| Ecosystem | 0 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 14 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Executes evaluation metrics using LLMs as judges by constructing structured prompts with evaluation schemas and routing them to any LLM provider (OpenAI, Anthropic, Ollama, etc.). Implements the G-Eval pattern with research-backed scoring templates that normalize outputs to 0-1 scales. The metric execution pipeline handles provider abstraction, caching of LLM responses, and deterministic scoring through configurable model selection and temperature control.
Unique: Implements provider-agnostic LLM-as-judge evaluation through a unified Model abstraction layer that supports OpenAI, Anthropic, Ollama, and custom providers with automatic schema-based prompt construction and response normalization. The metric execution pipeline includes built-in caching and deterministic scoring via configurable temperature/seed parameters.
vs alternatives: More flexible than Ragas (which is RAG-specific) and more comprehensive than LangSmith's basic scoring because it supports arbitrary LLM providers, includes 50+ research-backed metrics out-of-the-box, and provides full metric customization through the GEval base class.
Provides 50+ pre-built metrics covering general LLM quality (relevance, coherence, faithfulness), RAG-specific concerns (retrieval precision, context relevance), and conversation quality (turn-level relevance, conversation coherence). Each metric is implemented as a subclass of the Metric base class with built-in scoring logic that can use LLM-as-judge, statistical methods, or local NLP models. Metrics are composable and can be mixed in test runs to evaluate multiple dimensions simultaneously.
Unique: Combines research-backed metrics (G-Eval, RAGAS, BERTScore) with domain-specific implementations for RAG (retrieval precision, context relevance) and conversation quality (turn-level relevance, conversation coherence). Metrics are composable and can be evaluated in parallel within a single test run.
vs alternatives: More comprehensive than Ragas alone (which focuses only on RAG) and more specialized than generic LLM evaluation frameworks because it includes turn-level conversation metrics and multi-dimensional evaluation in a single framework.
Provides guardrail metrics to evaluate safety and compliance of LLM outputs, including toxicity detection, PII redaction, prompt injection detection, and bias assessment. Guardrails can be applied as pre-generation filters or post-generation validators. Integrates with external safety APIs (e.g., OpenAI Moderation) and local NLP models for offline evaluation.
Unique: Implements guardrail metrics for safety evaluation including toxicity, PII detection, prompt injection, and bias assessment. Supports both external APIs and local NLP models for flexible deployment.
vs alternatives: More comprehensive than single-purpose safety tools and more integrated than external safety APIs because it provides multiple guardrail types in a unified evaluation framework.
Generates adversarial test cases designed to expose weaknesses in LLM applications through systematic perturbation of inputs (e.g., typos, paraphrasing, edge cases). Red teaming metrics evaluate robustness by measuring how outputs change under adversarial conditions. Supports both automated generation and manual specification of adversarial scenarios.
Unique: Implements red teaming through systematic input perturbation (typos, paraphrasing, edge cases) and robustness metrics that measure output sensitivity to adversarial conditions. Supports both automated generation and manual specification.
vs alternatives: More systematic than ad-hoc adversarial testing and more integrated than standalone red teaming tools because it provides automated perturbation generation and robustness metrics within the evaluation framework.
Provides utilities for systematic prompt optimization by running evaluations across multiple prompt variants and comparing results. Supports A/B testing of prompts, model versions, and hyperparameters. Results are aggregated and compared to identify the best-performing variant. Integrates with the Confident AI platform for historical tracking of prompt iterations.
Unique: Provides A/B testing framework for prompt variants with automatic evaluation comparison and statistical significance testing. Results are tracked in Confident AI platform for historical analysis.
vs alternatives: More systematic than manual prompt testing and more integrated than standalone A/B testing tools because it combines prompt evaluation with statistical comparison and historical tracking.
Provides a command-line interface (deepeval CLI) for running evaluations, managing datasets, and configuring projects. Supports configuration files (deepeval.json) for project settings, environment variables for API keys, and provider configuration management. CLI commands enable running evaluations without writing Python code, making it accessible to non-developers.
Unique: Implements a CLI interface for running evaluations and managing projects without Python code. Supports configuration files and environment variables for flexible deployment.
vs alternatives: More accessible than Python-only APIs and more flexible than fixed configuration because it provides both CLI and programmatic interfaces with support for configuration files and environment variables.
Defines evaluation test cases as structured Python dataclasses (LLMTestCase, ConversationalTestCase) that capture input, expected output, actual output, and context. The framework provides schema validation, serialization to JSON/CSV, and dataset-level operations (filtering, splitting, versioning). Test cases can be created manually, loaded from files, or generated synthetically using LLM-based data generation.
Unique: Implements typed test case dataclasses (LLMTestCase, ConversationalTestCase) with built-in serialization and validation, allowing seamless integration with evaluation pipelines. Supports both single-turn and multi-turn conversation test cases with turn-level metadata.
vs alternatives: More structured than ad-hoc JSON files and more flexible than fixed CSV schemas because it provides Python-native dataclasses with validation, serialization, and dataset-level operations.
Orchestrates the execution of test cases against metrics using the evaluate() function, which handles parallel metric execution, result aggregation, and test run persistence. The execution engine manages metric scheduling, error handling, and result caching. Test runs are tracked with metadata (timestamp, model version, dataset version) and can be compared across iterations to detect regressions.
Unique: Implements a test run orchestration engine that executes metrics in parallel, aggregates results, and persists them to the Confident AI platform with full metadata tracking (model version, dataset version, timestamp). Includes built-in caching to avoid redundant metric evaluations.
vs alternatives: More integrated than running metrics manually and more scalable than sequential evaluation because it handles parallel execution, result aggregation, and persistence in a single abstraction.
+6 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 deepeval at 24/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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