GitHub Issues & Pull Requests Scraper

Extract public GitHub issues and pull requests from repositories or GitHub search queries.

Use this actor to monitor open-source demand, support pain, competitor roadmaps, bug reports, feature requests, and pull request activity.

What does GitHub Issues & Pull Requests Scraper do?

This actor turns public GitHub issue and pull request search results into a clean dataset.

It accepts repository names such as microsoft/vscode.

It also accepts GitHub issue search queries such as repo:microsoft/vscode is:issue label:bug.

The output includes issue and pull request metadata, labels, author details, timestamps, comments, reactions, and source-query provenance.

Who is it for?

DevRel teams

Track what developers ask for across important open-source projects.

Product managers

Find repeated bugs, integration gaps, feature requests, and migration pain.

Support teams

Monitor public issues mentioning your product, API, SDK, or competitor.

Founders and growth teams

Discover high-intent leads from public problems and requests.

AI and data teams

Collect structured issue and pull request records for RAG, classification, or trend analysis.

Why use this actor?

GitHub search is powerful, but manual monitoring is slow.

This actor saves repeatable searches as exportable data.

You can schedule it, connect it to integrations, or call it from code.

You get consistent fields instead of copying records by hand.

Ready-to-run examples

• Track open Apify SDK JavaScript issues
• Find Kubernetes good first issues
• Monitor open React bug reports
• Review closed VS Code bugs
• Research API rate-limit issues
• Track dependency-update pull requests

View all 50 ready-to-run examples

What data can you extract?

Field	Description
repository	Repository that owns the issue or pull request
type	issue or pull_request
number	GitHub issue or pull request number
title	Record title
bodyExcerpt	Short plain-text body excerpt
state	Open or closed state
url	Public GitHub URL
apiUrl	GitHub API URL
authorLogin	Author username
authorProfileUrl	Author profile URL
labels	Label names
assignees	Assigned usernames
milestone	Milestone title
commentsCount	Number of comments
reactions	Reaction counts
createdAt	Creation timestamp
updatedAt	Last update timestamp
closedAt	Close timestamp
mergedAt	Merge timestamp when available
sourceQuery	Query used to find the record
sourceRepository	Repository input that generated the query
scrapedAt	Actor scrape timestamp

Pricing

This Actor uses Apify pay-per-event pricing. The prices below come from the current Actor pricing configuration. Apify public plans map to Store discount tiers, so the table shows both the user-facing plan context and the pricing tier name. The final price shown in Apify depends on the user account plan and any custom agreement.

Event	What is charged	Price
start	One-time fee per run	$0.005

Event	What is charged	Free / no discount	Starter / Bronze	Scale / Silver	Business / Gold	Custom / Platinum	Custom / Diamond
item	One public GitHub issue or pull request saved to the default dataset.	$0.02905 / 1,000	$0.02526 / 1,000	$0.01971 / 1,000	$0.01516 / 1,000	$0.01011 / 1,000	$0.00707 / 1,000

Apify may also charge platform usage for compute, storage, proxies, or data transfer outside this Actor pricing. Check the Actor run and the Apify Pricing tab for the exact cost shown to your account.

Input overview

You can use repositories, search queries, or both. If both lists are omitted, the Actor runs the documented microsoft/vscode example with the normal maxItems limit.

If you provide both, the actor processes all sources in one run until maxItems is reached.

Repositories

Use repository names in owner/name format.

Examples:

• microsoft/vscode
• nodejs/node
• facebook/react

Repository URLs are also accepted.

Search queries

Use GitHub issue search syntax.

Examples:

• repo:microsoft/vscode is:issue label:bug
• kubernetes error "good first issue"
• org:vercel is:pr state:open

Example input

{
  "repositories": ["microsoft/vscode"],
  "searchQueries": [],
  "state": "open",
  "includeIssues": true,
  "includePullRequests": true,
  "labels": [],
  "maxItems": 25
}

Input settings

Setting	JSON key	Type / default	Description
Repositories	repositories	array, up to 100	Public GitHub repositories as owner/name or repository URLs. If both source lists are omitted, microsoft/vscode is used.
Search queries	searchQueries	array, up to 100	Raw GitHub issue-search queries. Repository and query inputs can be combined.
State	state	open by default	Include open, closed, or all records. A conflicting state already written in a raw query fails clearly.
Include issues	includeIssues	boolean, default true	Include issue records.
Include pull requests	includePullRequests	boolean, default true	Include pull request records.
Labels	labels	array, up to 50	Label filters appended to generated searches.
Updated since	since	date string	Include records updated on or after this date.
Maximum records	maxItems	integer, default 20	Total saved-row limit across all sources; each GitHub search query is capped at 1,000 matches.
GitHub token	githubToken	secret string	Optional token for higher GitHub API rate limits on public-data runs.

