TuringLang · penelopeysm · Jan 2, 2026 · Mar 12, 2026 · Mar 12, 2026 · Mar 12, 2026
diff --git a/Project.toml b/Project.toml
@@ -20,11 +20,11 @@ Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
 DynamicPPL = "366bfd00-2699-11ea-058f-f148b4cae6d8"
 EllipticalSliceSampling = "cad2338a-1db2-11e9-3401-43bc07c9ede2"
+FlexiChains = "4a37a8b9-6e57-4b92-8664-298d46e639f7"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 Libtask = "6f1fad26-d15e-5dc8-ae53-837a1d7b8c9f"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
-MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
 OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
@@ -40,6 +40,7 @@ StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
 
 [weakdeps]
 DynamicHMC = "bbc10e6e-7c05-544b-b16e-64fede858acb"
+MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
 
 [extensions]
 TuringDynamicHMCExt = "DynamicHMC"
@@ -63,6 +64,7 @@ DocStringExtensions = "0.8, 0.9"
 DynamicHMC = "3.4"
 DynamicPPL = "0.40.6"
 EllipticalSliceSampling = "0.5, 1, 2"
+FlexiChains = "0.4"
 ForwardDiff = "0.10.3, 1"
 Libtask = "0.9.14"
 LinearAlgebra = "1"

diff --git a/docs/make.jl b/docs/make.jl
@@ -11,6 +11,7 @@ links = InterLinks(
     "AbstractMCMC" => "https://turinglang.org/AbstractMCMC.jl/stable/",
     "ADTypes" => "https://sciml.github.io/ADTypes.jl/stable/",
     "AdvancedVI" => "https://turinglang.org/AdvancedVI.jl/stable/",
+    "FlexiChains" => "https://pysm.dev/FlexiChains.jl/stable/",
     "OrderedCollections" => "https://juliacollections.github.io/OrderedCollections.jl/stable/",
     "Distributions" => "https://juliastats.org/Distributions.jl/stable/",
 )

diff --git a/docs/src/api.md b/docs/src/api.md
@@ -2,12 +2,8 @@
 
 ## Module-wide re-exports
 
-Turing.jl directly re-exports the entire public API of the following packages:
-
-  - [Distributions.jl](https://juliastats.org/Distributions.jl)
-  - [MCMCChains.jl](https://turinglang.org/MCMCChains.jl)
-
-Please see the individual packages for their documentation.
+Turing.jl directly re-exports the entire public API of [Distributions.jl](https://juliastats.org/Distributions.jl).
+Please see its documentation for more details.
 
 ## Individual exports and re-exports
 
@@ -49,13 +45,14 @@ even though [`Prior()`](@ref) is actually defined in the `Turing.Inference` modu
 
 ### Inference
 
-| Exported symbol   | Documentation                                                             | Description                               |
-|:----------------- |:------------------------------------------------------------------------- |:----------------------------------------- |
-| `sample`          | [`StatsBase.sample`](https://turinglang.org/docs/usage/sampling-options/) | Sample from a model                       |
-| `MCMCThreads`     | [`AbstractMCMC.MCMCThreads`](@extref)                                     | Run MCMC using multiple threads           |
-| `MCMCDistributed` | [`AbstractMCMC.MCMCDistributed`](@extref)                                 | Run MCMC using multiple processes         |
-| `MCMCSerial`      | [`AbstractMCMC.MCMCSerial`](@extref)                                      | Run MCMC using without parallelism        |
-| `loadstate`       | [`Turing.Inference.loadstate`](@ref)                                      | Load saved state from `MCMCChains.Chains` |
+| Exported symbol   | Documentation                                                             | Description                         |
+|:----------------- |:------------------------------------------------------------------------- |:----------------------------------- |
+| `sample`          | [`StatsBase.sample`](https://turinglang.org/docs/usage/sampling-options/) | Sample from a model                 |
+| `MCMCThreads`     | [`AbstractMCMC.MCMCThreads`](@extref)                                     | Run MCMC using multiple threads     |
+| `MCMCDistributed` | [`AbstractMCMC.MCMCDistributed`](@extref)                                 | Run MCMC using multiple processes   |
+| `MCMCSerial`      | [`AbstractMCMC.MCMCSerial`](@extref)                                      | Run MCMC using without parallelism  |
+| `loadstate`       | [`Turing.Inference.loadstate`](@ref)                                      | Load saved state from an MCMC chain |
+| `VNChain`         | n/a                                                                       | Alias for `FlexiChain{VarName}`     |
 
