| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 15 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Provides a Markdown-based template system that organizes prompts into discrete sections (Profile, Rules, Workflow, Initialization) using a Role Template pattern. The framework enforces a hierarchical structure similar to object-oriented programming, where each role definition includes metadata (author, version, language), capability descriptions, behavioral constraints, and execution workflows. This enables prompts to be authored, versioned, and maintained as reusable code artifacts rather than ad-hoc text.
Unique: Introduces the Role Template pattern as a first-class abstraction for prompt engineering, treating prompts as software artifacts with Profile/Rules/Workflow/Initialization sections — a design pattern not found in ad-hoc prompt engineering or competing frameworks like Prompt Engineering Guide or OpenAI's prompt examples
vs alternatives: Enables prompt reusability and team collaboration at scale through structured templates, whereas traditional prompt engineering relies on scattered tips and manual iteration without systematic organization
Designs prompts in a provider-agnostic format that can be executed across GPT-4, Claude, Gemini, Qwen, Doubao, and other LLMs without modification. The framework abstracts away provider-specific syntax and API differences, allowing a single Role Template to be deployed to multiple LLM backends. This is achieved through standardized section definitions (Profile, Rules, Workflow) that map to universal LLM instruction patterns rather than provider-specific prompt formats.
Unique: Explicitly supports 6+ LLM providers (GPT-4, Claude, Gemini, Qwen, Doubao, etc.) through a single template format, whereas most prompt frameworks are designed for a single provider or require provider-specific syntax branches
vs alternatives: Reduces vendor lock-in and enables provider switching without prompt rewriting, unlike provider-specific frameworks like OpenAI's prompt engineering guide or Claude's prompt library which are optimized for single providers
Enables composition of multiple Role Templates into prompt chains where the output of one prompt becomes the input to the next, creating multi-step reasoning or processing pipelines. Prompt chains are orchestrated sequences of prompts that work together to solve complex problems by breaking them into smaller, manageable steps. This allows complex tasks to be decomposed into reusable prompt components that can be chained together in different combinations.
Unique: Enables composition of Role Templates into chains where output from one prompt feeds into the next, creating reusable multi-step reasoning pipelines, whereas most prompt frameworks treat individual prompts as isolated units
vs alternatives: Allows prompt reuse across different chain compositions through structured template design, whereas traditional approaches require custom orchestration code for each chain variation
Implements SOM (Self-Organizing Map) prompting patterns integrated with SAM (Specialized Agent Model) concepts, enabling prompts to organize and structure information hierarchically. SOM prompting allows prompts to define how information should be organized and processed, while SAM integration enables specialization of agents for specific tasks. This pattern enables complex information organization and agent specialization within the prompt structure itself.
Unique: Integrates advanced SOM (Self-Organizing Map) and SAM (Specialized Agent Model) patterns as documented patterns within the LangGPT framework, enabling complex information organization and agent specialization within prompts
vs alternatives: Provides documented patterns for advanced information organization and agent specialization, whereas most prompt frameworks focus on basic instruction patterns without support for hierarchical organization or agent specialization
Enables definition of multiple roles that can interact and collaborate within a single prompt or prompt chain, creating multi-agent scenarios where different roles have different perspectives, capabilities, or responsibilities. Multi-role collaboration patterns allow roles to be composed together to solve problems that require multiple specialized perspectives or capabilities. This enables complex collaborative reasoning where different roles contribute their expertise to reach conclusions.
Unique: Formalizes multi-role collaboration as a documented pattern within LangGPT, enabling roles to be composed together for collaborative reasoning, whereas most prompt frameworks treat roles as isolated entities
vs alternatives: Enables structured multi-role collaboration patterns within the prompt framework itself, whereas traditional approaches require custom orchestration code to coordinate multiple roles
Provides comprehensive documentation of prompt design principles, common patterns, and anti-patterns that guide effective prompt engineering within the LangGPT framework. This includes guidance on structuring prompts, avoiding common pitfalls, and applying proven patterns for different use cases. The documentation serves as a knowledge base that helps users apply the framework effectively and avoid common mistakes.
Unique: Provides comprehensive, structured documentation of prompt design principles and patterns specific to the LangGPT framework, enabling users to learn and apply best practices systematically
vs alternatives: Offers framework-specific guidance on prompt design principles and patterns, whereas general prompt engineering resources lack structure and framework-specific context
Provides pre-built example prompts and templates for common use cases including content generation, code generation, fitness planning, and other domains. These examples serve as starting points for users to understand how to apply the LangGPT framework to their specific problems, reducing the learning curve and enabling faster prompt development. Examples demonstrate best practices and patterns in action.
Unique: Provides domain-specific example templates (content generation, code generation, fitness planning) that demonstrate LangGPT patterns in action, enabling users to learn by example and customize for their needs
vs alternatives: Offers concrete, customizable examples for common use cases, whereas most prompt frameworks provide abstract guidance without domain-specific templates
Supports variable placeholders within prompts that can be dynamically substituted at runtime, enabling parameterized prompt generation without manual text editing. Variables are defined using a syntax that integrates with the Role Template structure, allowing prompts to accept user input, context data, or system parameters. This enables the same prompt template to be reused across different inputs and contexts by simply changing variable values rather than rewriting the entire prompt.
Unique: Integrates variable substitution as a first-class feature within the Role Template structure, allowing variables to be defined in Profile/Rules/Workflow sections and referenced throughout the prompt, rather than treating variables as an afterthought or requiring external templating engines
vs alternatives: Enables prompt parameterization without external templating libraries like Jinja2, keeping variable logic within the LangGPT framework itself and maintaining prompt portability across providers
+7 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 LangGPT at 37/100. LangGPT leads on quality, while TrendRadar is stronger on adoption and ecosystem.
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