Running Local AI Coding Agents with Gemma 4 and Ollama

Cloud-hosted coding agents have dominated AI-assisted programming for years. That era is ending. Running local AI models is finally practical.

Google released Gemma 4 on April 2, 2026. It’s an open model designed for local use. Gemma 4 comes in multiple sizes, including the E2B and the E4B variants. The E4B model weighs in at about 9.6 GB and supports a massive 128,000 token context window. This means it can handle large codebases and complex workflows without choking.

Ollama serves as the runtime environment for these models. Installing Ollama is straightforward—use winget on Windows or curl on Linux. After setup, Ollama runs a local server on your machine, eliminating the need for cloud calls. This lets developers write, explain, and manipulate code files entirely offline.

OpenCode acts as the agent interface. It supports connections to both cloud and local models. When running locally, OpenCode links to Ollama’s API endpoint at http://localhost:11434/v1. Configuration happens through an easy opencode.json file specifying the model and provider. This flexibility allows seamless switching between local and cloud environments.

ProtoAgent offers a terminal-based assistant built on ProtoLink, a Python framework that separates the brain (Python) from the face (Rust) via PyO3 bindings. ProtoAgent uses a three-node topology: Architect for orchestration, Explorer for search, and Coder for synthesis. This division of labor improves efficiency and clarity.

Small local models can struggle with complex instructions and heavy context. Nikos Maroulis warns that the “God Prompt” is a trap for small models. Removing unnecessary choices and constraints helps models perform better. ProtoAgent uses a deterministic context with a database called Context Loom, which indexes project files, symbols, imports, headings, content fingerprints, and Git state. This beats traditional filesystem searches in speed and reliability.

The ProtoLink A2A specification breaks workflows into stages managed by dedicated agents. Each stage outputs editable markdown files, allowing human review and fine-tuning. The Interpretable Context Methodology (ICM) replaces complex orchestration with a clear filesystem structure. This approach keeps workflows transparent and easier to debug.

The setup is simple: install Ollama, pull the Gemma 4 model, and configure OpenCode to connect locally. This local stack supports writing, explaining, and editing code without cloud dependencies. It’s a decisive step toward truly private and responsive AI coding assistants.

Sebastian Raschka, PhD, calls ProtoAgent a laboratory for exploring local coding models’ limits and strengths. The technology isn’t perfect, but it proves local AI coding agents can now rival cloud solutions. For developers tired of cloud latency and privacy concerns, this shift is a game changer.