Adoption and Ecosystem Health: A Longitudinal Analysis of Open-Source Multi-Agent Frameworks

2026-07-02

AI Summary

This study addresses the limitations of GitHub stars as a proxy for ecosystem health in open-source multi-agent frameworks, which can mislead engineering decisions. Analyzing over 800,000 stars, 70,000 pull requests, 86,000 commits, and nearly one million users across 15 prominent frameworks from late 2022 to early 2026, the work evaluates ecosystem vitality through awareness, adoption, and retention. It introduces novel metrics—contributor density, cross-framework engagement, and contribution retention—and combines longitudinal analysis, user behavior tracking, cross-repository contribution graphs, and retention curve modeling. The findings reveal a significant disconnect between star counts and community activity, underscoring LangChain’s role as critical shared infrastructure: high-star projects like AutoGPT exhibit markedly lower contributor density than LangChain; most contributors churn within 30 days of first contribution, stabilizing by 90 days; and frameworks with low visibility but high contributor density demonstrate greater depth of real-world adoption.

Abstract

Since ChatGPT's launch in November 2022, open-source agentic frameworks have proliferated, making framework selection important for engineering teams while obscured by popularity signals such as GitHub stars. This paper analyzes 15 major open-source AI agent framework repositories from late 2022 to early 2026, using 808,042 stars, 73,997 pull requests, 86,241 commits, and 987,330 user profiles to assess ecosystem health across awareness, adoption, and retention. Three findings emerge. First, headline popularity is unreliable. Star counts reflect hype cycles and inorganic activity. AutoGPT gained 111,967 stars in one month but converted fewer than 9 contributors per 1,000 stars, defined as contributor density in this research, compared with LangChain's 41. Lower-profile frameworks such as Pydantic-AI show higher contributor density, indicating deeper adoption. Second, mapping awareness against adoption shows that visibility and engagement diverge. MetaGPT and LangFlow have contributor density ratios below 5 even with their high visibility. Openai-agents-python's limited contributor base suggests institutional backing alone does not ensure community depth. By analyzing cross-framework contribution, we discover that LangChain functions as a shared infrastructure, attracting 82.5% of cross-ecosystem contributors. Third, retention drops most steeply in the first 30 days of initial contribution and stabilizes near 90 days. Overall, ecosystem health is better measured by contributor density, cross-ecosystem engagement, and retention than by stars alone. These metrics offer teams a more robust basis for framework evaluation.

Problem

• open-source AI agent frameworks
• ecosystem health
• framework adoption
• contributor density
• GitHub stars

Innovation

• contributor density
• ecosystem health
• cross-ecosystem engagement
• open-source AI agent frameworks
• retention analysis