The API of Agents.jl is defined on top of the fundamental structures AgentBasedModel, Space, AbstractAgent which are described in the Tutorial page. In this page we list the remaining API functions, which constitute the bulk of Agents.jl functionality.

Agent/model retrieval and access

getindex(model::ABM, id::Integer)

Return an agent given its ID.

getproperty(model::ABM, :prop)

Return a property with name :prop from the current model, assuming the model properties are either a dictionary with key type Symbol or a Julia struct. For example, if a model has the set of properties Dict(:weight => 5, :current => false), retrieving these values can be obtained via model.weight.

The property names :agents, :space, :scheduler, :properties, :maxid are internals and should not be accessed by the user.

seed!(model [, seed])

Reseed the random number pool of the model with the given seed or a random one, when using a pseudo-random number generator like MersenneTwister.

random_agent(model) → agent

Return a random agent from the model.

random_agent(model, condition) → agent

Return a random agent from the model that satisfies condition(agent) == true. The function generates a random permutation of agent IDs and iterates through them. If no agent satisfies the condition, nothing is returned instead.


Return an iterator over all agent IDs of the model.


Available spaces

Here we list the spaces that are available "out of the box" from Agents.jl. To create your own, see Creating a new space type.

Discrete spaces


Create a GraphSpace instance that is underlined by an arbitrary graph from Graphs.jl. GraphSpace represents a space where each node (i.e. position) of a graph can hold an arbitrary amount of agents, and each agent can move between the nodes of the graph. The position type for this space is Int, use GraphAgent for convenience.

Graphs.nv and Graphs.ne can be used in a model with a GraphSpace to obtain the number of nodes or edges in the graph. The underlying graph can be altered using add_vertex! and rem_vertex!.

An example using GraphSpace is SIR model for the spread of COVID-19.

If you want to model social networks, where each agent is equivalent with a node of a graph, you're better of using nothing as the model space, and using a graph from Graphs.jl directly in the model parameters, as shown in the Social networks with Graphs.jl integration example.

Distance specification

In functions like nearby_ids, distance for GraphSpace means the degree of neighbors in the graph (thus distance is always an integer). For example, for r=2 includes first and second degree neighbors. For 0 distance, the search occurs only on the origin node.

In functions like nearby_ids the keyword neighbor_type=:default can be used to select differing neighbors depending on the underlying graph directionality type.

  • :default returns neighbors of a vertex (position). If graph is directed, this is equivalent to :out. For undirected graphs, all options are equivalent to :out.
  • :all returns both :in and :out neighbors.
  • :in returns incoming vertex neighbors.
  • :out returns outgoing vertex neighbors.
GridSpace(d::NTuple{D, Int}; periodic = true, metric = :chebyshev)

Create a GridSpace that has size given by the tuple d, having D ≥ 1 dimensions. Optionally decide whether the space will be periodic and what will be the distance metric. The position type for this space is NTuple{D, Int}, use GridAgent for convenience. Valid positions have indices in the range 1:d[i] for the i-th dimension.

An example using GridSpace is Schelling's segregation model.

Distance specification

The typical terminology when searching neighbors in agent based modelling is "Von Neumann" neighborhood or "Moore" neighborhoods. However, because Agents.jl provides a much more powerful infastructure for finding neighbors, both in arbitrary dimensions but also of arbitrary neighborhood size, this established terminology is no longer appropriate. Instead, distances that define neighborhoods are specified according to a proper metric space, that is both well defined for any distance, and applicable to any dimensionality.

The allowed metrics are (and see docs online for a plotted example):

  • :chebyshev metric means that the r-neighborhood of a position are all positions within the hypercube having side length of 2*floor(r) and being centered in the origin position. This is similar to "Moore" for r = 1 and two dimensions.

  • :manhattan metric means that the r-neighborhood of a position are all positions whose cartesian indices have Manhattan distance ≤ r from the cartesian index of the origin position. This similar to "Von Neumann" for r = 1 and two dimensions.

  • :euclidean metric means that the r-neighborhood of a position are all positions whose cartesian indices have Euclidean distance ≤ r from the cartesian index of the origin position.

Advanced dimension-dependent distances in Chebyshev metric

If metric = :chebyshev, some advanved specification of distances is allowed when providing r to functions like nearby_ids.

  1. r::NTuple{D,Int} such as r = (5, 2). This would mean a distance of 5 in the first dimension and 2 in the second. This can be useful when different coordinates in the space need to be searched with different ranges, e.g., if the space corresponds to a full building, with the third dimension the floor number.
  2. r::Vector{Tuple{Int,UnitRange{Int}}} such as r = [(1, -1:1), (3, 1:2)]. This allows explicitly specifying the difference between position indices in each specified dimension. The example r = [(1, -1:1), (3, 1:2)] when given to e.g., nearby_ids, would search dimension 1 one step of either side of the current position (as well as the current position since 0 ∈ -1:1) and would search the third dimension one and two positions above current. Unspecified dimensions (like the second in this example) are searched throughout all their possible ranges.

See the Battle Royale example for usage of this advanced specification of dimension-dependent distances where one dimension is used as a categorical one.

GridSpaceSingle(d::NTuple{D, Int}; periodic = true, metric = :chebyshev)

This is a specialized version of GridSpace that allows only one agent per position, and utilizes this knowledge to offer significant performance gains versus GridSpace.

This space reserves agent ID = 0 for internal usage. Agents should be initialized with non-zero IDs, either positive or negative. This is not checked internally.

All arguments and keywords behave exactly as in GridSpace.


Here is a specification of how the metrics look like:

Continuous spaces

ContinuousSpace(extent::NTuple{D, <:Real}; kwargs...)

Create a D-dimensional ContinuousSpace in range 0 to (but not including) extent. Your agent positions (field pos) must be of type NTuple{D, <:Real}, and it is strongly recommend that agents also have a field vel::NTuple{D, <:Real} to use in conjunction with move_agent!. Use ContinuousAgent for convenience.

ContinuousSpace is a representation of agent dynamics on a continuous medium where agent position, orientation, and speed, are true floats. In addition, support is provided for representing spatial properties in a model that contains a ContinuousSpace. Spatial properties (which typically are contained in the model properties) can either be functions of the position vector, f(pos) = value, or AbstractArrays, representing discretizations of spatial data that may not be available in analytic form. In the latter case, the position is automatically mapped into the discretization represented by the array. Use get_spatial_property to access spatial properties in conjuction with ContinuousSpace.

