diff --git a/HISTORY.md b/HISTORY.md
index 374946f4c..ff0af9377 100644
--- a/HISTORY.md
+++ b/HISTORY.md
@@ -1,3 +1,19 @@
+# 0.42.1
+
+SMC and PG can now be used for models with keyword arguments, albeit with one requirement: the user must mark the model function as being able to produce.
+For example, if the model is
+
+```julia
+@model foo(x; y) = a ~ Normal(x, y)
+```
+
+then before samping from this with SMC or PG, you will have to run
+
+```julia
+using Libtask;
+Libtask.@might_produce(foo);
+```
+
 # 0.42.0
 
 ## DynamicPPL 0.39
diff --git a/Project.toml b/Project.toml
index 2831cd331..26f870b17 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,6 +1,6 @@
 name = "Turing"
 uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
-version = "0.42.0"
+version = "0.42.1"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
@@ -52,7 +52,7 @@ AbstractPPL = "0.11, 0.12, 0.13"
 Accessors = "0.1"
 AdvancedHMC = "0.8.3"
 AdvancedMH = "0.8.9"
-AdvancedPS = "0.7"
+AdvancedPS = "0.7.2"
 AdvancedVI = "0.6"
 BangBang = "0.4.2"
 Bijectors = "0.14, 0.15"
@@ -65,7 +65,7 @@ DynamicHMC = "3.4"
 DynamicPPL = "0.39.1"
 EllipticalSliceSampling = "0.5, 1, 2"
 ForwardDiff = "0.10.3, 1"
-Libtask = "0.9.3"
+Libtask = "0.9.5"
 LinearAlgebra = "1"
 LogDensityProblems = "2"
 MCMCChains = "5, 6, 7"
@@ -86,3 +86,6 @@ julia = "1.10.8"
 
 [extras]
 DynamicHMC = "bbc10e6e-7c05-544b-b16e-64fede858acb"
+
+[sources]
+AdvancedPS = {url = "https://github.com/TuringLang/AdvancedPS.jl", rev = "py/kwargs"}
diff --git a/src/mcmc/particle_mcmc.jl b/src/mcmc/particle_mcmc.jl
index 585d906cb..aff64a707 100644
--- a/src/mcmc/particle_mcmc.jl
+++ b/src/mcmc/particle_mcmc.jl
@@ -19,11 +19,13 @@ struct ParticleMCMCContext{R<:AbstractRNG} <: DynamicPPL.AbstractContext
     rng::R
 end
 
-struct TracedModel{V<:AbstractVarInfo,M<:Model,E<:Tuple} <: AdvancedPS.AbstractGenericModel
+struct TracedModel{V<:AbstractVarInfo,M<:Model,T<:Tuple,NT<:NamedTuple} <:
+       AdvancedPS.AbstractTuringLibtaskModel
     model::M
     varinfo::V
-    evaluator::E
     resample::Bool
+    fargs::T
+    kwargs::NT
 end
 
 function TracedModel(
@@ -31,11 +33,8 @@ function TracedModel(
 )
     model = DynamicPPL.setleafcontext(model, ParticleMCMCContext(rng))
     args, kwargs = DynamicPPL.make_evaluate_args_and_kwargs(model, varinfo)
-    isempty(kwargs) || error(
-        "Particle sampling methods do not currently support models with keyword arguments.",
-    )
-    evaluator = (model.f, args...)
-    return TracedModel(model, varinfo, evaluator, resample)
+    fargs = (model.f, args...)
+    return TracedModel(model, varinfo, resample, fargs, kwargs)
 end
 
 function AdvancedPS.advance!(
@@ -53,16 +52,16 @@ function AdvancedPS.delete_retained!(trace::TracedModel)
     # In such a case, we need to ensure that when we continue sampling (i.e.
     # the next time we hit tilde_assume!!), we don't use the values in the 
     # reference particle but rather sample new values.
-    return TracedModel(trace.model, trace.varinfo, trace.evaluator, true)
+    return TracedModel(trace.model, trace.varinfo, true, trace.fargs, trace.kwargs)
 end
 
