Github Ranking AI
Github Ranking AI ranks GitHub's top AI repositories daily by stars and forks across 14 AI subfields.
If you want one bookmark that answers "what's actually big in open-source AI right now," this is it — free, daily, and split into categories GitHub Trending doesn't offer. The trade-off is real: what you see is a snapshot of cumulative stars and forks, not growth velocity, history, or exportable data. Reach for it to orient quickly, then go to the repos themselves for anything deeper.
Verified 6d ago · liveness 65/100 · cite: rightaichoice.com/tools/github-ranking-ai
- Developers scanning which AI frameworks and tools lead each subfield today
- Researchers and analysts monitoring open-source AI momentum without an account
- Comparing a project's star and fork prominence against direct rivals
- Journalists or newsletter writers pulling current AI repo leaderboard numbers
- Anyone needing historical trend charts or star-growth velocity over time
- Teams that require an API or data export to feed dashboards and monitoring
- Users who want to filter by language, license, or recent activity
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
Skip Github Ranking AI if you need advanced search filters, historical trend analytics, or API access for programmatic use; it's a simple daily snapshot, not a deep analytics tool.
Github Ranking AI is free with no hidden costs. It's ideal for individuals and small teams who want a quick pulse on AI open-source trends without paying for premium analytics platforms. It complements free tools like GitHub Trending but adds AI-specific categorization.
In short
Github Ranking AI — Github Ranking AI ranks GitHub's top AI repositories daily by stars and forks across 14 AI subfields. Best for Developers scanning which AI frameworks and tools lead each subfield today, Researchers and analysts monitoring open-source AI momentum without an account, Comparing a project's star and fork prominence against direct rivals. Free to use.
What people actually say about Github Ranking AI — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
56 mentions across 4 sources (YouTube, App Store, GitHub, Lemmy) · researched Jul 6, 2026.
Average across the 4 sources that answered — each source counts once, not each post.
- +Completely free with no registration or API key required.
- +Daily updated rankings ensure freshness of data.
- +Covers diverse AI subfields like LLM, RAG, and Agents.
- +Open source on GitHub, promoting transparency and trust.
- +Simple, intuitive interface with clear top-10 previews.
- −No API available for automated data fetching.
- −Lacks advanced filters like by language or recent commits.
- −User feedback across major platforms is very sparse.
- −Only top 100 per category – may miss niche or new repos.
- −No personalized or customizable ranking views.
- • None – the tool is entirely free with no hidden charges.
Viability Score
How well maintained and how widely used is Github Ranking AI? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Daily automatic ranking of GitHub AI repositories by star count
- Separate rankings for 14 AI subfields including LLM, ChatGPT and AI Agents
- Top-10 preview table per category with a link to the top-100 list
- Repo details shown: stars, forks, language, open issues and description
- Last commit date displayed for every ranked repository
- Last Automatic Update Time stamp shown at the top of the page
- Bilingual interface in English and Chinese
- Table of contents navigation across all AI categories
- Fork-count ranking alongside star-count ranking
- No registration, login or API key needed
- Open source project hosted on GitHub
- Static, fast-loading page readable without JavaScript-heavy UI
About Github Ranking AI
Github Ranking AI is a free, auto-updating leaderboard of the most-starred AI repositories on GitHub. Instead of one generic trend feed, it splits the ecosystem into 14 categories — LLM, ChatGPT, OpenAI, DeepSeek, LLaMA, Chatbot, AI Agents, Claude, RAG, Mistral, Transformer, MoE, AGI and Generative_AI — so you can see who leads each niche rather than who wins the whole site. It's aimed at developers, researchers, and analysts who want a fast read on open-source AI momentum without opening a dozen repo pages. Every category opens with a top-10 preview table showing project name, star count, fork count, language, open issues, description, and last commit date, with a link through to the full top-100 list. A prominent "Last Automatic Update Time" stamp (2026-09-22 in the current scrape) tells you exactly how fresh the numbers are, so you never misread a stale leaderboard as today's. The interface is bilingual — English and Chinese — and needs no registration or API key. The whole project is itself open source on GitHub, so the ranking logic is inspectable rather than a black box, and the site is basically a static page, which keeps it fast to load. Against GitHub's own Trending page, the pitch is narrower and deeper: AI-only, category-split, and star/fork-ranked rather than a rolling velocity feed. Against commercial AI directories, it stays free and neutral, but deliberately shallow — no historical trend charts, no saved watchlists, no API. Treat it as a morning pulse check, not an analytics platform.
