diff --git a/.fernignore b/.fernignore
index 670c4ceb..718b7c3a 100644
--- a/.fernignore
+++ b/.fernignore
@@ -1,17 +1,19 @@
 # Specify files that shouldn't be modified by Fern
 
-src/humanloop/eval_utils
+
+## Custom code
+
+src/humanloop/evals
 src/humanloop/prompt_utils.py
 src/humanloop/client.py
+src/humanloop/overload.py
+src/humanloop/context.py
 mypy.ini
 README.md
-
-# Directories used by SDK decorators
-
-src/humanloop/utilities
+src/humanloop/decorators
 src/humanloop/otel
 
-# Tests
+## Tests
 
 tests/
 
diff --git a/poetry.lock b/poetry.lock
index 1d31dfea..010258ef 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand.
 
 [[package]]
 name = "annotated-types"
@@ -6,6 +6,7 @@ version = "0.7.0"
 description = "Reusable constraint types to use with typing.Annotated"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
     {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
@@ -17,6 +18,7 @@ version = "0.49.0"
 description = "The official Python library for the anthropic API"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "anthropic-0.49.0-py3-none-any.whl", hash = "sha256:bbc17ad4e7094988d2fa86b87753ded8dce12498f4b85fe5810f208f454a8375"},
     {file = "anthropic-0.49.0.tar.gz", hash = "sha256:c09e885b0f674b9119b4f296d8508907f6cff0009bc20d5cf6b35936c40b4398"},
@@ -41,6 +43,7 @@ version = "4.8.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
+groups = ["main", "dev"]
 files = [
     {file = "anyio-4.8.0-py3-none-any.whl", hash = "sha256:b5011f270ab5eb0abf13385f851315585cc37ef330dd88e27ec3d34d651fd47a"},
     {file = "anyio-4.8.0.tar.gz", hash = "sha256:1d9fe889df5212298c0c0723fa20479d1b94883a2df44bd3897aa91083316f7a"},
@@ -54,155 +57,28 @@ typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
 
 [package.extras]
 doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1) ; python_version >= \"3.10\"", "uvloop (>=0.21) ; platform_python_implementation == \"CPython\" and platform_system != \"Windows\" and python_version < \"3.14\""]
 trio = ["trio (>=0.26.1)"]
 
-[[package]]
-name = "asgiref"
-version = "3.8.1"
-description = "ASGI specs, helper code, and adapters"
-optional = false
-python-versions = ">=3.8"
-files = [
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-    {file = "asgiref-3.8.1.tar.gz", hash = "sha256:c343bd80a0bec947a9860adb4c432ffa7db769836c64238fc34bdc3fec84d590"},
-]
-
-[package.dependencies]
-typing-extensions = {version = ">=4", markers = "python_version < \"3.11\""}
-
-[package.extras]
-tests = ["mypy (>=0.800)", "pytest", "pytest-asyncio"]
-
 [[package]]
 name = "attrs"
 version = "25.1.0"
 description = "Classes Without Boilerplate"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "attrs-25.1.0-py3-none-any.whl", hash = "sha256:c75a69e28a550a7e93789579c22aa26b0f5b83b75dc4e08fe092980051e1090a"},
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 ]
 
 [package.extras]
-benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
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+benchmark = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
+cov = ["cloudpickle ; platform_python_implementation == \"CPython\"", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
+dev = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
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+tests = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
+tests-mypy = ["mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\""]
 
 [[package]]
 name = "certifi"
@@ -210,6 +86,7 @@ version = "2025.1.31"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
+groups = ["main", "dev"]
 files = [
     {file = "certifi-2025.1.31-py3-none-any.whl", hash = "sha256:ca78db4565a652026a4db2bcdf68f2fb589ea80d0be70e03929ed730746b84fe"},
     {file = "certifi-2025.1.31.tar.gz", hash = "sha256:3d5da6925056f6f18f119200434a4780a94263f10d1c21d032a6f6b2baa20651"},
@@ -221,6 +98,7 @@ version = "3.4.1"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
 files = [
     {file = "charset_normalizer-3.4.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:91b36a978b5ae0ee86c394f5a54d6ef44db1de0815eb43de826d41d21e4af3de"},
     {file = "charset_normalizer-3.4.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7461baadb4dc00fd9e0acbe254e3d7d2112e7f92ced2adc96e54ef6501c5f176"},
@@ -316,108 +194,13 @@ files = [
     {file = "charset_normalizer-3.4.1.tar.gz", hash = "sha256:44251f18cd68a75b56585dd00dae26183e102cd5e0f9f1466e6df5da2ed64ea3"},
 ]
 
-[[package]]
-name = "chroma-hnswlib"
-version = "0.7.6"
-description = "Chromas fork of hnswlib"
-optional = false
-python-versions = "*"
-files = [
-    {file = "chroma_hnswlib-0.7.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:f35192fbbeadc8c0633f0a69c3d3e9f1a4eab3a46b65458bbcbcabdd9e895c36"},
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-    {file = "chroma_hnswlib-0.7.6-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:fe91f018b30452c16c811fd6c8ede01f84e5a9f3c23e0758775e57f1c3778871"},
-    {file = "chroma_hnswlib-0.7.6-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e6c0e627476f0f4d9e153420d36042dd9c6c3671cfd1fe511c0253e38c2a1039"},
-    {file = "chroma_hnswlib-0.7.6-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3e9796a4536b7de6c6d76a792ba03e08f5aaa53e97e052709568e50b4d20c04f"},
-    {file = "chroma_hnswlib-0.7.6-cp39-cp39-win_amd64.whl", hash = "sha256:d30e2db08e7ffdcc415bd072883a322de5995eb6ec28a8f8c054103bbd3ec1e0"},
-    {file = "chroma_hnswlib-0.7.6.tar.gz", hash = "sha256:4dce282543039681160259d29fcde6151cc9106c6461e0485f57cdccd83059b7"},
-]
-
-[package.dependencies]
-numpy = "*"
-
-[[package]]
-name = "chromadb"
-version = "0.6.3"
-description = "Chroma."
-optional = false
-python-versions = ">=3.9"
-files = [
-    {file = "chromadb-0.6.3-py3-none-any.whl", hash = "sha256:4851258489a3612b558488d98d09ae0fe0a28d5cad6bd1ba64b96fdc419dc0e5"},
-    {file = "chromadb-0.6.3.tar.gz", hash = "sha256:c8f34c0b704b9108b04491480a36d42e894a960429f87c6516027b5481d59ed3"},
-]
-
-[package.dependencies]
-bcrypt = ">=4.0.1"
-build = ">=1.0.3"
-chroma-hnswlib = "0.7.6"
-fastapi = ">=0.95.2"
-grpcio = ">=1.58.0"
-httpx = ">=0.27.0"
-importlib-resources = "*"
-kubernetes = ">=28.1.0"
-mmh3 = ">=4.0.1"
-numpy = ">=1.22.5"
-onnxruntime = ">=1.14.1"
-opentelemetry-api = ">=1.2.0"
-opentelemetry-exporter-otlp-proto-grpc = ">=1.2.0"
-opentelemetry-instrumentation-fastapi = ">=0.41b0"
-opentelemetry-sdk = ">=1.2.0"
-orjson = ">=3.9.12"
-overrides = ">=7.3.1"
-posthog = ">=2.4.0"
-pydantic = ">=1.9"
-pypika = ">=0.48.9"
-PyYAML = ">=6.0.0"
-rich = ">=10.11.0"
-tenacity = ">=8.2.3"
-tokenizers = ">=0.13.2"
-tqdm = ">=4.65.0"
-typer = ">=0.9.0"
-typing_extensions = ">=4.5.0"
-uvicorn = {version = ">=0.18.3", extras = ["standard"]}
-
-[[package]]
-name = "click"
-version = "8.1.8"
-description = "Composable command line interface toolkit"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "click-8.1.8-py3-none-any.whl", hash = "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2"},
-    {file = "click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a"},
-]
-
-[package.dependencies]
-colorama = {version = "*", markers = "platform_system == \"Windows\""}
-
 [[package]]
 name = "cohere"
 version = "5.14.0"
 description = ""
 optional = false
 python-versions = "<4.0,>=3.9"
+groups = ["dev"]
 files = [
     {file = "cohere-5.14.0-py3-none-any.whl", hash = "sha256:286b4ff66f9a59c06a30b8819fb8b2aee3354dc2f7dc83f19519da771e598a5e"},
     {file = "cohere-5.14.0.tar.gz", hash = "sha256:fdbf2d4c54049b74c8f79ff8ed6fd149c4c2055d3b5eae57b9c70716e65c78fd"},
@@ -440,6 +223,8 @@ version = "0.4.6"
 description = "Cross-platform colored terminal text."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
+groups = ["dev"]
+markers = "sys_platform == \"win32\" or platform_system == \"Windows\""
 files = [
     {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
@@ -451,6 +236,7 @@ version = "15.0.1"
 description = "Colored terminal output for Python's logging module"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+groups = ["dev"]
 files = [
     {file = "coloredlogs-15.0.1-py2.py3-none-any.whl", hash = "sha256:612ee75c546f53e92e70049c9dbfcc18c935a2b9a53b66085ce9ef6a6e5c0934"},
     {file = "coloredlogs-15.0.1.tar.gz", hash = "sha256:7c991aa71a4577af2f82600d8f8f3a89f936baeaf9b50a9c197da014e5bf16b0"},
@@ -462,30 +248,13 @@ humanfriendly = ">=9.1"
 [package.extras]
 cron = ["capturer (>=2.4)"]
 
-[[package]]
-name = "deepdiff"
-version = "8.3.0"
-description = "Deep Difference and Search of any Python object/data. Recreate objects by adding adding deltas to each other."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "deepdiff-8.3.0-py3-none-any.whl", hash = "sha256:838acf1b17d228f4155bcb69bb265c41cbb5b2aba2575f07efa67ad9b9b7a0b5"},
-    {file = "deepdiff-8.3.0.tar.gz", hash = "sha256:92a8d7c75a4b26b385ec0372269de258e20082307ccf74a4314341add3d88391"},
-]
-
-[package.dependencies]
-orderly-set = ">=5.3.0,<6"
-
-[package.extras]
-cli = ["click (==8.1.8)", "pyyaml (==6.0.2)"]
-optimize = ["orjson"]
-
 [[package]]
 name = "deprecated"
 version = "1.2.18"
 description = "Python @deprecated decorator to deprecate old python classes, functions or methods."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
+groups = ["main"]
 files = [
     {file = "Deprecated-1.2.18-py2.py3-none-any.whl", hash = "sha256:bd5011788200372a32418f888e326a09ff80d0214bd961147cfed01b5c018eec"},
     {file = "deprecated-1.2.18.tar.gz", hash = "sha256:422b6f6d859da6f2ef57857761bfb392480502a64c3028ca9bbe86085d72115d"},
@@ -495,7 +264,7 @@ files = [
 wrapt = ">=1.10,<2"
 
 [package.extras]
-dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "setuptools", "tox"]
+dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "setuptools ; python_version >= \"3.12\"", "tox"]
 
 [[package]]
 name = "distro"
@@ -503,28 +272,20 @@ version = "1.9.0"
 description = "Distro - an OS platform information API"
 optional = false
 python-versions = ">=3.6"
+groups = ["main", "dev"]
 files = [
     {file = "distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2"},
     {file = "distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed"},
 ]
 
-[[package]]
-name = "durationpy"
-version = "0.9"
-description = "Module for converting between datetime.timedelta and Go's Duration strings."
-optional = false
-python-versions = "*"
-files = [
-    {file = "durationpy-0.9-py3-none-any.whl", hash = "sha256:e65359a7af5cedad07fb77a2dd3f390f8eb0b74cb845589fa6c057086834dd38"},
-    {file = "durationpy-0.9.tar.gz", hash = "sha256:fd3feb0a69a0057d582ef643c355c40d2fa1c942191f914d12203b1a01ac722a"},
-]
-
 [[package]]
 name = "exceptiongroup"
 version = "1.2.2"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
+markers = "python_version < \"3.11\""
 files = [
     {file = "exceptiongroup-1.2.2-py3-none-any.whl", hash = "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b"},
     {file = "exceptiongroup-1.2.2.tar.gz", hash = "sha256:47c2edf7c6738fafb49fd34290706d1a1a2f4d1c6df275526b62cbb4aa5393cc"},
@@ -533,32 +294,13 @@ files = [
 [package.extras]
 test = ["pytest (>=6)"]
 
-[[package]]
-name = "fastapi"
-version = "0.115.11"
-description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "fastapi-0.115.11-py3-none-any.whl", hash = "sha256:32e1541b7b74602e4ef4a0260ecaf3aadf9d4f19590bba3e1bf2ac4666aa2c64"},
-    {file = "fastapi-0.115.11.tar.gz", hash = "sha256:cc81f03f688678b92600a65a5e618b93592c65005db37157147204d8924bf94f"},
-]
-
-[package.dependencies]
-pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<2.1.0 || >2.1.0,<3.0.0"
-starlette = ">=0.40.0,<0.47.0"
-typing-extensions = ">=4.8.0"
-
-[package.extras]
-all = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=3.1.5)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.18)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
-standard = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "jinja2 (>=3.1.5)", "python-multipart (>=0.0.18)", "uvicorn[standard] (>=0.12.0)"]
-
 [[package]]
 name = "fastavro"
 version = "1.10.0"
 description = "Fast read/write of AVRO files"
 optional = false
 python-versions = ">=3.9"
+groups = ["dev"]
 files = [
     {file = "fastavro-1.10.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:1a9fe0672d2caf0fe54e3be659b13de3cad25a267f2073d6f4b9f8862acc31eb"},
     {file = "fastavro-1.10.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:86dd0410770e0c99363788f0584523709d85e57bb457372ec5c285a482c17fe6"},
@@ -605,6 +347,7 @@ version = "3.17.0"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.9"
+groups = ["dev"]
 files = [
     {file = "filelock-3.17.0-py3-none-any.whl", hash = "sha256:533dc2f7ba78dc2f0f531fc6c4940addf7b70a481e269a5a3b93be94ffbe8338"},
     {file = "filelock-3.17.0.tar.gz", hash = "sha256:ee4e77401ef576ebb38cd7f13b9b28893194acc20a8e68e18730ba9c0e54660e"},
@@ -613,7 +356,7 @@ files = [
 [package.extras]
 docs = ["furo (>=2024.8.6)", "sphinx (>=8.1.3)", "sphinx-autodoc-typehints (>=3)"]
 testing = ["covdefaults (>=2.3)", "coverage (>=7.6.10)", "diff-cover (>=9.2.1)", "pytest (>=8.3.4)", "pytest-asyncio (>=0.25.2)", "pytest-cov (>=6)", "pytest-mock (>=3.14)", "pytest-timeout (>=2.3.1)", "virtualenv (>=20.28.1)"]
-typing = ["typing-extensions (>=4.12.2)"]
+typing = ["typing-extensions (>=4.12.2) ; python_version < \"3.11\""]
 
 [[package]]
 name = "flatbuffers"
@@ -621,6 +364,7 @@ version = "25.2.10"
 description = "The FlatBuffers serialization format for Python"
 optional = false
 python-versions = "*"
+groups = ["dev"]
 files = [
     {file = "flatbuffers-25.2.10-py2.py3-none-any.whl", hash = "sha256:ebba5f4d5ea615af3f7fd70fc310636fbb2bbd1f566ac0a23d98dd412de50051"},
     {file = "flatbuffers-25.2.10.tar.gz", hash = "sha256:97e451377a41262f8d9bd4295cc836133415cc03d8cb966410a4af92eb00d26e"},
@@ -632,6 +376,7 @@ version = "2025.2.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
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 tqdm = ["tqdm"]
 
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-
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 version = "0.18.0"
 description = "The official Python library for the groq API"
 optional = false
 python-versions = ">=3.8"
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-
-[package.extras]
-protobuf = ["grpcio-tools (>=1.70.0)"]
-
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 name = "h11"
 version = "0.14.0"
 description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
 optional = false
 python-versions = ">=3.7"
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 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
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@@ -824,67 +464,13 @@ http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
 trio = ["trio (>=0.22.0,<1.0)"]
 
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-    {file = "httptools-0.6.4.tar.gz", hash = "sha256:4e93eee4add6493b59a5c514da98c939b244fce4a0d8879cd3f466562f4b7d5c"},
-]
-
-[package.extras]
-test = ["Cython (>=0.29.24)"]
-
 [[package]]
 name = "httpx"
 version = "0.28.1"
 description = "The next generation HTTP client."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad"},
     {file = "httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc"},
@@ -897,7 +483,7 @@ httpcore = "==1.*"
 idna = "*"
 
 [package.extras]
-brotli = ["brotli", "brotlicffi"]
+brotli = ["brotli ; platform_python_implementation == \"CPython\"", "brotlicffi ; platform_python_implementation != \"CPython\""]
 cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
@@ -909,6 +495,7 @@ version = "0.4.0"
 description = "Consume Server-Sent Event (SSE) messages with HTTPX."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "httpx-sse-0.4.0.tar.gz", hash = "sha256:1e81a3a3070ce322add1d3529ed42eb5f70817f45ed6ec915ab753f961139721"},
     {file = "httpx_sse-0.4.0-py3-none-any.whl", hash = "sha256:f329af6eae57eaa2bdfd962b42524764af68075ea87370a2de920af5341e318f"},
@@ -920,6 +507,7 @@ version = "0.29.2"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
+groups = ["dev"]
 files = [
     {file = "huggingface_hub-0.29.2-py3-none-any.whl", hash = "sha256:c56f20fca09ef19da84dcde2b76379ecdaddf390b083f59f166715584953307d"},
     {file = "huggingface_hub-0.29.2.tar.gz", hash = "sha256:590b29c0dcbd0ee4b7b023714dc1ad8563fe4a68a91463438b74e980d28afaf3"},
@@ -954,6 +542,7 @@ version = "10.0"
 description = "Human friendly output for text interfaces using Python"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+groups = ["dev"]
 files = [
     {file = "humanfriendly-10.0-py2.py3-none-any.whl", hash = "sha256:1697e1a8a8f550fd43c2865cd84542fc175a61dcb779b6fee18cf6b6ccba1477"},
     {file = "humanfriendly-10.0.tar.gz", hash = "sha256:6b0b831ce8f15f7300721aa49829fc4e83921a9a301cc7f606be6686a2288ddc"},
@@ -968,6 +557,7 @@ version = "3.10"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.6"
+groups = ["main", "dev"]
 files = [
     {file = "idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3"},
     {file = "idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9"},
@@ -982,6 +572,7 @@ version = "8.4.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "importlib_metadata-8.4.0-py3-none-any.whl", hash = "sha256:66f342cc6ac9818fc6ff340576acd24d65ba0b3efabb2b4ac08b598965a4a2f1"},
     {file = "importlib_metadata-8.4.0.tar.gz", hash = "sha256:9a547d3bc3608b025f93d403fdd1aae741c24fbb8314df4b155675742ce303c5"},
@@ -993,29 +584,7 @@ zipp = ">=0.5"
 [package.extras]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 perf = ["ipython"]
-test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
-
-[[package]]
-name = "importlib-resources"
-version = "6.5.2"
-description = "Read resources from Python packages"
-optional = false
-python-versions = ">=3.9"
-files = [
-    {file = "importlib_resources-6.5.2-py3-none-any.whl", hash = "sha256:789cfdc3ed28c78b67a06acb8126751ced69a3d5f79c095a98298cd8a760ccec"},
-    {file = "importlib_resources-6.5.2.tar.gz", hash = "sha256:185f87adef5bcc288449d98fb4fba07cea78bc036455dd44c5fc4a2fe78fed2c"},
-]
-
-[package.dependencies]
-zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
-
-[package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
-cover = ["pytest-cov"]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-enabler = ["pytest-enabler (>=2.2)"]
-test = ["jaraco.test (>=5.4)", "pytest (>=6,!=8.1.*)", "zipp (>=3.17)"]
-type = ["pytest-mypy"]
+test = ["flufl.flake8", "importlib-resources (>=1.3) ; python_version < \"3.9\"", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
 
 [[package]]
 name = "iniconfig"
@@ -1023,6 +592,7 @@ version = "2.0.0"
 description = "brain-dead simple config-ini parsing"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"},
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
@@ -1034,6 +604,7 @@ version = "0.8.2"
 description = "Fast iterable JSON parser."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "jiter-0.8.2-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:ca8577f6a413abe29b079bc30f907894d7eb07a865c4df69475e868d73e71c7b"},
     {file = "jiter-0.8.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b25bd626bde7fb51534190c7e3cb97cee89ee76b76d7585580e22f34f5e3f393"},
@@ -1119,6 +690,7 @@ version = "4.23.0"
 description = "An implementation of JSON Schema validation for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "jsonschema-4.23.0-py3-none-any.whl", hash = "sha256:fbadb6f8b144a8f8cf9f0b89ba94501d143e50411a1278633f56a7acf7fd5566"},
     {file = "jsonschema-4.23.0.tar.gz", hash = "sha256:d71497fef26351a33265337fa77ffeb82423f3ea21283cd9467bb03999266bc4"},
@@ -1140,6 +712,7 @@ version = "2024.10.1"
 description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
 optional = false
 python-versions = ">=3.9"
+groups = ["dev"]
 files = [
     {file = "jsonschema_specifications-2024.10.1-py3-none-any.whl", hash = "sha256:a09a0680616357d9a0ecf05c12ad234479f549239d0f5b55f3deea67475da9bf"},
     {file = "jsonschema_specifications-2024.10.1.tar.gz", hash = "sha256:0f38b83639958ce1152d02a7f062902c41c8fd20d558b0c34344292d417ae272"},
@@ -1148,74 +721,13 @@ files = [
 [package.dependencies]
 referencing = ">=0.31.0"
 
-[[package]]
-name = "kubernetes"
-version = "32.0.1"
-description = "Kubernetes python client"
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "kubernetes-32.0.1-py2.py3-none-any.whl", hash = "sha256:35282ab8493b938b08ab5526c7ce66588232df00ef5e1dbe88a419107dc10998"},
-    {file = "kubernetes-32.0.1.tar.gz", hash = "sha256:42f43d49abd437ada79a79a16bd48a604d3471a117a8347e87db693f2ba0ba28"},
-]
-
-[package.dependencies]
-certifi = ">=14.05.14"
-durationpy = ">=0.7"
-google-auth = ">=1.0.1"
-oauthlib = ">=3.2.2"
-python-dateutil = ">=2.5.3"
-pyyaml = ">=5.4.1"
-requests = "*"
-requests-oauthlib = "*"
-six = ">=1.9.0"
-urllib3 = ">=1.24.2"
-websocket-client = ">=0.32.0,<0.40.0 || >0.40.0,<0.41.dev0 || >=0.43.dev0"
-
-[package.extras]
-adal = ["adal (>=1.0.2)"]
-
-[[package]]
-name = "markdown-it-py"
-version = "3.0.0"
-description = "Python port of markdown-it. Markdown parsing, done right!"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "markdown-it-py-3.0.0.tar.gz", hash = "sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb"},
-    {file = "markdown_it_py-3.0.0-py3-none-any.whl", hash = "sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1"},
-]
-
-[package.dependencies]
-mdurl = ">=0.1,<1.0"
-
-[package.extras]
-benchmarking = ["psutil", "pytest", "pytest-benchmark"]
-code-style = ["pre-commit (>=3.0,<4.0)"]
-compare = ["commonmark (>=0.9,<1.0)", "markdown (>=3.4,<4.0)", "mistletoe (>=1.0,<2.0)", "mistune (>=2.0,<3.0)", "panflute (>=2.3,<3.0)"]
-linkify = ["linkify-it-py (>=1,<3)"]
-plugins = ["mdit-py-plugins"]
-profiling = ["gprof2dot"]
-rtd = ["jupyter_sphinx", "mdit-py-plugins", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"]
-testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
-
-[[package]]
-name = "mdurl"
-version = "0.1.2"
-description = "Markdown URL utilities"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8"},
-    {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"},
-]
-
 [[package]]
 name = "mmh3"
 version = "5.1.0"
 description = "Python extension for MurmurHash (MurmurHash3), a set of fast and robust hash functions."
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "mmh3-5.1.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:eaf4ac5c6ee18ca9232238364d7f2a213278ae5ca97897cafaa123fcc7bb8bec"},
     {file = "mmh3-5.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:48f9aa8ccb9ad1d577a16104834ac44ff640d8de8c0caed09a2300df7ce8460a"},
@@ -1308,23 +820,13 @@ plot = ["matplotlib (==3.10.0)", "pandas (==2.2.3)"]
 test = ["pytest (==8.3.4)", "pytest-sugar (==1.0.0)"]
 type = ["mypy (==1.14.1)"]
 
-[[package]]
-name = "monotonic"
-version = "1.6"
-description = "An implementation of time.monotonic() for Python 2 & < 3.3"
-optional = false
-python-versions = "*"
-files = [
-    {file = "monotonic-1.6-py2.py3-none-any.whl", hash = "sha256:68687e19a14f11f26d140dd5c86f3dba4bf5df58003000ed467e0e2a69bca96c"},
-    {file = "monotonic-1.6.tar.gz", hash = "sha256:3a55207bcfed53ddd5c5bae174524062935efed17792e9de2ad0205ce9ad63f7"},
-]
-
 [[package]]
 name = "mpmath"
 version = "1.3.0"
 description = "Python library for arbitrary-precision floating-point arithmetic"
 optional = false
 python-versions = "*"
+groups = ["dev"]
 files = [
     {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"},
     {file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"},
@@ -1333,7 +835,7 @@ files = [
 [package.extras]
 develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"]
 docs = ["sphinx"]
-gmpy = ["gmpy2 (>=2.1.0a4)"]
+gmpy = ["gmpy2 (>=2.1.0a4) ; platform_python_implementation != \"PyPy\""]
 tests = ["pytest (>=4.6)"]
 
 [[package]]
@@ -1342,6 +844,7 @@ version = "1.0.1"
 description = "Optional static typing for Python"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "mypy-1.0.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:71a808334d3f41ef011faa5a5cd8153606df5fc0b56de5b2e89566c8093a0c9a"},
     {file = "mypy-1.0.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:920169f0184215eef19294fa86ea49ffd4635dedfdea2b57e45cb4ee85d5ccaf"},
@@ -1388,6 +891,7 @@ version = "1.0.0"
 description = "Type system extensions for programs checked with the mypy type checker."
 optional = false
 python-versions = ">=3.5"
+groups = ["dev"]
 files = [
     {file = "mypy_extensions-1.0.0-py3-none-any.whl", hash = "sha256:4392f6c0eb8a5668a69e23d168ffa70f0be9ccfd32b5cc2d26a34ae5b844552d"},
     {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
@@ -1399,6 +903,7 @@ version = "1.26.4"
 description = "Fundamental package for array computing in Python"
 optional = false
 python-versions = ">=3.9"
+groups = ["dev"]
 files = [
     {file = "numpy-1.26.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9ff0f4f29c51e2803569d7a51c2304de5554655a60c5d776e35b4a41413830d0"},
     {file = "numpy-1.26.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2e4ee3380d6de9c9ec04745830fd9e2eccb3e6cf790d39d7b98ffd19b0dd754a"},
@@ -1438,28 +943,13 @@ files = [
     {file = "numpy-1.26.4.tar.gz", hash = "sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010"},
 ]
 
-[[package]]
-name = "oauthlib"
-version = "3.2.2"
-description = "A generic, spec-compliant, thorough implementation of the OAuth request-signing logic"
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "oauthlib-3.2.2-py3-none-any.whl", hash = "sha256:8139f29aac13e25d502680e9e19963e83f16838d48a0d71c287fe40e7067fbca"},
-    {file = "oauthlib-3.2.2.tar.gz", hash = "sha256:9859c40929662bec5d64f34d01c99e093149682a3f38915dc0655d5a633dd918"},
-]
-
-[package.extras]
-rsa = ["cryptography (>=3.0.0)"]
-signals = ["blinker (>=1.4.0)"]
-signedtoken = ["cryptography (>=3.0.0)", "pyjwt (>=2.0.0,<3)"]
-
 [[package]]
 name = "onnxruntime"
 version = "1.19.2"
 description = "ONNX Runtime is a runtime accelerator for Machine Learning models"
 optional = false
 python-versions = "*"
+groups = ["dev"]
 files = [
     {file = "onnxruntime-1.19.2-cp310-cp310-macosx_11_0_universal2.whl", hash = "sha256:84fa57369c06cadd3c2a538ae2a26d76d583e7c34bdecd5769d71ca5c0fc750e"},
     {file = "onnxruntime-1.19.2-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:bdc471a66df0c1cdef774accef69e9f2ca168c851ab5e4f2f3341512c7ef4666"},
@@ -1502,6 +992,7 @@ version = "1.65.4"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "openai-1.65.4-py3-none-any.whl", hash = "sha256:15566d46574b94eae3d18efc2f9a4ebd1366d1d44bfc1bdafeea7a5cf8271bcb"},
     {file = "openai-1.65.4.tar.gz", hash = "sha256:0b08c58625d556f5c6654701af1023689c173eb0989ce8f73c7fd0eb22203c76"},
@@ -1527,6 +1018,7 @@ version = "1.27.0"
 description = "OpenTelemetry Python API"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "opentelemetry_api-1.27.0-py3-none-any.whl", hash = "sha256:953d5871815e7c30c81b56d910c707588000fff7a3ca1c73e6531911d53065e7"},
     {file = "opentelemetry_api-1.27.0.tar.gz", hash = "sha256:ed673583eaa5f81b5ce5e86ef7cdaf622f88ef65f0b9aab40b843dcae5bef342"},
@@ -1536,46 +1028,13 @@ files = [
 deprecated = ">=1.2.6"
 importlib-metadata = ">=6.0,<=8.4.0"
 
