Methodology
How we rank AI agents
Evidence signals, tiered indexing, and honest limits. How AgentCrush scores and ranks the AI agent ecosystem.
Evidence, not popularity
AgentCrush ranks AI agents using verifiable public signals, not self-reported claims or popularity votes. An agent does not appear in /rankings by being well-known — it gets there by generating real evidence that can be independently checked.
Today 39 agents are evidence-ranked and over 1,186 are indexed. Both counts grow automatically as agents accumulate signals and as new agents are submitted or discovered.
How tiers work
Every agent on AgentCrush is assigned a tier based on current evidence coverage:
evidence_ranked
Sufficient signal across multiple evidence categories to support a reliable public rank. These agents appear in /rankings with a full score breakdown and evidence chips.
indexed
Tracked and discoverable in /explore, but does not yet meet the evidence threshold for /rankings. Over 1,186 agents are indexed today. Coverage is live and growing.
archived
Not publicly surfaced. Used for duplicate entries, sunset projects, and manual curation. Not promoted or discoverable.
Live scoring signals
The following signals are active in the v2 scoring model. Each agent score is a weighted combination of whichever signals have data available. The evidence chips visible on /rankings show which signals contributed to each position.
GitHub activity
Stars, commits, release frequency, and contributor volume on the primary repository.
Package usage
Download counts from npm, PyPI, and similar registries. Available for agents with published packages.
Ecosystem relationships
Integration links, framework adoption, and dependency depth in the AgentCrush relationship graph.
HN discourse
Hacker News mentions, technical discussions, and launch posts.
Trust state
Verified identity, claim status, and tier history recorded on the agent profile.
Signals are only included in a score when real data exists. An agent missing a signal is not penalized — the remaining signals are reweighted proportionally.
Signals in progress or planned
AgentCrush is transparent about what is not yet live. The following signals are in the pipeline but not fully active in scoring today:
Dependency graph hardening
Relationship edge coverage is expanding. Dependency depth is partially active; broader graph coverage is being added over time.
Docs quality scoring
Documentation completeness and freshness as a scoring input. Under active development; not yet included in public scores.
Reddit discourse signal
Community discussion on r/MachineLearning and related subreddits is a planned signal. Currently blocked pending Reddit API access approval.
Native AgentCrush signals
Profile views, search frequency, watchlist counts, and claim activity are planned future inputs. Not included in scoring today.
Indexed vs evidence-ranked
AgentCrush indexes broadly to support discovery: new projects, early-stage agents, niche tools, and historical data all belong in the index. The /explore directory is intentionally wider than /rankings.
An indexed agent is never penalized for not being ranked. It remains discoverable, linkable, and profile-complete. As evidence accumulates — new releases, growing downloads, ecosystem integrations — the scoring pipeline automatically reassesses eligibility.
Tier promotion (indexed to evidence_ranked) happens through the weekly scoring pipeline. An agent that meets the threshold during a Sunday run will appear in /rankings the following week.
Update cadence
Signal collection
Ecosystem signals — GitHub, package registries, HN — are refreshed on a scheduled cadence by the AgentCrush pipeline workers running on the VPS.
Score computation
Scores are recomputed when new signal data arrives. The v2 model weights are deterministic: the same inputs always produce the same score.
Tier promotion
The indexed to evidence_ranked promotion pipeline runs weekly on Sundays. An agent meeting the evidence threshold will be promoted on the next run.
v2 stability monitoring
The v2 scoring model is being monitored for week-over-week consistency. Until several consecutive runs confirm stability, the legacy global_rank remains the canonical reference for displayed rankings.
Historical record
AgentCrush tracks daily movement over time, so rankings are not just a current snapshot — they become a historical view of how agent activity, evidence, and reputation change. Daily records have accumulated since April 2026.
Machine-readable trust data
Rank, score, tier, and verification state are available via x402-protected API endpoints — no API key or subscription required. Payments settle in USDC on Base mainnet per call.
/api/agent/:handle/trust-summary$0.02Current rank, score, tier, archetype, and verified state.
/api/agent/:handle/history$0.0230-day rank and score history with trend summary.
/api/agent/:handle/verification-status$0.005Tier, verified flag, and claim status only. Lightweight check.