2026-08-12 –, Room 2
The Julia SciML ecosystem is a collection of hundreds of packages. Keeping the whole system up to date can be quite the task, with dependencies releasing breaking versions weekly and having to track down CI failures. Over the last year a multi-agent system was developed to help with a lot of the maintenance burden. The goal of this talk is to share the details of this system so that other Julia package ecosystems can iterate on the idea and adopt similar mechanisms.
Right now the system is kept private, but it will be opened sourced before the talk. It needs an audit to ensure no secret keys are leaked.
The core system has 48 concurrent agents which trigger individual bots and cycle through different behaviors to cover many standard maintenance problems. Right now the system has the following bot profiles that are orchestrated with specific purposes:
- CI Health, Check Tests master branch CI, diagnoses and fixes failures
- Random Issue Solver, Investigates open issues (prioritizes bug label)
- Dependency Update, Handles dependency update PRs
- Min Version Bump, Bumps minimum versions in compat
- Docs Improvement, Improves documentation
- Static Improvement, Static analysis improvements
- Performance Improvement, Performance optimizations
- Interface Check, Checks package interfaces
- Precompilation, Improvement Improves precompilation
- Version Bump, Checks for version releases
- Explicit Imports, Adds explicit imports
- Deprecation Fix, Fixes deprecation warnings
- Benchmark Check, Checks SciMLBenchmarks.jl
Dr. Chris Rackauckas is the VP of Modeling and Simulation at JuliaHub, the Director of Scientific Research at Pumas-AI, Co-PI of the Julia Lab at MIT, and the lead developer of the SciML Open Source Software Organization. For his work in mechanistic machine learning, his work is credited for the 15,000x acceleration of NASA Launch Services simulations and recently demonstrated a 60x-570x acceleration over Modelica tools in HVAC simulation, earning Chris the US Air Force Artificial Intelligence Accelerator Scientific Excellence Award. See more at https://chrisrackauckas.com/. He is the lead developer of the Pumas project and has received a top presentation award at every ACoP in the last 3 years for improving methods for uncertainty quantification, automated GPU acceleration of nonlinear mixed effects modeling (NLME), and machine learning assisted construction of NLME models with DeepNLME. For these achievements, Chris received the Emerging Scientist award from ISoP.