See also Continuous space exclusives on the online docs for more functionality. An example using continuous space is the Flocking model.

Distance specification

Distances specified by r in functions like nearby_ids are always based on the Euclidean distance between two points in ContinuousSpace.

In ContinuousSpace nearby_* searches are accelerated using a grid system, see discussion around the keyword spacing below. nearby_ids is not an exact search, but can be a possible over-estimation, including agent IDs whose distance slightly exceeds r with "slightly" being as much as spacing. If you want exact searches use the slower nearby_ids_exact.


  • periodic = true: Whether the space is periodic or not. If set to false an error will occur if an agent's position exceeds the boundary.
  • spacing::Real = minimum(extent)/20: Configures an internal compartment spacing that is used to accelerate nearest neighbor searches like nearby_ids. The compartments are actually a full instance of GridSpace in which agents move. All dimensions in extent must be completely divisible by spacing. There is no best choice for the value of spacing and if you need optimal performance it's advised to set up a benchmark over a range of choices. The finer the spacing, the faster and more accurate the inexact version of nearby_ids becomes. However, a finer spacing also means slower move_agent!, as agents change compartments more often.
  • update_vel!: A function, update_vel!(agent, model) that updates the agent's velocity before the agent has been moved, see move_agent!. You can of course change the agents' velocities during the agent interaction, the update_vel! functionality targets spatial force fields acting on the agents individually (e.g. some magnetic field). If you use update_vel!, the agent type must have a field vel::NTuple{D, <:Real}.
OpenStreetMapSpace(path::AbstractString; kwargs...)

Create a space residing on the Open Street Map (OSM) file provided via path. This space represents the underlying map as a continuous entity choosing accuracy over performance. The map is represented as a graph, consisting of nodes connected by edges. Nodes are not necessarily intersections, and there may be multiple nodes on a road joining two intersections. Agents move along the available roads of the map using routing, see below.

The functionality related to Open Street Map spaces is in the submodule OSM. An example of its usage can be found in Zombie Outbreak in a City.

The OSMAgent

The base properties for an agent residing on an OSMSpace are as follows:

mutable struct Agent <: AbstractAgent

Current position tuple is represented as (first intersection index, second intersection index, distance travelled). The indices are the indices of the nodes of the graph that internally represents the map. Functions like OSM.nearest_node or OSM.nearest_road can help find those node indices from a (lon, lat) real world coordinate. The distance travelled is in the units of weight_type. This ensures that the map is a continuous kind of space, as an agent can truly be at any possible point on an existing road.

Use OSMAgent for convenience.

Obtaining map files

Maps files can be downloaded using the functions provided by LightOSM.jl. Agents.jl also re-exports OSM.download_osm_network, the main function used to download maps and provides a test map in OSM.test_map. An example usage to download the map of London to "london.json":

    place_name = "London",
    save_to_file_location = "london.json"

The length of an edge between two nodes is specified in the units of the map's weight_type as listed in the documentation for LightOSM.OSMGraph. The possible weight_types are:

  • :distance: The distance in kilometers of an edge
  • :time: The time in hours to travel along an edge at the maximum speed allowed on that road
  • :lane_efficiency: Time scaled by number of lanes

The default weight_type used is :distance.

All kwargs are propagated to LightOSM.graph_from_file.

Routing with OSM

You can use plan_route! or plan_random_route!. To actually move along a planned route use move_along_route!.


Adding agents

add_agent!(agent::AbstractAgent [, pos], model::ABM) → agent

Add the agent to the model in the given position. If pos is not given, the agent is added to a random position. The agent's position is always updated to match position, and therefore for add_agent! the position of the agent is meaningless. Use add_agent_pos! to use the agent's position.

The type of pos must match the underlying space position type.

add_agent!([pos,] model::ABM, args...; kwargs...) → newagent

Create and add a new agent to the model using the constructor of the agent type of the model. Optionally provide a position to add the agent to as first argument, which must match the space position type.

This function takes care of setting the agent's id and position. The extra provided args... and kwargs... are propagated to other fields of the agent constructor (see example below).

add_agent!([pos,] A::Type, model::ABM, args...; kwargs...) → newagent

Use this version for mixed agent models, with A the agent type you wish to create (to be called as A(id, pos, args...; kwargs...)), because it is otherwise not possible to deduce a constructor for A.


using Agents
mutable struct Agent <: AbstractAgent
Agent(id, pos; w=0.5, k=false) = Agent(id, pos, w, k) # keyword constructor
model = ABM(Agent, GraphSpace(complete_digraph(5)))

add_agent!(model, 1, 0.5, true) # incorrect: id/pos is set internally
add_agent!(model, 0.5, true) # correct: w becomes 0.5
add_agent!(5, model, 0.5, true) # add at position 5, w becomes 0.5
add_agent!(model; w = 0.5) # use keywords: w becomes 0.5, k becomes false
add_agent_pos!(agent::AbstractAgent, model::ABM) → agent

Add the agent to the model at the agent's own position.

nextid(model::ABM) → id

Return a valid id for creating a new agent with it.

random_position(model) → pos

Return a random position in the model's space (always with appropriate Type).


Moving agents

move_agent!(agent [, pos], model::ABM) → agent

Move agent to the given position, or to a random one if a position is not given. pos must have the appropriate position type depending on the space type.

The agent's position is updated to match pos after the move.

move_agent!(agent::A, model::ABM{<:ContinuousSpace,A}, dt::Real)

Propagate the agent forwards one step according to its velocity, after updating the agent's velocity (if configured using update_vel!, see ContinuousSpace).

For this continuous space version of move_agent!, the "time evolution" is a trivial Euler scheme with dt the step size, i.e. the agent position is updated as agent.pos += agent.vel * dt.

Unlike move_agent!(agent, [pos,] model), this function respects the space size. For non-periodic spaces, agents will walk up to, but not reach, the space extent. For periodic spaces movement properly wraps around the extent.

walk!(agent, direction::NTuple, model; ifempty = true)

Move agent in the given direction respecting periodic boundary conditions. For non-periodic spaces, agents will walk to, but not exceed the boundary value. Available for both AbstractGridSpace and ContinuousSpaces.

