Ollama on Steroids with OpenCode: A Headless Multi Agent Coding Workflow

2026-06-21 · Source: Artificial Intelligence on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

A new headless multi-agent coding workflow, dubbed "Ollama on Steroids with OpenCode," addresses the challenge of reproducibility and transparency in AI coding demos. This system integrates OpenCode for workflow orchestration, Ollama for model routing, MiniMax for planning and implementation, and GLM for review and signoff. It establishes a structured engineering loop where agents handle distinct tasks, ensuring every prompt generates local session folders with logs and test evidence. The workflow prioritizes privacy by keeping prompts and session outputs local, and mandates human intervention before any code commits or pushes, solving the problem of understanding "what exactly happened" in AI-generated code.

Key takeaway

For MLOps Engineers building AI-powered development tools, you should adopt a multi-agent architecture to ensure reproducibility and auditability in your coding workflows. Implement distinct agents for planning, implementation, and review, leveraging tools like OpenCode for orchestration and Ollama for model routing. Crucially, establish local session logging for all prompts and outputs, and enforce human review before any code commits to maintain control and privacy over your AI-generated assets.

Key insights

A multi-agent system provides a reproducible, auditable, and private workflow for AI-driven code generation.

Principles

• AI coding workflows require transparency and reproducibility.
• Task-specific agents enhance the reliability of AI development loops.
• Local session logging and human oversight are critical for AI code integrity.

Method

The workflow uses MiniMax for planning/implementation, GLM for review/signoff, OpenCode for orchestration, and Ollama for model routing, maintaining local session folders for all outputs.

In practice

• Implement distinct AI agents for planning, coding, and review.
• Configure local session folders for prompt, log, and test evidence storage.
• Integrate OpenCode for multi-agent workflow orchestration.

Topics

• Multi-agent systems
• AI coding workflow
• Ollama
• OpenCode
• LLM orchestration
• Code reproducibility
• AI development privacy

Code references

• pillaiharish/opencode-ollama-steroids

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence on Medium.