{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-westworld-simulation","slug":"westworld-simulation","name":"“Westworld” simulation","type":"repo","url":"https://theolvs.github.io/westworld/","page_url":"https://unfragile.ai/westworld-simulation","categories":["ai-agents"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-westworld-simulation__cap_0","uri":"capability://automation.workflow.multi.agent.environment.initialization.and.configuration","name":"multi-agent environment initialization and configuration","description":"Initializes a simulation environment with configurable agent populations, spatial boundaries, and environmental parameters. The system uses a declarative configuration approach to define agent types, counts, initial positions, and behavioral parameters, then instantiates the simulation world with these specifications. Supports heterogeneous agent types within a single environment and allows runtime parameter adjustment before simulation execution.","intents":["Set up a simulation with multiple agent types and define their initial population sizes and spatial distribution","Configure environmental constraints like world boundaries, resource availability, and environmental dynamics","Create reproducible simulation scenarios by specifying seed values and parameter configurations"],"best_for":["researchers prototyping multi-agent systems and emergent behavior studies","game developers building NPC ecosystems and crowd simulation systems","teams modeling organizational dynamics, market competition, or social systems"],"limitations":["Configuration is typically static at initialization — dynamic agent spawning/removal during simulation may require custom extensions","No built-in validation of configuration consistency — invalid parameter combinations may cause runtime failures","Scalability limited by memory constraints; typical deployments handle 100-10,000 agents depending on agent complexity"],"requires":["JavaScript/TypeScript runtime environment","Understanding of agent-based modeling concepts","Configuration file or programmatic API access"],"input_types":["JSON configuration objects","programmatic API calls with parameter objects"],"output_types":["initialized simulation environment object","agent registry with instantiated agent instances"],"categories":["automation-workflow","multi-agent-systems"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-westworld-simulation__cap_1","uri":"capability://automation.workflow.discrete.time.step.simulation.execution","name":"discrete-time step simulation execution","description":"Executes the simulation by advancing time in discrete steps, where each step triggers perception, decision-making, and action phases for all agents in sequence or parallel. The execution engine manages the simulation loop, coordinates agent state updates, handles collision detection and interaction resolution, and maintains temporal consistency across the agent population. Supports configurable step duration and execution modes (synchronous or asynchronous).","intents":["Run a simulation forward in time and observe how agent behaviors and interactions evolve across multiple time steps","Synchronize agent actions so that all agents perceive the world state at the same logical time before acting","Pause, resume, or step through simulation execution for debugging and analysis"],"best_for":["researchers analyzing emergent behavior patterns and system-level dynamics","game developers implementing turn-based or frame-based NPC behavior systems","educators building interactive simulations for teaching complex systems concepts"],"limitations":["Synchronous execution mode can create performance bottlenecks with large agent populations; asynchronous mode may introduce subtle ordering dependencies","No built-in time-scaling or variable step-size support — all steps are uniform duration","State consistency guarantees depend on proper agent implementation; race conditions possible in parallel execution without careful synchronization"],"requires":["initialized simulation environment with agents","agent implementations with perception and action methods","JavaScript/TypeScript runtime with sufficient memory for agent state"],"input_types":["step count or duration parameter","execution mode configuration (sync/async)"],"output_types":["updated agent state objects","simulation event logs","interaction records"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-westworld-simulation__cap_10","uri":"capability://code.generation.editing.extensible.agent.type.system.with.inheritance","name":"extensible agent type system with inheritance","description":"Provides a class-based or prototype-based system for defining agent types with shared properties, behaviors, and state management. Agents can inherit from base classes or mixins to reuse common functionality, and custom agent types can override or extend inherited methods. The system supports multiple inheritance or composition patterns to combine behaviors from different agent archetypes.","intents":["Define reusable agent templates that can be instantiated with different parameters","Create specialized agent types (predators, prey, resources, obstacles) with type-specific