This tutorial series is designed for Python developers who want to empower their AI agents with real-time web access, enabling agents to utilize up-to-date information as context. Live web information is critical for AI agents tasked with performing research, answering questions accurately, monitoring trends, or providing up-to-date recommendations. You'll learn how to build AI agents that search the web, extract valuable content, navigate websites intelligently, and integrate real-time web information into private knowledge bases.
This tutorial series follows a step-by-step learning path with three stand-alone tutorials:
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In tutorial #1, we'll cover the basics of web access.
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In tutorial #2, we'll build a web agent that can search, scrape, and crawl the web.
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Finally, In tutorial #3, we'll develop a system that combines real-time web information with private knowledge base data.
📁 agent-with-tavily-web-access/
├── 📓 search-extract-crawl.ipynb # Tutorial notebook 1
├── 📓 web-agent-tutorial.ipynb # Tutorial notebook 2
├── 📓 hybrid-agent-tutorial.ipynb # Tutorial notebook 3
├── 📁 assets/ # Diagrams and screenshots
│ ├── 🖼️ web-agent.svg
| ├── 🖼️ hybrid.svg
| ├── 🖼️ api-key.png
│ └── 🖼️ sign-up.png
├── 📁 supplemental/ # Supplemental materials
│ ├── 📓 vectorize_tutorial.ipynb # Vectorize your own documents
│ ├── 📁 docs/ # Sample CRM documents
│ └── 📁 db/ # Pre-built Chroma vector database
└── 📄 README.md # This file