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AI In IndustryTop 8 Best Agent Based Modelling Software of 2026
Discover top agent based modelling software to accelerate simulations. Explore options and find the best fit for your needs today.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
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Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
AnyLogic
Integrated multi-paradigm modeling across agents, system dynamics, and discrete-event timing
Built for teams building integrated agent and process simulations with experiment-driven analysis.
NetLogo
BehaviorSpace parameter sweeps with automated experiment runs and output collection
Built for teaching and research teams prototyping ABMs with strong visualization and parameter sweeps.
Mesa
DataCollector for recording agent and model variables across simulation steps
Built for researchers and engineers building Python ABM models with custom analysis.
Related reading
Comparison Table
This comparison table reviews agent based modelling tools such as AnyLogic, NetLogo, Mesa, Repast Simphony, and GAMA, plus other commonly used platforms. It summarizes key differences that affect model development and execution, including supported languages, visualization and experiment workflows, scalability features, and integration options.
| # | Tool | Category | Overall | Features | Ease of Use | Value |
|---|---|---|---|---|---|---|
| 1 | AnyLogic AnyLogic builds agent-based, discrete-event, and system dynamics simulations with automated code generation and model deployment workflows for industrial use. | enterprise | 8.5/10 | 9.0/10 | 7.8/10 | 8.7/10 |
| 2 | NetLogo NetLogo runs agent-based models in a small programming language and supports experiments, metrics collection, and interactive visualization for simulation studies. | open-source | 7.8/10 | 8.0/10 | 8.3/10 | 6.9/10 |
| 3 | Mesa Mesa provides a Python framework for building agent-based models with scheduling, data collection, and experiment-oriented tooling. | python framework | 8.2/10 | 8.6/10 | 8.0/10 | 7.7/10 |
| 4 | Repast Simphony Repast Simphony delivers Java-based agent-based modeling with rich scheduling, projection tools, and integration with the broader Repast ecosystem. | research toolkit | 7.1/10 | 7.4/10 | 6.6/10 | 7.3/10 |
| 5 | GAMA GAMA supports agent-based modeling with spatial simulation, GIS data handling, and scenario execution across desktop and batch runs. | spatial ABM | 8.3/10 | 8.7/10 | 7.9/10 | 8.3/10 |
| 6 | FLAME GPU FLAME GPU uses GPU acceleration to run large-scale agent-based simulations with parallel execution and practical interfaces for industrial research. | gpu-accelerated | 8.0/10 | 8.8/10 | 7.2/10 | 7.8/10 |
| 7 | MASON MASON is a Java simulation toolkit for fast agent-based models with careful scheduling and scalable performance for desktop execution. | java toolkit | 7.3/10 | 8.0/10 | 6.8/10 | 7.0/10 |
| 8 | NetLogo Web (NetLogo models in browser) NetLogo Web packages NetLogo models for web execution so agent-based simulations can run in a browser environment. | deployment | 7.4/10 | 7.3/10 | 8.1/10 | 6.8/10 |
AnyLogic builds agent-based, discrete-event, and system dynamics simulations with automated code generation and model deployment workflows for industrial use.
NetLogo runs agent-based models in a small programming language and supports experiments, metrics collection, and interactive visualization for simulation studies.
Mesa provides a Python framework for building agent-based models with scheduling, data collection, and experiment-oriented tooling.
Repast Simphony delivers Java-based agent-based modeling with rich scheduling, projection tools, and integration with the broader Repast ecosystem.
GAMA supports agent-based modeling with spatial simulation, GIS data handling, and scenario execution across desktop and batch runs.
FLAME GPU uses GPU acceleration to run large-scale agent-based simulations with parallel execution and practical interfaces for industrial research.
MASON is a Java simulation toolkit for fast agent-based models with careful scheduling and scalable performance for desktop execution.
NetLogo Web packages NetLogo models for web execution so agent-based simulations can run in a browser environment.
AnyLogic
enterpriseAnyLogic builds agent-based, discrete-event, and system dynamics simulations with automated code generation and model deployment workflows for industrial use.
