TrendRadar
开源AI舆情监控与热点筛选工具，聚合多平台热点并以自然语言智能推送
如果你能接受自托管，TrendRadar 在中文热点聚合这条线上给得很实在：数十个平台加 RSS 汇入同一工作流，+required、!excluded、/regex/ 语法与 AI 自然语言打分让你不必再逐条翻热搜，日报/实时排行/增量三种模式配合飞书、钉钉、企业微信等 9 个渠道落地到团队群。代价是运维：Docker、GitHub Actions 或 uv 三选一都由你维护，采集按调度间隔进行。与慧科、清博这类托管商业舆情服务相比，TrendRadar 把数据和配置完全交回你手里，且项目以 GPL-3.0 开源发布。
Verified 10d ago · liveness 50/100 · cite: rightaichoice.com/tools/trendradar
- 愿意自托管的个人开发者
- 预算有限的小团队舆情监测者
- 需要中文多平台热点聚合的市场与内容研究者
- 想用 AI 过滤替代手工翻热搜的信息过载用户
- 没有 Docker 或命令行基础、希望开箱即用的用户
- 需要秒级实时预警的场景（采集按调度间隔进行）
- 以非中文信息源为主的团队
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
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Skip TrendRadar if you don't want to run and maintain your own collector — it ships as Docker, GitHub Actions or local uv deployments you operate yourself.
TrendRadar 以 GPL-3.0 开源发布，官网将 Docker、GitHub Actions 与本地 uv 三种部署并列，成本主要由你的基础设施和所选 AI 模型 token 承担，而非许可费；相比慧科、清博这类托管商业舆情服务，它是把预算从订阅费转移到自运维上。
In short
TrendRadar — 开源AI舆情监控与热点筛选工具，聚合多平台热点并以自然语言智能推送. Best for 愿意自托管的个人开发者, 预算有限的小团队舆情监测者, 需要中文多平台热点聚合的市场与内容研究者. Free to use.
What's new in TrendRadar
Checked yesterdayAcross the latest 3 updates: 3 feature updates.
What people actually say about TrendRadar — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
1 mentions across 1 source (Hacker News) · researched Jul 3, 2026.
Average across the 1 source that answered — each source counts once, not each post.
- +Auto-finds trending X posts within topics you care about.
- +Writes comments in your voice with chosen tone and sentiment.
- +Solo indie hacker building in public—transparent development.
- +Freemium pricing lowers entry barrier for beginners.
- +Supports Docker deployment for data self-hosting.
- −No independent user feedback on accuracy or quality.
- −Auto-commenting may feel spammy and harm authenticity.
- −X API changes could break core functionality.
- −Single Hacker News post suggests very early stage.
- −Scope described in HN (X auto-commenter) is narrower than tagline.
Viability Score
How well maintained and how widely used is TrendRadar? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- 多平台热点聚合（知乎、抖音、bilibili、微博、百度等数十个平台）
- RSS/Atom 订阅源采集与推送（v4.5+）
- 关键词精准筛选（+required、!excluded、/regex/、@count）
- AI 自然语言兴趣筛选与自动评分
- AI 情感分析与跨平台连接洞察
- AI 多语言翻译，批量处理以节省 token
- 趋势跟踪：排名变化监测、自动识别上升与下降趋势
- MCP 聊天分析服务，只读数据查询，官网列 17 个分析工具
- 三种推送模式：日报摘要、实时排名、增量监控
- 9 个推送渠道：企业微信、个人微信、飞书、钉钉、Telegram、邮件、ntfy、Bark、Slack、通用 Webhook
- 增强 HTML 报告（v6.6+，暗黑模式、搜索、快捷键、宽屏）
- 统一调度器 timeline.yaml，按时间段编排任务（v6.0+），工作日/周末自动切换
- 可视化配置编辑器，Web 界面在线编辑配置