-[[package]]
-name = "opentelemetry-exporter-otlp-proto-common"
-version = "1.27.0"
-description = "OpenTelemetry Protobuf encoding"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "opentelemetry_exporter_otlp_proto_common-1.27.0-py3-none-any.whl", hash = "sha256:675db7fffcb60946f3a5c43e17d1168a3307a94a930ecf8d2ea1f286f3d4f79a"},
-    {file = "opentelemetry_exporter_otlp_proto_common-1.27.0.tar.gz", hash = "sha256:159d27cf49f359e3798c4c3eb8da6ef4020e292571bd8c5604a2a573231dd5c8"},
-]
-
-[package.dependencies]
-opentelemetry-proto = "1.27.0"
-
-[[package]]
-name = "opentelemetry-exporter-otlp-proto-grpc"
-version = "1.27.0"
-description = "OpenTelemetry Collector Protobuf over gRPC Exporter"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "opentelemetry_exporter_otlp_proto_grpc-1.27.0-py3-none-any.whl", hash = "sha256:56b5bbd5d61aab05e300d9d62a6b3c134827bbd28d0b12f2649c2da368006c9e"},
-    {file = "opentelemetry_exporter_otlp_proto_grpc-1.27.0.tar.gz", hash = "sha256:af6f72f76bcf425dfb5ad11c1a6d6eca2863b91e63575f89bb7b4b55099d968f"},
-]
-
-[package.dependencies]
-deprecated = ">=1.2.6"
-googleapis-common-protos = ">=1.52,<2.0"
-grpcio = ">=1.0.0,<2.0.0"
-opentelemetry-api = ">=1.15,<2.0"
-opentelemetry-exporter-otlp-proto-common = "1.27.0"
-opentelemetry-proto = "1.27.0"
-opentelemetry-sdk = ">=1.27.0,<1.28.0"
-
 [[package]]
 name = "opentelemetry-instrumentation"
 version = "0.48b0"
 description = "Instrumentation Tools & Auto Instrumentation for OpenTelemetry Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "opentelemetry_instrumentation-0.48b0-py3-none-any.whl", hash = "sha256:a69750dc4ba6a5c3eb67986a337185a25b739966d80479befe37b546fc870b44"},
     {file = "opentelemetry_instrumentation-0.48b0.tar.gz", hash = "sha256:94929685d906380743a71c3970f76b5f07476eea1834abd5dd9d17abfe23cc35"},
@@ -1592,6 +1051,7 @@ version = "0.33.9"
 description = "OpenTelemetry Anthropic instrumentation"
 optional = false
 python-versions = "<4,>=3.9"
+groups = ["main"]
 files = [
     {file = "opentelemetry_instrumentation_anthropic-0.33.9-py3-none-any.whl", hash = "sha256:443fc46d7de9d95a86efebb4de1119672ba86f6da113cc7e1bb8129ce9978439"},
     {file = "opentelemetry_instrumentation_anthropic-0.33.9.tar.gz", hash = "sha256:1866e832a777cfd407f83b3782f0788e702a9ede02eaaf7b6680d32f0c03d1e2"},
@@ -1603,33 +1063,13 @@ opentelemetry-instrumentation = ">=0.48b0,<0.49"
 opentelemetry-semantic-conventions = ">=0.48b0,<0.49"
 opentelemetry-semantic-conventions-ai = "0.4.2"
 
-[[package]]
-name = "opentelemetry-instrumentation-asgi"
-version = "0.48b0"
-description = "ASGI instrumentation for OpenTelemetry"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "opentelemetry_instrumentation_asgi-0.48b0-py3-none-any.whl", hash = "sha256:ddb1b5fc800ae66e85a4e2eca4d9ecd66367a8c7b556169d9e7b57e10676e44d"},
-    {file = "opentelemetry_instrumentation_asgi-0.48b0.tar.gz", hash = "sha256:04c32174b23c7fa72ddfe192dad874954968a6a924608079af9952964ecdf785"},
-]
-
-[package.dependencies]
-asgiref = ">=3.0,<4.0"
-opentelemetry-api = ">=1.12,<2.0"
-opentelemetry-instrumentation = "0.48b0"
-opentelemetry-semantic-conventions = "0.48b0"
-opentelemetry-util-http = "0.48b0"
-
-[package.extras]
-instruments = ["asgiref (>=3.0,<4.0)"]
-
 [[package]]
 name = "opentelemetry-instrumentation-bedrock"
 version = "0.33.9"
 description = "OpenTelemetry Bedrock instrumentation"
 optional = false
 python-versions = "<4,>=3.9"
+groups = ["main"]
 files = [
     {file = "opentelemetry_instrumentation_bedrock-0.33.9-py3-none-any.whl", hash = "sha256:b6e1ac590b3c0c5bb1df0266feb9d6e349df396d4b3d1a0da5377cb8e6e16816"},
     {file = "opentelemetry_instrumentation_bedrock-0.33.9.tar.gz", hash = "sha256:4441e5f2093edb1cbcd05298a39d180ea88d6efeb1bbe355886a97a57f6b542e"},
@@ -1648,6 +1088,7 @@ version = "0.33.9"
 description = "OpenTelemetry Cohere instrumentation"
 optional = false
 python-versions = "<4,>=3.9"
+groups = ["main"]
 files = [
     {file = "opentelemetry_instrumentation_cohere-0.33.9-py3-none-any.whl", hash = "sha256:a94ab72d0c438a154236f9907acee1a07f581408dbd8b06f0cb9301ef29b656b"},
     {file = "opentelemetry_instrumentation_cohere-0.33.9.tar.gz", hash = "sha256:931f24768337026a933cb7dd4850530e0545772f08abaf37f4664f1e768b73db"},
@@ -1659,33 +1100,13 @@ opentelemetry-instrumentation = ">=0.48b0,<0.49"
 opentelemetry-semantic-conventions = ">=0.48b0,<0.49"
 opentelemetry-semantic-conventions-ai = "0.4.2"
 
-[[package]]
-name = "opentelemetry-instrumentation-fastapi"
-version = "0.48b0"
-description = "OpenTelemetry FastAPI Instrumentation"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "opentelemetry_instrumentation_fastapi-0.48b0-py3-none-any.whl", hash = "sha256:afeb820a59e139d3e5d96619600f11ce0187658b8ae9e3480857dd790bc024f2"},
-    {file = "opentelemetry_instrumentation_fastapi-0.48b0.tar.gz", hash = "sha256:21a72563ea412c0b535815aeed75fc580240f1f02ebc72381cfab672648637a2"},
-]
-
-[package.dependencies]
-opentelemetry-api = ">=1.12,<2.0"
-opentelemetry-instrumentation = "0.48b0"
-opentelemetry-instrumentation-asgi = "0.48b0"
-opentelemetry-semantic-conventions = "0.48b0"
-opentelemetry-util-http = "0.48b0"
-
-[package.extras]
-instruments = ["fastapi (>=0.58,<1.0)"]
-
 [[package]]
 name = "opentelemetry-instrumentation-groq"
 version = "0.33.9"
 description = "OpenTelemetry Groq instrumentation"
 optional = false
 python-versions = "<4,>=3.9"
+groups = ["main"]
 files = [
     {file = "opentelemetry_instrumentation_groq-0.33.9-py3-none-any.whl", hash = "sha256:52256832c06f9d1ba8c11efce0854f012e7900c313e410a02c8feb85b0e35407"},
     {file = "opentelemetry_instrumentation_groq-0.33.9.tar.gz", hash = "sha256:d83201c516a760fdc478413b855c6d9fb1aed48eb8d4166fa2dc7c762058f6b1"},
@@ -1703,6 +1124,7 @@ version = "0.33.9"
 description = "OpenTelemetry OpenAI instrumentation"
 optional = false
 python-versions = "<4,>=3.9"
+groups = ["main"]
 files = [
     {file = "opentelemetry_instrumentation_openai-0.33.9-py3-none-any.whl", hash = "sha256:9a54ec31a66c212cd42b7f02701beecea4068effdf227b11c96fecfbc6544f40"},
     {file = "opentelemetry_instrumentation_openai-0.33.9.tar.gz", hash = "sha256:5989a6049e63a09a6e9d699c077f7bbc932c0bda5a08f9ec0f4e88fd0c38d8b7"},
@@ -1721,6 +1143,7 @@ version = "0.33.9"
 description = "OpenTelemetry Replicate instrumentation"
 optional = false
 python-versions = "<4,>=3.9"
+groups = ["main"]
 files = [
     {file = "opentelemetry_instrumentation_replicate-0.33.9-py3-none-any.whl", hash = "sha256:cf2a0b83dfd150cb7a6827d405b088ed0a46beec7f652bfcc4acb5ffd3d2044a"},
     {file = "opentelemetry_instrumentation_replicate-0.33.9.tar.gz", hash = "sha256:e18f2ce224ae1efc2158263aaec6c7b487d7498da9a08d1a594df484e86fce88"},
@@ -1734,17 +1157,18 @@ opentelemetry-semantic-conventions-ai = "0.4.2"
 
 [[package]]
 name = "opentelemetry-proto"
-version = "1.27.0"
+version = "1.30.0"
 description = "OpenTelemetry Python Proto"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
-    {file = "opentelemetry_proto-1.27.0-py3-none-any.whl", hash = "sha256:b133873de5581a50063e1e4b29cdcf0c5e253a8c2d8dc1229add20a4c3830ace"},
-    {file = "opentelemetry_proto-1.27.0.tar.gz", hash = "sha256:33c9345d91dafd8a74fc3d7576c5a38f18b7fdf8d02983ac67485386132aedd6"},
+    {file = "opentelemetry_proto-1.30.0-py3-none-any.whl", hash = "sha256:c6290958ff3ddacc826ca5abbeb377a31c2334387352a259ba0df37c243adc11"},
+    {file = "opentelemetry_proto-1.30.0.tar.gz", hash = "sha256:afe5c9c15e8b68d7c469596e5b32e8fc085eb9febdd6fb4e20924a93a0389179"},
 ]
 
 [package.dependencies]
-protobuf = ">=3.19,<5.0"
+protobuf = ">=5.0,<6.0"
 
 [[package]]
 name = "opentelemetry-sdk"
@@ -1752,6 +1176,7 @@ version = "1.27.0"
 description = "OpenTelemetry Python SDK"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "opentelemetry_sdk-1.27.0-py3-none-any.whl", hash = "sha256:365f5e32f920faf0fd9e14fdfd92c086e317eaa5f860edba9cdc17a380d9197d"},
     {file = "opentelemetry_sdk-1.27.0.tar.gz", hash = "sha256:d525017dea0ccce9ba4e0245100ec46ecdc043f2d7b8315d56b19aff0904fa6f"},
@@ -1768,6 +1193,7 @@ version = "0.48b0"
 description = "OpenTelemetry Semantic Conventions"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "opentelemetry_semantic_conventions-0.48b0-py3-none-any.whl", hash = "sha256:a0de9f45c413a8669788a38569c7e0a11ce6ce97861a628cca785deecdc32a1f"},
     {file = "opentelemetry_semantic_conventions-0.48b0.tar.gz", hash = "sha256:12d74983783b6878162208be57c9effcb89dc88691c64992d70bb89dc00daa1a"},
@@ -1783,138 +1209,19 @@ version = "0.4.2"
 description = "OpenTelemetry Semantic Conventions Extension for Large Language Models"
 optional = false
 python-versions = "<4,>=3.9"
+groups = ["main"]
 files = [
     {file = "opentelemetry_semantic_conventions_ai-0.4.2-py3-none-any.whl", hash = "sha256:0a5432aacd441eb7dbdf62e0de3f3d90ed4f69595b687a6dd2ccc4c5b94c5861"},
     {file = "opentelemetry_semantic_conventions_ai-0.4.2.tar.gz", hash = "sha256:90b969c7d838e03e30a9150ffe46543d8e58e9d7370c7221fd30d4ce4d7a1b96"},
 ]
 
-[[package]]
-name = "opentelemetry-util-http"
-version = "0.48b0"
-description = "Web util for OpenTelemetry"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "opentelemetry_util_http-0.48b0-py3-none-any.whl", hash = "sha256:76f598af93aab50328d2a69c786beaedc8b6a7770f7a818cc307eb353debfffb"},
-    {file = "opentelemetry_util_http-0.48b0.tar.gz", hash = "sha256:60312015153580cc20f322e5cdc3d3ecad80a71743235bdb77716e742814623c"},
-]
-
-[[package]]
-name = "orderly-set"
-version = "5.3.0"
-description = "Orderly set"
-optional = false
-python-versions = ">=3.8"
-files = [
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-]
-
-[[package]]
-name = "orjson"
-version = "3.10.15"
-description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
-optional = false
-python-versions = ">=3.8"
-files = [
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-optional = false
-python-versions = ">=3.6"
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-
 [[package]]
 name = "packaging"
 version = "24.2"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
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@@ -1926,6 +1233,7 @@ version = "2.2.3"
 description = "Powerful data structures for data analysis, time series, and statistics"
 optional = false
 python-versions = ">=3.9"
+groups = ["dev"]
 files = [
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 description = "parse() is the opposite of format()"
 optional = false
 python-versions = "*"
+groups = ["main", "dev"]
 files = [
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 description = "Simplifies to build parse types based on the parse module"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,>=2.7"
+groups = ["dev"]
 files = [
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@@ -2033,9 +1343,9 @@ parse = {version = ">=1.18.0", markers = "python_version >= \"3.0\""}
 six = ">=1.15"
 
 [package.extras]
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+develop = ["build (>=0.5.1)", "coverage (>=4.4)", "pylint", "pytest (<5.0) ; python_version < \"3.0\"", "pytest (>=5.0) ; python_version >= \"3.0\"", "pytest-cov", "pytest-html (>=1.19.0)", "ruff ; python_version >= \"3.7\"", "setuptools", "setuptools-scm", "tox (>=2.8,<4.0)", "twine (>=1.13.0)", "virtualenv (<20.22.0) ; python_version <= \"3.6\"", "virtualenv (>=20.0.0) ; python_version > \"3.6\"", "wheel"]
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-testing = ["pytest (<5.0)", "pytest (>=5.0)", "pytest-html (>=1.19.0)"]
+testing = ["pytest (<5.0) ; python_version < \"3.0\"", "pytest (>=5.0) ; python_version >= \"3.0\"", "pytest-html (>=1.19.0)"]
 
 [[package]]
 name = "pluggy"
@@ -2043,6 +1353,7 @@ version = "1.5.0"
 description = "plugin and hook calling mechanisms for python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
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@@ -2052,49 +1363,25 @@ files = [
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 testing = ["pytest", "pytest-benchmark"]
 
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-description = "Integrate PostHog into any python application."
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-monotonic = ">=1.5"
-python-dateutil = ">2.1"
-requests = ">=2.7,<3.0"
-six = ">=1.5"
-
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-
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 name = "protobuf"
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+version = "5.29.3"
 description = ""
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
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+    {file = "protobuf-5.29.3-cp39-cp39-win_amd64.whl", hash = "sha256:6ce8cc3389a20693bfde6c6562e03474c40851b44975c9b2bf6df7d8c4f864da"},
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+    {file = "protobuf-5.29.3.tar.gz", hash = "sha256:5da0f41edaf117bde316404bad1a486cb4ededf8e4a54891296f648e8e076620"},
 ]
 
 [[package]]
@@ -2103,6 +1390,7 @@ version = "19.0.1"
 description = "Python library for Apache Arrow"
 optional = false
 python-versions = ">=3.9"
+groups = ["dev"]
 files = [
     {file = "pyarrow-19.0.1-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:fc28912a2dc924dddc2087679cc8b7263accc71b9ff025a1362b004711661a69"},
     {file = "pyarrow-19.0.1-cp310-cp310-macosx_12_0_x86_64.whl", hash = "sha256:fca15aabbe9b8355800d923cc2e82c8ef514af321e18b437c3d782aa884eaeec"},
@@ -2151,37 +1439,13 @@ files = [
 [package.extras]
 test = ["cffi", "hypothesis", "pandas", "pytest", "pytz"]
 
-[[package]]
-name = "pyasn1"
-version = "0.6.1"
-description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs (X.208)"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "pyasn1-0.6.1-py3-none-any.whl", hash = "sha256:0d632f46f2ba09143da3a8afe9e33fb6f92fa2320ab7e886e2d0f7672af84629"},
-    {file = "pyasn1-0.6.1.tar.gz", hash = "sha256:6f580d2bdd84365380830acf45550f2511469f673cb4a5ae3857a3170128b034"},
-]
-
-[[package]]
-name = "pyasn1-modules"
-version = "0.4.1"
-description = "A collection of ASN.1-based protocols modules"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "pyasn1_modules-0.4.1-py3-none-any.whl", hash = "sha256:49bfa96b45a292b711e986f222502c1c9a5e1f4e568fc30e2574a6c7d07838fd"},
-    {file = "pyasn1_modules-0.4.1.tar.gz", hash = "sha256:c28e2dbf9c06ad61c71a075c7e0f9fd0f1b0bb2d2ad4377f240d33ac2ab60a7c"},
-]
-
-[package.dependencies]
-pyasn1 = ">=0.4.6,<0.7.0"
-
 [[package]]
 name = "pydantic"
 version = "2.10.6"
 description = "Data validation using Python type hints"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "pydantic-2.10.6-py3-none-any.whl", hash = "sha256:427d664bf0b8a2b34ff5dd0f5a18df00591adcee7198fbd71981054cef37b584"},
     {file = "pydantic-2.10.6.tar.gz", hash = "sha256:ca5daa827cce33de7a42be142548b0096bf05a7e7b365aebfa5f8eeec7128236"},
@@ -2194,7 +1458,7 @@ typing-extensions = ">=4.12.2"
 
 [package.extras]
 email = ["email-validator (>=2.0.0)"]
-timezone = ["tzdata"]
+timezone = ["tzdata ; python_version >= \"3.9\" and platform_system == \"Windows\""]
 
 [[package]]
 name = "pydantic-core"
@@ -2202,6 +1466,7 @@ version = "2.27.2"
 description = "Core functionality for Pydantic validation and serialization"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "pydantic_core-2.27.2-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:2d367ca20b2f14095a8f4fa1210f5a7b78b8a20009ecced6b12818f455b1e9fa"},
     {file = "pydantic_core-2.27.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:491a2b73db93fab69731eaee494f320faa4e093dbed776be1a829c2eb222c34c"},
@@ -2308,47 +1573,14 @@ files = [
 [package.dependencies]
 typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
 
-[[package]]
-name = "pygments"
-version = "2.19.1"
-description = "Pygments is a syntax highlighting package written in Python."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "pygments-2.19.1-py3-none-any.whl", hash = "sha256:9ea1544ad55cecf4b8242fab6dd35a93bbce657034b0611ee383099054ab6d8c"},
-    {file = "pygments-2.19.1.tar.gz", hash = "sha256:61c16d2a8576dc0649d9f39e089b5f02bcd27fba10d8fb4dcc28173f7a45151f"},
-]
-
-[package.extras]
-windows-terminal = ["colorama (>=0.4.6)"]
-
-[[package]]
-name = "pypika"
-version = "0.48.9"
-description = "A SQL query builder API for Python"
-optional = false
-python-versions = "*"
-files = [
-    {file = "PyPika-0.48.9.tar.gz", hash = "sha256:838836a61747e7c8380cd1b7ff638694b7a7335345d0f559b04b2cd832ad5378"},
-]
-
-[[package]]
-name = "pyproject-hooks"
-version = "1.2.0"
-description = "Wrappers to call pyproject.toml-based build backend hooks."
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "pyproject_hooks-1.2.0-py3-none-any.whl", hash = "sha256:9e5c6bfa8dcc30091c74b0cf803c81fdd29d94f01992a7707bc97babb1141913"},
-    {file = "pyproject_hooks-1.2.0.tar.gz", hash = "sha256:1e859bd5c40fae9448642dd871adf459e5e2084186e8d2c2a79a824c970da1f8"},
-]
-
 [[package]]
 name = "pyreadline3"
 version = "3.5.4"
 description = "A python implementation of GNU readline."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "sys_platform == \"win32\""
 files = [
     {file = "pyreadline3-3.5.4-py3-none-any.whl", hash = "sha256:eaf8e6cc3c49bcccf145fc6067ba8643d1df34d604a1ec0eccbf7a18e6d3fae6"},
     {file = "pyreadline3-3.5.4.tar.gz", hash = "sha256:8d57d53039a1c75adba8e50dd3d992b28143480816187ea5efbd5c78e6c885b7"},
@@ -2363,6 +1595,7 @@ version = "7.4.4"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "pytest-7.4.4-py3-none-any.whl", hash = "sha256:b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8"},
     {file = "pytest-7.4.4.tar.gz", hash = "sha256:2cf0005922c6ace4a3e2ec8b4080eb0d9753fdc93107415332f50ce9e7994280"},
@@ -2385,6 +1618,7 @@ version = "0.23.8"
 description = "Pytest support for asyncio"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "pytest_asyncio-0.23.8-py3-none-any.whl", hash = "sha256:50265d892689a5faefb84df80819d1ecef566eb3549cf915dfb33569359d1ce2"},
     {file = "pytest_asyncio-0.23.8.tar.gz", hash = "sha256:759b10b33a6dc61cce40a8bd5205e302978bbbcc00e279a8b61d9a6a3c82e4d3"},
@@ -2403,6 +1637,7 @@ version = "1.7.0"
 description = "Adds the ability to retry flaky tests in CI environments"
 optional = false
 python-versions = ">=3.9"
+groups = ["dev"]
 files = [
     {file = "pytest_retry-1.7.0-py3-none-any.whl", hash = "sha256:a2dac85b79a4e2375943f1429479c65beb6c69553e7dae6b8332be47a60954f4"},
     {file = "pytest_retry-1.7.0.tar.gz", hash = "sha256:f8d52339f01e949df47c11ba9ee8d5b362f5824dff580d3870ec9ae0057df80f"},
@@ -2420,6 +1655,7 @@ version = "2.9.0.post0"
 description = "Extensions to the standard Python datetime module"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+groups = ["dev"]
 files = [
     {file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"},
     {file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"},
@@ -2434,6 +1670,7 @@ version = "1.0.1"
 description = "Read key-value pairs from a .env file and set them as environment variables"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "python-dotenv-1.0.1.tar.gz", hash = "sha256:e324ee90a023d808f1959c46bcbc04446a10ced277783dc6ee09987c37ec10ca"},
     {file = "python_dotenv-1.0.1-py3-none-any.whl", hash = "sha256:f7b63ef50f1b690dddf550d03497b66d609393b40b564ed0d674909a68ebf16a"},
@@ -2448,6 +1685,7 @@ version = "2025.1"
 description = "World timezone definitions, modern and historical"
 optional = false
 python-versions = "*"
+groups = ["dev"]
 files = [
     {file = "pytz-2025.1-py2.py3-none-any.whl", hash = "sha256:89dd22dca55b46eac6eda23b2d72721bf1bdfef212645d81513ef5d03038de57"},
     {file = "pytz-2025.1.tar.gz", hash = "sha256:c2db42be2a2518b28e65f9207c4d05e6ff547d1efa4086469ef855e4ab70178e"},
@@ -2459,6 +1697,7 @@ version = "6.0.2"
 description = "YAML parser and emitter for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "PyYAML-6.0.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0a9a2848a5b7feac301353437eb7d5957887edbf81d56e903999a75a3d743086"},
     {file = "PyYAML-6.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:29717114e51c84ddfba879543fb232a6ed60086602313ca38cce623c1d62cfbf"},
@@ -2521,6 +1760,7 @@ version = "0.36.2"
 description = "JSON Referencing + Python"
 optional = false
 python-versions = ">=3.9"
+groups = ["dev"]
 files = [
     {file = "referencing-0.36.2-py3-none-any.whl", hash = "sha256:e8699adbbf8b5c7de96d8ffa0eb5c158b3beafce084968e2ea8bb08c6794dcd0"},
     {file = "referencing-0.36.2.tar.gz", hash = "sha256:df2e89862cd09deabbdba16944cc3f10feb6b3e6f18e902f7cc25609a34775aa"},
@@ -2537,6 +1777,7 @@ version = "2024.11.6"
 description = "Alternative regular expression module, to replace re."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "regex-2024.11.6-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:ff590880083d60acc0433f9c3f713c51f7ac6ebb9adf889c79a261ecf541aa91"},
     {file = "regex-2024.11.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:658f90550f38270639e83ce492f27d2c8d2cd63805c65a13a14d36ca126753f0"},
@@ -2640,6 +1881,7 @@ version = "1.0.4"
 description = "Python client for Replicate"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "replicate-1.0.4-py3-none-any.whl", hash = "sha256:f568f6271ff715067901b6094c23c37373bbcfd7de0ff9b85e9c9ead567e09e7"},
     {file = "replicate-1.0.4.tar.gz", hash = "sha256:f718601863ef1f419aa7dcdab1ea8770ba5489b571b86edf840cd506d68758ef"},
@@ -2657,6 +1899,7 @@ version = "2.32.3"
 description = "Python HTTP for Humans."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "requests-2.32.3-py3-none-any.whl", hash = "sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6"},
     {file = "requests-2.32.3.tar.gz", hash = "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760"},
@@ -2672,49 +1915,13 @@ urllib3 = ">=1.21.1,<3"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
-[[package]]
-name = "requests-oauthlib"
-version = "2.0.0"
-description = "OAuthlib authentication support for Requests."
-optional = false
-python-versions = ">=3.4"
-files = [
-    {file = "requests-oauthlib-2.0.0.tar.gz", hash = "sha256:b3dffaebd884d8cd778494369603a9e7b58d29111bf6b41bdc2dcd87203af4e9"},
-    {file = "requests_oauthlib-2.0.0-py2.py3-none-any.whl", hash = "sha256:7dd8a5c40426b779b0868c404bdef9768deccf22749cde15852df527e6269b36"},
-]
-
-[package.dependencies]
-oauthlib = ">=3.0.0"
-requests = ">=2.0.0"
-
-[package.extras]
-rsa = ["oauthlib[signedtoken] (>=3.0.0)"]
-
-[[package]]
-name = "rich"
-version = "13.9.4"
-description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
-optional = false
-python-versions = ">=3.8.0"
-files = [
-    {file = "rich-13.9.4-py3-none-any.whl", hash = "sha256:6049d5e6ec054bf2779ab3358186963bac2ea89175919d699e378b99738c2a90"},
-    {file = "rich-13.9.4.tar.gz", hash = "sha256:439594978a49a09530cff7ebc4b5c7103ef57baf48d5ea3184f21d9a2befa098"},
-]
-
-[package.dependencies]
-markdown-it-py = ">=2.2.0"
-pygments = ">=2.13.0,<3.0.0"
-typing-extensions = {version = ">=4.0.0,<5.0", markers = "python_version < \"3.11\""}
-
-[package.extras]
-jupyter = ["ipywidgets (>=7.5.1,<9)"]
-
 [[package]]
 name = "rpds-py"
 version = "0.23.1"
 description = "Python bindings to Rust's persistent data structures (rpds)"
 optional = false
 python-versions = ">=3.9"
+groups = ["dev"]
 files = [
     {file = "rpds_py-0.23.1-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:2a54027554ce9b129fc3d633c92fa33b30de9f08bc61b32c053dc9b537266fed"},
     {file = "rpds_py-0.23.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b5ef909a37e9738d146519657a1aab4584018746a18f71c692f2f22168ece40c"},
@@ -2821,26 +2028,13 @@ files = [
     {file = "rpds_py-0.23.1.tar.gz", hash = "sha256:7f3240dcfa14d198dba24b8b9cb3b108c06b68d45b7babd9eefc1038fdf7e707"},
 ]
 
-[[package]]
-name = "rsa"
-version = "4.9"
-description = "Pure-Python RSA implementation"
-optional = false
-python-versions = ">=3.6,<4"
-files = [
-    {file = "rsa-4.9-py3-none-any.whl", hash = "sha256:90260d9058e514786967344d0ef75fa8727eed8a7d2e43ce9f4bcf1b536174f7"},
-    {file = "rsa-4.9.tar.gz", hash = "sha256:e38464a49c6c85d7f1351b0126661487a7e0a14a50f1675ec50eb34d4f20ef21"},
-]
-
-[package.dependencies]
-pyasn1 = ">=0.1.3"
-
 [[package]]
 name = "ruff"
 version = "0.5.7"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "ruff-0.5.7-py3-none-linux_armv6l.whl", hash = "sha256:548992d342fc404ee2e15a242cdbea4f8e39a52f2e7752d0e4cbe88d2d2f416a"},
     {file = "ruff-0.5.7-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:00cc8872331055ee017c4f1071a8a31ca0809ccc0657da1d154a1d2abac5c0be"},
@@ -2868,30 +2062,20 @@ version = "75.8.2"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "setuptools-75.8.2-py3-none-any.whl", hash = "sha256:558e47c15f1811c1fa7adbd0096669bf76c1d3f433f58324df69f3f5ecac4e8f"},
     {file = "setuptools-75.8.2.tar.gz", hash = "sha256:4880473a969e5f23f2a2be3646b2dfd84af9028716d398e46192f84bc36900d2"},
 ]
 
