{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-llm-spend-guard","slug":"llm-spend-guard","name":"llm-spend-guard","type":"mcp","url":"https://ali-raza-arain.github.io/llm-spend-guard/","page_url":"https://unfragile.ai/llm-spend-guard","categories":["mcp-servers"],"tags":["llm","openai","anthropic","gemini","token","budget","cost","guard","rate-limit","claude","ai","gpt","token-budget","llm-cost","api-cost-control","spending-limit","token-limit","ai-cost-management","gpt-4","gpt-4o"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-llm-spend-guard__cap_0","uri":"capability://data.processing.analysis.real.time.token.consumption.tracking.across.multiple.llm.providers","name":"real-time token consumption tracking across multiple llm providers","description":"Intercepts and monitors token usage in real-time by wrapping API calls to OpenAI, Anthropic Claude, and Google Gemini, tracking input/output tokens per request and maintaining cumulative counters. Uses provider-specific token counting libraries (tiktoken for OpenAI, custom counters for Anthropic/Gemini) to calculate costs before responses are returned, enabling immediate visibility into consumption patterns without post-hoc analysis.","intents":["I need to see exactly how many tokens each API call is consuming across different LLM providers","I want to track cumulative token usage in real-time as my application makes requests","I need to understand per-request token breakdown (input vs output) to optimize prompts"],"best_for":["Node.js developers building multi-provider LLM applications","teams managing shared API budgets across development and production","startups optimizing LLM costs before scaling"],"limitations":["Token counting accuracy depends on provider library versions — may diverge from actual billing if libraries are outdated","Real-time tracking adds synchronous overhead to request/response cycle; no async batching for cost calculation","Does not account for batch API pricing or volume discounts that providers may apply"],"requires":["Node.js 14+","Valid API keys for OpenAI, Anthropic, or Google Gemini","npm package installed (248k+ weekly downloads)"],"input_types":["API request parameters (messages, model, temperature, etc.)","API responses from LLM providers"],"output_types":["token count (input tokens, output tokens)","cost estimate (USD)","cumulative usage metrics"],"categories":["data-processing-analysis","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llm-spend-guard__cap_1","uri":"capability://safety.moderation.enforced.per.request.token.budget.limits.with.automatic.rejection","name":"enforced per-request token budget limits with automatic rejection","description":"Validates incoming requests against configurable per-request token budgets before sending to LLM APIs, rejecting calls that would exceed limits and throwing typed errors. Implements budget checking by calculating estimated input tokens from the request payload and comparing against a configured threshold, preventing over-budget requests from reaching the API and incurring charges.","intents":["I want to reject API calls that would exceed a per-request token limit to prevent runaway costs","I need to enforce maximum context window usage per request to avoid hitting model limits","I want to fail fast with clear errors when a request would violate budget constraints"],"best_for":["production applications with strict per-request cost caps","multi-tenant systems where each tenant has individual token budgets","teams preventing accidental expensive requests (e.g., large file uploads as context)"],"limitations":["Budget enforcement is based on estimated input tokens only — does not predict output token consumption, so total request cost may still exceed budget","No graceful degradation: requests are hard-rejected rather than truncated or re-routed to cheaper models","Requires manual configuration per request type; no automatic learning of typical token usage patterns"],"requires":["Node.js 14+","llm-spend-guard npm package","configured token limit value (integer)"],"input_types":["request configuration object (messages, model, parameters)"],"output_types":["boolean (pass/fail)","error object with rejection reason"],"categories":["safety-moderation","cost-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llm-spend-guard__cap_2","uri":"capability://safety.moderation.cumulative.session.level.spending.limit.enforcement","name":"cumulative session-level spending limit enforcement","description":"Tracks total token spending across all requests within a session or time window and enforces a cumulative budget ceiling, rejecting new requests when the session total would exceed the configured limit. Maintains an in-memory accumulator of costs per session, comparing each new request's estimated cost against remaining budget and blocking requests that would push the session over the threshold.","intents":["I want to set a total spending cap for an entire user session or conversation","I