How to Build a Local AI Coding Agent with Gemma 4: Complete Guide
By Braincuber Team
Published on April 20, 2026
Local LLMs have reached a turning point. With Gemma 4 long context window, native multimodal support, and the accessibility of Ollama, it is now practical to run a capable agentic coding assistant entirely on your own machine.
What You will Learn:
- Understanding Gemma 4 model family and variants
- Setting up Ollama for local inference
- Installing and configuring Gradio for the UI
- Building a split-pane code editor and chat interface
- Implementing agentic tool use for code execution
- Adding multimodal support for images and files
- Running a fully local AI coding assistant
What Is Gemma 4?
Gemma 4 is Google DeepMind open-weights model family, designed for both local deployment and research. It builds on the Gemma lineage with improved instruction following, longer context windows, and native multimodal input handling.
Models like Gemma-4-26B(MOE) and Gemma-4-31B achieve Elo scores comparable to much larger models, indicating strong performance-per-parameter. The 31B model currently ranks as 3rd open model in the world on the Arena AI text leaderboard.
The Gemma 4 Model Family
| Model | Architecture | Active Params | Context Length |
|---|---|---|---|
| Gemma-4-31B | Dense Transformer | 31B | 256K tokens |
| Gemma-4-26B-A4B | MoE (128 Experts) | 3.8B | 256K tokens |
| Gemma-4-E4B | Dense Transformer | 4.5B effective | 128K tokens |
| Gemma-4-E2B | Dense Transformer | 2.3B effective | 128K tokens |
Model Selection
In this tutorial, we use the gemma4:e4b (9.6GB) variant via Ollama which is a quantized version well-suited for local inference on consumer hardware.
Running Gemma 4 via Ollama
Ollama handles model downloading, quantization, serving and provides an OpenAI-compatible HTTP API. For this tutorial, Ollama acts as the inference backend, and our app communicates with it over localhost:11434.
curl -fsSL https://ollama.com/install.sh | sh
ollama pull gemma4:e4b
ollama serve
The above commands installs Ollama locally using the official install script. We then download the gemma4:e4b model variant locally for on device inference. Finally, ollama serve starts the Ollama server, enabling our app to send requests to the model.
Build a Coding Assistant with Ollama
In this section, we will build the code assistant step by step. At a high level, here is what the app does:
- Accepts a natural-language message in the chat panel, with optional image or file attachments
- Injects the current editor code as context for the model
- Sends a streaming request to Gemma 4 via Ollama /api/chat endpoint
- Optionally calls tools (code execution, math evaluation) in an agentic loop
- Pushes extracted code blocks from the response into the live editor
Install Dependencies
Install Gradio, requests, and Pillow for the UI and HTTP communication.
pip install gradio requests pillow
Configuration and Constants
Define imports, model name, Ollama base URL, supported languages, and the default system prompt.
Create a file called app.py and add the following imports and configuration:
Define Agentic Tools
Create tools for code execution and mathematical calculations that the model can call during inference.
The assistant can operate in an agentic mode where it calls tools during inference. We define two tools in the standard function-calling schema that Ollama supports: run_code and calculate.
Tool Execution Layer
Implement the functions that execute code and evaluate math expressions safely.
Safety Design
The code execution uses a 5-second timeout, output is capped at 3,000 characters, and the math evaluator uses eval with __builtins__ set to empty to prevent access to dangerous functions.
Helper Utilities
Create utility functions for image encoding, file handling, and code extraction.
Core Chat Streaming Generator
Build the main function that handles conversation flow, tool calls, and streaming responses.
The chat function builds conversation history, enriches user input with context (code, files, images), sends streaming requests to Ollama, optionally executes tools in agentic mode, and yields partial responses for real-time UI updates.
Gradio UI Layout
Create a split-pane interface with code editor on the left and chat panel on the right.
Split-Pane Layout
Code workspace on the left (55%) and chat panel on the right (45%) for seamless workflow between coding and AI assistance.
Multimodal Input
Support for images (vision) and code/text files as context, enabling the model to reason over multiple modalities.
Event Wiring
Connect UI components to the chat function for interactive responses.
Theming and CSS
Add custom theme and CSS for a polished, GitHub-inspired look with light and dark mode support.
Launch the Application
Start the Gradio app and access it at localhost:7860.
Access the App
Navigate to http://localhost:7860 once the app is running. The ollama_ok() check will warn you on startup if Ollama is not running.
Conclusion
In this tutorial, we built a fully local AI coding assistant using Gemma 4, Ollama, and Gradio. The app supports multimodal input, real tool use, streaming responses, and a live code editor, all running on your own machine without any external API.
The whole-context approach of injecting the editor code directly into every prompt is simpler than RAG for a single-file workflow and works particularly well for tasks like explaining, refactoring, or extending code you are actively editing.
Next Steps
- Add a file tree panel to support multi-file projects
- Add support for additional Ollama models via a dropdown
- Persist conversation history to disk so sessions survive app restarts
Frequently Asked Questions
Does this require a GPU?
Not necessarily. Gemma 4 e4b is a quantized model that runs on CPU, though a GPU will significantly improve the inference speed.
What is the difference between agentic mode and regular chat?
In regular mode, the model streams text only. In agentic mode, the model can call run_code or calculate, check the results, and incorporate them before finishing its response.
How does the editor update automatically?
The extract_last_code_block helper function scans the assistant response for the last fenced code block and pushes it to the editor after streaming completes.
What happens if Ollama is not running?
The ollama_ok check catches this on startup and on every chat submission. If Ollama is not reachable, the chat returns a formatted error message rather than crashing.
Can I use other models with this app?
Yes, you can modify the MODEL constant to use any model available in Ollama, such as llama3, mistral, or other models from the Ollama library.
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