diff --git a/pymc/distributions/multivariate.py b/pymc/distributions/multivariate.py
index 3edba7936e..defd92c87d 100644
--- a/pymc/distributions/multivariate.py
+++ b/pymc/distributions/multivariate.py
@@ -1294,17 +1294,12 @@ def _LKJCholeksyCovRV_logp(op, values, rng, n, eta, sd_dist, **kwargs):
     det_invjac = pt.log(corr_diag) - idx * pt.log(sd_vals)
     det_invjac = det_invjac.sum()
 
-    # TODO: _lkj_normalizing_constant currently requires `eta` and `n` to be constants
+    # TODO: _lkj_normalizing_constant currently requires `n` to be a constant
     try:
         n = int(get_underlying_scalar_constant_value(n))
     except NotScalarConstantError:
         raise NotImplementedError("logp only implemented for constant `n`")
 
-    try:
-        eta = float(get_underlying_scalar_constant_value(eta))
-    except NotScalarConstantError:
-        raise NotImplementedError("logp only implemented for constant `eta`")
-
     norm = _lkj_normalizing_constant(eta, n)
 
     return norm + logp_lkj + logp_sd + det_invjac
diff --git a/tests/distributions/test_multivariate.py b/tests/distributions/test_multivariate.py
index b1ea31975b..40f345e5c9 100644
--- a/tests/distributions/test_multivariate.py
+++ b/tests/distributions/test_multivariate.py
@@ -975,6 +975,30 @@ def test_no_warning_logp(self):
             warnings.simplefilter("error")
             m.logp()
 
+    def test_lkj_cholesky_cov_symbolic_eta(self):
+        with pm.Model() as model:
+            eta = pm.HalfNormal("eta", sigma=1.0)
+            sd_dist = pm.Exponential.dist(1.0)
+            chol = pm.LKJCholeskyCov("chol", eta=eta, n=3, sd_dist=sd_dist)
+
+            logp_fn = model.compile_logp()
+            dlogp_fn = model.compile_dlogp()
+
+        ip = model.initial_point()
+        logp_val = logp_fn(ip)
+        assert np.isfinite(logp_val)
+
+        dlogp_val = dlogp_fn(ip)
+        assert dlogp_val.size == 7
+        assert np.all(np.isfinite(dlogp_val))
+
+        with pm.Model() as m_invalid:
+            n_sym = pt.lscalar("n")
+            sd_dist = pm.Exponential.dist(1.0)
+            pm.LKJCholeskyCov("chol_invalid", eta=2.0, n=n_sym, sd_dist=sd_dist, compute_corr=False)
+            with pytest.raises(NotImplementedError, match="logp only implemented for constant `n`"):
+                m_invalid.logp()
+
     @pytest.mark.parametrize(
         "sd_dist",
         [