deepset-ai--haystack/docs-website/versioned_docs/version-2.29/pipeline-components/connectors/githubissuecommenter.mdx

---
title: "GitHubIssueCommenter"
id: githubissuecommenter
slug: "/githubissuecommenter"
description: "This component posts comments to GitHub issues using the GitHub API."
---

# GitHubIssueCommenter

This component posts comments to GitHub issues using the GitHub API.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | After a Chat Generator that provides the comment text to post or right at the beginning of a pipeline |
| **Mandatory init variables** | `github_token`: GitHub personal access token. Can be set with `GITHUB_TOKEN` env var. |
| **Mandatory run variables** | `url`: A GitHub issue URL  <br /> <br />`comment`: Comment text to post |
| **Output variables** | `success`: Boolean indicating whether the comment was posted successfully |
| **API reference** | [GitHub](/reference/integrations-github) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/github |
| **Package name** | `github-haystack` |

</div>

## Overview

`GitHubIssueCommenter` takes a GitHub issue URL and comment text, then posts the comment to the specified issue.

The component requires authentication with a GitHub personal access token since posting comments is an authenticated operation.

### Authorization

This component requires GitHub authentication with a personal access token. You can set the token using the `GITHUB_TOKEN` environment variable, or pass it directly during initialization via the `github_token` parameter.

To create a personal access token, visit [GitHub's token settings page](https://github.com/settings/tokens). Make sure to grant the appropriate permissions for repository access and issue management.

### Installation

Install the GitHub integration with pip:

```shell
pip install github-haystack
```

## Usage

:::info[Repository Placeholder]

To run the following code snippets, you need to replace the `owner/repo` with your own GitHub repository name.
:::

### On its own

Basic usage with environment variable authentication:

```python
from haystack_integrations.components.connectors.github import GitHubIssueCommenter

commenter = GitHubIssueCommenter()
result = commenter.run(
    url="https://github.com/owner/repo/issues/123",
    comment="Thanks for reporting this issue! We'll look into it.",
)

print(result)
```

```bash
{'success': True}
```

### In a pipeline

The following pipeline analyzes a GitHub issue and automatically posts a response:

```python
from haystack import Pipeline
from haystack.components.builders.chat_prompt_builder import ChatPromptBuilder
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage
from haystack_integrations.components.connectors.github import (
    GitHubIssueViewer,
    GitHubIssueCommenter,
)

issue_viewer = GitHubIssueViewer()
issue_commenter = GitHubIssueCommenter()

prompt_template = [
    ChatMessage.from_system(
        "You are a helpful assistant that analyzes GitHub issues and creates appropriate responses.",
    ),
    ChatMessage.from_user(
        "Based on the following GitHub issue:\n"
        "{% for document in documents %}"
        "{% if document.meta.type == 'issue' %}"
        "**Issue Title:** {{ document.meta.title }}\n"
        "**Issue Description:** {{ document.content }}\n"
        "{% endif %}"
        "{% endfor %}\n"
        "Generate a helpful response comment for this issue. Keep it professional and concise.",
    ),
]

prompt_builder = ChatPromptBuilder(template=prompt_template, required_variables="*")
llm = OpenAIChatGenerator(model="gpt-4o-mini")

pipeline = Pipeline()
pipeline.add_component("issue_viewer", issue_viewer)
pipeline.add_component("prompt_builder", prompt_builder)
pipeline.add_component("llm", llm)
pipeline.add_component("issue_commenter", issue_commenter)

pipeline.connect("issue_viewer.documents", "prompt_builder.documents")
pipeline.connect("prompt_builder.prompt", "llm.messages")
pipeline.connect("llm.replies", "issue_commenter.comment")

issue_url = "https://github.com/owner/repo/issues/123"
result = pipeline.run(
    data={"issue_viewer": {"url": issue_url}, "issue_commenter": {"url": issue_url}},
)

print(f"Comment posted successfully: {result['issue_commenter']['success']}")
```

```
Comment posted successfully: True
```