Pair Gemini CLI and OpenCode for Better Code

Why Pick One AI Coding Assistant When You Can Use Both?

The AI coding assistant landscape has become a battleground of tradeoffs. Gemini CLI excels at rapid feature generation and scaffolding, while OpenCode's models shine at catching edge cases, security flaws, and subtle logic errors. Most developers bounce between them, never fully satisfied with either alone.

But there's a better approach: orchestrate both tools into a unified workflow using specialized agents. Here's a breakdown of a three-agent system that leverages Gemini CLI as the orchestration layer while tapping OpenCode for rigorous code review — giving you speed and quality without compromise.

The Core Idea: Specialization Over Generalization

Instead of asking one AI to do everything — write code, review it, test it, and document it — this workflow assigns each task to the model best suited for it. Think of it like a small engineering team where each member has a defined role.

The setup relies on Gemini CLI's agent orchestration capabilities, specifically its .agents directory structure. By defining three distinct agent configurations, you create a pipeline where code flows from generation through validation before it ever hits your main branch.

The Three-Agent Setup

Agent 1: The Implementer (Gemini)

This agent handles the heavy lifting of code generation. Powered by Gemini's fast inference, it takes feature requests, user stories, or bug descriptions and produces working implementations quickly. Its system prompt is tuned for speed and completeness — it generates full functions, classes, and modules rather than snippets.

The key configuration here is setting the agent's context window to include your project's architecture docs and coding standards. This keeps generated code consistent with your existing codebase from the start.

Agent 2: The Reviewer (OpenCode)

Once the Implementer produces code, it's automatically handed off to the Reviewer agent. This agent calls OpenCode's models — which tend to be more deliberate and analytical — to perform a thorough code review.

The Reviewer checks for edge cases the Implementer might have missed: null pointer risks, race conditions, improper error handling, and security vulnerabilities. Its system prompt explicitly instructs it to be adversarial — to assume the code has bugs and find them.

Agent 3: The Synthesizer (Gemini)

The third agent takes the Reviewer's feedback and the original implementation, then produces a final, refined version. Running on Gemini again for speed, it merges the best of both worlds: the Implementer's complete solution with the Reviewer's corrections and improvements.

This agent also generates inline documentation and test suggestions based on the edge cases the Reviewer identified.

Setting It Up in Practice

The workflow lives in your project's .agents directory. Each agent gets its own YAML configuration file defining its model, system prompt, temperature settings, and handoff triggers.

A typical flow looks like this:

1. Run gemini-cli implement 'Add user authentication with OAuth 2.0'
2. The Implementer agent generates the feature code
3. Output automatically pipes to the Reviewer agent via OpenCode's API
4. The Reviewer returns annotated feedback
5. The Synthesizer merges everything into a final pull-request-ready diff

The entire cycle typically completes in under two minutes for a medium-complexity feature — compared to 10-15 minutes of manually switching between tools and copying context back and forth.

Why This Works Better Than a Single Tool

The fundamental insight is that code generation and code review require different 'mindsets,' even for AI models. Models optimized for fast, creative generation tend to take shortcuts on edge cases. Models tuned for analysis and critique tend to be slower and sometimes overly conservative in their suggestions.

By separating these concerns, you get faster iteration without sacrificing code quality. Early adopters of this pattern report catching 30-40% more edge-case bugs compared to using either tool alone, while maintaining the rapid development pace Gemini CLI is known for.

Limitations to Keep in Mind

This approach isn't without friction. Managing API keys for two different services adds complexity. Context can sometimes be lost in handoffs between agents, particularly for very large codebases. And the cost of running three agent passes per feature does add up — though it's still significantly cheaper than the bugs you'd ship otherwise.

What's Next for Multi-Agent Coding Workflows

As AI coding tools mature, expect more developers to adopt multi-model workflows rather than betting on a single provider. Google's Gemini CLI already supports custom agent definitions natively, and OpenCode's open-source architecture makes it easy to integrate as a review layer.

The era of 'pick one AI assistant' is ending. The future belongs to developers who treat AI models like team members — assigning each one the role it plays best. If you haven't experimented with multi-agent coding workflows yet, this three-agent pattern is a practical place to start.