 [package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.8.0)"]
-core = ["importlib_metadata (>=6)", "jaraco.collections", "jaraco.functools (>=4)", "jaraco.text (>=3.7)", "more_itertools", "more_itertools (>=8.8)", "packaging", "packaging (>=24.2)", "platformdirs (>=4.2.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\"", "ruff (>=0.8.0) ; sys_platform != \"cygwin\""]
+core = ["importlib_metadata (>=6) ; python_version < \"3.10\"", "jaraco.collections", "jaraco.functools (>=4)", "jaraco.text (>=3.7)", "more_itertools", "more_itertools (>=8.8)", "packaging", "packaging (>=24.2)", "platformdirs (>=4.2.2)", "tomli (>=2.0.1) ; python_version < \"3.11\"", "wheel (>=0.43.0)"]
 cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
 enabler = ["pytest-enabler (>=2.2)"]
-test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.7.2)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
-type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.14.*)", "pytest-mypy"]
-
-[[package]]
-name = "shellingham"
-version = "1.5.4"
-description = "Tool to Detect Surrounding Shell"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "shellingham-1.5.4-py2.py3-none-any.whl", hash = "sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686"},
-    {file = "shellingham-1.5.4.tar.gz", hash = "sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de"},
-]
+test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21) ; python_version >= \"3.9\" and sys_platform != \"cygwin\"", "jaraco.envs (>=2.2)", "jaraco.path (>=3.7.2)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf ; sys_platform != \"cygwin\"", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
+type = ["importlib_metadata (>=7.0.2) ; python_version < \"3.10\"", "jaraco.develop (>=7.21) ; sys_platform != \"cygwin\"", "mypy (==1.14.*)", "pytest-mypy"]
 
 [[package]]
 name = "six"
@@ -2899,6 +2083,7 @@ version = "1.17.0"
 description = "Python 2 and 3 compatibility utilities"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+groups = ["dev"]
 files = [
     {file = "six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274"},
     {file = "six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81"},
@@ -2910,35 +2095,19 @@ version = "1.3.1"
 description = "Sniff out which async library your code is running under"
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
 files = [
     {file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"},
     {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
 ]
 
-[[package]]
-name = "starlette"
-version = "0.46.0"
-description = "The little ASGI library that shines."
-optional = false
-python-versions = ">=3.9"
-files = [
-    {file = "starlette-0.46.0-py3-none-any.whl", hash = "sha256:913f0798bd90ba90a9156383bcf1350a17d6259451d0d8ee27fc0cf2db609038"},
-    {file = "starlette-0.46.0.tar.gz", hash = "sha256:b359e4567456b28d473d0193f34c0de0ed49710d75ef183a74a5ce0499324f50"},
-]
-
-[package.dependencies]
-anyio = ">=3.6.2,<5"
-typing-extensions = {version = ">=3.10.0", markers = "python_version < \"3.10\""}
-
-[package.extras]
-full = ["httpx (>=0.27.0,<0.29.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.18)", "pyyaml"]
-
 [[package]]
 name = "sympy"
 version = "1.13.3"
 description = "Computer algebra system (CAS) in Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "sympy-1.13.3-py3-none-any.whl", hash = "sha256:54612cf55a62755ee71824ce692986f23c88ffa77207b30c1368eda4a7060f73"},
     {file = "sympy-1.13.3.tar.gz", hash = "sha256:b27fd2c6530e0ab39e275fc9b683895367e51d5da91baa8d3d64db2565fec4d9"},
@@ -2950,27 +2119,13 @@ mpmath = ">=1.1.0,<1.4"
 [package.extras]
 dev = ["hypothesis (>=6.70.0)", "pytest (>=7.1.0)"]
 
-[[package]]
-name = "tenacity"
-version = "9.0.0"
-description = "Retry code until it succeeds"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "tenacity-9.0.0-py3-none-any.whl", hash = "sha256:93de0c98785b27fcf659856aa9f54bfbd399e29969b0621bc7f762bd441b4539"},
-    {file = "tenacity-9.0.0.tar.gz", hash = "sha256:807f37ca97d62aa361264d497b0e31e92b8027044942bfa756160d908320d73b"},
-]
-
-[package.extras]
-doc = ["reno", "sphinx"]
-test = ["pytest", "tornado (>=4.5)", "typeguard"]
-
 [[package]]
 name = "tiktoken"
 version = "0.9.0"
 description = "tiktoken is a fast BPE tokeniser for use with OpenAI's models"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "tiktoken-0.9.0-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:586c16358138b96ea804c034b8acf3f5d3f0258bd2bc3b0227af4af5d622e382"},
     {file = "tiktoken-0.9.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:d9c59ccc528c6c5dd51820b3474402f69d9a9e1d656226848ad68a8d5b2e5108"},
@@ -3018,6 +2173,7 @@ version = "0.21.0"
 description = ""
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "tokenizers-0.21.0-cp39-abi3-macosx_10_12_x86_64.whl", hash = "sha256:3c4c93eae637e7d2aaae3d376f06085164e1660f89304c0ab2b1d08a406636b2"},
     {file = "tokenizers-0.21.0-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:f53ea537c925422a2e0e92a24cce96f6bc5046bbef24a1652a5edc8ba975f62e"},
@@ -3050,6 +2206,8 @@ version = "2.2.1"
 description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version < \"3.11\""
 files = [
     {file = "tomli-2.2.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:678e4fa69e4575eb77d103de3df8a895e1591b48e740211bd1067378c69e8249"},
     {file = "tomli-2.2.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:023aa114dd824ade0100497eb2318602af309e5a55595f76b626d6d9f3b7b0a6"},
@@ -3091,6 +2249,7 @@ version = "4.67.1"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "tqdm-4.67.1-py3-none-any.whl", hash = "sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2"},
     {file = "tqdm-4.67.1.tar.gz", hash = "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2"},
@@ -3106,29 +2265,13 @@ notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
 
-[[package]]
-name = "typer"
-version = "0.15.2"
-description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "typer-0.15.2-py3-none-any.whl", hash = "sha256:46a499c6107d645a9c13f7ee46c5d5096cae6f5fc57dd11eccbbb9ae3e44ddfc"},
-    {file = "typer-0.15.2.tar.gz", hash = "sha256:ab2fab47533a813c49fe1f16b1a370fd5819099c00b119e0633df65f22144ba5"},
-]
-
-[package.dependencies]
-click = ">=8.0.0"
-rich = ">=10.11.0"
-shellingham = ">=1.3.0"
-typing-extensions = ">=3.7.4.3"
-
 [[package]]
 name = "types-jsonschema"
 version = "4.23.0.20241208"
 description = "Typing stubs for jsonschema"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "types_jsonschema-4.23.0.20241208-py3-none-any.whl", hash = "sha256:87934bd9231c99d8eff94cacfc06ba668f7973577a9bd9e1f9de957c5737313e"},
     {file = "types_jsonschema-4.23.0.20241208.tar.gz", hash = "sha256:e8b15ad01f290ecf6aea53f93fbdf7d4730e4600313e89e8a7f95622f7e87b7c"},
@@ -3137,12 +2280,25 @@ files = [
 [package.dependencies]
 referencing = "*"
 
+[[package]]
+name = "types-protobuf"
+version = "5.29.1.20250208"
+description = "Typing stubs for protobuf"
+optional = false
+python-versions = ">=3.9"
+groups = ["dev"]
+files = [
+    {file = "types_protobuf-5.29.1.20250208-py3-none-any.whl", hash = "sha256:c5f8bfb4afdc1b5cbca1848f2c8b361a2090add7401f410b22b599ef647bf483"},
+    {file = "types_protobuf-5.29.1.20250208.tar.gz", hash = "sha256:c1acd6a59ab554dbe09b5d1fa7dd701e2fcfb2212937a3af1c03b736060b792a"},
+]
+
 [[package]]
 name = "types-python-dateutil"
 version = "2.9.0.20241206"
 description = "Typing stubs for python-dateutil"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "types_python_dateutil-2.9.0.20241206-py3-none-any.whl", hash = "sha256:e248a4bc70a486d3e3ec84d0dc30eec3a5f979d6e7ee4123ae043eedbb987f53"},
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@@ -3154,6 +2310,7 @@ version = "2.32.0.20250306"
 description = "Typing stubs for requests"
 optional = false
 python-versions = ">=3.9"
+groups = ["dev"]
 files = [
     {file = "types_requests-2.32.0.20250306-py3-none-any.whl", hash = "sha256:25f2cbb5c8710b2022f8bbee7b2b66f319ef14aeea2f35d80f18c9dbf3b60a0b"},
     {file = "types_requests-2.32.0.20250306.tar.gz", hash = "sha256:0962352694ec5b2f95fda877ee60a159abdf84a0fc6fdace599f20acb41a03d1"},
@@ -3168,6 +2325,7 @@ version = "4.12.2"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
     {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
@@ -3179,6 +2337,7 @@ version = "2025.1"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
+groups = ["dev"]
 files = [
     {file = "tzdata-2025.1-py2.py3-none-any.whl", hash = "sha256:7e127113816800496f027041c570f50bcd464a020098a3b6b199517772303639"},
     {file = "tzdata-2025.1.tar.gz", hash = "sha256:24894909e88cdb28bd1636c6887801df64cb485bd593f2fd83ef29075a81d694"},
@@ -3190,277 +2349,25 @@ version = "2.3.0"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.9"
+groups = ["main", "dev"]
 files = [
     {file = "urllib3-2.3.0-py3-none-any.whl", hash = "sha256:1cee9ad369867bfdbbb48b7dd50374c0967a0bb7710050facf0dd6911440e3df"},
     {file = "urllib3-2.3.0.tar.gz", hash = "sha256:f8c5449b3cf0861679ce7e0503c7b44b5ec981bec0d1d3795a07f1ba96f0204d"},
 ]
 
 [package.extras]
-brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+brotli = ["brotli (>=1.0.9) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\""]
 h2 = ["h2 (>=4,<5)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
-[[package]]
-name = "uvicorn"
-version = "0.34.0"
-description = "The lightning-fast ASGI server."
-optional = false
-python-versions = ">=3.9"
-files = [
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-[package.dependencies]
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-h11 = ">=0.8"
-httptools = {version = ">=0.6.3", optional = true, markers = "extra == \"standard\""}
-python-dotenv = {version = ">=0.13", optional = true, markers = "extra == \"standard\""}
-pyyaml = {version = ">=5.1", optional = true, markers = "extra == \"standard\""}
-typing-extensions = {version = ">=4.0", markers = "python_version < \"3.11\""}
-uvloop = {version = ">=0.14.0,<0.15.0 || >0.15.0,<0.15.1 || >0.15.1", optional = true, markers = "(sys_platform != \"win32\" and sys_platform != \"cygwin\") and platform_python_implementation != \"PyPy\" and extra == \"standard\""}
-watchfiles = {version = ">=0.13", optional = true, markers = "extra == \"standard\""}
-websockets = {version = ">=10.4", optional = true, markers = "extra == \"standard\""}
-
-[package.extras]
-standard = ["colorama (>=0.4)", "httptools (>=0.6.3)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.14.0,!=0.15.0,!=0.15.1)", "watchfiles (>=0.13)", "websockets (>=10.4)"]
-
-[[package]]
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-version = "0.21.0"
-description = "Fast implementation of asyncio event loop on top of libuv"
-optional = false
-python-versions = ">=3.8.0"
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-    {file = "websockets-15.0.1.tar.gz", hash = "sha256:82544de02076bafba038ce055ee6412d68da13ab47f0c60cab827346de828dee"},
-]
-
 [[package]]
 name = "wrapt"
 version = "1.17.2"
 description = "Module for decorators, wrappers and monkey patching."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "wrapt-1.17.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:3d57c572081fed831ad2d26fd430d565b76aa277ed1d30ff4d40670b1c0dd984"},
     {file = "wrapt-1.17.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b5e251054542ae57ac7f3fba5d10bfff615b6c2fb09abeb37d2f1463f841ae22"},
@@ -3549,20 +2456,21 @@ version = "3.21.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"},
     {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"},
 ]
 
 [package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
 cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 enabler = ["pytest-enabler (>=2.2)"]
-test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
+test = ["big-O", "importlib-resources ; python_version < \"3.9\"", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
 type = ["pytest-mypy"]
 
 [metadata]
-lock-version = "2.0"
+lock-version = "2.1"
 python-versions = ">=3.9,<4"
-content-hash = "a4ea51a34af494df07d810d923e6856a0418fb138c6357ba0cf8358713440219"
+content-hash = "8841da1852d6bbeddd7c87b36fe7d3d64e1aacf550a1e6cfdfaa9a1bc3f76301"
diff --git a/pyproject.toml b/pyproject.toml
index eb121646..93b6e192 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -35,7 +35,6 @@ Repository = 'https://github.com/humanloop/humanloop-python'
 
 [tool.poetry.dependencies]
 python = ">=3.9,<4"
-deepdiff = "^8.2.0"
 httpx = ">=0.21.2"
 httpx-sse = "0.4.0"
 mmh3 = "^5.1.0"
@@ -51,15 +50,17 @@ parse = ">=1"
 pydantic = ">= 1.9.2"
 pydantic-core = "^2.18.2"
 typing_extensions = ">= 4.0.0"
+opentelemetry-proto = "^1.30.0"
+protobuf = "^5.29.3"
 
 [tool.poetry.dev-dependencies]
 mypy = "1.0.1"
 pytest = "^7.4.0"
+types-protobuf = "^5.29.1.20250208"
 pytest-asyncio = "^0.23.5"
 python-dateutil = "^2.9.0"
 types-python-dateutil = "^2.9.0.20240316"
 anthropic = ">=0.37.1"
-chromadb = "^0.6.3"
 cohere = "^5.11.2"
 groq = ">=0.11.0"
 jsonschema = "^4.23.0"
@@ -88,4 +89,4 @@ line-length = 120
 
 [build-system]
 requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"
+build-backend = "poetry.core.masonry.api"
\ No newline at end of file
diff --git a/src/humanloop/client.py b/src/humanloop/client.py
index 3b04e080..dbdb898b 100644
--- a/src/humanloop/client.py
+++ b/src/humanloop/client.py
@@ -1,7 +1,6 @@
 import os
 import typing
-from typing import List, Optional, Sequence
-from typing_extensions import Unpack
+from typing import Any, List, Optional, Sequence
 
 import httpx
 from opentelemetry.sdk.resources import Resource
@@ -9,16 +8,15 @@
 from opentelemetry.trace import Tracer
 
 from humanloop.core.client_wrapper import SyncClientWrapper
-from humanloop.eval_utils.run import prompt_call_evaluation_aware
-from humanloop.utilities.types import DecoratorPromptKernelRequestParams
 
-from humanloop.eval_utils import log_with_evaluation_context, run_eval
-from humanloop.eval_utils.types import Dataset, Evaluator, EvaluatorCheck, File
+from humanloop.evals import run_eval
+from humanloop.evals.types import Dataset, Evaluator, EvaluatorCheck, File
 
 from humanloop.base_client import AsyncBaseHumanloop, BaseHumanloop
-from humanloop.utilities.flow import flow as flow_decorator_factory
-from humanloop.utilities.prompt import prompt as prompt_decorator_factory
-from humanloop.utilities.tool import tool as tool_decorator_factory
+from humanloop.overload import overload_call, overload_log
+from humanloop.decorators.flow import flow as flow_decorator_factory
+from humanloop.decorators.prompt import prompt_decorator_factory
+from humanloop.decorators.tool import tool_decorator_factory as tool_decorator_factory
 from humanloop.environment import HumanloopEnvironment
 from humanloop.evaluations.client import EvaluationsClient
 from humanloop.otel import instrument_provider
@@ -26,11 +24,13 @@
 from humanloop.otel.processor import HumanloopSpanProcessor
 from humanloop.prompt_utils import populate_template
 from humanloop.prompts.client import PromptsClient
-from humanloop.requests.flow_kernel_request import FlowKernelRequestParams
-from humanloop.requests.tool_kernel_request import ToolKernelRequestParams
 
 
 class ExtendedEvalsClient(EvaluationsClient):
+    """
+    Provides high-level utilities for running Evaluations on the local runtime.
+    """
+
     client: BaseHumanloop
 
     def __init__(
@@ -54,7 +54,7 @@ def run(
         :param name: the name of the Evaluation to run. If it does not exist, a new Evaluation will be created under your File.
         :param dataset: the dataset to map your function over to produce the outputs required by the Evaluation.
         :param evaluators: define how judgments are provided for this Evaluation.
-        :param workers: the number of threads to process datapoints using your function concurrently.
+        :param workers: Number of concurrent threads for processing datapoints.
         :return: per Evaluator checks.
         """
         if self.client is None:
@@ -71,6 +71,10 @@ def run(
 
 
 class ExtendedPromptsClient(PromptsClient):
+    """
+    Adds utility for populating Prompt template inputs.
+    """
+
     populate_template = staticmethod(populate_template)  # type: ignore [assignment]
 
 
@@ -94,16 +98,14 @@ def __init__(
         opentelemetry_tracer_provider: Optional[TracerProvider] = None,
         opentelemetry_tracer: Optional[Tracer] = None,
     ):
-        """See docstring of :func:`BaseHumanloop.__init__(...)`
-
-        This class extends the base client with custom evaluation utilities
-        and decorators for declaring Files in code.
+        """
+        Extends the base client with custom evaluation utilities and
+        decorators for declaring Files in code.
 
-        The Humanloop SDK File decorators use OpenTelemetry internally. You can provide a
-        TracerProvider and a Tracer if you'd like to integrate them with your existing
-        telemetry system. Otherwise, an internal TracerProvider will be used.
-        If you provide only the `TraceProvider`, the SDK will log under a Tracer
-        named `humanloop.sdk`.
+        The Humanloop SDK File decorators use OpenTelemetry internally.
+        You can provide a TracerProvider and a Tracer to integrate
+        with your existing telemetry system. If not provided,
+        an internal TracerProvider will be used.
         """
         super().__init__(
             base_url=base_url,
@@ -120,9 +122,11 @@ def __init__(
         self.prompts = ExtendedPromptsClient(client_wrapper=self._client_wrapper)
 
         # Overload the .log method of the clients to be aware of Evaluation Context
-        self.prompts = log_with_evaluation_context(client=self.prompts)
-        self.prompts = prompt_call_evaluation_aware(client=self.prompts)
-        self.flows = log_with_evaluation_context(client=self.flows)
+        # and the @flow decorator providing the trace_id
+        self.prompts = overload_log(client=self.prompts)
+        self.prompts = overload_call(client=self.prompts)
+        self.flows = overload_log(client=self.flows)
+        self.tools = overload_log(client=self.tools)
 
         if opentelemetry_tracer_provider is not None:
             self._tracer_provider = opentelemetry_tracer_provider
@@ -136,10 +140,7 @@ def __init__(
             )
         instrument_provider(provider=self._tracer_provider)
         self._tracer_provider.add_span_processor(
-            HumanloopSpanProcessor(
-                client=self,
-                exporter=HumanloopSpanExporter(client=self),
-            ),
+            HumanloopSpanProcessor(exporter=HumanloopSpanExporter(client=self)),
         )
 
         if opentelemetry_tracer is None:
@@ -150,23 +151,13 @@ def __init__(
     def prompt(
         self,
         *,
-        path: Optional[str] = None,
-        **prompt_kernel: Unpack[DecoratorPromptKernelRequestParams],  # type: ignore
+        path: str,
     ):
-        """Decorator for declaring a [Prompt](https://humanloop.com/docs/explanation/prompts) in code.
-
-        The decorator intercepts calls to LLM provider APIs and creates
-        a new Prompt file based on the hyperparameters used in the call.
-        If a hyperparameter is specified in the `@prompt` decorator, then
-        they override any value intercepted from the LLM provider call.
-
-        If the [Prompt](https://humanloop.com/docs/explanation/prompts) already exists
-        on the specified path, a new version will be upserted when any of the above change.
-
-        Here's an example of declaring a (Prompt)[https://humanloop.com/docs/explanation/prompts] in code:
+        """Auto-instrument LLM provider and create [Prompt](https://humanloop.com/docs/explanation/prompts)
+        Logs on Humanloop from them.
 
         ```python
-        @prompt(template="You are an assistant on the following topics: {{topics}}.")
+        @prompt(path="My Prompt")
         def call_llm(messages):
             client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
             return client.chat.completions.create(
@@ -176,49 +167,55 @@ def call_llm(messages):
                 max_tokens=200,
                 messages=messages,
             ).choices[0].message.content
-        ```
 
-        This will create a [Prompt](https://humanloop.com/docs/explanation/prompts] with the following attributes:
-
-        ```python
+        Calling the function above creates a new Log on Humanloop
+        against this Prompt version:
         {
+            provider: "openai",
             model: "gpt-4o",
             endpoint: "chat",
-            template: "You are an assistant on the following topics: {{topics}}.",
-            provider: "openai",
             max_tokens: 200,
             temperature: 0.8,
             frequency_penalty: 0.5,
         }
+        ```
 
-        Every call to the decorated function will create a Log against the Prompt. For example:
-
-        ```python
-        call_llm(messages=[
-            {"role": "system", "content": "You are an assistant on the following topics: finance."}
-            {"role": "user", "content": "What can you do?"}
-        ])
+        If a different model, endpoint, or hyperparameter is used, a new
+        Prompt version is created. For example:
         ```
+        @humanloop_client.prompt(path="My Prompt")
+        def call_llm(messages):
+            client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+            client.chat.completions.create(
+                model="gpt-4o-mini",
+                temperature=0.5,
+            ).choices[0].message.content
 
-        The Prompt Log will be created with the following inputs:
-        ```python
+            client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))
+            client.messages.create(
+                model="claude-3-5-sonnet-20240620",
+                temperature=0.5,
+            ).content
+
+        Calling this function will create two versions of the same Prompt:
         {
-            "inputs": {
-                "topics": "finance"
-            },
-            messages: [
-                {"role": "system", "content": "You are an assistant on the following topics: finance."}
-                {"role": "user", "content": "What can you do?"}
-            ]
-            "output": "Hello, I'm an assistant that can help you with anything related to finance."
+            provider: "openai",
+            model: "gpt-4o-mini",
+            endpoint: "chat",
+            max_tokens: 200,
+            temperature: 0.5,
+            frequency_penalty: 0.5,
         }
-        ```
 
-        The decorated function should return a string or the output should be JSON serializable. If
-        the output cannot be serialized, TypeError will be raised.
+        {
+            provider: "anthropic",
+            model: "claude-3-5-sonnet-20240620",
+            endpoint: "messages",
+            temperature: 0.5,
+        }
 
-        If the function raises an exception, the log created by the function will have the output
-        field set to None and the error field set to the string representation of the exception.
+        And one Log will be added to each version of the Prompt.
+        ```
 
         :param path: The path where the Prompt is created. If not
             provided, the function name is used as the path and the File
@@ -226,39 +223,31 @@ def call_llm(messages):
 
         :param prompt_kernel: Attributes that define the Prompt. See `class:DecoratorPromptKernelRequestParams`
         """
-        return prompt_decorator_factory(
-            opentelemetry_tracer=self._opentelemetry_tracer,
-            path=path,
-            **prompt_kernel,
-        )
+        return prompt_decorator_factory(path=path)
 
     def tool(
         self,
         *,
-        path: Optional[str] = None,
-        **tool_kernel: Unpack[ToolKernelRequestParams],  # type: ignore
+        path: str,
+        attributes: Optional[dict[str, Any]] = None,
+        setup_values: Optional[dict[str, Any]] = None,
     ):
-        """Decorator for declaring a [Tool](https://humanloop.com/docs/explanation/tools) in code.
-
-        The decorator inspects the wrapped function's source code, name,
-        argument type hints and docstring to infer the values that define
-        the [Tool](https://humanloop.com/docs/explanation/tools).
+        """Manage [Tool](https://humanloop.com/docs/explanation/tools) Files through code.
 
-        If the [Tool](https://humanloop.com/docs/explanation/tools) already exists
-        on the specified path, a new version will be upserted when any of the
-        above change.
+        The decorator inspects the wrapped function's source code to infer the Tool's
+        JSON Schema. If the function declaration changes, a new Tool version
+        is upserted with an updated JSON Schema.
 
-        Here's an example of declaring a [Tool](https://humanloop.com/docs/explanation/tools) in code:
+        For example:
 
         ```python
-        @tool
+        # Adding @tool on this function
+        @humanloop_client.tool(path="calculator")
         def calculator(a: int, b: Optional[int]) -> int:
             \"\"\"Add two numbers together.\"\"\"
             return a + b
-        ```
 
