Agentic CLI: Top 10 Open Source Tools in 2025

Explore top free and open-source agentic CLI tools that offer LLM integration, prompt chaining, and code editing capabilities, all within a terminal-based workflow.

What are CLI-based coding agents?

CLI-based coding agents are conversational, prompt-driven AI tools that operate entirely in the terminal. 

These integrate models like GPT-4, Claude, or Gemini into your development workflow, allowing you to generate, edit, and refactor code without leaving the command line.

Most support models come from providers such as OpenAI, Anthropic, xAI, and Google.

CLI agents vs GUI-based coding tools

Unlike GUI-based tools like Cursor, Replit, Windsurf, which present a visual interface for code suggestions and approvals, CLI-based agents run natively in the terminal. From the command line,  you give a prompt, the agent suggests a change, and you approve or reject it.

In most cases, changes are automatically applied and committed to Git if the configuration is set. This makes CLI-based coding agents useful for:

• Version-controlled coding workflows
• Terminal-based or headless development setups
• Use of local or self-hosted LLMs
• Prompt-driven, scriptable automation workflows

Open source CLI-based coding agents

Fully agentic CLI tools: 

Tools that support conversational prompting, multi-turn interaction, code editing, and context retention. 

• Aider (GitHub stars: 35.2k) — Standout feature: Seamless Git-integrated inline editing
  
  Aider is one of the most complete agentic CLI tools, supporting conversational prompting, multi-turn refinement, multi-file awareness, and Git-based code tracking. Best for deep code collaboration in the terminal.

• Cline (GitHub stars: 47.2k) — Standout feature: Lightweight conversational file editing
  
  Cline offers multi-turn interactions and file-level updates through LLMs. While it lacks persistent memory across sessions, it excels in rapid, focused development workflows.

• Amazon Q Developer (AWS Q CLI) (GitHub stars: 0.99k) — Standout feature: Session-aware, file-specific prompts  
  
  AWS Q Developer supports conversational input with strong session memory and file-aware responses, making it effective for multi-step tasks like debugging, code reviews, and structured development.

• Codex CLI (GitHub stars: 30.5k) — Standout feature: Multi-mode interactions (suggest, edit, run)
  
  Codex CLI enables users to choose how the agent interacts — from suggestions to direct edits or auto-runs — all within the context of a session and file, offering flexibility for various development workflows.

• Continue (CLI mode) (GitHub stars: 28k) — Standout feature: IDE-class editing in terminal
  
  Continue provides prompt-driven, guided file edits along with memory retention during sessions. It’s designed to work fluidly in both CLI and IDE contexts, bridging structured and flexible agentic interaction.

• Refact CLI (GitHub stars: 2.6k) — Standout feature: Single-session, prompt-based inline edits
  
  Refact brings conversational prompting and file-level edits to the terminal. Best for quick, single-session updates. It does not retain state across sessions but focuses on local changes.

Partially agentic: 

Tools that support some agentic capabilities (e.g., conversational prompts or code editing), but lack full multi-turn interaction or context retention. No git integration except tabby-cli. Sorted based on level of agentic capabilities.

• AIChat (GitHub stars: 7.3k) – Rust CLI assistant for coding tasks.

• Claude Code (GitHub stars: 18.1k) – Anthropic’s Claude integrated into a terminal-based coding assistant. Best for vice coding and small-scale tasks.

• tabby-cli (GitHub stars: 64.6k) – Self-hosted Rust-based CLI coding assistant focusing on privacy.

• Plandex (GitHub stars: 13.9k) – Go-based CLI coding assistant.

• OpenHands (OpenDevin) (GitHub stars: 60.0k) – Autonomous task agent that executes multi-step development tasks without conversational refinement.

• AskCodi (not open source) – JavaScript-based CLI AI coding assistant.

• Gemini CLI (GitHub stars: 55.0k) – CLI wrapper for Google’s Gemini LLM, designed for one-shot code generation or natural language queries.

• Claude Squad (GitHub stars: 2.6k) – Terminal-based AI coding assistant with basic prompt support.

• Codai (GitHub stars: 0.34k) – Terminal and session-based AI coding assistant with prompt-driven code generation.

• GPTMe (GitHub stars: 3.9k) – Lightweight CLI assistant for one-shot prompt responses.

3 Leading command-line coding agents

Cline

Cline is a terminal-based AI assistant; it is highly capable, but still dependent on thoughtful guidance. 

Among Aider and Claude Code, Cline offers the most agentic experience. It can run commands, modify files, and iterate on test results autonomously. It supports any LLM via OpenRouter or local models and shows real-time credit usage.

What does Cline offer?

