GitHub Trending Scraper & Repo Trend Tracker

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GitHub Trending scraper and repository trend tracker for monitoring trending repositories, trending developers, star velocity, rank momentum, language shifts, and weekly breakout digests. Built for developers, venture capitalists, tech scouts, recruiters, and open-source advocates who need structured data on the latest developer trends.

Best first run

{
"language":"any",
"timeRange":"daily",
"maxRepos":25,
"enableVelocityTracking":true,
"enableAiAnalysis":false
}

Start with daily trending repositories across all languages. Schedule the same input daily to build star-velocity history, then add generateWeeklyDigest: true once you want a weekly open-source trend report.

GitHub Repository Trend Tracker

Use this GitHub repository trend tracker to identify open-source projects gaining developer attention before they show up in vendor reports, newsletters, or annual ecosystem retrospectives. Each run returns repository names, owners, URLs, descriptions, languages, star counts, stars gained, forks, contributors, developer profiles, and momentum fields in structured JSON.

This is useful when you need more than a one-time GitHub Trending export. Scheduled runs create a repeatable open-source intelligence feed for tech radar, VC deal flow, developer-relations sourcing, recruiting, and competitive research.

What it does

This actor scrapes GitHub Trending repositories and Trending Developers pages and transforms leaderboard snapshots into a continuous momentum feed. Each run extracts repo names, descriptions, languages, total stars, stars gained in the period, fork counts, and contributor lists - structured and clean, ready for dashboards, databases, or downstream pipelines.

The standout capability is cross-run velocity tracking. Scheduled runs annotate each repo with velocity_score, stars_per_hour, and a momentum label - accelerating, steady, or fading - turning a point-in-time snapshot into an ongoing signal. The same style of developer-trend intelligence that commercial platforms sell in expensive subscriptions is available here at $0.003 per repo.

Schedule it daily and enable Weekly Breakout Digest to get an aggregated narrative across the last N days of snapshots: sustained risers (genuinely trending vs. one-day blips), one-hit wonders, dying stars, and emerging-language share shifts. When AI analysis is on, that digest becomes a full VC-analyst narrative - headline, executive summary, breakout-winner rationales, language-shift signals, and a 3-5 bullet action-item list for tech scouts.

Filter by 16 programming languages and daily/weekly/monthly time ranges. Flip scrapeDevelopers to also pull the Trending Developers leaderboard for recruiter sourcing and developer-relations outreach.

Replaces: daily manual GitHub checks, expensive developer-trend intelligence subscriptions, and ad-hoc monitoring spreadsheets. Goes beyond what free tools like GitHub Stars, Star History, and the annual Octoverse report offer - those give you point-in-time charts or yearly retrospectives, while this actor delivers scheduled cross-run velocity, accelerating/fading flags, and breakout digests on the cadence your workflow actually needs.

Features

Complete Repository Data Extraction - For each trending repository, the actor extracts the full name (owner/repo), repo owner, description, programming language, total star count, stars gained in the selected period, total forks, and a list of contributors who built the project. Every data point is cleaned and normalized for immediate use in spreadsheets, dashboards, and databases.

Trending Developers Scraping - Flip scrapeDevelopers on to also extract the GitHub Trending Developers leaderboard for the same language and time range. Each developer item includes username, display name, profile URL, avatar, popular repo name, popular repo URL, and popular repo description - perfect for developer-relations outreach, recruiter sourcing, and open-source sponsorship research. Repository and developer items are emitted to the same dataset with distinct type fields ("repository" vs "developer") so you can filter them downstream.