need to prevent a single user or tenant from consuming more than their monthly/daily token allocation","I want to enforce hard spending limits across multiple requests in a single session"],"best_for":["SaaS applications with per-user token quotas","chatbot platforms with session-based billing","multi-tenant systems enforcing per-customer spending caps"],"limitations":["In-memory tracking means budgets are lost on process restart — requires external persistence for production use","No built-in session expiration or time-window reset logic; requires manual session lifecycle management","Does not handle concurrent requests well — race conditions possible if multiple requests are evaluated simultaneously against the same budget"],"requires":["Node.js 14+","llm-spend-guard npm package","configured cumulative spending limit (USD or token count)","session identifier or context"],"input_types":["session ID or user context","request cost estimate"],"output_types":["boolean (budget exceeded or not)","remaining budget (USD or tokens)","error with budget exhaustion details"],"categories":["safety-moderation","cost-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llm-spend-guard__cap_3","uri":"capability://data.processing.analysis.multi.provider.cost.calculation.with.unified.pricing.model","name":"multi-provider cost calculation with unified pricing model","description":"Converts token counts to USD costs using provider-specific pricing tables (OpenAI GPT-4/GPT-4o, Anthropic Claude variants, Google Gemini tiers), normalizing costs across providers into a single currency for comparison and aggregation. Implements a pricing registry that maps model names to per-token input/output rates, calculating costs as (input_tokens × input_rate) + (output_tokens × output_rate) and supporting multiple model variants per provider.","intents":["I want to compare costs across different LLM providers to choose the cheapest option for a task","I need to calculate total spending in USD across requests to different models and providers","I want to understand the cost breakdown (input vs output tokens) for optimization"],"best_for":["teams evaluating multiple LLM providers for cost-effectiveness","applications dynamically routing requests to cheapest available model","finance/ops teams reporting on LLM spending across the organization"],"limitations":["Pricing tables are static and must be manually updated when providers change rates — no automatic price feed integration","Does not account for volume discounts, enterprise pricing, or batch API discounts","Pricing accuracy depends on keeping model lists current; new model variants may not be recognized"],"requires":["Node.js 14+","llm-spend-guard npm package with pricing data","model names that match provider naming conventions"],"input_types":["model name (string)","input token count (integer)","output token count (integer)"],"output_types":["cost in USD (number)","cost breakdown (input cost, output cost)","cost per token (number)"],"categories":["data-processing-analysis","cost-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llm-spend-guard__cap_4","uri":"capability://tool.use.integration.provider.agnostic.api.wrapper.with.transparent.cost.injection","name":"provider-agnostic api wrapper with transparent cost injection","description":"Wraps LLM API calls (OpenAI, Anthropic, Google Gemini) with a unified interface that transparently injects token counts and cost data into responses without modifying the underlying API contract. Uses middleware/decorator pattern to intercept requests before sending to providers and responses after receiving, enriching response objects with usage metadata (tokens, cost) while preserving the original provider response structure.","intents":["I want to add cost tracking to my existing LLM code without rewriting API calls","I need a drop-in wrapper that works with existing OpenAI/Anthropic/Gemini client libraries","I want cost data attached to every API response for logging and analysis"],"best_for":["teams migrating existing code to add cost controls","developers wanting minimal code changes to enable budget tracking","applications using multiple LLM providers with a single abstraction layer"],"limitations":["Wrapper adds latency to every request (token counting and cost calculation overhead)","Does not support streaming responses in all cases — cost calculation may be delayed until stream completes","Requires the underlying provider client library to be installed separately; adds dependency management complexity"],"requires":["Node.js 14+","llm-spend-guard npm package","OpenAI, Anthropic, or Google client library (version-specific compatibility required)"],"input_types":["provider API request (OpenAI ChatCompletion, Anthropic Message, Google GenerateContent)"],"output_types":["enriched provider response with added usage and cost fields","original provider response structure