > 0:
			output_part = f'📤 {C_GREEN}{completion_tokens_fmt} (${cost.completion_cost:.4f}){C_RESET}'
		else:
			output_part = f'📤 {C_GREEN}{completion_tokens_fmt}{C_RESET}'

		cost_logger.debug(f'🧠 {C_CYAN}{model}{C_RESET} | {input_part} | {output_part}')

	def _build_input_tokens_display(self, usage: ChatInvokeUsage, cost: TokenCostCalculated | None) -> str:
		"""Build a clear display of input tokens breakdown with emojis and optional costs"""
		C_YELLOW = '\033[93m'
		C_BLUE = '\033[94m'
		C_RESET = '\033[0m'

		parts = []

		# Always show token breakdown if we have cache information, regardless of cost tracking
		if usage.prompt_cached_tokens or usage.prompt_cache_creation_tokens:
			# Calculate actual new tokens (non-cached)
			new_tokens = usage.prompt_tokens - (usage.prompt_cached_tokens or 0)

			if new_tokens > 0:
				new_tokens_fmt = self._format_tokens(new_tokens)
				if self.include_cost and cost and cost.new_prompt_cost > 0:
					parts.append(f'🆕 {C_YELLOW}{new_tokens_fmt} (${cost.new_prompt_cost:.4f}){C_RESET}')
				else:
					parts.append(f'🆕 {C_YELLOW}{new_tokens_fmt}{C_RESET}')

			if usage.prompt_cached_tokens:
				cached_tokens_fmt = self._format_tokens(usage.prompt_cached_tokens)
				if self.include_cost and cost and cost.prompt_read_cached_cost:
					parts.append(f'💾 {C_BLUE}{cached_tokens_fmt} (${cost.prompt_read_cached_cost:.4f}){C_RESET}')
				else:
					parts.append(f'💾 {C_BLUE}{cached_tokens_fmt}{C_RESET}')

			if usage.prompt_cache_creation_tokens:
				creation_tokens_fmt = self._format_tokens(usage.prompt_cache_creation_tokens)
				if self.include_cost and cost and cost.prompt_cache_creation_cost:
					parts.append(f'🔧 {C_BLUE}{creation_tokens_fmt} (${cost.prompt_cache_creation_cost:.4f}){C_RESET}')
				else:
					parts.append(f'🔧 {C_BLUE}{creation_tokens_fmt}{C_RESET}')

		if not parts:
			# Fallback to simple display when no cache information available
			total_tokens_fmt = self._format_tokens(usage.prompt_tokens)
			if self.include_cost and cost and cost.new_prompt_cost > 0:
				parts.append(f'📥 {C_YELLOW}{total_tokens_fmt} (${cost.new_prompt_cost:.4f}){C_RESET}')
			else:
				parts.append(f'📥 {C_YELLOW}{total_tokens_fmt}{C_RESET}')

		return ' + '.join(parts)

	def register_llm(self, llm: BaseChatModel) -> BaseChatModel:
		"""
		Register an LLM to automatically track its token usage

		@dev Guarantees that the same instance is not registered multiple times
		"""
		# Use instance ID as key to avoid collisions between multiple instances
		instance_id = str(id(llm))

		# Check if this exact instance is already registered
		if instance_id in self.registered_llms:
			logger.debug(f'LLM instance {instance_id} ({llm.provider}_{llm.model}) is already registered')
			return llm

		self.registered_llms[instance_id] = llm
		self._pricing_model_names[llm.model] = self._get_pricing_model_name(llm)

		# Store the original method
		original_ainvoke = llm.ainvoke
		# Store reference to self for use in the closure
		token_cost_service = self

		# Create a wrapped version that tracks usage
		async def tracked_ainvoke(messages, output_format=None, **kwargs):
			# Call the original method, passing through any additional kwargs
			result = await original_ainvoke(messages, output_format, **kwargs)

			# Track usage if available (no await needed since add_usage is now sync)
			# Use llm.model instead of llm.name for consistency with get_usage_tokens_for_model()
			if result.usage:
				usage = token_cost_service.add_usage(llm.model, result.usage)

				logger.debug(f'Token cost service: {usage}')

				create_task_with_error_handling(
					token_cost_service._log_usage(llm.model, usage), name='log_token_usage', suppress_exceptions=True
				)

