SurfSense Agent工作流
经 AI Skill Hub 精选评估，SurfSense Agent工作流 获评「强烈推荐」。在 GitHub 上收获超过 14.2k 颗 Star，这款Agent工作流在功能完整性、社区活跃度和易用性方面表现出色，AI 评分 8.2 分，适合有一定技术背景的用户使用。
📚 深度解析
SurfSense Agent工作流 工作流的设计遵循"最小配置，最大复用"原则：核心逻辑已经封装好，用户只需配置自己的 API Key 和业务参数即可快速上手。工作流内置错误处理和重试机制，在网络波动或 API 限速等情况下仍能稳定运行，适合作为生产环境的自动化基础设施。
在实际部署时，建议先在测试环境中运行 3-5 次，验证各个环节的输出结果符合预期，再部署到生产环境。AI Skill Hub 评分 8.2 分，是同类 Agent 工作流中的精选推荐。
📋 工具概览
SurfSense Agent工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排，将复杂的多步骤任务拆解为清晰的自动化流程，实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成，适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
📖 中文文档
SurfSense Agent工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排，将复杂的多步骤任务拆解为清晰的自动化流程，实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成，适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
- 可视化 Agent 工作流编排，无需编写复杂代码
- 支持多步骤自动化任务链，实现全流程无人值守
- 与外部 API、数据库和第三方服务无缝集成
- 内置错误处理与自动重试机制，保障稳定运行
- 提供可复用的自动化模板，快速在同类场景部署
- 自动化日常重复性工作，将精力集中于创造性任务
- 构建数据采集 → 处理 → 输出的完整自动化管线
- 实现跨平台、跨系统的数据流转和业务协同
# 方式一：pip 安装（推荐）
pip install surfsense
# 方式二：虚拟环境安装（推荐生产环境）
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install surfsense
# 方式三：从源码安装（获取最新功能）
git clone https://github.com/MODSetter/SurfSense
cd SurfSense
pip install -e .
# 验证安装
python -c "import surfsense; print('安装成功')"
- 访问 GitHub 仓库获取工作流文件
- 在对应平台（Dify / Flowise / Make 等）中找到「导入工作流」功能
- 上传工作流文件
- 按照提示配置必要的环境变量和 API Key
- 运行测试确认流程正常后投入使用
# 命令行使用
surfsense --help
# 基本用法
surfsense input_file -o output_file
# Python 代码中调用
import surfsense
# 示例
result = surfsense.process("input")
print(result)
# surfsense 配置文件示例（config.yml） app: name: "surfsense" debug: false log_level: "INFO" # 运行时指定配置文件 surfsense --config config.yml # 或通过环境变量配置 export SURFSENSE_API_KEY="your-key" export SURFSENSE_OUTPUT_DIR="./output"
简介
<a href="https://www.surfsense.com/"><img width="1584" height="396" alt="SurfSense, the open-source NotebookLM alternative for open web research" src="https://github.com/user-attachments/assets/9361ef58-1753-4b6e-b275-5020d8847261" /></a>
English | Español | Português | हिन्दी | 简体中文
</div> <div align="center"> <a href="https://trendshift.io/repositories/13606" target="_blank"><img src="https://trendshift.io/api/badge/repositories/13606" alt="MODSetter%2FSurfSense | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a> </div>
Feature requests and future
SurfSense is actively being developed. While it's not yet production-ready, you can help us speed up the process.
Join the SurfSense Discord and help shape the future of SurfSense!
Quick start
Video Agent Sample
https://github.com/user-attachments/assets/012a7ffa-6f76-4f06-9dda-7632b470057a
Podcast Agent Sample
https://github.com/user-attachments/assets/a0a16566-6967-4374-ac51-9b3e07fbecd7
SurfSense: The Open-Source NotebookLM Alternative for Open Web Research
SurfSense is the open-source NotebookLM alternative for AI agents, an open web research platform with live data connectors. Your agents research the live web with structured data from Reddit, YouTube, Instagram, TikTok, Amazon, Walmart, Google Maps, Google Search, Indeed, and any page on the open web, through one REST API or MCP server. Scheduled and event-triggered agents turn what they find into briefs and alerts, and a built-in knowledge base keeps every finding searchable with citations.
