diff --git a/CLAUDE.md b/CLAUDE.md
index ed53c3f..5b6d370 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -93,8 +93,8 @@ simulate_dataset() → Simulate states (with optional policy effects)
   computation using Kalman filtering. The debug variant is not jitted and returns
   intermediate results (residuals, contributions, filtered states).
 - **constraints.py**: Generates parameter constraints (bounds, equalities from stagemap,
-  fixed values) for optimization. Exports `get_constraints_dicts()`,
-  `constraints_dicts_to_om()`, `enforce_fixed_constraints()`, `add_bounds()`.
+  fixed values) for optimization. Exports `get_constraints()`,
+  `enforce_fixed_constraints()`, `add_bounds()`, `FixedConstraintWithValue`.
 - **parse_params.py**: Converts flat parameter vectors to structured model parameters.
   Exports `create_parsing_info()` and `parse_params()`.
 - **params_index.py**: Builds the `pd.MultiIndex` for the params DataFrame via
@@ -194,7 +194,8 @@ These are not in `__all__` but are imported directly by application projects:
 - `skillmodels.process_model.process_model` — central to all application code
 - `skillmodels.types.ProcessedModel`, `EndogenousFactorsInfo`
 - `skillmodels.decorators.register_params` — essential for custom transition functions
-- `skillmodels.constraints.constraints_dicts_to_om`, `enforce_fixed_constraints`
+- `skillmodels.constraints.get_constraints`, `enforce_fixed_constraints`,
+  `FixedConstraintWithValue`, `select_by_loc`
 - `skillmodels.utilities.extract_factors`, `update_parameter_values`
 - `skillmodels.process_data.pre_process_data`
 - `skillmodels.correlation_heatmap.get_measurements_corr`, `get_quasi_scores_corr`,
diff --git a/pixi.lock b/pixi.lock
index 08153c9..031ce22 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -34,7 +34,7 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2026.2.25-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-2.0.0-py314h4a8dc5f_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.5-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda
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@@ -78,7 +78,7 @@ environments:
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.2-h33c6efd_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.3-h33c6efd_0.conda
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@@ -171,7 +171,7 @@ environments:
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/prek-0.3.5-hb17b654_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/prek-0.3.6-hb17b654_0.conda
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@@ -316,7 +316,7 @@ environments:
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-      - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.5-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.6-pyhd8ed1ab_0.conda
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.2-h33c6efd_0.conda
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+      - conda: https://conda.anaconda.org/conda-forge/linux-64/prek-0.3.6-hb17b654_0.conda
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-      - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.5-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.6-pyhd8ed1ab_0.conda
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@@ -769,7 +769,7 @@ environments:
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-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/icu-78.2-hef89b57_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/icu-78.3-hef89b57_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.11-pyhd8ed1ab_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.3.0-pyhd8ed1ab_0.conda
@@ -3097,7 +3097,7 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/prek-0.3.5-h6fdd925_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/prek-0.3.6-h6fdd925_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/prometheus_client-0.24.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/prompt-toolkit-3.0.52-pyha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/psutil-7.2.2-py314ha14b1ff_0.conda
@@ -3222,7 +3222,7 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2026.2.25-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/cffi-2.0.0-py314h5a2d7ad_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.5-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.3-pyhe01879c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/contourpy-1.3.3-py314hf309875_4.conda
@@ -3322,7 +3322,7 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/win-64/prek-0.3.5-h18a1a76_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/win-64/prek-0.3.6-h18a1a76_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/prometheus_client-0.24.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/prompt-toolkit-3.0.52-pyha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/psutil-7.2.2-py314hc5dbbe4_0.conda
@@ -3990,17 +3990,17 @@ packages:
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   size: 294731
   timestamp: 1761203441365
-- conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.5-pyhd8ed1ab_0.conda
-  sha256: 05ea76a016c77839b64f9f8ec581775f6c8a259044bd5b45a177e46ab4e7feac
-  md5: beb628209b2b354b98203066f90b3287
+- conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.6-pyhd8ed1ab_0.conda
+  sha256: d86dfd428b2e3c364fa90e07437c8405d635aa4ef54b25ab51d9c712be4112a5
+  md5: 49ee13eb9b8f44d63879c69b8a40a74b
   depends:
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   license: MIT
   license_family: MIT
   purls:
   - pkg:pypi/charset-normalizer?source=compressed-mapping
-  size: 53210
-  timestamp: 1772816516728
+  size: 58510
+  timestamp: 1773660086450
 - pypi: https://files.pythonhosted.org/packages/b7/9f/d73dfb85d7a5b1a56a99adc50f2074029468168c970ff5daeade4ad819e4/choreographer-1.2.1-py3-none-any.whl
   name: choreographer
   version: 1.2.1
@@ -4100,9 +4100,9 @@ packages:
   - pkg:pypi/contourpy?source=hash-mapping
   size: 247437
   timestamp: 1769155978556
-- conda: https://conda.anaconda.org/conda-forge/linux-64/coverage-7.13.4-py314h67df5f8_0.conda
-  sha256: b84aa99886610e0c3856ee1b577fe5c2552a5677bb7d281b4a86e79248813898
-  md5: 6c7efc167cee337d9c41200506d022b8
+- conda: https://conda.anaconda.org/conda-forge/linux-64/coverage-7.13.5-py314h67df5f8_0.conda
+  sha256: cf5f98a291c3a5489cb299bae38711d5dc21b88a00df981f3b1528781e18c909
+  md5: 78f547b78ace7541c4f54c4268ac9d2e
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=14
@@ -4112,12 +4112,12 @@ packages:
   license: Apache-2.0
   license_family: APACHE
   purls:
-  - pkg:pypi/coverage?source=hash-mapping
-  size: 412776
-  timestamp: 1770720660175
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/coverage-7.13.4-py314h6e9b3f0_0.conda
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-  md5: 96c7bf4f2b5010d29604a62e91e634bb
+  - pkg:pypi/coverage?source=compressed-mapping
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+  timestamp: 1773761119353
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/coverage-7.13.5-py314h6e9b3f0_0.conda
+  sha256: 808ebcb57027251f379f84e53a3755d2851918f78bdd512d131afe40ca64a041
+  md5: cdbafe4a3e605024e7372c9580f9d734
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@@ -4127,12 +4127,12 @@ packages:
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   license_family: APACHE
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-  - pkg:pypi/coverage?source=hash-mapping
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-- conda: https://conda.anaconda.org/conda-forge/win-64/coverage-7.13.4-py314h2359020_0.conda
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-  md5: ac7417dc27e19765fd50f547d9a9e445
+  - pkg:pypi/coverage?source=compressed-mapping
+  size: 412458
+  timestamp: 1773761280047
+- conda: https://conda.anaconda.org/conda-forge/win-64/coverage-7.13.5-py314h2359020_0.conda
+  sha256: 80a6a7be7eef784b8314a4cb563563c654e2180a0b2b31b232f79b2e7334aaf2
+  md5: 849f0bd5b83d4fd59b41202b21bb3ca2
   depends:
   - python >=3.14,<3.15.0a0
   - python_abi 3.14.* *_cp314
@@ -4143,9 +4143,9 @@ packages:
   license: Apache-2.0
   license_family: APACHE
   purls:
-  - pkg:pypi/coverage?source=hash-mapping
-  size: 438221
-  timestamp: 1770720521756
+  - pkg:pypi/coverage?source=compressed-mapping
+  size: 438927
+  timestamp: 1773760993379
 - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.3-py314hd8ed1ab_101.conda
   noarch: generic
   sha256: 91b06300879df746214f7363d6c27c2489c80732e46a369eb2afc234bcafb44c
@@ -4893,28 +4893,26 @@ packages:
   - pkg:pypi/hyperframe?source=hash-mapping
   size: 17397
   timestamp: 1737618427549
-- conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.2-h33c6efd_0.conda
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-  md5: 186a18e3ba246eccfc7cff00cd19a870
+- conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.3-h33c6efd_0.conda
+  sha256: fbf86c4a59c2ed05bbffb2ba25c7ed94f6185ec30ecb691615d42342baa1a16a
+  md5: c80d8a3b84358cb967fa81e7075fbc8a
   depends:
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   - libgcc >=14
   - libstdcxx >=14
   license: MIT
-  license_family: MIT
   purls: []
-  size: 12728445
-  timestamp: 1767969922681
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/icu-78.2-hef89b57_0.conda
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-  md5: 114e6bfe7c5ad2525eb3597acdbf2300
+  size: 12723451
+  timestamp: 1773822285671
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/icu-78.3-hef89b57_0.conda
+  sha256: 3a7907a17e9937d3a46dfd41cffaf815abad59a569440d1e25177c15fd0684e5
+  md5: f1182c91c0de31a7abd40cedf6a5ebef
   depends:
   - __osx >=11.0
   license: MIT
-  license_family: MIT
   purls: []
-  size: 12389400
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+  size: 12361647
+  timestamp: 1773822915649
 - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.11-pyhd8ed1ab_0.conda
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@@ -7167,7 +7165,7 @@ packages:
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/markupsafe?source=compressed-mapping
+  - pkg:pypi/markupsafe?source=hash-mapping
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   timestamp: 1772445227915
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/markupsafe-3.0.3-py314h6e9b3f0_1.conda
@@ -7331,9 +7329,9 @@ packages:
   - pkg:pypi/mdurl?source=hash-mapping
   size: 14465
   timestamp: 1733255681319
-- conda: https://conda.anaconda.org/conda-forge/linux-64/memray-1.19.1-py314hef15ded_3.conda
-  sha256: 43801200d3b8dcaa1f9ab47f527c9fe94028780b2760173a240e132e25be2194
-  md5: cc1bee6de727d07ce2dad51a5e8364b9
+- conda: https://conda.anaconda.org/conda-forge/linux-64/memray-1.19.2-py314hef15ded_0.conda
+  sha256: 575b877b4ad4f93c44532e99b4dd8fe402ce34bff63f87b420f141e81f647de6
+  md5: 2951224e244e0e4a27e2c4b24b76ffba
   depends:
   - python
   - rich >=11.2.0
@@ -7343,17 +7341,17 @@ packages:
   - libgcc >=14
   - __glibc >=2.17,<3.0.a0
   - lz4-c >=1.10.0,<1.11.0a0
-  - elfutils >=0.194,<0.195.0a0
-  - libunwind >=1.8.3,<1.9.0a0
   - python_abi 3.14.* *_cp314
+  - libunwind >=1.8.3,<1.9.0a0
+  - elfutils >=0.194,<0.195.0a0
   license: Apache-2.0 AND BSD-3-Clause
   purls:
   - pkg:pypi/memray?source=hash-mapping
-  size: 1824670
-  timestamp: 1765821568349
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/memray-1.19.1-py314habef2a7_3.conda
-  sha256: 912a462c888a867a22e6ebf0607ad3a42d078260cafc7d4f25654989a17f41ac
-  md5: f8c08fd9eb42146b0489d42069fb270e
+  size: 1845157
+  timestamp: 1773493681427
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/memray-1.19.2-py314h44d60dd_0.conda
+  sha256: 5e053a64dbcdc98bd470f358f3fce1cde9b9fe362280a87cb66f1587e9f09e26
+  md5: f29f08f053a687bcfe09089f8a410bc9
   depends:
   - python
   - rich >=11.2.0
@@ -7362,13 +7360,13 @@ packages:
   - python 3.14.* *_cp314
   - __osx >=11.0
   - libcxx >=19
-  - lz4-c >=1.10.0,<1.11.0a0
   - python_abi 3.14.* *_cp314
+  - lz4-c >=1.10.0,<1.11.0a0
   license: Apache-2.0 AND BSD-3-Clause
   purls:
   - pkg:pypi/memray?source=hash-mapping
-  size: 1721669
-  timestamp: 1765821674618
+  size: 1744827
+  timestamp: 1773493881871
 - conda: https://conda.anaconda.org/conda-forge/noarch/mistune-3.2.0-pyhcf101f3_0.conda
   sha256: d3fb4beb5e0a52b6cc33852c558e077e1bfe44df1159eb98332d69a264b14bae
   md5: b11e360fc4de2b0035fc8aaa74f17fd6
@@ -8492,9 +8490,9 @@ packages:
   - pkg:pypi/pluggy?source=compressed-mapping
   size: 25877
   timestamp: 1764896838868
-- conda: https://conda.anaconda.org/conda-forge/linux-64/prek-0.3.5-hb17b654_0.conda
-  sha256: d1e626c747c75ad7403eb6021a9781f96a2293ce79726e493c0691c1a8c9ee2e
-  md5: e965d5ef067ec4e1b4ee9d92760f4f03
+- conda: https://conda.anaconda.org/conda-forge/linux-64/prek-0.3.6-hb17b654_0.conda
+  sha256: a26627790776987421ecb130240dfd5c26e706d6811e173f7bdf3029bec13e1e
+  md5: 903cc9fafd676d3c13d9c1e71a52231a
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=14
@@ -8503,11 +8501,11 @@ packages:
   license: MIT
   license_family: MIT
   purls: []
-  size: 5584276
-  timestamp: 1773071198111
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/prek-0.3.5-h6fdd925_0.conda
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-  md5: 9328b05853b2044f8fd9cdb9e3ae532d
+  size: 5638792
+  timestamp: 1773659796309
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/prek-0.3.6-h6fdd925_0.conda
+  sha256: 9acb2a629c5cf8b423532a18bd385fa1e2b06b587ced8055e0075d2232a6903a
+  md5: 4b7ebf6d9d829849c264171a00cb7395
   depends:
   - __osx >=11.0
   constrains:
@@ -8515,11 +8513,11 @@ packages:
   license: MIT
   license_family: MIT
   purls: []
-  size: 5185941
-  timestamp: 1773071302258
-- conda: https://conda.anaconda.org/conda-forge/win-64/prek-0.3.5-h18a1a76_0.conda
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-  md5: e775b274325df883f90fb4a6b57579bc
+  size: 5203524
+  timestamp: 1773660234368
+- conda: https://conda.anaconda.org/conda-forge/win-64/prek-0.3.6-h18a1a76_0.conda
+  sha256: 31984152d475d6854a64786b9308dd180e2a4b014fea519b4959513e51dcab26
+  md5: 042c6b6426dbeef69ec7aef3864427bf
   depends:
   - vc >=14.3,<15
   - vc14_runtime >=14.44.35208
@@ -8527,8 +8525,8 @@ packages:
   license: MIT
   license_family: MIT
   purls: []
-  size: 5893739
-  timestamp: 1773071227406
+  size: 5913998
+  timestamp: 1773659816255
 - conda: https://conda.anaconda.org/conda-forge/noarch/prometheus_client-0.24.1-pyhd8ed1ab_0.conda
   sha256: 75b2589159d04b3fb92db16d9970b396b9124652c784ab05b66f584edc97f283
   md5: 7526d20621b53440b0aae45d4797847e
@@ -9404,7 +9402,7 @@ packages:
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/scipy?source=compressed-mapping
+  - pkg:pypi/scipy?source=hash-mapping
   size: 13986990
   timestamp: 1771881110844
 - conda: https://conda.anaconda.org/conda-forge/win-64/scipy-1.17.1-py314h221f224_0.conda
@@ -9499,10 +9497,10 @@ packages:
   timestamp: 1753199211006
 - pypi: ./
   name: skillmodels
-  version: 0.0.24.dev297+gb1606b889.d20260318
-  sha256: c1f639ce783d330e50caa5f38c2ac5567c7cab2b2f932f6e58fe17c662684892
+  version: 0.0.24.dev302+g350c3b9e5.d20260318
+  sha256: c3e259ba2e68a9ccf4593eecfac180a8d2db8d26b757a19d3784ca917060e6f6
   requires_dist:
-  - dags>=0.5
+  - dags>=0.5.1
   - jax>=0.9
   - jupyter-book>=2
   - kaleido>=1.2
@@ -9995,7 +9993,7 @@ packages:
   license: Apache-2.0
   license_family: Apache
   purls:
-  - pkg:pypi/unicodedata2?source=compressed-mapping
+  - pkg:pypi/unicodedata2?source=hash-mapping
   size: 406126
   timestamp: 1770909191618
 - pypi: https://files.pythonhosted.org/packages/dd/1a/5d9a402b39ec892d856bbdd9db502ff73ce28cdf4aff72eb1ce1d6843506/universal_pathlib-0.3.10-py3-none-any.whl
diff --git a/pyproject.toml b/pyproject.toml
index 5a29b2c..49566e3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -29,7 +29,7 @@ classifiers = [
 ]
 dynamic = [ "version" ]
 dependencies = [
-  "dags>=0.5",
+  "dags>=0.5.1",
   "jax>=0.9",
   "jupyter-book>=2",
   "kaleido>=1.2",
diff --git a/src/skillmodels/constraints.py b/src/skillmodels/constraints.py
index 9319da6..4285ac0 100644
--- a/src/skillmodels/constraints.py
+++ b/src/skillmodels/constraints.py
@@ -1,4 +1,4 @@
-"""List of constraints for a model, which can be converted to optimagic constraints."""
+"""Constraint objects for a model specification."""
 