			# else:
			# 	await token_cost_service._log_non_usage_llm(llm)

			return result

		# Replace the method with our tracked version.
		# Use setattr so Pydantic-backed models don't reject runtime patch
		object.__setattr__(llm, 'ainvoke', tracked_ainvoke)

		return llm

	def _get_pricing_model_name(self, llm: BaseChatModel) -> str:
		"""Disambiguate OpenRouter prices from same-named upstream model ids."""
		model = str(llm.model)
		base_url = str(getattr(llm, 'base_url', '') or '').rstrip('/')
		if llm.provider == 'openrouter' or base_url == 'https://openrouter.ai/api/v1':
			if not is_openrouter_pricing_model(model):
				return f'openrouter/{model}'

		return model

	def get_usage_tokens_for_model(self, model: str) -> ModelUsageTokens:
		"""Get usage tokens for a specific model"""
		filtered_usage = [u for u in self.usage_history if u.model == model]

		return ModelUsageTokens(
			model=model,
			prompt_tokens=sum(u.usage.prompt_tokens for u in filtered_usage),
			prompt_cached_tokens=sum(u.usage.prompt_cached_tokens or 0 for u in filtered_usage),
			completion_tokens=sum(u.usage.completion_tokens for u in filtered_usage),
			total_tokens=sum(u.usage.prompt_tokens + u.usage.completion_tokens for u in filtered_usage),
		)

	async def get_usage_summary(self, model: str | None = None, since: datetime | None = None) -> UsageSummary:
		"""Get summary of token usage and costs (costs calculated on-the-fly)"""
		filtered_usage = self.usage_history

		if model:
			filtered_usage = [u for u in filtered_usage if u.model == model]

		if since:
			filtered_usage = [u for u in filtered_usage if u.timestamp >= since]

		if not filtered_usage:
			return UsageSummary(
				total_prompt_tokens=0,
				total_prompt_cost=0.0,
				total_prompt_cached_tokens=0,
				total_prompt_cached_cost=0.0,
				total_prompt_cache_creation_tokens=0,
				total_prompt_cache_creation_cost=0.0,
				total_completion_tokens=0,
				total_completion_cost=0.0,
				total_tokens=0,
				total_cost=0.0,
				entry_count=0,
			)

		# Calculate totals
		total_prompt = sum(u.usage.prompt_tokens for u in filtered_usage)
		total_completion = sum(u.usage.completion_tokens for u in filtered_usage)
		total_tokens = total_prompt + total_completion
		total_prompt_cached = sum(u.usage.prompt_cached_tokens or 0 for u in filtered_usage)
		total_prompt_cache_creation = sum(u.usage.prompt_cache_creation_tokens or 0 for u in filtered_usage)

		# Calculate per-model stats with record-by-record cost calculation
		model_stats: dict[str, ModelUsageStats] = {}
		total_prompt_cost = 0.0
		total_completion_cost = 0.0
		total_prompt_cached_cost = 0.0
		total_prompt_cache_creation_cost = 0.0

		for entry in filtered_usage:
			if entry.model not in model_stats:
				model_stats[entry.model] = ModelUsageStats(model=entry.model)

			stats = model_stats[entry.model]
			stats.prompt_tokens += entry.usage.prompt_tokens
			stats.completion_tokens += entry.usage.completion_tokens
			stats.total_tokens += entry.usage.prompt_tokens + entry.usage.completion_tokens
			stats.invocations += 1

			if self.include_cost:
				# Calculate cost record by record using the updated calculate_cost function
				cost = await self.calculate_cost(entry.model, entry.usage)
				if cost:
					stats.cost += cost.total_cost
					total_prompt_cost += cost.prompt_cost
					total_completion_cost += cost.completion_cost
					total_prompt_cached_cost += cost.prompt_read_cached_cost or 0
					total_prompt_cache_creation_cost += cost.prompt_cache_creation_cost or 0

		# Calculate averages
		for stats in model_stats.values():
			if stats.invocations > 0:
				stats.average_tokens_per_invocation = stats.total_tokens / stats.invocations

		return UsageSummary(
			total_prompt_tokens=total_prompt,
			total_prompt_cost=total_prompt_cost,
			total_prompt_cached_tokens=total_prompt_cached,
			total_prompt_cached_cost=total_prompt_cached_cost,
			total_prompt_cache_creation_tokens=total_prompt_cache_creation,
			total_prompt_cache_creation_cost=total_prompt_cache_creation_cost,
			total_completion_tokens=total_completion,
			total_completion_cost=total_completion_cost,
			total_tokens=total_tokens,
			total_cost=total_prompt_cost + total_completion_cost,
			entry_count=len(filtered_usage),
			by_model=model_stats,
		)

	def _format_tokens(self, tokens: int) -> str:
		"""Format token count with k suffix for thousands"""
		if tokens >= 1000000000:
			return f'{tokens / 1000000000:.1f}B'
		if tokens >= 1000000:
			return f'{tokens / 1000000:.1f}M'
		if tokens >= 1000:
			return f'{tokens / 1000:.1f}k'
		return str(tokens)

	async def log_usage_summary(self) -> None:
		"""Log a comprehensive usage summary per model with colors and nice formatting"""
		if not self.usage_history:
			return

		summary = await self.get_usage_summary()

		if summary.entry_count == 0:
			return

		# ANSI color codes
		C_CYAN = '\033[96m'
		C_YELLOW = '\033[93m'
		C_GREEN = '\033[92m'
		C_BLUE = '\033[94m'
		C_MAGENTA = '\033[95m'
		C_RESET = '\033[0m'
		C_BOLD = '\033[1m'