Integrated multi-paradigm modeling across agents, system dynamics, and discrete-event timing
AnyLogic stands out for combining agent-based modeling with system dynamics and discrete-event modeling in a single project environment. It supports visual flow construction for process logic plus a model layer for agents, state variables, and interactions. Simulation runs can be instrumented with experiments, parameter sweeps, and statistical outputs aimed at decision and scenario analysis. The tool’s strength centers on building connected models where agents and resources interact with event timing and feedback loops.
Pros
- Unified agent-based, system dynamics, and discrete-event modeling in one workspace
- Agent populations, behaviors, and interaction rules are modeled with clear constructs
- Experimentation tools support parameter sweeps and output collection for comparisons
- Strong built-in visualization and monitoring for entities, variables, and states
- Java-based extensibility enables custom logic and specialized behaviors
Cons
- Modeling workflows require learning multiple paradigms and syntax conventions
- Large models can become complex to manage and troubleshoot during iteration
- Some advanced customization takes more engineering effort than visual-only tools
Best For
Teams building integrated agent and process simulations with experiment-driven analysis
More related reading
NetLogo
open-sourceNetLogo runs agent-based models in a small programming language and supports experiments, metrics collection, and interactive visualization for simulation studies.
BehaviorSpace parameter sweeps with automated experiment runs and output collection
NetLogo stands out with its model-first workflow that couples an interactive simulation workspace with agent-based primitives for fast experimentation. It supports breeds, agent messaging, spatial grids, and network-style links so researchers can represent local interactions and emergent system behavior. Built-in plotting, monitors, and BehaviorSpace enable systematic parameter sweeps tied directly to model runs. It remains strongest for educational and research prototypes that prioritize visualization and iterative tuning over large-scale production deployment.
Pros
- Immediate visualization with interactive sliders, monitors, and plots during model runs
- Agent breeds, links, and grids cover common ABM structures like swarms and spatial diffusion
- BehaviorSpace automates parameter sweeps and collects outputs for comparative analysis
- Rich library of sample models supports rapid learning and benchmarking
Cons
- Performance is limited for very large agent counts compared with compiled ABM frameworks
- Modeling large data pipelines and external integrations is more manual than in data-centric tools
- Reusability across projects can be harder due to tight coupling of UI and model logic
Best For
Teaching and research teams prototyping ABMs with strong visualization and parameter sweeps
Mesa
python frameworkMesa provides a Python framework for building agent-based models with scheduling, data collection, and experiment-oriented tooling.
DataCollector for recording agent and model variables across simulation steps
Mesa stands out for clean, Python-native agent based modeling with a focus on extensibility. It provides core simulation building blocks like schedulers, agent classes, and data collection utilities that support running many steps and tracking outcomes. The model runs and experiment scaffolding are testable and reproducible using the same Python code base, which helps structure research workflows. Visualization and analysis are supported through external plotting and interoperability rather than a single integrated GUI.
Pros
- Python-first API with explicit Agent and Model abstractions
- Flexible schedulers enable varied activation strategies and step semantics
- Built-in data collection supports structured time series and metrics
Cons
- Visualization requires external tooling instead of built-in dashboards
- Large-scale performance needs extra engineering for many agents
- Model calibration and statistical inference are not part of the core toolkit
Best For
Researchers and engineers building Python ABM models with custom analysis
More related reading
Repast Simphony
research toolkitRepast Simphony delivers Java-based agent-based modeling with rich scheduling, projection tools, and integration with the broader Repast ecosystem.
Built-in grid and continuous space support with Repast-driven agent scheduling and context management
Repast Simphony stands out for its integrated modeling environment that combines scenario building, scheduling, and visualization support for agent-based simulations. It provides a structured workflow for defining agents, behaviors, and context via the Repast simulation API and a GUI-driven model builder. Core capabilities include discrete-time scheduling, spatial modeling with grids and continuous space, and built-in data collection and charting hooks during runs. Java-based models support reproducible experimentation by separating model logic from run configuration and outputs.
