The Gemini CLI offers different interaction modes designed for various use cases and workflows, each optimized for specific types of development tasks and user preferences. The chat mode provides an interactive, conversational interface where you can have extended discussions with the AI, maintaining context across multiple exchanges throughout your development session. This mode is ideal for exploratory work, debugging sessions, or when you need to iterate on ideas through back-and-forth conversation. The chat interface remembers your previous questions and responses, allowing you to build upon earlier discussions without having to repeat context or re-explain your project structure. This persistent memory makes it particularly valuable for complex problem-solving scenarios where you might need to explore multiple approaches or refine your understanding of a codebase over time.
The CLI supports both interactive chat mode (launched with gemini chat or gemini --interactive) and single query mode where you can provide direct prompts using gemini "your question here". In single query mode, you get immediate responses without entering a persistent chat session, making it perfect for quick questions, one-off code generation tasks, or integration into scripts and automation workflows. This mode is particularly useful for CI/CD pipelines, automated code review processes, or when you need to embed AI assistance into existing development tools. The single query approach also allows for efficient batch processing of multiple requests without the overhead of maintaining interactive sessions, making it ideal for developers who prefer command-line efficiency over conversational interfaces.
The tool also features an agent mode that uses a reason-and-act (ReAct) loop with built-in tools and MCP servers to complete complex multi-step tasks like fixing bugs, creating new features, and improving test coverage. This agentic approach allows Gemini to break down complex requests into manageable steps, execute actions, observe results, and adapt its approach based on outcomes. The agent mode is particularly powerful because it can coordinate multiple tools, make decisions based on intermediate results, and even recover from failed implementation paths by trying alternative approaches. The different modes provide flexibility for various development scenarios, from quick one-off queries to complex project management tasks, allowing developers to choose the interaction style that best fits their workflow and the complexity of their current task.