Cross-Run Velocity & Momentum Tracking - On by default. Scheduled runs compare the current star count with the previous run for the same language and time range, then annotate every repo with:

• velocity_score - stars gained since the last run
• stars_per_hour - velocity normalized by the time elapsed between snapshots
• momentum - one of "new" (first appearance), "accelerating" (gaining stars >=25% faster than the previous period), "steady", or "fading" (<=60% of the prior period)
• previous_stars_total, previous_scraped_at, hours_since_last_snapshot - provenance fields for auditing

Starting with the second run on the same filter combination, the actor also pushes a single breakout_summary dataset item listing the top-5 rising repos by stars_per_hour (excluding fading entries). This converts the actor from a point-in-time snapshot into an ongoing momentum detector. Velocity tracking has no extra charge and is free to disable via enableVelocityTracking: false if you only want one-shot snapshots.

Weekly Breakout Digest - Flip generateWeeklyDigest on to emit a single weekly_breakout_digest dataset item per run that aggregates the last N days of snapshot history (default 7 days, configurable up to 60) into a breakout report. Every digest includes:

• sustained_risers - top-10 repos present across many snapshots that kept gaining stars across every appearance (what's really trending vs. what's a one-day blip)
• one_hit_wonders - repos that appeared in exactly one snapshot with a meaningful gain, then vanished (viral moments, launch-day bumps)
• dying_stars - repos whose per-snapshot gains are monotonically decreasing (projects losing developer interest, shipped a bad release, or got outcompeted)
• emerging_languages - languages whose share of trending repos grew between the oldest and newest snapshot in the window
• breakout_patterns - raw (snapshot_at, stars_total, stars_gained) series for the top-10 risers, suitable for sparkline or chart rendering downstream

When enableAiAnalysis is also on, the digest includes a VC-analyst narrative generated from the aggregates - headline, executive summary, sustained-risers analysis, breakout winners list with "why it matters" rationales, one-hit-wonder read, fading-stars analysis, language-shift signal, investment thesis, and a 3-5 bullet action-item list for tech scouts. Charged as weekly-digest-generated at $0.10 per digest. Requires at least 2 prior snapshots on the same (language, timeRange) filter within the window - schedule daily runs for a week before enabling.

Flexible Language and Time Range Filters - Filter trending repositories by 16 popular programming languages including Python, JavaScript, TypeScript, Rust, Go, Java, C++, C#, Ruby, Swift, Kotlin, PHP, R, Scala, and Shell. Choose between daily, weekly, or monthly trending periods to match your analysis needs.

Spoken Language Support - Optionally filter by the spoken language of the repository README. Use standard language codes like "en" for English or "zh" for Chinese to find trending projects accessible in your preferred language.

AI-Powered Trend Analysis - Enable the built-in AI analysis to generate a comprehensive report that identifies trending categories, emerging technologies, investment-worthy projects, developer recommendations, and market themes. The AI examines all scraped repositories together to find patterns that are impossible to spot manually.

Multiple AI Providers - Choose OpenRouter, Anthropic Claude, Google AI Gemini, OpenAI GPT, or self-hosted Ollama for AI analysis. Specify a model on your chosen platform or use the defaults.

Pay-Per-Event Pricing - Only pay for what you use. Each scraped repository costs $0.003, each developer $0.003, each AI analysis report $0.05, and each weekly digest $0.10. No monthly fees, no minimum commitments.

Fast public-page extraction - GitHub Trending is lightweight and public, so runs are fast, low-cost, and reliable for scheduled monitoring.

Proxy Support - Use Apify's built-in proxy infrastructure or configure your own proxies for reliable access to GitHub trending pages from any location.

Use Cases

Developers and Engineering Teams - Stay on top of the tools and libraries gaining traction in your language ecosystem. Discover new frameworks before they go mainstream. Use weekly runs to keep a pulse on what the open-source community is building and adopting.

Venture Capitalists and Investors - Identify projects with explosive star velocity that signal strong developer interest and potential commercial opportunity. The AI analysis highlights investment-worthy projects and market themes to inform your deal flow. The weekly digest VC-analyst narrative surfaces investment theses you can act on immediately.

Tech Scouts and Analysts - Generate structured trend reports for technology landscape analysis. Track how programming languages, frameworks, and tools rise and fall in popularity over time by running the actor on a schedule across different filters.