Pros
- Integrated model definition, scheduling, and spatial contexts for ABM experiments
- Strong Java-based extensibility for custom agent logic and behaviors
- Built-in data collection and runtime hooks for charts and outputs
- Supports visualization to debug agent movement and interactions
Cons
- Java-centric workflow adds overhead compared with non-coding ABM tools
- Learning the Repast API and scheduling patterns takes time
- Visualization and output customization can feel restrictive for complex dashboards
Best For
Researchers building Java ABM models needing scheduling, space, and run-time instrumentation
GAMA
spatial ABMGAMA supports agent-based modeling with spatial simulation, GIS data handling, and scenario execution across desktop and batch runs.
Native GIS integration with spatially explicit agent movement and interaction
GAMA stands out as an agent-based modeling environment that combines spatial modeling, GIS integration, and scenario execution in one workflow. It supports a full modeling lifecycle with iterative development, experiment automation, and visualization built into the platform. The modeling language enables agent behavior, environment dynamics, and data collection tied to simulation runs. Strong spatial and GIS tooling makes it especially effective for geographically grounded agent simulations.
Pros
- Deep GIS and spatial layers support agent interactions in real geography
- Built-in experiment management streamlines parameter sweeps and batch runs
- Integrated visualization and data collection reduce external tooling needs
- Modeling language supports clear agent, spatial, and rule-based logic
Cons
- Learning the modeling language and syntax takes sustained practice
- Large models can strain performance without careful optimization
- Advanced workflows may require specialized understanding of spatial operations
Best For
Teams building spatially grounded agent simulations with automated experiments
More related reading
FLAME GPU
gpu-acceleratedFLAME GPU uses GPU acceleration to run large-scale agent-based simulations with parallel execution and practical interfaces for industrial research.
GPU parallel agent execution via FLAME GPU’s GPU backend
FLAME GPU targets high-performance agent-based modelling by executing agent rules on the GPU, which enables very large populations and fast iteration. The workflow centers on building agent logic and simulation dynamics with a toolchain that supports multi-physics style state updates like fields and agent interactions. Visual debugging and analysis are supported through GPU-focused outputs, which makes it easier to inspect spatial behavior and emergent patterns. The result is a strong fit for researchers and teams that need throughput for parameter sweeps and spatially resolved phenomena.
Pros
- GPU execution scales agent counts far beyond CPU-based models
- Agent state updates and neighborhood interactions align with spatial ABM needs
- Toolchain supports debugging and visualizing emergent agent behavior
- Performance enables rapid parameter sweeps and scenario testing
Cons
- GPU-oriented development adds complexity compared with simple ABM tools
- Modeling patterns that fit GPU kernels require careful data layout
- Debugging performance issues can be harder than debugging logic
Best For
Teams building GPU-accelerated spatial ABMs for large-scale simulations
MASON
java toolkitMASON is a Java simulation toolkit for fast agent-based models with careful scheduling and scalable performance for desktop execution.
Discrete-event scheduler and repeatable simulation stepping via MASON’s simulation core
MASON is a Java-based agent-based modeling toolkit centered on fast discrete-event simulation and clean separation of agents, environment, and scheduling. It provides a built-in simulation scheduler, repeatable step logic, and efficient state updates suitable for crowd, network, and ecological models. Its core design favors extensibility through user-written agent classes and custom data collection rather than GUI-centric modeling. The software is well aligned with research workflows that need control over performance, determinism, and experiment replication.
Pros
- High-performance discrete-event simulation with a flexible scheduler
- Deterministic runs using explicit seeding and controlled stepping
- Strong extensibility via custom agents and repeatable model runs
Cons
- Java-centric workflow requires coding for core modeling and experiments
- Visualization support is secondary to simulation internals
- Model reuse and configuration tooling are limited compared to GUI tools
Best For
Researchers building Java-based ABM simulations needing performance and scheduler control
More related reading
NetLogo Web (NetLogo models in browser)
deploymentNetLogo Web packages NetLogo models for web execution so agent-based simulations can run in a browser environment.
Browser-native execution of NetLogo models with interactive interface controls
NetLogo Web runs NetLogo models directly in a browser, which removes local installation barriers for sharing agent-based simulations. It supports interactive visualization tied to NetLogo model logic, including interface elements like sliders and buttons. Users can publish models as web artifacts and execute them in the client with the same agent rules used in desktop NetLogo.