 ### Samplers
 

diff --git a/ext/TuringMCMCChainsExt.jl b/ext/TuringMCMCChainsExt.jl
@@ -0,0 +1,44 @@
+module TuringMCMCChainsExt
+
+using Turing
+using Turing: AbstractMCMC
+using MCMCChains: MCMCChains
+
+"""
+    loadstate(chain::MCMCChains.Chains)
+
+Load the final state of the sampler from a `MCMCChains.Chains` object.
+
+To save the final state of the sampler, you must use `sample(...; save_state=true)`. If this
+argument was not used during sampling, calling `loadstate` will throw an error.
+"""
+function Turing.Inference.loadstate(chain::MCMCChains.Chains)
+    if !haskey(chain.info, :samplerstate)
+        throw(
+            ArgumentError(
+                "the chain object does not contain the final state of the sampler; to save the final state you must sample with `save_state=true`",
+            ),
+        )
+    end
+    return chain.info[:samplerstate]
+end
+
+function AbstractMCMC.bundle_samples(
+    samples::Vector{<:Vector},
+    model::DynamicPPL.Model,
+    spl::Emcee,
+    state::EmceeState,
+    ::Type{MCMCChains.Chains},
+    kwargs...,
+)
+    n_walkers = _get_n_walkers(spl)
+    chains = map(1:n_walkers) do i
+        this_walker_samples = [s[i] for s in samples]
+        AbstractMCMC.bundle_samples(
+            this_walker_samples, model, spl, state, MCMCChains.Chains; kwargs...
+        )
+    end
+    return AbstractMCMC.chainscat(chains...)
+end
+
+end
diff --git a/src/Turing.jl b/src/Turing.jl
@@ -4,7 +4,7 @@ using Reexport, ForwardDiff
 using Bijectors, StatsFuns, SpecialFunctions
 using Statistics, LinearAlgebra
 using Libtask
-@reexport using Distributions, MCMCChains
+@reexport using Distributions
 using Compat: pkgversion
 
 using AdvancedVI: AdvancedVI
@@ -14,6 +14,7 @@ using LogDensityProblems: LogDensityProblems
 using StatsAPI: StatsAPI
 using StatsBase: StatsBase
 using AbstractMCMC
+using FlexiChains
 
 using Printf: Printf
 using Random: Random
@@ -181,6 +182,8 @@ export
     loadstate,
     # kwargs in SMC
     might_produce,
-    @might_produce
+    @might_produce,
+    # FlexiChains re-export
+    VNChain
 
 end
diff --git a/src/mcmc/Inference.jl b/src/mcmc/Inference.jl
@@ -16,6 +16,7 @@ using DynamicPPL:
     Model,
     DefaultContext
 using Distributions, Libtask, Bijectors
+using FlexiChains: FlexiChains, VNChain
 using LinearAlgebra
 using ..Turing: PROGRESS, Turing
 using StatsFuns: logsumexp
@@ -35,7 +36,6 @@ import AdvancedPS
 import EllipticalSliceSampling
 import LogDensityProblems
 import Random
-import MCMCChains
 import StatsBase: predict
 
 export Hamiltonian,
@@ -62,7 +62,10 @@ export Hamiltonian,
     init_strategy,
     loadstate
 