behaviors","Share common functionality across agent types without code duplication"],"best_for":["developers building complex simulations with many agent types","researchers implementing heterogeneous agent populations with shared base behaviors","teams maintaining large codebases where code reuse is critical"],"limitations":["Deep inheritance hierarchies can become difficult to understand and maintain; composition is often preferable but requires more boilerplate","Method resolution order (MRO) in multiple inheritance can create subtle bugs if not carefully managed","No built-in support for dynamic agent type creation at runtime; agent types are typically defined statically"],"requires":["JavaScript/TypeScript class system or prototype chain","understanding of OOP principles and inheritance patterns"],"input_types":["agent class definitions","inheritance specifications (parent class, mixins)"],"output_types":["agent instances with inherited properties and methods","agent type registry"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-westworld-simulation__cap_11","uri":"capability://tool.use.integration.event.driven.agent.communication.and.messaging","name":"event-driven agent communication and messaging","description":"Enables agents to communicate through an event or message-passing system where agents can emit events and subscribe to event types. The system maintains an event queue, delivers messages to subscribed agents, and ensures message ordering and delivery guarantees. Supports both direct agent-to-agent messaging and broadcast events that reach all interested agents.","intents":["Allow agents to communicate without direct references, reducing coupling and enabling dynamic agent networks","Implement group behaviors where agents coordinate through shared events or signals","Model realistic communication constraints like message delay or bandwidth limitations"],"best_for":["researchers modeling communication-based coordination and swarm behavior","game developers implementing NPC communication and group tactics","teams building loosely-coupled agent systems where agents shouldn't know about each other"],"limitations":["Event-driven systems can be difficult to debug because message flow is implicit and non-obvious","No built-in support for message ordering guarantees across multiple agents; race conditions possible if not carefully managed","Event queue can grow unbounded if agents emit events faster than they're consumed"],"requires":["event emitter/listener infrastructure","event type definitions","agent subscription management"],"input_types":["event objects with type and payload","event subscriptions (agent, event type)"],"output_types":["delivered messages to subscribed agents","event delivery logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-westworld-simulation__cap_2","uri":"capability://planning.reasoning.agent.behavior.definition.and.policy.execution","name":"agent behavior definition and policy execution","description":"Provides a framework for defining agent behaviors through policy functions that map perceived state to actions. Agents execute their assigned policies each simulation step, receiving a perception object containing local environmental state and returning action commands. The system supports behavior composition, where agents can switch between multiple policies based on conditions, and includes built-in support for common behavior patterns like movement, interaction, and state transitions.","intents":["Define how individual agents perceive their environment and decide what actions to take each simulation step","Implement complex behaviors by composing simpler behavior primitives or switching between behavior modes based on agent state","Test different behavioral strategies and compare their effects on system-level outcomes"],"best_for":["AI researchers implementing reinforcement learning agents or rule-based behavior systems","game developers creating NPC behavior trees or state machines","social scientists modeling human decision-making and organizational behavior"],"limitations":["Policy functions are synchronous — no built-in support for asynchronous decision-making or external API calls within agent perception/action cycles","No native support for learning or adaptation — agents execute static policies unless explicitly implemented","Perception is limited to what the framework provides; custom perception logic requires extending the agent class"],"requires":["JavaScript/TypeScript function definitions","understanding of agent state representation","simulation environment with perception infrastructure"],"input_types":["perception object (local state, neighbor positions, environmental data)","agent internal state"],"output_types":["action objects (movement vectors, interaction targets, state changes)","behavior transition signals"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-westworld-simulation__cap_3","uri":"capability://data.processing.analysis.spatial.environment.representation.and.neighbor.queries","name":"spatial environment representation and neighbor queries","description":"Maintains a spatial representation of the environment (typically grid-based or continuous coordinate space) and provides efficient