agents-radar
agents-radar tracks over 10,000 GitHub repositories with 5,000+ stars across 22 categories, scoring them on popularity, growth momentum, maintenance activity, and community health to recommend optimal tools for AI agents. Data updates daily for real-time intelligence. AI agent developers use it to identify vetted open-source components for building robust tech stacks.
Overview
agents-radar delivers real-time tech stack intelligence tailored for AI agents. It monitors more than 10,000 GitHub repositories exceeding 5,000 stars, categorized into 22 areas like machine learning frameworks, data processing, and deployment tools. Scores are computed from popularity metrics, recent growth trends, commit frequency, issue resolution rates, and contributor engagement, enabling precise tool recommendations. All data refreshes daily to reflect the latest repository states.
Key Capabilities
- Repository Tracking: Queries 10,000+ high-star GitHub repos across 22 categories, providing metadata on stars, forks, and updates.
- Scoring System: Evaluates repos using four factors—popularity (star/fork counts), growth momentum (recent gains), maintenance activity (commits/PRs/issues), and community health (contributors/discussions)—to rank tools quantitatively.
- Tool Recommendations: Returns top-ranked repositories by category, filtered by score thresholds, for direct integration into AI agent development workflows.
- Daily Updates: Automates data ingestion from GitHub API to ensure scores and rankings stay current without manual intervention.
Use Cases
- An AI developer building a multi-modal agent queries recommendations for "data processing" category to select repos like LangChain or Haystack based on top scores for momentum and maintenance.
- A team prototyping agentic workflows uses repo tracking to compare security tools in the "security" category, prioritizing those with high community health scores.
- Researchers scouting frameworks fetch scored lists from "machine learning" category to integrate trending repos like LlamaIndex into experiments.
- During tech stack audits, query growth momentum scores across categories to migrate from stagnant repos to actively maintained alternatives.
Who This Is For
AI agent builders, machine learning engineers, and developers assembling open-source tech stacks. Ideal for those needing data-driven decisions on GitHub tools without manual repo hunting.