Behind the Verdict
Most AI discovery tools try to sell you something. This one is a static, open-source page that just tells you which AI repos have the most stars and forks today, and there's a certain honesty in that. The category split is the part I'd actually miss if it disappeared: seeing ollama top the LLaMA list while hermes-agent leads ChatGPT and deepseek-harness leads DeepSeek tells you more about each niche's gravity than one merged ranking ever could. Where it earns a permanent tab is the freshness stamp. Rankings that quietly age are worse than no rankings, and this site prints its last automatic update time right at the top, so you can decide in one glance whether the data is worth your time. Pick this when you're doing a quick scan — evaluating a new framework, checking whether a project you're considering is actually adopted, or prepping a talk or article about open-source AI. It's also handy for spotting repos you've never heard of sitting next to the names everyone already knows. The bilingual build helps if you or your team reads Chinese. Pass on it if you need to chart a repo's growth over time, export a dataset, or filter by language, license, or activity. Cumulative stars favor old projects, so a repo with fewer stars but steep recent momentum can hide below the top 10 — the last-commit column is your only hint of life. There's also no API, which rules it out for automated monitoring or feeding a dashboard. The closest alternative is GitHub's own Trending page. Trending is broader and reflects recent activity; Github Ranking AI is AI-only and cumulative, which makes it better for benchmarking prominence within a subfield and worse for catching something the day it starts climbing. If you want velocity plus history, you'll need a different tool — or scrape the
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Real-world workflow fit
Concrete scenarios for the personas Github Ranking AI actually fits — and what changes day-one when you adopt it.
You're building a RAG pipeline and want to see which libraries are gaining traction. Open Github Ranking AI, go to the RAG category, and scan the top-10 repos for star counts and last commit dates to gauge community activity.
Outcome: You quickly identify the most popular RAG frameworks like LangChain and Dify, and can click through to their GitHub pages to evaluate further.
You need to cite the most influential AI projects in a paper. Check the LLM and AGI categories for the top-starred repos such as AutoGPT and hermes-agent, and note their star counts as evidence of impact.
Outcome: You gather up-to-date popularity data without manual GitHub browsing, saving hours of research time.
You want to contribute to a high-visibility AI project. Scan the AI Agents category for repos like OpenHands and LobeHub, check their open issues count to find easy entry points, then click through to the GitHub repo.
Outcome: You find a promising project with manageable issues to tackle, and the popularity metrics indicate your contribution will have wide visibility.
Use Cases
- Discover trending AI repositories for inspiration or adoption
- Benchmark personal projects against popular GitHub repos
- Stay updated on the hottest AI tools and frameworks
- Find open-source alternatives to proprietary AI solutions
- Identify potential repositories for contribution or forking
Limitations
- Github Ranking AI is a daily-updated static ranking page of GitHub AI repositories by stars and forks across 14 AI subfields; each category previews the top 10 with a link to the top-100 list.
- It is a reference/ranking directory rather than an interactive AI tool: no model of its own is named on the page, and it displays repository metadata (stars, forks, language, open issues, description, last commit) only.
- Data is refreshed once per day, as shown by the Last Automatic Update Time stamp.
as of 2026-08-31
Verification history
We have re-verified Github Ranking AI 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Github Ranking AI tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Any individual or team that wants a free, daily-updated overview of popular AI GitHub repositories without registration or cost.
What this tier adds
Starting tier and only tier: provides access to all rankings, top-100 lists, and bilingual interface at no cost.
Where the pricing makes sense
The company stage and team size where Github Ranking AI's pricing actually pencils out — and where peers do it cheaper.
Github Ranking AI is free with no hidden costs. It's ideal for individuals and small teams who want a quick pulse on AI open-source trends without paying for premium analytics platforms. It complements free tools like GitHub Trending but adds AI-specific categorization.
Setup time & first value
How long it actually takes to get something useful out of Github Ranking AI — broken out by persona, not the marketing-page minute.
No setup required. You can access the tool immediately at the website. For developers who want to run it locally, the open-source code on GitHub can be cloned and run in minutes.
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Github Ranking AI”, and we withheld 6: 6 did not mention Github Ranking AI. We are showing none, because we could not prove any of them are about Github Ranking AI.
Official links
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Featured Head-to-Head Comparisons
Github Ranking Ai vs Surge Ai
Choose Github Ranking AI if you want a free, quick pulse on trending AI open-source projects. Choose Surge AI if you are building frontier models and need expert human evaluations for RLHF, red teaming, or complex benchmarks—backed by recent work with Microsoft and novel benchmarks like Antidote. They serve entirely different needs: discovery vs. deep alignment.
Github Ranking Ai vs Praktika
Choose Github Ranking AI if you're a developer hunting for trending open-source AI projects—it's free and laser-focused on GitHub repo rankings. Choose Praktika if you're an intermediate language learner who wants immersive speaking practice with instant AI feedback, though note its premium pricing and mobile-only limitation.
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Frequently Asked Questions
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