sklearn:
  package_info:
    repo: https://github.com/scikit-learn/scikit-learn/tree/HEAD
    pip_release: "scikit-learn"
    install_dev: |
      uv pip install --system git+https://github.com/scikit-learn/scikit-learn.git

  models:
    minimum: "1.5.1"
    maximum: "1.9.0"
    run: |
      pytest tests/sklearn/test_sklearn_model_export.py

  autologging:
    minimum: "1.5.1"
    maximum: "1.9.0"
    requirements:
      ">= 0.0.0": ["matplotlib", "polars"]
    run: |
      pytest tests/sklearn/test_sklearn_autolog.py

      # Ensure sklearn autologging works without matplotlib
      pip uninstall -q -y matplotlib
      pytest tests/sklearn/test_sklearn_autolog_without_matplotlib.py

pytorch:
  package_info:
    repo: https://github.com/pytorch/pytorch/tree/HEAD
    pip_release: "torch"
    module_name: "torch"
    install_dev: |
      uv pip install --system --upgrade --pre torch -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html

  models:
    minimum: "2.4.0"
    maximum: "2.12.0"
    requirements:
      ">= 0.0.0": ["torchvision", "scikit-learn"]
      ">= 1.8": ["transformers"]
      # transformers 5.x trips torch.library.infer_schema on EmbeddingBag.forward
      # for torch < 2.5 (annotations stored as strings under `from __future__`).
      # https://github.com/huggingface/transformers/issues/42352
      "< 2.5": ["transformers<5"]
      # transformers 5.10.x references torch.float8_e8m0fnu at import time, which
      # only exists in torch >= 2.7, so importing any model fails on older torch.
      # Fixed (guarded) in transformers 5.11.0, so exclude only the broken 5.10
      # line and keep testing newer transformers on torch 2.5/2.6.
      ">= 2.5, < 2.7": ["transformers!=5.10.*"]
    run: |
      pytest tests/pytorch/test_pytorch_model_export.py tests/pytorch/test_pytorch_metric_value_conversion_utils.py

  autologging:
    minimum: "2.4.0"
    maximum: "2.12.0"
    requirements:
      ">= 0.0.0": ["tensorboard"]
    run: |
      pytest tests/pytorch/test_tensorboard_autolog.py

pytorch-lightning:
  package_info:
    repo: https://github.com/Lightning-AI/pytorch-lightning/tree/HEAD
    pip_release: "pytorch-lightning"
    module_name: "lightning"
    install_dev: |
      export PACKAGE_NAME=pytorch
      uv pip install --system git+https://github.com/PytorchLightning/pytorch-lightning.git

  autologging:
    minimum: "2.3.1"
    maximum: "2.6.5"
    requirements:
      ">= 0.0.0":
        [
          "scikit-learn",
          "torchvision",
          "protobuf<4.0.0",
          "tensorboard",
          "greenlet<3",
          "pytorch-forecasting",
        ]
    run: |
      pytest tests/pytorch/test_pytorch_autolog.py

  models:
    minimum: "2.3.1"
    maximum: "2.6.5"
    requirements:
      ">= 0.0.0": ["pytorch-forecasting"]
    run: |
      pytest tests/pytorch/test_forecasting_model.py

keras:
  package_info:
    repo: https://github.com/keras-team/keras/tree/HEAD
    pip_release: "keras"
    install_dev: |
      uv pip install --system --upgrade git+https://github.com/keras-team/keras.git

  models:
    minimum: "3.4.0"
    maximum: "3.14.1"
    requirements:
      ">= 3.0.0": ["jax[cpu]>0.4"]
      "< 3.10.0": ["jax[cpu]<0.6"]
    run: |
      export KERAS_BACKEND=jax
      pytest tests/keras/test_callback.py

  autologging:
    minimum: "3.4.0"
    maximum: "3.14.1"
    requirements:
      ">= 3.0.0": ["jax[cpu]>0.4"]
      "< 3.10.0": ["jax[cpu]<0.6"]
    run: |
      export KERAS_BACKEND=jax
      pytest tests/keras/test_autolog.py

tensorflow:
  package_info:
    repo: https://github.com/tensorflow/tensorflow/tree/HEAD
    pip_release: "tensorflow"
    install_dev: |
      uv pip install --system --pre tf-nightly

  models:
    minimum: "2.16.2"
    maximum: "2.21.0"
    requirements:
      # Requirements to run tests for keras
      ">= 0.0.0": ["scikit-learn", "pyspark", "pyarrow", "transformers!=4.38.0,!=4.38.1"]
      ">= 2.21.0": ["tensorboard"]
    run: |
      pytest \
        tests/tensorflow/test_tensorflow2_core_model_export.py \
        tests/tensorflow/test_tensorflow2_metric_value_conversion_utils.py \
        tests/tensorflow/test_keras_model_export.py \
        tests/tensorflow/test_keras_pyfunc_model_works_with_all_input_types.py

  autologging:
    minimum: "2.16.2"
    maximum: "2.21.0"
    requirements:
      "== dev": ["scikit-learn"]
      ">= 2.21.0": ["tensorboard"]
    run: |
      pytest tests/tensorflow/test_tensorflow2_autolog.py

xgboost:
  package_info:
    repo: https://github.com/dmlc/xgboost/tree/HEAD
    pip_release: "xgboost"
    install_dev: |
      uv pip install --system git+https://github.com/dmlc/xgboost.git#subdirectory=python-package

