Session brief
Vercel AI SDK Masterclass: From Fundamentals to Deep Research
Overview
This talk demonstrates how to build AI agents using Vercel's AI SDK, progressing from fundamental concepts to a practical deep research project. It highlights the SDK's unified interface for model switching, tool-use capabilities for interacting with the external world, and structured output generation for creating type-safe data. The deep research example showcases how to break down complex tasks into agentic workflows, combining web searching, result analysis, and recursive query generation to produce comprehensive reports.
Who should watch
- AI engineers and builders looking to create agentic applications.
- Product Managers interested in the capabilities of modern AI SDKs.
- Developers seeking to integrate LLMs for complex research and data synthesis tasks.
- Those interested in building applications that can autonomously perform multi-step processes.
Key takeaways
- The Vercel AI SDK offers a unified interface to easily switch between different language models by changing a single line of code.
- Tool calling allows LLMs to interact with external functions or APIs, enabling agents to perform actions beyond text generation.
- The `max_steps` parameter enables multi-step agent execution, where the model can chain tool calls and receive results iteratively until a final text output is generated.
- Structured outputs, particularly using `generate_object` with Zod schemas, provide type-safe data generation, simplifying data handling and validation.
- A deep research workflow can be constructed by recursively breaking down a query into sub-queries, searching the web, analyzing results for learnings and follow-up questions, and synthesizing information into a final report.
- The `search_and_process` function demonstrates an agentic loop that searches the web, evaluates result relevance, and either proceeds with relevant findings or requests more specific queries.
- The `generate_learnings` function extracts key insights and follow-up questions from relevant search results, facilitating deeper research.
- Recursive calls with depth and breadth parameters allow for controlled exploration of complex topics, managing the scope of information gathering.
Notable quotes
*The AI SDK makes this accessible with the sources property.*
*While tools may seem complicated, at the core, it's a very simple idea.*
*max_steps can seem again a little bit confusing but what it is at its core is that if the language model decides to generate a tool call and therefore there is a tool result we are going to send that tool result alongside the previous conversation context back to the model and trigger another generation.*
Unofficial community note. Prefer the recording for nuance.