-        This will create a [Tool](https://humanloop.com/docs/explanation/tools) with the following attributes:
-        ```python
+        # Creates a Tool with this JSON Schema:
         {
             strict: True,
             function: {
@@ -276,55 +265,37 @@ def calculator(a: int, b: Optional[int]) -> int:
         }
         ```
 
-        Every call to the decorated function will create a Log against the Tool. For example:
-
-        ```python
-        calculator(a=1, b=2)
-        ```
-
-        Will create the following Log:
-
-        ```python
-        {
-            "inputs": {
-                a: 1,
-                b: 2
-            },
-            "output": 3
-        }
-        ```
+        The return value of the decorated function must be JSON serializable.
 
-        The decorated function should return a string or the output should be JSON serializable. If
-        the output cannot be serialized, TypeError will be raised.
+        If the function raises an exception, the created Log will have `output`
+        set to null, and the `error` field populated.
 
-        If the function raises an exception, the log created by the function will have the output
-        field set to None and the error field set to the string representation of the exception.
+        :param path: The path of the File in the Humanloop workspace.
 
-        :param path: The path to the Tool. If not provided, the function name
-            will be used as the path and the File will be created in the root
-            of your organization's workspace.
+        :param setup_values: Values needed to setup the Tool, defined in [JSON Schema](https://json-schema.org/)
 
-        :param tool_kernel: Attributes that define the Tool. See `class:ToolKernelRequestParams`
+        :param attributes: Additional fields to describe the Tool. Helpful to separate Tool versions from each other with details on how they were created or used.
         """
         return tool_decorator_factory(
             opentelemetry_tracer=self._opentelemetry_tracer,
             path=path,
-            **tool_kernel,
+            attributes=attributes,
+            setup_values=setup_values,
         )
 
     def flow(
         self,
         *,
-        path: Optional[str] = None,
-        **flow_kernel: Unpack[FlowKernelRequestParams],  # type: ignore
+        path: str,
+        attributes: Optional[dict[str, Any]] = None,
     ):
-        """Decorator for declaring a [Flow](https://humanloop.com/docs/explanation/flows) in code.
+        """Trace SDK logging calls through [Flows](https://humanloop.com/docs/explanation/flows).
+
+        Use it as the entrypoint of your LLM feature. Logging calls like `prompts.call(...)`,
+        `tools.call(...)`, or other Humanloop decorators will be automatically added to the trace.
 
-        A [Flow](https://humanloop.com/docs/explanation/flows) wrapped callable should
-        be used as the entrypoint of your LLM feature. Call other functions wrapped with
-        Humanloop decorators to create a trace of Logs on Humanloop.
+        For example:
 
-        Here's an example of declaring a [Flow](https://humanloop.com/docs/explanation/flows) in code:
         ```python
         @prompt(template="You are an assistant on the following topics: {{topics}}.")
         def call_llm(messages):
@@ -338,7 +309,7 @@ def call_llm(messages):
             ).choices[0].message.content
 
         @flow(attributes={"version": "v1"})
-        def entrypoint():
+        def agent():
             while True:
                 messages = []
                 user_input = input("You: ")
@@ -350,28 +321,29 @@ def entrypoint():
                 print(f"Assistant: {response}")
         ```
 
-        In this example, the Flow instruments a conversational agent where the
-        Prompt defined in `call_llm` is called multiple times in a loop. Calling
-        `entrypoint` will create a Flow Trace under which multiple Prompt Logs
-        will be nested, allowing you to track the whole conversation session
-        between the user and the assistant.
+        Each call to agent will create a trace corresponding to the conversation
+        session. Multiple Prompt Logs will be created as the LLM is called. They
+        will be added to the trace, allowing you to see the whole conversation
+        in the UI.
 
-        The decorated function should return a string or the output should be JSON serializable. If
-        the output cannot be serialized, TypeError will be raised.
+        If the function returns a ChatMessage-like object, the Log will
+        populate the `output_message` field. Otherwise, it will serialize
+        the return value and populate the `output` field.
 
-        If the function raises an exception, the log created by the function will have the output
-        field set to None and the error field set to the string representation of the exception.
+        If an exception is raised, the output fields will be set to None
+        and the error message will be set in the Log's `error` field.
 
         :param path: The path to the Flow. If not provided, the function name
             will be used as the path and the File will be created in the root
             of your organization workspace.
 
-        :param flow_kernel: Attributes that define the Flow. See `class:ToolKernelRequestParams`
+        :param attributes: Additional fields to describe the Flow. Helpful to separate Flow versions from each other with details on how they were created or used.
         """
         return flow_decorator_factory(
+            client=self,
             opentelemetry_tracer=self._opentelemetry_tracer,
             path=path,
-            **flow_kernel,
+            attributes=attributes,
         )
 
 
diff --git a/src/humanloop/context.py b/src/humanloop/context.py
new file mode 100644
index 00000000..d3b96f33
--- /dev/null
+++ b/src/humanloop/context.py
@@ -0,0 +1,173 @@
+from contextlib import contextmanager
+from dataclasses import dataclass
+import threading
+from typing import Any, Callable, Generator, Literal, Optional
+from opentelemetry import context as context_api
+
+from humanloop.error import HumanloopRuntimeError
+from humanloop.otel.constants import (
+    HUMANLOOP_CONTEXT_EVALUATION,
+    HUMANLOOP_CONTEXT_DECORATOR,
+    HUMANLOOP_CONTEXT_TRACE_ID,
+)
+
+
+def get_trace_id() -> Optional[str]:
+    # Use threading.get_ident() to ensure the context is unique to the current thread
+    key = str(hash((HUMANLOOP_CONTEXT_TRACE_ID, threading.get_ident())))
+    return context_api.get_value(key=key)  # type: ignore [return-value]
+
+
+@contextmanager
+def set_trace_id(flow_log_id: str) -> Generator[None, None, None]:
+    # Use threading.get_ident() to ensure the context is unique to the current thread
+    key = str(hash((HUMANLOOP_CONTEXT_TRACE_ID, threading.get_ident())))
+    token = context_api.attach(context_api.set_value(key=key, value=flow_log_id))
+    yield
+    context_api.detach(token=token)
+
+
+@dataclass
+class DecoratorContext:
+    path: str
+    type: Literal["prompt", "tool", "flow"]
+    version: dict[str, Any]
+
+
+@contextmanager
+def set_decorator_context(
+    decorator_context: DecoratorContext,
+) -> Generator[DecoratorContext, None, None]:
+    # Use threading.get_ident() to ensure the context is unique to the current thread
+    key = str(hash((HUMANLOOP_CONTEXT_DECORATOR, threading.get_ident())))
+    reset_token = context_api.attach(
+        context_api.set_value(
+            key=key,
+            value=decorator_context,
+        )
+    )
+    yield decorator_context
+    context_api.detach(token=reset_token)
+
+
+def get_decorator_context() -> Optional[DecoratorContext]:
+    # Use threading.get_ident() to ensure the context is unique to the current thread
+    key = str(hash((HUMANLOOP_CONTEXT_DECORATOR, threading.get_ident())))
+    return context_api.get_value(key)  # type: ignore [return-value]
+
+
+class EvaluationContext:
+    """
+    Represents the context for evaluating a specific data point within a run.
+
+    This class integrates with the OpenTelemetry (OTEL) runtime context API to distribute data points across threads.
+    Each thread processes one data point by invoking a callable and subsequently logs the results against a run.
+
+    Attributes:
+        source_datapoint_id (str): The unique identifier of the source data point.
+        run_id (str): The unique identifier of the evaluation run.
+        file_id (str): The identifier of the file associated with the evaluation.
+        path (str): The file path associated with the evaluation.
+        _logged (bool): Tracks whether logging has already occurred in this context to ensure only the first log is counted.
+        _callback (Callable[[str], None]): A callback function to be executed when logging occurs.
+    """
+
+    source_datapoint_id: str
+    run_id: str
+    file_id: str
+    path: str
+    _logged: bool
+    _callback: Callable[[str], None]
+    _context_log_belongs_eval_file: bool
+
+    def __init__(
+        self,
+        source_datapoint_id: str,
+        run_id: str,
+        eval_callback: Callable[[str], None],
+        file_id: str,
+        path: str,
+    ):
+        self.source_datapoint_id = source_datapoint_id
+        self.run_id = run_id
+        self._callback = eval_callback
+        self.file_id = file_id
+        self.path = path
+        self._logged = False
+        self._context_log_belongs_eval_file = False
+
+    @property
+    def logged(self) -> bool:
+        """
+        Return true if the current datapoint has been evaluated already.
+        """
+        return self._logged
+
+    def log_args_with_context(
+        self,
+        log_args: dict[str, Any],
+        path: Optional[str] = None,
+        file_id: Optional[str] = None,
+    ) -> tuple[dict[str, Any], Optional[Callable[[str], None]]]:
+        """
+        Logs arguments within the evaluation context if the path or file ID matches.
+
+        This method ensures that if multiple logs are made against the same file, only the first one
+        is considered toward the evaluation run. If a log has already been made, subsequent calls
+        will return the log arguments without adding evaluation-specific metadata.
+
+        Args:
+            log_args (dict[str, Any]): The log arguments to be recorded.
+            path (Optional[str]): The file path for logging (if applicable).
+            file_id (Optional[str]): The file ID for logging (if applicable).
+
+        Returns:
+            tuple[dict[str, Any], Optional[Callable[[str], None]]]:
+                - Updated log arguments with additional context information if applicable.
+                - A callback function if logging belongs to the evaluation file, otherwise None.
+
+        Raises:
+            HumanloopRuntimeError: If neither `path` nor `file_id` is provided.
+        """
+        if path is None and file_id is None:
+            raise HumanloopRuntimeError("Internal error: Evaluation context called without providing a path or file_id")
+
+        # Ensure only the first log against the same file is considered
+        if self._logged:
+            return log_args, None
+
+        if self.path is not None and self.path == path:
+            self._logged = True
+            self._context_log_belongs_eval_file = True
+            return {
+                **log_args,
+                "source_datapoint_id": self.source_datapoint_id,
+                "run_id": self.run_id,
+            }, self._callback
+        elif self.file_id is not None and self.file_id == file_id:
+            self._logged = True
+            self._context_log_belongs_eval_file = True
+            return {
+                **log_args,
+                "source_datapoint_id": self.source_datapoint_id,
+                "run_id": self.run_id,
+            }, self._callback
+        else:
+            return log_args, None
+
+
+@contextmanager
+def set_evaluation_context(
+    evaluation_context: EvaluationContext,
+) -> Generator[None, None, None]:
+    # Use threading.get_ident() to ensure the context is unique to the current thread
+    key = str(hash((HUMANLOOP_CONTEXT_EVALUATION, threading.get_ident())))
+    reset_token = context_api.attach(context_api.set_value(key, evaluation_context))
+    yield
+    context_api.detach(token=reset_token)
+
+
+def get_evaluation_context() -> Optional[EvaluationContext]:
+    # Use threading.get_ident() to ensure the context is unique to the current thread
+    key = str(hash((HUMANLOOP_CONTEXT_EVALUATION, threading.get_ident())))
+    return context_api.get_value(key)  # type: ignore [return-value]
diff --git a/src/humanloop/utilities/__init__.py b/src/humanloop/decorators/__init__.py
similarity index 100%
rename from src/humanloop/utilities/__init__.py
rename to src/humanloop/decorators/__init__.py
diff --git a/src/humanloop/decorators/flow.py b/src/humanloop/decorators/flow.py
new file mode 100644
index 00000000..b43b1b7d
--- /dev/null
+++ b/src/humanloop/decorators/flow.py
@@ -0,0 +1,133 @@
+import logging
+from functools import wraps
+from typing import Any, Callable, Optional, TypeVar
+from typing_extensions import ParamSpec
+
+from opentelemetry.trace import Span, Tracer
+
+from humanloop.base_client import BaseHumanloop
+from humanloop.context import (
+    DecoratorContext,
+    get_trace_id,
+    set_decorator_context,
+    set_trace_id,
+)
+from humanloop.evals.run import HumanloopRuntimeError
+from humanloop.types.chat_message import ChatMessage
+from humanloop.decorators.helpers import bind_args
+from humanloop.evals.types import File
+from humanloop.otel.constants import (
+    HUMANLOOP_FILE_TYPE_KEY,
+    HUMANLOOP_LOG_KEY,
+    HUMANLOOP_FILE_PATH_KEY,
+    HUMANLOOP_FLOW_SPAN_NAME,
+)
+from humanloop.otel.helpers import process_output, write_to_opentelemetry_span
+from humanloop.requests import FlowKernelRequestParams as FlowDict
+from humanloop.types.flow_log_response import FlowLogResponse
+
+logger = logging.getLogger("humanloop.sdk")
+
+
+P = ParamSpec("P")
+R = TypeVar("R")
+
+
+def flow(
+    client: "BaseHumanloop",
+    opentelemetry_tracer: Tracer,
+    path: str,
+    attributes: Optional[dict[str, Any]] = None,
+):
+    flow_kernel = {"attributes": attributes or {}}
+
+    def decorator(func: Callable[P, R]) -> Callable[P, Optional[R]]:
+        decorator_path = path or func.__name__
+        file_type = "flow"
+
+        @wraps(func)
+        def wrapper(*args: P.args, **kwargs: P.kwargs) -> Optional[R]:
+            span: Span
+            with set_decorator_context(
+                DecoratorContext(
+                    path=decorator_path,
+                    type="flow",
+                    version=flow_kernel,
+                )
+            ) as decorator_context:
+                with opentelemetry_tracer.start_as_current_span(HUMANLOOP_FLOW_SPAN_NAME) as span:  # type: ignore
+                    span.set_attribute(HUMANLOOP_FILE_PATH_KEY, decorator_path)
+                    span.set_attribute(HUMANLOOP_FILE_TYPE_KEY, file_type)
+                    trace_id = get_trace_id()
+                    func_args = bind_args(func, args, kwargs)
+
+                    # Create the trace ahead so we have a parent ID to reference
+                    init_log_inputs = {
+                        "inputs": {k: v for k, v in func_args.items() if k != "messages"},
+                        "messages": func_args.get("messages"),
+                        "trace_parent_id": trace_id,
+                    }
+                    this_flow_log: FlowLogResponse = client.flows._log(  # type: ignore [attr-defined]
+                        path=decorator_context.path,
+                        flow=decorator_context.version,
+                        log_status="incomplete",
+                        **init_log_inputs,
+                    )
+
+                    with set_trace_id(this_flow_log.id):
+                        func_output: Optional[R]
+                        log_output: Optional[str]
+                        log_error: Optional[str]
+                        log_output_message: Optional[ChatMessage]
+                        try:
+                            func_output = func(*args, **kwargs)
+                            if (
+                                isinstance(func_output, dict)
+                                and len(func_output.keys()) == 2
+                                and "role" in func_output
+                                and "content" in func_output
+                            ):
+                                log_output_message = func_output  # type: ignore [assignment]
+                                log_output = None
+                            else:
+                                log_output = process_output(func=func, output=func_output)
+                                log_output_message = None
+                            log_error = None
+                        except HumanloopRuntimeError as e:
+                            # Critical error, re-raise
+                            client.logs.delete(id=this_flow_log.id)
+                            span.record_exception(e)
+                            raise e
+                        except Exception as e:
+                            logger.error(f"Error calling {func.__name__}: {e}")
+                            log_output = None
+                            log_output_message = None
+                            log_error = str(e)
+                            func_output = None
+
+                        updated_flow_log = {
+                            "log_status": "complete",
+                            "output": log_output,
+                            "error": log_error,
+                            "output_message": log_output_message,
+                            "id": this_flow_log.id,
+                        }
+                        # Write the Flow Log to the Span on HL_LOG_OT_KEY
+                        write_to_opentelemetry_span(
+                            span=span,  # type: ignore [arg-type]
+                            key=HUMANLOOP_LOG_KEY,
+                            value=updated_flow_log,  # type: ignore
+                        )
+                        # Return the output of the decorated function
+                        return func_output  # type: ignore [return-value]
+
+        wrapper.file = File(  # type: ignore
+            path=decorator_path,
+            type=file_type,  # type: ignore [arg-type, typeddict-item]
+            version=FlowDict(**flow_kernel),  # type: ignore
+            callable=wrapper,
+        )
+
+        return wrapper
+
+    return decorator
diff --git a/src/humanloop/utilities/helpers.py b/src/humanloop/decorators/helpers.py
similarity index 100%
rename from src/humanloop/utilities/helpers.py
rename to src/humanloop/decorators/helpers.py
diff --git a/src/humanloop/decorators/prompt.py b/src/humanloop/decorators/prompt.py
new file mode 100644
index 00000000..5e99cab0
--- /dev/null
+++ b/src/humanloop/decorators/prompt.py
@@ -0,0 +1,44 @@
+from functools import wraps
+import logging
+
+from typing_extensions import ParamSpec
+from typing import Callable, TypeVar
+
+from humanloop.context import DecoratorContext, set_decorator_context
+from humanloop.evals.types import File
+
+logger = logging.getLogger("humanloop.sdk")
+
+P = ParamSpec("P")
+R = TypeVar("R")
+
+
+def prompt_decorator_factory(path: str):
+    def decorator(func: Callable[P, R]) -> Callable[P, R]:
+        @wraps(func)
+        def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
+            with set_decorator_context(
+                DecoratorContext(
+                    path=path,
+                    type="prompt",
+                    version={
+                        # TODO: Implement a reverse-lookup of the template
+                        "template": None,
+                    },
+                )
+            ):
+                output = func(*args, **kwargs)
+                return output
+
+        wrapper.file = File(  # type: ignore [attr-defined]
+            path=path,
+            type="prompt",
+            version={  # type: ignore [typeddict-item]
+                "template": None,
+            },
+            callable=wrapper,
+        )
+
+        return wrapper
+
+    return decorator
diff --git a/src/humanloop/utilities/tool.py b/src/humanloop/decorators/tool.py
similarity index 85%
rename from src/humanloop/utilities/tool.py
rename to src/humanloop/decorators/tool.py
index f0de6a1d..1fddc3d6 100644
--- a/src/humanloop/utilities/tool.py
+++ b/src/humanloop/decorators/tool.py
@@ -7,21 +7,22 @@
 from dataclasses import dataclass
 from functools import wraps
 from inspect import Parameter
-from typing import Any, Callable, Literal, Mapping, Optional, Sequence, TypedDict, Union
+from typing import Any, Callable, Literal, Mapping, Optional, Sequence, TypeVar, TypedDict, Union
+from typing_extensions import ParamSpec
 
 from opentelemetry.trace import Tracer
-from typing_extensions import Unpack
 
-from humanloop.eval_utils.run import HumanloopUtilityError
-from humanloop.utilities.helpers import bind_args
-from humanloop.eval_utils import File
+from humanloop.context import get_evaluation_context, get_trace_id
+from humanloop.decorators.helpers import bind_args
+from humanloop.evals import File
+from humanloop.evals.run import HumanloopRuntimeError
 from humanloop.otel.constants import (
     HUMANLOOP_FILE_KEY,
     HUMANLOOP_FILE_TYPE_KEY,
     HUMANLOOP_LOG_KEY,
-    HUMANLOOP_PATH_KEY,
+    HUMANLOOP_FILE_PATH_KEY,
 )
-from humanloop.otel.helpers import jsonify_if_not_string, write_to_opentelemetry_span
+from humanloop.otel.helpers import process_output, write_to_opentelemetry_span
 from humanloop.requests.tool_function import ToolFunctionParams
 from humanloop.requests.tool_kernel_request import ToolKernelRequestParams
 
@@ -31,75 +32,90 @@
 logger = logging.getLogger("humanloop.sdk")
 
 
-def tool(
+P = ParamSpec("P")
+R = TypeVar("R")
+
+
+def tool_decorator_factory(
     opentelemetry_tracer: Tracer,
-    path: Optional[str] = None,
-    **tool_kernel: Unpack[ToolKernelRequestParams],  # type: ignore
+    path: str,
+    attributes: Optional[dict[str, Any]] = None,
+    setup_values: Optional[dict[str, Any]] = None,
 ):
-    def decorator(func: Callable):
-        enhanced_tool_kernel = _build_tool_kernel(
+    def decorator(func: Callable[P, R]) -> Callable[P, Optional[R]]:
+        file_type = "tool"
+
+        tool_kernel = _build_tool_kernel(
             func=func,
-            attributes=tool_kernel.get("attributes"),
-            setup_values=tool_kernel.get("setup_values"),
+            attributes=attributes,
+            setup_values=setup_values,
             strict=True,
         )
 
-        # Mypy complains about adding attribute on function, but it's nice UX
-        func.json_schema = enhanced_tool_kernel["function"]  # type: ignore
+        # Mypy complains about adding attribute on function, but it's nice DX
+        func.json_schema = tool_kernel["function"]  # type: ignore
 
         @wraps(func)
-        def wrapper(*args, **kwargs):
+        def wrapper(*args: P.args, **kwargs: P.kwargs) -> Optional[R]:
+            evaluation_context = get_evaluation_context()
+            if evaluation_context is not None:
+                if evaluation_context.path == path:
+                    raise HumanloopRuntimeError("Tools cannot be evaluated with the `evaluations.run()` utility.")
             with opentelemetry_tracer.start_as_current_span("humanloop.tool") as span:
                 # Write the Tool Kernel to the Span on HL_FILE_OT_KEY
-                span.set_attribute(HUMANLOOP_PATH_KEY, path if path else func.__name__)
-                span.set_attribute(HUMANLOOP_FILE_TYPE_KEY, "tool")
-                if enhanced_tool_kernel:
-                    write_to_opentelemetry_span(
-                        span=span,
-                        key=f"{HUMANLOOP_FILE_KEY}.tool",
-                        value=enhanced_tool_kernel,
-                    )
+                write_to_opentelemetry_span(
+                    span=span,  # type: ignore [arg-type]
+                    key=HUMANLOOP_FILE_KEY,
+                    value=tool_kernel,  # type: ignore [arg-type]
+                )
+                span.set_attribute(HUMANLOOP_FILE_PATH_KEY, path)
+                span.set_attribute(HUMANLOOP_FILE_TYPE_KEY, file_type)
+
+                log_inputs: dict[str, Any] = bind_args(func, args, kwargs)
+                log_error: Optional[str]
+                log_output: str
 
-                # Call the decorated function
+                func_output: Optional[R]
                 try:
-                    output = func(*args, **kwargs)
-                    output_stringified = jsonify_if_not_string(
+                    func_output = func(*args, **kwargs)
+                    log_output = process_output(
                         func=func,
-                        output=output,
+                        output=func_output,
                     )
-                    error = None
-                except HumanloopUtilityError as e:
+                    log_error = None
+                except HumanloopRuntimeError as e:
+                    # Critical error, re-raise
                     raise e
                 except Exception as e:
                     logger.error(f"Error calling {func.__name__}: {e}")
                     output = None
-                    output_stringified = jsonify_if_not_string(
+                    log_output = process_output(
                         func=func,
                         output=output,
                     )
-                    error = str(e)
+                    log_error = str(e)
 
-                # Populate known Tool Log attributes
+                # Populate Tool Log attributes
                 tool_log = {
-                    "inputs": bind_args(func, args, kwargs),
-                    "output": output_stringified,
-                    "error": error,
+                    "inputs": log_inputs,
+                    "output": log_output,
+                    "error": log_error,
+                    "trace_parent_id": get_trace_id(),
                 }
-
                 # Write the Tool Log to the Span on HL_LOG_OT_KEY
                 write_to_opentelemetry_span(
-                    span=span,
+                    span=span,  # type: ignore [arg-type]
                     key=HUMANLOOP_LOG_KEY,
-                    value=tool_log,
+                    value=tool_log,  # type: ignore [arg-type]
                 )
 
                 # Return the output of the decorated function
-                return output
+                return func_output
 
         wrapper.file = File(  # type: ignore
-            path=path if path else func.__name__,
-            type="tool",
-            version=enhanced_tool_kernel,
+            path=path,
+            type=file_type,  # type: ignore [arg-type, typeddict-item]
+            version=tool_kernel,
             callable=wrapper,
         )
 
diff --git a/src/humanloop/error.py b/src/humanloop/error.py
new file mode 100644
index 00000000..beff349c
--- /dev/null
+++ b/src/humanloop/error.py
@@ -0,0 +1,18 @@
+from typing import Optional
+
+
+class HumanloopRuntimeError(Exception):
+    """
+    SDK custom code handles exceptions by populating Logs' `error` field.
+
+    This exception signals an error severe enough to crash the execution
+    e.g. illegal use of decorators.
+    """
+
+    def __init__(self, message: Optional[str] = None):
+        self.message = message
+
+    def __str__(self) -> str:
+        if self.message is None:
+            return super().__str__()
+        return self.message
diff --git a/src/humanloop/eval_utils/__init__.py b/src/humanloop/eval_utils/__init__.py
deleted file mode 100644
index ac5a5eba..00000000
--- a/src/humanloop/eval_utils/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .run import log_with_evaluation_context, run_eval
-from .types import File
-
-__all__ = ["run_eval", "log_with_evaluation_context", "File"]
diff --git a/src/humanloop/eval_utils/context.py b/src/humanloop/eval_utils/context.py
deleted file mode 100644
index dd10e26c..00000000
--- a/src/humanloop/eval_utils/context.py
+++ /dev/null
@@ -1,126 +0,0 @@
-from contextvars import ContextVar
-from dataclasses import dataclass
-from typing import Any, Callable
-from opentelemetry.trace import Tracer
-
-
-@dataclass
-class EvaluationContext:
-    """Context Log to Humanloop.
-
-    Per datapoint state that is set when an Evaluation is ran.
-    """
-
-    """Required for associating a Log with the Evaluation Run."""
-    source_datapoint_id: str
-
-    """Overloaded .log method call."""
-    upload_callback: Callable[[str], None]
-
-    """ID of the evaluated File."""
-    file_id: str
-
-    """Path of the evaluated File."""
-    path: str
-
-    """Required for associating a Log with the Evaluation Run."""
-    run_id: str
-
-    """Whether a log has been made for this datapoint/ run_id pair."""
-    logged: bool
-
-
-_EVALUATION_CONTEXT_VAR: ContextVar[EvaluationContext] = ContextVar("__EVALUATION_CONTEXT")
-
-_UnsafeContextRead = RuntimeError("Attempting to read from thread Context when variable was not set.")
-
-
-def set_evaluation_context(context: EvaluationContext):
-    _EVALUATION_CONTEXT_VAR.set(context)
-
-
-def get_evaluation_context() -> EvaluationContext:
-    try:
-        return _EVALUATION_CONTEXT_VAR.get()
-    except LookupError:
-        raise _UnsafeContextRead
-
-
-def evaluation_context_set() -> bool:
-    try:
-        _EVALUATION_CONTEXT_VAR.get()
-        return True
-    except LookupError:
-        return False
-
-
-def log_belongs_to_evaluated_file(log_args: dict[str, Any]) -> bool:
-    try:
-        evaluation_context: EvaluationContext = _EVALUATION_CONTEXT_VAR.get()
-        return evaluation_context.file_id == log_args.get("id") or evaluation_context.path == log_args.get("path")
-    except LookupError:
-        # Not in an evaluation context
-        return False
-
-
-def is_evaluated_file(file_path) -> bool:
-    try:
-        evaluation_context = _EVALUATION_CONTEXT_VAR.get()
-        return evaluation_context.path == file_path
-    except LookupError:
-        raise _UnsafeContextRead
-
-
-@dataclass
-class PromptUtilityContext:
-    tracer: Tracer
-    _in_prompt_utility: int
-
-    @property
-    def in_prompt_utility(self) -> bool:
-        return self._in_prompt_utility > 0
-
-
-_PROMPT_UTILITY_CONTEXT_VAR: ContextVar[PromptUtilityContext] = ContextVar("__PROMPT_UTILITY_CONTEXT")
-
-
-def in_prompt_utility_context() -> bool:
-    try:
-        return _PROMPT_UTILITY_CONTEXT_VAR.get().in_prompt_utility
-    except LookupError:
-        return False
-
-
-def set_prompt_utility_context(tracer: Tracer):
-    global _PROMPT_UTILITY_CONTEXT_VAR
-    try:
-        prompt_utility_context = _PROMPT_UTILITY_CONTEXT_VAR.get()
-        # Already set, push another context
-        prompt_utility_context._in_prompt_utility += 1
-        _PROMPT_UTILITY_CONTEXT_VAR.set(prompt_utility_context)
-    except LookupError:
-        _PROMPT_UTILITY_CONTEXT_VAR.set(
-            PromptUtilityContext(
-                tracer=tracer,
-                _in_prompt_utility=1,
-            )
-        )
-
-
-def get_prompt_utility_context() -> PromptUtilityContext:
-    try:
-        return _PROMPT_UTILITY_CONTEXT_VAR.get()
-    except LookupError:
-        raise _UnsafeContextRead
-
-
-def unset_prompt_utility_context():
-    global _PROMPT_UTILITY_CONTEXT_VAR_TOKEN
-    try:
-        prompt_utility_context = _PROMPT_UTILITY_CONTEXT_VAR.get()
-        if prompt_utility_context._in_prompt_utility >= 1:
-            prompt_utility_context._in_prompt_utility -= 1
-        else:
-            raise ValueError("No matching unset_prompt_utility_context() call.")
-    except LookupError:
-        raise _UnsafeContextRead
diff --git a/src/humanloop/evals/__init__.py b/src/humanloop/evals/__init__.py
new file mode 100644
index 00000000..61f97716
--- /dev/null
+++ b/src/humanloop/evals/__init__.py
@@ -0,0 +1,4 @@
+from .run import run_eval
+from .types import File
+
+__all__ = ["run_eval", "File"]
diff --git a/src/humanloop/eval_utils/run.py b/src/humanloop/evals/run.py
similarity index 72%
rename from src/humanloop/eval_utils/run.py
