Agentic Very Much! Adoption of Coding Agent in New GitHub Projects
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
In previous work, we investigated the adoption of coding agents in GitHub projects, finding that it was very significant. This study follows this line of work, but analyses new projects, that were created after the previous study. In this new sample, we find that the adoption of coding agents is more than twice as high. We also find that the adoption is significantly more intensive, as the proportion of AI-assisted commits is sensibly higher, despite strong signs that we do not detect all of it.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 3 of its 3 references that are in the map. First posted on arXiv 5 Jun 2026.
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3 of 3 references.
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- 2026Promises, Perils, and (Timely) Heuristics for Mining Coding Agent Activity26 Jan
- 2026Agentic Much? Adoption of Coding Agents on GitHub26 Jan
Other references · 1
- 2021Sampling Projects in GitHub for MSR Studiescited by 21
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- 2026Decoupling Code Complexity from Newcomer Participation: A Causal Study of AI Coding Agent Adoption in OSS
- 2026Detecting AI Coding Agents in Open Source: A Validated Multi-Method Census of 180 Million Repositories