Cline is a CLI-based AI coding assistant focused on agentic test-driven workflows and iterative development.  It offers:

• File-aware editing – Reads, writes, and modifies local code files directly.
  
• Command execution – Runs tests, linters, and shell commands; responds to outputs.
  
• Git integration – Commits changes, tracks diffs, and resolves merge conflicts.
  
• Agentic behavior – Supports auto-approve mode for autonomous task execution.
  
• Flexible model support – Compatible with Claude, GPT, Mistral, and local LLMs via OpenRouter.
  
• Cost tracking – Displays real-time token usage and API spend per action.
  
• Context injection tools – Accepts structured input via @file, @folder, @url, and @problems.

Best application areas

TDD-Driven development with immediate feedback:

Cline is highly capable when working with interfaces and unit tests. 

Integrates seamlessly with test frameworks to enable a real-time, test-first workflow. Developers write a test, and Cline responds by generating code to pass it, executing the test suite immediately to verify success or failure.

This shortens the feedback loop:

• Failing tests are caught instantly
  
• Cline adapts based on results without needing manual prompting
  
• Errors are addressed iteratively through structured guidance

Bug fixing via iterative prompts:

When confronting a bug or unexpected output, Cline’s workflow—”edit, run, fix”—reads your tests, applies updates, runs the tool, interprets errors, and adjusts accordingly. 

This provides a back-and-forth loop for seamless fixing.

Source: Cline¹

End-to-end execution and validation:

Unlike Aider and Claude Code, Cline goes beyond static code editing by executing terminal commands, opening browsers, and interpreting live output. This allows for end-to-end validation within the same workflow, eliminating the need to manually copy code or test results between tools.

Safe refactoring and controlled code modification:

Cline supports a Plan/Act mode that separates intent from execution. It first proposes a strategy, then waits for user approval before applying changes. This helps prevent unintended edits and enforces architectural consistency.

Source: Cline²

Automated checkpointing:

After each operation, Cline creates a lightweight snapshot of the codebase. These checkpoints allow easy rollback without affecting Git history

Source:Cline³

Pricing & runtime behavior

Cline itself is open source and free to use, but its cost depends on the underlying LLM provider. When used with OpenRouter, sessions typically cost $1–$3 per hour, depending on model choice and task complexity. A session of hours of continuous coding may run around $6.⁴

Limitations

• No guardrails in auto-approve mode – Can repeat the same mistakes if left running without supervision
• Trusts flawed test cases – If a test case is wrong, Cline will try to make it pass anyway, even if the outcome doesn’t make sense
• Guess-based debugging – Tends to retry solutions instead of gathering new information unless prompted for debug output
• No persistent memory – Doesn’t retain architectural knowledge or task history. Needs all important context reloaded each time
• No automatic rollback – Failed attempts must be manually reverted; lacks built-in branching or version checkpointing

Claude Code

Source: Anthropic⁵

Claude Code is an agentic CLI interface for Claude 3.5 and 3.7, enabling direct interaction with your local codebase. While it supports file edits, bug fixes, merge resolution, and test execution, Claude Code remains lightweight and less integrated than tools like Cursor.

What does Claude Code offer?

Claude Code allows you to point to a directory and activate the assistant using a claude command. It can:

• Edit files and fix bugs
• Answer architectural or code-related questions
• Run and debug tests or linting
  Manage Git: review history, resolve merges, create commits and PRs
• Support agent-like behaviors (e.g., task chaining, searching, troubleshooting)

Best application areas

Vibe coding:

Claude Code stands out in more conversational workflows. It supports a “vibe coding” style where code is shaped through natural language rather than structured navigation. Developers interact more than they write, often dictating high-level ideas and allowing the model to iterate or self-correct. 

The trade-off is lower precision and potentially higher cost due to longer sessions and inflated context. 

This approach is particularly best for:

• Solo developers and indie hackers
• Rapid prototyping 
• Projects where the architecture is still evolving

 Lightweight projects:

Claude Code lacks file selection or tagging mechanisms. It relies on the user to describe context manually, which becomes inefficient as codebase size or complexity increases. It works best when the scope is small. This simplifies setup but limits precision and scalability.

It performs less effectively in cases such as:

• Large repositories
  
• Projects with strict architectural conventions
  
• Tasks that require consistent cross-file context alignment

Pricing & runtime behavior

Users report spending ~$3–$5/hour depending on context size and prompt complexity. Costs can quickly add up, especially when working with large codebases or running longer workflows. While there is a /cost command, there is no upfront control over spending or session limits.⁶

Instead, Cursor’s $20/month plan has a small fraction of usage. Tools like Bolt or Lovable even offer similar capabilities for free.

