AGENTS.md is a simple, open format for guiding coding agents. Think of it as a README for agents: a dedicated, predictable place to provide context and instructions to help AI coding agents work on your project.
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.
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.
A typical AGENTS.md file is organized into intuitive sections:
This section provides quick-start guidance for setting up the local environment. For example:
pnpm dlx turbo run where <project_name> to locate packages efficiently.pnpm install --filter <project_name> to integrate them into the workspace.pnpm create vite@latest <project_name> -- --template react-ts for TypeScript support.These tips ensure agents can mimic human developers' setup processes accurately.
To maintain code quality, this part details how to execute and validate tests:
.github/workflows.pnpm turbo run test --filter <project_name>.pnpm vitest run -t "<test name>".pnpm lint --filter <project_name> after changes.Agents are instructed to add or update tests for any modifications, promoting robust, self-sustaining codebases.
Standardizing contributions is crucial for team projects:
[<project_name>] <Title>.This structure minimizes merge conflicts and enforces consistency.
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:
pnpm install.pnpm run dev.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.
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.