  models:
    minimum: "2.1.0"
    maximum: "3.2.0"
    requirements:
      ">= 0.0.0": ["scikit-learn"]
    run: |
      pytest tests/xgboost/test_xgboost_model_export.py

  autologging:
    minimum: "2.1.0"
    maximum: "3.2.0"
    requirements:
      ">= 0.0.0": ["scikit-learn", "matplotlib"]
    run: |
      pytest tests/xgboost/test_xgboost_autolog.py

lightgbm:
  package_info:
    repo: https://github.com/microsoft/LightGBM/tree/HEAD
    pip_release: "lightgbm"
    install_dev: |
      git clone --recursive https://github.com/microsoft/LightGBM --depth=1 --branch master /tmp/LightGBM
      cd /tmp/LightGBM
      bash build-python.sh install

  models:
    minimum: "4.5.0"
    maximum: "4.6.0"
    requirements:
      ">= 0.0.0": ["scikit-learn"]
    run: |
      pytest tests/lightgbm/test_lightgbm_model_export.py

  autologging:
    minimum: "4.5.0"
    maximum: "4.6.0"
    requirements:
      ">= 0.0.0": ["scikit-learn", "matplotlib", "polars"]
    run: |
      pytest tests/lightgbm/test_lightgbm_autolog.py

catboost:
  package_info:
    pip_release: "catboost"

  models:
    minimum: "1.2.6"
    maximum: "1.2.10"
    requirements:
      ">= 0.0.0": ["scikit-learn"]
      "< 1.3": ["numpy<2"]
    run: |
      pytest tests/catboost/test_catboost_model_export.py

onnx:
  package_info:
    repo: https://github.com/onnx/onnx/tree/HEAD
    pip_release: "onnx"
    install_dev: |
      # This workflow describes how to build a wheel for Linux:
      # https://github.com/onnx/onnx/blob/bf3f63ece5cadea625265a42e17dd685f4cf17eb/.github/workflows/release_linux.yml
      tmp_dir=$(mktemp -d)
      git clone --recurse-submodules --shallow-submodules --depth=1 https://github.com/onnx/onnx.git $tmp_dir
      cd $tmp_dir

      # Build wheel
      python_version=$(python -c 'import sys; print(".".join(map(str, sys.version_info[:2])))')
      docker run --rm -v $(pwd):/github/workspace --workdir /github/workspace --entrypoint bash \
        quay.io/pypa/manylinux_2_28_x86_64 .github/workflows/manylinux/entrypoint.sh $python_version manylinux_2_28_x86_64 pull_request

      # Install wheel
      uv pip install --system dist/*manylinux*.whl

  models:
    minimum: "1.17.0"
    maximum: "1.21.0"
    requirements:
      ">= 0.0.0": ["onnxruntime", "onnxscript", "torch", "scikit-learn"]
    run: |
      pytest tests/onnx/test_onnx_model_export.py

semantic_kernel:
  package_info:
    genai: true
    repo: https://github.com/microsoft/semantic-kernel/tree/HEAD/python
    pip_release: "semantic-kernel"
    install_dev: |
      uv pip install --system git+https://github.com/microsoft/semantic-kernel.git@main#subdirectory=python

  autologging:
    minimum: "1.34.0"
    maximum: "1.43.0"
    requirements:
      ">= 1.34.0": [
          "pydantic>=2.0,<2.12",
          # 1.99.2 ~ 1.99.8 have unintended breaking changes:
          # https://github.com/openai/openai-python/issues/2525
          "openai>=1.99.9",
        ]
      # semantic-kernel>=1.36.1 depends on azure-ai-agents>=1.2.0b3 (pre-release).
      # Explicitly specifying the prerelease requirement allows uv to resolve it without --prerelease=allow.
      ">= 1.36.1": ["azure-ai-agents>=1.2.0b3"]
    run: |
      pytest tests/semantic_kernel/test_semantic_kernel_autolog.py

spacy:
  package_info:
    repo: https://github.com/explosion/spaCy/tree/HEAD
    pip_release: "spacy"
    install_dev: |
      uv pip install --system git+https://github.com/explosion/spaCy.git

  models:
    minimum: "3.8.2"
    maximum: "3.8.14"
    run: |
      pytest tests/spacy/test_spacy_model_export.py

statsmodels:
  package_info:
    repo: https://github.com/statsmodels/statsmodels/tree/HEAD
    pip_release: "statsmodels"
    install_dev: |
      uv pip install --system git+https://github.com/statsmodels/statsmodels.git

  models:
    minimum: "0.14.3"
    maximum: "0.14.6"
    run: |
      pytest tests/statsmodels/test_statsmodels_model_export.py

  autologging:
    minimum: "0.14.3"
    maximum: "0.14.6"
    run: |
      pytest tests/statsmodels/test_statsmodels_autolog.py

spark:
  package_info:
    repo: https://github.com/apache/spark/tree/HEAD
    pip_release: "pyspark"
    module_name: "pyspark"
    install_dev: |
      temp_dir=$(mktemp -d)
      git clone --depth 1 https://github.com/apache/spark.git $temp_dir
      cd $temp_dir
      # -Pconnect -Phive is required by spark connect
      ./build/sbt -Pconnect -Phive clean package connect/assembly
      cd python/packaging/classic
      uv pip install --system '.[connect]'
      pyspark --version