The type of direction must be the same as the space position. AbstractGridSpace asks for Int, and ContinuousSpace for Float64 tuples, describing the walk distance in each direction. direction = (2, -3) is an example of a valid direction on a AbstractGridSpace, which moves the agent to the right 2 positions and down 3 positions. Agent velocity is ignored for this operation in ContinuousSpace.


  • ifempty will check that the target position is unoccupied and only move if that's true. Available only on AbstractGridSpace.

Example usage in Battle Royale.

walk!(agent, rand, model)

Invoke a random walk by providing the rand function in place of direction. For AbstractGridSpace, the walk will cover ±1 positions in all directions, ContinuousSpace will reside within [-1, 1].

get_direction(from, to, model::ABM)

Return the direction vector from the position from to position to taking into account periodicity of the space.


Movement with paths

For OpenStreetMapSpace, and GridSpace/ContinuousSpace using Pathfinding.Pathfinder, a special movement method is available.

plan_route!(agent, dest, model::ABM{<:OpenStreetMapSpace};
            return_trip = false, kwargs...) → success

Plan a route from the current position of agent to the location specified in dest, which can be an intersection or a point on a road. Overwrite any existing route.

If return_trip = true, a route will be planned from start ⟶ finish ⟶ start. All other keywords are passed to LightOSM.shortest_path.

Return true if a path to dest exists, and hence the route planning was successful. Otherwise return false. Specifying return_trip = true also requires the existence of a return path for a route to be planned.

plan_route!(agent, dest, pathfinder::AStar{D})

Calculate and store the shortest path to move the agent from its current position to dest (a position e.g. (1, 5) or (1.3, 5.2)) using the provided pathfinder.

Use this method in conjuction with move_along_route!.

plan_best_route!(agent, dests, pathfinder::AStar{D}; kwargs...)

Calculate, store, and return the best path to move the agent from its current position to a chosen destination taken from dests using pathfinder.

The condition = :shortest keyword retuns the shortest path which is shortest out of the possible destinations. Alternatively, the :longest path may also be requested.

Return the position of the chosen destination. Return nothing if none of the supplied destinations are reachable.

move_along_route!(agent, model::ABM{<:OpenStreetMapSpace}, distance::Real) → remaining

Move an agent by distance along its planned route. Units of distance are as specified by the underlying graph's weight_type. If the provided distance is greater than the distance to the end of the route, return the remaining distance. Otherwise, return 0. 0 is also returned if is_stationary(agent, model).

move_along_route!(agent, model::ABM{<:GridSpace{D}}, pathfinder::AStar{D})

Move agent for one step along the route toward its target set by plan_route!

For pathfinding in models with GridSpace.

If the agent does not have a precalculated path or the path is empty, it remains stationary.

move_along_route!(agent, model::ABM{<:ContinuousSpace{D}}, pathfinder::AStar{D}, speed, dt = 1.0)

Move agent for one step along the route toward its target set by plan_route! at the given speed and timestep dt.

For pathfinding in models with ContinuousSpace

If the agent does not have a precalculated path or the path is empty, it remains stationary.

is_stationary(agent, model)

Return true if agent has reached the end of its route, or no route has been set for it. Used in setups where using move_along_route! is valid.

is_stationary(agent, astar::AStar)

Same, but for pathfinding with A*.


Removing agents

kill_agent!(agent::AbstractAgent, model::ABM)
kill_agent!(id::Int, model::ABM)

Remove an agent from the model.

Pathfinding.kill_agent!(agent, model, pathfinder)

The same as kill_agent!(agent, model), but also removes the agent's path data from pathfinder.


Kill all the agents of the model.

genocide!(model::ABM, n::Int)

Kill the agents whose IDs are larger than n.

genocide!(model::ABM, IDs)

Kill the agents with the given IDs.

genocide!(model::ABM, f::Function)

Kill all agents where the function f(agent) returns true.

sample!(model::ABM, n [, weight]; kwargs...)

Replace the agents of the model with a random sample of the current agents with size n.

Optionally, provide a weight: Symbol (agent field) or function (input agent out put number) to weight the sampling. This means that the higher the weight of the agent, the higher the probability that this agent will be chosen in the new sampling.


  • replace = true : whether sampling is performed with replacement, i.e. all agents can

be chosen more than once.

Example usage in Wright-Fisher model of evolution.


Space utility functions

normalize_position(pos, model::ABM{<:Union{AbstractGridSpace,ContinuousSpace}})

Return the position pos normalized for the extents of the space of the given model. For periodic spaces, this wraps the position along each dimension, while for non-periodic spaces this clamps the position to the space extent.


Discrete space exclusives

positions(model::ABM{<:DiscreteSpace}) → ns

Return an iterator over all positions of a model with a discrete space.

positions(model::ABM{<:DiscreteSpace}, by::Symbol) → ns

Return all positions of a model with a discrete space, sorting them using the argument by which can be:

  • :random - randomly sorted
  • :population - positions are sorted depending on how many agents they accommodate. The more populated positions are first.
ids_in_position(position, model::ABM{<:DiscreteSpace})
ids_in_position(agent, model::ABM{<:DiscreteSpace})

Return the ids of agents in the position corresponding to position or position of agent.

id_in_position(pos, model::ABM{<:GridSpaceSingle}) → id

Return the agent ID in the given position. This will be 0 if there is no agent in this position.

This is similar to ids_in_position, but specialized for GridSpaceSingle. See also isempty.

agents_in_position(position, model::ABM{<:DiscreteSpace})
agents_in_position(agent, model::ABM{<:DiscreteSpace})

Return the agents in the position corresponding to position or position of agent.

fill_space!([A ,] model::ABM{<:DiscreteSpace,A}, args...; kwargs...)
fill_space!([A ,] model::ABM{<:DiscreteSpace,A}, f::Function; kwargs...)

Add one agent to each position in the model's space. Similarly with add_agent!, the function creates the necessary agents and the args...; kwargs... are propagated into agent creation. If instead of args... a function f is provided, then args = f(pos) is the result of applying f where pos is each position (tuple for grid, integer index for graph).

An optional first argument is an agent type to be created, and targets mixed agent models where the agent constructor cannot be deduced (since it is a union).


