| 0 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Routes requests across multiple LLM models organized into performance tiers (e.g., fast/cheap vs. slow/capable), selecting the appropriate tier based on request complexity or user-defined routing rules. Implements a decision tree that evaluates incoming prompts against tier criteria and selects the lowest-cost model capable of handling the request, reducing API spend while maintaining quality thresholds.
Unique: Implements explicit tier-based routing with fallback chains rather than simple load balancing, allowing developers to define semantic tiers (e.g., 'reasoning', 'classification', 'generation') and map them to specific models with cost/latency tradeoffs
vs alternatives: More granular than round-robin load balancing because it considers request characteristics and model capabilities, not just availability
Automatically cascades requests to alternative models when the primary model fails, times out, or returns an error. Maintains a fallback chain (e.g., GPT-4 → Claude → Llama) and transparently retries with the next model in sequence without requiring application-level retry logic, with configurable backoff and circuit-breaker patterns.
Unique: Encapsulates fallback logic as a first-class routing primitive rather than requiring application code to implement try-catch chains, with built-in circuit breaker to prevent cascading failures
vs alternatives: Simpler than manual retry logic in application code and more reliable than simple timeout-based retries because it understands provider-specific error semantics
Routes requests to models based on attached metadata (e.g., user tier, request priority, domain) rather than just request content. Evaluates metadata against routing rules at request time to select the optimal model, enabling use cases like 'premium users get GPT-4, free users get GPT-3.5' or 'code generation requests use specialized models'. Metadata can be attached by middleware or application logic before routing.
Unique: Decouples routing decisions from request content by using explicit metadata, allowing non-technical operators to define routing policies without code changes
vs alternatives: More flexible than content-based routing because it enables business logic (user tier, priority) to drive model selection without analyzing prompt content
Provides a single API surface for interacting with multiple LLM providers (OpenAI, Anthropic, Ollama, etc.) by normalizing their different request/response formats into a common schema. Handles provider-specific quirks (token limits, parameter names, response structures) transparently, allowing applications to switch providers without code changes. Implements adapter pattern with provider-specific implementations for each API.
Unique: Implements provider abstraction as a routing concern rather than a separate SDK, allowing routing decisions and provider abstraction to be co-located in the same decision point
vs alternatives: More integrated than standalone abstraction libraries (like LangChain) because routing and provider selection happen together, reducing context switching
Monitors model availability in real-time by tracking request success/failure rates and response times, automatically removing models from rotation when they exceed error thresholds or timeout consistently. Implements circuit breaker pattern that temporarily disables failing models and periodically tests them for recovery, preventing cascading failures and wasted API calls to unavailable endpoints.
Unique: Integrates circuit breaker as a native routing concern rather than a separate middleware, allowing availability decisions to influence tier selection in real-time
vs alternatives: More responsive than manual health checks because it reacts to actual request failures rather than periodic probes
Groups multiple requests destined for the same model and sends them in batch operations where supported (e.g., OpenAI Batch API), reducing per-request overhead and API costs. Tracks costs per model and aggregates them for billing/analytics, providing visibility into which models are consuming budget. Implements batching with configurable window sizes and timeout thresholds to balance latency vs. cost savings.
Unique: Couples request batching with cost aggregation, providing both latency optimization and financial visibility in a single primitive
vs alternatives: More integrated than separate batching and billing systems because cost is tracked at the routing layer where batching decisions are made
Automatically optimizes prompts before sending to models by truncating context, removing redundant information, or reformatting based on model token limits and capabilities. Tracks token usage per request and model, enforcing hard limits to prevent exceeding context windows. Implements strategies like sliding window context, summarization, or hierarchical chunking to fit large contexts into model limits while preserving semantic meaning.
Unique: Integrates token management into the routing layer rather than requiring application code to handle context limits, with automatic optimization strategies
vs alternatives: More proactive than error-based truncation because it prevents token limit errors before they occur
Collects latency, throughput, and quality metrics for each model in the routing configuration, enabling data-driven decisions about tier assignments and fallback ordering. Provides built-in benchmarking tools to compare models on representative workloads, with support for custom evaluation metrics. Stores historical performance data to identify trends and detect performance regressions.
Unique: Provides built-in benchmarking as a first-class feature rather than requiring external tools, with metrics directly tied to routing decisions
vs alternatives: More integrated than standalone benchmarking tools because results directly inform tier assignments and fallback ordering
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 @kb-labs/llm-router at 27/100.
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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.
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