  models:
    minimum: "3.2.1"
    maximum: "4.1.2"
    java:
      "<= 3.4.1": "11"
    # NB: Allow unreleased maximum versions for the pyspark package to support models and
    # autologging use cases in environments where newer versions of pyspark are available
    # prior to their release on PyPI (e.g. Databricks)
    allow_unreleased_max_version: True
    requirements:
      ">= 0.0.0": ["boto3", "scikit-learn", "pyarrow", "numpy<2"]
      "< 3.4.0": ["pandas<2"]
      ">= 3.5.0": ["torch<2.6.0", "scikit-learn", "torcheval"]
      ">= 3.5.0, < dev": ["pyspark[connect]"]
    run: |
      SAGEMAKER_OUT=$(mktemp)
      if mlflow sagemaker build-and-push-container --no-push --mlflow-home . > $SAGEMAKER_OUT 2>&1; then
        echo "Sagemaker container build succeeded.";
      else
        echo "Sagemaker container build failed, output:";
        cat $SAGEMAKER_OUT;
        exit 1
      fi
      pytest tests/spark --ignore tests/spark/autologging --ignore tests/spark/test_spark_connect_model_export.py

      if [[ $(python -c "from packaging.version import Version;import pyspark;print(Version(pyspark.__version__) >= Version('3.5'))") = "True" ]]; then
        pytest tests/spark/test_spark_connect_model_export.py;
      fi

  autologging:
    minimum: "3.3.0"
    maximum: "4.1.2"
    java:
      "<= 3.4.1": "11"
    # NB: Allow unreleased maximum versions for the pyspark package to support models and
    # autologging use cases in environments where newer versions of pyspark are available
    # prior to their release on PyPI (e.g. Databricks)
    allow_unreleased_max_version: True
    requirements:
      ">= 0.0.0": ["scikit-learn", "numpy<2"]
      "< 3.4.0": ["pandas<2"]
    run: |
      # Build Java package
      # Pyspark 4.0 ('dev' version) exclusively supports Scala 2.13
      if [[ $(python -c "import pyspark;from packaging.version import Version;print(Version(pyspark.__version__).major >= 4)") = "True" ]]; then
        pushd mlflow/java/spark_2.13
      else
        pushd mlflow/java/spark_2.12
      fi
      mvn package -DskipTests -q
      popd

      # `test_spark_disable_autologging.py` is flaky if it runs with other tests
      # so run it individually
      pytest tests/spark/autologging/datasource/test_spark_disable_autologging.py

      # NB: Running each .py file individually is important (instead of
      # 'pytest tests/spark/autologging') not to share SparkSession across tests
      # `test_spark_datasource_autologging_crossframework.py` fails when run with other tests
      pytest tests/spark/autologging/datasource/test_spark_datasource_autologging_crossframework.py

      find tests/spark/autologging -name 'test*.py' | grep -v crossframework | grep -v test_spark_disable_autologging | xargs -L 1 pytest

prophet:
  package_info:
    pip_release: "prophet"

  models:
    minimum: "1.1.6"
    maximum: "1.3.0"
    requirements:
      # Prophet does not handle numpy 2 yet. https://github.com/facebook/prophet/issues/2595
      ">= 0.0.0": ["numpy<2"]
      # cmdstanpy>=1.3.0 is not compatible with prophet<=1.2.0: https://github.com/facebook/prophet/issues/2697
      "<= 1.2.0": ["cmdstanpy<1.3.0"]
      # Avoid holidays 0.25 due to https://github.com/dr-prodigy/python-holidays/issues/1200 on
      # older version of prophet. Compatibility updates have been released as part of the 1.1.4
      # release of prophet.
    run: |
      pytest tests/prophet/test_prophet_model_export.py

pmdarima:
  package_info:
    pip_release: "pmdarima"
    # TODO: Uncomment once https://github.com/alkaline-ml/pmdarima/issues/582 is fixed.
    # install_dev: |
    #   # pip install Cython
    #   # pip install git+https://github.com/alkaline-ml/pmdarima.git

  models:
    minimum: "2.1.0"
    maximum: "2.1.1"
    requirements:
      ">= 0.0.0": [
          "prophet",
          # Avoid holidays 0.25 due to https://github.com/dr-prodigy/python-holidays/issues/1200
          "holidays!=0.25",
          # TODO: Try the latest version of moto (https://github.com/getmoto/moto/issues/8498) and remove this pin
          "boto3<1.36",
        ]
    run: |
      pytest tests/pmdarima/test_pmdarima_model_export.py

h2o:
  package_info:
    pip_release: "h2o"
  models:
    minimum: "3.46.0.4"
    maximum: "3.46.0.11"
    run: |
      pytest tests/h2o

shap:
  package_info:
    pip_release: "shap"
  models:
    minimum: "0.46.0"
    maximum: "0.52.0"
    unsupported:
      # shap >= 0.52.0 dropped Python 3.10/3.11 support and requires Python >= 3.12.
      # The cross version test CI only runs Python 3.10/3.11 (the setup-python action
      # rejects other versions), so mark these releases unsupported until CI can run
      # Python 3.12.
      - ">= 0.52.0"
    run: |
      pytest tests/shap