Return true if there are any positions in the model without agents.

random_empty(model::ABM{<:DiscreteSpace}, cutoff = 0.998)

Return a random position without any agents, or nothing if no such positions exist. cutoff switches the search algorithm from probabalistic to a filter. Specifically, when clamp(nagents(model)/total_positions, 0.0, 1.0) < cutoff, then the algorithm is probabilistic.

add_agent_single!(agent, model::ABM{<:DiscreteSpace}) → agent

Add the agent to a random position in the space while respecting a maximum of one agent per position, updating the agent's position to the new one.

This function does nothing if there aren't any empty positions.

add_agent_single!(model::ABM{<:DiscreteSpace}, properties...; kwargs...)

Same as add_agent!(model, properties...; kwargs...) but ensures that it adds an agent into a position with no other agents (does nothing if no such position exists).

move_agent_single!(agent, model::ABM{<:DiscreteSpace}; cutoff) → agent

Move agent to a random position while respecting a maximum of one agent per position. If there are no empty positions, the agent won't move.

The keyword cutoff = 0.998 is sent to random_empty.

isempty(position, model::ABM{<:DiscreteSpace})

Return true if there are no agents in position.


GraphSpace exclusives

add_edge!(model::ABM{<:GraphSpace},  args...; kwargs...)

Add a new edge (relationship between two positions) to the graph. Returns a boolean, true if the operation was successful.

args and kwargs are directly passed to the add_edge! dispatch that acts the underlying graph type.

rem_edge!(model::ABM{<:GraphSpace}, n, m)

Remove an edge (relationship between two positions) from the graph. Returns a boolean, true if the operation was successful.

rem_vertex!(model::ABM{<:GraphSpace}, n::Int)

Remove node (i.e. position) n from the model's graph. All agents in that node are killed.

Warning: Graphs.jl (and thus Agents.jl) swaps the index of the last node with that of the one to be removed, while every other node remains as is. This means that when doing rem_vertex!(n, model) the last node becomes the n-th node while the previous n-th node (and all its edges and agents) are deleted.


ContinuousSpace exclusives

nearby_ids_exact(x, model, r = 1)

Return an iterator over agent IDs nearby x (a position or an agent). Only valid for ContinuousSpace models. Use instead of nearby_ids for a slower, but 100% accurate version. See ContinuousSpace for more details.

nearest_neighbor(agent, model::ABM{<:ContinuousSpace}, r) → nearest

Return the agent that has the closest distance to given agent. Return nothing if no agent is within distance r.

get_spatial_property(pos::NTuple{D, Float64}, property::AbstractArray, model::ABM)

Convert the continuous agent position into an appropriate index of property, which represents some discretization of a spatial field over a ContinuousSpace. Then, return property[index]. To get the index directly, for e.g. mutating the property in-place, use get_spatial_index.

get_spatial_property(pos::NTuple{D, Float64}, property::Function, model::ABM)

Literally equivalent with property(pos, model), provided just for syntax consistency.

get_spatial_index(pos, property::AbstractArray, model::ABM)

Convert the continuous agent position into an appropriate index of property, which represents some discretization of a spatial field over a ContinuousSpace.

The dimensionality of property and the continuous space do not have to match. If property has lower dimensionalty than the space (e.g. representing some surface property in 3D space) then the front dimensions of pos will be used to index.

interacting_pairs(model, r, method; scheduler = model.scheduler) → piter

Return an iterator that yields unique pairs of agents (a, b) that are close neighbors to each other, within some interaction radius r.

This function is usefully combined with model_step!, when one wants to perform some pairwise interaction across all pairs of close agents once (and does not want to trigger the event twice, both with a and with b, which would be unavoidable when using agent_step!). This means, that if a pair (a, b) exists, the pair (b, a) is not included in the iterator!

Use piter.pairs to get a vector of pair IDs from the iterator.

The argument method provides three pairing scenarios

  • :all: return every pair of agents that are within radius r of each other, not only the nearest ones.
  • :nearest: agents are only paired with their true nearest neighbor (existing within radius r). Each agent can only belong to one pair, therefore if two agents share the same nearest neighbor only one of them (sorted by distance, then by next id in scheduler) will be paired.
  • :types: For mixed agent models only. Return every pair of agents within radius r (similar to :all), only capturing pairs of differing types. For example, a model of Union{Sheep,Wolf} will only return pairs of (Sheep, Wolf). In the case of multiple agent types, e.g. Union{Sheep, Wolf, Grass}, skipping pairings that involve Grass, can be achived by a scheduler that doesn't schedule Grass types, i.e.: scheduler(model) = (a.id for a in allagents(model) if !(a isa Grass)).

The following keywords can be used:

  • scheduler = model.scheduler, which schedulers the agents during iteration for finding pairs. Especially in the :nearest case, this is important, as different sequencing for the agents may give different results (if b is the nearest agent for a, but a is not the nearest agent for b, whether you get the pair (a, b) or not depends on whether a was scheduelr first or not).
  • nearby_f = nearby_ids_exact is the function that decides how to find nearby IDs in the :all, :types cases. Must be nearby_ids_exact or nearby_ids.

Example usage in https://juliadynamics.github.io/AgentsExampleZoo.jl/dev/examples/growing_bacteria/.

Better performance with CellListMap.jl

Notice that in most applications that interacting_pairs is useful, there is significant (10x-100x) performance gain to be made by integrating with CellListMap.jl. Checkout the Integrating Agents.jl with CellListMap.jl integration example for how to do this.

elastic_collision!(a, b, f = nothing) → happened

Resolve a (hypothetical) elastic collision between the two agents a, b. They are assumed to be disks of equal size touching tangentially. Their velocities (field vel) are adjusted for an elastic collision happening between them. This function works only for two dimensions. Notice that collision only happens if both disks face each other, to avoid collision-after-collision.

If f is a Symbol, then the agent property f, e.g. :mass, is taken as a mass to weight the two agents for the collision. By default no weighting happens.

One of the two agents can have infinite "mass", and then acts as an immovable object that specularly reflects the other agent. In this case momentum is not conserved, but kinetic energy is still conserved.

Return a boolean encoding whether the collision happened.

Example usage in Continuous space social distancing.

euclidean_distance(a, b, model::ABM)

Return the euclidean distance between a and b (either agents or agent positions), respecting periodic boundary conditions (if in use). Works with any space where it makes sense: currently AbstractGridSpace and ContinuousSpace.