 import functools
 import warnings
@@ -21,18 +21,50 @@
 )
 
 
-def get_constraints_dicts(
+def select_by_loc(params: pd.DataFrame, loc: Any) -> pd.DataFrame:  # noqa: ANN401
+    """Select parameters by location."""
+    return params.loc[loc]
+
+
+@dataclass(frozen=True)
+class FixedConstraintWithValue(om.FixedConstraint):
+    """Fixed constraint that carries the target value and parameter location.
+
+    `om.FixedConstraint` fixes parameters at their start values but does not carry a
+    target value. This wrapper adds `loc` (the parameter location in the params
+    DataFrame) and `value` (the value to set before optimization).
+    """
+
+    loc: pd.MultiIndex | tuple | str | None = None
+    """Parameter location in the params DataFrame."""
+    value: float | None = None
+    """Value to enforce on the parameter."""
+
+    def __post_init__(self) -> None:
+        """Validate that `loc` and `value` are not None and derive `selector`."""
+        if self.loc is None:
+            msg = "loc must not be None"
+            raise TypeError(msg)
+        if self.value is None:
+            msg = "value must not be None"
+            raise TypeError(msg)
+        object.__setattr__(
+            self,
+            "selector",
+            functools.partial(select_by_loc, loc=self.loc),
+        )
+
+
+def get_constraints(
     dimensions: Dimensions,
     labels: Labels,
     anchoring_info: Anchoring,
     update_info: pd.DataFrame,
     normalizations: Mapping[str, Normalizations],
     endogenous_factors_info: EndogenousFactorsInfo,
-) -> list[dict]:
+) -> list[om.constraints.Constraint]:
     """Generate constraints implied by the model specification.
 
-    The result can easily be converted to optimagic-style constraints.
-
     Args:
         dimensions: Dimensional information like n_states, n_periods, n_controls,
             n_mixtures. See :ref:`dimensions`.
@@ -46,47 +78,38 @@ def get_constraints_dicts(
         endogenous_factors_info: Information about endogenous factors in the model.
 
     Returns:
-        A list of constraints dictionaries with entries:
-        - "type": str, one of "fixed", "equality", "probability", "increasing",
-            "pairwise_equality". Must map to an optimagic constraint, see
-            :func:`constraints_dicts_to_om`.
-        - "loc": The location of the affected row(s) in the params DataFrame
-        - "value": float, only present if type is "fixed"
-        - "description": str, optional description of the constraint
+        List of optimagic constraint objects.
 
     """
-    constraints_dicts = []
+    constraints: list[om.constraints.Constraint] = []
 
-    constraints_dicts += _get_normalization_constraints(
+    constraints += _get_normalization_constraints(
         normalizations=normalizations, factors=labels.latent_factors
     )
-    constraints_dicts += _get_mixture_weights_constraints(dimensions.n_mixtures)
-    constraints_dicts += _get_stage_constraints(
+    constraints += _get_mixture_weights_constraints(dimensions.n_mixtures)
+    constraints += _get_stage_constraints(
         stagemap=labels.aug_stagemap,
         stages=labels.aug_stages,
     )
-    constraints_dicts += _get_constant_factors_constraints(labels=labels)
-    constraints_dicts += _get_initial_states_constraints(
+    constraints += _get_constant_factors_constraints(labels=labels)
+    constraints += _get_initial_states_constraints(
         n_mixtures=dimensions.n_mixtures,
         factors=labels.latent_factors,
     )
-    constraints_dicts += _get_transition_constraints(labels=labels)
-    constraints_dicts += _get_anchoring_constraints(
+    constraints += _get_transition_constraints(labels=labels)
+    constraints += _get_anchoring_constraints(
         update_info=update_info,
         controls=labels.controls,
         anchoring_info=anchoring_info,
         periods=labels.aug_periods,
     )
     if endogenous_factors_info.has_endogenous_factors:
-        constraints_dicts += _get_constraints_for_augmented_periods(
+        constraints += _get_constraints_for_augmented_periods(
             labels=labels,
             endogenous_factors_info=endogenous_factors_info,
         )
 
-    for i, c in enumerate(constraints_dicts):
-        c["id"] = i
-
-    return constraints_dicts
+    return constraints
 
 
 def add_bounds(params: pd.DataFrame, bounds_distance: float) -> pd.DataFrame:
@@ -142,7 +165,7 @@ def _is_diagonal_entry(ind_tup: tuple[str, ...]) -> bool:
 def _get_normalization_constraints(
     normalizations: Mapping[str, Normalizations],
     factors: tuple[str, ...],
-) -> list[dict]:
+) -> list[om.constraints.Constraint]:
     """List of constraints to enforce normalizations.
 
     Args:
@@ -150,102 +173,83 @@ def _get_normalization_constraints(
         factors: Tuple of factor names to process.
 
     Returns:
-        constraints_dicts
+        List of constraint objects.
 
     """
-    msg = "This constraint was generated because of an explicit normalization."
     periods = range(len(normalizations[factors[0]].loadings))
 
-    constraints_dicts = []
+    constraints: list[om.constraints.Constraint] = []
     for factor in factors:
         for period in periods:
             for meas, normval in normalizations[factor].loadings[period].items():
-                constraints_dicts.append(
-                    {
-                        "loc": ("loadings", period, meas, factor),
-                        "type": "fixed",
-                        "value": normval,
-                        "description": msg,
-                    }
-                )
+                loc = ("loadings", period, meas, factor)
+                constraints.append(FixedConstraintWithValue(loc=loc, value=normval))
             for meas, normval in normalizations[factor].intercepts[period].items():
-                constraints_dicts.append(
-                    {
-                        "loc": ("controls", period, meas, "constant"),
-                        "type": "fixed",
-                        "value": normval,
-                        "description": msg,
-                    }
-                )
+                loc = ("controls", period, meas, "constant")
+                constraints.append(FixedConstraintWithValue(loc=loc, value=normval))
 
-    return constraints_dicts
+    return constraints
 
 
-def _get_mixture_weights_constraints(n_mixtures: int) -> list[dict]:
+def _get_mixture_weights_constraints(
+    n_mixtures: int,
+) -> list[om.constraints.Constraint]:
     """Constrain mixture weights to be between 0 and 1 and sum to 1."""
+    loc = "mixture_weights"
     if n_mixtures == 1:
-        msg = "Set the mixture weight to 1 if there is only one mixture element."
-        constraints_dicts = [
-            {
-                "loc": "mixture_weights",
-                "type": "fixed",
-                "value": 1.0,
-                "description": msg,
-            },
-        ]
-    else:
-        msg = "Ensure that weights are between 0 and 1 and sum to 1."
-        constraints_dicts = [
-            {"loc": "mixture_weights", "type": "probability", "description": msg}
+        return [
+            FixedConstraintWithValue(loc=loc, value=1.0),
         ]
-    return constraints_dicts
+    return [
+        om.ProbabilityConstraint(selector=functools.partial(select_by_loc, loc=loc))
+    ]
 
 
 def _get_stage_constraints(
     stagemap: tuple[int, ...],
     stages: tuple[int, ...],
-) -> list[dict]:
+) -> list[om.constraints.Constraint]:
     """Equality constraints for transition and shock parameters within stages.
 
