| Ecosystem | 0 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Executes the same prompt across multiple LLM providers (OpenAI, Anthropic, etc.) in a single experiment run by implementing a polymorphic Experiment base class that abstracts provider-specific API calls. Each provider gets a concrete implementation (OpenAIChatExperiment, AnthropicExperiment) that handles authentication, request formatting, and response parsing, allowing developers to compare outputs side-by-side without writing provider-specific code.
Unique: Implements a polymorphic Experiment base class with concrete provider implementations (OpenAIChatExperiment, etc.) that abstracts away provider-specific API details, allowing identical test code to run against different LLMs without conditional logic or provider detection
vs alternatives: Simpler than building custom integrations for each provider and more flexible than single-provider tools like OpenAI's playground, as it unifies comparison logic across any provider with a Python SDK
Generates a full factorial experiment matrix by accepting prompt templates with variable placeholders and a dictionary of parameter values, then expanding all combinations (e.g., 3 prompts × 2 models × 4 temperature values = 24 test cases). The harness system orchestrates these expanded experiments, executing each combination and collecting results in a unified output table for systematic evaluation of prompt variations.
Unique: Implements automatic cartesian product expansion of prompt templates and parameters through the Harness system, generating all combinations declaratively without manual loop nesting, and provides unified result collection across the entire experiment matrix
vs alternatives: More systematic than manual prompt iteration and less error-prone than hand-written nested loops; provides structured result collection that tools like LangSmith require custom code to achieve
Calculates estimated and actual costs for experiments based on token counts, model pricing, and API usage, providing cost breakdowns per model, prompt, and parameter combination. Developers can set cost budgets, receive warnings when approaching limits, and analyze cost-effectiveness of different prompt variations relative to quality metrics.
Unique: Integrates cost estimation and tracking into the experiment framework, calculating costs based on token counts and model pricing, and providing cost breakdowns per parameter combination without requiring external cost tracking tools
vs alternatives: More integrated than manual cost calculation and provider dashboards; enables cost-aware experiment design and optimization that tools like LangSmith require custom analysis to achieve
Supports running multiple experiment instances in sequence or parallel, aggregating results across runs and computing statistical summaries (mean, std dev, confidence intervals) for each metric. Developers can run the same experiment multiple times to account for model variability and generate robust performance estimates with statistical confidence.
Unique: Extends the experiment framework to support batch execution with automatic result aggregation and statistical analysis, computing confidence intervals and summary statistics across multiple runs without requiring external statistical tools
vs alternatives: More integrated than manual result aggregation and statistical analysis; enables robust model evaluation with statistical confidence that single-run experiments cannot provide
Applies a registry of evaluation functions (scorers) to experiment results after execution, computing metrics like BLEU, ROUGE, semantic similarity, or custom business logic. The evaluation step is decoupled from execution, allowing developers to define custom scorer functions that accept model outputs and reference answers, then aggregate scores across all experiment runs for comparative analysis.
Unique: Decouples evaluation from execution through a pluggable scorer registry, allowing custom evaluation functions to be applied post-hoc to any experiment results without modifying experiment code, and supports both built-in metrics (BLEU, ROUGE) and user-defined scorers
vs alternatives: More flexible than hardcoded evaluation in experiment classes and more accessible than building custom evaluation pipelines; integrates seamlessly with experiment results without requiring external evaluation frameworks
Provides a browser-based UI (built with Streamlit or similar) that allows non-technical users to test prompts interactively without writing code. The playground loads experiment definitions from Python files, exposes UI controls for parameter adjustment, executes experiments on-demand, and displays results with visualizations, enabling rapid iteration and exploration of prompt behavior.
Unique: Wraps the core Experiment system in a Streamlit-based web interface that automatically generates UI controls from experiment parameters, enabling non-technical users to run experiments without code while maintaining full access to the underlying evaluation and visualization capabilities
vs alternatives: More accessible than command-line tools and Jupyter notebooks for non-technical users; faster iteration than rebuilding UI for each experiment type, though less customizable than purpose-built web applications
Extends the Experiment system to test vector databases (Pinecone, Weaviate, Chroma, etc.) by implementing VectorDatabaseExperiment subclasses that handle embedding generation, vector storage, and retrieval evaluation. Developers can compare retrieval quality across different databases, embedding models, and query strategies using the same experiment framework as LLM testing.
Unique: Extends the polymorphic Experiment base class to support vector database testing with the same prepare/run/evaluate/visualize workflow as LLM experiments, enabling unified comparison of retrieval systems across different providers and embedding models
vs alternatives: Unifies RAG evaluation with LLM evaluation in a single framework, whereas most tools require separate testing pipelines for retrieval and generation; supports multiple vector database providers without provider-specific code
Generates tabular and graphical visualizations of experiment results using matplotlib and pandas, supporting exports to CSV, JSON, and HTML formats. The visualization step is built into the experiment workflow, automatically creating comparison charts, heatmaps, and summary tables that highlight differences across parameter combinations and model outputs.
Unique: Integrates visualization and export as a built-in step in the experiment workflow (prepare/run/evaluate/visualize), automatically generating comparison tables and charts without requiring separate visualization code, and supports multiple output formats from a single experiment run
vs alternatives: More convenient than manual result export and visualization; less flexible than dedicated BI tools but requires no external dependencies or data pipeline setup
+4 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 prompttools at 22/100.
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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