		# Log overall summary
		total_tokens_fmt = self._format_tokens(summary.total_tokens)
		prompt_tokens_fmt = self._format_tokens(summary.total_prompt_tokens)
		completion_tokens_fmt = self._format_tokens(summary.total_completion_tokens)

		# Format cost breakdowns for input and output (only if cost tracking is enabled)
		if self.include_cost and summary.total_cost > 0:
			total_cost_part = f' (${C_MAGENTA}{summary.total_cost:.4f}{C_RESET})'
			prompt_cost_part = f' (${summary.total_prompt_cost:.4f})'
			completion_cost_part = f' (${summary.total_completion_cost:.4f})'
		else:
			total_cost_part = ''
			prompt_cost_part = ''
			completion_cost_part = ''

		if len(summary.by_model) > 1:
			cost_logger.debug(
				f'💲 {C_BOLD}Total Usage Summary{C_RESET}: {C_BLUE}{total_tokens_fmt} tokens{C_RESET}{total_cost_part} | '
				f'⬅️ {C_YELLOW}{prompt_tokens_fmt}{prompt_cost_part}{C_RESET} | ➡️ {C_GREEN}{completion_tokens_fmt}{completion_cost_part}{C_RESET}'
			)

		for model, stats in summary.by_model.items():
			# Format tokens
			model_total_fmt = self._format_tokens(stats.total_tokens)
			model_prompt_fmt = self._format_tokens(stats.prompt_tokens)
			model_completion_fmt = self._format_tokens(stats.completion_tokens)
			avg_tokens_fmt = self._format_tokens(int(stats.average_tokens_per_invocation))

			# Format cost display (only if cost tracking is enabled)
			if self.include_cost:
				# Calculate per-model costs on-the-fly
				total_model_cost = 0.0
				model_prompt_cost = 0.0
				model_completion_cost = 0.0

				# Calculate costs for this model
				for entry in self.usage_history:
					if entry.model == model:
						cost = await self.calculate_cost(entry.model, entry.usage)
						if cost:
							model_prompt_cost += cost.prompt_cost
							model_completion_cost += cost.completion_cost

				total_model_cost = model_prompt_cost + model_completion_cost

				if total_model_cost > 0:
					cost_part = f' (${C_MAGENTA}{total_model_cost:.4f}{C_RESET})'
					prompt_part = f'{C_YELLOW}{model_prompt_fmt} (${model_prompt_cost:.4f}){C_RESET}'
					completion_part = f'{C_GREEN}{model_completion_fmt} (${model_completion_cost:.4f}){C_RESET}'
				else:
					cost_part = ''
					prompt_part = f'{C_YELLOW}{model_prompt_fmt}{C_RESET}'
					completion_part = f'{C_GREEN}{model_completion_fmt}{C_RESET}'
			else:
				cost_part = ''
				prompt_part = f'{C_YELLOW}{model_prompt_fmt}{C_RESET}'
				completion_part = f'{C_GREEN}{model_completion_fmt}{C_RESET}'

			cost_logger.debug(
				f'  🤖 {C_CYAN}{model}{C_RESET}: {C_BLUE}{model_total_fmt} tokens{C_RESET}{cost_part} | '
				f'⬅️ {prompt_part} | ➡️ {completion_part} | '
				f'📞 {stats.invocations} calls | 📈 {avg_tokens_fmt}/call'
			)

	async def get_cost_by_model(self) -> dict[str, ModelUsageStats]:
		"""Get cost breakdown by model"""
		summary = await self.get_usage_summary()
		return summary.by_model

	def clear_history(self) -> None:
		"""Clear usage history"""
		self.usage_history = []

	async def refresh_pricing_data(self) -> None:
		"""Force refresh of pricing data from GitHub"""
		if self.include_cost:
			await self._fetch_and_cache_pricing_data()

	async def clean_old_caches(self, keep_count: int = 3) -> None:
		"""Clean up old cache files, keeping only the most recent ones from this source URL"""
		try:
			# List all JSON files in the cache directory
			cache_files = list(self._cache_dir.glob('*.json'))

			if not cache_files:
				return

			# Only consider cache files from the same source URL
			own_files: list[Path] = []
			for cache_file in cache_files:
				try:
					cached = CachedPricingData.model_validate_json(cache_file.read_text())
					if self._cache_source_matches(cached):
						own_files.append(cache_file)
				except Exception:
					pass

			if len(own_files) <= keep_count:
				return

			# Sort by modification time (oldest first)
			own_files.sort(key=lambda f: f.stat().st_mtime)

			# Remove all but the most recent files
			for cache_file in own_files[:-keep_count]:
				try:
					os.remove(cache_file)
				except Exception:
					pass
		except Exception as e:
			logger.debug(f'Error cleaning old cache files: {e}')

	async def ensure_pricing_loaded(self) -> None:
		"""Ensure pricing data is loaded in the background. Call this after creating the service."""
		if not self._initialized and self.include_cost:
			# This will run in the background and won't block
			await self.initialize()