preserved"],"categories":["tool-use-integration","abstraction-layer"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llm-spend-guard__cap_5","uri":"capability://safety.moderation.configurable.alert.thresholds.for.spending.anomalies","name":"configurable alert thresholds for spending anomalies","description":"Monitors spending patterns and triggers alerts when costs exceed configured thresholds (per-request, per-session, or per-time-window), enabling proactive detection of budget overruns or unexpected usage spikes. Implements threshold comparison logic that evaluates current spending against configured limits and emits events or callbacks when thresholds are crossed, supporting multiple alert levels (warning, critical) and custom handlers.","intents":["I want to be notified when a single request costs more than expected","I need alerts when a user's session spending exceeds a warning threshold","I want to trigger custom actions (log, email, Slack) when spending anomalies are detected"],"best_for":["production systems requiring cost monitoring and alerting","teams with shared LLM budgets needing visibility into spending","applications integrating cost alerts into existing monitoring/observability stacks"],"limitations":["Alerts are reactive (triggered after threshold is crossed) rather than predictive — cannot prevent overspending, only notify","No built-in integration with external alerting systems (Slack, PagerDuty, etc.) — requires custom handlers","Alert state is in-memory; alerts may be lost on process restart without external persistence"],"requires":["Node.js 14+","llm-spend-guard npm package","configured threshold values (USD or token count)","optional: custom alert handler function"],"input_types":["threshold configuration (number)","current spending (number)","alert level (warning, critical)"],"output_types":["alert event with spending details","callback/handler invocation","structured alert object"],"categories":["safety-moderation","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llm-spend-guard__cap_6","uri":"capability://automation.workflow.token.budget.reset.and.time.window.management","name":"token budget reset and time-window management","description":"Manages budget reset schedules (daily, weekly, monthly) and time-window-based quota enforcement, automatically resetting cumulative spending counters at configured intervals and supporting sliding-window or fixed-window quota models. Implements timer-based reset logic that clears session budgets or resets global counters at specified times, enabling per-period spending limits without manual intervention.","intents":["I want to reset user token budgets daily/weekly/monthly automatically","I need to enforce monthly spending caps that reset on the 1st of each month","I want sliding-window quotas where budgets refresh based on time elapsed"],"best_for":["SaaS applications with per-user monthly/daily token quotas","multi-tenant systems with time-based billing periods","applications with recurring budget cycles (e.g., free tier resets)"],"limitations":["Reset timing is local to the Node.js process — distributed systems need external coordination (Redis, database) to sync resets across instances","No built-in persistence of reset schedules; resets are lost on process restart unless explicitly saved","Timezone handling is not built-in; requires manual configuration for multi-region deployments"],"requires":["Node.js 14+","llm-spend-guard npm package","configured reset interval (daily, weekly, monthly, or custom cron)","optional: external state store for distributed reset coordination"],"input_types":["reset interval (string: 'daily', 'weekly', 'monthly', or cron expression)","budget amount (USD or tokens)","session or user ID"],"output_types":["reset confirmation (boolean)","next reset time (timestamp)","reset event"],"categories":["automation-workflow","cost-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llm-spend-guard__cap_7","uri":"capability://data.processing.analysis.detailed.usage.logging.and.audit.trail.generation","name":"detailed usage logging and audit trail generation","description":"Records comprehensive logs of all API calls, token usage, costs, and budget decisions (approvals/rejections) with timestamps and context, enabling audit trails and usage analytics. Implements structured logging that captures request metadata (model, user, session), token counts (input/output), costs, and budget enforcement decisions, supporting multiple log destinations (console, file, external services) via configurable handlers.","intents":["I need an audit trail of all LLM API calls for compliance and cost reconciliation","I want to analyze usage patterns to identify optimization opportunities","I need to debug why a request was rejected due to budget limits"],"best_for":["regulated industries requiring API usage audit trails","teams analyzing LLM spending patterns for cost optimization","multi-tenant systems needing per-customer usage reports"],"limitations":["Logging adds I/O overhead to every request — can impact latency if logs are written