Pros
- Runs NetLogo models inside a browser for quick sharing and classroom demos
- Browser UI supports sliders and buttons that drive simulation parameters
- Uses the familiar NetLogo agent model and visualization workflow
Cons
- Web execution limits access to the full NetLogo desktop ecosystem
- Browser sandboxing constrains file IO and system-level integrations
- Large models can face performance limits compared with desktop runs
Best For
Teaching and collaboration teams sharing interactive NetLogo agent simulations in-browser
Conclusion
After evaluating 8 ai in industry, AnyLogic stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right Agent Based Modelling Software
This buyer’s guide helps teams select agent based modelling software by mapping concrete capabilities to real modelling and deployment needs across AnyLogic, NetLogo, Mesa, Repast Simphony, GAMA, FLAME GPU, MASON, and NetLogo Web. It also covers how scheduling, spatial handling, experiment automation, and performance constraints affect outcomes when building ABMs. The guide connects common pitfalls like toolchain complexity and scaling limits to specific tools so shortlists stay focused on requirements.
What Is Agent Based Modelling Software?
Agent based modelling software builds simulations where individual agents follow interaction rules and state updates inside a simulated environment. These tools solve problems like emergent behavior in networks, crowd dynamics with discrete-event timing, and spatial diffusion using grids or continuous space. Typical outputs include time series metrics, parameter sweep comparisons, and visual monitoring of entities and variables during runs. Examples in this category include AnyLogic for integrated agent plus system dynamics and discrete-event workflows, and Mesa for Python-native agent and model abstractions with structured data collection across steps.
Key Features to Look For
These capabilities determine how fast models can be built, how reliably experiments can be repeated, and how well results can be measured across scenarios.
Multi-paradigm modelling workspace for connected experiments
AnyLogic supports agent-based modelling together with system dynamics and discrete-event timing in a single project environment, which fits teams building feedback loops across paradigms. This connected workflow also supports experiment-driven comparisons using parameter sweeps and statistical outputs so scenario results stay comparable.
Automated parameter sweeps with experiment execution and output collection
NetLogo uses BehaviorSpace to automate parameter sweeps and collect outputs tied directly to model runs, which suits iterative study design. GAMA also includes built-in experiment management that streamlines parameter sweeps and batch execution so spatial scenarios can be tested repeatedly without manual reruns.
Agent and model data collection built into the simulation workflow
Mesa includes a DataCollector that records agent and model variables across simulation steps, which supports structured metrics without relying on external logging. MASON provides custom data collection hooks tied to repeatable discrete-event stepping, which helps research teams record outcomes with deterministic control.
Spatial modelling with grids and continuous space plus runtime scheduling
Repast Simphony includes built-in grid and continuous space support with Repast-driven agent scheduling and context management, which is suited for ABMs that combine movement with scheduled behaviors. MASON also provides a discrete-event scheduler with efficient state updates that supports crowd, network, and ecological models where timing control matters.
Native GIS integration for geographically grounded simulations
GAMA offers native GIS and spatial layers so agents can interact with real geographic inputs through spatially explicit movement and rules. This built-in GIS support reduces reliance on external GIS pipelines because the platform connects spatial layers to agent behaviors and data collection.
GPU acceleration for high-throughput, large agent populations
FLAME GPU executes agent rules on the GPU so it scales agent counts far beyond CPU-based models. Teams that need fast iteration for parameter sweeps and spatially resolved phenomena use FLAME GPU to increase throughput while visual debugging and GPU-focused outputs help inspect emergent patterns.
How to Choose the Right Agent Based Modelling Software
A practical selection path starts with the simulation style, then checks experiment automation, then validates scaling and integration requirements against the tool’s execution model.
Match the modelling paradigm and scheduling semantics
AnyLogic is the strongest fit for projects that need agent-based logic plus system dynamics and discrete-event timing in one workflow. Mesa fits teams who want Python-native agent and model abstractions with explicit scheduler control, while MASON fits Java teams that prioritize a discrete-event scheduler with repeatable step logic.
