# \[ANN\] RxInfer.jl v6.0: faster startup, modular rule packages and Reactant support

**URL:** <https://discourse.julialang.org/t/ann-rxinfer-jl-v6-0-faster-startup-modular-rule-packages-and-reactant-support/142721>\
**Category:** Package Announcements\
**Tags:** inference, bayesian-inference, gaussian-process, rxinfer, probablistic\
**Created:** [November 3, 2026, 8:24am UTC](https://discourse.julialang.org/t/ann-rxinfer-jl-v6-0-faster-startup-modular-rule-packages-and-reactant-support/142721 "2026-11-03T08:24:24Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![bvdmitri](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/bvdmitri/32/22935_2.png) [@bvdmitri](https://discourse.julialang.org/u/bvdmitri)\
**Post date:** [November 3, 2026, 8:24am UTC](https://discourse.julialang.org/t/ann-rxinfer-jl-v6-0-faster-startup-modular-rule-packages-and-reactant-support/142721/1 "2026-11-03T08:24:24Z")

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Hi everyone,

We’re glad to announce **RxInfer 6** , the new major release of our package for Bayesian inference by reactive message passing.

We have been running it internally in BIASlab for a while before the release. On our tests, cold-start performance on a Raspberry Pi is nearly twice as fast as before, and we see overall performance improvements.

### Highlights

- **Precompiled common models.** The first `infer` call on common model types drops from about 7 s to under 0.5 s. Other models start 40 to 70% faster.
- **Modular rule packages.** RxInfer now runs on ReactiveMP v7. Message passing rules live in separate registered packages built on [MessagePassingRulesBase.jl](https://github.com/ReactiveBayes/MessagePassingRulesBase.jl), so you only load the nodes you use. You can also reuse these well-tested update rules to build your own message passing engine.
- **Reactant.jl support.** `infer` can now be compiled with Reactant. In our Kalman filter example, a compiled step runs in about 10 μs, 20× faster than standard RxInfer: [Compiling Rxinfer With Reactant · RxInfer.jl Examples](https://examples.rxinfer.com/categories/making_inference_faster/compiling_rxinfer_with_reactant/#Compiling-RxInfer-with-Reactant)
- **Cleaner internals.** The internal API was revised, unused features were removed, and previously unspecified behaviour is now documented and tested.
- **New options.** `logscales = true`, engine `diagnostics`, a rule `context` (e.g. a custom `rng` for sampling rules), and free energy tracing via callbacks and TensorBoard.

### Breaking changes

Models that only use built-in nodes mostly run as before. Notable changes:

- `meta` is renamed to `algorithm` (`where { algorithm = ... }`, `@algorithm`). The old names still work with a deprecation warning.
- `where { dependencies = ... }` is replaced by `where { initial_messages = ... }`.
- Optim.jl is now an extension. Load `using Optim` for continuous point-mass constraints.
- ContinuousTransition, Pólya, BIFM and Flow nodes need their package loaded explicitly.
- Custom nodes and rules use the new `@define_factor_node` and `@define_message_update_rule` macros.

If you’re coming from v5, please read the migration guides first:

- RxInfer v5 to v6: [Migration from v5 to v6 · RxInfer.jl](https://docs.rxinfer.com/stable/manuals/migration/v5-to-v6/#migration-5-to-6)
- ReactiveMP v6 to v7 (for custom nodes and rules): [v6 to v7 · ReactiveMP.jl](https://reactivebayes.github.io/ReactiveMP.jl/stable/migration-guides/v6-to-v7/)

### Links

- Docs: [https://docs.rxinfer.com](https://docs.rxinfer.com)
- Code: [GitHub - ReactiveBayes/RxInfer.jl: Julia package for automated Bayesian inference on a factor graph with reactive message passing · GitHub](https://github.com/ReactiveBayes/RxInfer.jl)
- Changelog: [RxInfer.jl/CHANGELOG.md at main · ReactiveBayes/RxInfer.jl · GitHub](https://github.com/ReactiveBayes/RxInfer.jl/blob/main/CHANGELOG.md)

 ![RxInfer Logo](https://global.discourse-cdn.com/julialang/original/3X/d/2/d2a357fdc30606b940d13b66afd0aa82f76a4674.svg)

Feedback, issues and questions are very welcome here or on GitHub!