| 0 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
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.
Unique: Provides unified token tracking abstraction across three major LLM providers (OpenAI, Anthropic, Google) with provider-specific token counting libraries integrated directly, rather than requiring manual per-provider instrumentation or external monitoring services
vs alternatives: Simpler than building custom instrumentation per provider and faster than post-hoc cost analysis tools because it tracks tokens at request-time before responses are fully processed
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.
Unique: Implements synchronous pre-flight validation that rejects requests before API calls are made, using provider-specific token estimation rather than generic heuristics, ensuring budget compliance at the request boundary
vs alternatives: More cost-effective than rate-limiting or quota systems because it prevents expensive requests from being sent to the API at all, rather than charging and then blocking
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.
Unique: Maintains per-session cost accumulators that persist across multiple requests within a session, enabling cumulative budget enforcement without external state stores, using in-memory tracking with optional persistence hooks
vs alternatives: Simpler to implement than external quota systems (no database required for basic use) but trades off durability and concurrency safety for ease of integration
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.
Unique: Provides a unified pricing abstraction that normalizes costs across three major providers (OpenAI, Anthropic, Google) with provider-specific rate tables, enabling direct cost comparison without manual lookup or external pricing APIs
vs alternatives: More accurate than generic cost estimation because it uses actual provider pricing tables rather than averages, and faster than querying external pricing APIs because rates are bundled with the library
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.
Unique: Implements a transparent wrapper pattern that enriches provider responses with cost metadata without modifying the underlying API contract, preserving compatibility with existing provider SDKs and allowing drop-in integration
vs alternatives: Less invasive than forking provider libraries or building custom clients because it wraps existing clients, and more flexible than using provider-native cost tracking because it works across multiple providers with a unified interface
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.
Unique: Provides configurable multi-level alert thresholds (per-request, per-session, per-window) with custom handler callbacks, enabling integration into existing monitoring stacks without requiring external services
vs alternatives: More immediate than provider-native billing alerts (which may lag by hours/days) because it triggers in real-time as requests are made, and more flexible than fixed-rate limiting because thresholds are configurable
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.
Unique: Provides built-in time-window management with configurable reset intervals (daily, weekly, monthly) and automatic counter reset, eliminating manual budget reset logic and supporting multiple quota models without external schedulers
vs alternatives: Simpler than building custom cron-based resets because reset logic is built-in, and more reliable than manual reset endpoints because resets are automatic and time-based
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.
Unique: Provides built-in structured logging of all budget decisions and API calls with configurable handlers, capturing both approvals and rejections with full context, enabling compliance-grade audit trails without external logging infrastructure
vs alternatives: More comprehensive than provider-native usage logs because it captures budget enforcement decisions and rejections, and more flexible than external logging services because logs are generated locally with full context
+1 more capabilities
Crawls 11+ heterogeneous platforms (Zhihu, Weibo, Bilibili, Twitter, Reddit, HackerNews, etc.) and RSS feeds using platform-specific scrapers, normalizes disparate data schemas into a unified NewsItem model, and deduplicates content across sources using fuzzy title matching and URL canonicalization. The system maintains platform-specific metadata (rank, heat value, engagement metrics) while presenting a single normalized feed, enabling cross-platform trend detection that would be invisible within individual platform silos.
Unique: Implements platform-specific crawler modules with unified NewsItem schema and fuzzy deduplication across 11+ heterogeneous sources (Chinese + international), rather than relying on single-platform APIs or generic RSS parsing. Maintains platform-specific metadata (rank × 0.6 + frequency × 0.3 + platform hot value × 0.1) for weighted hotspot scoring.
vs alternatives: Covers more platforms (especially Chinese social media) with deeper metadata extraction than generic RSS aggregators, and provides unified deduplication across sources unlike single-platform monitoring tools.
Implements a multi-stage filtering pipeline that matches news items against user-defined keywords using regex patterns, required word lists, and excluded word lists. The system applies frequency-based scoring (keyword occurrence count) combined with platform hotspot weights to rank filtered results. Configuration is stored in frequency_words.txt with support for regex patterns, AND/OR/NOT boolean operators, and per-keyword weighting. Filtering occurs at collection time (reducing storage) and again at report generation time (enabling dynamic reconfiguration without re-crawling).
Unique: Combines regex pattern matching with frequency-based scoring and platform hotspot weighting (rank × 0.6 + frequency × 0.3 + platform hot value × 0.1) in a two-stage pipeline (collection-time and report-time filtering). Supports dynamic reconfiguration without re-crawling by applying filters at report generation.
TrendRadar scores higher at 58/100 vs llm-spend-guard at 39/100.
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vs alternatives: More flexible than simple keyword matching (supports regex and boolean logic) and more efficient than semantic filtering (no LLM overhead), making it suitable for real-time filtering at scale.