Choose an experimentation workflow that matches how results get compared
NetLogo is built for parameter sweep studies using BehaviorSpace, which automates experiment runs and output collection for comparative analysis. GAMA also supports built-in experiment management for iterative development, batch runs, and scenario automation, while AnyLogic provides experiment tools for parameter sweeps and statistical outputs.
Validate spatial depth and environment data needs early
Repast Simphony supports both grids and continuous space plus spatial context management, which helps when movement and proximity must be expressed precisely. GAMA is the better match when simulations rely on GIS inputs and spatially grounded agent movement, and FLAME GPU is the better match when spatial ABMs must run with very large populations.
Plan for model performance and scaling constraints
FLAME GPU targets large-scale runs by executing on the GPU, so it fits throughput-heavy scenario testing with huge agent counts. NetLogo can face performance limits for very large agent counts compared with compiled ABM frameworks, and Mesa and Repast Simphony may require extra engineering to reach large-scale performance when many agents are involved.
Confirm integration and sharing requirements for stakeholders
NetLogo Web supports running NetLogo models directly in a browser with interface controls like sliders and buttons, which fits teaching and collaboration workflows that need quick sharing. AnyLogic supports Java-based extensibility for custom logic and specialized behaviors, and Mesa supports reproducible Python-based experiments that plug into external plotting and analysis.
Who Needs Agent Based Modelling Software?
Different ABM platforms target different production needs like integrated multi-paradigm modelling, spatial GIS depth, or GPU-scale throughput.
Teams integrating agent logic with process dynamics and discrete-event timing
AnyLogic fits teams building connected models where agents and resources interact with event timing and feedback loops, which is why it supports agent populations plus system dynamics plus discrete-event constructs. It also includes experiment tools for parameter sweeps and statistical outputs for decision and scenario analysis.
Teaching and research groups that prioritize interactive visualization and fast iteration
NetLogo excels at immediate visualization with interactive sliders, monitors, and plots during model runs, and it provides BehaviorSpace for automated parameter sweeps with output collection. NetLogo Web adds browser-native execution with interactive interface controls for classroom and collaboration sharing.
Researchers and engineers building Python ABMs that need structured metrics capture
Mesa fits Python-first workflows with explicit Agent and Model abstractions and a DataCollector for recording agent and model variables across simulation steps. It also supports testable and reproducible experiments using the same Python code base, which helps research pipelines.
Java-focused teams that need discrete-event control or Repast ecosystem integration
Repast Simphony is suited to Java ABM projects that need grid and continuous space plus Repast-driven scheduling, visualization support, and runtime hooks for charts and outputs. MASON fits Java research simulations that demand high-performance discrete-event simulation with deterministic runs through explicit seeding.
Spatially grounded projects that combine agents with GIS inputs
GAMA fits simulations that require native GIS integration, spatial layers, and spatially explicit agent movement and interaction. Its built-in experiment management supports scenario execution across desktop and batch runs while keeping visualization and data collection inside the platform.
Large-scale spatial ABMs that require GPU throughput for scenario sweeps
FLAME GPU fits teams that need very large agent populations and fast iteration by running agent rules on the GPU. Its GPU parallel agent execution and GPU-focused debugging support help inspect spatial behavior and emergent patterns during high-throughput parameter sweeps.
Common Mistakes to Avoid
Selection mistakes usually come from ignoring tool-specific workflow constraints like GUI versus code control, or from underestimating scaling and integration friction for large models.
Choosing a tool without matching the required modelling paradigm
AnyLogic supports agent-based modelling plus system dynamics and discrete-event timing, so it is the wrong fit to ignore when those paradigms must be connected. Mesa stays Python-native but leans on external visualization, so it can slow teams that expect an all-in-one dashboard experience without extra tooling.
Underestimating scaling limits with large agent counts
NetLogo can face performance limits for very large agent counts compared with compiled ABM frameworks, which can break throughput targets for scenario testing. FLAME GPU specifically targets GPU execution for large populations, so it is the better match for high-throughput spatial ABMs.
Forgetting that visualization and output customization vary by platform
Repast Simphony includes visualization and chart hooks, but output customization can feel restrictive for complex dashboards, which can add iteration time. MASON treats visualization as secondary to simulation internals, so teams needing rich interactive dashboards may prefer NetLogo or AnyLogic for built-in monitoring.
