Master Mentat with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Install Mentat via pip or clone from GitHub repository for development setup Configure OpenAI API key either as environment variable or through command
line options Navigate to your project directory and run 'mentat' with specific files or directories as arguments Describe your desired changes in natural language and review the proposed modifications Apply changes and test functionality to ensure correctness across all modified files
💡 Quick Start: Follow these 2 steps in order to get up and running with Mentat quickly.
Explore the key features that make Mentat powerful for coding agents workflows.
AI understands relationships between files and makes coordinated edits across the entire codebase to implement complex changes correctly. Mentat tracks imports, function signatures, and architectural patterns to ensure consistency across all modified files in a single operation.
Refactor a core data model and have Mentat automatically update all controllers, views, tests, and documentation that reference the modified model structure.
Translate high-level feature descriptions into precise code implementations that span multiple files and maintain architectural consistency. Powered by OpenAI models, the tool interprets intent and produces production-quality code following your project's existing conventions.
Describe a new user authentication feature in plain English and have Mentat implement the database models, API routes, middleware, and frontend components needed.
Deep understanding of code dependencies, imports, and function signatures ensures changes maintain correctness across the entire project. Mentat traces call graphs and reference patterns to update all dependent code when you modify a shared interface.
Change a function signature and have Mentat automatically update all calling code, import statements, and related tests to maintain compatibility.
Terminal-based workflow integrates with existing development tools and can be automated as part of CI/CD processes. The CLI accepts file path arguments, supports interactive sessions, and outputs structured diffs that can be piped to other tools.
Integrate Mentat into build scripts and automated workflows to perform routine refactoring and maintenance tasks across large codebases.
Respects .gitignore patterns and integrates with Git workflows to avoid processing build artifacts, dependencies, or sensitive files. The tool can scope its context to specific branches, commits, or working tree changes for targeted operations.
Run Mentat on a feature branch to implement changes that respect existing repository structure while ignoring node_modules, build outputs, and other excluded directories.
Mentat focuses on coordinated multi-file editing from the command line, while GitHub Copilot ($10/month) provides inline single-line and multi-line suggestions within IDEs like VS Code and JetBrains. Mentat understands entire project context and implements complex changes across multiple files simultaneously, whereas Copilot is optimized for real-time autocompletion as you type within a single file. Choose Mentat for large refactoring tasks; choose Copilot for day-to-day coding speed within an editor.
Mentat itself is free and open-source under the MIT license, but it requires an OpenAI API key which charges based on token usage. As a rough guide, GPT-4o costs approximately $2.50 per million input tokens and $10 per million output tokens, while GPT-4 Turbo costs approximately $10/$30 per million tokens. A typical refactoring session processing 10,000–50,000 tokens of context might cost $0.05–$1.00 with GPT-4o. Larger sessions with full 128K context on GPT-4 Turbo could reach $2.00–$5.00. Refer to OpenAI's pricing page for the latest rates.
Mentat is limited by the LLM's token context window, so it works best when focused on specific files or directories rather than entire massive codebases at once. You can target specific areas using file path arguments, and Mentat respects .gitignore patterns to exclude irrelevant files. With GPT-4 Turbo's 128K token context window, you can process substantial portions of a project in a single session, but extremely large monorepos may need to be broken into focused segments for best results.
Mentat processes your code locally on your machine and only sends necessary context to OpenAI's API for processing during active sessions. As an open-source MIT-licensed project, the entire codebase is auditable on GitHub for security review, and no code is permanently stored on external servers beyond OpenAI's standard API data handling policies. You can review exactly what data is sent by examining the source code, and .gitignore patterns are respected to avoid sending sensitive files.
Mentat supports any programming language that GPT-4 understands, which includes Python, JavaScript, TypeScript, Java, C#, Go, Rust, Ruby, PHP, Swift, Kotlin, C/C++, and dozens of others. It also handles configuration files (YAML, JSON, TOML), markup languages (HTML, Markdown), shell scripts, and SQL. Performance is generally strongest for languages well-represented in the model's training data, particularly Python, JavaScript, and TypeScript, with diminishing returns for less common or newer languages.
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Tutorial updated March 2026