paddle:
  package_info:
    pip_release: "paddlepaddle"
  models:
    minimum: "2.6.2"
    maximum: "3.3.1"
    requirements:
    run: |
      pytest tests/paddle/test_paddle_model_export.py
  autologging:
    minimum: "2.6.2"
    maximum: "3.3.1"
    requirements:
    run: |
      pytest tests/paddle/test_paddle_autolog.py

transformers:
  package_info:
    repo: https://github.com/huggingface/transformers/tree/HEAD
    pip_release: "transformers"
    install_dev: |
      uv pip install --system git+https://github.com/huggingface/transformers
  models:
    minimum: "4.42.0"
    maximum: "5.10.2"
    test_every_n_versions: 4
    unsupported: [
        # Avoid this patch: https://github.com/huggingface/transformers/pull/29032
        ">=4.38.0,<4.39.0",
        # https://github.com/huggingface/transformers/issues/38269
        "== 4.52.1",
        "== 4.52.2",
        # 5.0.0 broke the TAPAS table-QA and Whisper ASR pipelines; fixed in later 5.x patches.
        # https://github.com/huggingface/transformers/issues/43792
        "== 5.0.0",
      ]
    requirements:
      ">= 0.0.0": [
          "datasets",
          # 0.22.0 is broken: https://github.com/huggingface/huggingface_hub/issues/2157
          "huggingface_hub!=0.22.0",
          "hf_transfer",
          "torch",
          "torchvision",
          "tensorflow",
          "peft", # optimized fine-tuning library developed by Hugging Face
          "accelerate", # required for large torch models where weights will not fit in RAM
          "librosa", # required for transformers audio pipelines for bitrate conversion
          "ffmpeg", # required for transformers audio pipelines for audio byte to numpy conversion
          "sentencepiece", # required for transformers text2text generation pipeline
          "protobuf<=3.20.3", # fix error `Couldn't build proto file into descriptor pool: duplicate file name sentencepiece_model.proto`
          "typing_extensions>=4.6.0", # fix error for SqlAlchemy == 2.0.25 cannot import name 'TypeAliasType' from 'typing_extensions'
          # TODO: Try the latest version of moto (https://github.com/getmoto/moto/issues/8498) and remove this pin
          "boto3<1.36",
        ]
      # peft >= 0.14 relies on classes from newer versions of transformers
      "< 4.45": ["peft<0.14.0"]
      # peft >= 0.18 requires transformers.modeling_layers which was added in transformers 4.52.0
      "< 4.52": ["peft<0.18.0"]
      ">= 4.38.2": ["tf-keras"] # Transformers doesn't support Keras 3.0. tf-keras needs to be installed.
      "< 4.52.0.dev0": ["accelerate<1"]
    pre_test: |
      sudo apt-get update
      sudo apt-get install -y ffmpeg
      export PYTHONPATH=./
      python tests/transformers/helper.py
    run: |
      # Run all Transformers tests except autologging
      pytest tests/transformers --ignore tests/transformers/test_transformers_autolog.py
      uv pip uninstall --system accelerate
      pytest tests/transformers/test_transformers_model_export.py -k "test_transformers_pt_model_save_dependencies_without_accelerate"
  autologging:
    minimum: "4.42.0"
    maximum: "5.10.2"
    test_every_n_versions: 4
    unsupported: [
        # https://github.com/huggingface/transformers/issues/38269
        "== 4.52.1",
        "== 4.52.2",
      ]
    requirements:
      ">= 0.0.0": [
          "datasets",
          # 0.22.0 is broken: https://github.com/huggingface/huggingface_hub/issues/2157
          "huggingface_hub!=0.22.0",
          "hf_transfer",
          "torch",
          "torchvision",
          "tensorflow",
          "optuna",
          # TODO: Try the latest version of moto (https://github.com/getmoto/moto/issues/8498) and remove this pin
          "boto3<1.36",
        ]
      ">= 4.38.2": ["tf-keras"] # Transformers doesn't support Keras 3.0. tf-keras needs to be installed.
      # sentence-transformers 5.4 passes `processing_class` to transformers Trainer,
      # which was added in transformers 4.46.0
      "< 4.46": ["sentence-transformers<5.4"]
      "< 4.52.0.dev0": ["accelerate<1"]
      # transformers 5.x requires accelerate>=1.1.0 for Trainer
      ">= 5.0": ["accelerate>=1.1.0"]
      # setfit is incompatible with transformers 5.x (imports removed `default_logdir` from training_args)
      "< 5.0": ["setfit"]
    run: |
      pytest tests/transformers/test_transformers_autolog.py

diffusers:
  package_info:
    repo: https://github.com/huggingface/diffusers/tree/HEAD
    pip_release: "diffusers"
    install_dev: |
      uv pip install --system git+https://github.com/huggingface/diffusers
  models:
    minimum: "0.37.0"
    maximum: "0.38.0"
    requirements:
      ">= 0.0.0": ["transformers", "safetensors", "accelerate", "peft", "torch"]
      # diffusers>=0.38.0 depends on safetensors>=0.8.0rc0 (pre-release).
      # Explicitly specifying the prerelease requirement allows uv to resolve it without --prerelease=allow.
      ">= 0.38.0": ["safetensors>=0.8.0rc0"]
    run: |
      pytest tests/diffusers/test_diffusers_model_export.py

