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Building AI Agents with Local Small Language Models

This article explains how to build a fully functional AI agent that runs locally on your machine using small language models, with no internet connection or API costs. It covers the concepts of AI agents and SLMs, the advantages of local deployment, setting up Ollama and Python libraries, step-by-step agent construction, adding memory and tools, and discusses the limitations of SLMs.

SourceMachine Learning MasteryAuthor: Shittu Olumide
Building AI Agents with Local Small Language Models
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Under the source content permission, this page provides an editorial summary, key points, and a link to the original article.

Key points and analysis

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Key points

  • AI agents are programs that use language models to reason and take actions, going beyond simple chatbots.
  • Small language models like Phi-3 and Mistral 7B can run on standard hardware, offering privacy and zero API costs.
  • Using Ollama and LangChain, you can quickly set up a local AI agent with tool calling and conversation memory.
  • Local SLMs have limitations in accuracy, speed, and complex reasoning, making them ideal for prototyping, learning, and privacy-sensitive projects.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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