Recruiters and Talent Acquisition - Find the developers behind trending projects. The built-by data includes contributor usernames and profile links, helping you identify top talent actively building popular open-source software. Enable scrapeDevelopers to get the full GitHub Trending Developers leaderboard.

CTOs and Technical Decision Makers - Evaluate which technologies are gaining community momentum before committing your team to a technology stack. Use the AI analysis to understand broader ecosystem dynamics and make informed build-vs-buy decisions.

Open-Source Advocates and Community Managers - Monitor trending projects in your domain to identify collaboration opportunities, competing projects, and emerging community needs. Use scheduled runs to build a historical record of open-source trends.

News and Research Publishers - Generate weekly "State of Open Source" digests automatically. The VC-analyst narrative output is ready to paste into newsletters, analyst briefings, or internal knowledge bases with minimal editing.

Input

CLI aliases: programmingLanguage / lang for language; since for timeRange; maxItems for maxRepos; spokenLanguageCode for spokenLanguage; includeDevelopers for scrapeDevelopers.

Pricing:

Example cost calculations:

• 25 repos (daily trending, default): 25 x $0.003 = $0.075
• 25 repos + AI analysis: $0.075 + $0.05 = $0.125
• 25 repos + 25 developers + AI analysis: $0.15 + $0.05 = $0.20
• 100 repos + AI analysis: 100 x $0.003 + $0.05 = $0.35
• Daily scheduled run (25 repos + AI, 30 days): 30 x $0.125 = $3.75/month
• Weekly digest (25 repos + AI + digest, weekly): $0.125 + $0.10 = $0.225/week ~ $0.98/month

Output

Each trending repository produces a dataset item with this structure:

{
"type":"repository",
"rank":1,
"repo_name":"exampleuser/awesome-project",
"owner":"exampleuser",
"author":"exampleuser",
"repo_url":"https://github.com/exampleuser/awesome-project",
"url":"https://github.com/exampleuser/awesome-project",
"description":"A blazing fast tool for building modern web applications",
"language":"TypeScript",
"programming_language":"TypeScript",
"stars_total":15234,
"stars_period_count":342,
"stars_gained":342,
"stars_period_label":"342 stars today",
"forks_total":1205,
"built_by":[
{
"username":"contributor1",
"avatar_url":"https://avatars.githubusercontent.com/u/12345?s=40&v=4",
"profile_url":"https://github.com/contributor1"
}
],
"velocity_score":180,
"stars_per_hour":7.5,
"momentum":"accelerating",
"previous_stars_total":15054,
"previous_scraped_at":"2026-04-10T12:00:00Z",
"hours_since_last_snapshot":24.0,
"scraped_at":"2026-04-11T12:00:00Z"
}

On the first run for a given (language, timeRange) combination the velocity fields are null / momentum="new" - momentum data fills in from the second run onward. When the previous snapshot is available, a single breakout_summary item is also emitted per run:

{
"type":"breakout_summary",
"language_filter":"python",
"time_range":"daily",
"snapshot_at":"2026-04-11T12:00:00Z",
"previous_snapshot_at":"2026-04-10T12:00:00Z",
"breakout_count":5,
"top_breakouts":[
{
"repo_name":"exampleuser/awesome-project",
"url":"https://github.com/exampleuser/awesome-project",
"stars_total":15234,
"stars_gained":342,
"velocity_score":180,
"stars_per_hour":7.5,
"momentum":"accelerating",
"language":"TypeScript",
"description":"A blazing fast tool for building modern web applications"
}
]
}

When scrapeDevelopers=true, developer items are also emitted:

