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Agents.jl is a pure Julia framework for agent-based modeling (ABM): a computational simulation methodology where autonomous agents react to their environment (including other agents) given a predefined set of rules. Some major highlights of Agents.jl are:

  1. It is fast (faster than MASON, NetLogo, or Mesa)
  2. It is simple: has a very short learning curve and requires writing minimal code
  3. Has an extensive interface of thousands of out-of-the box possible agent actions
  4. Straightforwardly allows simulations on Open Street Maps
  5. Allows both traditional discrete-time ABM simulations as well as continuous time "event queue based" ABM simulations.

The simplicity of Agents.jl is due to the intuitive space-agnostic modelling approach we have implemented: agent actions are specified using generically named functions (such as "move agent" or "find nearby agents") that do not depend on the actual space the agents exist in, nor on the properties of the agents themselves. Overall this leads to ultra fast model prototyping where even changing the space the agents live in is matter of only a couple of lines of code.

More information and an extensive list of features can be found in the documentation, which you can either find online or build locally by running the docs/make.jl file.


If you use this package in a publication, or simply want to refer to it, please cite the paper below:

  doi = {10.1177/00375497211068820},
  url = {},
  year = {2022},
  month = jan,
  publisher = {{SAGE} Publications},
  pages = {003754972110688},
  author = {George Datseris and Ali R. Vahdati and Timothy C. DuBois},
  title = {Agents.jl: a performant and feature-full agent-based modeling software of minimal code complexity},
  journal = {{SIMULATION}},
  volume = {0},
  number = {0},
Star us on GitHub!

If you have found this package useful, please consider starring it on GitHub. This gives us an accurate lower bound of the (satisfied) user count.

Latest news: Agents.jl v6.0

This is a new major release of Agents.jl with great new content and massive performance increases all across the board! Please see the online CHANGELOG for a full list of changes. The most noteworthy ones are:

  • A new @multiagent macro allows to run multi-agent simulations much more efficiently.
  • A new experimental model type EventQueueABM has been implemented. It operates in continuous time through the scheduling of events at arbitrary time points. It is a generalization of "Gillespie-like" models.
  • AgentBasedModel defines an API that can be extended by other models.
  • Stronger inheritance capabilities in @agent.
  • Manually setting or altering the ids of agents is no longer allowed. The agent id is now considered a read-only field, and is set internally by Agents.jl.


Software quality

  • Free and open source.
  • Small learning curve due to intuitive design based on a modular space-agnostic function-based modelling implementation.
  • Extremely high performance when compared to other open source frameworks, routinely being 100x faster versus other ABM frameworks (proof)
  • User-created models typically have much smaller source code versus implementations in other open source ABM frameworks (proof)
  • High quality, extensive documentation featuring tutorials, example ABM implementations, an extra zoo of ABM examples, and integration examples with other Julia packages

Agent based modelling

  • Universal model structure where agents are identified by a unique id: AgentBasedModel.
  • Extendable API that provides out of the box thousands of possible agent actions.
  • Support for many types of space: arbitrary graphs, regular grids, continuous space
  • Support for simulations on Open Street Maps including support for utilizing the road's max speed limit, finding nearby agents/roads/destinations and pathfinding
  • Multi-agent support, for interactions between disparate agent species
  • Scheduler interface (with default schedulers), making it easy to activate agents in a specific order (e.g. by the value of some property)
  • Automatic data collection in a DataFrame at desired intervals
  • Aggregating collected data during model evolution
  • Distributed computing
  • Batch running and batch data collection
  • Extensive pathfinding capabilities in continuous or discrete spaces
  • Customizable visualization support for all kinds of models via the Makie ecosystem: publication-quality graphics and video output
  • Interactive applications for any agent based models, which are created with only 5 lines of code and look like this:

Getting started

To install Agents.jl, launch Julia and then run this command:

using Pkg; Pkg.add("Agents")

To learn how to use Agents.jl, please visit the Tutorial before anything else.

Use the latest released version

After adding Agents.jl to your project, please check if the most up to date stable version has been installed. The versions of the installed packages in the project can be checked by running Pkg.status(). Only the latest version of Agents.jl provides all the features described in this documentation. It is generally advised against using earlier versions as they will likely only work partially and are not supported anymore.

Design philosophy of Agents.jl

Agents.jl was designed with the following philosophy in mind:

Simple to learn and use, yet extendable and highly performant, allowing for fast and scalable model creation and evolution.

