Gemini CLI
AIOpen SourceOpen-source command-line agent that gives direct, terminal-first access to Google Gemini models for chat, scripting, and automation.
-(0 Reviews)
Free Available
Starting from Free
Gemini CLI
AIOpen Source
Open-source command-line agent that gives direct, terminal-first access to Google Gemini models for chat, scripting, and automation.
-(0 Reviews)
Free Available
Starting from Free
About Gemini CLI
Gemini CLI is an open-source, terminal-first agent that connects your command line to Google’s Gemini models. It provides interactive multi-turn chat, non-interactive/scripting modes, file attachments, session management, and extensions to route requests to Gemini or community MCP servers. The tool is designed for developers and power users who prefer terminal workflows, enabling piping, scripted prompts, and session persistence without requiring a separate runtime. Its value lies in lightweight, direct access to Gemini from the shell, extensibility via community plugins and MCP connectors, and an emphasis on portability and low operational overhead.
Key Features
Interactive Chat: Provides a shell-like, multi-turn conversational interface to Gemini models with prompt history, system messages, and live input for developer workflows.
Non-Interactive / Scripting Mode: Automatically detects piped input or supports flags (e.g., -e) to run prompts and attachments from scripts and CI pipelines for automation and batch processing.
File Attachments & Input Handling: Accepts files and piped content as text attachments to supply context or data to models, enabling tasks like document analysis or code generation from local files.
Session Management: Stores and manages chat sessions, allowing users to resume conversations, persist context across runs, and organize interactions for repeated workflows.
Extensibility & MCP Support: Supports community MCP servers and extensions so organizations can route requests, add custom connectors, or extend functionality through plugins and contributions.
Lightweight & Portable Implementations: Community implementations (C, Go, etc.) emphasize portability and minimal runtime dependencies for fast, native CLI performance across platforms.
Scripting-friendly Commands: Rich set of subcommands and flags for embedding into shell scripts, invoking model calls programmatically, and integrating into developer toolchains.
Terminal-based access to Gemini models (interactive chat and multi-turn conversations)
Non-interactive / scripting mode (auto-entered when piping or using -e flag)
Model selection and control via commands or system prompts
Support for file attachments and piping content as text attachments
Session management for conversational state
Embeddings generation and comparison (in some implementations)
SQLite-backed storage and analysis capabilities (community implementations)
Extensible via MCP servers and user-provided extensions
Cross-platform executables and source builds (community-provided binaries for Windows, macOS, Linux)
Open-source licensing (Apache-2.0 for official repo; various community licenses exist)
Use Cases
Terminal-first Development Assistance: Use Gemini CLI as an in-terminal coding assistant to generate, refactor, and review code directly from the shell and integrate outputs into local projects.
Automated CI/CD Prompts: Embed non-interactive CLI calls in CI pipelines or scripts to run model-based checks, generate release notes, or produce code snippets during automated workflows.
Document and Code Analysis from Files: Pipe or attach local documents and code files to the CLI to request summaries, explanations, or transformations grounded in the file content.
Interactive Troubleshooting and Pairing: Maintain conversational sessions to iteratively debug issues, experiment with solutions, and preserve context across multiple terminal sessions.
Lightweight Local Prototyping: Quickly prototype agent behaviors and terminal workflows without needing a full web UI—use the CLI to test prompts, integrations, and session logic.
Community-driven Extensions and MCP Routing: Route requests through self-hosted MCP servers or use community extensions to customize model routing, privacy controls, or enterprise integrations.
Developer terminal workflows: quick prompts, code generation, and debugging assistance directly from shell
Scripting and automation: batch requests, piping input to models, and integrating LLM results into shell scripts
Interactive drafting and editing: writing, summarization, and multi-turn document editing in terminal sessions
Data processing: generating and comparing embeddings for small-scale similarity search (via CLI commands)
Experimentation and extension: building custom MCP servers or extensions to tailor model behavior
Screenshots
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