Sidecar: Multi-Model Subagent Tool

Parallel LLM conversations are managed through subagent sidecars that delegate tasks to specialized models, keeping your primary chat clean by folding back only summarized results. This prevents deep explorations from consuming your main context.

Yes, you can delegate specialized code generation and reasoning tasks to different models like Gemini and GPT-4. The tool supports configurable agent types allowing you to match the LLM to the specific analysis or implementation requirement.

To spawn LLM subagents for debugging, you need Node.js and the OpenCode CLI installed in your environment. These dependencies enable the interactive and headless modes required to manage parallel agent conversations.

Context management works by isolating deep explorations within a sidecar conversation and returning only the summarized results to your primary chat. This ensures complex delegated tasks do not pollute your main workflow context.

The best way to handle complex coding problems across multiple AI models is spawning a sidecar with a model optimized for your specific module task. This allows parallel processing while you continue your main task uninterrupted.

Headless mode for multi-model agent processing requires Node.js and OpenCode CLI, limiting usage to environments supporting these dependencies. Agent types must be explicitly configured for analysis or implementation tasks in headless operations.