AGENTS.md: A Simple, Open Format for Guiding Coding Agents
AGENTS.md is an innovative and straightforward open format designed specifically to assist AI coding agents in understanding and contributing to software projects. By creating a standardized file—much like a traditional README.md but tailored for AI interactions—this format ensures that essential project information is easily accessible and interpretable by automated agents. It addresses a key challenge in AI-assisted development: providing clear, structured guidance that helps agents navigate complex codebases without ambiguity.
Core Purpose and Benefits
At its heart, AGENTS.md serves as a centralized hub for project-specific instructions. Developers can outline details such as development environment setup, testing protocols, and pull request (PR) guidelines in a predictable structure. This not only streamlines human-AI collaboration but also reduces errors in AI-generated code by giving agents the context they need upfront. For instance, it might detail how to install dependencies using tools like pnpm in a monorepo setup or specify exact commands for running tests with Vitest.
The format's simplicity is one of its standout features—no complex schemas or tools required. It's just a Markdown file placed at the root of your repository, making it instantly compatible with any AI agent that can parse Markdown. This openness encourages widespread adoption across diverse projects, from small scripts to large-scale applications. With over 9,500 GitHub stars, it has gained significant traction in the developer community, highlighting its practical value in accelerating AI-driven workflows.
Key Sections and Examples
A typical AGENTS.md file is organized into intuitive sections:
Dev Environment Tips
This section provides quick-start guidance for setting up the local environment. For example:
- Use
pnpm dlx turbo run where <project_name>to locate packages efficiently. - Install new packages with
pnpm install --filter <project_name>to integrate them into the workspace. - Bootstrap new React projects using
pnpm create vite@latest <project_name> -- --template react-tsfor TypeScript support.
These tips ensure agents can mimic human developers' setup processes accurately.
Testing Instructions
To maintain code quality, this part details how to execute and validate tests:
- Locate CI workflows in
.github/workflows. - Run full test suites with
pnpm turbo run test --filter <project_name>. - For focused testing, use
pnpm vitest run -t "<test name>". - Always resolve linting issues with
pnpm lint --filter <project_name>after changes.
Agents are instructed to add or update tests for any modifications, promoting robust, self-sustaining codebases.
PR Instructions
Standardizing contributions is crucial for team projects:
- Follow a title format like
[<project_name>] <Title>. - Ensure all lint and test checks pass before committing.
This structure minimizes merge conflicts and enforces consistency.
Implementation and Accessibility
The project repository includes a basic Next.js website at https://agents.md, which offers an engaging explanation of the format's goals, complete with real-world examples. To explore it locally:
- Install dependencies:
pnpm install. - Run the dev server:
pnpm run dev. - Visit http://localhost:3000 in your browser.
This companion site makes AGENTS.md approachable for newcomers, while the GitHub repo (https://github.com/agentsmd/agents.md) hosts the source code and community contributions.
Why It Matters for AI Development
In an era where AI agents like those powered by large language models are increasingly integral to coding, AGENTS.md fills a critical gap. It empowers developers to harness AI more effectively, fostering innovation while maintaining control over project standards. Whether you're building a solo app or collaborating in a large team, this format reduces friction and boosts productivity, positioning it as a must-have tool in modern software engineering.
