Machine learning is a promising field to augment and potentially replace part of the event recon-
struction of high-energy physics experiments. This is partly due to the fact that many machine-
learning algorithms offer relatively easy portability to heterogeneous hardware and thus could
play an important role in controlling the computing budget of future experiments. In addition, the
capability of machine-learning-based approaches to tackle nonlinear problems can improve perfor-
mance. Particularly, the track reconstruction problem has been addressed in the past with several
machine-learning-based attempts, largely facilitated by the two highly resonant machine-learning
challenges (TrackML). The Exa.TrkX project has developed a track-finding pipeline based on
graph neural networks that has shown good performance when applied to the TrackML detector.
We present the technical integration of the Exa.TrkX pipeline into the framework of the ACTS
(A Common Tracking Software) project. We further present our efforts to apply the pipeline to
the OpenDataDetector, a model of a more realistic detector that supersedes the TrackML detector.
The tracking performance in this setup is compared to that of the ACTS standard track finder, the
Combinatorial Kalman Filter.