- LiteLLM 统一接口支持 100+ AI 模型（DeepSeek、OpenAI、Gemini、Ollama 等）
- 三种部署方式：Docker、GitHub Actions、本地 uv
About TrendRadar
TrendRadar 是一款开源、AI 驱动的舆情监控与趋势分析工具，用来解决信息过载。它把知乎、抖音、bilibili、微博、百度等数十个主流平台的热点汇聚到一个工作流里，v4.5 起还支持 RSS/Atom 订阅源。筛选上提供 +required、!excluded、/regex/、@count 等高级关键词语法，你也可以用自然语言描述兴趣，由 AI 自动分类打分，只推送你真正关心的内容。分析层包含 AI 情感与跨平台连接洞察、多语言翻译（批量处理以节省 token），并通过 LiteLLM 统一接口支持 100+ 模型，可切换 DeepSeek、OpenAI、Gemini、Ollama。系统有日报摘要、实时排名、增量监控三种推送模式，覆盖企业微信、个人微信、飞书、钉钉、Telegram、邮件、ntfy、Bark、Slack 与通用 Webhook 共 9 个渠道。部署可选 Docker、GitHub Actions 或本地 uv，数据落在 SQLite 或 S3 兼容存储；v6.0 起用 timeline.yaml 统一调度，v6.6 起增强 HTML 报告支持暗黑模式、搜索与快捷键，另有可视化配置编辑器免去手写 YAML。它适合愿意自托管、以中文信息源为主的个人开发者、小团队和预算有限的舆情监测者。
Behind the Verdict
TrendRadar 的价值在于把通常要拼五六个脚本的事情收进了一条流水线：NewsNow API 拉主流平台热点、RSS/Atom 补自定义源、关键词与 AI 过滤、AI 分析翻译、最后并排推送到 9 个渠道。配置面也做得比多数开源爬虫友好——可视化编辑器直接改配置，timeline.yaml 按时间段编排采集/分析/推送，工作日与周末可自动切换，这一点对固定作息做每日简报的人特别省事。 真正拉开差距的是 AI 那层：不需要你手写正则，用自然语言写兴趣就能让模型分类评分；情感与跨平台连接洞察用来追踪某个产品口碑的涨落；多语言翻译做了批量处理来省 token。模型侧走 LiteLLM 统一接口，DeepSeek、OpenAI、Gemini、Ollama 之间可以换，不自建模型服务也能跑。MCP 聊天分析服务是独立进程、只读查询，官网列的是 17 个分析工具（架构图里写 21+），可以对话式地问趋势和跨平台关联。 短板同样清楚。它是自托管项目，采集按调度间隔跑，不要指望秒级预警；平台源以中文为主，非中文场景要靠 RSS 自己补；GitHub Actions 部署虽然零服务器成本，但每个 7 天周期需要一次签到保活。项目由 sansan 维护并采用 GPL-3.0 许可，属于活跃开源项目，但你需要自行评估长期维护的可持续性，并准备好 Docker 或命令行基础。
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Real-world workflow fit
Concrete scenarios for the personas TrendRadar actually fits — and what changes day-one when you adopt it.
用 docker compose up 起一个容器，在可视化配置编辑器里把关键词规则和 AI 兴趣描述填好，再把飞书与企业微信群机器人填进推送渠道，让 timeline.yaml 按工作时间段编排采集与分析。
Outcome: 每天固定时段收到一份含暗黑模式与搜索功能的 HTML 简报，热点、情感与跨平台关联都在里面，团队不必再各自刷热搜。
把知乎、抖音、bilibili、微博的热点源与几个行业 RSS 一起接入，用 AI 自然语言兴趣描述让模型给候选话题打分，只保留高相关项，走 Telegram 或邮件推送给自己。
Outcome: 选题池从『全网热搜』收窄成每天几十条高相关线索，减少了逐条翻榜的时间。
针对某个产品名设置 +required 与 !excluded 规则做精筛，开启 AI 情感分析与多语言翻译，接入 MCP 聊天分析服务用自然语言追问趋势与跨平台连接。
Outcome: 能用对话方式查排名变化与情感走向，而不是导出数据后手工做表。
Use Cases
- 汇总知乎、抖音、微博、B站等平台热点，用关键词规则筛出与业务相关的选题
- 每天定时生成 HTML 版行业简报，通过飞书、钉钉或邮件推送到团队群
- 用 AI 情感分析与跨平台连接洞察追踪某个产品口碑的涨落
- 用自然语言描述兴趣让 AI 分类打分，只接收真正关心的动态
- 多语言翻译与摘要，辅助做跨境或全球化调研
- 通过 MCP 聊天分析服务对话式查询趋势变化与跨平台关联
Models Under the Hood
as of 2026-09-09
Limitations
自托管需要 Docker 或命令行基础，GitHub Actions 部署还需要每个 7 天周期签到保活；平台源以中文为主，非中文内容要靠自建 RSS 补充；数据采集按调度间隔进行，因此推送存在延迟，不适合秒级告警；推送渠道虽是 9 个并行，但每个渠道的配置与凭据都由你自己维护；AI 功能依赖你自己接入的模型服务（LiteLLM 支持 DeepSeek、OpenAI、Gemini、Ollama 等），token 成本与可用性随所选提供方而定。
as of 2026-09-28
Verification history
We have re-verified TrendRadar 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where TrendRadar's pricing actually pencils out — and where peers do it cheaper.