rename to src/humanloop/evals/run.py
index 1ccc991d..25c2228b 100644
--- a/src/humanloop/eval_utils/run.py
+++ b/src/humanloop/evals/run.py
@@ -17,9 +17,8 @@
 import sys
 import threading
 import time
-import types
 import typing
-from concurrent.futures import ThreadPoolExecutor
+from concurrent.futures import Future, ThreadPoolExecutor
 from datetime import datetime
 from functools import partial
 from logging import INFO
@@ -37,25 +36,17 @@
 
 from humanloop import EvaluatorResponse, FlowResponse, PromptResponse, ToolResponse
 from humanloop.core.api_error import ApiError
-from humanloop.eval_utils.context import (
+from humanloop.context import (
     EvaluationContext,
     get_evaluation_context,
-    evaluation_context_set,
-    get_prompt_utility_context,
-    in_prompt_utility_context,
-    log_belongs_to_evaluated_file,
     set_evaluation_context,
 )
-from humanloop.eval_utils.types import Dataset, Evaluator, EvaluatorCheck, File
+from humanloop.error import HumanloopRuntimeError
+from humanloop.evals.types import Dataset, Evaluator, EvaluatorCheck, File
 
 # We use TypedDicts for requests, which is consistent with the rest of the SDK
 from humanloop.evaluators.client import EvaluatorsClient
 from humanloop.flows.client import FlowsClient
-from humanloop.otel.constants import (
-    HUMANLOOP_INTERCEPTED_HL_CALL_RESPONSE,
-    HUMANLOOP_INTERCEPTED_HL_CALL_SPAN_NAME,
-)
-from humanloop.otel.helpers import write_to_opentelemetry_span
 from humanloop.prompts.client import PromptsClient
 from humanloop.requests import CodeEvaluatorRequestParams as CodeEvaluatorDict
 from humanloop.requests import ExternalEvaluatorRequestParams as ExternalEvaluator
@@ -74,17 +65,15 @@
 from humanloop.types import NumericEvaluatorStatsResponse as NumericStats
 from humanloop.types import PromptKernelRequest as Prompt
 from humanloop.types import ToolKernelRequest as Tool
-from humanloop.types.create_evaluator_log_response import CreateEvaluatorLogResponse
-from humanloop.types.create_flow_log_response import CreateFlowLogResponse
-from humanloop.types.create_prompt_log_response import CreatePromptLogResponse
-from humanloop.types.create_tool_log_response import CreateToolLogResponse
 from humanloop.types.datapoint_response import DatapointResponse
 from humanloop.types.dataset_response import DatasetResponse
 from humanloop.types.evaluation_run_response import EvaluationRunResponse
-from humanloop.types.prompt_call_response import PromptCallResponse
+from humanloop.types.flow_log_response import FlowLogResponse
+from humanloop.types.log_response import LogResponse
 from humanloop.types.run_stats_response import RunStatsResponse
 from pydantic import ValidationError
 
+
 if typing.TYPE_CHECKING:
     from humanloop.client import BaseHumanloop
 
@@ -114,115 +103,6 @@
 CLIENT_TYPE = TypeVar("CLIENT_TYPE", PromptsClient, ToolsClient, FlowsClient, EvaluatorsClient)
 
 
-class HumanloopUtilityError(Exception):
-    def __init__(self, message):
-        self.message = message
-
-    def __str__(self):
-        return self.message
-
-
-def prompt_call_evaluation_aware(client: PromptsClient) -> PromptsClient:
-    client._call = client.call
-
-    def _overload_call(self, **kwargs) -> PromptCallResponse:
-        if in_prompt_utility_context():
-            kwargs = {**kwargs, "save": False}
-
-            try:
-                response = self._call(**kwargs)
-                response = typing.cast(PromptCallResponse, response)
-            except Exception as e:
-                # TODO: Bug found in backend: not specifying a model 400s but creates a File
-                raise HumanloopUtilityError(message=str(e)) from e
-
-            response_copy = response.dict()
-            prompt_utility_context = get_prompt_utility_context()
-            for idx, _ in enumerate(response_copy.get("logs", [])):
-                del response_copy["logs"][idx]["created_at"]
-            for idx, _ in enumerate(response_copy["prompt"].get("environments", [])):
-                del response_copy["prompt"]["environments"][idx]["created_at"]
-            del response_copy["prompt"]["last_used_at"]
-            del response_copy["prompt"]["updated_at"]
-            del response_copy["prompt"]["created_at"]
-            del response_copy["start_time"]
-            del response_copy["end_time"]
-
-            with prompt_utility_context.tracer.start_as_current_span(HUMANLOOP_INTERCEPTED_HL_CALL_SPAN_NAME) as span:
-                write_to_opentelemetry_span(
-                    span=span,
-                    key=HUMANLOOP_INTERCEPTED_HL_CALL_RESPONSE,
-                    value=response_copy,
-                )
-            return response
-        else:
-            return self._call(**kwargs)
-
-    # Replace the original log method with the overloaded one
-    client.call = types.MethodType(_overload_call, client)
-    # Return the client with the overloaded log method
-    logger.debug("Overloaded the .log method of %s", client)
-    return client
-
-
-def log_with_evaluation_context(client: CLIENT_TYPE) -> CLIENT_TYPE:
-    """
-    Wrap the `log` method of the provided Humanloop client to use EVALUATION_CONTEXT.
-
-    This makes the overloaded log actions be aware of whether the created Log is
-    part of an Evaluation (e.g. one started by eval_utils.run_eval).
-    """
-    # Copy the original log method in a hidden attribute
-    client._log = client.log
-
-    def _overload_log(
-        self, **kwargs
-    ) -> Union[
-        CreatePromptLogResponse,
-        CreateToolLogResponse,
-        CreateFlowLogResponse,
-        CreateEvaluatorLogResponse,
-    ]:
-        if evaluation_context_set():
-            evaluation_context = get_evaluation_context()
-        else:
-            evaluation_context = None
-
-        if log_belongs_to_evaluated_file(log_args=kwargs) and evaluation_context and not evaluation_context.logged:
-            for attribute in ["source_datapoint_id", "run_id"]:
-                kwargs[attribute] = getattr(evaluation_context, attribute)
-
-            # Call the original .log method
-            logger.debug(
-                "Logging %s inside _overloaded_log on Thread %s",
-                kwargs,
-                evaluation_context,
-                threading.get_ident(),
-            )
-
-        try:
-            response = self._log(**kwargs)
-        except Exception as e:
-            error_message = str(e).replace("\n", " ")
-            sys.stderr.write(f"{RED}Failed to log: {error_message[:100]}...{RESET}\n")
-            raise e
-
-        # Notify the run_eval utility about one Log being created
-        if log_belongs_to_evaluated_file(log_args=kwargs):
-            evaluation_context = get_evaluation_context()
-            evaluation_context.logged = True
-            evaluation_context.upload_callback(log_id=response.id)
-            set_evaluation_context(evaluation_context)
-
-        return response
-
-    # Replace the original log method with the overloaded one
-    client.log = types.MethodType(_overload_log, client)
-    # Return the client with the overloaded log method
-    logger.debug("Overloaded the .call method of %s", client)
-    return client
-
-
 def run_eval(
     client: "BaseHumanloop",
     file: File,
@@ -245,39 +125,63 @@ def run_eval(
     evaluators_worker_pool = ThreadPoolExecutor(max_workers=workers)
 
     file_ = _file_or_file_inside_hl_utility(file)
-    is_decorator = _callable_is_hl_utility(file_)
     type_ = _get_file_type(file_)
     function_ = _get_file_callable(file_, type_)
+    if hasattr(function_, "file"):
+        decorator_type = function_.file["type"]  # type: ignore [attr-defined, union-attr]
+        if decorator_type != type_:
+            raise HumanloopRuntimeError(
+                "The type of the decorated function does not match the type of the file. Expected `%s`, got `%s`."
+                % (type_.capitalize(), decorator_type.capitalize())
+            )
 
-    hl_file = _upsert_file(file=file_, type=type_, client=client)
-    hl_dataset = _upsert_dataset(dataset=dataset, client=client)
-    local_evaluators = _upsert_local_evaluators(
-        evaluators=evaluators,
-        client=client,
-        function=function_,
-        type=type_,
-    )
+    try:
+        hl_file = _upsert_file(file=file_, type=type_, client=client)
+    except ValidationError as e:
+        sys.stdout.write(f"{RED}Error in your `file` argument:\n\n{e}{RESET}")
+        return []
+    except Exception as e:
+        sys.stdout.write(f"{RED}Error in your `file` argument:\n\n{e}{RESET}")
+        return []
+    try:
+        hl_dataset = _upsert_dataset(dataset=dataset, client=client)
+    except Exception as e:
+        sys.stdout.write(f"{RED}Error in your `dataset` argument:\n\n{e}{RESET}")
+        return []
+    try:
+        local_evaluators = _upsert_local_evaluators(
+            evaluators=evaluators,  # type: ignore [arg-type]
+            client=client,
+            function=function_,
+            type=type_,
+        )
+    except Exception as e:
+        sys.stdout.write(f"{RED}Error in your `evaluators` argument:\n\n{e}{RESET}")
+        return []
     _assert_dataset_evaluators_fit(hl_dataset, local_evaluators)
 
     evaluation, run = _get_new_run(
         client=client,
         evaluation_name=name,
-        evaluators=evaluators,
+        evaluators=evaluators,  # type: ignore [arg-type]
         hl_file=hl_file,
         hl_dataset=hl_dataset,
         function=function_,
     )
 
-    def handle_exit_signal(signum, frame):
-        sys.stderr.write(
-            f"\n{RED}Received signal {signum}, cancelling Evaluation and shutting down threads...{RESET}\n"
-        )
+    def _cancel_evaluation():
         client.evaluations.update_evaluation_run(
             id=evaluation.id,
             run_id=run.id,
             status="cancelled",
         )
         evaluators_worker_pool.shutdown(wait=False)
+
+    def handle_exit_signal(signum, frame):
+        sys.stderr.write(
+            f"\n{RED}Received signal {signum}, cancelling Evaluation and shutting down threads...{RESET}\n"
+        )
+        _cancel_evaluation()
         sys.exit(signum)
 
     signal.signal(signal.SIGINT, handle_exit_signal)
@@ -315,61 +219,82 @@ def upload_callback(log_id: str):
                     log_id=log_id,
                     datapoint=dp,
                     local_evaluators=local_evaluators,
-                    file_type=hl_file.type,
+                    file_type=hl_file.type,  # type: ignore [arg-type]
                     progress_bar=_PROGRESS_BAR,
                 )
 
             # Set the Evaluation Context for current datapoint
-            set_evaluation_context(
+            with set_evaluation_context(
                 EvaluationContext(
                     source_datapoint_id=dp.id,
-                    upload_callback=upload_callback,
+                    eval_callback=upload_callback,
                     file_id=hl_file.id,
                     run_id=run.id,
                     path=hl_file.path,
-                    logged=False,
                 )
-            )
-
-            log_func = _get_log_func(
-                client=client,
-                file_type=hl_file.type,
-                file_id=hl_file.id,
-                version_id=hl_file.version_id,
-                run_id=run.id,
-            )
-            start_time = datetime.now()
-            try:
-                output = _call_function(function_, hl_file.type, dp)
-                evaluation_context = get_evaluation_context()
-                if not evaluation_context.logged and not is_decorator:
-                    # function_ did not Log against the source_datapoint_id/ run_id pair
-                    # so we need to create a Log
-                    log_func(
-                        inputs=dp.inputs,
-                        output=output,
-                        start_time=start_time,
-                        end_time=datetime.now(),
-                        source_datapoint_id=dp.id,
-                        run_id=run.id,
-                    )
-            except Exception as e:
-                log_func(
-                    inputs=dp.inputs,
-                    error=str(e),
-                    source_datapoint_id=dp.id,
+            ):
+                log_func = _get_log_func(
+                    client=client,
+                    file_type=hl_file.type,  # type: ignore [arg-type]
+                    file_id=hl_file.id,
+                    version_id=hl_file.version_id,
                     run_id=run.id,
-                    start_time=start_time,
-                    end_time=datetime.now(),
-                )
-                error_message = str(e).replace("\n", " ")
-                sys.stderr.write(
-                    f"\n{RED}Your {hl_file.type}'s `callable` failed for Datapoint: {dp.id}. Error: {error_message[:100]}...{RESET}\n"
                 )
+                start_time = datetime.now()
+                evaluation_context = get_evaluation_context()
+                if evaluation_context is None:
+                    raise HumanloopRuntimeError(
+                        "Internal error: evaluation context is not set while processing a datapoint."
+                    )
+                try:
+                    output = _call_function(function_, hl_file.type, dp)  # type: ignore [arg-type]
+                    if not evaluation_context.logged:
+                        # function_ did not Log against the source_datapoint_id/ run_id pair
+                        # so we need to create a Log
+                        log = log_func(
+                            **{
+                                "inputs": dp.inputs,
+                                "output": output,
+                                "start_time": start_time,
+                                "end_time": datetime.now(),
+                                "source_datapoint_id": dp.id,
+                                "run_id": run.id,
+                                "log_status": "complete",
+                            }
+                        )
+                        evaluation_context._callback(log.id)
+                except HumanloopRuntimeError as e:
+                    raise e
+                except Exception as e:
+                    log = log_func(
+                        **{
+                            "inputs": dp.inputs,
+                            "error": str(e),
+                            "source_datapoint_id": dp.id,
+                            "run_id": run.id,
+                            "start_time": start_time,
+                            "end_time": datetime.now(),
+                            "log_status": "complete",
+                        }
+                    )
+                    evaluation_context._callback(log.id)
+                    error_message = _get_error_message(e, length_limit=True)
+                    sys.stderr.write(
+                        f"\n{RED}Evaluated callable failed for Datapoint `{dp.id}`:\n{error_message}{RESET}\n"
+                    )
 
+        futures: list[Future] = []
         with ThreadPoolExecutor(max_workers=workers) as executor:
             for datapoint in hl_dataset.datapoints:
-                executor.submit(_process_datapoint, datapoint)
+                futures.append(executor.submit(_process_datapoint, datapoint))
+
+        for future in futures:
+            try:
+                future.result()
+            except Exception as e:
+                sys.stderr.write(f"\n{RED}Error processing datapoint:\n{_get_error_message(e)}{RESET}\n")
+                _cancel_evaluation()
+                return []
 
     stats = _wait_for_evaluation_to_complete(
         client=client,
@@ -385,7 +310,7 @@ def upload_callback(log_id: str):
         client=client,
         evaluation=evaluation,
         stats=stats,
-        evaluators=evaluators,
+        evaluators=evaluators,  # type: ignore [arg-type]
         run=run,
     )
     evaluators_worker_pool.shutdown(wait=False)
@@ -443,7 +368,7 @@ class _LocalEvaluator:
 
 def _callable_is_hl_utility(file: File) -> bool:
     """Check if a File is a decorated function."""
-    return hasattr(file["callable"], "file")
+    return hasattr(file.get("callable", {}), "file")
 
 
 def _wait_for_evaluation_to_complete(
@@ -467,10 +392,11 @@ def _wait_for_evaluation_to_complete(
             if not waiting_for_local_evals_message_printed:
                 sys.stderr.write("\n\nWaiting for Evaluators on Humanloop runtime...\n")
                 waiting_for_local_evals_message_printed = True
-            sys.stderr.write(stats.progress)
+            sys.stderr.write(stats.progress)  # type: ignore [arg-type]
             # Move the cursor up in stderr a number of lines equal to the number of lines in stats.progress
-            sys.stderr.write("\033[A" * (stats.progress.count("\n")))
+            # sys.stderr.write("\033[A" * (stats.progress.count("\n")))
             time.sleep(5)
+
     return stats
 
 
@@ -521,33 +447,48 @@ def _get_checks(
 
 def _file_or_file_inside_hl_utility(file: File) -> File:
     if _callable_is_hl_utility(file):
-        # When the decorator inside `file` is a decorated function,
-        # we need to validate that the other parameters of `file`
-        # match the attributes of the decorator
-        inner_file: File = file["callable"].file
+        inner_file: File = file["callable"].file  # type: ignore [misc, attr-defined]
         if "path" in file and inner_file["path"] != file["path"]:
-            raise ValueError(
-                "`path` attribute specified in the `file` does not match the File path of the decorated function."
+            raise HumanloopRuntimeError(
+                "`path` attribute specified in the `file` does not match the path of the decorated function. "
+                f"Expected `{inner_file['path']}`, got `{file['path']}`."
             )
-        if "version" in file and inner_file["version"] != file["version"]:
-            raise ValueError(
-                "`version` attribute in the `file` does not match the File version of the decorated function."
+        if "id" in file:
+            raise HumanloopRuntimeError(
+                "Do not specify an `id` attribute in `file` argument when using a decorated function."
             )
+        if "version" in file:
+            if inner_file["type"] != "prompt":
+                raise HumanloopRuntimeError(
+                    f"Do not specify a `version` attribute in `file` argument when using a {inner_file['type'].capitalize()} decorated function."
+                )
         if "type" in file and inner_file["type"] != file["type"]:
-            raise ValueError(
-                "`type` attribute of `file` argument does not match the File type of the decorated function."
+            raise HumanloopRuntimeError(
+                "Attribute `type` of `file` argument does not match the file type of the decorated function. "
+                f"Expected `{inner_file['type']}`, got `{file['type']}`."
             )
         if "id" in file:
-            raise ValueError("Do not specify an `id` attribute in `file` argument when using a decorated function.")
+            raise HumanloopRuntimeError(
+                "Do not specify an `id` attribute in `file` argument when using a decorated function."
+            )
         # file on decorated function holds at least
         # or more information than the `file` argument
         file_ = copy.deepcopy(inner_file)
+        if file_["type"] == "prompt":
+            sys.stdout.write(
+                f"{YELLOW}"
+                "The @prompt decorator will not spy on provider calls when passed to `evaluations.run()`. "
+                "Using the `version` in `file` argument instead.\n"
+                f"{RESET}"
+            )
+            # TODO: document this
+            file_["version"] = file["version"]
     else:
         file_ = copy.deepcopy(file)
 
-    # Raise error if one of path or id not provided
+    # Raise error if neither path nor id is provided
     if not file_.get("path") and not file_.get("id"):
-        raise ValueError("You must provide a path or id in your `file`.")
+        raise HumanloopRuntimeError("You must provide a path or id in your `file`.")
 
     return file_
 
@@ -555,14 +496,14 @@ def _file_or_file_inside_hl_utility(file: File) -> File:
 def _get_file_type(file: File) -> FileType:
     # Determine the `type` of the `file` to Evaluate - if not `type` provided, default to `flow`
     try:
-        type_ = typing.cast(FileType, file.pop("type"))
+        type_ = typing.cast(FileType, file.pop("type"))  # type: ignore [arg-type, misc]
         sys.stdout.write(
             f"{CYAN}Evaluating your {type_} function corresponding to `{file.get('path') or file.get('id')}` on Humanloop{RESET}\n\n"
         )
         return type_ or "flow"
     except KeyError as _:
         type_ = "flow"
-        sys.stdout.write(f"{CYAN}No `file` type specified, defaulting to flow.{RESET}\n")
+        sys.stdout.write(f"{YELLOW}No `file` type specified, defaulting to flow.{RESET}\n")
         return type_
 
 
@@ -574,7 +515,7 @@ def _get_file_callable(file: File, type_: FileType) -> Optional[Callable]:
             raise ValueError("You must provide a `callable` for your Flow `file` to run a local eval.")
         else:
             sys.stdout.write(
-                f"No `callable` provided for your {type_} file - will attempt to generate logs on Humanloop.\n"
+                f"{CYAN}No `callable` provided for your {type_} file - will attempt to generate logs on Humanloop.\n\n{RESET}"
             )
     return function_
 
@@ -593,29 +534,20 @@ def _upsert_file(
         except ValidationError:
             flow_version = {"attributes": version}
             file_dict = {**file, **flow_version}
-        hl_file = client.flows.upsert(**file_dict)
+        hl_file = client.flows.upsert(**file_dict)  # type: ignore [arg-type, assignment]
 
     elif type == "prompt":
-        try:
-            Prompt.model_validate(version)
-        except ValidationError as error_:
-            sys.stdout.write(f"Invalid Prompt `version` in your `file` request. \n\nValidation error: \n{error_}")
-            raise error_
-        try:
-            hl_file = client.prompts.upsert(**file_dict)
-        except ApiError as error_:
-            raise error_
+        # Will throw error if version is invalid
+        Prompt.model_validate(version)
+        hl_file = client.prompts.upsert(**file_dict)  # type: ignore [arg-type, assignment]
 
     elif type == "tool":
-        try:
-            Tool.model_validate(version)
-        except ValidationError as error_:
-            sys.stdout.write(f"Invalid Tool `version` in your `file` request. \n\nValidation error: \n{error_}")
-            raise error_
-        hl_file = client.tools.upsert(**file_dict)
+        # Will throw error if version is invalid
+        Tool.model_validate(version)
+        hl_file = client.tools.upsert(**file_dict)  # type: ignore [arg-type, assignment]
 
     elif type == "evaluator":
-        hl_file = client.evaluators.upsert(**file_dict)
+        hl_file = client.evaluators.upsert(**file_dict)  # type: ignore [arg-type, assignment]
 
     else:
         raise NotImplementedError(f"Unsupported File type: {type}")
@@ -667,11 +599,17 @@ def _upsert_local_evaluators(
                     attributes={"code": inspect.getsource(eval_function)},
                     evaluator_type="external",
                 )
-                evaluator = client.evaluators.upsert(
-                    id=evaluator_request.get("id"),
-                    path=evaluator_request.get("path"),
-                    spec=spec,
-                )
+                try:
+                    evaluator = client.evaluators.upsert(
+                        id=evaluator_request.get("id"),
+                        path=evaluator_request.get("path"),
+                        spec=spec,
+                    )
+                except Exception as error_:
+                    sys.stdout.write(
+                        f"Error upserting Evaluator {evaluator_request.get('path') or evaluator_request.get('id')} on Humanloop:\n\n{error_}"
+                    )
+                    raise error_
                 local_evaluators.append(_LocalEvaluator(hl_evaluator=evaluator, function=eval_function))
     return local_evaluators
 
@@ -688,7 +626,7 @@ def _assert_dataset_evaluators_fit(
             break
     if requires_target:
         missing_target = 0
-        for _datapoint in hl_dataset.datapoints:
+        for _datapoint in hl_dataset.datapoints:  # type: ignore [union-attr]
             if not _datapoint.target:
                 missing_target += 1
         if missing_target > 0:
@@ -700,7 +638,7 @@ def _assert_dataset_evaluators_fit(
 
 def _get_new_run(
     client: "BaseHumanloop",
-    evaluation_name: str,
+    evaluation_name: Optional[str],
     evaluators: list[Evaluator],
     hl_file: Union[PromptResponse, FlowResponse, ToolResponse, EvaluatorResponse],
     hl_dataset: DatasetResponse,
@@ -719,7 +657,7 @@ def _get_new_run(
         if error_.status_code == 409:
             evals = client.evaluations.list(file_id=hl_file.id, size=50)
             for page in evals.iter_pages():
-                evaluation = next((e for e in page.items if e.name == evaluation_name), None)
+                evaluation = next((e for e in page.items if e.name == evaluation_name), None)  # type: ignore [union-attr]
         else:
             raise error_
         if not evaluation:
@@ -760,11 +698,11 @@ def _call_function(
 
 def _get_log_func(
     client: "BaseHumanloop",
-    file_type: FileType,
+    file_type: Literal["flow", "prompt"],
     file_id: str,
     version_id: str,
     run_id: str,
-) -> Callable:
+) -> Callable[..., LogResponse]:
     """Returns the appropriate log function pre-filled with common parameters."""
     log_request = {
         # TODO: why does the Log `id` field refer to the file ID in the API?
@@ -774,13 +712,9 @@ def _get_log_func(
         "run_id": run_id,
     }
     if file_type == "flow":
-        return partial(client.flows.log, **log_request, log_status="complete")
+        return partial(client.flows._log, **log_request)  # type: ignore [attr-defined]
     elif file_type == "prompt":
-        return partial(client.prompts.log, **log_request)
-    elif file_type == "evaluator":
-        return partial(client.evaluators.log, **log_request)
-    elif file_type == "tool":
-        return partial(client.tools.log, **log_request)
+        return partial(client.prompts._log, **log_request)  # type: ignore [attr-defined]
     else:
         raise NotImplementedError(f"Unsupported File version: {file_type}")
 
@@ -794,7 +728,7 @@ def _get_score_from_evaluator_stat(
         if stat.total_logs:
             score = round(stat.num_true / stat.total_logs, 2)
     elif isinstance(stat, NumericStats):
-        score = round(stat.mean, 2)
+        score = round(stat.mean, 2)  # type: ignore [arg-type]
     else:
         raise ValueError(f"Unsupported Evaluator Stat type: {type(stat)}")
     return score
@@ -811,7 +745,7 @@ def _get_evaluator_stats_by_path(
         evaluators_by_id[evaluator_stat.evaluator_version_id].version.path: evaluator_stat
         for evaluator_stat in stat.evaluator_stats
     }
-    return evaluator_stats_by_path
+    return evaluator_stats_by_path  # type: ignore [return-value]
 
 
 def _check_evaluation_threshold(
@@ -826,14 +760,14 @@ def _check_evaluation_threshold(
     evaluator_stats_by_path = _get_evaluator_stats_by_path(
         stat=next(
             (stat for stat in stats.run_stats if stat.run_id == run_id),
-            None,
+            None,  # type: ignore [arg-type]
         ),
         evaluation=evaluation,
     )
     if evaluator_path in evaluator_stats_by_path:
         evaluator_stat = evaluator_stats_by_path[evaluator_path]
         score = _get_score_from_evaluator_stat(stat=evaluator_stat)
-        if score >= threshold:
+        if score >= threshold:  # type: ignore [operator]
             sys.stderr.write(
                 f"{GREEN}✅ Latest eval [{score}] above threshold [{threshold}] for evaluator {evaluator_path}.{RESET}"
             )
@@ -863,7 +797,7 @@ def _check_evaluation_improvement(
     latest_evaluator_stats_by_path = _get_evaluator_stats_by_path(
         stat=next(
             (stat for stat in stats.run_stats if stat.run_id == run_id),
-            None,
+            None,  # type: ignore [arg-type]
         ),
         evaluation=evaluation,
     )
@@ -893,6 +827,20 @@ def _check_evaluation_improvement(
         raise ValueError(f"Evaluator {evaluator_path} not found in the stats.")
 
 
+def _get_error_message(e: Exception, length_limit: bool = False) -> str:
+    import traceback
+
+    # Get the full traceback
+    trace_info = traceback.format_exc()
+
+    # Extract the last 200 characters of the traceback
+    last_trace_part = (
+        (trace_info[-200:] + "..." if len(trace_info) > 200 else trace_info) if length_limit else trace_info
+    )
+
+    return f"\n{last_trace_part}"
+
+
 def _run_local_evaluators(
     client: "BaseHumanloop",
     log_id: str,
@@ -947,18 +895,16 @@ def _run_local_evaluators(
                 _ = client.evaluators.log(
                     parent_id=log_id,
                     id=local_evaluator.id,
-                    error=str(e),
+                    error=_get_error_message(e, length_limit=True),
                     start_time=start_time,
                     end_time=datetime.now(),
                 )
-                error_message = str(e).replace("\n", " ")
-                sys.stderr.write(
-                    f"{RED}Evaluator {local_evaluator.path} failed with error {error_message[:100]}...{RESET}\n"
-                )
+                error_message = _get_error_message(e, length_limit=True)
+                sys.stderr.write(f"{RED}Evaluator `{local_evaluator.path}` failed: {error_message}{RESET}\n")
     except Exception as e:
-        error_message = str(e).replace("\n", " ")
+        error_message = _get_error_message(e, length_limit=True)
         sys.stderr.write(
-            f"{RED}Failed to run local Evaluators for source datapoint {datapoint.dict()['id'] if datapoint else None}: {error_message[:100]}...{RESET}\n"
+            f"{RED}Failed to run local Evaluators for source datapoint `{datapoint.dict()['id'] if datapoint else None}`:\n{error_message}{RESET}\n"
         )
         pass
     finally:
diff --git a/src/humanloop/eval_utils/types.py b/src/humanloop/evals/types.py
similarity index 94%
rename from src/humanloop/eval_utils/types.py
rename to src/humanloop/evals/types.py
index 845a8542..86c3dc64 100644
--- a/src/humanloop/eval_utils/types.py
+++ b/src/humanloop/evals/types.py
@@ -19,9 +19,13 @@
 )
 
 # Responses are Pydantic models and we leverage them for improved request validation
-from humanloop.types import UpdateDatesetAction as UpdateDatasetAction  # TODO: fix original type typo
+from humanloop.types import (
+    UpdateDatesetAction as UpdateDatasetAction,
+)  # TODO: fix original type typo
 
-EvaluatorDict = Union[CodeEvaluatorDict, LLMEvaluatorDict, HumanEvaluatorDict, ExternalEvaluator]
+EvaluatorDict = Union[
+    CodeEvaluatorDict, LLMEvaluatorDict, HumanEvaluatorDict, ExternalEvaluator
+]
 Version = Union[FlowDict, PromptDict, ToolDict, EvaluatorDict]
 FileType = Literal["flow", "prompt", "tool", "evaluator"]
 
@@ -38,7 +42,7 @@ class Identifiers(TypedDict):
 class File(Identifiers):
     """A File on Humanloop (Flow, Prompt, Tool, Evaluator)."""
 
-    type: NotRequired[FileType]
+    type: Literal["flow", "prompt"]
     """The type of File this callable relates to on Humanloop."""
     version: NotRequired[Version]
     """The contents uniquely define the version of the File on Humanloop."""
diff --git a/src/humanloop/otel/constants.py b/src/humanloop/otel/constants.py
index ef6ac223..8a28572f 100644
--- a/src/humanloop/otel/constants.py
+++ b/src/humanloop/otel/constants.py
@@ -2,9 +2,14 @@
 HUMANLOOP_FILE_KEY = "humanloop.file"
 # Attribute name prefix on Humanloop spans for log-related attributes
 HUMANLOOP_LOG_KEY = "humanloop.log"
+HUMANLOOP_LOG_ID_KEY = "humanloop.log_id"
 HUMANLOOP_FILE_TYPE_KEY = "humanloop.file.type"
-HUMANLOOP_PATH_KEY = "humanloop.file.path"