neighbor/proximity queries for agents. The system tracks agent positions, updates spatial indices as agents move, and allows agents to query nearby entities within a specified radius or grid neighborhood. Uses spatial partitioning (e.g., quadtrees, grid cells) to optimize query performance from O(n) to O(log n) or O(1) depending on implementation.","intents":["Allow agents to efficiently find nearby agents or environmental features without scanning the entire population","Implement local interaction rules where agents only interact with neighbors within a certain distance","Support spatial analysis and visualization of agent distributions and clustering patterns"],"best_for":["simulations with 1000+ agents where naive O(n²) interaction checking becomes prohibitive","researchers studying spatial patterns, clustering, and self-organization","game developers implementing local perception and interaction systems for NPCs"],"limitations":["Grid-based spatial indexing introduces quantization artifacts and edge effects at cell boundaries","Continuous coordinate spaces require careful handling of floating-point precision and rounding errors","Dynamic spatial index updates add computational overhead; performance degrades if agents move very frequently"],"requires":["agent position data (2D or 3D coordinates)","environment bounds definition","spatial index data structure (grid, quadtree, or similar)"],"input_types":["query position (agent location)","search radius or neighborhood definition"],"output_types":["list of nearby agent objects","distance values to neighbors","spatial grid cell contents"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-westworld-simulation__cap_4","uri":"capability://automation.workflow.agent.to.agent.interaction.and.collision.resolution","name":"agent-to-agent interaction and collision resolution","description":"Detects when agents occupy the same or overlapping space and executes interaction logic to resolve collisions or trigger behaviors. The system identifies collision pairs using spatial queries, applies interaction rules (e.g., agents merge, repel, exchange resources), and updates agent state accordingly. Supports both hard constraints (agents cannot occupy same space) and soft interactions (agents influence each other without physical collision).","intents":["Prevent agents from occupying the same location and handle the physical consequences of collision","Trigger special behaviors when agents meet (e.g., predator-prey interactions, social bonding, resource exchange)","Model realistic physical constraints like pushing, bouncing, or merging behaviors"],"best_for":["ecological simulations with predator-prey dynamics or resource competition","social simulations modeling group formation and interaction","game environments with physics-based NPC interactions"],"limitations":["Collision resolution order matters — resolving collisions sequentially can produce different outcomes than simultaneous resolution, creating non-determinism","No built-in support for complex multi-body collisions or constraint satisfaction","Interaction logic is typically hardcoded per agent type; scaling to many agent types requires significant code duplication"],"requires":["spatial indexing system for collision detection","agent position and size/radius data","interaction rule definitions"],"input_types":["collision pair (two agent objects)","interaction type or rule identifier"],"output_types":["updated agent positions","modified agent state (health, resources, etc.)","interaction event records"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-westworld-simulation__cap_5","uri":"capability://data.processing.analysis.agent.state.persistence.and.history.tracking","name":"agent state persistence and history tracking","description":"Records agent state changes across simulation steps, maintaining a history of agent attributes, positions, and interactions. The system captures snapshots of agent state at configurable intervals or on-demand, allowing post-simulation analysis and visualization of agent trajectories and behavior evolution. Supports filtering and querying historical data to extract specific agent properties or interaction sequences.","intents":["Analyze how individual agent behavior and state evolve over the course of a simulation","Reconstruct agent trajectories and interaction sequences for debugging or visualization","Extract statistical summaries of agent properties across time for research analysis"],"best_for":["researchers analyzing emergent behavior patterns and system dynamics","educators creating visualizations of simulation evolution","developers debugging agent behavior and interaction issues"],"limitations":["Full state history can consume significant memory for large simulations with many agents and long durations","No built-in compression or sampling strategies — storing every step for every agent quickly becomes prohibitive","Historical data is immutable after recording; no support for retroactive state modifications or time-travel debugging"],"requires":["simulation execution with state tracking enabled","storage capacity proportional to (agents × steps × state_size)","query interface or data export functionality"],"input_types":["agent state objects","timestamp or step number","filter criteria (agent ID, property name, etc.)"],"output_types":["state snapshots (JSON or structured format)","time-series data for visualization","statistical summaries (mean, variance, etc.)