TrendRadar 以 GPL-3.0 开源发布，官网将 Docker、GitHub Actions 与本地 uv 三种部署并列，成本主要由你的基础设施和所选 AI 模型 token 承担，而非许可费；相比慧科、清博这类托管商业舆情服务，它是把预算从订阅费转移到自运维上。
Setup time & first value
How long it actually takes to get something useful out of TrendRadar — broken out by persona, not the marketing-page minute.
Docker：拉镜像后 docker compose up 即可启动，熟悉容器的人通常十几分钟走完首次配置。GitHub Actions：用模板建独立仓库、把敏感配置写进 Secrets，一般半小时内跑通第一次定时执行，之后每个 7 天周期需要签到保活。本地 uv：环境自动管理，适合调试，熟悉 Python 的人当天可跑通。可视化配置编辑器让 YAML 不必手写，是第一件事就能省时间的地方。
Switching to or from TrendRadar
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From 慧科等托管商业舆情服务：先并行跑 TrendRadar 的热点聚合与关键词规则，用 HTML 日报比对覆盖度后再决定是否切换主力
- →From 自建爬虫脚本：把原本的抓取列表换成 NewsNow API 多平台源，筛选逻辑改写为 +required、!excluded、/regex/ 语法
- →From 纯 RSS 阅读器：保留原有 RSS/Atom 源接入同一工作流，再叠加 AI 打分与 9 渠道推送
- →From Airtable/表格手工汇总：用 timeline.yaml 统一调度采集、分析与推送，替代每日人工整理
- ↗To 慧科、清博等托管商业舆情服务：当团队不再想负责 Docker 与调度运维，转向含托管支持的商业订阅
- ↗To 通用 RSS 阅读器：只保留少量固定订阅源时，可退回轻量阅读器
- ↗To 自研采集管道：当需要完全定制的数据源与存储模型，可基于 TrendRadar 的思路重写
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “TrendRadar”, and we withheld 6: 6 could not be judged, because “TrendRadar” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about TrendRadar.
Official links
Tools that pair well with TrendRadar
Common stack mates teams adopt alongside TrendRadar, with the specific reason each pairing earns its keep.
Kagi
Kagi is a paid, ad-free private search engine with a private AI assistant grounded in its own results.
Marvin User Research
Marvin is an AI-native customer insights platform that runs AI-moderated interviews and delivers cited answers from your research repository.
AlphaSense
AlphaSense turns 500M+ premium finance documents into cited, traceable market intelligence for investment, strategy, and legal teams.
Featured Head-to-Head Comparisons
Trendradar vs Weglot
Choose Weglot if you need to instantly launch a multilingual website with brand-consistent AI translation and strong SEO support. Choose TrendRadar if your priority is monitoring Chinese social media and news trends with AI-powered filtering and multi-channel alerts. They serve completely different needs.
Trendradar vs Chili Piper
Chili Piper and TrendRadar serve completely different needs: Chili Piper is a high-cost enterprise sales tool for instant meeting booking from B2B traffic; TrendRadar is a freemium AI-powered trend monitoring tool for market research and content creation. Buy Chili Piper if you have high inbound B2B traffic and Salesforce, and need to convert visitors into meetings instantly. Buy TrendRadar if you need to aggregate and analyze multi-platform trends with AI, and prefer a self-hosted option.
Trendradar vs Screenplayiq
ScreenplayIQ and TrendRadar serve completely different needs. ScreenplayIQ is a niche tool for English feature film professionals who want data-driven script analysis and box office forecasts. TrendRadar is a broad Chinese-market trend monitoring tool for tracking social media, news, and RSS feeds with AI summaries and multi-channel alerts. Choose based on your domain: film production or trend intelligence.
Alternatives to TrendRadar
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Kagi
Kagi is a paid, ad-free private search engine with a private AI assistant grounded in its own results.
Marvin User Research
Marvin is an AI-native customer insights platform that runs AI-moderated interviews and delivers cited answers from your research repository.
AlphaSense
AlphaSense turns 500M+ premium finance documents into cited, traceable market intelligence for investment, strategy, and legal teams.
Frequently Asked Questions
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