There are multiple examples that highlight this core design principle, that one will quickly encounter when scanning through our API page. Here we just give two quick examples: first, there exists a universal function nearby_agents, which returns the agents nearby a given agent and within a given "radius". What is special for this function, which is allowed by Julia's Multiple Dispatch, is that nearby_agents will work for any space type the model has, reducing the learning curve of finding neighbors in ABMs made with Agents.jl. An even better example is perhaps our treatment of spaces. A user may create an entirely new kind of space (e.g. one representing a planet, or whatever else) by only extending 5 functions, as discussed in our Creating a new space type documentation.

Many other agent-based modeling frameworks have been constructed to ease the process of building and analyzing ABMs (see e.g. here for an outdated review), spanning a varying degree of complexity. In the page ABM Framework Comparison we compare how our design philosophy puts us into comparison with other well accepted ABM software. Fascinatingly, even though the main focus of Agents.jl is simplicity and ease of use, it outperforms all software we compared it with.

Crash course on agent based modeling

An agent-based (or individual-based) model is a computational simulation of autonomous agents that react to their environment (including other agents) given a predefined set of rules [1]. ABMs have been adopted and studied in a variety of research disciplines. One reason for their popularity is that they enable a relaxation of many simplifying assumptions usually made by mathematical models. Relaxing such assumptions of a "perfect world" can change a model's behavior [2].

Agent-based models are increasingly recognized as a useful approach for studying complex systems [3,4,5,6]. Complex systems cannot be fully understood using traditional mathematical tools which aggregate the behavior of elements in a system. The behavior of a complex system depends on both the behavior of and interactions between its elements (agents). Small changes in the input to complex systems or the behavior of its agents can lead to large changes in outcome. That is to say, a complex system's behavior is nonlinear, and that it is not only the sum of the behavior of its elements. Use of ABMs have become feasible after the availability of computers and has been growing ever since, especially in modeling biological and economic systems, and has extended to social studies and archaeology.

An ABM consists of autonomous agents that behave given a set of rules. A classic example of an ABM is Schelling's segregation model, which we implement as an example here. This model uses a regular grid and defines agents at random positions on the grid. Agents can be from different social groups. Agents are happy/unhappy based on the fraction of their neighbors that belong to the same group as they are. If they are unhappy, they keep moving to new locations until they are happy. Schelling's model shows that even small preferences of agents to have neighbors belonging to the same group (e.g. preferring that at least 30% of neighbors to be in the same group) could lead to total segregation of neighborhoods. This is an example of emergent behavior from simple interactions of agents that can only be captured in an agent-based model.

Getting help

You're looking for support for Agents.jl? Look no further! Here's some things you can do to resolve your questions about Agents.jl:

  1. Read the online documentation! It is likely that the thing you want to know is already documented, so use the search bar and search away!
  2. Chat with us in the channel #dynamics-bridged in the Julia Slack!
  3. Post a question in the Julia discourse in the category “Modelling and simulations”, using “agent” as a tag!
  4. If you believe that you have encountered unexpected behavior or a bug in Agents.jl, then please do open an issue on our GitHub page providing a minimal working example!


Any contribution to Agents.jl is welcome! For example you can:

  • Add new feature or improve an existing one (plenty to choose from the "Issues" page)
  • Improve the existing documentation
  • Add new example ABMs into our existing pool of examples
  • Report bugs and suggestions in the Issues page

Have a look at contributor's guide of the SciML organization for some good information on contributing to Julia packages!


If you use this package in work that leads to a publication, then please cite the paper below:

  doi = {10.1177/00375497211068820},
  url = {},
  year = {2022},
  month = jan,
  publisher = {{SAGE} Publications},
  pages = {003754972110688},
  author = {George Datseris and Ali R. Vahdati and Timothy C. DuBois},
  title = {Agents.jl: a performant and feature-full agent-based modeling software of minimal code complexity},
  journal = {{SIMULATION}},
  volume = {0},
  number = {0},