[!NOTE] 📢 A note for our NotebookLM-alternative users For the past couple of months we built SurfSense as the best general research agent for your own knowledge, and that chapter earned us a community we are genuinely proud of. Agentic tools like Claude, OpenCode, Hermes, and OpenClaw have now proven that agents are the future, and reasoning over a static index is becoming something every capable agent does out of the box. What agents still lack is live data from the places where answers actually live, and the workflows around it. That is where we are pointing all of our energy: giving agents the primitives to research the open web. Nothing you rely on is going away. Your knowledge base, chat with citations, reports, podcasts, presentations, automations, and collaborative chats all keep working, and self-hosting stays free and open source. Read the full announcement on our changelog.
How SurfSense compares
SurfSense is the only open-source product that combines a NotebookLM-style research workspace for people with live-data primitives for agents. Here is how that stacks up against each class of tool.
vs browser agents (Browserbase, Browser Use). Browser agents drive a real browser with an LLM in the loop — the right tool when a task needs clicking, logging in, or filling forms. But most research is read-only retrieval, and for retrieval the LLM-in-a-browser loop costs you minutes and thousands of tokens per page. A SurfSense connector call is one HTTP request: seconds, deterministic, and zero tokens spent deciding where to click.
vs scraping APIs (Firecrawl). Scraping APIs are great at turning a generic page into markdown, but a markdown blob still leaves your agent parsing structure out of prose, and they degrade on bot-protected platforms like Reddit, TikTok, and Instagram. SurfSense connectors return platform-native structured items — posts, comments, transcripts, reviews — and bill only for items actually returned; failed calls are never billed.
vs search APIs (Exa, Tavily, Parallel). Search APIs answer from a web index, which is the right tool for "find me pages about X." They cannot pull a Reddit thread's comments, TikTok reactions, YouTube transcripts, or Google Maps reviews — the places where the answer often actually lives.
vs scraper marketplaces (Apify). Marketplaces give you thousands of community actors, each with its own schema, quality, and pricing. SurfSense is one typed API and one MCP server with an agent harness and a research workspace behind it, and it is open source.
SurfSense vs Google NotebookLM
Still comparing us as a NotebookLM alternative? Here is the honest breakdown.
| Feature | Google NotebookLM | SurfSense |
|---|---|---|
| **Live web data for agents** | No | Reddit, YouTube, Instagram, TikTok, Amazon, Walmart, Google Maps, Google Search, Indeed, and web crawl connectors via REST API and MCP |
| **MCP server** | No | Every connector exposed as a native agent tool, plus bring-your-own MCP servers with one-click OAuth apps |
| **Sources per Notebook** | 50 (Free) to 600 (Ultra, $249.99/mo) | Unlimited |
| **Number of Notebooks** | 100 (Free) to 500 (paid tiers) | Unlimited |
| **Source Size Limit** | 500,000 words / 200MB per source | No limit |
| **Pricing** | Free tier; Pro $19.99/mo, Ultra $249.99/mo | Free and open source to self-host; cloud is pay as you go with $5 free credit |
| **LLM Support** | Google Gemini only | 100+ LLMs via OpenAI spec & LiteLLM |
| **Embedding Models** | Google only | 6,000+ embedding models, all major rerankers |
| **Local / Private LLMs** | Not available | Full support (vLLM, Ollama), your data stays yours |
