# Detecting AI Coding Agents in Open Source: A Validated Multi-Method Census of 180 Million Repositories

arXiv: https://arxiv.org/abs/2606.24429 · HTML: https://mlsanity.com/paper/2606.24429/

## 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 13 of its 35 references that are in the map.
- First posted on arXiv 23 Jun 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 13 of 13)

- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [Agentic Much? Adoption of Coding Agents on GitHub](https://mlsanity.com/paper/2601.18341/) (2026)
- [Zero-Shot Detection of Machine-Generated Codes](https://mlsanity.com/paper/2310.05103/) (2023)
- [Speed at the Cost of Quality: How Cursor AI Increases Short-Term Velocity and Long-Term Complexity in Open-Source Projects](https://mlsanity.com/paper/2511.04427/) (2025)
- [Composer 2 Technical Report](https://mlsanity.com/paper/2603.24477/) (2026)
- [AIDev: Studying AI Coding Agents on GitHub](https://mlsanity.com/paper/2602.09185/) (2026)
- [Detecting and Characterizing Bots that Commit Code](https://mlsanity.com/paper/2003.03172/) (2020)
- [A ground-truth dataset and classification model for detecting bots in GitHub issue and PR comments](https://mlsanity.com/paper/2010.03303/) (2020)
- [World of Code: Enabling a Research Workflow for Mining and Analyzing the Universe of Open Source VCS data](https://mlsanity.com/paper/2010.16196/) (2020)
- [A Complete Set of Related Git Repositories Identified via Community Detection Approaches Based on Shared Commits](https://mlsanity.com/paper/2002.02707/) (2020)
- [A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits](https://mlsanity.com/paper/2003.08349/) (2020)
- [Detecting LLM-generated Code with Subtle Modification by Adversarial Training](https://mlsanity.com/paper/2507.13123/) (2025)

## Abstract (arXiv)

Generative AI coding agents are entering the open-source supply chain, yet their diverse and often invisible traces leave their prevalence poorly understood. We introduce a multi-layered detection framework that integrates configuration-file scanning, commit-message analysis, author-identity matching, and bot-signature lookup across World of Code (180M+ Git repositories), classifying agent traces into four behavioral types. No single method captures more than a fraction of activity: multi-method detection identifies 850,157 Claude Code commits in one snapshot, of which bot-account lookup_the signal most adoption studies rely on_recovers only 28,154 (3.3%), a 30x relative-recall gap, so single-signal prevalence estimates are biased low by at least this factor. Every detection pattern is hand-validated (495 labels) with per-cell precision and Wilson confidence intervals. Across snapshots from December 2024 to April 2026, commit-attributed agents generate over 320,000 commits per month; Claude Code leads (886,122 commits across 17,295 projects) and dominates silent, configuration-file-only adoption (21,078 projects). Compared against an independent pull-request census (AIDev), the two channels capture nearly disjoint agent populations_a PR census misses 79% of commit-detected Claude Code adopters and essentially all Codex adopters_and different kinds of work: PR-deployed cloud agents (Codex, Cursor) surface as feature work, while commit-deployed in-editor agents (Claude Code, OpenHands, Aider) surface as maintenance. The observed work profile follows deployment and detection mode rather than the tool itself, so no single channel is representative.