| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
The Planner component uses an LLM to automatically decompose complex user queries into subtasks and generates a directed acyclic graph (DAG) representing task dependencies. It parses LLM outputs via StreamingGraphParser to extract task nodes, their input/output relationships, and execution constraints, enabling identification of parallelizable work without manual specification.
Unique: Uses LLM-in-the-loop planning with streaming graph parsing to generate executable task DAGs on-the-fly, rather than requiring users to manually specify task dependencies or using fixed rule-based decomposition. The Planner can generate plans incrementally and stream tasks to the executor before the full plan is complete.
vs alternatives: More flexible than rule-based task decomposition (e.g., ReAct) because it adapts to problem structure via LLM reasoning, and faster than sequential function calling because it identifies parallelizable tasks automatically.
The TaskFetchingUnit and Executor components implement a scheduler that respects task dependencies from the generated DAG, executing independent tasks concurrently while blocking dependent tasks until their inputs are available. The system maintains a task queue, tracks completion status, and collects results for aggregation, enabling wall-clock latency reduction through parallelism.
Unique: Implements a dependency-aware scheduler that extracts parallelism from task DAGs generated by the Planner, executing tasks concurrently while respecting input dependencies. Unlike sequential function calling (standard ReAct), this enables multiple independent tool calls to run simultaneously with automatic dependency resolution.
vs alternatives: Reduces latency vs sequential function calling by 2-5x on multi-hop tasks with independent branches; more efficient than naive parallel execution because it respects dependencies and doesn't execute tasks prematurely.
LLMCompiler can be integrated with ReAct (Reasoning + Acting) patterns where the agent iteratively reasons about the current state, decides on actions (tool calls), observes results, and repeats. This enables adaptive behavior where the agent can adjust its strategy based on intermediate observations.
Unique: Integrates ReAct-style iterative reasoning with LLMCompiler's parallel execution, enabling the agent to combine planned parallelism with reactive decision-making based on intermediate observations.
vs alternatives: More flexible than pure planning because it allows mid-execution strategy changes; more efficient than pure ReAct because it exploits parallelism in independent tasks.
When enabled, streaming mode allows the TaskFetchingUnit to begin executing tasks as soon as they are generated by the Planner's LLM output stream, without waiting for the complete plan. The StreamingGraphParser incrementally parses LLM tokens into task objects, enabling pipelined planning and execution that reduces time-to-first-result and overall latency.
Unique: Implements streaming graph parsing that converts LLM token streams into executable task objects on-the-fly, enabling the executor to begin work before the Planner finishes generating the full plan. This pipelined approach reduces end-to-end latency by overlapping planning and execution phases.
vs alternatives: Faster than batch planning (wait for full plan before execution) because it starts execution immediately; more responsive than traditional ReAct which waits for full LLM output before parsing.
LLMCompiler abstracts LLM provider differences through a unified model interface (src/utils/model_utils.py) that supports OpenAI, Azure OpenAI, Friendli, and vLLM backends. The framework handles provider-specific API calls, token counting, and response parsing, allowing users to swap providers without changing orchestration logic.
Unique: Provides a unified interface abstracting OpenAI, Azure OpenAI, Friendli, and vLLM with provider-agnostic method signatures, allowing the Planner and Executor to remain provider-agnostic while supporting both closed-source and open-source models.
vs alternatives: More flexible than frameworks tied to a single provider (e.g., LangChain's OpenAI-centric design); enables cost optimization by switching providers without code changes.
LLMCompiler can generate new execution plans based on results from previous attempts, incorporating execution history and intermediate results as context to the Planner. This enables the system to adapt when initial plans fail or produce unsatisfactory results, using feedback to refine task decomposition.
Unique: Enables the Planner to generate new execution plans conditioned on previous execution results and failures, treating replanning as a first-class capability rather than an error recovery afterthought. This allows the system to learn from execution and adapt decomposition strategies.
vs alternatives: More adaptive than single-shot planning because it incorporates execution feedback; more efficient than naive retry because it generates new plans rather than re-executing the same failed plan.
LLMCompiler maintains a registry of available tools with structured schemas defining inputs, outputs, and descriptions. The Planner uses these schemas to generate valid function calls, and the Executor uses them to invoke tools with proper argument binding. This schema-driven approach ensures type safety and enables the LLM to reason about tool capabilities.
Unique: Implements a schema-driven tool registry where tools are defined with structured input/output schemas that the Planner uses to generate valid function calls. This enables type-safe, schema-validated function calling without manual argument binding.
vs alternatives: More structured than string-based tool descriptions (e.g., ReAct with natural language tool specs); enables validation and type checking that reduces runtime errors.
The LLMCompilerAgent component collects results from all executed tasks and synthesizes them into a final answer using the LLM. It maintains a mapping of task IDs to results, passes this context to the LLM, and generates a coherent response that incorporates all intermediate findings.
Unique: Uses the LLM itself to synthesize results from parallel task execution, treating synthesis as an LLM-powered reasoning step rather than simple concatenation. This enables intelligent interpretation and integration of diverse task outputs.
vs alternatives: More intelligent than template-based result aggregation because it uses LLM reasoning to synthesize and interpret results; more flexible than fixed aggregation logic.
+3 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 LLMCompiler at 37/100.
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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