Caesar.jl is a comprehensive Julia library for non-Gaussian, multimodal factor graph inference primarily targeting SLAM (Simultaneous Localization and Mapping) and robot perception. It implements the mm-iSAMv2 algorithm, supporting both parametric and non-parametric belief representations (KDE), incremental smoothing and mapping, multi-hypothesis data association, and on-manifold inference. The library provides a rich set of variable and factor types (e.g., Pose2, Point2, BearingRange, Pose2Pose2) and integrates with various sensor modalities via weak extensions: AprilTags, image features, point clouds (PCL), beamforming (synthetic aperture sonar), and ZMQ-based multi-language services. The architecture is a monolithic Julia package with a clear public API (ExportAPI.jl) and a factor graph construction pattern (initfg, addVariable!, addFactor!, solveTree!). It relies heavily on the external IncrementalInference.jl, RoME.jl, and DistributedFactorGraphs.jl packages. While the core framework is production-caliber, the repository includes a large number of experimental and research examples (marine, wheeled, aerial) that are often unmaintained, resulting in moderate technical drift. The library is actively maintained (CI, CompatHelper, TagBot) but contains deprecated APIs and Julia version sensitivity.