Search filters

State

Choose open, closed, or all.

Include issues

Enable this to save GitHub issues.

Include pull requests

Enable this to save GitHub pull requests.

Labels

Add label names such as bug, documentation, or good first issue.

The actor appends these filters to generated repository searches.

Updated since

Use since to collect records updated after a date.

This is useful for scheduled monitoring.

Output example

{
  "repository": "microsoft/vscode",
  "type": "issue",
  "number": 123,
  "title": "Example issue title",
  "state": "open",
  "url": "https://github.com/microsoft/vscode/issues/123",
  "authorLogin": "octocat",
  "labels": ["bug"],
  "commentsCount": 4,
  "createdAt": "2026-01-01T00:00:00Z",
  "updatedAt": "2026-01-02T00:00:00Z"
}

How to scrape a repository

1. Open the actor input form.
2. Add one or more repositories.
3. Choose state and issue / pull request options.
4. Set maxItems.
5. Run the actor.
6. Export the dataset as JSON, CSV, Excel, or via API.

How to monitor a keyword

1. Add a GitHub search query.
2. Include words, labels, organizations, or repositories.
3. Set since for incremental monitoring.
4. Schedule the actor daily or weekly.
5. Send new rows to your destination system.

Tips for better results

💡 Use repository-specific queries for precise output.

💡 Use labels to focus on bugs, feature requests, or onboarding tasks.

💡 Use since for recurring jobs so each run stays small.

💡 Start with maxItems: 25 before scaling up.

💡 Add a GitHub token only when you need higher rate limits.

Rate limits

Public GitHub data can be fetched without a token for small runs.

GitHub applies rate limits to unauthenticated API usage.

For larger monitoring workflows, provide a GitHub token in the optional secret input.

The token is used only to request public API data with a higher rate limit.

GitHub search returns at most 1,000 matches for each query. maxItems remains a total cap across all sources, so larger values are useful only when you provide multiple repositories or search queries. Each run stores a RUN_SUMMARY key-value record that identifies incomplete search results and sources that may have reached the per-query cap.

Invalid GitHub search queries and conflicting state filters fail clearly instead of returning a successful empty dataset. If GitHub rate limits a request, the actor retries a bounded number of times and then fails with guidance to wait or provide githubToken.

GitHub search returns at most 1,000 matches for each query. maxItems remains a total cap across all sources, so larger values are useful only when you provide multiple repositories or search queries. Each run stores a RUN_SUMMARY key-value record that identifies incomplete search results and sources that may have reached the per-query cap.

Invalid GitHub search queries and conflicting state filters fail clearly instead of returning a successful empty dataset. If GitHub rate limits a request, the actor retries a bounded number of times and then fails with guidance to wait or provide githubToken.

Integrations

Send dataset rows to Google Sheets for manual review.

Send issue records to Slack or Discord for support triage.

Load pull request data into a warehouse for engineering analytics.

Use webhooks to trigger workflows when new matching issues appear.

Feed output into LLM classification or RAG pipelines.

API usage

Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('fetch_cat/github-issues-pull-requests-scraper').call({
  repositories: ['microsoft/vscode'],
  state: 'open',
  maxItems: 25
});
console.log(run.defaultDatasetId);

Python

from apify_client import ApifyClient
import os

client = ApifyClient(os.environ['APIFY_TOKEN'])
run = client.actor('fetch_cat/github-issues-pull-requests-scraper').call(run_input={
    'repositories': ['microsoft/vscode'],
    'state': 'open',
    'maxItems': 25,
})
print(run['defaultDatasetId'])

cURL

curl -X POST "https://api.apify.com/v2/acts/fetch_cat~github-issues-pull-requests-scraper/runs?token=$APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  -d '{"repositories":["microsoft/vscode"],"state":"open","maxItems":25}'

MCP usage

You can run this actor from Apify MCP tools.