synchronously","No built-in log retention or cleanup — logs can grow unbounded without external log management","Structured log format is library-specific; requires custom parsing for integration with external log aggregation systems"],"requires":["Node.js 14+","llm-spend-guard npm package","optional: external log destination (file, database, logging service)"],"input_types":["API request metadata","token counts and costs","budget decision (approved/rejected)"],"output_types":["structured log entry (JSON or text)","audit trail (array of log entries)","usage report (aggregated metrics)"],"categories":["data-processing-analysis","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llm-spend-guard__cap_8","uri":"capability://safety.moderation.error.handling.and.budget.exhaustion.recovery","name":"error handling and budget exhaustion recovery","description":"Provides typed error objects and recovery strategies when budgets are exhausted, including graceful degradation options (fallback models, request truncation, queuing) and error callbacks for custom handling. Implements error classification (budget exceeded, invalid model, API error) with structured error objects that include remaining budget, suggested actions, and recovery hints.","intents":["I want to handle budget exhaustion gracefully instead of crashing the application","I need to fall back to a cheaper model when the primary model exceeds budget","I want custom error handling logic when a request is rejected due to budget limits"],"best_for":["production applications requiring resilience to budget exhaustion","systems with fallback strategies (cheaper models, degraded service)","applications needing custom error handling and recovery logic"],"limitations":["Fallback strategies (model switching, truncation) are not automatic — require manual configuration and implementation","Error recovery is synchronous; no built-in async retry logic or exponential backoff","No automatic request queuing or deferral — rejected requests are immediately failed rather than retried later"],"requires":["Node.js 14+","llm-spend-guard npm package","optional: custom error handler function","optional: fallback model configuration"],"input_types":["error type (budget exceeded, invalid model, etc.)","request context (model, tokens, cost)"],"output_types":["typed error object with details","recovery suggestion (fallback model, truncation, etc.)","remaining budget and reset time"],"categories":["safety-moderation","error-handling"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":51,"verified":false,"data_access_risk":"high","permissions":["Node.js 14+","Valid API keys for OpenAI, Anthropic, or Google Gemini","npm package installed (248k+ weekly downloads)","llm-spend-guard npm package","configured token limit value (integer)","configured cumulative spending limit (USD or token count)","session identifier or context","llm-spend-guard npm package with pricing data","model names that match provider naming conventions","OpenAI, Anthropic, or Google client library (version-specific compatibility required)"],"failure_modes":["Token counting accuracy depends on provider library versions — may diverge from actual billing if libraries are outdated","Real-time tracking adds synchronous overhead to request/response cycle; no async batching for cost calculation","Does not account for batch API pricing or volume discounts that providers may apply","Budget enforcement is based on estimated input tokens only — does not predict output token consumption, so total request cost may still exceed budget","No graceful degradation: requests are hard-rejected rather than truncated or re-routed to cheaper models","Requires manual configuration per request type; no automatic learning of typical token usage patterns","In-memory tracking means budgets are lost on process restart — requires external persistence for production use","No built-in session expiration or time-window reset logic; requires manual session lifecycle management","Does not handle concurrent requests well — race conditions possible if multiple requests are evaluated simultaneously against the same budget","Pricing tables are static and must be manually updated when providers change rates — no automatic price feed integration","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7391520052143256,"quality":0.43,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-05-03T14:04:47.473Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":248159,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=llm-spend-guard","compare_url":"https://unfragile.ai/compare?artifact=llm-spend-guard"}},"signature":"xRFV9QnaTNTpDr0zDbghJ2wW0LnxF9l3ftDhqmmklGF94APNvi37SSfpRQssMpnKn9kXEykw9Hb1iqCzNauECg==","signedAt":"2026-06-20T22:08:49.891Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llm-spend-guard","artifact":"https://unfragile.ai/llm-spend-guard","verify":"https://unfragile.ai/api/v1/verify?slug=llm-spend-guard","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}