How to Implement Tool Calling with Gemma 4 and Python

2026-04-13 · Source: MachineLearningMastery.com - Machinelearningmastery.com · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

This article details how to construct a local, privacy-focused tool-calling agent utilizing the Gemma 4 model family and Ollama. It introduces Google's Gemma 4 models, highlighting their Apache 2.0 license, native support for agentic workflows, and ability to generate structured JSON outputs for function calls. The piece explains tool calling as a mechanism for language models to interact with external functions, transforming them into dynamic autonomous agents. Specifically, it focuses on using Ollama as a local inference runner with the `gemma4:e2b` model, an Edge 2 billion parameter variant optimized for mobile and IoT devices, which retains multimodal and function-calling capabilities without requiring a GPU. The implementation relies on standard Python libraries, defining tools as Python functions and mapping them to Ollama-compliant JSON schemas to guide the model's function invocation.

Key takeaway

For AI Engineers building privacy-first applications, leveraging Gemma 4 models with Ollama for local tool calling offers a robust solution. This approach allows you to deploy dynamic agents on consumer hardware, bypassing cloud dependencies and API costs while maintaining strict data privacy. Consider integrating this architecture to enable complex, real-world interactions directly on edge devices, expanding the utility of small language models.

Key insights

Gemma 4 and Ollama enable local, privacy-first tool-calling agents for dynamic, real-world interactions.

Principles

• Tool calling transforms static models into dynamic agents.
• JSON schema guides model function call generation.
• Local inference enhances privacy and reduces costs.

Method

Define Python functions as tools, map them to JSON schemas, pass user queries and tool registry to Ollama, execute model-requested tool calls, and feed results back for final response generation.

In practice

• Use `gemma4:e2b` for edge device tool calling.
• Implement tools with standard Python libraries.
• Map Python functions to Ollama-compliant JSON schemas.

Topics

• Gemma 4
• Tool Calling
• Ollama
• Local AI Agents
• Function Calling

Code references

• mmmayo13/gemma_4_tool_calling

Best for: AI Engineer, Machine Learning Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by MachineLearningMastery.com - Machinelearningmastery.com.