Setting Up a Local Coding Agent with Open-Source Models

A practical tutorial for configuring a fully independent local coding agent without reliance on proprietary services. The system combines a locally served LLM with a coding harness capable of reading files, making edits, running commands, and verifying changes. This approach offers complete transparency, operates at minimal cost (hardware and electricity only), and remains entirely under the user's control, allowing arbitrary modifications to the harness. The article outlines several advantages of local setups over proprietary services like GPT in Codex or Claude Code: predictable fixed costs after hardware investment, model reproducibility without unexpected updates, and offline operation capability. Privacy considerations are also addressed—for example, processing receipts locally rather than sending data to OpenAI or Anthropic. The implementation focuses primarily on Qwen3.6 35B-A3B paired with the Qwen-Code harness, chosen because this model is specifically optimized for that environment. The Qwen3.6 requires approximately 30-40 GB of RAM and downloads about 22 GB, currently offering the best performance in its size class according to recent benchmarks. The setup remains compatible with other popular harnesses like Claude Code and Codex, as well as alternative models such as Cohere's North Mini Code 1.0. The tutorial details using Ollama as an efficient model serving framework, selected for its installation simplicity and cross-platform compatibility. Ollama provides optimized local model serving while optionally enabling access to cloud-hosted models when necessary. The guide includes practical instructions for model downloading and performance evaluation, featuring a benchmark script to measure speed (tokens per second) and memory usage across different context sizes.