Getting Started with OpenCode for Local AI Coding Agent Setup
OpenCode Local Setup Guide
ByteShape has published a getting started guide for using OpenCode as a fully local AI coding agent. OpenCode is a terminal-based coding agent that can write, edit, and run code using local or remote models.
Setup Process
The guide covers setup across multiple platforms:
- Mac
- Linux
- Windows (using WSL2)
Model Integration Options
The tutorial shows how to run models locally with three different tools:
- LM Studio (CLI)
- llama.cpp
- Ollama
The guide specifically mentions using ByteShape's optimized models, though it notes you can use other models as well.
Configuration Steps
The workflow includes:
- Exposing an OpenAI-compatible API endpoint
- Configuring OpenCode to work as a coding agent
The goal is to make the full workflow approachable for those new to this space, focusing on making the setup fully local and practical.
📖 Read the full source: r/LocalLLaMA
👀 See Also
Building Claude Skills to Automate Cognitive Processes
Claude Code includes a built-in skill-creator that lets you build AI-powered skills by describing processes in natural language instead of writing code. The source describes creating a startup validation skill that reduced a 2-day manual process to 15 minutes.
How to Secure Claude Cowork with a Proxy Layer: Practical Guide
A walkthrough on setting up a proxy layer to observe and secure Claude Cowork's behavior, published by General Analysis team.
Three-layer memory architecture for persistent OpenClaw agent context
A developer built a 3-layer memory system on top of OpenClaw's infrastructure to prevent agents from starting each session without context. The architecture includes L1 workspace files injected every turn, L2 semantic memory search, and L3 reference documents opened on demand.
OpenClaw Agent Cost Analysis: From $340 to $112 Monthly with Five Optimizations
A developer tracked 18,000 API calls across four OpenClaw agents for 30 days, finding 70% of tasks didn't need GPT-4.1. By implementing prompt caching, shortening system prompts, batching analytics, switching to cheaper models, and adding max token limits, costs dropped from $340 to $112 monthly.