GSoC 2026 ideas

[EwoutH]

Let's start the discussion on 2026 GSoC ideas!

2025 ideas can be found here. Ones leftover from last year:

• Behavioral framework: This project aims to develop a unified behavior and state management system for Mesa, addressing the current limitation of simple discrete-time steps by enabling continuous state changes, time-consuming tasks, and sophisticated decision-making. By creating a comprehensive framework that integrates task priority scheduling, interruption capabilities, and behavioral theories, the initiative will allow modelers to simulate realistic agent behaviors—such as resource depletion and parallel activities—more effectively. The expected outcome is a robust API and event system that empowers users to build complex, theoretically grounded agent-based models with greater ease and flexibility.
  • Relevant discussions and groundwork include #2529, #2526, #2538, and PR #2547.
• Unifying geospatial Support in Mesa: This initiative seeks to merge the standalone mesa-geo(https://github.com/projectmesa/mesa-geo) package directly into the core Mesa library as a mesa.geo module, resolving compatibility issues arising from their separate evolution and simplifying dependency management. By leveraging Mesa's new experimental cell and continuous space architectures, the project will create a unified spatial modeling framework that supports GIS functionality, coordinate transformations, and standard file formats like GeoJSON within a consistent API. The consolidation aims to make advanced geospatial modeling a first-class feature, ensuring that property layers and spatial visualizations work seamlessly across all Mesa projects.
  • Key technical context includes the experimental cell space, continuous space implementation, and the Space conceptual model.
• Mesa Blocks: Mesa Blocks proposes the development of a low-code/no-code extension designed to help users rapidly assemble and run custom agent-based models using a visual drag-and-drop interface. Recognizing the difficulty in building complex models from scratch, this project aims to provide a system of reusable, customizable building blocks that allow users to quickly prototype valid models while retaining the ability to modify the underlying logic. The ultimate goal is to create a prototype that democratizes access to agent-based modeling, allowing for faster decision-making and model exploration through a user-friendly, modular environment.
  • Historical context and inspiration can be found in previous ecosystem attempts and Reusable Building Blocks for ABMS.

What more ideas and ambitions do we have?

[EwoutH]

We did talk about performance optimization in the past, and a Mesa performance optimization project might be interesting this year.

Mesa's scalability to millions of agents depends on efficient core operations in AgentSet, spatial grids, and event scheduling. This project could systematically identify and address performance bottlenecks across the library. The first phase involves comprehensive profiling of Mesa's example models (Boltzmann Wealth, Schelling, Wolf-Sheep, Flocking) using tools like cProfile, py-spy, and memory_profiler to create a performance baseline and identify hotspots. Likely candidates include AgentSet operations (shuffle_do, select, groupby, agg), grid neighbor lookups, and PropertyLayer modifications.

The second phase could explore optimization strategies: expanding NumPy vectorization for batch agent operations, restructuring data layouts for cache efficiency, and evaluating Rust acceleration via PyO3 for compute-intensive components like spatial indexing, large-scale shuffling, and event queue management. Rust is particularly promising for operations with clear data boundaries (grids, coordinate math) where Python object overhead can be avoided.

Deliverables could include a reproducible low-level benchmarking suite, documented performance improvements with before/after comparisons, and potentially a mesa-accel optional dependency for Rust-accelerated components. The contributor should have Python profiling experience and interest in either NumPy internals or Rust/PyO3 (though the discovery phase will determine which optimization strategies yield the best results for Mesa's specific workloads).

@Ben-geo @adamamer20 I'm also curious if there are lessons or techniques of mesa-frames transferable to the main library.

[madhavik-2005]

These project ideas look great! I'm really interested in getting involved with Mesa for GSoC 2026.
I've read through the candidate guide, and it's clear that building models first is the way to go. So that's my plan - dive into mesa-examples, build some stuff, and actually experience what works and what's frustrating. I want my proposal to come from real hands-on experience, not just theory.

The Behavioral Framework project really caught my attention. I find it fascinating how individual agents making their own decisions can lead to complex emergent behaviors in the system. What excites me most is the challenge of taking these theoretical behavioral models - things like BDI or needs-based architectures - and turning them into something practical that people can actually use. It's basically building the "brain" for agents, which is pretty cool.
My plan for the next few months:

• Work through the tutorial and build models that run into the behavioral limitations we're trying to solve
• Contribute some models to mesa-examples
• Deep dive into the discussions in Continuous States #2529, Tasks #2526, and Behavioral Framework #2538
• Learn more about behavior trees, GOAP, BDI architectures, and related concepts
• Try building a simple proof-of-concept for a Task or State management system

One question: which existing Mesa models would you recommend looking at to see how people currently work around things like time-consuming tasks, competing priorities, or continuous state changes? I'd love to understand the current pain points from real examples.

[EwoutH]

Thanks for reading up before asking questions, and then asking targeted, informed questions. That's appreciated!

Most of our models can be improved in this regard, here are some examples:

• Wolf–Sheep Predation: A classic predator–prey model where wolves and sheep move, eat, and reproduce while tracking continuous energy levels. It highlights how even simple agents require juggling multiple drives (survival, movement, reproduction) using hand-coded logic. Pain point: Multi-step actions (e.g., hunting) must be faked with per-tick rules because Mesa lacks task duration or priority management.
• Schelling Segregation: Agents evaluate local similarity and relocate when dissatisfied, producing emergent spatial patterns from threshold-based decisions. It’s a clean example of stateful decision-making driven by continuous dissatisfaction metrics. Pain point: Moves are instantaneous and there’s no concept of gradual action, making it impossible to model multi-step relocation or inertia.
• Epstein's Civil Violence: Citizens and cops interact based on grievance, risk, and legitimacy, approximating BDI-like reasoning through inline formulas. It’s a strong case of complex motivations crammed into a single step function. Pain point: The behavior architecture collapses into bulky conditional logic because Mesa offers no structure for beliefs, desires, or competing goals.
• Boids (Flocking): Agents compute steering forces (alignment, cohesion, separation) continuously, creating smooth flocking. This model stresses continuous state updates and layered behaviors in every tick. Pain point: All behaviors are blended manually each step instead of modularized, showing the need for stackable behavior components or continuous behavior nodes.
• Sugarscape: Agents consume resources, metabolize energy, move based on local gradients, and even trade in extended versions. It demonstrates rich, multi-variable needs that demand arbitration between competing goals. Pain point: All needs (food, movement, vision, trading) get stuffed into one big step method, illustrating how badly Mesa lacks goals, tasks, or behavior trees.

You can find them in https://github.com/projectmesa/mesa/tree/main/mesa/examples

So note there can be two distinct goals:

• Make existing complex behavior easier to implement
• Allow new, previously impossible, behaviour

Project can address either one of these or both.

[madhavik-2005]

This is really helpful, thanks! I'll start implementing these models and dig into the pain points. If I get stuck or have questions along the way, I'll reach out.