Example usage in the Flocking model.

manhattan_distance(a, b, model::ABM)

Return the manhattan distance between a and b (either agents or agent positions), respecting periodic boundary conditions (if in use). Works with any space where it makes sense: currently AbstractGridSpace and ContinuousSpace.


OpenStreetMapSpace exclusives


Submodule for functionality related to OpenStreetMapSpace. See the docstring of the space for more info.

OSM.lonlat(pos, model)
OSM.lonlat(agent, model)

Return (longitude, latitude) of current road or intersection position.

OSM.nearest_node(lonlat::Tuple{Float64,Float64}, model::ABM{<:OpenStreetMapSpace})

Return the nearest intersection position to (longitude, latitude). Quicker, but less precise than OSM.nearest_road.

OSM.nearest_road(lonlat::Tuple{Float64,Float64}, model::ABM{<:OpenStreetMapSpace})

Return a location on a road nearest to (longitude, latitude). Slower, but more precise than OSM.nearest_node.


Similar to random_position, but rather than providing only intersections, this method returns a location somewhere on a road heading in a random direction.

OSM.plan_random_route!(agent, model::ABM{<:OpenStreetMapSpace}; kwargs...) → success

Plan a new random route for the agent, by selecting a random destination and planning a route from the agent's current position. Overwrite any existing route.

The keyword limit = 10 specifies the limit on the number of attempts at planning a random route, as no connection may be possible given the random destination. Return true if a route was successfully planned, false otherwise. All other keywords are passed to plan_route!

OSM.road_length(start::Int, finish::Int, model)
OSM.road_length(pos::Tuple{Int,Int,Float64}, model)

Return the road length between two intersections. This takes into account the direction of the road, so OSM.road_length(pos_1, pos_2, model) may not be the same as OSM.road_length(pos_2, pos_1, model). Units of the returned quantity are as specified by the underlying graph's weight_type. If start and finish are the same or pos[1] and pos[2] are the same, then return 0.

OSM.route_length(agent, model::ABM{<:OpenStreetMapSpace})

Return the length of the route planned for the given agent, correctly taking into account the amount of route already traversed by the agent. Return 0 if is_stationary(agent, model).

OSM.same_position(a::Tuple{Int,Int,Float64}, b::Tuple{Int,Int,Float64}, model::ABM{<:OpenStreetMapSpace})

Return true if the given positions a and b are (approximately) identical

OSM.same_road(a::Tuple{Int,Int,Float64}, b::Tuple{Int,Int,Float64})

Return true if both points lie on the same road of the graph


Download a small test map of Göttingen as an artifact. Return a path to the downloaded file.

Using this map requires network_type = :none to be passed as a keyword to OSMSpace. The unit of distance used for this map is :time.


Downloads an OpenStreetMap network by querying with a place name, bounding box, or centroid point.


  • download_method::Symbol: Download method, choose from :place_name, :bbox or :point.
  • network_type::Symbol=:drive: Network type filter, pick from :drive, :drive_service, :walk, :bike, :all, :all_private, :none, :rail
  • metadata::Bool=false: Set true to return metadata.
  • download_format::Symbol=:json: Download format, either :osm, :xml or json.
  • save_to_file_location::Union{String,Nothing}=nothing: Specify a file location to save downloaded data to disk.

Required Kwargs for each Download Method


  • place_name::String: Any place name string used as a search argument to the Nominatim API.


  • minlat::AbstractFloat: Bottom left bounding box latitude coordinate.
  • minlon::AbstractFloat: Bottom left bounding box longitude coordinate.
  • maxlat::AbstractFloat: Top right bounding box latitude coordinate.
  • maxlon::AbstractFloat: Top right bounding box longitude coordinate.


  • point::GeoLocation: Centroid point to draw the bounding box around.
  • radius::Number: Distance (km) from centroid point to each bounding box corner.


  • polygon::AbstractVector: Vector of longitude-latitude pairs.

Network Types

  • :drive: Motorways excluding private and service ways.
  • :drive_service: Motorways including private and service ways.
  • :walk: Walkways only.
  • :bike: Cycleways only.
  • :all: All motorways, walkways and cycleways excluding private ways.
  • :all_private: All motorways, walkways and cycleways including private ways.
  • :none: No network filters.
  • :rail: Railways excluding proposed and platform.


  • Union{XMLDocument,Dict{String,Any}}: OpenStreetMap network data parsed as either XML or Dictionary object depending on the download method.

Nearby Agents

nearby_ids(position, model::ABM, r = 1; kwargs...) → ids

Return an iterable over the IDs of the agents within distance r (inclusive) from the given position. The position must match type with the spatial structure of the model. The specification of what "distance" means depends on the space, hence it is explained in each space's documentation string. Keyword arguments are space-specific and also described in each space's documentation string.

nearby_ids always includes IDs with 0 distance to position.

nearby_ids(agent::AbstractAgent, model::ABM, r=1)

Same as nearby_ids(agent.pos, model, r) but the iterable excludes the given agent's id.

nearby_agents(agent, model::ABM, r = 1; kwargs...) -> agent

Return an iterable of the agents near the position of the given agent.

The value of the argument r and possible keywords operate identically to nearby_ids.

nearby_positions(position, model::ABM{<:DiscreteSpace}, r=1; kwargs...)

Return an iterable of all positions within "radius" r of the given position (which excludes given position). The position must match type with the spatial structure of the model.

The value of r and possible keywords operate identically to nearby_ids.

This function only exists for discrete spaces with a finite amount of positions.

nearby_positions(position, model::ABM{<:OpenStreetMapSpace}; kwargs...) → positions

For OpenStreetMapSpace this means "nearby intersections" and operates directly on the underlying graph of the OSM, providing the intersection nodes nearest to the given position.

nearby_positions(agent::AbstractAgent, model::ABM, r=1)

Same as nearby_positions(agent.pos, model, r).

random_nearby_id(agent, model::ABM, r = 1; kwargs...) → id

Return the id of a random agent near the position of the given agent using an optimized algorithm from Reservoir sampling. Return nothing if no agents are nearby.

The value of the argument r and possible keywords operate identically to nearby_ids.

random_nearby_agent(agent, model::ABM, r = 1; kwargs...) → agent

Return the a random agent near the position of the given agent. Return nothing if no agent is nearby.