     Args:
         stagemap: map aug_periods to aug_stages
         stages: aug_stages
+
     Returns:
-        constraints_dicts
+        List of constraint objects.
 
     """
-    msg = (
-        "This constraint was generated because all involved periods belong to stage {}."
-    )
-    constraints_dicts = []
+    constraints: list[om.constraints.Constraint] = []
 
-    stages_to_periods = {stage: [] for stage in stages}
+    stages_to_periods: dict[int, list[int]] = {stage: [] for stage in stages}
     for aug_period, stage in enumerate(stagemap):
         stages_to_periods[stage].append(aug_period)
 
-    for stage, stage_periods in stages_to_periods.items():
+    for stage_periods in stages_to_periods.values():
         if len(stage_periods) > 1:
             loc_trans = [("transition", p) for p in stage_periods]
             loc_q = [("shock_sds", p) for p in stage_periods]
-            constraints_dicts.append(
-                {
-                    "loc": loc_trans,
-                    "type": "pairwise_equality",
-                    "description": msg.format(stage),
-                },
+            constraints.append(
+                om.PairwiseEqualityConstraint(
+                    selectors=[
+                        functools.partial(select_by_loc, loc=loc) for loc in loc_trans
+                    ],
+                ),
             )
-            constraints_dicts.append(
-                {
-                    "loc": loc_q,
-                    "type": "pairwise_equality",
-                    "description": msg.format(stage),
-                },
+            constraints.append(
+                om.PairwiseEqualityConstraint(
+                    selectors=[
+                        functools.partial(select_by_loc, loc=loc) for loc in loc_q
+                    ],
+                ),
             )
 
-    return constraints_dicts
+    return constraints
 
 
-def _get_constant_factors_constraints(labels: Labels) -> list[dict]:
+def _get_constant_factors_constraints(
+    labels: Labels,
+) -> list[om.constraints.Constraint]:
     """Fix shock variances of constant factors to zero.
 
     Args:
@@ -253,29 +257,24 @@ def _get_constant_factors_constraints(labels: Labels) -> list[dict]:
             factors, periods, controls, stagemap and stages. See :ref:`labels`
 
     Returns:
-        constraints_dicts
+        List of constraint objects.
 
     """
-    constraints_dicts = []
+    constraints: list[om.constraints.Constraint] = []
     for f, factor in enumerate(labels.latent_factors):
         if labels.transition_names[f] == "constant":
-            msg = f"This constraint was generated because {factor} is constant."
             for aug_period in labels.aug_periods[:-1]:
-                constraints_dicts.append(
-                    {
-                        "loc": ("shock_sds", aug_period, factor, "-"),
-                        "type": "fixed",
-                        "value": 0.0,
-                        "description": msg,
-                    },
+                loc = ("shock_sds", aug_period, factor, "-")
+                constraints.append(
+                    FixedConstraintWithValue(loc=loc, value=0.0),
                 )
-    return constraints_dicts
+    return constraints
 
 
 def _get_initial_states_constraints(
     n_mixtures: int,
     factors: tuple[str, ...],
-) -> list[dict]:
+) -> list[om.constraints.Constraint]:
     """Enforce that the x values of the first factor are increasing.
 
     Otherwise the model would only be identified up to the order of the start factors.
@@ -285,28 +284,23 @@ def _get_initial_states_constraints(
         factors: the latent factors of the model
 
     Returns:
-        constraints_dicts
+        List of constraint objects.
 
     """
-    msg = (
-        "This constraint enforces an ordering on the initial means of the states "
-        "across the components of the factor distribution. This is necessary to ensure "
-        "uniqueness of the maximum likelihood estimator."
-    )
-
     if n_mixtures > 1:
         locs = [
             ("initial_states", 0, f"mixture_{emf}", factors[0])
             for emf in range(n_mixtures)
         ]
-        constraints_dicts = [{"loc": locs, "type": "increasing", "description": msg}]
-    else:
-        constraints_dicts = []
-
-    return constraints_dicts
+        return [
+            om.IncreasingConstraint(selector=functools.partial(select_by_loc, loc=locs))
+        ]
+    return []
 
 
-def _get_transition_constraints(labels: Labels) -> list[dict]:
+def _get_transition_constraints(
+    labels: Labels,
+) -> list[om.constraints.Constraint]:
     """Collect possible constraints on transition parameters.
 
     Args:
@@ -314,31 +308,31 @@ def _get_transition_constraints(labels: Labels) -> list[dict]:
             factors, periods, controls, stagemap and stages. See :ref:`labels`
 
     Returns:
-        constraints_dicts
+        List of constraint objects.
 
     """
-    constraints_dicts = []
+    constraints: list[om.constraints.Constraint] = []
     for f, factor in enumerate(labels.latent_factors):
         tname = labels.transition_names[f]
-        msg = f"This constraint is inherent to the {tname} production function."
         for aug_period in labels.aug_periods[:-1]:
             funcname = f"constraints_{tname}"
             if func := getattr(t_f_module, funcname, False):
-                c = func(  # ty: ignore[call-non-callable]
-                    factor=factor, factors=labels.all_factors, aug_period=aug_period
+                constraints.append(
+                    func(  # ty: ignore[call-non-callable]
+                        factor=factor,
+                        factors=labels.all_factors,
+                        aug_period=aug_period,
+                    )
                 )
-                if "description" not in c:
-                    c["description"] = msg
-                constraints_dicts.append(c)
-    return constraints_dicts
+    return constraints
 
 
-def _get_anchoring_constraints(
+def _get_anchoring_constraints(  # noqa: C901
     update_info: pd.DataFrame,
     controls: tuple[str, ...],
     anchoring_info: Anchoring,
     periods: tuple[int, ...],
-) -> list[dict]:
+) -> list[om.constraints.Constraint]:
     """Constraints on anchoring parameters.
 
     Args:
@@ -349,42 +343,34 @@ def _get_anchoring_constraints(
         periods: Period of the model
 
     Returns:
-        constraints_dicts
+        List of constraint objects.
 
     """
     anchoring_updates = update_info[update_info["purpose"] == "anchoring"].index
 
-    constraints_dicts = []
+    constraints: list[om.constraints.Constraint] = []
     if not anchoring_info.free_constant:
-        msg = (
-            "This constraint was generated because free_constant in the anchoring "
-            "section of the model specification is set to False."
-        )
         locs = []
         for period, meas in anchoring_updates:
             locs.append(("controls", period, meas, "constant"))
-        constraints_dicts.append(
-            {"loc": locs, "type": "fixed", "value": 0, "description": msg},
-        )
+        if locs:
+            loc = tuple(locs)
+            constraints.append(
+                FixedConstraintWithValue(loc=loc, value=0),
+            )
 
     if not anchoring_info.free_controls:
-        msg = (
-            "This constraint was generated because free_controls in the anchoring "
-            "section of the model specification is set to False."
-        )
         ind_tups = []
         for period, meas in anchoring_updates:
             for cont in [c for c in controls if c != "constant"]:
                 ind_tups.append(("controls", period, meas, cont))
-        constraints_dicts.append(
-            {"loc": ind_tups, "type": "fixed", "value": 0, "description": msg},
-        )
+        if ind_tups:
+            loc = tuple(ind_tups)
+            constraints.append(
+                FixedConstraintWithValue(loc=loc, value=0),
+            )
 
     if not anchoring_info.free_loadings:
-        msg = (
-            "This constraint was generated because free_loadings in the anchoring "
-            "section of the model specification is set to False."
-        )
         ind_tups = []
         for period in periods:
             for factor in anchoring_info.factors:
@@ -392,17 +378,19 @@ def _get_anchoring_constraints(
                 meas = f"{outcome}_{factor}"
                 ind_tups.append(("loadings", period, meas, factor))
 
-        constraints_dicts.append(
-            {"loc": ind_tups, "type": "fixed", "value": 1, "description": msg},
-        )
+        if ind_tups:
+            loc = tuple(ind_tups)
+            constraints.append(
+                FixedConstraintWithValue(loc=loc, value=1),
+            )
 
-    return [c for c in constraints_dicts if c["loc"] != []]
+    return constraints
 
 
 def _get_constraints_for_augmented_periods(
     labels: Labels,
     endogenous_factors_info: EndogenousFactorsInfo,
-) -> list[dict]:
+) -> list[om.constraints.Constraint]:
     """Constraints for augmented periods.
 