 function AdvancedPS.reset_model(trace::TracedModel)
     return trace
 end
 
-function Libtask.TapedTask(taped_globals, model::TracedModel; kwargs...)
+function Libtask.TapedTask(taped_globals, model::TracedModel)
     return Libtask.TapedTask(
-        taped_globals, model.evaluator[1], model.evaluator[2:end]...; kwargs...
+        taped_globals, model.fargs[1], model.fargs[2:end]...; model.kwargs...
     )
 end
 
@@ -124,6 +123,7 @@ function AbstractMCMC.sample(
 )
     check_model && _check_model(model, sampler)
     error_if_threadsafe_eval(model)
+    check_model_kwargs(model)
     # need to add on the `nparticles` keyword argument for `initialstep` to make use of
     return AbstractMCMC.mcmcsample(
         rng,
@@ -138,6 +138,31 @@ function AbstractMCMC.sample(
     )
 end
 
+function check_model_kwargs(model::DynamicPPL.Model)
+    if !isempty(model.defaults)
+        # If there are keyword arguments, we need to check that the user has
+        # accounted for this by overloading `might_produce`.
+        might_produce = Libtask.might_produce(typeof((Core.kwcall, NamedTuple(), model.f)))
+        if !might_produce
+            io = IOBuffer()
+            ctx = IOContext(io, :color => true)
+            print(
+                ctx,
+                "Models with keyword arguments need special treatment to be used" *
+                " with particle methods. Please run:\n\n",
+            )
+            printstyled(
+                ctx,
+                "    using Libtask; Libtask.@might_produce($(model.f))";
+                bold=true,
+                color=:blue,
+            )
+            print(ctx, "\n\nbefore sampling from this model with particle methods.\n")
+            error(String(take!(io)))
+        end
+    end
+end
+
 function Turing.Inference.initialstep(
     rng::AbstractRNG,
     model::DynamicPPL.Model,
@@ -146,6 +171,7 @@ function Turing.Inference.initialstep(
     nparticles::Int,
     kwargs...,
 )
+    check_model_kwargs(model)
     # Reset the VarInfo.
     vi = DynamicPPL.setacc!!(vi, ProduceLogLikelihoodAccumulator())
     vi = DynamicPPL.empty!!(vi)
@@ -254,6 +280,7 @@ function Turing.Inference.initialstep(
     rng::AbstractRNG, model::DynamicPPL.Model, spl::PG, vi::AbstractVarInfo; kwargs...
 )
     error_if_threadsafe_eval(model)
+    check_model_kwargs(model)
     vi = DynamicPPL.setacc!!(vi, ProduceLogLikelihoodAccumulator())
 
