diff --git a/bin/environment-local.yml b/bin/environment-local.yml
index a20c5cf98..19f902936 100644
--- a/bin/environment-local.yml
+++ b/bin/environment-local.yml
@@ -7,7 +7,7 @@ dependencies:
   - cosmosis=3.24.*
   - dask=2024.5.*
   - dm-tree=0.1.*
-  - firecrown=1.7.*
+  - firecrown=1.14.*
   - fitsio=1.3.*
   - h5py=3.15.*=mpi_mpich_*
   - healpy=1.18.*
diff --git a/bin/perlmutter-install.sh b/bin/perlmutter-install.sh
index c65e8e621..1eaf1e67b 100755
--- a/bin/perlmutter-install.sh
+++ b/bin/perlmutter-install.sh
@@ -12,7 +12,9 @@ mamba activate ./conda
 
 # we manually install firecrown as we have to remove numcosmo to avoid clashes
 FIRECROWN_DIR=firecrown-tmp-${RANDOM}
-git clone --branch v1.7.5 https://github.com/LSSTDESC/firecrown  ${FIRECROWN_DIR}
+
+# TODO: check if this is still needed.
+git clone --branch v1.14.0 https://github.com/LSSTDESC/firecrown  ${FIRECROWN_DIR}
 cd ${FIRECROWN_DIR}
 sed -i '/numcosmo/d' setup.cfg
 pip install .
diff --git a/txpipe/test/test_firecrown.py b/txpipe/test/test_firecrown.py
new file mode 100644
index 000000000..36ac46dc8
--- /dev/null
+++ b/txpipe/test/test_firecrown.py
@@ -0,0 +1,31 @@
+from ..utils.theory import theory_3x2pt, fill_empty_sacc
+import pyccl
+import sacc
+import numpy as np
+
+
+def make_sacc():
+    sacc_data = sacc.Sacc()
+    nbin_source = 2
+    nbin_lens = 3
+    z = np.linspace(0.0, 2.0, 200)
+    mu_source = [0.6, 1.0]
+    mu_lens = [0.2, 0.3, 0.4]
+    sigma = 0.1
+    for i in range(nbin_source):
+        Nz = np.exp(-0.5 * ((z - mu_source[i]) / sigma) ** 2)
+        sacc_data.add_tracer("NZ", f"source_{i}", z, Nz)
+    for i in range(nbin_lens):
+        Nz = np.exp(-0.5 * ((z - mu_lens[i]) / sigma) ** 2)
+        sacc_data.add_tracer("NZ", f"lens_{i}", z, Nz)
+    return sacc_data
+
+
+
+def test_firecrown_theory():
+    sacc_data = make_sacc()
+    ell_values = np.geomspace(10, 2000, 20)
+    fill_empty_sacc(sacc_data, ell_values=ell_values)
+    cosmo = pyccl.CosmologyVanillaLCDM()
+    theory_sacc = theory_3x2pt(cosmo, sacc_data)
+    print(theory_sacc.get_mean())
\ No newline at end of file
diff --git a/txpipe/utils/theory.py b/txpipe/utils/theory.py
index 0856d4185..be39598d7 100755
--- a/txpipe/utils/theory.py
+++ b/txpipe/utils/theory.py
@@ -2,7 +2,6 @@
 import numpy as np
 import warnings
 import yaml
-import pathlib
 
 # same convention as elsewhere
 SHEAR_SHEAR = 0
@@ -12,6 +11,27 @@
 default_theory_model = str(pathlib.Path(__file__).parents[0] / "theory_model.py")
 
 
+def _make_firecrown_build_parameters(sacc_data):
+    """Create Firecrown build parameters compatible with multiple versions.
+
+    Firecrown >=1.14 expects a NamedParameters instance, while some legacy
+    likelihood scripts still access build parameters with dict-style syntax.
+    """
+    try:
+        from firecrown.likelihood.likelihood import NamedParameters
+    except ImportError:
+        return {"sacc_data": sacc_data}
+
+    class TxBuildParameters(NamedParameters):
+        def __getitem__(self, key):
+            return self.data[key]
+
+        def get(self, key, default=None):
+            return self.data.get(key, default)
+
+    return TxBuildParameters({"sacc_data": sacc_data})
+
+
 def ccl_read_yaml(filename, **kwargs):
     import pyccl as ccl
 