     - Carry forward states from uneven periods to even periods
@@ -418,10 +406,10 @@ def _get_constraints_for_augmented_periods(
             relationship to augmented periods.
 
     Returns:
-        constraints_dicts
+        List of constraint objects.
 
     """
-    constraints_dicts = []
+    constraints: list[om.constraints.Constraint] = []
     for f, factor in enumerate(labels.latent_factors):
         tname = labels.transition_names[f]
         if tname == "constant":
@@ -443,135 +431,26 @@ def _get_constraints_for_augmented_periods(
         ]
         for aug_period in aug_periods_to_constrain:
             if func := getattr(t_f_module, f"identity_constraints_{tname}", False):
-                constraints_dicts += func(  # ty: ignore[call-non-callable]
+                constraints += func(  # ty: ignore[call-non-callable]
                     factor=factor,
                     aug_period=aug_period,
                     all_factors=labels.all_factors,
                 )
         for aug_period in aug_periods_to_constrain[:-1]:
-            constraints_dicts.append(
-                {
-                    "loc": ("shock_sds", aug_period, factor, "-"),
-                    "type": "fixed",
-                    "value": endogenous_factors_info.bounds_distance,
-                    "description": "Identity constraint.",
-                }
-            )
-
-    return constraints_dicts
-
-
-def select_by_loc(params: pd.DataFrame, loc: Any) -> pd.DataFrame:  # noqa: ANN401
-    """Select parameters by location."""
-    return params.loc[loc]
-
-
-@dataclass(frozen=True)
-class SkillmodelsPairwiseEqualityConstraint(om.PairwiseEqualityConstraint):
-    """Thin wrapper around om.PairwiseEqualityConstraint.
-
-    Adds fields to preserve information from the internal constraints dictionary.
-    """
-
-    loc: pd.MultiIndex | tuple | str | None = None
-    description: str | None = None
-    type: str = "Just to be able to use **constraints_dict"
-    id: int | None = None
-
-
-@dataclass(frozen=True)
-class SkillmodelsFixedConstraint(om.FixedConstraint):
-    """Thin wrapper around om.FixedConstraint.
-
-    Adds fields to preserve information from the internal constraints dictionary.
-    """
-
-    loc: pd.MultiIndex | tuple | str | None = None
-    description: str | None = None
-    type: str = "Just to be able to use **constraints_dict"
-    id: int | None = None
-    value: float | None = None
-
-
-@dataclass(frozen=True)
-class SkillmodelsEqualityConstraint(om.EqualityConstraint):
-    """Thin wrapper around om.EqualityConstraint.
-
-    Adds fields to preserve information from the internal constraints dictionary.
-    """
-
-    loc: pd.MultiIndex | tuple | str | None = None
-    description: str | None = None
-    type: str = "Just to be able to use **constraints_dict"
-    id: int | None = None
-
-
-@dataclass(frozen=True)
-class SkillmodelsProbabilityConstraint(om.ProbabilityConstraint):
-    """Thin wrapper around om.ProbabilityConstraint.
-
-    Adds fields to preserve information from the internal constraints dictionary.
-    """
-
-    loc: pd.MultiIndex | tuple | str | None = None
-    description: str | None = None
-    type: str = "Just to be able to use **constraints_dict"
-    id: int | None = None
-
-
-@dataclass(frozen=True)
-class SkillmodelsIncreasingConstraint(om.IncreasingConstraint):
-    """Thin wrapper around om.IncreasingConstraint.
-
-    Adds fields to preserve information from the internal constraints dictionary.
-    """
-
-    loc: pd.MultiIndex | tuple | str | None = None
-    description: str | None = None
-    type: str = "Just to be able to use **constraints_dict"
-    id: int | None = None
-
-
-def constraints_dicts_to_om(
-    constraints_dicts: list[dict],
-) -> list[om.constraints.Constraint]:
-    """Convert constraints provided in dictionary form to optimagic constraints.
-
-    Args:
-        constraints_dicts: see :ref:`get_constraints_dicts`.
-
-    Returns:
-        List of optimagic constraints.
-    """
-    om_style = []
-    for c_d in constraints_dicts:
-        if c_d["type"] == "pairwise_equality":
-            om_style.append(
-                SkillmodelsPairwiseEqualityConstraint(
-                    selectors=[
-                        functools.partial(select_by_loc, loc=loc) for loc in c_d["loc"]
-                    ],
-                    **c_d,
+            loc = ("shock_sds", aug_period, factor, "-")
+            constraints.append(
+                FixedConstraintWithValue(
+                    loc=loc,
+                    value=endogenous_factors_info.bounds_distance,
                 )
             )
-        else:
-            sel = functools.partial(select_by_loc, loc=c_d["loc"])
-            if c_d["type"] == "fixed":
-                om_style.append(SkillmodelsFixedConstraint(selector=sel, **c_d))
-            elif c_d["type"] == "equality":
-                om_style.append(SkillmodelsEqualityConstraint(selector=sel, **c_d))
-            elif c_d["type"] == "probability":
-                om_style.append(SkillmodelsProbabilityConstraint(selector=sel, **c_d))
-            elif c_d["type"] == "increasing":
-                om_style.append(SkillmodelsIncreasingConstraint(selector=sel, **c_d))
-            else:
-                raise TypeError(c_d["type"])
-    return om_style
+
+    return constraints
 
 
 def enforce_fixed_constraints(
     params_template: pd.DataFrame,
-    constraints_dicts: list[dict[str, Any]],
+    constraints: list[om.constraints.Constraint],
 ) -> pd.DataFrame:
     """Enforce fixed constraints on params_template.
 
@@ -580,7 +459,7 @@ def enforce_fixed_constraints(
 
     Args:
         params_template: see :ref:`params_df`.
-        constraints_dicts: see :ref:`get_constraints_dicts`.
+        constraints: list of optimagic constraint objects.
 