The documentation of Agents.jl was built using these direct dependencies,
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and using this machine and Julia version.
Julia Version 1.10.2
Commit bd47eca2c8a (2024-03-01 10:14 UTC)
Build Info:
  Official release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 4 × AMD EPYC 7763 64-Core Processor
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  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)
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A more complete overview of all dependencies and their versions is also provided.
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  [82ae8749] StatsAPI v1.7.0
  [2913bbd2] StatsBase v0.34.3
  [4c63d2b9] StatsFuns v1.3.1
  [7792a7ef] StrideArraysCore v0.5.3
  [892a3eda] StringManipulation v0.3.4
  [09ab397b] StructArrays v0.6.18
  [8e1ec7a9] SumTypes v0.5.6
  [2efcf032] SymbolicIndexingInterface v0.3.15
  [3783bdb8] TableTraits v1.0.1
  [bd369af6] Tables v1.11.1
  [62fd8b95] TensorCore v0.1.1
  [1c621080] TestItems v0.1.1
  [8290d209] ThreadingUtilities v0.5.2
 [731e570b] TiffImages v0.6.8
  [a759f4b9] TimerOutputs v0.5.23
  [3bb67fe8] TranscodingStreams v0.10.7
  [d5829a12] TriangularSolve v0.1.21
  [410a4b4d] Tricks v0.1.8
  [981d1d27] TriplotBase v0.1.0
  [781d530d] TruncatedStacktraces v1.4.0
  [5c2747f8] URIs v1.5.1
  [3a884ed6] UnPack v1.0.2
  [1cfade01] UnicodeFun v0.4.1
  [3d5dd08c] VectorizationBase v0.21.65
  [19fa3120] VertexSafeGraphs v0.2.0
  [ea10d353] WeakRefStrings v1.4.2
 [efce3f68] WoodburyMatrices v0.5.6
  [76eceee3] WorkerUtilities v1.6.1
  [6e34b625] Bzip2_jll v1.0.8+1
  [4e9b3aee] CRlibm_jll v1.0.1+0
  [83423d85] Cairo_jll v1.18.0+1
  [5ae413db] EarCut_jll v2.2.4+0
  [2e619515] Expat_jll v2.5.0+0
 [b22a6f82] FFMPEG_jll v4.4.2+2
  [f5851436] FFTW_jll v3.3.10+0
  [a3f928ae] Fontconfig_jll v2.13.93+0
  [d7e528f0] FreeType2_jll v2.13.1+0
  [559328eb] FriBidi_jll v1.0.10+0
  [0656b61e] GLFW_jll v3.3.9+0
  [78b55507] Gettext_jll v0.21.0+0
  [61579ee1] Ghostscript_jll v9.55.0+4
 [f8c6e375] Git_jll v2.36.1+2
  [7746bdde] Glib_jll v2.80.0+0
  [3b182d85] Graphite2_jll v1.3.14+0
  [528830af] Gumbo_jll v0.10.2+0
  [2e76f6c2] HarfBuzz_jll v2.8.1+1
 [c73af94c] ImageMagick_jll v6.9.11+4
  [905a6f67] Imath_jll v3.1.7+0
  [1d5cc7b8] IntelOpenMP_jll v2024.0.2+0
  [aacddb02] JpegTurbo_jll v3.0.2+0
  [c1c5ebd0] LAME_jll v3.100.1+0
  [88015f11] LERC_jll v3.0.0+1
  [1d63c593] LLVMOpenMP_jll v15.0.7+0
  [dd4b983a] LZO_jll v2.10.1+0
 [e9f186c6] Libffi_jll v3.2.2+1
  [d4300ac3] Libgcrypt_jll v1.8.7+0
  [7e76a0d4] Libglvnd_jll v1.6.0+0
  [7add5ba3] Libgpg_error_jll v1.42.0+0
  [94ce4f54] Libiconv_jll v1.17.0+0
  [4b2f31a3] Libmount_jll v2.39.3+0
 [89763e89] Libtiff_jll v4.4.0+0
  [38a345b3] Libuuid_jll v2.39.3+1
 [d3a379c0] LittleCMS_jll v2.12.0+0
  [856f044c] MKL_jll v2024.0.0+0
  [e7412a2a] Ogg_jll v1.3.5+1