@@ -175,18 +195,18 @@ def theory_3x2pt(
 
     # Use the FireCrown machinery to compute the likelihood and as
     # a by-product the theory
-    build_parameters = {
-        "sacc_data": sacc_data,
-    }
+    build_parameters = _make_firecrown_build_parameters(sacc_data)
 
     # These stages are a copy of what is done inside the FireCrown connectors
     likelihood, tools = load_likelihood_from_script(theory_model, build_parameters)
 
-    systematic_params = {**make_bias_parameters(bias, sacc_data, cosmo)}
+    cosmo_params = {k: v for (k, v) in cosmo.to_dict().items() if isinstance(v, (int, float))}
+    params_values = {**make_bias_parameters(bias, sacc_data, cosmo), **cosmo_params}
     # Apply the systematics parameters
-    likelihood.update(ParamsMap(systematic_params))
-    tools.update(ParamsMap(systematic_params))
-    tools.prepare(cosmo)
+    params = ParamsMap(params_values)
+    likelihood.update(params)
+    tools.update(params)
+    tools.prepare()
 
     # Ask the likelihood for a theory vector. We don't want
     # to print the actual likelihood because for many applications
diff --git a/txpipe/utils/theory_model.py b/txpipe/utils/theory_model.py
index c76064bc5..e7a28b1ba 100644
--- a/txpipe/utils/theory_model.py
+++ b/txpipe/utils/theory_model.py
@@ -1,23 +1,46 @@
-import os
 import pathlib
 
-import firecrown.likelihood.gauss_family.statistic.source.weak_lensing as wl
-import firecrown.likelihood.gauss_family.statistic.source.number_counts as nc
-from firecrown.likelihood.gauss_family.statistic.two_point import TwoPoint
-from firecrown.likelihood.gauss_family.gaussian import ConstGaussian
+try:
+    from firecrown.likelihood.weak_lensing import WeakLensing
+    from firecrown.likelihood.number_counts import LinearBiasSystematic, NumberCounts
+    from firecrown.likelihood.two_point import TwoPoint
+    from firecrown.likelihood.gaussian import ConstGaussian
+except ImportError:
+    import firecrown.likelihood.gauss_family.statistic.source.weak_lensing as wl
+    import firecrown.likelihood.gauss_family.statistic.source.number_counts as nc
+    from firecrown.likelihood.gauss_family.statistic.two_point import TwoPoint
+    from firecrown.likelihood.gauss_family.gaussian import ConstGaussian
+
+    WeakLensing = wl.WeakLensing
+    LinearBiasSystematic = nc.LinearBiasSystematic
+    NumberCounts = nc.NumberCounts
+
 from firecrown.modeling_tools import ModelingTools
 import sacc
-import pyccl as ccl
 import numpy as np
 
 
+def _get_build_parameter(build_parameters, key):
+    """Read build parameters from either a dict-like object or NamedParameters."""
+    try:
+        return build_parameters[key]
+    except (KeyError, TypeError):
+        pass
+
+    data = getattr(build_parameters, "data", None)
+    if data is not None and key in data:
+        return data[key]
+
+    raise KeyError(key)
+
+
 def build_likelihood(build_parameters):
     """
     This is a generic 3x2pt theory model, but with only
     bias as a systematic.
 
     """
-    sacc_data = build_parameters["sacc_data"]
+    sacc_data = _get_build_parameter(build_parameters, "sacc_data")
 
     # items in the build_parameters are supposed to be
     # just str, int, etc, not complicated parameters.
@@ -41,10 +64,10 @@ def build_likelihood(build_parameters):
 
     for tracer_name in sacc_data.tracers.keys():
         if tracer_name.startswith("source"):
-            tr = wl.WeakLensing(sacc_tracer=tracer_name)
+            tr = WeakLensing(sacc_tracer=tracer_name)
         elif tracer_name.startswith("lens"):
-            bias_systematic = nc.LinearBiasSystematic(sacc_tracer=tracer_name)
-            tr = nc.NumberCounts(sacc_tracer=tracer_name, systematics=[bias_systematic])
+            bias_systematic = LinearBiasSystematic(sacc_tracer=tracer_name)
+            tr = NumberCounts(sacc_tracer=tracer_name, systematics=[bias_systematic])
         else:
             raise ValueError(f"Unknown tracer in sacc file, non-3x2pt: {tracer_name}")
         sources[tracer_name] = tr