     Returns:
         pd.DataFrame: modified copy of params_template
@@ -591,9 +470,9 @@ def enforce_fixed_constraints(
             "ignore",
             message="indexing past lexsort depth may impact performance.",
         )
-        for constraint in constraints_dicts:
-            if constraint["type"] == "fixed":
-                params.loc[constraint["loc"], "value"] = constraint["value"]
+        for constraint in constraints:
+            if isinstance(constraint, FixedConstraintWithValue):
+                params.loc[constraint.loc, "value"] = constraint.value
 
     # Setting via loc may expand the index, so reduce to the original index
     return params.loc[params_template.index].astype(float)
diff --git a/src/skillmodels/maximization_inputs.py b/src/skillmodels/maximization_inputs.py
index 31d0cda..4d3dcd3 100644
--- a/src/skillmodels/maximization_inputs.py
+++ b/src/skillmodels/maximization_inputs.py
@@ -15,9 +15,8 @@
 import skillmodels.likelihood_function_debug as lfd
 from skillmodels.constraints import (
     add_bounds,
-    constraints_dicts_to_om,
     enforce_fixed_constraints,
-    get_constraints_dicts,
+    get_constraints,
 )
 from skillmodels.kalman_filters import calculate_sigma_scaling_factor_and_weights
 from skillmodels.model_spec import ModelSpec
@@ -165,7 +164,7 @@ def debug_loglike(params: pd.DataFrame) -> dict[str, Any]:
         tmp["value"] = float(tmp["value"])
         return process_debug_data(debug_data=tmp, model=processed_model)
 
-    _constraints_dicts = get_constraints_dicts(
+    constraints = get_constraints(
         dimensions=processed_model.dimensions,
         labels=processed_model.labels,
         anchoring_info=processed_model.anchoring,
@@ -174,8 +173,6 @@ def debug_loglike(params: pd.DataFrame) -> dict[str, Any]:
         endogenous_factors_info=processed_model.endogenous_factors_info,
     )
 
-    constraints = constraints_dicts_to_om(_constraints_dicts)
-
     params_template = pd.DataFrame(columns=["value"], index=p_index)
     params_template = add_bounds(
         params=params_template,
@@ -183,7 +180,7 @@ def debug_loglike(params: pd.DataFrame) -> dict[str, Any]:
     )
     params_template = enforce_fixed_constraints(
         params_template=params_template,
-        constraints_dicts=_constraints_dicts,
+        constraints=constraints,
     )
     if not params_template.index.equals(p_index):
         raise ValueError("params_template index is not equal to p_index")
diff --git a/src/skillmodels/transition_functions.py b/src/skillmodels/transition_functions.py
index 057ff96..9e38461 100644
--- a/src/skillmodels/transition_functions.py
+++ b/src/skillmodels/transition_functions.py
@@ -27,12 +27,23 @@
 
 """
 
+import functools
 from itertools import combinations
+from typing import TYPE_CHECKING, Any
 
 import jax
 import jax.numpy as jnp
+import optimagic as om
 from jax import Array
 
+if TYPE_CHECKING:
+    from skillmodels.constraints import FixedConstraintWithValue
+
+
+def select_by_loc(params: Any, loc: Any) -> Any:  # noqa: ANN401
+    """Select parameters by location."""
+    return params.loc[loc]
+
 
 def linear(states: Array, params: Array) -> Array:
     """Linear production function where the constant is the last parameter."""
@@ -50,20 +61,16 @@ def identity_constraints_linear(
     factor: str,
     aug_period: int,
     all_factors: tuple[str, ...],
-) -> list[dict]:
+) -> list[FixedConstraintWithValue]:
     """Identity constraints for linear transition function."""
-    constraints_dicts = []
+    from skillmodels.constraints import FixedConstraintWithValue  # noqa: PLC0415
+
+    constraints: list[FixedConstraintWithValue] = []
     for regressor in params_linear(all_factors):
         val = 1.0 if factor == regressor else 0.0
-        constraints_dicts.append(
-            {
-                "loc": ("transition", aug_period, factor, regressor),
-                "type": "fixed",
-                "value": val,
-                "description": "Identity constraint.",
-            }
-        )
-    return constraints_dicts
+        loc = ("transition", aug_period, factor, regressor)
+        constraints.append(FixedConstraintWithValue(loc=loc, value=val))
+    return constraints
 
 
 def translog(states: Array, params: Array) -> Array:
@@ -102,20 +109,16 @@ def identity_constraints_translog(
     factor: str,
     aug_period: int,
     all_factors: tuple[str, ...],
-) -> list[dict]:
+) -> list[FixedConstraintWithValue]:
     """Identity constraints for translog transition function."""
-    constraints_dicts = []
+    from skillmodels.constraints import FixedConstraintWithValue  # noqa: PLC0415
+
+    constraints: list[FixedConstraintWithValue] = []
     for regressor in params_translog(all_factors):
         val = 1.0 if factor == regressor else 0.0
-        constraints_dicts.append(
-            {
-                "loc": ("transition", aug_period, factor, regressor),
-                "type": "fixed",
-                "value": val,
-                "description": "Identity constraint.",
-            }
-        )
-    return constraints_dicts
+        loc = ("transition", aug_period, factor, regressor)
+        constraints.append(FixedConstraintWithValue(loc=loc, value=val))
+    return constraints
 
 
 def log_ces(states: Array, params: Array) -> Array:
@@ -142,18 +145,18 @@ def constraints_log_ces(
     factor: str,
     factors: tuple[str, ...],
     aug_period: int,
-) -> dict:
+) -> om.constraints.Constraint:
     """Constraints for log_ces production function."""
     names = params_log_ces(factors)
     loc = [("transition", aug_period, factor, name) for name in names[:-1]]
-    return {"loc": loc, "type": "probability"}
+    return om.ProbabilityConstraint(selector=functools.partial(select_by_loc, loc=loc))
 
 
 def identity_constraints_log_ces(
     factors: tuple[str, ...],
     aug_period: int,
     all_factors: tuple[str, ...],
-) -> list[dict]:
+) -> list[om.constraints.Constraint]:
     """Identity constraints for log_ces."""
     raise NotImplementedError
 
@@ -193,7 +196,7 @@ def identity_constraints_robust_translog(
     factor: str,
     aug_period: int,
     all_factors: tuple[str, ...],
-) -> list[dict]:
+) -> list[FixedConstraintWithValue]:
     """Identity constraints for robust_translog."""
     return identity_constraints_translog(
         factor=factor, aug_period=aug_period, all_factors=all_factors
@@ -222,20 +225,16 @@ def identity_constraints_linear_and_squares(
     factor: str,
     aug_period: int,
     all_factors: tuple[str, ...],
-) -> list[dict]:
+) -> list[FixedConstraintWithValue]:
     """Identity constraints for linear_and_squares transition function."""
-    constraints_dicts = []
+    from skillmodels.constraints import FixedConstraintWithValue  # noqa: PLC0415
+
+    constraints: list[FixedConstraintWithValue] = []
     for regressor in params_linear_and_squares(all_factors):
         val = 1.0 if factor == regressor else 0.0
-        constraints_dicts.append(
-            {
-                "loc": ("transition", aug_period, factor, regressor),
-                "type": "fixed",
-                "value": val,
-                "description": "Identity constraint.",
-            }
-        )
-    return constraints_dicts
+        loc = ("transition", aug_period, factor, regressor)
+        constraints.append(FixedConstraintWithValue(loc=loc, value=val))
+    return constraints
 
 
 def log_ces_general(states: Array, params: Array) -> Array:
@@ -263,6 +262,6 @@ def identity_constraints_log_ces_general(
     factors: tuple[str, ...],
     aug_period: int,
     all_factors: tuple[str, ...],
-) -> list[dict]:
+) -> list[om.constraints.Constraint]:
     """Identity constraints for log_ces_general."""
     raise NotImplementedError
diff --git a/tests/test_constraints.py b/tests/test_constraints.py
index 6bb52c4..fede762 100644
--- a/tests/test_constraints.py
+++ b/tests/test_constraints.py
@@ -1,13 +1,16 @@
 """Tests for constraints."""
 
 from types import MappingProxyType
+from typing import Any
 
 import numpy as np
+import optimagic as om
 import pandas as pd
 import pytest
 from pandas.testing import assert_frame_equal
 
 from skillmodels.constraints import (
+    FixedConstraintWithValue,
     _get_anchoring_constraints,
     _get_constant_factors_constraints,
     _get_constraints_for_augmented_periods,
@@ -17,14 +20,29 @@
     _get_stage_constraints,
     _get_transition_constraints,
     add_bounds,
-    constraints_dicts_to_om,
-    get_constraints_dicts,
+    get_constraints,
 )
 from skillmodels.process_model import process_model
 from skillmodels.test_data.simplest_augmented_model import SIMPLEST_AUGMENTED_MODEL
 from skillmodels.types import Anchoring, Labels, Normalizations
 