{
"type":"developer",
"rank":1,
"username":"octocat",
"name":"The Octocat",
"profile_url":"https://github.com/octocat",
"url":"https://github.com/octocat",
"avatar_url":"https://avatars.githubusercontent.com/u/583231?s=96&v=4",
"popular_repo":"octocat/Hello-World",
"popular_repo_url":"https://github.com/octocat/Hello-World",
"popular_repo_description":"My first repository on GitHub!",
"time_range":"daily",
"period_label":"today",
"scraped_at":"2026-04-22T12:00:00Z"
}

When generateWeeklyDigest=true and the rolling history has at least 2 snapshots in the window, a weekly_breakout_digest item is emitted (narrative is populated only when enableAiAnalysis is also on):

{
"type":"weekly_breakout_digest",
"language_filter":"python",
"time_range":"daily",
"window_days":7,
"snapshots_in_window":7,
"oldest_snapshot_at":"2026-04-15T12:00:00+00:00",
"newest_snapshot_at":"2026-04-22T12:00:00+00:00",
"sustained_risers":[
{
"repo_name":"exampleuser/awesome-project",
"appearances":7,
"total_stars_gained":2100,
"first_stars_total":15054,
"last_stars_total":17154,
"stars_delta":2100,
"first_seen_at":"2026-04-15T12:00:00+00:00",
"last_seen_at":"2026-04-22T12:00:00+00:00"
}
],
"one_hit_wonders":[{"repo_name":"flash/pan","seen_at":"2026-04-17T12:00:00+00:00","stars_total":842,"stars_gained":420}],
"dying_stars":[{"repo_name":"old/project","appearances":4,"peak_stars_gained":210,"final_stars_gained":15,"drop_pct":92.9}],
"emerging_languages":[{"language":"Rust","oldest_share_pct":16.0,"newest_share_pct":32.0,"share_delta_pct":16.0}],
"breakout_patterns":[{"repo_name":"exampleuser/awesome-project","total_stars_gained":2100,"appearances":7,"series":[{"snapshot_at":"2026-04-15T12:00:00+00:00","stars_total":15054,"stars_gained":280}]}],
"narrative":{
"headline":"Rust's share of trending doubled this week, led by infra-layer tooling",
"executive_summary":"...",
"sustained_risers_narrative":"...",
"breakout_winners":["..."],
"investment_thesis":"...",
"action_items":["..."]
},
"generated_at":"2026-04-22T12:01:00+00:00"
}

When AI analysis is enabled, an additional dataset item is produced:

{
"type":"trend_analysis",
"repos_analyzed":25,
"language_filter":"any",
"time_range":"daily",
"trending_categories":["AI/ML Tools","Developer Productivity","Web Frameworks"],
"emerging_technologies":["..."],
"most_active_languages":["Python","TypeScript","Rust"],
"investment_worthy_projects":["..."],
"ecosystem_insights":"...",
"developer_recommendations":["..."],
"market_themes":["..."],
"technology_patterns":["..."],
"opportunity_signals":["..."],
"generated_at":"2026-04-11T12:01:00Z"
}

Performance: GitHub Trending is lightweight, so the actor usually completes in under 10 seconds for scraping alone. AI analysis adds 10-30 seconds depending on the provider and model chosen. Total execution time is typically under 40 seconds, with minimal Apify compute cost.

Note: The GitHub trending page typically shows a maximum of 25 repositories per language/time-range combination. Setting maxRepos above 25 will only return more results if GitHub displays more than 25 on the page. Trending data is a snapshot in time - schedule regular runs to build your own historical dataset.