openai:
  package_info:
    genai: true
    repo: https://github.com/openai/openai-python/tree/HEAD
    pip_release: "openai"
    install_dev: |
      uv pip install --system git+https://github.com/openai/openai-python
  models:
    minimum: "1.87.0"
    maximum: "2.41.0"
    requirements:
      ">= 0.0.0": [
          "pyspark",
          "tiktoken",
          "aiohttp",
          "tenacity",
          # Required to run tests/openai/mock_openai.py
          "fastapi",
          "uvicorn",
        ]
    run: |
      pytest tests/openai --ignore-glob="*autolog.py"
    # Our CI runs tests for every minor version of the library by default, which results in
    # many test tasks for openai. Reducing the number of testing only for every 10 version.
    test_every_n_versions: 10
  autologging:
    minimum: "1.87.0"
    maximum: "2.41.0"
    requirements:
      ">= 0.0.0": [
          "tiktoken",
          "tenacity",
          # Required to run tests/openai/mock_openai.py
          "fastapi",
          "uvicorn",
        ]
      # minimum version supported by openai-agents>=0.2.0
      ">= 1.93.1": ["openai-agents"]
    run: |
      pytest tests/openai/*_autolog.py
    test_every_n_versions: 10
    test_tracing_sdk: true

dspy:
  package_info:
    genai: true
    repo: https://github.com/stanfordnlp/dspy/tree/HEAD
    pip_release: "dspy"
    install_dev: |
      uv pip install --system git+https://github.com/stanfordnlp/dspy.git
  models:
    minimum: "3.0.0"
    maximum: "3.2.1"
    requirements:
      ">= 0.0.0": ["openai"]
    run: |
      pytest tests/dspy --ignore tests/dspy/test_dspy_autolog.py
  autologging:
    minimum: "3.0.0"
    maximum: "3.2.1"
    requirements:
      ">= 0.0.0": ["openai"]
    run: |
      pytest tests/dspy/test_dspy_autolog.py
    test_tracing_sdk: true

langchain:
  package_info:
    genai: true
    repo: https://github.com/langchain-ai/langchain/tree/HEAD/libs/langchain
    pip_release: "langchain"
    install_dev: |
      uv pip install --system git+https://github.com/langchain-ai/langchain#subdirectory=libs/langchain_v1
  models:
    # Where the large package update was made (langchain-core, community, ...)
    minimum: "0.3.26"
    maximum: "1.3.4"
    unsupported: [
        # Chain.save() broken in 0.3.28 due to model_dump() regression: https://github.com/langchain-ai/langchain/issues/35665
        "== 0.3.28",
      ]
    requirements:
      ">= 0.0.0": [
          "pyspark",
          "transformers",
          "torch",
          "torchvision",
          "openai",
          "google-search-results",
          "psutil",
          "faiss-cpu",
          "langchain-experimental",
          "numexpr",
          "hf_transfer",
          "langchain-huggingface",
          "langchain-openai",
          "langgraph",
          # Required to run tests/openai/mock_openai.py
          "fastapi",
          "uvicorn",
          # Required for testing Databricks dependency extraction
          "databricks-langchain",
          # Pin <0.74: 0.74 renamed databricks-vectorsearch to databricks-ai-search and
          # turned `databricks.vector_search.*` into a re-export that drops
          # `VectorSearchIndex`. Both this dependency-extraction test AND databricks-langchain
          # itself import that symbol from `databricks.vector_search.client` at import time,
          # so 0.74 breaks the whole langchain matrix. Revisit once databricks-langchain
          # moves to databricks-ai-search.
          "databricks-vectorsearch<0.74",
          # Required for testing uc tools
          "unitycatalog-langchain",
        ]
      ">= 1.0.0": [
          # required for testing legacy modules
          "langchain-classic",
        ]
      # `langgraph-prebuilt >= 1.0.9` imports `ExecutionInfo`/`ServerInfo` from
      # `langgraph.runtime`, which only exist in `langgraph >= 1.1.5`. `langchain < 1.2`
      # pins `langgraph < 1.1.0`, so hold `langgraph-prebuilt` back to a compatible version.
      "< 1.2": ["langgraph-prebuilt<1.0.9"]
    pre_test: |
      # Installing both pyspark and databricks-connect causes a conflict
      pip uninstall -y databricks-connect
      uv pip install --system --no-deps --force-reinstall pyspark
    run: |
      # Some dependencies above includes 'mlflow-skinny' but not full 'mlflow', such as databricks-vectorsearch.
      # This causes installation of mlflow-skinny from stable version, while 'mlflow' points to the dev version,
      # resulting in a weird module conflict issue.
      uv pip install --system './libs/skinny'
      # Run all langchain tests except autologging
      pytest tests/langchain --ignore tests/langchain/test_langchain_autolog.py
  autologging:
    minimum: "0.3.26"
    maximum: "1.3.4"
    requirements:
      ">= 0.0.0": [
          "openai",
          "google-search-results",
          "faiss-cpu",
          # Required to run tests/openai/mock_openai.py
          "fastapi",
          "uvicorn",
          # Some model logging/loading requires langchain community
          "langchain-community",
          # Need to bump FasAPI version to support pydantic v2
          "fastapi>=0.100.0",
          "langchain-openai>=0.2.0",
        ]
      ">= 1.0.0": ["langchain-classic"]
      # `langgraph-prebuilt >= 1.0.9` imports `ExecutionInfo`/`ServerInfo` from
      # `langgraph.runtime`, which only exist in `langgraph >= 1.1.5`. `langchain < 1.2`
      # pins `langgraph < 1.1.0`, so hold `langgraph-prebuilt` back to a compatible version.
      "< 1.2": ["langgraph-prebuilt<1.0.9"]
    pre_test: |
      # Installing both pyspark and databricks-connect causes a conflict
      pip uninstall -y databricks-connect
      uv pip install --system --no-deps --force-reinstall pyspark
    run: |
      pytest tests/langchain/test_langchain_autolog.py

