Top 10 Community GitHub Repositories (Last 30 Days) for AWS + AI + Agentic + Blockchain

Scenario

Your engineering team wants to quickly identify high-signal, community-built repositories in the AWS + AI + agentic + blockchain space to accelerate prototyping.

Scope and methodology

• Collection date: May 14, 2026
• Window: April 14, 2026 to May 14, 2026 (inclusive)
• Source: GitHub Search API (/search/repositories)
• Ranking basis: stars (descending), then recency
• Community filter: excluded major vendor-owned repos (for example aws, aws-samples, awslabs, amazon, microsoft, google, openai, anthropic)

Reproducible query approach

curl -s "https://api.github.com/search/repositories?q=aws+ai+agentic+created:2026-04-14..2026-05-14&sort=stars&order=desc&per_page=100"

Invoke-RestMethod -Uri "https://api.github.com/search/repositories?q=aws+ai+agentic+created:2026-04-14..2026-05-14&sort=stars&order=desc&per_page=100" -Headers @{ 'User-Agent'='research-script' }

Use additional queries (RAG, MCP, blockchain-agent, verifiable-RAG) and deduplicate by full_name.

Optional AWS data pipeline for weekly tracking

export AWS_REGION=us-east-1
export PROJECT=github-trending-intel
export ACCOUNT_ID=$(aws sts get-caller-identity --query Account --output text)
export BUCKET=${PROJECT}-${ACCOUNT_ID}-${AWS_REGION}

aws s3api create-bucket --bucket "$BUCKET" --region "$AWS_REGION"
aws s3 cp top_repos_2026-05-14.json s3://$BUCKET/snapshots/

$env:AWS_REGION = "us-east-1"
$env:PROJECT = "github-trending-intel"
$env:ACCOUNT_ID = (aws sts get-caller-identity --query Account --output text)
$env:BUCKET = "$($env:PROJECT)-$($env:ACCOUNT_ID)-$($env:AWS_REGION)"

aws s3api create-bucket --bucket $env:BUCKET --region $env:AWS_REGION
aws s3 cp top_repos_2026-05-14.json "s3://$($env:BUCKET)/snapshots/"

Top 10 community repositories

\*Stars are point-in-time values captured on 2026-05-14 and will change.

Fast screening checklist (before adoption)

• Maintainer activity in last 14 days
• Clear license (MIT, Apache-2.0, etc.)
• No hardcoded secrets in examples
• CI/test presence
• Documentation quality and architecture clarity
• Security posture (dependency hygiene, scoped credentials)

Practical evaluation pipeline on AWS

Sample scoring script

from dataclasses import dataclass

@dataclass
class RepoScoreInput:
    stars: int
    days_since_update: int
    has_license: bool
    has_ci: bool


def score_repo(x: RepoScoreInput) -> float:
    freshness = max(0, 30 - x.days_since_update)
    license_bonus = 10 if x.has_license else -10
    ci_bonus = 15 if x.has_ci else -5
    return (x.stars * 1.0) + freshness + license_bonus + ci_bonus

Security, monitoring, and cost for this workflow

Security:

• store GitHub tokens in Secrets Manager
• rate-limit outbound API calls
• isolate research pipelines in non-production account

Monitoring:

• track API failures, data freshness, duplicate rates
• alert on sudden ranking volatility

Cost optimization:

• daily batch pulls instead of frequent polling
• deduplicate and compress snapshots in S3 parquet

Production checklist

• Query methodology versioned in repo
• Inclusion/exclusion policy documented
• Legal/license review process defined
• Weekly delta report automated
• Adoption experiments tied to measurable outcomes

References

• https://docs.github.com/en/rest/search/search#search-repositories
• https://github.com/github/docs/blob/main/content/search-github/searching-on-github/searching-for-repositories.md