Use this tool URL pattern:

https://mcp.apify.com/?tools=fetch_cat/github-issues-pull-requests-scraper

Add it to Claude Code with:

$claude mcp add apify-github-issues "https://mcp.apify.com/?tools=fetch_cat/github-issues-pull-requests-scraper"

For a JSON MCP configuration, use:

{
  "mcpServers": {
    "apify-github-issues": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://mcp.apify.com/?tools=fetch_cat/github-issues-pull-requests-scraper"
      ]
    }
  }
}

Example MCP prompts

Use these prompts after connecting the actor through MCP.

Example prompt ideas:

Claude Code prompt

"Use the Apify GitHub Issues & Pull Requests Scraper tool for microsoft/vscode and summarize the top open bugs."

Claude Desktop prompt

"Use the MCP GitHub issues scraper to find recent open pull requests in nodejs/node and group them by likely topic."

Agent workflow prompt

"With the Apify MCP tool, monitor GitHub issues mentioning our SDK every day and send high-priority records to Slack."

Common workflows

Competitive intelligence

Track public issues in competitor repositories.

Look for bug patterns, integration gaps, and roadmap signals.

DevRel monitoring

Find developers asking for help with your ecosystem.

Prioritize responses by comments, labels, or recency.

Product discovery

Collect feature requests and pain points from open-source communities.

Classify titles and body excerpts with your preferred AI model.

Engineering analytics

Export pull request metadata for reporting on activity and review flow.

Combine with other GitHub or CI data sources.

Troubleshooting

The run says GitHub rate limit reached

Use a smaller maxItems, wait for the reset window, or provide a GitHub token.

My query returns no data

Check the query in GitHub search first, then copy the same syntax into searchQueries.

I only want issues, not pull requests

Set includeIssues to true and includePullRequests to false.

I only want pull requests

Set includeIssues to false and includePullRequests to true.

Data quality notes

The actor returns public GitHub metadata available through GitHub search.

Some fields may be empty when GitHub does not provide that value.

Body text is saved as an excerpt to keep rows compact.

Use url to open the full public issue or pull request.

Legality and responsible use

This actor extracts public GitHub issue and pull request data.

You are responsible for using the data in accordance with GitHub's terms, privacy expectations, and applicable laws.

Do not use the output for spam, harassment, or abusive automation.

Respect users and project maintainers.

Related actors

Explore related Apify actors from fetch_cat for developer intelligence and open-source research.

• GitHub Profile Scraper
• GitHub Repository Search Scraper
• GitHub Trending Scraper
• GitHub Contributor Email Scraper

Use GitHub issue data together with other public web datasets to enrich product and market research.

FAQ

Does this require a GitHub account?

No for small public-data runs.

A token is optional for higher rate limits.

Can it access private repositories?

No. This actor is designed for public GitHub issues and pull requests.

Can I schedule it?

Yes. Use Apify schedules and the since field for recurring monitoring.

Can I export to CSV?

Yes. Apify datasets can be exported as CSV, JSON, Excel, XML, RSS, or through the API.

Can I scrape comments?

The first version saves comment counts, not full comment threads.

Can I combine multiple repositories?

Yes. Add multiple repository names to repositories.

Can I combine repositories and search queries?

Yes. The actor processes both input sources in one run.

Support

Report a bug from the Actor page when a run fails or the output looks wrong. Include:

• Run ID or run URL
• Input JSON
• Expected output
• Actual output
• One reproducible public URL or repository/search query

Privacy and data handling

This Actor only requests the permissions needed to run the input you provide. It uses your input (such as URLs, search terms, identifiers, filters, and limits) only to fetch the requested public data from the relevant source site or API for this Actor, then writes results to your Apify dataset/key-value store.

Data may pass through Apify platform services and Apify Proxy during the run, and requests are sent only to the target site or public data provider required for this Actor's results. FetchCat does not send your inputs or outputs to advertising networks, data brokers, or model-training services, and does not retain run data outside Apify storage after the run except when you explicitly share run details for transient support debugging.

You are responsible for using this Actor lawfully, respecting the target site's terms, and avoiding unnecessary personal or sensitive data in inputs. Review the output before storing, sharing, or combining it with other data.