    test_tracing_sdk: true

langgraph:
  package_info:
    genai: true
    repo: https://github.com/langchain-ai/langgraph/tree/HEAD/libs/langgraph
    pip_release: "langgraph"
    install_dev: |
      uv pip install --system git+https://github.com/langchain-ai/langgraph#subdirectory=libs/langgraph
      # Force-reinstall prebuilt to ensure dev version compatibility and prevent pipeline failures
      uv pip install --system --force-reinstall --no-deps git+https://github.com/langchain-ai/langgraph#subdirectory=libs/prebuilt

  models:
    minimum: "0.4.9"
    maximum: "1.2.4"
    requirements:
      ">= 0.0.0": [
          "langchain",
          "langchain_openai",
          # Required to run tests/openai/mock_openai.py
          "fastapi",
          "uvicorn",
        ]
      ">= 0.3.0": ["langgraph-prebuilt"]
      # `langgraph == 0.4.*` is incompatible with `langgraph-prebuilt >= 0.5`
      "== 0.4.*": ["langgraph-prebuilt<0.5"]
      # `langgraph-prebuilt >= 1.0.9` imports `ExecutionInfo`/`ServerInfo` from
      # `langgraph.runtime`, which only exist in `langgraph >= 1.1.5`.
      "< 1.1.5": ["langgraph-prebuilt<1.0.9"]
    run: |
      pytest tests/langgraph --ignore tests/langgraph/test_langgraph_autolog.py

  autologging:
    minimum: "0.4.9"
    maximum: "1.2.4"
    requirements:
      ">= 0.0.0": [
          "langchain",
          "langchain_openai",
          # Required to run tests/openai/mock_openai.py
          "fastapi",
          "uvicorn",
        ]
      ">= 0.3.0": ["langgraph-prebuilt"]
      # `langgraph == 0.4.*` is incompatible with `langgraph-prebuilt >= 0.5`
      "== 0.4.*": ["langgraph-prebuilt<0.5"]
      # `langgraph-prebuilt >= 1.0.9` imports `ExecutionInfo`/`ServerInfo` from
      # `langgraph.runtime`, which only exist in `langgraph >= 1.1.5`.
      "< 1.1.5": ["langgraph-prebuilt<1.0.9"]
    run: |
      pytest tests/langgraph/test_langgraph_autolog.py
    test_tracing_sdk: true

llama_index:
  package_info:
    genai: true
    repo: https://github.com/run-llama/llama_index/tree/HEAD
    pip_release: "llama-index"
    module_name: "llama_index.core"
    install_dev: |
      uv pip install --system git+https://github.com/run-llama/llama_index.git
  models:
    # New event/span framework is fully implemented in 0.10.44
    minimum: "0.12.43"
    maximum: "0.14.22"
    unsupported:
      # 0.14.17+ requires llama-index-llms-openai>=0.7 which conflicts with
      # llama-index-llms-databricks (requires llama-index-llms-openai-like<0.6)
      - ">= 0.14.17"
    requirements:
      ">= 0.0.0": [
          # Versions 1.99.2 - 1.99.8 contain regressions:
          # https://github.com/openai/openai-python/issues/2525
          "openai>=1.99.9",
          # Required to test Databricks integration
          "llama-index-llms-databricks",
          "llama-index-embeddings-databricks",
          # Required to run tests/openai/mock_openai.py
          "fastapi",
          "uvicorn",
        ]
      # llama-index-vector-stores-qdrant 0.7+ requires qdrant-client>=1.13 which
      # conflicts with llama-index 0.12.x dependency resolution.
      # TODO: qdrant-client 1.16 is currently broken with llama-index until
      # https://github.com/run-llama/llama_index/issues/20271 is resolved
      ">= 0.13.0": ["llama-index-vector-stores-qdrant", "qdrant-client<1.16.0"]
    run: pytest tests/llama_index --ignore tests/llama_index/test_llama_index_autolog.py --ignore tests/llama_index/test_llama_index_tracer.py
  autologging:
    minimum: "0.12.43"
    maximum: "0.14.22"
    unsupported:
      # 0.14.17+ requires llama-index-llms-openai>=0.7 which conflicts with
      # llama-index-llms-databricks (requires llama-index-llms-openai-like<0.6)
      - ">= 0.14.17"
    requirements:
      ">= 0.0.0": [
          # Versions 1.99.2 - 1.99.8 contain regressions:
          # https://github.com/openai/openai-python/issues/2525
          "openai>=1.99.9",
          # Required to run tests/openai/mock_openai.py
          "fastapi",
          "uvicorn",
        ]
    run: pytest tests/llama_index/test_llama_index_autolog.py tests/llama_index/test_llama_index_tracer.py
    test_tracing_sdk: true