Detects newly emerged topics by comparing current crawl results against historical data stored in the system. Topics are marked as 🆕 (new) if they appear for the first time in the current crawl or if their hotspot rank increased significantly compared to previous crawls. The system tracks topic emergence velocity (how quickly a topic rises in rankings) and flags topics with unusual acceleration. New topic detection is performed at report generation time, enabling dynamic detection without re-crawling. The system maintains a historical hotspot index for comparison.
Unique: Detects new topics by comparing current hotspot rankings against historical data, marking topics with significant rank increases as 🆕. Tracks emergence velocity to distinguish breaking news from sustained trends.
vs alternatives: More efficient than semantic similarity detection (no LLM overhead) and more accurate than simple first-appearance detection (accounts for re-emerging topics), but requires historical baseline data.
Provides a web-based UI for editing TrendRadar configuration files (config.yaml, frequency_words.txt, timeline.yaml) with real-time validation and preview. The editor supports: (1) syntax highlighting for YAML and regex, (2) validation of keyword patterns (regex compilation check), (3) preview of filtered results based on current keyword configuration, (4) drag-and-drop channel configuration, (5) schedule preview (shows next 10 execution times). Changes are validated before saving, preventing configuration errors. The editor is optional; users can edit config files directly.
Unique: Provides web-based configuration editor with real-time validation, regex preview, and schedule visualization. Enables non-technical users to configure TrendRadar without editing YAML files.
vs alternatives: More user-friendly than manual YAML editing and provides validation feedback, but adds operational complexity compared to file-based configuration.
Integrates LiteLLM to provide vendor-agnostic AI analysis and summarization of filtered news items. Users configure their preferred LLM provider (OpenAI, Anthropic, Ollama, local models, etc.) once in config.yaml, and the system automatically routes analysis requests to that provider. The AI analysis capability includes: (1) automated summarization of long articles into key points, (2) sentiment analysis (positive/negative/neutral), (3) trend prediction based on historical patterns, and (4) custom analysis prompts. Analysis results are cached to avoid redundant API calls and can be pushed directly to notification channels.
Unique: Uses LiteLLM abstraction layer to support any LLM provider (OpenAI, Anthropic, Ollama, local models) with single configuration, enabling provider switching without code changes. Caches analysis results to reduce redundant API calls and costs.
vs alternatives: More flexible than hardcoded OpenAI integration (supports any LiteLLM provider) and cheaper than dedicated sentiment analysis APIs (can use local models), but slower than rule-based sentiment analysis.
Leverages LiteLLM to translate news content from source languages (primarily Chinese) to target languages (English, etc.) on-demand. The system detects source language automatically (via langdetect or similar), caches translations to avoid re-translating identical content, and batches translation requests to reduce API calls. Translations are stored alongside original content, enabling bilingual reports and multi-language notification delivery. Translation can be triggered at collection time (all news) or report time (only filtered news).
Unique: Implements provider-agnostic translation via LiteLLM with automatic language detection, content-based caching, and batch request optimization. Stores translations alongside originals for bilingual report generation.
vs alternatives: More flexible than dedicated translation APIs (supports any LiteLLM provider) and cheaper than commercial translation services when using local models, but slower than specialized translation APIs.
Implements a notification abstraction layer supporting 9+ delivery channels (WeChat, WeWork, Feishu, Telegram, Email, ntfy, Bark, Slack, etc.). Each channel has a provider-specific formatter that converts normalized news items into channel-appropriate messages (e.g., WeChat card format, Telegram markdown, email HTML). The system batches notifications atomically—all news items for a report are sent as a single batch to each channel, ensuring consistency and reducing API calls. Message formatting respects channel constraints (character limits, attachment limits, etc.) and supports templating for customization.
Unique: Implements atomic message batching across 9+ heterogeneous channels with provider-specific formatters and constraint-aware truncation. Single configuration enables simultaneous delivery to WeChat, WeWork, Feishu, Telegram, Email, ntfy, Bark, Slack, etc. without code changes.
vs alternatives: Supports more channels (especially Chinese platforms like WeWork, Feishu) than generic notification services, and batching reduces API calls and spam compared to per-item notifications.
Exposes TrendRadar's data and analysis capabilities as an MCP server, enabling AI agents and LLM applications to query trends, perform analysis, and generate insights through natural language. The MCP server implements tools for: (1) querying filtered news by keyword/date/platform, (2) retrieving trend statistics and hotspot rankings, (3) running custom analysis on news subsets, (4) generating reports in various formats. Clients (Claude, other LLM agents) can invoke these tools via MCP protocol, enabling conversational exploration of trends without direct database access. The server maintains state across multiple requests, allowing multi-turn conversations about trends.
Unique: Implements full MCP server exposing trend data and analysis tools to LLM agents, enabling conversational queries and multi-turn analysis workflows. Maintains state across requests and supports complex tool invocations (filtering, analysis, report generation).
vs alternatives: Enables conversational access to trends (vs. API-only access) and integrates with LLM agent workflows (vs. standalone tools), but adds operational complexity compared to simple REST APIs.
+4 more capabilities