-# Required for the exporter to know when to mark the Flow Log as complete
-HUMANLOOP_FLOW_PREREQUISITES_KEY = "humanloop.flow.prerequisites"
-HUMANLOOP_INTERCEPTED_HL_CALL_SPAN_NAME = "humanloop_intercepted_hl_call"
-HUMANLOOP_INTERCEPTED_HL_CALL_RESPONSE = "intercepted_call_response"
+HUMANLOOP_FILE_PATH_KEY = "humanloop.file.path"
+# Opentelemetry context
+HUMANLOOP_CONTEXT_DECORATOR = "humanloop.context.decorator"
+HUMANLOOP_CONTEXT_TRACE_ID = "humanloop.context.flow.trace_id"
+HUMANLOOP_CONTEXT_EVALUATION = "humanloop.context.evaluation"
+
+HUMANLOOP_FLOW_SPAN_NAME = "humanloop.flow"
+HUMANLOOP_PROMPT_SPAN_NAME = "humanloop.prompt"
+HUMANLOOP_TOOL_SPAN_NAME = "humanloop.tool"
diff --git a/src/humanloop/otel/exporter.py b/src/humanloop/otel/exporter.py
deleted file mode 100644
index b04f58c5..00000000
--- a/src/humanloop/otel/exporter.py
+++ /dev/null
@@ -1,392 +0,0 @@
-import logging
-import threading
-
-import typing
-from queue import Empty as EmptyQueue
-from queue import Queue
-from threading import Thread
-from typing import Any, Optional, Sequence
-
-from opentelemetry.sdk.trace import ReadableSpan
-from opentelemetry.sdk.trace.export import SpanExporter, SpanExportResult
-
-from humanloop.core import ApiError as HumanloopApiError
-from humanloop.eval_utils.context import (
-    EvaluationContext,
-    evaluation_context_set,
-    get_evaluation_context,
-    set_evaluation_context,
-)
-from humanloop.otel.constants import (
-    HUMANLOOP_FILE_KEY,
-    HUMANLOOP_FILE_TYPE_KEY,
-    HUMANLOOP_FLOW_PREREQUISITES_KEY,
-    HUMANLOOP_LOG_KEY,
-)
-from humanloop.otel.helpers import is_humanloop_span, read_from_opentelemetry_span
-from humanloop.requests.flow_kernel_request import FlowKernelRequestParams
-from humanloop.requests.prompt_kernel_request import PromptKernelRequestParams
-from humanloop.requests.tool_kernel_request import ToolKernelRequestParams
-
-if typing.TYPE_CHECKING:
-    from humanloop.client import Humanloop
-
-
-logger = logging.getLogger("humanloop.sdk")
-
-
-class HumanloopSpanExporter(SpanExporter):
-    """Upload Spans created by SDK decorators to Humanloop.
-
-    Spans not created by Humanloop SDK decorators will be dropped.
-
-    Each Humanloop Span contains information about the File to log against and
-    the Log to create. We are using the .log actions that pass the kernel in the
-    request. This allows us to create new Versions if the decorated function
-    is changed.
-
-    The exporter uploads Spans top-to-bottom, where a Span is uploaded only after
-    its parent Span has been uploaded. This is necessary for Flow Traces, where
-    the parent Span is a Flow Log and the children are the Logs in the Trace.
-
-    The exporter keeps an upload queue and only uploads a Span if its direct parent has
-    been uploaded.
-    """
-
-    # NOTE: LLM Instrumentors will only intercept calls to the provider made via the
-    # official libraries e.g. import openai from openai. This is 100% the reason why
-    # prompt call is not intercepted by the Instrumentor. The way to fix this is likely
-    # overriding the hl_client.prompt.call utility. @James I'll do this since it will
-    # involve looking at the EvaluationContext deep magic.
-
-    DEFAULT_NUMBER_THREADS = 4
-
-    def __init__(
-        self,
-        client: "Humanloop",
-        worker_threads: Optional[int] = None,
-    ) -> None:
-        """Upload Spans created by SDK decorators to Humanloop.
-
-        Spans not created by Humanloop SDK decorators will be ignored.
-        """
-        super().__init__()
-        self._client = client
-        # Uploaded spans translate to a Log on Humanloop. The IDs are required to link Logs in a Flow Trace
-        self._span_to_uploaded_log_id: dict[int, Optional[str]] = {}
-        # Work queue for the threads uploading the spans
-        self._upload_queue: Queue = Queue()
-        # Worker threads to export the spans
-        self._threads: list[Thread] = [
-            Thread(
-                target=self._do_work,
-                daemon=True,
-            )
-            for _ in range(worker_threads or self.DEFAULT_NUMBER_THREADS)
-        ]
-        # Signals threads no more work will arrive and
-        # they should wind down after they empty the queue
-        self._shutdown: bool = False
-        # Init the upload threads
-        for thread in self._threads:
-            thread.start()
-            logger.debug("Exporter Thread %s started", thread.ident)
-        # Flow Log Span ID mapping to children Spans that must be uploaded first
-        self._spans_left_in_trace: dict[int, set[int]] = {}
-        self._traces: list[set[str]] = []
-
-    def export(self, spans: Sequence[ReadableSpan]) -> SpanExportResult:
-        if self._shutdown:
-            logger.warning("[HumanloopSpanExporter] Shutting down, not accepting new spans")
-            return SpanExportResult.FAILURE
-
-        for span in spans:
-            if not is_humanloop_span(span):
-                continue
-
-            self._upload_queue.put(
-                (
-                    span,
-                    get_evaluation_context() if evaluation_context_set() else None,
-                ),
-            )
-
-        return SpanExportResult.SUCCESS
-
-    def shutdown(self) -> None:
-        self._shutdown = True
-        for thread in self._threads:
-            thread.join(timeout=5)
-            logger.debug("[HumanloopSpanExporter] Exporter Thread %s joined", thread.ident)
-
-    def force_flush(self, timeout_millis: int = 5000) -> bool:
-        self._shutdown = True
-        for thread in self._threads:
-            thread.join(timeout=timeout_millis / 1000)
-        self._upload_queue.join()
-
-        return True
-
-    def _do_work(self):
-        """Upload spans to Humanloop.
-
-        Ran by worker threads. The threads use the self._shutdown flag to wait
-        for Spans to arrive. Setting a timeout on self._upload_queue.get() risks
-        shutting down the thread early as no Spans are produced e.g. while waiting
-        for user input into the instrumented feature or application.
-
-        Each thread will upload a Span to Humanloop, provided the Span has all its
-        dependencies uploaded. The dependency happens in a Flow Trace context, where
-        the Trace parent must be uploaded first. The Span Processor will send in Spans
-        bottoms-up, while the upload of a Trace happens top-down. If a Span did not
-        have its span uploaded yet, it will be re-queued to be uploaded later.
-        """
-
-        # Do work while the Exporter was not instructed to
-        # wind down or the queue is not empty
-        while self._upload_queue.qsize() > 0 or not self._shutdown:
-            thread_args: tuple[ReadableSpan, Optional[EvaluationContext]]  # type: ignore
-            try:
-                # Don't block or the thread will never be notified of the shutdown
-                thread_args = self._upload_queue.get(
-                    block=False,
-                )  # type: ignore
-            except EmptyQueue:
-                # Wait for the another span to arrive
-                continue
-
-            span_to_export, evaluation_context = thread_args
-            if evaluation_context is not None:
-                # Context variables are thread scoped
-                # One existed in the eval_run utility thread
-                # so it must be copied over to the current
-                # exporter thread
-                set_evaluation_context(evaluation_context)
-
-            if span_to_export.parent is None:
-                # Span cannot be part of a Flow trace
-                self._export_span_dispatch(span_to_export)
-                logger.debug(
-                    "[HumanloopSpanExporter] _do_work on Thread %s: Dispatching span %s %s",
-                    threading.get_ident(),
-                    span_to_export.context.span_id,
-                    span_to_export.name,
-                )
-
-            elif span_to_export.parent.span_id in self._span_to_uploaded_log_id:
-                # Span is part of a Flow trace and its parent has been uploaded
-                self._export_span_dispatch(span_to_export)
-
-            else:
-                # Requeue the Span and upload after its parent
-                self._upload_queue.put((span_to_export, evaluation_context))
-
-            # Notify the shared queue that we are done
-            # with the current head of the task queue
-            self._upload_queue.task_done()
-
-    def _export_span_dispatch(self, span: ReadableSpan) -> None:
-        """Call the appropriate BaseHumanloop.X.log based on the Span type."""
-        file_type = span._attributes.get(HUMANLOOP_FILE_TYPE_KEY)  # type: ignore
-        parent_span_id = span.parent.span_id if span.parent else None
-
-        while parent_span_id and self._span_to_uploaded_log_id.get(parent_span_id) is None:
-            logger.debug(
-                "[HumanloopSpanExporter] _export_span_dispatch on Thread %s Span %s %s waiting for parent %s to be uploaded",
-                threading.get_ident(),
-                span.context.span_id,
-                span.name,
-                parent_span_id,
-            )
-
-        logger.debug(
-            "[HumanloopSpanExporter] Exporting span %s with file type %s",
-            span,
-            file_type,
-        )
-
-        if file_type == "prompt":
-            self._export_prompt_span(span=span)
-        elif file_type == "tool":
-            self._export_tool_span(span=span)
-        elif file_type == "flow":
-            self._export_flow_span(span=span)
-        else:
-            raise NotImplementedError(f"Unknown span type: {file_type}")
-
-    def _export_prompt_span(self, span: ReadableSpan) -> None:
-        file_object: dict[str, Any] = read_from_opentelemetry_span(
-            span,
-            key=HUMANLOOP_FILE_KEY,
-        )
-        log_object: dict[str, Any] = read_from_opentelemetry_span(
-            span,
-            key=HUMANLOOP_LOG_KEY,
-        )
-        # NOTE: Due to OTEL conventions, attributes with value of None are removed
-        # on write to Span. If not present, instantiate these as empty
-        if "inputs" not in log_object:
-            log_object["inputs"] = {}
-        if "messages" not in log_object:
-            log_object["messages"] = []
-        if "tools" not in file_object["prompt"]:
-            file_object["prompt"]["tools"] = []
-
-        path: str = file_object["path"]
-        prompt: PromptKernelRequestParams = file_object["prompt"]
-
-        trace_parent_id = self._get_parent_in_trace(span)
-
-        if "attributes" not in prompt or not prompt["attributes"]:
-            prompt["attributes"] = {}
-
-        try:
-            log_response = self._client.prompts.log(
-                path=path,
-                prompt=prompt,
-                **log_object,
-                trace_parent_id=trace_parent_id,
-            )
-            self._span_to_uploaded_log_id[span.context.span_id] = log_response.id
-            if trace_parent_id is not None:
-                self._keep_track_of_trace(log_response.id, trace_parent_id)
-        except HumanloopApiError:
-            self._span_to_uploaded_log_id[span.context.span_id] = None
-        self._mark_span_as_uploaded(span_id=span.context.span_id)
-
-    def _export_tool_span(self, span: ReadableSpan) -> None:
-        file_object: dict[str, Any] = read_from_opentelemetry_span(
-            span,
-            key=HUMANLOOP_FILE_KEY,
-        )
-        log_object: dict[str, Any] = read_from_opentelemetry_span(
-            span,
-            key=HUMANLOOP_LOG_KEY,
-        )
-
-        path: str = file_object["path"]
-        tool: ToolKernelRequestParams = file_object["tool"]
-
-        # API expects an empty dictionary if user does not supply attributes
-        # NOTE: see comment in _export_prompt_span about OTEL conventions
-        if not tool.get("attributes"):
-            tool["attributes"] = {}
-        if not tool.get("setup_values"):
-            tool["setup_values"] = {}
-        if "parameters" in tool["function"] and "properties" not in tool["function"]["parameters"]:
-            tool["function"]["parameters"]["properties"] = {}
-
-        trace_parent_id = self._get_parent_in_trace(span)
-        try:
-            log_response = self._client.tools.log(
-                path=path,
-                tool=tool,
-                **log_object,
-                trace_parent_id=trace_parent_id,
-            )
-            self._span_to_uploaded_log_id[span.context.span_id] = log_response.id
-            if trace_parent_id is not None:
-                self._keep_track_of_trace(log_response.id, trace_parent_id)
-        except HumanloopApiError:
-            self._span_to_uploaded_log_id[span.context.span_id] = None
-        self._mark_span_as_uploaded(span_id=span.context.span_id)
-
-    def _export_flow_span(self, span: ReadableSpan) -> None:
-        file_object: dict[str, Any] = read_from_opentelemetry_span(
-            span,
-            key=HUMANLOOP_FILE_KEY,
-        )
-        log_object: dict[str, Any] = read_from_opentelemetry_span(
-            span,
-            key=HUMANLOOP_LOG_KEY,
-        )
-        # Spans that must be uploaded before the Flow Span is completed
-        # We instantiate the list of prerequisites from the attribute
-        # passed by the Processor. Each uploaded child in the trace
-        # will check if it's the last one and mark the Flow Log as complete
-        try:
-            prerequisites: list[int] = read_from_opentelemetry_span(  # type: ignore
-                span=span,
-                key=HUMANLOOP_FLOW_PREREQUISITES_KEY,
-            )
-            self._spans_left_in_trace[span.context.span_id] = set(prerequisites)
-        except KeyError:
-            # OTEL will drop falsy attributes, so if a Flow has no prerequisites
-            # the attribute will not be present
-            self._spans_left_in_trace[span.context.span_id] = set()
-
-        path: str = file_object["path"]
-        flow: FlowKernelRequestParams
-        if not file_object.get("flow"):
-            flow = {"attributes": {}}
-        else:
-            flow = file_object["flow"]
-
-        trace_parent_id = self._get_parent_in_trace(span)
-
-        if "output" not in log_object:
-            log_object["output"] = None
-        try:
-            log_response = self._client.flows.log(
-                path=path,
-                flow=flow,
-                **log_object,
-                trace_parent_id=trace_parent_id,
-            )
-            if trace_parent_id is not None:
-                self._keep_track_of_trace(
-                    log_id=log_response.id,
-                    parent_log_id=trace_parent_id,
-                )
-            # Exporting a flow log creates a new trace
-            self._traces.append({log_response.id})
-            self._span_to_uploaded_log_id[span.get_span_context().span_id] = log_response.id
-        except HumanloopApiError as e:
-            logger.error(str(e))
-            self._span_to_uploaded_log_id[span.context.span_id] = None
-        self._mark_span_as_uploaded(span_id=span.context.span_id)
-
-    def _mark_span_as_uploaded(self, span_id: int) -> None:
-        """Mark a Span as uploaded for Flow trace completion.
-
-        If this Span corresponds to the last child in the Flow trace,
-        mark the Flow Log as complete.
-        """
-        for trace_head_span_id, spans_left in self._spans_left_in_trace.items():
-            if span_id in spans_left:
-                spans_left.remove(span_id)
-                self._mark_trace_complete_if_needed(trace_head_span_id=trace_head_span_id)
-                # Found the trace the span belongs to
-                # break from for loop
-                break
-
-    def _mark_trace_complete_if_needed(self, trace_head_span_id: int):
-        spans_to_complete = self._spans_left_in_trace[trace_head_span_id]
-        if len(spans_to_complete) == 0:
-            flow_log_id = self._span_to_uploaded_log_id[trace_head_span_id]
-            if flow_log_id is None:
-                # Uploading the head of the Flow trace failed
-                logger.error(
-                    "[HumanloopSpanExporter] Cannot complete Flow log %s, log ID is None",
-                    trace_head_span_id,
-                )
-            else:
-                self._client.flows.update_log(log_id=flow_log_id, log_status="complete")
-
-    def _keep_track_of_trace(self, log_id: str, parent_log_id: str):
-        found = False
-        for trace in self._traces:
-            if parent_log_id in trace:
-                trace.add(log_id)
-                found = True
-            if found:
-                break
-
-    def _get_parent_in_trace(self, span: ReadableSpan) -> Optional[str]:
-        if span.parent is None:
-            return None
-        parent_log_id = self._span_to_uploaded_log_id[span.parent.span_id]
-        for trace in self._traces:
-            if parent_log_id in trace:
-                return parent_log_id
-        return None
diff --git a/src/humanloop/otel/exporter/__init__.py b/src/humanloop/otel/exporter/__init__.py
new file mode 100644
index 00000000..681deb76
--- /dev/null
+++ b/src/humanloop/otel/exporter/__init__.py
@@ -0,0 +1,155 @@
+import logging
+
+import time
+import typing
+from queue import Empty as EmptyQueue
+from queue import Queue
+from threading import Thread
+from typing import Optional, Sequence
+
+from opentelemetry.sdk.trace import ReadableSpan
+from opentelemetry.sdk.trace.export import SpanExporter, SpanExportResult
+
+import requests
+from typing import Callable
+from humanloop.context import get_evaluation_context
+from humanloop.evals.run import HumanloopRuntimeError
+from humanloop.otel.constants import (
+    HUMANLOOP_FILE_TYPE_KEY,
+    HUMANLOOP_LOG_KEY,
+    HUMANLOOP_FILE_PATH_KEY,
+)
+from humanloop.otel.exporter.proto import serialize_span
+from humanloop.otel.helpers import (
+    read_from_opentelemetry_span,
+    write_to_opentelemetry_span,
+)
+
+
+if typing.TYPE_CHECKING:
+    from humanloop.client import Humanloop
+
+
+logger = logging.getLogger("humanloop.sdk")
+
+
+class HumanloopSpanExporter(SpanExporter):
+    DEFAULT_NUMBER_THREADS = 1
+
+    def __init__(
+        self,
+        client: "Humanloop",
+        worker_threads: Optional[int] = None,
+    ) -> None:
+        """Upload Spans created by SDK decorators to Humanloop.
+
+        Spans not created by Humanloop SDK decorators will be ignored.
+        """
+        super().__init__()
+        self._client = client
+        # Work queue for the threads uploading the spans
+        self._upload_queue: Queue = Queue()
+        # Worker threads to export the spans
+        self._threads: list[Thread] = [
+            Thread(
+                target=self._do_work,
+                daemon=True,
+            )
+            for _ in range(worker_threads or self.DEFAULT_NUMBER_THREADS)
+        ]
+        # Signals threads no more work will arrive and
+        # they should wind down after they empty the queue
+        self._shutdown: bool = False
+        # Init the upload threads
+        for thread in self._threads:
+            thread.start()
+            logger.debug("Exporter Thread %s started", thread.ident)
+
+    def export(self, spans: Sequence[ReadableSpan]) -> SpanExportResult:
+        if self._shutdown:
+            logger.warning("[HumanloopSpanExporter] Shutting down, not accepting new spans")
+            return SpanExportResult.FAILURE
+
+        for span in spans:
+            file_type = span.attributes.get(HUMANLOOP_FILE_TYPE_KEY)  # type: ignore [union-attr]
+            if file_type is None:
+                raise HumanloopRuntimeError("Internal error: Span does not have type set")
+
+            try:
+                log_args = read_from_opentelemetry_span(
+                    span=span,
+                    key=HUMANLOOP_LOG_KEY,
+                )
+                path = read_from_opentelemetry_span(
+                    span=span,
+                    key=HUMANLOOP_FILE_PATH_KEY,
+                )
+                evaluation_context = get_evaluation_context()
+                if evaluation_context is not None:
+                    kwargs_eval, eval_callback = evaluation_context.log_args_with_context(
+                        path=path,  # type: ignore [arg-type]
+                        log_args=log_args,  # type: ignore [arg-type]
+                    )
+                    write_to_opentelemetry_span(
+                        span=span,
+                        key=HUMANLOOP_LOG_KEY,
+                        value=kwargs_eval,
+                    )
+                else:
+                    eval_callback = None
+            except HumanloopRuntimeError as e:
+                raise e
+            except Exception:
+                # No log args in the span
+                eval_callback = None
+
+            self._upload_queue.put((span, eval_callback))
+
+        return SpanExportResult.SUCCESS
+
+    def shutdown(self) -> None:
+        self._shutdown = True
+        for thread in self._threads:
+            thread.join(timeout=5)
+            logger.debug("[HumanloopSpanExporter] Exporter Thread %s joined", thread.ident)
+
+    def force_flush(self, timeout_millis: int = 5000) -> bool:
+        self._shutdown = True
+        for thread in self._threads:
+            thread.join(timeout=timeout_millis / 1000)
+        self._upload_queue.join()
+
+        return True
+
+    def _do_work(self):
+        # Do work while the Exporter was not instructed to
+        # wind down or the queue is not empty
+        while self._upload_queue.qsize() > 0 or not self._shutdown:
+            thread_args: tuple[ReadableSpan, Optional[Callable[[str], None]]]  # type: ignore
+            try:
+                # Don't block or the thread will never be notified of the shutdown
+                thread_args = self._upload_queue.get(block=False)  # type: ignore
+            except EmptyQueue:
+                # Wait for another span to arrive
+                time.sleep(0.1)
+                continue
+
+            span_to_export, eval_context_callback = thread_args
+
+            response = requests.post(
+                f"{self._client._client_wrapper.get_base_url()}/import/otel/v1/traces",
+                headers={
+                    **self._client._client_wrapper.get_headers(),
+                },
+                data=serialize_span(span_to_export),
+            )
+            print("RECV", span_to_export.attributes, response.json(), response.status_code)
+            if response.status_code != 200:
+                pass
+            else:
+                if eval_context_callback:
+                    print("HELLO")
+                    log_id = response.json()["records"][0]
+                    eval_context_callback(log_id)
+
+            self._upload_queue.task_done()
diff --git a/src/humanloop/otel/exporter/proto.py b/src/humanloop/otel/exporter/proto.py
new file mode 100644
index 00000000..85dde94b
--- /dev/null
+++ b/src/humanloop/otel/exporter/proto.py
@@ -0,0 +1,76 @@
+from opentelemetry.proto.common.v1.common_pb2 import KeyValue, AnyValue, InstrumentationScope
+from opentelemetry.proto.trace.v1.trace_pb2 import (
+    TracesData,
+    ResourceSpans,
+    ScopeSpans,
+    Span as ProtoBufferSpan,
+)
+from google.protobuf.json_format import MessageToJson
+
+from opentelemetry.sdk.trace import ReadableSpan
+from humanloop.otel.helpers import is_llm_provider_call
+
+
+def serialize_span(span_to_export: ReadableSpan) -> str:
+    """
+    Serialize a span into format compatible with the /otel backend endpoint.
+    """
+    payload = TracesData(
+        resource_spans=[
+            ResourceSpans(
+                scope_spans=[
+                    ScopeSpans(
+                        scope=InstrumentationScope(
+                            name="humanloop.sdk.provider"
+                            if is_llm_provider_call(span_to_export)
+                            else "humanloop.sdk.decorator",
+                            version="0.1.0",
+                        ),
+                        spans=[
+                            ProtoBufferSpan(
+                                trace_id=span_to_export.context.trace_id.to_bytes(length=16, byteorder="big"),
+                                span_id=span_to_export.context.span_id.to_bytes(length=8, byteorder="big"),
+                                name=span_to_export.name,
+                                kind={
+                                    0: ProtoBufferSpan.SpanKind.SPAN_KIND_INTERNAL,
+                                    1: ProtoBufferSpan.SpanKind.SPAN_KIND_SERVER,
+                                    2: ProtoBufferSpan.SpanKind.SPAN_KIND_CLIENT,
+                                    3: ProtoBufferSpan.SpanKind.SPAN_KIND_PRODUCER,
+                                    4: ProtoBufferSpan.SpanKind.SPAN_KIND_CONSUMER,
+                                }[span_to_export.kind.value],
+                                start_time_unix_nano=span_to_export.start_time,  # type: ignore [attr-defined, arg-type]
+                                end_time_unix_nano=span_to_export.end_time,  # type: ignore [attr-defined, arg-type]
+                                attributes=[
+                                    KeyValue(
+                                        key=key,
+                                        value=AnyValue(string_value=str(value)),
+                                    )
+                                    for key, value in span_to_export.attributes.items()  # type: ignore [union-attr]
+                                ],
+                                dropped_attributes_count=span_to_export.dropped_attributes,
+                                dropped_events_count=span_to_export.dropped_events,
+                                dropped_links_count=span_to_export.dropped_links,
+                                links=[
+                                    ProtoBufferSpan.Link(
+                                        trace_id=link.context.trace_id.to_bytes(length=16, byteorder="big"),
+                                        span_id=link.context.span_id.to_bytes(length=8, byteorder="big"),
+                                        attributes=[
+                                            KeyValue(
+                                                key=key,
+                                                value=AnyValue(string_value=str(value)),
+                                            )
+                                            for key, value in link.attributes.items()  # type: ignore [union-attr]
+                                        ],
+                                    )
+                                    for link in span_to_export.links
+                                ],
+                                events=[],
+                            )
+                        ],
+                    )
+                ]
+            )
+        ]
+    )
+
+    return MessageToJson(payload)
diff --git a/src/humanloop/otel/helpers.py b/src/humanloop/otel/helpers.py
index 094f6e46..37ca8cea 100644
--- a/src/humanloop/otel/helpers.py
+++ b/src/humanloop/otel/helpers.py
@@ -5,7 +5,6 @@
 from opentelemetry.trace import SpanKind
 from opentelemetry.util.types import AttributeValue
 
-from humanloop.otel.constants import HUMANLOOP_INTERCEPTED_HL_CALL_SPAN_NAME
 
 NestedDict = dict[str, Union["NestedDict", AttributeValue]]
 NestedList = list[Union["NestedList", NestedDict]]
@@ -178,12 +177,6 @@ def read_from_opentelemetry_span(span: ReadableSpan, key: str = "") -> NestedDic
             # Remove the key prefix and the first dot
             to_process.append((span_key, span_value))
 
-    if not to_process:
-        if key == "":
-            # Empty span attributes
-            return result
-        raise KeyError(f"Key {key} not found in span attributes")
-
     for span_key, span_value in to_process:  # type: ignore
         parts = span_key.split(".")
         len_parts = len(parts)
@@ -264,10 +257,6 @@ def is_llm_provider_call(span: ReadableSpan) -> bool:
     )
 
 
-def is_intercepted_call(span: ReadableSpan) -> bool:
-    return span.name == HUMANLOOP_INTERCEPTED_HL_CALL_SPAN_NAME
-
-
 def is_humanloop_span(span: ReadableSpan) -> bool:
     """Check if the Span was created by the Humanloop SDK."""
     return span.name.startswith("humanloop.")
@@ -285,7 +274,7 @@ def module_is_installed(module_name: str) -> bool:
     return True
 
 
-def jsonify_if_not_string(func: Callable, output: Any) -> str:
+def process_output(func: Callable, output: Any) -> str:
     if not isinstance(output, str):
         try:
             output = json.dumps(output)
diff --git a/src/humanloop/otel/processor.py b/src/humanloop/otel/processor.py
index a70baf97..c04922ba 100644
--- a/src/humanloop/otel/processor.py
+++ b/src/humanloop/otel/processor.py
@@ -1,252 +1,54 @@
-from concurrent.futures import ThreadPoolExecutor
 import logging
-from collections import defaultdict
-import time
-from typing import Any, TypedDict
 
-from opentelemetry.sdk.trace import ReadableSpan
+
+from opentelemetry.sdk.trace import ReadableSpan, Span
 from opentelemetry.sdk.trace.export import SimpleSpanProcessor, SpanExporter
-from pydantic import ValidationError as PydanticValidationError
 
+from humanloop.context import get_decorator_context, get_evaluation_context, get_trace_id
 from humanloop.otel.constants import (
     HUMANLOOP_FILE_KEY,
     HUMANLOOP_FILE_TYPE_KEY,
-    HUMANLOOP_FLOW_PREREQUISITES_KEY,
     HUMANLOOP_LOG_KEY,
+    HUMANLOOP_FILE_PATH_KEY,
 )
-from humanloop.otel.helpers import (
-    is_humanloop_span,
-    is_llm_provider_call,
-    read_from_opentelemetry_span,
-    write_to_opentelemetry_span,
-)
-from humanloop.types.prompt_kernel_request import PromptKernelRequest
+from humanloop.otel.helpers import is_llm_provider_call
 
 logger = logging.getLogger("humanloop.sdk")
 
 
-class CompletableSpan(TypedDict):
-    span: ReadableSpan
-    complete: bool
-
-
 class HumanloopSpanProcessor(SimpleSpanProcessor):
-    """Enrich Humanloop spans with data from their children spans.
-
-    The decorators add Instrumentors to the OpenTelemetry TracerProvider
-    that log interactions with common LLM libraries. These Instrumentors
-    produce Spans which contain information that can be used to enrich the
-    Humanloop File Kernels.
-
-    For example, Instrumentors for LLM provider libraries intercept
-    hyperparameters used in the API call to the model to build the
-    Prompt File definition when using the @prompt decorator.
-
-    Spans created that are not created by Humanloop decorators, such as
-    those created by the Instrumentors mentioned above, will be passed
-    to the Exporter as they are.
-    """
-
     def __init__(self, exporter: SpanExporter) -> None:
         super().__init__(exporter)
-        # span parent to span children map
-        self._children: dict[int, list[CompletableSpan]] = defaultdict(list)
-        # List of all span IDs that are contained in a Flow trace
-        # They are passed to the Exporter as a span attribute
-        # so the Exporter knows when to complete a trace
-        self._prerequisites: dict[int, list[int]] = {}
-        self._executor = ThreadPoolExecutor(max_workers=4)
 
-    def shutdown(self):
-        self._executor.shutdown()
-        return super().shutdown()
-
-    def on_start(self, span, parent_context=None):
-        span_id = span.context.span_id
-        parent_span_id = span.parent.span_id if span.parent else None
-        if span.name == "humanloop.flow":
-            self._prerequisites[span_id] = []
-        if parent_span_id and is_humanloop_span(span):
-            for trace_head, all_trace_nodes in self._prerequisites.items():
-                if parent_span_id == trace_head or parent_span_id in all_trace_nodes:
-                    all_trace_nodes.append(span_id)
-                    break
-        # Handle stream case: when Prompt instrumented function calls a provider with streaming: true
-        # The instrumentor span will end only when the ChunksResponse is consumed, which can happen
-        # after the span created by the Prompt utility finishes. To handle this, we register all instrumentor