-const DEFAULT_CHAIN_TYPE = MCMCChains.Chains
+const DEFAULT_CHAIN_TYPE = VNChain
+
+# Extended in chains extensions
+function loadstate end
 
 include("abstractmcmc.jl")
 include("repeat_sampler.jl")

diff --git a/src/mcmc/abstractmcmc.jl b/src/mcmc/abstractmcmc.jl
@@ -107,25 +107,6 @@ function AbstractMCMC.sample(
     )
 end
 
-"""
-    loadstate(chain::MCMCChains.Chains)
-
-Load the final state of the sampler from a `MCMCChains.Chains` object.
-
-To save the final state of the sampler, you must use `sample(...; save_state=true)`. If this
-argument was not used during sampling, calling `loadstate` will throw an error.
-"""
-function loadstate(chain::MCMCChains.Chains)
-    if !haskey(chain.info, :samplerstate)
-        throw(
-            ArgumentError(
-                "the chain object does not contain the final state of the sampler; to save the final state you must sample with `save_state=true`",
-            ),
-        )
-    end
-    return chain.info[:samplerstate]
-end
-
 # TODO(penelopeysm): Remove initialstep and generalise MCMC sampling procedures
 function initialstep end
 

diff --git a/src/mcmc/emcee.jl b/src/mcmc/emcee.jl
@@ -118,18 +118,18 @@ function AbstractMCMC.step(
 end
 
 function AbstractMCMC.bundle_samples(
-    samples::Vector{<:Vector},
-    model::AbstractModel,
+    samples::Vector{<:AbstractVector},
+    model::DynamicPPL.Model,
     spl::Emcee,
     state::EmceeState,
-    chain_type::Type{MCMCChains.Chains};
+    chain_type::Type{VNChain};
     kwargs...,
 )
     n_walkers = _get_n_walkers(spl)
     chains = map(1:n_walkers) do i
         this_walker_samples = [s[i] for s in samples]
         AbstractMCMC.bundle_samples(
-            this_walker_samples, model, spl, state, chain_type; kwargs...
+            this_walker_samples, model, spl, state, VNChain; kwargs...
         )
     end
     return AbstractMCMC.chainscat(chains...)

diff --git a/test/Project.toml b/test/Project.toml
@@ -15,14 +15,14 @@ Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 DynamicHMC = "bbc10e6e-7c05-544b-b16e-64fede858acb"
 DynamicPPL = "366bfd00-2699-11ea-058f-f148b4cae6d8"
 FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
+FlexiChains = "4a37a8b9-6e57-4b92-8664-298d46e639f7"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 HypothesisTests = "09f84164-cd44-5f33-b23f-e6b0d136a0d5"
 Libtask = "6f1fad26-d15e-5dc8-ae53-837a1d7b8c9f"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
 LogDensityProblemsAD = "996a588d-648d-4e1f-a8f0-a84b347e47b1"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
-MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
 Mooncake = "da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 OptimizationBBO = "3e6eede4-6085-4f62-9a71-46d9bc1eb92b"
@@ -54,15 +54,14 @@ Combinatorics = "1"
 DifferentiationInterface = "0.7"
 Distributions = "0.25"
 DynamicHMC = "2.1.6, 3.0"
-DynamicPPL = "0.40.6"
+DynamicPPL = "0.40.13"
 FiniteDifferences = "0.10.8, 0.11, 0.12"
 ForwardDiff = "0.10.12 - 0.10.32, 0.10, 1"
 HypothesisTests = "0.11"
 Libtask = "0.9.14"
 LinearAlgebra = "1"
 LogDensityProblems = "2"
 LogDensityProblemsAD = "1.4"
-MCMCChains = "7.3.0"
 Mooncake = "0.4.182, 0.5"
 Optimization = "3, 4, 5"
 OptimizationBBO = "0.1, 0.2, 0.3, 0.4"