Quick Start

The simplest way to run this actor with default settings (daily trending, any language, 25 repos):

{
"language":"any",
"timeRange":"daily",
"maxRepos":25,
"enableAiAnalysis":false
}

To scrape Python trending repos this week with AI analysis via OpenRouter:

{
"language":"python",
"timeRange":"weekly",
"maxRepos":50,
"enableAiAnalysis":true,
"llmProvider":"openrouter",
"openrouterApiKey":"YOUR_OPENROUTER_API_KEY"
}

For the full weekly-digest setup (scheduled daily, emits a VC-analyst weekly digest every run once a week of history has accumulated):

{
"language":"python",
"timeRange":"daily",
"maxRepos":25,
"enableVelocityTracking":true,
"generateWeeklyDigest":true,
"digestWindowDays":7,
"enableAiAnalysis":true,
"llmProvider":"openrouter",
"openrouterApiKey":"YOUR_OPENROUTER_API_KEY"
}

Scheduling tip: Use Apify's built-in scheduler to run this actor daily at the same UTC hour. Keep the language and timeRange parameters identical across all scheduled runs - this is what allows the velocity tracker and weekly digest to accumulate meaningful history. Each unique (language, timeRange) combination is tracked in its own independent snapshot slot.

Getting started with AI analysis: OpenRouter is the cheapest option (free tier available). Create an account at openrouter.ai, copy your API key, and paste it into the openrouterApiKey field. The default model (google/gemini-2.0-flash-001) produces high-quality analysis at minimal cost.

Getting started with the weekly digest: Enable generateWeeklyDigest: true and schedule daily runs. The digest will not appear on the first run (no history yet). After the second daily run you will see a weekly_breakout_digest item in the dataset. By the seventh daily run you have a full "this week" view including all five aggregated signals (sustained risers, one-hit wonders, dying stars, emerging languages, and breakout pattern series).

Use in automated open-source intelligence workflows

GitHub Trending Scraper returns structured JSON that fits tech-radar, VC deal-flow, developer-relations, recruiting, and newsletter workflows. Common patterns include:

• Send breakout_summary or weekly_breakout_digest records to Slack, Notion, Airtable, n8n, Make, or a data warehouse.
• Select outputConnectors to send the same digest directly through a user-authorized Slack, Notion, GitHub, Sheets, or CRM connector without managing third-party credentials in this actor.
• Track Python, TypeScript, Rust, Go, or AI repositories daily and alert when star velocity accelerates.
• Pair trending repos with News Monitor to see whether developer momentum is turning into press coverage.
• Pair maintainer and project URLs with Contact Extractor for developer-relations or partnership research.

Example workflow prompt for an internal automation:

Track GitHub Trending for Python and Rust every morning. Alert the tech-radar channel when a repository is accelerating, include the maintainer profiles, and save the weekly breakout digest for our open-source strategy notes.

Related actors for cross-referencing:

• News Monitor - track which GitHub trending repos are also generating media coverage
• Reddit Scraper - cross-reference with developer community sentiment on programming subreddits
• Product Hunt Scraper - identify repos that also launched on Product Hunt
• Contact Extractor - extract contact details from trending repo maintainers for dev-relations outreach

Troubleshooting

Actor returns 0 repos despite a valid language filter

GitHub Trending can return an empty page when GitHub is under maintenance or temporarily rate-limiting anonymous requests. Wait 15 minutes and retry. If the issue recurs across multiple runs, GitHub may have changed the page layout - check the actor changelog for an updated version. Using Apify proxy (proxyConfiguration.useApifyProxy: true) can reduce repeat rate limits on high-volume schedules.

Language filter returns results for the wrong language or no results

Language names are case-insensitive but must match GitHub's internal slug exactly. Common mistakes: "c++" works but "cpp" does not; "c#" works but "csharp" does not; "javascript" works but "js" does not. Check the parameter table in the Input section for the full accepted list. Unrecognized language strings cause GitHub to return the full all-language trending page rather than an error.

timeRange aliases produce unexpected windows

The actor normalizes common aliases such as "today", "day", "week", "this-week", "month", and "this-month" to GitHub's three supported values: "daily", "weekly", and "monthly". If a value is still unknown, the actor safely falls back to "daily" so the run remains functional.

Velocity and momentum fields are null on the second run

Cross-run velocity requires the exact same (language, timeRange) combination to be used in both runs. If you change language from "python" to "any" or timeRange from "daily" to "weekly" between runs, a new snapshot slot is created and no prior baseline exists. Keep these parameters identical across scheduled runs. The first run for any filter combination always emits momentum: "new" and null velocity fields - data appears from the second run onward.