-        # spans belonging to a Humanloop span, and their parent will wait for them to complete in onEnd before
-        # exporting the Humanloop span.
-        if parent_span_id and _is_instrumentor_span(span):
-            if parent_span_id not in self._children:
-                self._children[parent_span_id] = []
-            self._children[parent_span_id].append(
-                {
-                    "span": span,
-                    "complete": False,
-                }
-            )
-
-    def on_end(self, span: ReadableSpan) -> None:
-        if is_humanloop_span(span=span):
-            # Wait for children to complete asynchronously
-            self._executor.submit(self._wait_for_children, span=span)
-        elif span.parent is not None and _is_instrumentor_span(span):
-            # If this is one of the children spans waited upon, update its completion status
-
-            # Updating the child span status
-            self._children[span.parent.span_id] = [
-                child if child["span"].context.span_id != span.context.span_id else {"span": span, "complete": True}
-                for child in self._children[span.parent.span_id]
-            ]
-
-            # Export the instrumentor span
-            self.span_exporter.export([span])
-        else:
-            # Unknown span, pass it to the Exporter
-            self.span_exporter.export([span])
-
-    def _wait_for_children(self, span: ReadableSpan):
-        """Wait for all children spans to complete before processing the Humanloop span."""
-        span_id = span.context.span_id
-        while not all(child["complete"] for child in self._children[span_id]):
-            # TODO: This assumes that the children spans will complete
-            # The LLM provider might fail; address in future
-            logger.debug(
-                "[HumanloopSpanProcessor] Span %s %s waiting for children to complete: %s",
-                span_id,
-                span.name,
-                self._children[span_id],
-            )
-        # All instrumentor spans have arrived, we can process the
-        # Humanloop parent span owning them
-        if span.name == "humanloop.flow":
-            write_to_opentelemetry_span(
-                span=span,
-                key=HUMANLOOP_FLOW_PREREQUISITES_KEY,
-                value=self._prerequisites[span_id],
-            )
-            del self._prerequisites[span_id]
-        logger.debug("[HumanloopSpanProcessor] Dispatching span %s %s", span_id, span.name)
-        _process_span_dispatch(span, [child["span"] for child in self._children[span_id]])
-        # Release references
-        del self._children[span_id]
-        # Pass Humanloop span to Exporter
-        logger.debug("[HumanloopSpanProcessor] Sending span %s %s to exporter", span_id, span.name)
+    def on_start(self, span: Span, parent_context=...):
+        """Called when a Span is started."""
+        if is_llm_provider_call(span):
+            decorator_context = get_decorator_context()
+            if decorator_context and decorator_context.type == "prompt":
+                path, template = (
+                    decorator_context.path,
+                    decorator_context.version["template"],
+                )
+                span.set_attribute(HUMANLOOP_FILE_PATH_KEY, path)
+                span.set_attribute(HUMANLOOP_FILE_TYPE_KEY, "prompt")
+                if template:
+                    span.set_attribute(
+                        f"{HUMANLOOP_FILE_KEY}.template",
+                        template,
+                    )
+            trace_id = get_trace_id()
+            if trace_id:
+                span.set_attribute(f"{HUMANLOOP_LOG_KEY}.trace_parent_id", trace_id)
+
+    def on_end(self, span: ReadableSpan):
+        """Called when a Span finishes recording."""
+        if is_llm_provider_call(span):
+            decorator_context = get_decorator_context()
+            if decorator_context is None or decorator_context.type != "prompt":
+                # User made a provider call outside a @prompt context, ignore the span
+                return
+            evaluation_context = get_evaluation_context()
+            if evaluation_context is not None and evaluation_context.path == decorator_context.path:
+                # User made a provider call inside a @prompt context, ignore the span
+                return
         self.span_exporter.export([span])
-
-
-def _is_instrumentor_span(span: ReadableSpan) -> bool:
-    """Determine if the span contains information of interest for Spans created by Humanloop decorators."""
-    # At the moment we only enrich Spans created by the Prompt decorators
-    # As we add Instrumentors for other libraries, this function must
-    # be expanded
-    return is_llm_provider_call(span=span)
-
-
-def _process_span_dispatch(span: ReadableSpan, children_spans: list[ReadableSpan]):
-    file_type = span.attributes[HUMANLOOP_FILE_TYPE_KEY]  # type: ignore
-
-    # Processing common to all Humanloop File types
-    if span.start_time:
-        span._attributes[f"{HUMANLOOP_LOG_KEY}.start_time"] = span.start_time / 1e9  # type: ignore
-    if span.end_time:
-        span._attributes[f"{HUMANLOOP_LOG_KEY}.end_time"] = span.end_time / 1e9  # type: ignore
-        span._attributes[f"{HUMANLOOP_LOG_KEY}.created_at"] = span.end_time / 1e9  # type: ignore
-
-    # Processing specific to each Humanloop File type
-    if file_type == "prompt":
-        _process_prompt(prompt_span=span, children_spans=children_spans)
-        return
-    elif file_type == "tool":
-        pass
-    elif file_type == "flow":
-        pass
-    else:
-        logger.error(
-            "[HumanloopSpanProcessor] Unknown Humanloop File span %s %s",
-            span.context.span_id,
-            span.name,
-        )
-
-
-def _process_prompt(prompt_span: ReadableSpan, children_spans: list[ReadableSpan]):
-    if len(children_spans) == 0:
-        return
-    for child_span in children_spans:
-        if is_llm_provider_call(child_span):
-            _enrich_prompt_kernel(prompt_span, child_span)
-            _enrich_prompt_log(prompt_span, child_span)
-            # NOTE: @prompt decorator expects a single LLM provider call
-            # to happen in the function. If there are more than one, we
-            # ignore the rest
-            break
-
-
-def _enrich_prompt_kernel(prompt_span: ReadableSpan, llm_provider_call_span: ReadableSpan):
-    hl_file: dict[str, Any] = read_from_opentelemetry_span(prompt_span, key=HUMANLOOP_FILE_KEY)
-    gen_ai_object: dict[str, Any] = read_from_opentelemetry_span(llm_provider_call_span, key="gen_ai")
-    llm_object: dict[str, Any] = read_from_opentelemetry_span(llm_provider_call_span, key="llm")
-
-    prompt: dict[str, Any] = hl_file.get("prompt", {})  # type: ignore
-
-    # Check if the Prompt Kernel keys were assigned default values
-    # via the @prompt arguments. Otherwise, use the information
-    # from the intercepted LLM provider call
-    prompt["model"] = prompt.get("model") or gen_ai_object.get("request", {}).get("model", None)
-    if prompt["model"] is None:
-        raise ValueError("Could not infer required parameter `model`. Please provide it in the @prompt decorator.")
-    prompt["endpoint"] = prompt.get("endpoint") or llm_object.get("request", {}).get("type")
-    prompt["provider"] = prompt.get("provider") or gen_ai_object.get("system", None)
-    if prompt["provider"]:
-        # Normalize provider name; Interceptors output the names with
-        # different capitalization e.g. OpenAI instead of openai
-        prompt["provider"] = prompt["provider"].lower()
-    prompt["temperature"] = prompt.get("temperature") or gen_ai_object.get("request", {}).get("temperature", None)
-    prompt["top_p"] = prompt.get("top_p") or gen_ai_object.get("request", {}).get("top_p", None)
-    prompt["max_tokens"] = prompt.get("max_tokens") or gen_ai_object.get("request", {}).get("max_tokens", None)
-    prompt["presence_penalty"] = prompt.get("presence_penalty") or llm_object.get("presence_penalty", None)
-    prompt["frequency_penalty"] = prompt.get("frequency_penalty") or llm_object.get("frequency_penalty", None)
-    prompt["tools"] = prompt.get("tools", [])
-
-    try:
-        # Validate the Prompt Kernel
-        PromptKernelRequest.model_validate(obj=prompt)
-    except PydanticValidationError as e:
-        logger.error(
-            "[HumanloopSpanProcessor] Could not validate Prompt Kernel extracted from span: %s %s. Error: %s",
-            prompt_span.context.span_id,
-            prompt_span.name,
-            e,
-        )
-
-    # Write the enriched Prompt Kernel back to the span
-    hl_file["prompt"] = prompt
-    write_to_opentelemetry_span(
-        span=prompt_span,
-        key=HUMANLOOP_FILE_KEY,
-        # hl_file was modified in place via prompt_kernel reference
-        value=hl_file,
-    )
-
-
-def _enrich_prompt_log(prompt_span: ReadableSpan, llm_provider_call_span: ReadableSpan):
-    try:
-        hl_log: dict[str, Any] = read_from_opentelemetry_span(prompt_span, key=HUMANLOOP_LOG_KEY)
-    except KeyError:
-        hl_log = {}
-    gen_ai_object: dict[str, Any] = read_from_opentelemetry_span(llm_provider_call_span, key="gen_ai")
-
-    # TODO: Seed not added by Instrumentors in provider call
-
-    if "output_tokens" not in hl_log:
-        hl_log["output_tokens"] = gen_ai_object.get("usage", {}).get("completion_tokens")
-    if len(gen_ai_object.get("completion", [])) > 0:
-        hl_log["finish_reason"] = gen_ai_object["completion"][0].get("finish_reason")
-    hl_log["messages"] = gen_ai_object.get("prompt")
-
-    write_to_opentelemetry_span(
-        span=prompt_span,
-        key=HUMANLOOP_LOG_KEY,
-        # hl_log was modified in place
-        value=hl_log,
-    )
diff --git a/src/humanloop/otel/processor/__init__.py b/src/humanloop/otel/processor/__init__.py
deleted file mode 100644
index bc4e5940..00000000
--- a/src/humanloop/otel/processor/__init__.py
+++ /dev/null
@@ -1,237 +0,0 @@
-from dataclasses import dataclass
-import logging
-from collections import defaultdict
-from typing import Optional
-import typing
-
-from opentelemetry.sdk.trace import ReadableSpan
-from opentelemetry.sdk.trace.export import SimpleSpanProcessor, SpanExporter
-
-from humanloop.base_client import BaseHumanloop
-from humanloop.otel.constants import (
-    HUMANLOOP_FILE_TYPE_KEY,
-    HUMANLOOP_FLOW_PREREQUISITES_KEY,
-    HUMANLOOP_INTERCEPTED_HL_CALL_SPAN_NAME,
-    HUMANLOOP_LOG_KEY,
-)
-from humanloop.otel.helpers import (
-    is_humanloop_span,
-    is_llm_provider_call,
-    write_to_opentelemetry_span,
-)
-from humanloop.otel.processor.prompts import enhance_prompt_span
-
-if typing.TYPE_CHECKING:
-    from humanloop.base_client import BaseHumanloop
-
-
-logger = logging.getLogger("humanloop.sdk")
-
-
-# NOTE: Source of bugs, refactor to dataclass for type safety
-# Instead of accessing via "key"
-@dataclass
-class DependantSpan:
-    span: ReadableSpan
-    finished: bool
-
-
-class HumanloopSpanProcessor(SimpleSpanProcessor):
-    """Enrich Humanloop spans with data from their children spans.
-
-    The decorators add Instrumentors to the OpenTelemetry TracerProvider
-    that log interactions with common LLM libraries. These Instrumentors
-    produce Spans which contain information that can be used to enrich the
-    Humanloop File Kernels.
-
-    For example, Instrumentors for LLM provider libraries intercept
-    hyperparameters used in the API call to the model to build the
-    Prompt File definition when using the @prompt decorator.
-
-    Spans created that are not created by Humanloop decorators, such as
-    those created by the Instrumentors mentioned above, will be passed
-    to the Exporter as they are.
-    """
-
-    def __init__(
-        self,
-        exporter: SpanExporter,
-        client: "BaseHumanloop",
-    ) -> None:
-        super().__init__(exporter)
-        # span parent to span children map
-        self._dependencies: dict[int, list[DependantSpan]] = defaultdict(list)
-        self._waiting: dict[int, ReadableSpan] = {}
-        # List of all span IDs that are contained in a Flow trace
-        # They are passed to the Exporter as a span attribute
-        # so the Exporter knows when to complete a trace
-        self._spans_to_complete_flow_trace: dict[int, list[int]] = {}
-        self._client = client
-
-    def shutdown(self):
-        return super().shutdown()
-
-    def on_start(self, span, parent_context=None):
-        """Hook executed on Span creation.
-
-        Used for two cases:
-            1. Complete the Flow trace after all Logs inside have been uploaded. The Flow trace
-                spans are created bottoms-up. By the time the Flow span reaches the on_end hook,
-                all spans inside have been passed to the Exporter. We attach the list of span IDs
-                to the Flow span as an attribute, so the Exporter knows what spans (Logs) must
-                be uploaded before the Flow trace is completed
-            2. Instrument streaming Prompt decorated functions. The Instrumentor span will end only
-                when the ChunksResponse is consumed, while the Prompt-decorated span will end when
-                the function returns.
-        """
-        self._track_flow_traces(span)
-        self._add_dependency_to_await(span)
-
-    def on_end(self, span: ReadableSpan) -> None:
-        span_id = span.context.span_id
-        if is_humanloop_span(span=span):
-            if not self._must_wait(span):
-                self._send_to_exporter(
-                    span=span,
-                    dependencies=[dependency.span for dependency in self._dependencies[span.context.span_id]],
-                )
-            else:
-                # Must wait for dependencies
-                self._waiting[span_id] = span
-            return
-
-        if self._is_dependency(span):
-            self._mark_dependency_arrival(span)
-            self._send_to_exporter(span, [])
-
-            waiting_span = self._get_waiting_parent(span)
-            if waiting_span is not None:
-                self._send_to_exporter(
-                    span=span,
-                    dependencies=[dependency.span for dependency in self._dependencies[span.context.span_id]],
-                )
-            return
-
-        # Be unopinionated and pass all other spans to Exporter
-        self._send_to_exporter(span=span, dependencies=[])
-
-    def _must_wait(self, span: ReadableSpan) -> bool:
-        if span.context.span_id not in self._dependencies:
-            return False
-        if all([dependency.finished for dependency in self._dependencies[span.context.span_id]]):
-            return False
-        return True
-
-    def _get_waiting_parent(self, span: ReadableSpan) -> Optional[ReadableSpan]:
-        # We know this span has a parent, need to satisfy the type checker
-        parent_span_id = span.parent.span_id  # type: ignore
-        if parent_span_id in self._waiting:
-            if all([dependency.finished for dependency in self._dependencies[parent_span_id]]):
-                waiting_span = self._waiting[parent_span_id]
-                del self._dependencies[parent_span_id]
-                del self._waiting[parent_span_id]
-                return waiting_span
-        return None
-
-    def _add_dependency_to_await(self, span: ReadableSpan):
-        # We know this span has a parent, need to satisfy the type checker
-        if self._is_dependency(span):
-            parent_span_id = span.parent.span_id  # type: ignore
-            self._dependencies[parent_span_id].append(DependantSpan(span=span, finished=False))
-
-    def _track_flow_traces(self, span: ReadableSpan):
-        span_id = span.context.span_id
-
-        if span.name == "humanloop.flow":
-            # Head of a trace
-            self._spans_to_complete_flow_trace[span_id] = []
-
-        parent_span_id = span.parent.span_id if span.parent else None
-        if parent_span_id and is_humanloop_span(span):
-            # Log belongs to a trace, keep track of it
-            for trace_head, all_trace_nodes in self._spans_to_complete_flow_trace.items():
-                if parent_span_id == trace_head or parent_span_id in all_trace_nodes:
-                    all_trace_nodes.append(span_id)
-                    break
-
-    def _mark_dependency_arrival(self, span: ReadableSpan):
-        span_id = span.context.span_id
-        # We know this span has a parent, need to satisfy type checker
-        parent_span_id = span.parent.span_id  # type: ignore
-        self._dependencies[parent_span_id] = [
-            dependency if dependency.span.context.span_id != span_id else DependantSpan(span=span, finished=True)
-            for dependency in self._dependencies[parent_span_id]
-        ]
-
-    def _send_to_exporter(
-        self,
-        span: ReadableSpan,
-        dependencies: list[ReadableSpan],
-    ):
-        """
-        Write attributes to the Humanloop spans depending on their type
-        """
-
-        if is_humanloop_span(span):
-            # Processing common to all Humanloop File types
-            self._write_start_end_times(span=span)
-
-            # Processing specific to each Humanloop File type
-            file_type = span.attributes[HUMANLOOP_FILE_TYPE_KEY]  # type: ignore
-            span_id = span.context.span_id
-            if file_type == "prompt":
-                enhance_prompt_span(
-                    client=self._client,
-                    prompt_span=span,
-                    dependencies=dependencies,
-                )
-            elif file_type == "tool":
-                # No extra processing needed
-                pass
-            elif file_type == "flow":
-                trace = self._spans_to_complete_flow_trace.get(span_id, [])
-                write_to_opentelemetry_span(
-                    span=span,
-                    key=HUMANLOOP_FLOW_PREREQUISITES_KEY,
-                    value=trace,
-                )
-            else:
-                logger.error(
-                    "[HumanloopSpanProcessor] Unknown Humanloop File span %s %s",
-                    span_id,
-                    span.name,
-                )
-
-        self.span_exporter.export([span])
-
-        # Cleanup
-        span_id = span.context.span_id
-        if span_id in self._waiting:
-            del self._waiting[span_id]
-        if span_id in self._dependencies:
-            del self._dependencies[span_id]
-        if span_id in self._spans_to_complete_flow_trace:
-            del self._spans_to_complete_flow_trace[span_id]
-
-    @classmethod
-    def _is_dependency(cls, span: ReadableSpan) -> bool:
-        """Determine if the span contains information of interest for Spans created by Humanloop decorators."""
-        # At the moment we only enrich Spans created by the Prompt decorators
-        # As we add Instrumentors for other libraries, this function must
-        # be expanded
-        return span.parent is not None and (
-            is_llm_provider_call(span=span) or span.name == HUMANLOOP_INTERCEPTED_HL_CALL_SPAN_NAME
-        )
-
-    @classmethod
-    def _write_start_end_times(cls, span: ReadableSpan):
-        if span.start_time:
-            # NOTE: write_to_otel_span and read_from_otel_span have extra behavior
-            # OTEL canonical way to write keys is to use the dot notation, as below
-            # The 2 utilities encapsulate this behavior, allowing the dev to write
-            # complex objects.
-            # See doc-strings in humanloop.otel.helpers for more information
-            span._attributes[f"{HUMANLOOP_LOG_KEY}.start_time"] = span.start_time / 1e9  # type: ignore
-        if span.end_time:
-            span._attributes[f"{HUMANLOOP_LOG_KEY}.end_time"] = span.end_time / 1e9  # type: ignore
-            span._attributes[f"{HUMANLOOP_LOG_KEY}.created_at"] = span.end_time / 1e9  # type: ignore
diff --git a/src/humanloop/otel/processor/prompts.py b/src/humanloop/otel/processor/prompts.py
deleted file mode 100644
index 2606427c..00000000
--- a/src/humanloop/otel/processor/prompts.py
+++ /dev/null
@@ -1,204 +0,0 @@
-import deepdiff  # type: ignore [import]
-import logging
-from typing import Any
-import typing
-from opentelemetry.sdk.trace import ReadableSpan
-from pydantic import ValidationError as PydanticValidationError
-
-from humanloop.eval_utils.run import HumanloopUtilityError
-from humanloop.otel.constants import (
-    HUMANLOOP_FILE_KEY,
-    HUMANLOOP_INTERCEPTED_HL_CALL_RESPONSE,
-    HUMANLOOP_LOG_KEY,
-    HUMANLOOP_PATH_KEY,
-)
-from humanloop.otel.helpers import (
-    is_intercepted_call,
-    is_llm_provider_call,
-    read_from_opentelemetry_span,
-    write_to_opentelemetry_span,
-)
-from humanloop.types.prompt_kernel_request import PromptKernelRequest
-
-if typing.TYPE_CHECKING:
-    from humanloop.client import BaseHumanloop
-
-logger = logging.getLogger("humanloop.sdk")
-
-
-def enhance_prompt_span(client: "BaseHumanloop", prompt_span: ReadableSpan, dependencies: list[ReadableSpan]):
-    """Add information from the LLM provider span to the Prompt span.
-
-    We are passing a list of children spans to the Prompt span, but more than one
-    is undefined behavior.
-    """
-    if len(dependencies) == 0:
-        return
-    for child_span in dependencies:
-        if is_llm_provider_call(child_span):
-            _enrich_prompt_kernel(prompt_span, child_span)
-            _enrich_prompt_log(prompt_span, child_span)
-            # NOTE: @prompt decorator expects a single LLM provider call
-            # to happen in the function. If there are more than one, we
-            # ignore the rest
-            break
-        elif is_intercepted_call(child_span):
-            _enrich_prompt_kernel_from_intercepted_call(client, prompt_span, child_span)
-            _enrich_prompt_log_from_intercepted_call(prompt_span, child_span)
-            break
-        else:
-            raise NotImplementedError(
-                f"Span {child_span.context.span_id} is not a recognized LLM provider call or intercepted call."
-            )
-
-
-def _enrich_prompt_kernel_from_intercepted_call(
-    client: "BaseHumanloop",
-    prompt_span: ReadableSpan,
-    intercepted_call_span: ReadableSpan,
-):
-    intercepted_response: dict[str, Any] = read_from_opentelemetry_span(
-        intercepted_call_span,
-        key=HUMANLOOP_INTERCEPTED_HL_CALL_RESPONSE,
-    )
-    hl_file = read_from_opentelemetry_span(
-        span=prompt_span,
-        key=HUMANLOOP_FILE_KEY,
-    )
-    hl_path = read_from_opentelemetry_span(
-        span=prompt_span,
-        key=HUMANLOOP_PATH_KEY,
-    )
-    prompt: dict[str, Any] = hl_file.get("prompt", {})  # type: ignore
-
-    for key, value_from_utility in {**prompt, "path": hl_path}.items():
-        if key not in intercepted_response["prompt"]:
-            continue
-
-        if "values_changed" in deepdiff.DeepDiff(
-            value_from_utility,
-            intercepted_response["prompt"][key],
-            ignore_order=True,
-        ):
-            # TODO: We want this behavior?
-            # save=False in overloaded prompt_call will still create the File
-            # despite not saving the log, so we rollback the File
-            file_id = intercepted_response["prompt"]["id"]
-            client.prompts.delete(id=file_id)
-            raise HumanloopUtilityError(
-                f"The prompt.call() {key} argument does not match the one provided in the decorator"
-            )
-
-    for key in intercepted_response["prompt"].keys():
-        if key not in prompt:
-            prompt[key] = intercepted_response["prompt"][key]
-
-    try:
-        # Validate the Prompt Kernel
-        PromptKernelRequest.model_validate(obj=prompt)  # type: ignore
-    except PydanticValidationError as e:
-        logger.error(
-            "[HumanloopSpanProcessor] Could not validate Prompt Kernel extracted from span: %s %s. Error: %s",
-            prompt_span.context.span_id,
-            prompt_span.name,
-            e,
-        )
-
-    hl_file["prompt"] = prompt
-    write_to_opentelemetry_span(
-        span=prompt_span,
-        key=HUMANLOOP_FILE_KEY,
-        value=hl_file,
-    )
-
-
-def _enrich_prompt_log_from_intercepted_call(prompt_span: ReadableSpan, intercepted_call_span: ReadableSpan):
-    hl_log: dict[str, Any] = read_from_opentelemetry_span(
-        span=prompt_span,
-        key=HUMANLOOP_LOG_KEY,
-    )
-    response: dict[str, Any] = read_from_opentelemetry_span(
-        intercepted_call_span,
-        key=HUMANLOOP_INTERCEPTED_HL_CALL_RESPONSE,
-    )
-    hl_log["output_tokens"] = response["logs"][0]["output_tokens"]
-    hl_log["finish_reason"] = response["logs"][0]["finish_reason"]
-    hl_log["output_message"] = response["logs"][0]["output_message"]
-
-    write_to_opentelemetry_span(
-        span=prompt_span,
-        key=HUMANLOOP_LOG_KEY,
-        # hl_log was modified in place
-        value=hl_log,
-    )
-
-
-def _enrich_prompt_kernel(prompt_span: ReadableSpan, llm_provider_call_span: ReadableSpan):
-    hl_file: dict[str, Any] = read_from_opentelemetry_span(prompt_span, key=HUMANLOOP_FILE_KEY)
-    gen_ai_object: dict[str, Any] = read_from_opentelemetry_span(llm_provider_call_span, key="gen_ai")
-    llm_object: dict[str, Any] = read_from_opentelemetry_span(llm_provider_call_span, key="llm")
-
-    prompt: dict[str, Any] = hl_file.get("prompt", {})  # type: ignore
-
-    # Check if the Prompt Kernel keys were assigned default values
-    # via the @prompt arguments. Otherwise, use the information
-    # from the intercepted LLM provider call
-    prompt["model"] = prompt.get("model") or gen_ai_object.get("request", {}).get("model", None)
-    if prompt["model"] is None:
-        raise ValueError("Could not infer required parameter `model`. Please provide it in the @prompt decorator.")
-    prompt["endpoint"] = prompt.get("endpoint") or llm_object.get("request", {}).get("type")
-    prompt["provider"] = prompt.get("provider") or gen_ai_object.get("system", None)
-    if prompt["provider"]:
-        # Normalize provider name; Interceptors output the names with
-        # different capitalization e.g. OpenAI instead of openai
-        prompt["provider"] = prompt["provider"].lower()
-    prompt["temperature"] = prompt.get("temperature") or gen_ai_object.get("request", {}).get("temperature", None)
-    prompt["top_p"] = prompt.get("top_p") or gen_ai_object.get("request", {}).get("top_p", None)
-    prompt["max_tokens"] = prompt.get("max_tokens") or gen_ai_object.get("request", {}).get("max_tokens", None)
-    prompt["presence_penalty"] = prompt.get("presence_penalty") or llm_object.get("presence_penalty", None)
-    prompt["frequency_penalty"] = prompt.get("frequency_penalty") or llm_object.get("frequency_penalty", None)
-    prompt["tools"] = prompt.get("tools", [])
-
-    try:
-        # Validate the Prompt Kernel
-        PromptKernelRequest.model_validate(obj=prompt)  # type: ignore
-    except PydanticValidationError as e:
-        logger.error(
-            "[HumanloopSpanProcessor] Could not validate Prompt Kernel extracted from span: %s %s. Error: %s",
-            prompt_span.context.span_id,
-            prompt_span.name,
-            e,
-        )
-
-    # Write the enriched Prompt Kernel back to the span
-    hl_file["prompt"] = prompt
-    write_to_opentelemetry_span(
-        span=prompt_span,
-        key=HUMANLOOP_FILE_KEY,
-        # hl_file was modified in place via prompt_kernel reference
-        value=hl_file,
-    )
-
-
-def _enrich_prompt_log(prompt_span: ReadableSpan, llm_provider_call_span: ReadableSpan):
-    try:
-        hl_log: dict[str, Any] = read_from_opentelemetry_span(prompt_span, key=HUMANLOOP_LOG_KEY)
-    except KeyError:
-        hl_log = {}
-    gen_ai_object: dict[str, Any] = read_from_opentelemetry_span(llm_provider_call_span, key="gen_ai")
-
-    # TODO: Seed not added by Instrumentors in provider call
-
-    if "output_tokens" not in hl_log:
-        hl_log["output_tokens"] = gen_ai_object.get("usage", {}).get("completion_tokens")
-    if len(gen_ai_object.get("completion", [])) > 0:
-        hl_log["finish_reason"] = gen_ai_object["completion"][0].get("finish_reason")
-    hl_log["messages"] = gen_ai_object.get("prompt")
-    # TODO: Need to fill in output_message
-
-    write_to_opentelemetry_span(
-        span=prompt_span,
-        key=HUMANLOOP_LOG_KEY,
-        # hl_log was modified in place
-        value=hl_log,
-    )
diff --git a/src/humanloop/overload.py b/src/humanloop/overload.py
new file mode 100644
index 00000000..b0c83215
--- /dev/null
+++ b/src/humanloop/overload.py
@@ -0,0 +1,124 @@
+import inspect
+import logging
+import types
+from typing import TypeVar, Union
+
+from humanloop.context import (
+    get_decorator_context,
+    get_evaluation_context,
+    get_trace_id,
+)
+from humanloop.evals.run import HumanloopRuntimeError
+
+from humanloop.evaluators.client import EvaluatorsClient
+from humanloop.flows.client import FlowsClient
+from humanloop.prompts.client import PromptsClient
+from humanloop.tools.client import ToolsClient
+from humanloop.types.create_evaluator_log_response import CreateEvaluatorLogResponse
+from humanloop.types.create_flow_log_response import CreateFlowLogResponse
+from humanloop.types.create_prompt_log_response import CreatePromptLogResponse
+from humanloop.types.create_tool_log_response import CreateToolLogResponse
+from humanloop.types.prompt_call_response import PromptCallResponse
+
+logger = logging.getLogger("humanloop.sdk")
+
+
+CLIENT_TYPE = TypeVar("CLIENT_TYPE", PromptsClient, FlowsClient, EvaluatorsClient, ToolsClient)
+
+
+def overload_log(client: CLIENT_TYPE) -> CLIENT_TYPE:
+    """
+    Wrap the `log` method of the provided Humanloop client to use EVALUATION_CONTEXT.
+
+    This makes the overloaded log actions be aware of whether the created Log is
+    part of an Evaluation (e.g. one started by eval_utils.run_eval).
+    """
+    # Copy the original log method in a hidden attribute
+    client._log = client.log  # type: ignore [attr-defined]
+
+    def _overload_log(
+        # It's safe to only consider kwargs since the original
+        # log method bans positional arguments
+        self,
+        **kwargs,
+    ) -> Union[
+        CreatePromptLogResponse,
+        CreateToolLogResponse,
+        CreateFlowLogResponse,
+        CreateEvaluatorLogResponse,
+    ]:
+        trace_id = get_trace_id()
+        if trace_id is not None and type(client) is FlowsClient:
+            context = get_decorator_context()
+            if context is None:
+                raise HumanloopRuntimeError("Internal error: trace_id context is set outside a decorator context.")
+            raise HumanloopRuntimeError(
+                f"Using `flows.log()` is not allowed: Flow decorator "
+                f"for File {context.path} manages the tracing and trace completion."
+            )
+        if trace_id is not None:
+            if "trace_parent_id" in kwargs:
+                logger.warning(
+                    "Ignoring trace_parent_id argument at line %d: the Flow decorator manages tracing.",
+                    inspect.currentframe().f_lineno,  # type: ignore [union-attr]
+                )
+            kwargs = {
+                **kwargs,
+                "trace_parent_id": trace_id,
+            }
+        evaluation_context = get_evaluation_context()
+        if evaluation_context is not None:
+            kwargs_eval, eval_callback = evaluation_context.log_args_with_context(
+                path=kwargs.get("path"), log_args=kwargs
+            )
+            try:
+                response = self._log(**kwargs_eval)
+            except Exception as e:
+                # Re-raising as HumanloopDecoratorError so the decorators don't catch it
+                raise HumanloopRuntimeError from e
+            if eval_callback is not None:
+                eval_callback(response.id)
+        else:
+            try:
+                response = self._log(**kwargs)
+            except Exception as e:
+                # Re-raising as HumanloopDecoratorError so the decorators don't catch it
+                raise HumanloopRuntimeError from e
+
+        return response
+
+    # Replace the original log method with the overloaded one