 
+def _to_dict(c: om.constraints.Constraint) -> dict[str, Any]:
+    """Convert a constraint object to a comparable dict for testing."""
+    if isinstance(c, FixedConstraintWithValue):
+        return {"loc": c.loc, "type": "fixed", "value": c.value}
+    if isinstance(c, om.PairwiseEqualityConstraint):
+        locs = [s.keywords["loc"] for s in c.selectors]  # ty: ignore[unresolved-attribute]
+        return {"loc": locs, "type": "pairwise_equality"}
+    if isinstance(c, om.ProbabilityConstraint):
+        return {"loc": c.selector.keywords["loc"], "type": "probability"}  # ty: ignore[unresolved-attribute]
+    if isinstance(c, om.IncreasingConstraint):
+        return {"loc": c.selector.keywords["loc"], "type": "increasing"}  # ty: ignore[unresolved-attribute]
+    if isinstance(c, om.EqualityConstraint):
+        return {"loc": c.selector.keywords["loc"], "type": "equality"}  # ty: ignore[unresolved-attribute]
+    raise TypeError(type(c))
+
+
 def test_add_bounds() -> None:
     ind_tups = [("shock_sds", i) for i in range(5)] + [
         ("meas_sds", 4),
@@ -85,26 +103,22 @@ def test_normalization_constraints() -> None:
     ]
 
     calculated = _get_normalization_constraints(norm, factors=("fac1", "fac2"))
-    for c in calculated:
-        del c["description"]
-
-    assert_list_equal_except_for_order(calculated, expected)
+    as_dicts = [_to_dict(c) for c in calculated]
+    assert_list_equal_except_for_order(as_dicts, expected)
 
 
 def test_mixture_weight_constraints_mixture() -> None:
     calculated = _get_mixture_weights_constraints(n_mixtures=2)
-    for c in calculated:
-        del c["description"]
+    as_dicts = [_to_dict(c) for c in calculated]
     expected = [{"loc": "mixture_weights", "type": "probability"}]
-    assert_list_equal_except_for_order(calculated, expected)
+    assert_list_equal_except_for_order(as_dicts, expected)
 
 
 def test_mixture_weight_constraints_normal() -> None:
     calculated = _get_mixture_weights_constraints(n_mixtures=1)
-    for c in calculated:
-        del c["description"]
+    as_dicts = [_to_dict(c) for c in calculated]
     expected = [{"loc": "mixture_weights", "type": "fixed", "value": 1.0}]
-    assert_list_equal_except_for_order(calculated, expected)
+    assert_list_equal_except_for_order(as_dicts, expected)
 
 
 def test_stage_constraints() -> None:
@@ -123,9 +137,8 @@ def test_stage_constraints() -> None:
     ]
 
     calculated = _get_stage_constraints(stagemap=stagemap, stages=stages)
-    for c in calculated:
-        del c["description"]
-    assert_list_equal_except_for_order(calculated, expected)
+    as_dicts = [_to_dict(c) for c in calculated]
+    assert_list_equal_except_for_order(as_dicts, expected)
 
 
 def test_stage_constraints_with_endogenous_factors() -> None:
@@ -151,9 +164,8 @@ def test_stage_constraints_with_endogenous_factors() -> None:
     ]
 
     calculated = _get_stage_constraints(stagemap=stagemap, stages=stages)
-    for c in calculated:
-        del c["description"]
-    assert_list_equal_except_for_order(calculated, expected)
+    as_dicts = [_to_dict(c) for c in calculated]
+    assert_list_equal_except_for_order(as_dicts, expected)
 
 
 def test_constant_factor_constraints() -> None:
@@ -178,9 +190,8 @@ def test_constant_factor_constraints() -> None:
     ]
 
     calculated = _get_constant_factors_constraints(labels)
-    for c in calculated:
-        del c["description"]
-    assert_list_equal_except_for_order(calculated, expected)
+    as_dicts = [_to_dict(c) for c in calculated]
+    assert_list_equal_except_for_order(as_dicts, expected)
 
 
 def test_initial_mean_constraints() -> None:
@@ -195,9 +206,8 @@ def test_initial_mean_constraints() -> None:
     expected = [{"loc": ind_tups, "type": "increasing"}]
 
     calculated = _get_initial_states_constraints(nmixtures, factors)
-    for c in calculated:
-        del c["description"]
-    assert_list_equal_except_for_order(calculated, expected)
+    as_dicts = [_to_dict(c) for c in calculated]
+    assert_list_equal_except_for_order(as_dicts, expected)
 
 
 def test_trans_coeff_constraints() -> None:
@@ -235,10 +245,8 @@ def test_trans_coeff_constraints() -> None:
         },
     ]
     calculated = _get_transition_constraints(labels)
-
-    for c in calculated:
-        del c["description"]
-    assert_list_equal_except_for_order(calculated, expected)
+    as_dicts = [_to_dict(c) for c in calculated]
+    assert_list_equal_except_for_order(as_dicts, expected)
 
 
 @pytest.fixture
@@ -287,22 +295,22 @@ def test_anchoring_constraints_for_constants(anch_uinfo) -> None:
         ignore_constant_when_anchoring=False,
     )
     calculated = _get_anchoring_constraints(anch_uinfo, (), anchoring_info, (0, 1))
+    as_dicts = [_to_dict(c) for c in calculated]
 
-    del calculated[0]["description"]
     expected = [
         {
-            "loc": [
+            "loc": (
                 ("controls", 0, "outcome_f1", "constant"),
                 ("controls", 0, "outcome_f2", "constant"),
                 ("controls", 1, "outcome_f1", "constant"),
                 ("controls", 1, "outcome_f2", "constant"),
-            ],
+            ),
             "type": "fixed",
             "value": 0,
         },
     ]
 
-    assert calculated == expected
+    assert as_dicts == expected
 
 
 def test_anchoring_constraints_for_controls(anch_uinfo) -> None:
@@ -321,13 +329,11 @@ def test_anchoring_constraints_for_controls(anch_uinfo) -> None:
         anchoring_info,
         (0, 1),
     )
-
-    for c_t in calculated:
-        del c_t["description"]
+    as_dicts = [_to_dict(c) for c in calculated]
 
     expected = [
         {
-            "loc": [
+            "loc": (
                 ("controls", 0, "outcome_f1", "c1"),
                 ("controls", 0, "outcome_f1", "c2"),
                 ("controls", 0, "outcome_f2", "c1"),
@@ -336,13 +342,13 @@ def test_anchoring_constraints_for_controls(anch_uinfo) -> None:
                 ("controls", 1, "outcome_f1", "c2"),
                 ("controls", 1, "outcome_f2", "c1"),
                 ("controls", 1, "outcome_f2", "c2"),
-            ],
+            ),
             "type": "fixed",
             "value": 0,
         },
     ]
 
-    assert calculated == expected
+    assert as_dicts == expected
 
 
 def test_anchoring_constraints_for_loadings(anch_uinfo) -> None:
@@ -356,24 +362,22 @@ def test_anchoring_constraints_for_loadings(anch_uinfo) -> None:
         ignore_constant_when_anchoring=False,
     )
     calculated = _get_anchoring_constraints(anch_uinfo, (), anchoring_info, (0, 1))
+    as_dicts = [_to_dict(c) for c in calculated]
 
     expected = [
         {
-            "loc": [
+            "loc": (
                 ("loadings", 0, "outcome_f1", "f1"),
                 ("loadings", 0, "outcome_f2", "f2"),
                 ("loadings", 1, "outcome_f1", "f1"),
                 ("loadings", 1, "outcome_f2", "f2"),
-            ],
+            ),
             "type": "fixed",
             "value": 1,
         },
     ]
 
-    for c_t in calculated:
-        del c_t["description"]
-
-    assert calculated == expected
+    assert as_dicts == expected
 
 
 def assert_list_equal_except_for_order(list1, list2) -> None:
@@ -388,10 +392,10 @@ def simplest_augmented_model():
     return process_model(SIMPLEST_AUGMENTED_MODEL)
 
