[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83658-en":3,"doc-seo-83658-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83658,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Adoption and Ecosystem Health: A Longitudinal Analysis of Open-Source Multi-Agent Frameworks","Since ChatGPT’s release in November 2022, open-source agentic frameworks have rapidly expanded, creating selection challenges for engineering teams where popularity signals like GitHub stars can mask true adoption. The study analyzes 15 major open-source AI agent framework repositories from late 2022 to early 2026 using stars, pull requests, commits, and  user-profile data to evaluate awareness, adoption, and retention. Results show star counts are unreliable, contributor density and cross-ecosystem engagement better reflect adoption depth, and retention declines sharply within the first 30 days.","Adoption and Ecosystem Health: A Longitudinal Analysis of Open-Source Multi-Agent Frameworks  \nXi Zhang, PhD Cisco Systems  \nPapi Menon Cisco Systems  \nVivian Chu Cisco Systems  \nKoray Cosguner, PhD Indiana University  \nAbstract  \nSince 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  \n808,042 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-agentspython’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.  \n1. Introduction  \nThe evolution of agentic AI frameworks reflects a broader industry understanding of deploying reliable AI in real-world settings, beyond mere software adoption [1] . Following ChatGPT’s public launch in late 2022 [2], the 2023–2024 period signaled the rise of autonomous agents, with frameworks like LangChain enabling composable LLM  \npipelines and AutoGPT pioneering autonomous agent loops for reasoning and acting [3] . Each wave of new frameworks directly addresses the shortcomings of previous versions, shifting from unrestricted autonomy to modular chaining, from single-agent to multi-agent systems [4], and from experimental prototypes to type-safe, observable, enterprise-ready solutions. As public interest in open-source agentic AI continues to grow, an increasing number of enterprise AI strategies now incorporate open-source frameworks as a core architectural component, making framework selection a consequential and understudied decision. Betting on the wrong ecosystem creates business risk, including inherited technical debt, a shrinking contributor base, and reduced support if a corporate backer changes direction.  \nMany metrics used to evaluate open-source AI projects are often misunderstood and susceptible to manipulation. For example, star counts tend to indicate popularity rather than actual adoption [5], while contributor numbers can include a single person's quick bug fix alongside years of ongoing effort [6] . The volume of pull requests may also be artificially increased through automated updates or AI-generated patches. Although these metrics might suggest rapid growth and a healthy ecosystem, closer examination reveals that sustained adoption varies considerably across frameworks and appears strongly associated with underlying architectural choices. Frameworks that achieve long-term, production-level adoption tend to address specific, tangible engineering challenges, suggesting that raw growth metrics alone are insufficient predictors of staying power. To examine this, the paper proceeds in three analytical layers: first, we assess community awareness ","cbCaieyjtzNY7YzU","https://ap.wps.com/l/cbCaieyjtzNY7YzU","pdf",3357213,2,1,24,"English","en",105,"# Introduction\n## Motivation and evaluation challenges\n## Analytical approach\n# Data\n## Data sources and collection\n## Repository selection","[{\"question\":\"Why are GitHub star counts considered unreliable for judging open-source agent framework adoption?\",\"answer\":\"Star counts mainly reflect hype cycles and inorganic activity rather than real contributor uptake. In the study, AutoGPT gained many stars quickly but converted fewer contributors per 1,000 stars than LangChain, while lower-profile frameworks showed higher contributor density.\"},{\"question\":\"Which metrics better capture ecosystem health than stars alone?\",\"answer\":\"Ecosystem health is better measured by contributor density, cross-ecosystem engagement, and contributor retention. The analysis also finds that LangChain acts as shared infrastructure attracting most cross-ecosystem contributors.\"},{\"question\":\"How does contributor retention change over time after initial contribution?\",\"answer\":\"Retention drops most steeply during the first 30 days after initial contribution, then stabilizes near the 90-day mark. This pattern supports evaluating sustainability over early post-onboarding dynamics rather than only initial growth.\"}]",1784189577,60,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"adoption-and-ecosystem-health-a-longitudinal-analysis-of-open-source-multi-agent-frameworks","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/adoption-and-ecosystem-health-a-longitudinal-analysis-of-open-source-multi-agent-frameworks/83658/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-24","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are GitHub star counts considered unreliable for judging open-source agent framework adoption?","Question",{"text":75,"@type":76},"Star counts mainly reflect hype cycles and inorganic activity rather than real contributor uptake. In the study, AutoGPT gained many stars quickly but converted fewer contributors per 1,000 stars than LangChain, while lower-profile frameworks showed higher contributor density.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which metrics better capture ecosystem health than stars alone?",{"text":80,"@type":76},"Ecosystem health is better measured by contributor density, cross-ecosystem engagement, and contributor retention. The analysis also finds that LangChain acts as shared infrastructure attracting most cross-ecosystem contributors.",{"name":82,"@type":73,"acceptedAnswer":83},"How does contributor retention change over time after initial contribution?",{"text":84,"@type":76},"Retention drops most steeply during the first 30 days after initial contribution, then stabilizes near the 90-day mark. This pattern supports evaluating sustainability over early post-onboarding dynamics rather than only initial growth.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":29,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]