The value of the argument r and possible keywords operate identically to nearby_ids.


A note on iteration

Most iteration in Agents.jl is dynamic and lazy, when possible, for performance reasons.

Dynamic means that when iterating over the result of e.g. the ids_in_position function, the iterator will be affected by actions that would alter its contents. Specifically, imagine the scenario

using Agents
# We don't need to make a new agent type here,
# we use the minimal agent for 4-dimensional grid spaces
model = ABM(GridAgent{4}, GridSpace((5, 5, 5, 5)))
add_agent!((1, 1, 1, 1), model)
add_agent!((1, 1, 1, 1), model)
add_agent!((2, 1, 1, 1), model)
for id in ids_in_position((1, 1, 1, 1), model)
    kill_agent!(id, model)
2-element Vector{Int64}:

You will notice that only 1 agent got killed. This is simply because the final state of the iteration of ids_in_position was reached unnaturally, because the length of its output was reduced by 1 during iteration. To avoid problems like these, you need to collect the iterator to have a non dynamic version.

Lazy means that when possible the outputs of the iteration are not collected and instead are generated on the fly. A good example to illustrate this is nearby_ids, where doing something like

a = random_agent(model)
sort!(nearby_ids(random_agent(model), model))

leads to error, since you cannot sort! the returned iterator. This can be easily solved by adding a collect in between:

a = random_agent(model)
sort!(collect(nearby_agents(a, model)))
1-element Vector{GridAgent{4}}:
 GridAgent{4}(2, (1, 1, 1, 1))

Higher-order interactions

There may be times when pair-wise, triplet-wise or higher interactions need to be accounted for across most or all of the model's agent population. The following methods provide an interface for such calculation.

These methods follow the conventions outlined above in A note on iteration.

iter_agent_groups(order::Int, model::ABM; scheduler = Schedulers.by_id)

Return an iterator over all agents of the model, grouped by order. When order = 2, the iterator returns agent pairs, e.g (agent1, agent2) and when order = 3: agent triples, e.g. (agent1, agent7, agent8). order must be larger than 1 but has no upper bound.

Index order is provided by the model scheduler by default, but can be altered with the scheduler keyword.

map_agent_groups(order::Int, f::Function, model::ABM; kwargs...)
map_agent_groups(order::Int, f::Function, model::ABM, filter::Function; kwargs...)

Applies function f to all grouped agents of an iter_agent_groups iterator. kwargs are passed to the iterator method. f must take the form f(NTuple{O,AgentType}), where the dimension O is equal to order.

Optionally, a filter function that accepts an iterable and returns a Bool can be applied to remove unwanted matches from the results. Note: This option cannot keep matrix order, so should be used in conjuction with index_mapped_groups to associate agent ids with the resultant data.

index_mapped_groups(order::Int, model::ABM; scheduler = Schedulers.by_id)
index_mapped_groups(order::Int, model::ABM, filter::Function; scheduler = Schedulers.by_id)

Return an iterable of agent ids in the model, meeting the filter criteria if used.


Minimal agent types

The @agent macro can be used to define new agent types from the minimal agent types that are listed below:

NoSpaceAgent <: AbstractAgent

The minimal agent struct for usage with nothing as space (i.e., no space). It has the field id::Int, and potentially other internal fields that are not documentated as part of the public API. See also @agent.

GridAgent{D} <: AbstractAgent

The minimal agent struct for usage with D-dimensional GridSpace. It has an additional pos::NTuple{D,Int} field. See also @agent.


Parameter scanning

paramscan(parameters, initialize; kwargs...) → adf, mdf

Perform a parameter scan of a ABM simulation output by collecting data from all parameter combinations into dataframes (one for agent data, one for model data). The dataframes columns are both the collected data (as in run!) but also the input parameter values used.

parameters is a dictionary with key type Symbol which contains various parameters that will be scanned over (as well as other parameters that remain constant). This function uses DrWatson's dict_list convention. This means that every entry of parameters that is a Vector contains many parameters and thus is scanned. All other entries of parameters that are not Vectors are not expanded in the scan.

The second argument initialize is a function that creates an ABM and returns it. It must accept keyword arguments which are the keys of the parameters dictionary. Since the user decides how to use input arguments to make an ABM, parameters can be used to affect model properties, space type and creation as well as agent properties, see the example below.


The following keywords modify the paramscan function:

  • include_constants::Bool = false: by default, only the varying parameters (Vector in parameters) will be included in the output DataFrame. If true, constant parameters (non-Vector in parameteres) will also be included.
  • parallel::Bool = false whether Distributed.pmap is invoked to run simulations in parallel. This must be used in conjunction with @everywhere (see Performance Tips).
  • showprogress::Bool = false whether a progressbar will be displayed to indicate % runs finished.

All other keywords are propagated into run!. Furthermore, agent_step!, model_step!, n are also keywords here, that are given to run! as arguments. Naturally, stepping functions and the number of time steps (agent_step!, model_step!, and n) and at least one of adata, mdata are mandatory. The adata, mdata lists shouldn't contain the parameters that are already in the parameters dictionary to avoid duplication.


A runnable example that uses paramscan is shown in Schelling's segregation model. There, we define

function initialize(; numagents = 320, griddims = (20, 20), min_to_be_happy = 3)
    space = GridSpaceSingle(griddims, periodic = false)
    properties = Dict(:min_to_be_happy => min_to_be_happy)
    model = ABM(SchellingAgent, space;
                properties = properties, scheduler = Schedulers.randomly)
    for n in 1:numagents
        agent = SchellingAgent(n, (1, 1), false, n < numagents / 2 ? 1 : 2)
        add_agent_single!(agent, model)
    return model

and do a parameter scan by doing:

happyperc(moods) = count(moods) / length(moods)
adata = [(:mood, happyperc)]

parameters = Dict(
    :min_to_be_happy => collect(2:5), # expanded
    :numagents => [200, 300],         # expanded
    :griddims => (20, 20),            # not Vector = not expanded

adf, _ = paramscan(parameters, initialize; adata, agent_step!, n = 3)

Data collection

The central simulation function is run!, which is mentioned in our Tutorial. But there are other functions that are related to simulations listed here. Specifically, these functions aid in making custom data collection loops, instead of using the run! function.

