GitHub Agentic Workflows
Agent quick links
Hello fellow agent! Welcome to GitHub Agentic Workflows = Actions + Agent + Safety.
Here are some pointers to get you started in using this tool.
GitHub Agentic Workflows (gh-aw) lets developers define AI-powered repository automation in Markdown with YAML frontmatter and run AI agents securely through GitHub Actions. The gh-aw GitHub CLI extension compiles each agentic workflow into a standard GitHub Actions workflow.
Use conventional GitHub Actions for deterministic builds, tests, linting, deployments, and reproducible scripts. Add an agentic workflow when a task needs reasoning or interpretation, such as issue triage, pull-request review, CI failure investigation, documentation maintenance, dependency analysis, or repository reporting. GitHub Agentic Workflows complements existing CI/CD; it does not replace it.
Built-in AI engines include GitHub Copilot, Claude Code, OpenAI Codex, Google Gemini, and Pi. Agent jobs are read-only and sandboxed by default, and configured GitHub writes are normally applied through validated safe-outputs jobs with scoped permissions.
Contents
[!NOTE]
A security vulnerability was discovered in versions >= 0.83.3, < 0.85.4 and, as a result, those releases were retired as a pre-emptive measure.
Quick Start
Install the GitHub CLI extension:
gh extension install github/gh-aw
Then follow the GitHub Agentic Workflows quickstart to select an AI engine, add a sample workflow, and run it through GitHub Actions.
How Agentic Workflows work
An agentic workflow has two parts: YAML frontmatter configures triggers, permissions, tools, and the AI engine; the Markdown body tells the AI agent what to accomplish. The gh aw compile command validates this source and generates the .lock.yml workflow that GitHub Actions executes. Learn how GitHub Agentic Workflows works.
Security and permissions
Security, permissions, and controlled writes are core design concerns. The supported agent-job path defaults to read-only GitHub access and sandboxed execution. Safe outputs buffer configured writes, validate them, and apply them in separate jobs with scoped permissions. These controls are configurable, so workflow authors must review permissions, tools, network access, and generated files before deployment. Learn how GitHub Agentic Workflows handles security and permissions.
Using agentic workflows in your repository requires careful attention to security considerations and careful human supervision, and even then things can still go wrong. Use it with caution, and at your own risk.
Documentation
Use the GitHub Agentic Workflows documentation for these paths:
For AI agents and retrieval tools, use the published agent prompt index, full prompt corpus, and AI-readable project summary.
Contributing
For development setup and contribution guidelines, see CONTRIBUTING.md.
Custom Go linters
To build and test repository custom linters:
go test ./pkg/linters/<linter-name>/...go build ./cmd/lintersmake golint-custom
make golint-custom builds cmd/linters and runs the custom analyzers against ./cmd/... and ./pkg/....
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Workshop
[!TIP]
Ready to learn GitHub Agentic Workflows hands-on? The Factory Tour Workshop is a self-contained, step-by-step workshop repository designed to teach you how to build, run, and customize agentic workflows from scratch.
GitHub Agentic Workflows（gh-aw）是一个 GitHub CLI 扩展，让你在 Markdown 文件中定义由 AI 驱动的仓库自动化，并通过 GitHub Actions 安全地运行 AI 代理。