     # Create a new set of particles
@@ -495,7 +522,7 @@ end
 # details of the compiler, we set a bunch of methods as might_produce = true. We start with
 # adding to ProduceLogLikelihoodAccumulator, which is what calls `produce`, and go up the
 # call stack.
-Libtask.might_produce(::Type{<:Tuple{typeof(DynamicPPL.accloglikelihood!!),Vararg}}) = true
+Libtask.@might_produce(DynamicPPL.accloglikelihood!!)
 function Libtask.might_produce(
     ::Type{
         <:Tuple{
@@ -507,15 +534,11 @@ function Libtask.might_produce(
 )
     return true
 end
-function Libtask.might_produce(
-    ::Type{<:Tuple{typeof(DynamicPPL.accumulate_observe!!),Vararg}}
-)
-    return true
-end
-Libtask.might_produce(::Type{<:Tuple{typeof(DynamicPPL.tilde_observe!!),Vararg}}) = true
-# Could the next two could have tighter type bounds on the arguments, namely a GibbsContext?
+Libtask.@might_produce(DynamicPPL.accumulate_observe!!)
+Libtask.@might_produce(DynamicPPL.tilde_observe!!)
+# Could tilde_assume!! have tighter type bounds on the arguments, namely a GibbsContext?
 # That's the only thing that makes tilde_assume calls result in tilde_observe calls.
-Libtask.might_produce(::Type{<:Tuple{typeof(DynamicPPL.tilde_assume!!),Vararg}}) = true
-Libtask.might_produce(::Type{<:Tuple{typeof(DynamicPPL.evaluate!!),Vararg}}) = true
-Libtask.might_produce(::Type{<:Tuple{typeof(DynamicPPL.init!!),Vararg}}) = true
+Libtask.@might_produce(DynamicPPL.tilde_assume!!)
+Libtask.@might_produce(DynamicPPL.evaluate!!)
+Libtask.@might_produce(DynamicPPL.init!!)
 Libtask.might_produce(::Type{<:Tuple{<:DynamicPPL.Model,Vararg}}) = true
diff --git a/test/Project.toml b/test/Project.toml
index 70bf16fcf..96bda0824 100644
--- a/test/Project.toml
+++ b/test/Project.toml
@@ -43,7 +43,7 @@ ADTypes = "1"
 AbstractMCMC = "5.9"
 AbstractPPL = "0.11, 0.12, 0.13"
 AdvancedMH = "0.8.9"
-AdvancedPS = "0.7"
+AdvancedPS = "0.7.2"
 AdvancedVI = "0.6"
 Aqua = "0.8"
 BangBang = "0.4"
@@ -77,3 +77,6 @@ StatsBase = "0.33, 0.34"
 StatsFuns = "0.9.5, 1"
 TimerOutputs = "0.5"
 julia = "1.10"
+
+[sources]
+AdvancedPS = {url = "https://github.com/TuringLang/AdvancedPS.jl", rev = "py/kwargs"}
diff --git a/test/mcmc/particle_mcmc.jl b/test/mcmc/particle_mcmc.jl
index f816fc43a..0f3353319 100644
--- a/test/mcmc/particle_mcmc.jl
+++ b/test/mcmc/particle_mcmc.jl
@@ -4,6 +4,7 @@ using ..Models: gdemo_default
 using ..SamplerTestUtils: test_chain_logp_metadata
 using AdvancedPS: ResampleWithESSThreshold, resample_systematic, resample_multinomial
 using Distributions: Bernoulli, Beta, Gamma, Normal, sample
+using Libtask: @might_produce
 using Random: Random
 using StableRNGs: StableRNG
 using Test: @test, @test_throws, @testset
@@ -162,9 +163,23 @@ end
     end
 
     # https://github.com/TuringLang/Turing.jl/issues/2007
-    @testset "keyword arguments not supported" begin
-        @model kwarg_demo(; x=2) = return x
-        @test_throws ErrorException sample(kwarg_demo(), PG(1), 10)
+    @testset "keyword argument handling" begin
+        @model function kwarg_demo(y; n=0.0)
+            x ~ Normal(n)
+            return y ~ Normal(x)
+        end
+        @test_throws "Models with keyword arguments" sample(kwarg_demo(5.0), PG(20), 10)
+
+        # Check that enabling `might_produce` does allow sampling
+        @might_produce kwarg_demo
+        chain = sample(StableRNG(468), kwarg_demo(5.0), PG(20), 1000)
+        @test chain isa MCMCChains.Chains
+        @test mean(chain[:x]) ≈ 2.5 atol = 0.2
+
+        # Check that the keyword argument's value is respected
+        chain2 = sample(StableRNG(468), kwarg_demo(5.0; n=10.0), PG(20), 1000)
+        @test chain2 isa MCMCChains.Chains
+        @test mean(chain2[:x]) ≈ 7.5 atol = 0.2
     end
 
     @testset "refuses to run threadsafe eval" begin