| **Self Hostable** | No | Yes, Docker one-liner or full Docker Compose |
| **Open Source** | No | Yes |
| **Knowledge Base Sources** | Google Drive, YouTube, websites | File uploads, Google Drive, OneDrive, Dropbox, local folder sync, and crawled pages |
| **File Format Support** | PDFs, Docs, Slides, Sheets, CSV, Word, EPUB, images, web URLs, YouTube | 50+ formats: documents, images, videos via LlamaCloud, Unstructured, or Docling (local) |
| **Search** | Semantic search | Hybrid semantic + full-text with hierarchical indices & reciprocal rank fusion |
| **Cited Answers** | Yes | Yes, Perplexity-style cited responses |
| **Agentic Architecture** | No | Yes, powered by [LangChain Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview) with planning, subagents, and file system access |
| **AI Automations & Agents** | No | Scheduled workflows, event triggers, and chat-built no-code automations with write-back to Notion, Slack, Linear & Jira |
| **Real-Time Multiplayer** | Shared notebooks with Viewer/Editor roles (no real-time chat) | RBAC with Owner / Admin / Editor / Viewer roles, real-time chat & comment threads |
| **Video Generation** | Cinematic Video Overviews via Veo 3 (Ultra only) | Available (NotebookLM is better here, actively improving) |
| **Presentation Generation** | Better looking slides but not editable | Editable, slide-based presentations |
| **Podcast Generation** | Audio Overviews with customizable hosts and languages | Available with multiple TTS providers (NotebookLM is better here, actively improving) |
| **Desktop App** | No | Native app with General Assist, Quick Assist, Screenshot Assist, and local folder sync |
成熟的开源AI工作流框架，14k stars印证其实用价值。隐私优先设计与Agent工作流功能结合，填补NotebookLM本地化空白，适合注重数据安全的专业团队。
- 构建多智能体协作系统的 Agent 开发者
- 构建企业知识库 / RAG 检索应用的团队
- 做语音类 AI 产品的开发者
- 生产部署优先使用 Docker Compose 隔离依赖，并挂载 volume 持久化数据
- 本地部署优先选 GGUF 量化模型，节省显存并保持响应速度
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- 容器内无法访问宿主机 localhost — 使用 host.docker.internal
- 显存不足直接 OOM — 优先降低 context 或换更小的量化模型
- Python 依赖冲突：建议用 venv / uv 隔离环境
- Docker：SurfSense 提供官方镜像，docker compose up 一键启动
- 本地部署：CPU 8GB 起，GPU 推荐 16GB+ 显存
- 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
⚡ 核心功能
- 可视化 Agent 工作流编排，无需编写复杂代码
- 支持多步骤自动化任务链，实现全流程无人值守
- 与外部 API、数据库和第三方服务无缝集成
- 内置错误处理与自动重试机制，保障稳定运行
- 提供可复用的自动化模板，快速在同类场景部署
- 构建多智能体协作系统的 Agent 开发者
- 构建企业知识库 / RAG 检索应用的团队
- 做语音类 AI 产品的开发者
- 生产部署优先使用 Docker Compose 隔离依赖，并挂载 volume 持久化数据
- 本地部署优先选 GGUF 量化模型，节省显存并保持响应速度
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- 容器内无法访问宿主机 localhost — 使用 host.docker.internal
- 显存不足直接 OOM — 优先降低 context 或换更小的量化模型
- Python 依赖冲突：建议用 venv / uv 隔离环境
👥 适合人群
🎯 使用场景
- 自动化日常重复性工作，将精力集中于创造性任务
- 构建数据采集 → 处理 → 输出的完整自动化管线
- 实现跨平台、跨系统的数据流转和业务协同
⚖️ 优点与不足
- +GitHub 14.2k Star，社区高度认可
- +Apache-2.0 协议，可免费商用
- +大幅减少重复性人工操作
- +可视化流程，清晰直观
- +可扩展性强，支持复杂场景
- −初始配置和调试需投入一定时间
- −强依赖外部服务的稳定性
- −复杂场景需具备一定技术基础
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
✅ Apache 2.0 — 宽松开源协议，可商用，需保留版权声明和 NOTICE 文件，含专利授权条款。
🔗 相关工具推荐
❓ 常见问题 FAQ
AI Skill Hub 点评：SurfSense Agent工作流 的核心功能完整，质量优秀。对于自动化工程师和运维人员来说，这是一个值得纳入个人工具库的选择。建议先在非生产环境试用，再逐步推广。
| 原始名称 | SurfSense |
| 原始描述 | 开源AI工作流：An open source, privacy focused alternative to NotebookLM for teams with no data。⭐14.2k · Python |
| Topics | AI工作流隐私保护Agent智能体开源工具浏览器扩展 |
| GitHub | https://github.com/MODSetter/SurfSense |
| License | Apache-2.0 |
| 语言 | Python |
收录时间：2026-05-14 · 更新时间：2026-05-16 · License：Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。
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