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GitHub Agentic Workflows (gh-aw)

GitHub Agentic Workflows (gh-aw) is a next-generation agentic development tool that goes beyond traditional, fixed automation rules, enabling repository management and automation tasks through natural language. This tool helps developers convert automation goals and rules written in natural language within Markdown documents into pipelines that can be directly executed in GitHub Actions. It's as if a developer provides a task list (Jira ticket or repository issue) in a Markdown document to a colleague, and a skilled virtual assistant fully understands it and modifies the actual source code or pulls requests.

GitHub Agentic Workflows (gh-aw) is a next-generation agentic development tool that goes beyond traditional fixed automation rules, enabling repository management and automation tasks through natural language. This tool allows developers to convert automation goals and rules written in natural language within Markdown documents into pipelines that can be directly executed in GitHub Actions. Just as a developer can provide a task list (Jira ticket or repository issue) in a Markdown document to a colleague, and a skilled virtual assistant fully understands it and modifies the actual source code or creates a pull request, gh-aw performs text-based instructions in real-time through AI agents within a secure and independent sandboxed environment.

In traditional CI/CD environments, even simple tasks like adding an issue label or sending a notification upon build failure required hardcoding dozens of lines of YAML pipeline scripts filled with API calls and conditional statements. Furthermore, introducing external AI agent frameworks like CrewAI or LangChain presented clear limitations in terms of security, as it required hosting separate, independent infrastructure and exposing repository access tokens externally. In contrast, gh-aw leverages existing GitHub Actions security boundaries and runner resources without building separate external infrastructure, allowing it to execute natural language-based workflows and perform precise, secure, read-only, and validated write operations under enhanced security controls, providing a significant advantage.

Researchers and developers can use gh-aw to fully automate complex and repetitive repository maintenance tasks. For example, when a specific test in the source code fails, causing a CI build to break, a gh-aw compiler can parse the build failure logs, activate an AI agent to diagnose the cause of the error, and automatically generate a source code modification proposal with a solution and create a pull request. Additionally, when managing open-source projects or internal libraries, it can analyze the content of numerous newly received issues in real-time, assign appropriate personnel, and attach priority-based classification labels, enabling the easy creation of a powerful autonomous collaboration pipeline that dramatically maximizes repository quality management and operational efficiency.

๐Ÿ’ป System Requirements

๐Ÿง RAM

0 (CPU only, as it relies on external LLM API calls, local GPU is not required)

๐Ÿ’พStorage

Approximately within 200MB for CLI and local compilation dependency installation

โšก Installation

4-1. Quick Start

gh extension install github/gh-aw gh aw init

4-2. Detailed Installation

1. GitHub CLI must be installed. 2. Install the gh-aw package using the GitHub CLI extension.

gh extension install github/gh-aw

3. Perform initialization in the local repository where you want to enable the agentic workflow.

gh aw init

4. Build the workflow file defined in Markdown into an action lock file (.lock.yml).

gh aw compile

FAQ

What is GitHub Agentic Workflows (gh-aw)?

GitHub Agentic Workflows (gh-aw) is a next-generation agentic development tool that goes beyond traditional fixed automation rules, enabling repository management and automation tasks through natural language. This tool allows developers to convert automation goals and rules written in natural language within Markdown documents into pipelines that can be directly executed in GitHub Actions. Just as a developer can provide a task list (Jira ticket or repository issue) in a Markdown document to a colleague, and a skilled virtual assistant fully understands it and modifies the actual source code or creates a pull request, gh-aw performs text-based instructions in real-time through AI agents within a secure and independent sandboxed environment. In traditional CI/CD environments, even simple tasks like adding an issue label or sending a notification upon build failure required hardcoding dozens of lines of YAML pipeline scripts filled with API calls and conditional statements. Furthermore, introducing external AI agent frameworks like CrewAI or LangChain presented clear limitations in terms of security, as it required hosting separate, independent infrastructure and exposing repository access tokens externally. In contrast, gh-aw leverages existing GitHub Actions security boundaries and runner resources without building separate external infrastructure, allowing it to execute natural language-based workflows and perform precise, secure, read-only, and validated write operations under enhanced security controls, providing a significant advantage. Researchers and developers can use gh-aw to fully automate complex and repetitive repository maintenance tasks. For example, when a specific test in the source code fails, causing a CI build to break, a gh-aw compiler can parse the build failure logs, activate an AI agent to diagnose the cause of the error, and automatically generate a source code modification proposal with a solution and create a pull request. Additionally, when managing open-source projects or internal libraries, it can analyze the content of numerous newly received issues in real-time, assign appropriate personnel, and attach priority-based classification labels, enabling the easy creation of a powerful autonomous collaboration pipeline that dramatically maximizes repository quality management and operational efficiency.

When should I use GitHub Agentic Workflows (gh-aw)?

GitHub Agentic Workflows (gh-aw) is a next-generation agentic development tool that goes beyond traditional, fixed automation rules, enabling repository management and automation tasks through natural language. This tool helps developers convert automation goals and rules written in natural language within Markdown documents into pipelines that can be directly executed in GitHub Actions. It's as if a developer provides a task list (Jira ticket or repository issue) in a Markdown document to a colleague, and a skilled virtual assistant fully understands it and modifies the actual source code or pulls requests.

๐Ÿ“„ Official Docs๐Ÿ™ GitHub

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