Building a Lightweight Research Agent with Gemma 4, Ollama, OpenAI Agents SDK, and Tavily MCP
English summary
This Towards Data Science article by Shuai Guo demonstrates building a lightweight research agent using Google's Gemma 4 local LLM served via Ollama. It integrates OpenAI Agents SDK for agent workflow orchestration and Tavily MCP as a web search tool. The guide provides a practical example of combining open-source local models with agent frameworks to create a tool-using agent that runs locally.
Chinese summary
这篇Towards Data Science文章由Shuai Guo撰写，演示了使用谷歌Gemma 4本地大语言模型（通过Ollama部署）构建轻量级研究代理。它集成了OpenAI Agents SDK进行代理工作流编排，并使用Tavily MCP作为网络搜索工具。该指南提供了将开源本地模型与代理框架相结合、创建本地运行工具使用代理的实践示例。
Key points
The tutorial builds a local research agent using Gemma 4, a large language model from Google.
该教程使用谷歌的大语言模型Gemma 4构建本地研究代理。
Ollama is used to serve the model locally, and OpenAI Agents SDK handles agent orchestration.
使用Ollama在本地提供模型服务，OpenAI Agents SDK负责代理编排。
Tavily MCP is integrated as a web search tool, enabling the agent to perform research tasks.
集成了Tavily MCP作为网络搜索工具，使代理能够执行研究任务。
The guide illustrates how to combine open-source local LLMs with modern agent frameworks.
该指南展示了如何将开源本地大语言模型与现代代理框架相结合。