ag2:
  package_info:
    genai: true
    pip_release: "ag2"
    module_name: "autogen"
  autologging:
    minimum: "0.9.3"
    maximum: "0.13.3"
    requirements:
      ">= 0.0.0": [
          # Required to run tests/openai/mock_openai.py
          "fastapi",
          "uvicorn",
          "openai",
          "numpy<2",
        ]
    run: pytest tests/ag2

autogen:
  package_info:
    genai: true
    pip_release: "autogen-agentchat"
    module_name: "autogen_agentchat"
  autologging:
    minimum: "0.6.2"
    maximum: "0.7.5"
    requirements:
      ">= 0.0.0": [
          # Required to run tests/openai/mock_openai.py
          "fastapi",
          "uvicorn",
          "autogen_ext",
          # TODO: remove this pin after https://github.com/pydantic/pydantic/issues/12348 is resolved
          "pydantic<2.12.0",
          # autogen-core pins opentelemetry-semantic-conventions==0.55b1, but the latest
          # opentelemetry-sdk imports a symbol (OTEL_SPAN_PARENT_ORIGIN) only present in
          # newer semconv. Keep the SDK on the matching 1.34.x line to avoid an ImportError.
          "opentelemetry-sdk<1.35",
        ]
    run: pytest tests/autogen
    test_tracing_sdk: true

gemini:
  package_info:
    genai: true
    repo: https://github.com/googleapis/python-genai/tree/HEAD
    pip_release: "google-genai"
    module_name: "google.genai"
    install_dev: |
      uv pip install --system git+https://github.com/googleapis/python-genai
  autologging:
    minimum: "1.21.0"
    maximum: "2.8.0"
    requirements:
    run: |
      # Install legacy gemini SDK to ensure the integration works for the legacy SDK
      uv pip install --system google-generativeai
      pytest tests/gemini
    test_tracing_sdk: true
    test_every_n_versions: 10

anthropic:
  package_info:
    genai: true
    repo: https://github.com/anthropics/anthropic-sdk-python/tree/HEAD
    pip_release: "anthropic"
    install_dev: |
      uv pip install --system git+https://github.com/anthropics/anthropic-sdk-python
  autologging:
    minimum: "0.55.0"
    maximum: "0.107.1"
    requirements:
    run: pytest tests/anthropic
    # Our CI runs tests for every minor version of the library by default, which results in
    # many test tasks for anthropic. Reducing the number of testing only for every 5 version.
    test_every_n_versions: 10
    test_tracing_sdk: true

crewai:
  package_info:
    genai: true
    repo: https://github.com/crewAIInc/crewAI/tree/HEAD/lib/crewai
    pip_release: "crewai"
    module_name: "crewai"
    install_dev: |
      uv pip install --system git+https://github.com/crewAIInc/crewAI#subdirectory=lib/crewai
  autologging:
    minimum: "0.134.0"
    maximum: "1.14.6"
    unsupported: ["==0.114.0"]
    requirements:
    run: pytest tests/crewai
    test_tracing_sdk: true
    test_every_n_versions: 20

agno:
  package_info:
    genai: true
    repo: https://github.com/agno-agi/agno/tree/HEAD/libs/agno
    pip_release: "agno"
    module_name: "agno"
    install_dev: |
      uv pip install --system git+https://github.com/agno-agi/agno.git#subdirectory=libs/agno
  autologging:
    minimum: "1.7.0"
    maximum: "2.6.12"
    requirements:
      ">= 0.0.0": ["anthropic", "yfinance"]
      ">= 2.0.0": ["opentelemetry-exporter-otlp", "openinference-instrumentation-agno"]
      # openinference-instrumentation-agno <= 0.1.30 calls wrap_function_wrapper(module=...),
      # but wrapt 2.0 renamed the kwarg to `target`. Pin wrapt<2 until we can move off 0.1.20.
      "<= 2.4.8": ["openinference-instrumentation-agno==0.1.20", "wrapt<2"]
    run: pytest tests/agno
    test_tracing_sdk: true

pydantic_ai:
  package_info:
    genai: true
    repo: https://github.com/pydantic/pydantic-ai/tree/HEAD
    pip_release: "pydantic-ai"
    module_name: "pydantic_ai"

    install_dev: |
      # Install the stable version first to install dependencies
      uv pip install --system pydantic-ai
      # We need to install deno to run test suite for MCPServer as deno is used to start the MCPServer
      curl -fsSL https://deno.land/install.sh | sh -s -- -y --no-modify-path
      # Update key libraries from source w/ no-deps
      uv pip install --system --no-deps \
      # We need to install pydantic_graph and pydantic_ai_slim as pydantic_ai is dependent on both of them
      # and we need to install them separately. Without pinning them separately would result in dependency
      # conflict errors and the installation of pydantic_ai would get failed.
      # Ref: https://github.com/pydantic/pydantic-ai/blob/main/pydantic_ai_slim/pyproject.toml#L45-L52
        "git+https://github.com/pydantic/pydantic-ai.git#egg=pydantic-graph&subdirectory=pydantic_graph" \
        "git+https://github.com/pydantic/pydantic-ai.git#egg=pydantic-ai-slim&subdirectory=pydantic_ai_slim" \
        "git+https://github.com/pydantic/pydantic-ai.git#egg=pydantic-ai"
  autologging:
    minimum: "0.2.19"
    # TODO: Bump past 1.94.0 once the tracing tests patch the right model request
    # entrypoint. pydantic-ai >= 1.96 no longer routes through
    # `InstrumentedModel.request`, so the mock is bypassed and tests hit the real API.
    maximum: "1.94.0"
    requirements:
      ">= 0.0.0": ["mcp"]
    run: pytest tests/pydantic_ai
    test_tracing_sdk: true
    test_every_n_versions: 10