Weekly digest item not appearing in the output dataset

The weekly_breakout_digest item requires three conditions: (1) generateWeeklyDigest: true, (2) at least 2 prior snapshots exist in the rolling window for the same (language, timeRange) filter, and (3) the snapshots fall within digestWindowDays of each other. On the first run there is no history - the digest only appears from the second run onward. Schedule the actor to run daily for at least two days before expecting a digest.

AI analysis fails or is skipped silently

If enableAiAnalysis: true but no analysis item appears in the dataset, the API key is missing or invalid. Provide it via the input field (e.g., openrouterApiKey) or the corresponding environment variable (OPENROUTER_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY, or OPENAI_API_KEY). The actor logs a warning and continues scraping repos without the AI step - it does not abort the run. Set enableAiAnalysis: false to suppress the warning.

How does the weekly breakout digest work?

When generateWeeklyDigest=true, the actor compares recent scheduled snapshots for the same language and time range, then emits a single weekly_breakout_digest item with sustained risers, one-hit wonders, dying stars, emerging languages, and breakout patterns. With enableAiAnalysis=true, the digest also includes a structured narrative for tech scouts. To get meaningful digests, schedule the actor to run daily on the same filter for at least a week - the digest needs at least 2 snapshots in the window and is strongest with 7 or more.

How often does GitHub update the trending page?

GitHub updates the trending page roughly every hour, though the exact frequency is not publicly documented. Running this actor more than once per hour for the same filter is unlikely to yield new data.

Can I scrape trending for languages not in the dropdown?

The preset list covers 16 popular languages. GitHub supports many more - you can enter any language name that GitHub recognizes in the language field, and the actor will attempt to use it in the URL.

Pricing

This actor uses pay-per-event pricing - you only pay for what you scrape.

vs. commercial alternatives: Eoris and Star-History.com offer chart views but no programmatic API or bulk export; Augur (Linux Foundation) and OSSF Scorecard focus on security, not trend velocity. This actor uses pay-per-event - $0.003/repository with no subscription, delivering structured JSON ready for dashboards, reports, and automation.

Scheduling and webhooks

Schedule daily GitHub Trending runs in Apify Console to track the developer ecosystem in real time. Use Apify's run-finished webhooks, or the Apify integrations in n8n and Make, to push today's trending repos into a Slack #dev-radar channel, Notion research database, or newsletter content queue the moment a run finishes. Typical pipeline: language=python daily trending -> Apify webhook -> n8n -> Slack digest of top 10 AI/ML repos every morning.

Legal and Compliance

GitHub Trending (https://github.com/trending) is publicly accessible. No authentication or login is required to view it. The data displayed - repository names, descriptions, star counts, and contributor information - is intentionally made public by the repository owners who chose to host their projects on GitHub as public repositories.

This actor accesses only publicly available GitHub Trending data. It does not access private repositories, user accounts, or authenticated endpoints.

Users of this actor should review GitHub's Terms of Service (https://docs.github.com/en/site-policy/github-terms/github-terms-of-service) and ensure their use case complies. Automated access to GitHub is subject to their acceptable use policies. Very high-frequency scraping may trigger GitHub's rate limiting - using Apify proxies is recommended for production use, and the actor enforces polite crawling with exponential backoff on rate-limit responses.

This actor is provided as-is for legitimate research, analysis, and monitoring purposes. Users are responsible for ensuring their use of the scraped data complies with all applicable laws and terms of service, including data protection regulations such as GDPR. While GitHub trending data consists primarily of public repository metadata rather than personal data, users should assess their own compliance obligations under applicable data protection laws. Developer username and avatar data (included in contributor lists and the trending developers feed) may constitute personal data under certain jurisdictions - users are responsible for handling this data appropriately.