  [18a262bb] OpenEXR_jll v3.1.4+0
 [643b3616] OpenJpeg_jll v2.4.0+0
 [9bd350c2] OpenSSH_jll v8.9.0+1
 [458c3c95] OpenSSL_jll v1.1.23+0
  [efe28fd5] OpenSpecFun_jll v0.5.5+0
  [91d4177d] Opus_jll v1.3.2+0
  [36c8627f] Pango_jll v1.52.1+0
  [30392449] Pixman_jll v0.42.2+0
  [f50d1b31] Rmath_jll v0.4.0+0
  [02c8fc9c] XML2_jll v2.12.6+0
  [aed1982a] XSLT_jll v1.1.34+0
  [4f6342f7] Xorg_libX11_jll v1.8.6+0
  [0c0b7dd1] Xorg_libXau_jll v1.0.11+0
  [935fb764] Xorg_libXcursor_jll v1.2.0+4
  [a3789734] Xorg_libXdmcp_jll v1.1.4+0
  [1082639a] Xorg_libXext_jll v1.3.4+4
  [d091e8ba] Xorg_libXfixes_jll v5.0.3+4
  [a51aa0fd] Xorg_libXi_jll v1.7.10+4
  [d1454406] Xorg_libXinerama_jll v1.1.4+4
  [ec84b674] Xorg_libXrandr_jll v1.5.2+4
  [ea2f1a96] Xorg_libXrender_jll v0.9.10+4
  [14d82f49] Xorg_libpthread_stubs_jll v0.1.1+0
  [c7cfdc94] Xorg_libxcb_jll v1.15.0+0
  [c5fb5394] Xorg_xtrans_jll v1.5.0+0
  [3161d3a3] Zstd_jll v1.5.6+0
  [9a68df92] isoband_jll v0.2.3+0
  [a4ae2306] libaom_jll v3.4.0+0
  [0ac62f75] libass_jll v0.15.1+0
  [f638f0a6] libfdk_aac_jll v2.0.2+0
  [b53b4c65] libpng_jll v1.6.43+1
  [47bcb7c8] libsass_jll v3.6.4+0
  [075b6546] libsixel_jll v1.10.3+0
  [f27f6e37] libvorbis_jll v1.3.7+1
  [1270edf5] x264_jll v2021.5.5+0
  [dfaa095f] x265_jll v3.5.0+0
  [0dad84c5] ArgTools v1.1.1
  [56f22d72] Artifacts
  [2a0f44e3] Base64
  [8bf52ea8] CRC32c
  [ade2ca70] Dates
  [8ba89e20] Distributed
  [f43a241f] Downloads v1.6.0
  [7b1f6079] FileWatching
  [9fa8497b] Future
  [b77e0a4c] InteractiveUtils
  [4af54fe1] LazyArtifacts
  [b27032c2] LibCURL v0.6.4
  [76f85450] LibGit2
  [8f399da3] Libdl
  [37e2e46d] LinearAlgebra
  [56ddb016] Logging
  [d6f4376e] Markdown
  [a63ad114] Mmap
  [ca575930] NetworkOptions v1.2.0
  [44cfe95a] Pkg v1.10.0
  [de0858da] Printf
  [9abbd945] Profile
  [3fa0cd96] REPL
  [9a3f8284] Random
  [ea8e919c] SHA v0.7.0
  [9e88b42a] Serialization
  [1a1011a3] SharedArrays
  [6462fe0b] Sockets
  [2f01184e] SparseArrays v1.10.0
  [10745b16] Statistics v1.10.0
  [4607b0f0] SuiteSparse
  [fa267f1f] TOML v1.0.3
  [a4e569a6] Tar v1.10.0
  [8dfed614] Test
  [cf7118a7] UUIDs
  [4ec0a83e] Unicode
  [e66e0078] CompilerSupportLibraries_jll v1.1.0+0
  [deac9b47] LibCURL_jll v8.4.0+0
  [e37daf67] LibGit2_jll v1.6.4+0
  [29816b5a] LibSSH2_jll v1.11.0+1
  [c8ffd9c3] MbedTLS_jll v2.28.2+1
  [14a3606d] MozillaCACerts_jll v2023.1.10
  [4536629a] OpenBLAS_jll v0.3.23+4
  [05823500] OpenLibm_jll v0.8.1+2
  [efcefdf7] PCRE2_jll v10.42.0+1
  [bea87d4a] SuiteSparse_jll v7.2.1+1
  [83775a58] Zlib_jll v1.2.13+1
  [8e850b90] libblastrampoline_jll v5.8.0+1
  [8e850ede] nghttp2_jll v1.52.0+1
  [3f19e933] p7zip_jll v17.4.0+2
Info Packages marked with  and  have new versions available. Those with  may be upgradable, but those with  are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`

You can also download the manifest file and the project file.