smolagents:
  package_info:
    genai: true
    repo: https://github.com/huggingface/smolagents/tree/HEAD
    pip_release: "smolagents"
    module_name: "smolagents"
    install_dev: |
      uv pip install --system "git+https://github.com/huggingface/smolagents"
  autologging:
    minimum: "1.19.0"
    maximum: "1.26.0"
    requirements:
      "< 1.21.0": ["duckduckgo-search"]
      # `duckduckgo_search` has been renamed to `ddgs`:
      # https://github.com/huggingface/smolagents/pull/1593
      ">= 1.21.0": ["ddgs"]
    run: pytest tests/smolagents
    test_tracing_sdk: true

strands:
  package_info:
    genai: true
    repo: https://github.com/strands-agents/sdk-python/tree/HEAD
    pip_release: "strands-agents"
    module_name: "strands"
    install_dev: |
      uv pip install --system "git+https://github.com/strands-agents/sdk-python"
  autologging:
    minimum: "1.4.0"
    maximum: "1.42.0"
    run: pytest tests/strands
    test_tracing_sdk: true

haystack:
  package_info:
    repo: https://github.com/deepset-ai/haystack/tree/HEAD
    pip_release: "haystack-ai"
    module_name: "haystack"
    install_dev: |
      uv pip install --system git+https://github.com/deepset-ai/haystack
  autologging:
    minimum: "2.2.2"
    maximum: "2.30.0"
    requirements:
    run: pytest tests/haystack
    test_tracing_sdk: true
    test_every_n_versions: 10

mistral:
  package_info:
    genai: true
    repo: https://github.com/mistralai/client-python/tree/HEAD
    pip_release: "mistralai"
    module_name: "mistralai"
    install_dev: |
      TMP_DIR=$(mktemp -d)
      git clone --depth 1 https://github.com/mistralai/client-python $TMP_DIR
      cd $TMP_DIR
      python scripts/prepare_readme.py
      uv pip install --system .
      rm -rf $TMP_DIR
  autologging:
    minimum: "1.9.1"
    maximum: "2.4.9"
    requirements:
      # mistralai 2.x hard-pins opentelemetry-semantic-conventions==0.60b1, which is only
      # paired with opentelemetry-sdk 1.39.x. mlflow's `.[extras]` install pulls in the
      # newest sdk, leaving an inconsistent api/sdk/semconv trio that breaks span start
      # with `TraceFlags.RANDOM_TRACE_ID` AttributeError. Hold the sdk back so the trio
      # stays aligned with mistralai's semconv pin.
      ">= 2.0.0": ["opentelemetry-sdk<1.40"]
    run: pytest tests/mistral
    test_tracing_sdk: true
    test_every_n_versions: 10

sentence_transformers:
  package_info:
    repo: https://github.com/UKPLab/sentence-transformers/tree/HEAD
    pip_release: "sentence-transformers"
    install_dev: |
      uv pip install --system git+https://github.com/UKPLab/sentence-transformers#egg=sentence-transformers
  models:
    minimum: "3.1.0"
    maximum: "5.5.1"
    requirements:
      ">= 0.0.0": [
          "pyspark",
          "torch>1.6",
          "transformers>4.25",
          "hf_transfer",
          # Required by a test model (nomic-ai/nomic-embed-text-v1.5)
          "einops",
          # TODO: Try the latest version of moto (https://github.com/getmoto/moto/issues/8498) and remove this pin
          "boto3<1.36",
          # Fix for https://github.com/huggingface/transformers/issues/37326
          "accelerate",
        ]
    run: |
      pytest tests/sentence_transformers/test_sentence_transformers_model_export.py

johnsnowlabs:
  package_info:
    pip_release: "johnsnowlabs"
  models:
    minimum: "5.4.0"
    maximum: "6.4.0"
    requirements:
      ">= 0.0.0": ["pandas<=1.5.3"]
    run: |
      if [ ! -z "$JOHNSNOWLABS_LICENSE_JSON" ]; then
        pytest tests/johnsnowlabs/test_johnsnowlabs_model_export.py
      else
        echo "Skipping tests due to missing license key"
      fi

groq:
  package_info:
    genai: true
    repo: https://github.com/groq/groq-python/tree/HEAD
    pip_release: "groq"
    install_dev: |
      uv pip install --system git+https://github.com/groq/groq-python
  autologging:
    minimum: "0.29.0"
    maximum: "1.4.0"
    requirements:
    run: pytest tests/groq
    test_tracing_sdk: true
    test_every_n_versions: 10

bedrock:
  package_info:
    genai: true
    pip_release: "boto3"
    module_name: "boto3"
  autologging:
    # BedrockRuntime client is added in boto3 1.33
    minimum: "1.38.37"
    maximum: "1.43.24"
    run: pytest tests/bedrock
    test_tracing_sdk: true