 
-def test_get_constraints_dicts_with_endogenous_factors(
+def test_get_constraints_with_endogenous_factors(
     simplest_augmented_model,
 ) -> None:
-    constraints = get_constraints_dicts(
+    constraints = get_constraints(
         update_info=simplest_augmented_model.update_info,
         labels=simplest_augmented_model.labels,
         dimensions=simplest_augmented_model.dimensions,
@@ -400,11 +404,14 @@ def test_get_constraints_dicts_with_endogenous_factors(
         endogenous_factors_info=simplest_augmented_model.endogenous_factors_info,
     )
     # Should contain augmented-period constraints
-    assert any(c.get("value") == 1e-08 for c in constraints)
+    assert any(
+        isinstance(c, FixedConstraintWithValue) and c.value == 1e-08
+        for c in constraints
+    )
 
 
-def test_constraints_dicts_to_om_type_dispatch(simplest_augmented_model) -> None:
-    constraints = get_constraints_dicts(
+def test_get_constraints_returns_om_objects(simplest_augmented_model) -> None:
+    constraints = get_constraints(
         update_info=simplest_augmented_model.update_info,
         labels=simplest_augmented_model.labels,
         dimensions=simplest_augmented_model.dimensions,
@@ -412,63 +419,9 @@ def test_constraints_dicts_to_om_type_dispatch(simplest_augmented_model) -> None
         normalizations=simplest_augmented_model.normalizations,
         endogenous_factors_info=simplest_augmented_model.endogenous_factors_info,
     )
-    om_constraints = constraints_dicts_to_om(constraints)
-    assert len(om_constraints) > 0
-
-
-def test_constraints_dicts_to_om_equality_type() -> None:
-    dicts = [
-        {
-            "loc": ("loadings", 0, "y1", "f1"),
-            "type": "equality",
-            "id": 0,
-            "description": "test",
-        },
-    ]
-    result = constraints_dicts_to_om(dicts)
-    assert len(result) == 1
-
-
-def test_constraints_dicts_to_om_probability_type() -> None:
-    dicts = [
-        {
-            "loc": "mixture_weights",
-            "type": "probability",
-            "id": 0,
-            "description": "test",
-        },
-    ]
-    result = constraints_dicts_to_om(dicts)
-    assert len(result) == 1
-
-
-def test_constraints_dicts_to_om_increasing_type() -> None:
-    dicts = [
-        {
-            "loc": [
-                ("initial_states", 0, "m0", "f1"),
-                ("initial_states", 0, "m1", "f1"),
-            ],
-            "type": "increasing",
-            "id": 0,
-            "description": "test",
-        },
-    ]
-    result = constraints_dicts_to_om(dicts)
-    assert len(result) == 1
-
-
-def test_constraints_dicts_to_om_unknown_type_raises() -> None:
-    dicts = [
-        {
-            "loc": ("loadings", 0, "y1", "f1"),
-            "type": "unknown_type",
-            "id": 0,
-            "description": "test",
-        },
-    ]
-    with pytest.raises(TypeError, match="unknown_type"):
-        constraints_dicts_to_om(dicts)
+    assert len(constraints) > 0
+    for c in constraints:
+        assert isinstance(c, om.constraints.Constraint)
 
 
 def test_get_constraints_for_augmented_periods(simplest_augmented_model) -> None:
@@ -476,8 +429,7 @@ def test_get_constraints_for_augmented_periods(simplest_augmented_model) -> None
         labels=simplest_augmented_model.labels,
         endogenous_factors_info=simplest_augmented_model.endogenous_factors_info,
     )
-    for c in calculated:
-        del c["description"]
+    as_dicts = [_to_dict(c) for c in calculated]
     expected = [
         {"loc": ("transition", 0, "fac1", "fac1"), "type": "fixed", "value": 1.0},
         {"loc": ("transition", 0, "fac1", "fac2"), "type": "fixed", "value": 0.0},
@@ -498,4 +450,4 @@ def test_get_constraints_for_augmented_periods(simplest_augmented_model) -> None
         {"loc": ("transition", 3, "fac2", "of"), "type": "fixed", "value": 0.0},
         {"loc": ("transition", 3, "fac2", "constant"), "type": "fixed", "value": 0.0},
     ]
-    assert_list_equal_except_for_order(calculated, expected)
+    assert_list_equal_except_for_order(as_dicts, expected)
diff --git a/tests/test_transition_functions.py b/tests/test_transition_functions.py
index 562dfbf..363f3bd 100644
--- a/tests/test_transition_functions.py
+++ b/tests/test_transition_functions.py
@@ -2,7 +2,7 @@
 
 import jax
 import jax.numpy as jnp
-import numpy as np
+import optimagic as om
 import pytest
 from numpy.testing import assert_array_almost_equal as aaae
 
@@ -222,11 +222,11 @@ def test_identity_constraints_linear() -> None:
     result = identity_constraints_linear("a", 0, all_factors)
     assert len(result) == 4  # 3 factors + constant
     # "a" regressor should be fixed at 1.0
-    assert np.isclose(result[0]["value"], 1.0)
-    assert result[0]["loc"] == ("transition", 0, "a", "a")
+    assert result[0].value == pytest.approx(1.0)
+    assert result[0].loc == ("transition", 0, "a", "a")
     # others should be 0.0
-    assert np.isclose(result[1]["value"], 0.0)
-    assert np.isclose(result[3]["value"], 0.0)  # constant
+    assert result[1].value == pytest.approx(0.0)
+    assert result[3].value == pytest.approx(0.0)  # constant
 
 
 def test_identity_constraints_translog() -> None:
@@ -235,26 +235,29 @@ def test_identity_constraints_translog() -> None:
     # Should have one constraint per translog param
     assert len(result) == len(params_translog(all_factors))
     # First constraint for "a" linear should be 1.0
-    assert np.isclose(result[0]["value"], 1.0)
+    assert result[0].value == pytest.approx(1.0)
     # All others should be 0.0
     for c in result[1:]:
-        assert np.isclose(c["value"], 0.0)
+        assert c.value == pytest.approx(0.0)
 
 
 def test_identity_constraints_robust_translog() -> None:
     all_factors = ("a", "b")
     result_robust = identity_constraints_robust_translog("a", 0, all_factors)
     result_translog = identity_constraints_translog("a", 0, all_factors)
-    assert result_robust == result_translog
+    assert len(result_robust) == len(result_translog)
+    for r, t in zip(result_robust, result_translog, strict=True):
+        assert r.loc == t.loc
+        assert r.value == t.value
 
 
 def test_identity_constraints_linear_and_squares() -> None:
     all_factors = ("a", "b", "c")
     result = identity_constraints_linear_and_squares("a", 0, all_factors)
     assert len(result) == len(params_linear_and_squares(all_factors))
-    assert np.isclose(result[0]["value"], 1.0)  # "a" linear
+    assert result[0].value == pytest.approx(1.0)  # "a" linear
     for c in result[1:]:
-        assert np.isclose(c["value"], 0.0)
+        assert c.value == pytest.approx(0.0)
 
 
 def test_identity_constraints_log_ces_raises() -> None:
@@ -269,9 +272,10 @@ def test_identity_constraints_log_ces_general_raises() -> None:
 
 def test_constraints_log_ces() -> None:
     result = constraints_log_ces("fac1", ("a", "b", "c"), 0)
-    assert result["type"] == "probability"
-    assert len(result["loc"]) == 3  # gamma constraints for a, b, c (not phi)
-    for loc in result["loc"]:
-        assert loc[0] == "transition"
-        assert loc[1] == 0
-        assert loc[2] == "fac1"
+    assert isinstance(result, om.ProbabilityConstraint)
+    loc = result.selector.keywords["loc"]  # ty: ignore[unresolved-attribute]
+    assert len(loc) == 3  # gamma constraints for a, b, c (not phi)
+    for entry in loc:
+        assert entry[0] == "transition"
+        assert entry[1] == 0
+        assert entry[2] == "fac1"