For example, the core loop of run! is just

df_agent = init_agent_dataframe(model, adata)
df_model = init_model_dataframe(model, mdata)

s = 0
while until(s, n, model)
  if should_we_collect(s, model, when)
      collect_agent_data!(df_agent, model, adata, s)
  if should_we_collect(s, model, when_model)
      collect_model_data!(df_model, model, mdata, s)
  step!(model, agent_step!, model_step!, 1)
  s += 1
return df_agent, df_model

(here until and should_we_collect are internal functions)

run! uses the following functions:

collect_agent_data!(df, model, properties, step = 0; obtainer = identity)

Collect and add agent data into df (see run! for the dispatch rules of properties and obtainer). step is given because the step number information is not known.

dataname(k) → name

Return the name of the column of the i-th collected data where k = adata[i] (or mdata[i]). dataname also accepts tuples with aggregate and conditional values.




Submodule containing all predefined schedulers of Agents.jl and the scheduling API. Schedulers have a very simple interface. They are functions that take as an input the ABM and return an iterator over agent IDs. Notice that this iterator can be a "true" iterator (non-allocated) or can be just a standard vector of IDs. You can define your own scheduler according to this API and use it when making an AgentBasedModel. You can also use the function schedule(model) to obtain the scheduled ID list, if you prefer to write your own step!-like loop.

See also Advanced scheduling for making more advanced schedulers.

Notice that schedulers can be given directly to model creation, and thus become the "default" scheduler a model uses, but they can just as easily be incorporated in a model_step! function as shown in Advanced stepping.


Predefined schedulers

Some useful schedulers are available below as part of the Agents.jl API:


A scheduler that activates all agents once per step in the order dictated by the agent's container, which is arbitrary (the keys sequence of a dictionary). This is the fastest way to activate all agents once per step.


A non-allocating scheduler that activates all agents agents at each step according to their id.


A non-allocating scheduler that activates all agents once per step in a random order. Different random ordering is used at each different step.


A non-allocating scheduler that at each step activates the agents in an order dictated by their property, with agents with greater property acting first. property can be a Symbol, which just dictates which field of the agents to compare, or a function which inputs an agent and outputs a real number.

Schedulers.ByType(shuffle_types::Bool, shuffle_agents::Bool, agent_union)

A non-allocating scheduler useful only for mixed agent models using Union types.

  • Setting shuffle_types = true groups by agent type, but randomizes the type order.

Otherwise returns agents grouped in order of appearance in the Union.

  • shuffle_agents = true randomizes the order of agents within each group, false returns

the default order of the container (equivalent to Schedulers.fastest).

  • agent_union is a Union of all valid agent types (as passed to ABM)
Schedulers.ByType((C, B, A), shuffle_agents::Bool)

A non-allocating scheduler that activates agents by type in specified order (since Unions are not order preserving). shuffle_agents = true randomizes the order of agents within each group.


Advanced scheduling

You can use Function-like objects to make your scheduling possible of arbitrary events. For example, imagine that after the n-th step of your simulation you want to fundamentally change the order of agents. To achieve this you can define

mutable struct MyScheduler
    n::Int # step number

and then define a calling method for it like so

function (ms::MyScheduler)(model::ABM)
    ms.n += 1 # increment internal counter by 1 each time its called
              # be careful to use a *new* instance of this scheduler when plotting!
    if ms.n < 10
        return allids(model) # order doesn't matter in this case
        ids = collect(allids(model))
        # filter all ids whose agents have `w` less than some amount
        filter!(id -> model[id].w < ms.w, ids)
        return ids

and pass it to e.g. step! by initializing it

ms = MyScheduler(100, 0.5)
step!(model, agentstep, modelstep, 100; scheduler = ms)

Ensemble runs and Parallelization

ensemblerun!(models::Vector, agent_step!, model_step!, n; kwargs...)

Perform an ensemble simulation of run! for all model ∈ models. Each model should be a (different) instance of an AgentBasedModel but probably initialized with a different random seed or different initial agent distribution. All models obey the same rules agent_step!, model_step! and are evolved for n.

Similarly to run! this function will collect data. It will furthermore add one additional column to the dataframe called :ensemble, which has an integer value counting the ensemble member. The function returns agent_df, model_df, models.

If you want to scan parameters and at the same time run multiple simulations at each parameter combination, simply use seed as a parameter, and use that parameter to tune the model's initial random seed and/or agent distribution.

See example usage in Schelling's segregation model.


The following keywords modify the ensemblerun! function:

  • parallel::Bool = false whether Distributed.pmap is invoked to run simulations in parallel. This must be used in conjunction with @everywhere (see Performance Tips).
  • showprogress::Bool = false whether a progressbar will be displayed to indicate % runs finished.

All other keywords are propagated to run! as-is.

ensemblerun!(generator, agent_step!, model_step!, n; kwargs...)

Generate many ABMs and propagate them into ensemblerun!(models, ...) using the provided generator which is a one-argument function whose input is a seed.

This method has additional keywords ensemble = 5, seeds = rand(UInt32, ensemble).


How to use Distributed

To use the parallel=true option of ensemblerun! you need to load Agents and define your fundamental types at all processors. How to do this is shown in Ensembles and distributed computing section of Schelling's Segregation Model example. See also the Performance Tips page for parallelization.



Submodule containing functionality for path-finding based on the A* algorithm. Currently available for GridSpace and ContinuousSpace. Discretization of ContinuousSpace is taken care of internally.

You can enable path-finding and set its options by creating an instance of a Pathfinding.AStar struct. This must be passed to the relevant pathfinding functions during the simulation. Call plan_route! to set the destination for an agent. This triggers the algorithm to calculate a path from the agent's current position to the one specified. You can alternatively use plan_best_route! to choose the best target from a list. Once a target has been set, you can move an agent one step along its precalculated path using the move_along_route! function.

Refer to the Maze Solver, Mountain Runners and Rabbit, Fox, Hawk examples using path-finding and see the available functions below as well.

Pathfinding.AStar(space; kwargs...)

Enables pathfinding for agents in the provided space (which can be a GridSpace or ContinuousSpace) using the A* algorithm. This struct must be passed into any pathfinding functions.

For ContinuousSpace, a walkmap or instance of PenaltyMap must be provided to specify the level of discretisation of the space.