Limitations:

Output & context handling

Claude Code struggles with precise context control. Unlike Cursor, which allows developers to include specific files, such as routers or schemas, for structured prompts, Claude relies solely on broad directory scans and natural language. 

This makes it less effective in modular or complex codebases.

In practice, this impacts output quality. When building a UI from scratch, Claude Code delivers weak layout and minimal visual hierarchy. 

Source: The Discourse⁷

Inconsistent code formatting

Claude Code sometimes outputs incomplete or fragmented code blocks.

Source:Reddit⁸

Other limitations:

• Not an IDE – No GUI, no file tree, no inline editor
  
• No image preprocessing – Large images may be rejected unless manually resized
  
• Mac-only (as of 2025) – Requires WSL or similar workaround on Windows
  
• No fine-grained file control – Cannot tag or selectively load files into context
  
• No upfront cost limits – Usage costs (~$3–$5/hr) accrue silently unless monitored

Aider

Source: aider⁹

Aider is one of the original open-source AI coding assistants and has influenced the development of many similar tools.

While its primary interface runs in the terminal, optional tools like a web UI and third-party VS Code extensions (such as “Aider Composer”) bring it closer to the experience offered by tools like Cursor, Windsurf, or Cline.

Aider is a good fit for developers who work with terminal-based workflows and need instruction-following AI for code editing and refactoring tasks.

Aider works best with LLMs such as Claude 3.7 Sonnet, DeepSeek R1, and OpenAI’s GPT-4o, o1, and o3-mini. It can also connect to local models.

What does Aider offer?

• AI-assisted code editing in terminal – Make live code changes using natural language, with version control through Git
  
• Refactoring and restructuring code – Automate tasks like renaming variables, splitting functions, or simplifying logic
  
• Multi-file and multi-language support – Edit and navigate across polyglot codebases (Python, JavaScript, C++, Go, etc.)
  
• Test generation and validation – Ask Aider to create or modify unit tests, or validate logic without leaving the terminal
  
• Editing config and documentation files – Apply changes to YAML, JSON, Markdown alongside code updates
  
• Style and convention enforcement – Specify coding standards (e.g., snake_case, PEP8) and let Aider apply them consistently
  
• Prompt-driven code reviews or cleanups – Review recent diffs or request improvements for specific files or blocks
  
• Offline or local LLM support – Use Aider with local models or private APIs for secure, offline development environments
  
• CLI-integrated code linting – Trigger linting workflows directly in your terminal session for fast iteration

Best application areas

Example chat transcripts: Here are sample transcripts that illustrate what it’s like to collaborate with Aider during real coding sessions:

Create a simple Flask app with Aider

In this example, Aider is asked to build a basic Flask web application with multiple endpoints. Aider created a new file app.py and generated a basic Flask application based on a simple user instruction:

“make a flask app with a /hello endpoint that returns hello world”

In another example, Aider is asked to create a new web route in a Flask app that accepts two numbers in the URL—such as /add/3/5—and returns their sum (in this case, 8). The prompt was:

“add an endpoint like /add/3/5 which returns the sum of the 2 numbers”,

Automatically updating documentation:

Aider can be used to automatically update documentation—such as usage instructions, README sections, or inline comments—based on the latest version of the code.

For example, when the user modifies a main() function, they can ask Aider to regenerate the relevant Usage docs to reflect those changes.

Source: aider¹⁰

Here, Aider

• Analyzed the changes in main.py
• Updated the CLI argument descriptions in README.md to reflect the latest defaults and options

Limitations:

• Occasional prompt misinterpretation – Context and variable usage aren’t always well handled; instructions may be misunderstood, especially if your request is vague or spans multiple files.
• Overwrites during sequential edits – May undo previous changes when applying multiple prompts in a row
• Clunky browser UI – Experimental web interface works, but lacks polish
• Limited agentic control – Lacks full autonomous workflows (e.g., long-run test-driven loops); it helps, but it’s no substitute for true agent mode
• Model-specific quirks – Using certain LLMs (e.g., GPT-4) can generate poor-quality or incorrect code until broken tasks are split into smaller steps

External Links

• 1. The Cline AI Assistant is Mesmerizing · mtlynch.io.
• 2. Plan & Act - Cline.
• 3. Checkpoints - Cline.
• 4. The Cline AI Assistant is Mesmerizing · mtlynch.io.
• 5. Introducing Claude Code - YouTube.
• 6. Reddit - The heart of the internet.
• 7. Anthropic Claude Code: Command Line AI Coding - Review & Analysis. The Discourse
• 8. Reddit - The heart of the internet.
• 9. Aider - AI Pair Programming in Your Terminal.
• 10. Create a simple flask app with aider | aider.