AGENTS.md is a simple, open-source file format designed to provide context and instructions for AI coding agents, similar to a README but tailored for automated assistance. It helps agents understand project specifics like build steps, testing commands, code styles, and security considerations, ensuring efficient collaboration between human developers and AI tools. Used by over 20,000 open-source projects, it promotes consistency and reduces clutter in human-focused documentation by offering a dedicated space for agent guidance.
Free
How to use AGENTS.md?
To use AGENTS.md, create a file at the root of your repository or in subdirectories for monorepos. Add sections covering project overview, build and test commands, code style guidelines, testing instructions, and security details. AI agents automatically parse this file to assist with coding tasks, such as generating code, running tests, and adhering to project conventions, streamlining development workflows and reducing manual setup.
AGENTS.md 's Core Features
Standard Markdown format for easy creation and editing, allowing developers to use familiar syntax without proprietary tools.
Predictable file placement at repository roots or nested directories, ensuring agents can reliably find and interpret instructions.
Compatibility with multiple AI coding agents like OpenAI Codex, Amp, and Cursor, fostering a broad ecosystem integration.
Support for detailed sections including build commands, testing protocols, and security guidelines, enhancing agent accuracy and efficiency.
Automatic precedence rules where the closest AGENTS.md file takes priority, enabling tailored instructions for different project parts.
Living documentation that can be updated over time, adapting to project changes and improving long-term maintainability.
Backward compatibility through symbolic links, facilitating smooth migration from existing documentation files.
AGENTS.md 's Use Cases
Open-source maintainers use AGENTS.md to provide consistent instructions for AI agents contributing to projects, reducing onboarding time and ensuring code quality across contributions.
Software engineers in large teams implement AGENTS.md in monorepos to give subproject-specific guidance, helping agents handle diverse codebases without confusion.
DevOps professionals integrate AGENTS.md into CI/CD pipelines, allowing AI agents to automate testing and deployment steps based on predefined commands.
Startup founders adopt AGENTS.md to scale development efforts with limited resources, leveraging AI for rapid prototyping and bug fixes.
Educators in coding bootcamps use AGENTS.md to teach students about AI-assisted development, providing clear examples for hands-on learning.
Security analysts include security considerations in AGENTS.md to guide AI agents in identifying and mitigating vulnerabilities during code generation.