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-westworld-simulation__cap_6","uri":"capability://image.visual.simulation.visualization.and.real.time.monitoring","name":"simulation visualization and real-time monitoring","description":"Provides visualization capabilities to display agent positions, movements, and interactions in real-time or from recorded history. The system renders agents as visual elements (circles, sprites, etc.) on a 2D canvas or 3D scene, updates positions each simulation step, and can overlay information like agent state, interaction networks, or heatmaps. Supports both live visualization during simulation execution and playback of recorded simulation data.","intents":["Watch simulation execution in real-time to observe emergent behavior and validate agent interactions","Replay recorded simulations to analyze specific events or behavior patterns","Create visualizations for presentations, papers, or educational materials"],"best_for":["researchers validating simulation correctness and observing emergent patterns","educators teaching complex systems concepts through interactive visualization","game developers prototyping NPC behaviors and crowd dynamics"],"limitations":["Real-time visualization adds computational overhead; simulations with 10,000+ agents may require frame rate reduction or agent culling","Canvas-based rendering limits visual complexity; 3D visualization requires additional libraries and expertise","No built-in support for custom visual representations — extending visualization requires modifying rendering code"],"requires":["HTML5 Canvas or WebGL context","agent position and state data","browser with graphics acceleration support"],"input_types":["agent objects with position and visual properties","simulation state snapshots","visualization configuration (colors, sizes, overlays)"],"output_types":["rendered canvas/WebGL output","interactive visualization with mouse/keyboard controls","exportable images or video frames"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-westworld-simulation__cap_7","uri":"capability://automation.workflow.parameter.sweep.and.batch.simulation.execution","name":"parameter sweep and batch simulation execution","description":"Enables running multiple simulation instances with different parameter configurations to study how system behavior varies with input parameters. The system generates parameter combinations (Cartesian product or custom sampling), executes simulations in parallel or sequence, and aggregates results for comparative analysis. Supports both exhaustive parameter sweeps and targeted sensitivity analysis.","intents":["Systematically explore how changing agent parameters, environment settings, or behavior rules affects simulation outcomes","Identify critical parameter ranges where system behavior transitions or becomes unstable","Generate datasets for machine learning models that predict simulation outcomes from parameters"],"best_for":["researchers conducting sensitivity analysis and parameter optimization","teams tuning simulation parameters for specific desired behaviors","scientists exploring phase transitions and bifurcations in complex systems"],"limitations":["Computational cost scales multiplicatively with parameter space size; 10 parameters with 10 values each = 10 billion simulations","No built-in support for adaptive sampling or Bayesian optimization — exhaustive grids are inefficient for high-dimensional spaces","Parallel execution requires careful management of random seeds to ensure reproducibility across runs"],"requires":["parameter definitions with ranges or discrete values","simulation execution framework","sufficient computational resources (CPU cores, memory) for parallel execution"],"input_types":["parameter configuration objects","parameter ranges or value lists","execution strategy (parallel, sequential, batch size)"],"output_types":["aggregated results across parameter combinations","comparative statistics and visualizations","parameter-outcome correlation data"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-westworld-simulation__cap_8","uri":"capability://automation.workflow.reproducible.random.number.generation.and.seeding","name":"reproducible random number generation and seeding","description":"Provides deterministic random number generation through explicit seed management, ensuring that simulations with identical parameters and seeds produce identical results. The system seeds the random number generator before each simulation run and allows specifying seeds at the global, agent, or behavior level. Supports both pseudo-random sequences (for reproducibility) and true randomness (for non-deterministic exploration).","intents":["Reproduce exact simulation results for debugging, validation, and peer review","Generate multiple independent random runs with different seeds to study stochastic variation","Create deterministic test cases for simulation validation and regression testing"],"best_for":["researchers