Open Source AI Selection Methodology
Top Open Source AI is a selection guide for open source AI projects in real-world application scenarios — committed to making practical open source AI easier to discover and adopt. We first decide whether a project belongs in the catalog, then compare listed projects using objective signals, helping AI users, developers, and technical leaders decide with clarity and accountability.
Three principles
- • Gate first, rank second — projects that fail inclusion never enter the catalog
- • Scenario-based taxonomy — one primary category per project; browse all scenarios or filter by scenario
- • Public criteria, visible reasons — inclusion and ranking logic are explainable
① Inclusion — what enters the catalog
Inclusion focuses on relevance to real-world open source AI — not only on whether a project is trending right now. The minimum star threshold is primarily used to define the catalog comparison scope.
Must have
- ✓ Recognizable open source license (OSI-approved preferred)
- ✓ AI / ML related: models, inference, RAG, agents, multimodal, MLOps, etc.
- ✓ Open core you can self-host, integrate, or use as infrastructure / SDK / framework
- ✓ Public GitHub repository with accessible code and metadata
- ✓ Minimum star threshold (e.g. 5000+; adjusted as coverage expands)
Preferred (strengthens the case)
- + Meaningful activity in the last 12 months
- + Clear README, license, and basic usage documentation
- + Standalone tool, framework, or product
Usually excluded
- ✗ Archived repositories
- ✗ Link indexes / awesome-style lists
- ✗ Study notes, roadmaps, or lists without a runnable product
- ✗ Forks with no independent value
- ✗ Not relevant to real-world open source AI
- ✗ Long abandoned or unmaintained
- ✗ Closed-source or SaaS-only
Based on what the repository actually is — not its name alone.
② Taxonomy — application scenarios
Each project gets one primary category (e.g. LLM inference, RAG, agent frameworks, MCP tooling, AI education) — the capability you are selecting, not a dump of GitHub topics. AI assists; editors confirm. New categories can be proposed when none fit. Project pages show an inclusion reason explaining scenario fit.
③ Evaluation & editorial review
New candidates are assessed using repo descriptions, topics, and public context. The assessment outcome determines immediate publication, editorial review, or decline.
| Assessment | Action | Public visibility |
|---|---|---|
| Clear scenario fit | Added to catalog | Visible on the website |
| Edge case | Assessed; published after editorial review | Hidden from listings until approved |
| Does not meet inclusion criteria | Not listed | Not in catalog |
| New scenario proposed | Awaiting editor confirmation | Not public until approved |
AI assists screening; our editorial team makes the final call. Listings may be corrected or removed when information changes.
④ Application fit — what we look for
Being listed does not mean “works out of the box.” The following dimensions appear on project detail pages for your review:
- • License & compliance — can teams evaluate and deploy it?
- • Maintenance — update cadence and community responsiveness
- • Community adoption — stars, forks, contributors
- • Deployability — clear install, deployment, or integration guidance
- • Scenario fit — solves a real problem in this category (see inclusion reason)
For detail-page application-fit context. List ordering is defined in Ranking.
⑤ Ranking — how lists are ordered
Rankings compare publicly listed projects only. “Top” means curated leaders after inclusion — not merely a global GitHub hype list.
Comparison scope
Rankings apply within your current browse and filter context:
| How you browse | Ranking scope |
|---|---|
| All scenarios | All listed projects |
| One or more selected scenarios | Listed projects in those scenarios |
| Tags, license, or other filters applied | Current filtered result set |
Browsing all scenarios helps discovery; for serious selection, filter to the relevant scenario first. Rank shown on a project page is always within its primary category.
Composite score (default)
By default we rank using weighted GitHub signals, compared within your current browse scope:
- • GitHub Stars (70%) — community adoption
- • Contributors (10%) — collaboration breadth
- • Update recency (20%) — more recent pushes score higher
You can also sort by a single metric:
- • Stars — community adoption
- • Forks — downstream usage
- • Last update — most recent first
Sponsored placements never change rank.
⑥ Trust & governance
A selection guide is only useful if it is explainable, correctable, and kept current.
- • Every publicly listed project shows an inclusion reason explaining scenario fit
- • Edge cases and new scenario categories are reviewed by our editorial team
- • Repo metrics refresh from GitHub on a fixed schedule
- • Anyone may submit corrections, category suggestions, listing requests, methodology feedback, or application-fit questions via the Contact page
- • Entries may be removed for license changes, abandonment, misfit, or duplicate mirrors
- • Material methodology changes will be posted here with a new version date
⑦ Limits & disclaimers
We provide selection guidance — not a substitute for your security review, performance testing, or compliance process.
- ! We do not perform source-code security audits or penetration testing
- ! We do not publish unified performance / quality benchmarks
- ! Stars reflect community attention — not real-world reliability or vendor support in your environment
- ! Scenario matching and categorization may be imperfect; validate important decisions against your own requirements
- ! GitHub data may lag; check the repository directly for critical decisions
Sponsorship vs. rankings
Sponsored placements provide extra visibility only. They do not change ranking algorithms or objective metrics. All paid partnerships are clearly disclosed.
Questions, suggestions, or objections about this methodology, or about applying projects in practice? Please contact us on the Contact page .
Methodology v1.0 · July 2026