  • diagonal_movement = true specifies if movement can be to diagonal neighbors of a tile, or only orthogonal neighbors. Only available for GridSpace
  • admissibility = 0.0 allows the algorithm to aprroximate paths to speed up pathfinding. A value of admissibility allows paths with at most (1+admissibility) times the optimal length.
  • walkmap = trues(size(space)) specifies the (un)walkable positions of the space. If specified, it should be a BitArray of the same size as the corresponding GridSpace. By default, agents can walk anywhere in the space.
  • cost_metric = DirectDistance{D}() is an instance of a cost metric and specifies the metric used to approximate the distance between any two points.

Utilization of all features of AStar occurs in the 3D Mixed-Agent Ecosystem with Pathfinding example.

Pathfinding.nearby_walkable(position, model::ABM{<:GridSpace{D}}, pathfinder::AStar{D}, r = 1)

Return an iterator over all nearby_positions within "radius" r of the given position (excluding position), which are walkable as specified by the given pathfinder.

Pathfinding.random_walkable(model, pathfinder::AStar{D})

Return a random position in the given model that is walkable as specified by the given pathfinder.

Pathfinding.random_walkable(pos, model::ABM{<:ContinuousSpace{D}}, pathfinder::AStar{D}, r = 1.0)

Return a random position within radius r of pos which is walkable, as specified by pathfinder. Return pos if no such position exists.


Pathfinding Metrics

Pathfinding.DirectDistance{D}([direction_costs::Vector{Int}]) <: CostMetric{D}

Distance is approximated as the shortest path between the two points, provided the walkable property of Pathfinding.AStar allows. Optionally provide a Vector{Int} that represents the cost of going from a tile to the neighboring tile on the i dimensional diagonal (default is 10√i).

If diagonal_movement=false in Pathfinding.AStar, neighbors in diagonal positions will be excluded. Cost defaults to the first value of the provided vector.

Pathfinding.MaxDistance{D}() <: CostMetric{D}

Distance between two tiles is approximated as the maximum of absolute difference in coordinates between them.

Pathfinding.PenaltyMap(pmap::Array{Int,D} [, base_metric::CostMetric]) <: CostMetric{D}

Distance between two positions is the sum of the shortest distance between them and the absolute difference in penalty.

A penalty map (pmap) is required. For pathfinding in GridSpace, this should be the same dimensions as the space. For pathfinding in ContinuousSpace, the size of this map determines the granularity of the underlying grid, and should agree with the size of the walkable map.

Distance is calculated using Pathfinding.DirectDistance by default, and can be changed by specifying base_metric.

An example usage can be found in Mountain Runners.


Building a custom metric is straightforward, if the provided ones do not suit your purpose. See the Developer Docs for details.

Save, Load, Checkpoints

There may be scenarios where interacting with data in the form of files is necessary. The following functions provide an interface to save/load data to/from files.

AgentsIO.save_checkpoint(filename, model::ABM)

Write the entire model to file specified by filename. The following points should be considered before using this functionality:

  • OpenStreetMap data is not saved. The path to the map should be specified when loading the model using the map keyword of AgentsIO.load_checkpoint.
  • Functions are not saved, including stepping functions, schedulers, and update_vel!. The last two can be provided to AgentsIO.load_checkpoint using the appropriate keyword arguments.
AgentsIO.load_checkpoint(filename; kwargs...)

Load the model saved to the file specified by filename.


  • scheduler = Schedulers.fastest specifies what scheduler should be used for the model.
  • warn = true can be used to disable warnings from type checks on the agent type.

ContinuousSpace specific:

  • update_vel! specifies a function that should be used to update each agent's velocity before it is moved. Refer to ContinuousSpace for details.

OpenStreetMapSpace specific:

  • map is a path to the OpenStreetMap to be used for the space. This is a required parameter if the space is OpenStreetMapSpace.
  • use_cache = false, trim_to_connected_graph = true refer to OpenStreetMapSpace
AgentsIO.populate_from_csv!(model, filename [, agent_type, col_map]; row_number_is_id, kwargs...)

Populate the given model using CSV data contained in filename. Use agent_type to specify the type of agent to create (In the case of multi-agent models) or a function that returns an agent to add to the model. The CSV row is splatted into the agent_type constructor/function.

col_map is a Dict{Symbol,Int} specifying a mapping of keyword-arguments to row number. If col_map is specified, the specified data is splatted as keyword arguments.

The keyword row_number_is_id = false specifies whether the row number will be passed as the first argument (or as id keyword) to agent_type.

Any other keyword arguments are forwarded to CSV.Rows. If the types keyword is not specified and agent_type is a struct, then the mapping from struct field to type will be used. Tuple{...} fields will be suffixed with _1, _2, ... similarly to AgentsIO.dump_to_csv

For example,

struct Foo <: AbstractAgent

model = ABM(Foo, ...)
AgentsIO.populate_from_csv!(model, "test.csv")

Here, types will be inferred to be

    :id => Int,
    :pos_1 => Int,
    :pos_2 => Int,
    :foo_1 => Int,
    :foo_2 => String,

It is not necessary for all these fields to be present as columns in the CSV. Any column names that match will be converted to the appropriate type. There should exist a constructor for Foo taking the appropriate combination of fields as parameters.

If "test.csv" contains the following columns: pos_1, pos_2, foo_1, foo_2, then model can be populated as AgentsIO.populate_from_csv!(model, "test.csv"; row_number_is_id = true).

AgentsIO.dump_to_csv(filename, agents [, fields]; kwargs...)

Dump agents to the CSV file specified by filename. agents is any iterable sequence of types, such as from allagents. fields is an iterable sequence of Symbols specifying which fields of each agent are dumped. If not explicitly specified, it is automatically inferred using eltype(agents). All kwargs... are forwarded to CSV.write.

All Tuple{...} fields are flattened to multiple columns suffixed by _1, _2... similarly to AgentsIO.populate_from_csv!

For example,

struct Foo <: AbstractAgent

model = ABM(Foo, ...)
AgentsIO.dump_to_csv("test.csv", allagents(model))

The resultant "test.csv" file will contain the following columns: id, pos_1, pos_2, foo_1, foo_2.


In case you require custom serialization for model properties, refer to the Developer Docs for details.