publishing simulation results that must be reproducible by others","developers testing simulation correctness across multiple runs","teams validating that code changes don't alter simulation behavior"],"limitations":["Reproducibility is fragile — changing agent execution order, adding logging, or modifying floating-point operations can break determinism","No built-in support for cross-platform reproducibility; different systems may produce different random sequences","Seeding at multiple levels (global, agent, behavior) can create confusion about which seed is actually used"],"requires":["random number generator with seed support","explicit seed specification in simulation configuration","consistent agent execution order across runs"],"input_types":["seed value (integer or string)","seed scope (global, per-agent, per-behavior)"],"output_types":["deterministic random number sequences","identical simulation results across runs with same seed"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-westworld-simulation__cap_9","uri":"capability://data.processing.analysis.performance.profiling.and.execution.metrics.collection","name":"performance profiling and execution metrics collection","description":"Collects runtime metrics including simulation step duration, agent computation time, spatial query performance, and memory usage. The system instruments key operations (perception, decision-making, collision detection) to measure execution time and resource consumption, then aggregates metrics across simulation steps. Supports both real-time metric reporting and post-simulation analysis.","intents":["Identify performance bottlenecks in agent behavior or simulation infrastructure","Measure how simulation performance scales with agent population size","Optimize agent implementations by comparing execution time before and after changes"],"best_for":["developers optimizing simulation performance for large agent populations","researchers studying computational complexity of agent-based models","teams profiling simulation overhead to determine feasible agent counts"],"limitations":["Profiling instrumentation adds overhead that distorts measurements; profiling results may not reflect production performance","No built-in support for distributed profiling across multiple machines","Memory profiling is limited to JavaScript heap; native memory usage (if using native modules) is not captured"],"requires":["simulation execution with profiling enabled","performance measurement infrastructure","sufficient logging/storage for metric collection"],"input_types":["operation/function to profile","profiling configuration (sampling rate, metrics to collect)"],"output_types":["execution time statistics (mean, percentiles, distribution)","memory usage snapshots","per-step performance breakdown"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["JavaScript/TypeScript runtime environment","Understanding of agent-based modeling concepts","Configuration file or programmatic API access","initialized simulation environment with agents","agent implementations with perception and action methods","JavaScript/TypeScript runtime with sufficient memory for agent state","JavaScript/TypeScript class system or prototype chain","understanding of OOP principles and inheritance patterns","event emitter/listener infrastructure","event type definitions"],"failure_modes":["Configuration is typically static at initialization — dynamic agent spawning/removal during simulation may require custom extensions","No built-in validation of configuration consistency — invalid parameter combinations may cause runtime failures","Scalability limited by memory constraints; typical deployments handle 100-10,000 agents depending on agent complexity","Synchronous execution mode can create performance bottlenecks with large agent populations; asynchronous mode may introduce subtle ordering dependencies","No built-in time-scaling or variable step-size support — all steps are uniform duration","State consistency guarantees depend on proper agent implementation; race conditions possible in parallel execution without careful synchronization","Deep inheritance hierarchies can become difficult to understand and maintain; composition is often preferable but requires more boilerplate","Method resolution order (MRO) in multiple inheritance can create subtle bugs if not carefully managed","No built-in support for dynamic agent type creation at runtime; agent types are typically defined statically","Event-driven systems can be difficult to debug because message flow is implicit and non-obvious","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.689Z","last_scraped_at":"2026-05-03T14:00:10.321Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=westworld-simulation","compare_url":"https://unfragile.ai/compare?artifact=westworld-simulation"}},"signature":"+esEEIgQ1arBfls+dXQ+g4x2PbwdFs7JeQdSo4jCDqeuDYGPcGQxlBebkLcNNnc00yErQ8cXIYQ/2HKj/lbRCQ==","signedAt":"2026-06-21T04:26:27.718Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/westworld-simulation","artifact":"https://unfragile.ai/westworld-simulation","verify":"https://unfragile.ai/api/v1/verify?slug=westworld-simulation","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}