Treating experiment automation as an afterthought
NetLogo’s BehaviorSpace automates parameter sweeps and output collection, so skipping that workflow leads to manual reruns and inconsistent comparisons. GAMA also manages experiments in-platform, while AnyLogic provides parameter sweep experiments and statistical outputs, so these capabilities should be validated during early prototyping.
How We Selected and Ranked These Tools
we evaluated each tool on three sub-dimensions. Features accounted for 0.4 of the score, ease of use accounted for 0.3 of the score, and value accounted for 0.3 of the score. The overall rating is the weighted average of those three dimensions using overall = 0.40 × features + 0.30 × ease of use + 0.30 × value. AnyLogic separated itself from lower-ranked tools by combining multi-paradigm modelling across agents, system dynamics, and discrete-event timing in one workspace, which directly strengthened the features dimension for integrated connected simulations.
Frequently Asked Questions About Agent Based Modelling Software
Which agent based modelling platform combines multiple paradigms like agents, system dynamics, and discrete-event timing in one project?
AnyLogic supports agent-based modeling alongside system dynamics and discrete-event modeling inside a single project workspace. This setup lets teams connect agent interactions with feedback loops and event timing, then run experiments and parameter sweeps for scenario analysis.
Which tool is best for building ABMs with a model-first workflow and rapid interactive exploration?
NetLogo is designed around a model-first workflow with built-in plotting, monitors, and an interface that drives interactive simulation tuning. BehaviorSpace enables automated parameter sweeps tied directly to NetLogo runs, which fits experimentation and research prototypes.
What platform is most suitable for Python-native agent based modelling with reproducible experiments?
Mesa is Python-native and organizes agent logic, schedulers, and data collection utilities in a reusable codebase. DataCollector records agent and model variables across steps, and the same Python scaffolding can be used for repeatable runs and experiment scaffolding.
Which ABM environment targets Java teams that need structured scheduling, spatial grids, and integrated visualization hooks?
Repast Simphony provides a GUI-driven model builder plus a structured workflow for agents, behaviors, and context. It includes discrete-time scheduling, support for grids and continuous space, and built-in data collection and charting hooks during runs.
Which agent based modelling software is strongest for geographically grounded simulations with GIS data?
GAMA includes native GIS integration and spatial modeling features that fit geographically explicit agent movement and interaction. The platform supports an end-to-end workflow with experiment automation and built-in visualization tied to the modeling language.
Which tool is designed for GPU acceleration when simulations must scale to very large agent populations?
FLAME GPU executes agent rules on the GPU, which supports very large populations and faster iteration for spatial ABMs. Its workflow includes GPU-oriented outputs for inspecting spatial behavior and emergent patterns, which helps with high-throughput parameter sweeps.
Which platform is best for deterministic, discrete-event simulations where agent logic must stay separate from scheduling?
MASON is centered on fast discrete-event simulation with a clean separation between agents, environment, and scheduling. Its simulation scheduler and repeatable step logic support research workloads that prioritize deterministic outcomes and controlled experimentation.
How can teams share an ABM so other users can run it in a browser without local installation?
NetLogo Web runs NetLogo models directly in a browser, which removes installation barriers for collaborative use. It preserves interactive visualization tied to model logic and supports interface controls like sliders and buttons for in-browser execution.
When a project needs frequent parameter sweeps tied to model execution, which tools provide built-in experiment automation?
NetLogo uses BehaviorSpace to automate parameter sweeps and link outputs to specific model runs. AnyLogic supports experiments and parameter sweeps with statistical outputs, while Mesa provides experiment scaffolding using the same Python code for structured repeatable runs.
Which ABM platforms provide native spatial modeling features and what additional integration might be required?
Repast Simphony includes both grid and continuous space support inside its modeling environment, and it pairs space with agent scheduling through the Repast API. GAMA adds GIS integration for real geographic inputs, while FLAME GPU focuses on spatially resolved behavior with GPU parallel execution that benefits from GPU-focused debugging outputs.
Tools reviewed
Referenced in the comparison table and product reviews above.
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