+    client.log = types.MethodType(_overload_log, client)  # type: ignore [assignment]
+    # Return the client with the overloaded log method
+    logger.debug("Overloaded the .call method of %s", client)
+    return client
+
+
+def overload_call(client: PromptsClient) -> PromptsClient:
+    client._call = client.call  # type: ignore [attr-defined]
+
+    def _overload_call(self, **kwargs) -> PromptCallResponse:
+        # None if not logging inside a decorator
+        trace_id = get_trace_id()
+        if trace_id is not None:
+            if "trace_parent_id" in kwargs:
+                logger.warning(
+                    "Ignoring trace_parent_id argument at line %d: the Flow decorator manages tracing.",
+                    inspect.currentframe().f_lineno,  # type: ignore [union-attr]
+                )
+            kwargs = {
+                **kwargs,
+                "trace_parent_id": trace_id,
+            }
+
+        try:
+            response = self._call(**kwargs)
+        except Exception as e:
+            # Re-raising as HumanloopDecoratorError so the decorators don't catch it
+            raise HumanloopRuntimeError from e
+
+        return response
+
+    # Replace the original log method with the overloaded one
+    client.call = types.MethodType(_overload_call, client)  # type: ignore [assignment]
+    return client
diff --git a/src/humanloop/utilities/flow.py b/src/humanloop/utilities/flow.py
deleted file mode 100644
index 70affd52..00000000
--- a/src/humanloop/utilities/flow.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import logging
-from functools import wraps
-from typing import Any, Callable, Mapping, Optional, Sequence
-
-from opentelemetry.sdk.trace import Span
-from opentelemetry.trace import Tracer
-from typing_extensions import Unpack
-
-from humanloop.eval_utils.run import HumanloopUtilityError
-from humanloop.utilities.helpers import bind_args
-from humanloop.eval_utils.types import File
-from humanloop.otel.constants import (
-    HUMANLOOP_FILE_KEY,
-    HUMANLOOP_FILE_TYPE_KEY,
-    HUMANLOOP_LOG_KEY,
-    HUMANLOOP_PATH_KEY,
-)
-from humanloop.otel.helpers import jsonify_if_not_string, write_to_opentelemetry_span
-from humanloop.requests import FlowKernelRequestParams as FlowDict
-from humanloop.requests.flow_kernel_request import FlowKernelRequestParams
-
-logger = logging.getLogger("humanloop.sdk")
-
-
-def flow(
-    opentelemetry_tracer: Tracer,
-    path: Optional[str] = None,
-    **flow_kernel: Unpack[FlowKernelRequestParams],  # type: ignore
-):
-    flow_kernel["attributes"] = {k: v for k, v in flow_kernel.get("attributes", {}).items() if v is not None}
-
-    def decorator(func: Callable):
-        @wraps(func)
-        def wrapper(*args: Sequence[Any], **kwargs: Mapping[str, Any]) -> Any:
-            span: Span
-            with opentelemetry_tracer.start_as_current_span("humanloop.flow") as span:  # type: ignore
-                span.set_attribute(HUMANLOOP_PATH_KEY, path if path else func.__name__)
-                span.set_attribute(HUMANLOOP_FILE_TYPE_KEY, "flow")
-
-                if flow_kernel:
-                    write_to_opentelemetry_span(
-                        span=span,
-                        key=f"{HUMANLOOP_FILE_KEY}.flow",
-                        value=flow_kernel,  # type: ignore
-                    )
-
-                # Call the decorated function
-                try:
-                    output = func(*args, **kwargs)
-                    output_stringified = jsonify_if_not_string(
-                        func=func,
-                        output=output,
-                    )
-                    error = None
-                except HumanloopUtilityError as e:
-                    raise e
-                except Exception as e:
-                    logger.error(f"Error calling {func.__name__}: {e}")
-                    output = None
-                    output_stringified = None
-                    error = str(e)
-
-                flow_log = {
-                    # TODO: Revisit and agree on
-                    "inputs": {k: v for k, v in bind_args(func, args, kwargs).items() if k != "messages"},
-                    "messages": bind_args(func, args, kwargs).get("messages"),
-                    "output": output_stringified,
-                    "error": error,
-                }
-
-                # Write the Flow Log to the Span on HL_LOG_OT_KEY
-                if flow_log:
-                    write_to_opentelemetry_span(
-                        span=span,
-                        key=HUMANLOOP_LOG_KEY,
-                        value=flow_log,  # type: ignore
-                    )
-
-            # Return the output of the decorated function
-            return output
-
-        wrapper.file = File(  # type: ignore
-            path=path if path else func.__name__,
-            type="flow",
-            version=FlowDict(**flow_kernel),  # type: ignore
-            callable=wrapper,
-        )
-
-        return wrapper
-
-    return decorator
diff --git a/src/humanloop/utilities/prompt.py b/src/humanloop/utilities/prompt.py
deleted file mode 100644
index b02dcde2..00000000
--- a/src/humanloop/utilities/prompt.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import logging
-from functools import wraps
-from typing import Any, Callable, Mapping, Optional, Sequence
-
-from opentelemetry.sdk.trace import Span
-from opentelemetry.trace import Tracer
-from typing_extensions import Unpack
-
-from humanloop.eval_utils.context import set_prompt_utility_context, unset_prompt_utility_context
-from humanloop.eval_utils.run import HumanloopUtilityError
-from humanloop.utilities.helpers import bind_args
-from humanloop.utilities.types import DecoratorPromptKernelRequestParams
-from humanloop.eval_utils import File
-from humanloop.otel.constants import (
-    HUMANLOOP_FILE_KEY,
-    HUMANLOOP_FILE_TYPE_KEY,
-    HUMANLOOP_LOG_KEY,
-    HUMANLOOP_PATH_KEY,
-)
-from humanloop.otel.helpers import jsonify_if_not_string, write_to_opentelemetry_span
-
-logger = logging.getLogger("humanloop.sdk")
-
-
-def prompt(
-    opentelemetry_tracer: Tracer,
-    path: Optional[str] = None,
-    # TODO: Template can be a list of objects?
-    **prompt_kernel: Unpack[DecoratorPromptKernelRequestParams],  # type: ignore
-):
-    def decorator(func: Callable):
-        @wraps(func)
-        def wrapper(*args: Sequence[Any], **kwargs: Mapping[str, Any]) -> Any:
-            set_prompt_utility_context(tracer=opentelemetry_tracer)
-            span: Span
-            with opentelemetry_tracer.start_as_current_span("humanloop.prompt") as span:  # type: ignore
-                span.set_attribute(HUMANLOOP_PATH_KEY, path if path else func.__name__)
-                span.set_attribute(HUMANLOOP_FILE_TYPE_KEY, "prompt")
-
-                if prompt_kernel:
-                    write_to_opentelemetry_span(
-                        span=span,
-                        key=f"{HUMANLOOP_FILE_KEY}.prompt",
-                        value={
-                            **prompt_kernel,  # type: ignore
-                            "attributes": prompt_kernel.get("attributes") or None,  # type: ignore
-                        },  # type: ignore
-                    )
-
-                # Call the decorated function
-                try:
-                    output = func(*args, **kwargs)
-                    output_stringified = jsonify_if_not_string(
-                        func=func,
-                        output=output,
-                    )
-                    error = None
-                except HumanloopUtilityError as e:
-                    raise e
-                except Exception as e:
-                    logger.error(f"Error calling {func.__name__}: {e}")
-                    output = None
-                    output_stringified = jsonify_if_not_string(
-                        func=func,
-                        output=output,
-                    )
-                    error = str(e)
-
-                prompt_log = {
-                    "inputs": {k: v for k, v in bind_args(func, args, kwargs).items() if k != "messages"},
-                    "messages": bind_args(func, args, kwargs).get("messages"),
-                    "output": output_stringified,
-                    "error": error,
-                }
-
-                write_to_opentelemetry_span(
-                    span=span,
-                    key=HUMANLOOP_LOG_KEY,
-                    value=prompt_log,  # type: ignore
-                )
-
-            # Return the output of the decorated function
-            unset_prompt_utility_context()
-            return output
-
-        wrapper.file = File(  # type: ignore
-            path=path if path else func.__name__,
-            type="prompt",
-            version={**prompt_kernel},  # type: ignore
-            callable=wrapper,
-        )
-
-        return wrapper
-
-    return decorator
diff --git a/src/humanloop/utilities/types.py b/src/humanloop/utilities/types.py
deleted file mode 100644
index f52f0178..00000000
--- a/src/humanloop/utilities/types.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from typing_extensions import NotRequired
-
-from humanloop.requests.prompt_kernel_request import PromptKernelRequestParams
-
-
-class DecoratorPromptKernelRequestParams(PromptKernelRequestParams):
-    """See :class:`PromptKernelRequestParams` for more information.
-
-    Allows the `model` field to be optional for Prompt decorator.
-    """
-
-    model: NotRequired[str]  # type: ignore
diff --git a/tests/conftest.py b/tests/conftest.py
index 033a2d09..80e3b336 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -98,10 +98,7 @@ def opentelemetry_hl_test_configuration(
     Humanloop Spans.
     """
     exporter = InMemorySpanExporter()
-    processor = HumanloopSpanProcessor(
-        exporter=exporter,
-        client=humanloop_client,
-    )
+    processor = HumanloopSpanProcessor(exporter=exporter)
     opentelemetry_test_provider.add_span_processor(processor)
     instrumentors: list[BaseInstrumentor] = [
         OpenAIInstrumentor(),
@@ -142,10 +139,7 @@ def opentelemetry_hl_with_exporter_test_configuration(
     """Configure OTel backend with HumanloopSpanProcessor and
     a HumanloopSpanExporter where HTTP calls are mocked.
     """
-    processor = HumanloopSpanProcessor(
-        exporter=hl_test_exporter,
-        client=humanloop_client,  # type: ignore [arg-type]
-    )
+    processor = HumanloopSpanProcessor(exporter=hl_test_exporter)
     opentelemetry_test_provider.add_span_processor(processor)
     instrumentor = OpenAIInstrumentor()
     instrumentor.instrument(tracer_provider=opentelemetry_test_provider)
diff --git a/tests/integration/chat_agent/__init__.py b/tests/integration/chat_agent/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/tests/integration/chat_agent/conftest.py b/tests/integration/chat_agent/conftest.py
deleted file mode 100644
index 2ca3c92e..00000000
--- a/tests/integration/chat_agent/conftest.py
+++ /dev/null
@@ -1,177 +0,0 @@
-from dataclasses import dataclass
-from typing import Any, Callable
-
-import pytest
-
-from humanloop.client import Humanloop
-from tests.conftest import APIKeys
-
-
-@dataclass
-class SurferAgentScenario:
-    calculator: Callable[[str, int, int], str]
-    pick_random_number: Callable[[], int]
-    call_agent: Callable[[list[str]], str]
-    agent_chat_workflow: Callable[[], str]
-
-
-@pytest.fixture()
-def surfer_agent_scenario_factory(
-    humanloop_client: Humanloop,
-    get_test_path: Callable[[str], str],
-    api_keys: APIKeys,
-) -> Callable[[bool], SurferAgentScenario]:
-    def factory(use_overload_call: bool) -> SurferAgentScenario:
-        import json
-        import random
-        from openai import OpenAI
-
-        TOPICS = ["math", "science"]
-        TONE = "groovy 80s surfer dude"
-        LLM_HYPERPARAMETERS = {
-            "temperature": 0.7,
-            "max_tokens": 200,
-            "top_p": 1,
-            "stop": "\n\n\n",
-            "presence_penalty": 0.5,
-            "frequency_penalty": 0.5,
-            "seed": 42,
-        }
-        PROMPT_TEMPLATE = (
-            "You are a helpful assistant knowledgeable on the following topics: {topics}. "
-            "When you reply you should use the following tone of voice: {tone}"
-        )
-
-        client = OpenAI(api_key=api_keys.openai)
-
-        @humanloop_client.tool(path=get_test_path("Calculator"))
-        def calculator(operation: str, num1: int, num2: int) -> float:
-            """Do arithmetic operations on two numbers."""
-            if operation == "add":
-                return num1 + num2
-            elif operation == "subtract":
-                return num1 - num2
-            elif operation == "multiply":
-                return num1 * num2
-            elif operation == "divide":
-                return num1 / num2
-            else:
-                raise NotImplementedError("Invalid operation")
-
-        @humanloop_client.tool(path=get_test_path("Random Number"))
-        def pick_random_number():
-            """Pick a random number between 1 and 100."""
-            return random.randint(1, 100)
-
-        @humanloop_client.prompt(
-            path=get_test_path("Agent Prompt"),
-            template=PROMPT_TEMPLATE,
-            tools=[
-                pick_random_number.json_schema,
-                calculator.json_schema,
-            ],
-        )
-        def call_agent(messages: list[dict[str, Any]]) -> str:  # type: ignore [call-arg]
-            if use_overload_call:
-                output = humanloop_client.prompts.call(
-                    path=get_test_path("Agent Prompt"),
-                    messages=messages,  # type: ignore [arg-type]
-                    prompt={  # type: ignore [arg-type]
-                        "model": "gpt-4o-mini",
-                        "tools": [
-                            # TODO: json_schema property should specify
-                            # an OpenAI/ sHL format or be aware of the context
-                            # it's called from; note the difference between
-                            # prompts.call and openai.chat.completions.create
-                            calculator.json_schema,
-                            pick_random_number.json_schema,
-                        ],
-                        **LLM_HYPERPARAMETERS,  # type: ignore
-                    },
-                )
-
-                if output.logs[0].output_message.tool_calls:  # type: ignore [union-attr]
-                    for tool_call in output.logs[0].output_message.tool_calls:  # type: ignore [union-attr]
-                        arguments = json.loads(tool_call.function.arguments)  # type: ignore [arg-type]
-                        if tool_call.function.name == "calculator":
-                            result = calculator(**arguments)
-
-                        elif tool_call.function.name == "pick_random_number":
-                            result = pick_random_number(**arguments)
-
-                        else:
-                            raise NotImplementedError("Invalid tool call")
-
-                        return f"[TOOL CALL: {tool_call.function.name}] {result}"
-
-                return output.logs[0].output_message.content  # type: ignore
-
-            else:
-                output = client.chat.completions.create(  # type: ignore [call-overload]
-                    model="gpt-4o-mini",
-                    messages=messages,
-                    # Use .json_schema property on decorated functions to easily access
-                    # the definition for function calls
-                    tools=[
-                        {
-                            "type": "function",
-                            "function": calculator.json_schema,
-                        },
-                        {
-                            "type": "function",
-                            "function": pick_random_number.json_schema,
-                        },
-                    ],
-                    **LLM_HYPERPARAMETERS,
-                )
-
-            # Check if tool calls are present in the output
-            if output.choices[0].message.tool_calls:  # type: ignore [attr-defined]
-                for tool_call in output.choices[0].message.tool_calls:  # type: ignore [attr-defined]
-                    arguments = json.loads(tool_call.function.arguments)
-                    if tool_call.function.name == "calculator":
-                        result = calculator(**arguments)
-
-                    elif tool_call.function.name == "pick_random_number":
-                        result = pick_random_number(**arguments)
-
-                    else:
-                        raise NotImplementedError("Invalid tool call")
-
-                    return f"[TOOL CALL: {tool_call.function.name}] {result}"
-
-            return output.choices[0].message.content  # type: ignore [attr-defined]
-
-        @humanloop_client.flow(path=get_test_path("Agent Workflow"))
-        def agent_chat_workflow():
-            messages = [
-                {
-                    "role": "system",
-                    "content": PROMPT_TEMPLATE.format(
-                        topics=" ".join(TOPICS),
-                        tone=TONE,
-                    ),
-                },
-            ]
-            input_output_pairs = []
-            while True:
-                user_input = input("You: ")
-                input_output = [user_input]
-                if user_input == "exit":
-                    break
-                messages.append({"role": "user", "content": user_input})
-                response = call_agent(messages=messages)
-                messages.append({"role": "assistant", "content": str(response)})
-                input_output.append(str(response))
-                print(f"Agent: {response}")
-                input_output_pairs.append(input_output)
-            return json.dumps(input_output_pairs)
-
-        return SurferAgentScenario(
-            calculator=calculator,
-            pick_random_number=pick_random_number,
-            call_agent=call_agent,
-            agent_chat_workflow=agent_chat_workflow,
-        )
-
-    return factory
diff --git a/tests/integration/chat_agent/test_chat_agent.py b/tests/integration/chat_agent/test_chat_agent.py
deleted file mode 100644
index f65538c3..00000000
--- a/tests/integration/chat_agent/test_chat_agent.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# """This script demonstrates instrumenting a simple conversational agent with function calling.
-
-# The example uses the Humanloop SDK to declare Files in code.
-
-# Type 'exit' to end the conversation.
-# """
-
-import time
-from typing import Callable, ContextManager, TextIO
-from unittest.mock import MagicMock, patch
-
-import pytest
-from humanloop import Humanloop
-from tests.conftest import DirectoryIdentifiers
-from tests.integration.chat_agent.conftest import SurferAgentScenario
-
-
-@pytest.mark.skip("skip for demo")
-@pytest.mark.parametrize("use_call", [False])
-@patch("builtins.input")
-def test_scenario_runs(
-    mocked_input: MagicMock,
-    surfer_agent_scenario_factory: Callable[[bool], SurferAgentScenario],
-    capture_stdout: ContextManager[TextIO],
-    humanloop_client: Humanloop,
-    test_directory: DirectoryIdentifiers,
-    use_call: bool,
-):
-    surfer_agent_scenario = surfer_agent_scenario_factory(use_call)
-    scenario_io = [
-        "How are you?",
-        "Tubular",
-        "exit",
-    ]
-    mocked_input.side_effect = scenario_io
-    with capture_stdout() as console_output:  # type: ignore [operator]
-        surfer_agent_scenario.agent_chat_workflow()
-
-    # Wait for the HL workspace to be updated
-    time.sleep(5)
-
-    lines = console_output.getvalue().splitlines()
-    assert len(lines) == 2
-    assert "Agent" in lines[0]
-
-    response = humanloop_client.directories.get(test_directory.id)
-    assert len(response.files) == 2
-    flow_file = [file for file in response.files if file.type == "flow"][0]
-    flow_logs = humanloop_client.logs.list(file_id=flow_file.id)
-    assert flow_logs.items and len(flow_logs.items) == 1
-    flow_log = flow_logs.items[0]
-    assert flow_log.trace_status == "complete"  # type: ignore
-    # List will not pass the children to the trace_children attribute
-    assert len(flow_log.trace_children) == 0  # type: ignore
-    response = humanloop_client.logs.get(flow_log.id)  # type: ignore [assignment]
-    if not isinstance(response, dict):
-        response = response.dict()  # type: ignore [assignment]
-    assert response["trace_status"] == "complete"  # type: ignore
-    assert len(response["trace_children"]) == 2  # type: ignore [index]
-    messages = response["trace_children"][1]["messages"]  # type: ignore [index]
-    assert len(messages) == 4
-    # Messages are in reverse order
-    if not use_call:
-        # TODO: Some special characters are dropped when
-        # using prompt.call inside
-        assert messages[2]["content"] == scenario_io[0]
-        assert messages[0]["content"] == scenario_io[1]
diff --git a/tests/integration/evaluate_medqa/__init__.py b/tests/integration/evaluate_medqa/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/tests/integration/evaluate_medqa/conftest.py b/tests/integration/evaluate_medqa/conftest.py
deleted file mode 100644
index 18f52d81..00000000
--- a/tests/integration/evaluate_medqa/conftest.py
+++ /dev/null
@@ -1,202 +0,0 @@
-from dataclasses import dataclass
-import json
-from typing import Callable
-import pytest
-
-import os
-import requests
-from humanloop.client import Humanloop
-
-from tests.assets import levenshtein, exact_match
-from tests.conftest import APIKeys
-
-
-@pytest.fixture(scope="session", autouse=True)
-def medqa_knowledge_base_path() -> str:
-    file_path = "tests/assets/medqa.parquet"
-    if not os.path.exists(file_path):
-        os.makedirs("tests/assets", exist_ok=True)
-        url = "https://github.com/humanloop/humanloop-cookbook/raw/refs/heads/main/assets/sources/textbooks.parquet"
-        response = requests.get(url)
-        with open(file_path, "wb+") as file:
-            file.write(response.content)
-    return file_path
-
-
-@pytest.fixture(scope="session", autouse=True)
-def medqa_dataset_path() -> str:
-    file_path = "tests/assets/datapoints.jsonl"
-    if not os.path.exists(file_path):
-        os.makedirs("tests/assets", exist_ok=True)
-        url = "https://raw.githubusercontent.com/humanloop/humanloop-cookbook/refs/heads/main/assets/datapoints.jsonl"
-        response = requests.get(url)
-        with open(file_path, "wb+") as file:
-            file.write(response.content)
-    return file_path
-
-
-@dataclass
-class MedQAScenario:
-    retrieval_tool: tuple[str, Callable[[str], str]]
-    call_model: tuple[str, Callable[..., str]]
-    ask_question: tuple[str, Callable[..., str]]
-    medqa_dataset_path: tuple[str, list[dict]]
-    levenshtein_path: str
-    exact_match_path: str
-
-
-@pytest.fixture()
-def evaluate_medqa_scenario_factory(
-    humanloop_client: "Humanloop",
-    get_test_path: Callable[[str], str],
-    api_keys: APIKeys,
-    medqa_knowledge_base_path: str,
-    medqa_dataset_path: str,
-) -> Callable[[bool], MedQAScenario]:
-    def factory(use_overload_call: bool) -> MedQAScenario:
-        import inspect
-
-        from chromadb import chromadb  # type: ignore
-        from openai import OpenAI
-        import pandas as pd  # type: ignore
-
-        chroma = chromadb.Client()
-        collection = chroma.get_or_create_collection(name="MedQA")
-        knowledge_base = pd.read_parquet(medqa_knowledge_base_path)
-        knowledge_base = knowledge_base.sample(10, random_state=42)
-        collection.add(
-            documents=knowledge_base["contents"].to_list(),
-            ids=knowledge_base["id"].to_list(),
-        )
-
-        openai = OpenAI(api_key=api_keys.openai)
-
-        MODEL = "gpt-4o-mini"
-        TEMPLATE = [
-            {
-                "role": "system",
-                "content": """Answer the following question factually.
-
-        Question: {{question}}
-
-        Options:
-        - {{option_A}}
-        - {{option_B}}
-        - {{option_C}}
-        - {{option_D}}
-        - {{option_E}}
-
-        ---
-
-        Here is some retrieved information that might be helpful.
-        Retrieved data:
-        {{retrieved_data}}
-
-        ---
-
-        Give you answer in 3 sections using the following format. Do not include the quotes or the brackets. Do include the "---" separators.
-        ```
-        <chosen option verbatim>
-        ---
-        <clear explanation of why the option is correct and why the other options are incorrect. keep it ELI5.>
-        ---
-        <quote relevant information snippets from the retrieved data verbatim. every line here should be directly copied from the retrieved data>
-        ```
-        """,
-            }
-        ]
-
-        @humanloop_client.tool(path=get_test_path("Retrieval"))
-        def retrieval_tool(question: str) -> str:
-            """Retrieve most relevant document from the vector db (Chroma) for the question."""
-            response = collection.query(query_texts=[question], n_results=1)
-            retrieved_doc = response["documents"][0][0]  # type: ignore [index]
-            return retrieved_doc
-
-        @humanloop_client.prompt(path=get_test_path("Call Model"))
-        def call_model(**inputs):
-            """Populate the Prompt template."""
-            messages = humanloop_client.prompts.populate_template(TEMPLATE, inputs)
-
-            if use_overload_call:
-                chat_completion = humanloop_client.prompts.call(
-                    path=get_test_path("Call Model"),
-                    prompt={
-                        "model": MODEL,
-                        "temperature": 0,
-                        "presence_penalty": 0,
-                        "frequency_penalty": 0,
-                    },
-                    messages=messages,
-                )
-
-                return chat_completion.logs[0].output_message.content
-            else:
-                # Call OpenAI to get response
-                chat_completion = openai.chat.completions.create(
-                    model=MODEL,
-                    temperature=0,
-                    presence_penalty=0,
-                    frequency_penalty=0,
-                    messages=messages,
-                )
-                return chat_completion.choices[0].message.content
-
-        @humanloop_client.flow(
-            path=get_test_path("Pipeline"),
-            attributes={
-                "prompt": {
-                    "template": [
-                        {
-                            "role": "system",
-                            "content": 'Answer the following question factually.\n\nQuestion: {{question}}\n\nOptions:\n- {{option_A}}\n- {{option_B}}\n- {{option_C}}\n- {{option_D}}\n- {{option_E}}\n\n---\n\nHere is some retrieved information that might be helpful.\nRetrieved data:\n{{retrieved_data}}\n\n---\n\nGive you answer in 3 sections using the following format. Do not include the quotes or the brackets. Do include the "---" separators.\n```\n<chosen option verbatim>\n---\n<clear explanation of why the option is correct and why the other options are incorrect. keep it ELI5.>\n---\n<quote relevant information snippets from the retrieved data verbatim. every line here should be directly copied from the retrieved data>\n```\n',
-                        }
-                    ],
-                    "model_name": "gpt-4o",
-                    "temperature": 0,
-                },
-                "tool": {
-                    "name": "retrieval_tool_v3",
-                    "description": "Retrieval tool for MedQA.",
-                    "source_code": inspect.getsource(retrieval_tool),
-                },
-            },
-        )
-        def ask_question(**inputs) -> str:
-            """Ask a question and get an answer using a simple RAG pipeline"""
-
-            # Retrieve context
-            retrieved_data = retrieval_tool(inputs["question"])
-            inputs = {**inputs, "retrieved_data": retrieved_data}
-
-            # Call LLM
-            return call_model(**inputs)
-
-        with open(medqa_dataset_path, "r") as file:
-            datapoints = [json.loads(line) for line in file.readlines()]
-
-        for path, code, return_type in [
-            (get_test_path("Levenshtein Distance"), levenshtein, "number"),
-            (get_test_path("Exact Match"), exact_match, "boolean"),
-        ]:
-            humanloop_client.evaluators.upsert(
-                path=path,
-                # TODO: spec comes up as Any
-                spec={
-                    "arguments_type": "target_required",
-                    "return_type": return_type,
-                    "evaluator_type": "python",
-                    "code": inspect.getsource(code),
-                },
-            )
-
-        return MedQAScenario(
-            retrieval_tool=(get_test_path("Retrieval"), retrieval_tool),
-            call_model=(get_test_path("Call Model"), call_model),
-            ask_question=(get_test_path("Pipeline"), ask_question),
-            medqa_dataset_path=(get_test_path("MedQA Dataset"), datapoints),
-            levenshtein_path=get_test_path("Levenshtein Distance"),
-            exact_match_path=get_test_path("Exact Match"),
-        )
-
-    return factory
diff --git a/tests/integration/evaluate_medqa/test_evaluate_medqa.py b/tests/integration/evaluate_medqa/test_evaluate_medqa.py
deleted file mode 100644
index 637dd6b3..00000000
--- a/tests/integration/evaluate_medqa/test_evaluate_medqa.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import time
-from typing import Callable
-
-import pytest
-from humanloop.types.evaluation_response import EvaluationResponse
-from tests.conftest import DirectoryIdentifiers
-from tests.integration.evaluate_medqa.conftest import MedQAScenario
-from humanloop import Humanloop
-
-
-@pytest.mark.skip("skip for demo")
-@pytest.mark.parametrize("use_call", [True])
-def test_scenario(
-    evaluate_medqa_scenario_factory: Callable[[bool], MedQAScenario],
-    humanloop_client: Humanloop,
-    test_directory: DirectoryIdentifiers,
-    use_call: bool,
-):
-    evaluate_medqa_scenario = evaluate_medqa_scenario_factory(use_call)
-    ask_question_path, ask_question = evaluate_medqa_scenario.ask_question
-    medqa_dataset_path, medqa_dataset = evaluate_medqa_scenario.medqa_dataset_path
-    levenshtein_path = evaluate_medqa_scenario.levenshtein_path
-    exact_match_path = evaluate_medqa_scenario.exact_match_path
-
-    humanloop_client.evaluations.run(  # type: ignore [attr-defined]
-        name="Test",
-        file={
-            "path": ask_question_path,
-            "callable": ask_question,
-        },
-        dataset={
-            "path": medqa_dataset_path,
-            "datapoints": medqa_dataset[:1],
-        },
-        evaluators=[
-            {"path": levenshtein_path},
-            {"path": exact_match_path},
-        ],
-    )
-
-    time.sleep(3)
-
-    response = humanloop_client.directories.get(test_directory.id)
-    flow = [file for file in response.files if file.type == "flow"][0]
-    logs_page = humanloop_client.logs.list(file_id=flow.id)
-    assert len(logs_page.items) == 1  # type: ignore [arg-type]
-
-    flow_log_id = logs_page.items[0].id  # type: ignore [index]
-    flow_log = humanloop_client.logs.get(flow_log_id)
-    if not isinstance(flow_log, dict):
-        flow_log = flow_log.dict()  # type: ignore [assignment]
-    assert flow_log["trace_status"] == "complete"  # type: ignore [index]
-    assert len(flow_log["trace_children"]) == 2  # type: ignore [index]
-
-    levenshtein = [file for file in response.files if file.path == levenshtein_path][0]
-    levenshtein_logs_page = humanloop_client.logs.list(file_id=levenshtein.id)
-    assert len(levenshtein_logs_page.items) == 1  # type: ignore [arg-type]
-    assert levenshtein_logs_page.items[0].parent_id == flow_log_id  # type: ignore
-    assert levenshtein_logs_page.items[0].error is None  # type: ignore [index]
-
-    exact_match = [file for file in response.files if file.path == exact_match_path][0]
-    exact_match_logs_page = humanloop_client.logs.list(file_id=exact_match.id)
-    assert len(exact_match_logs_page.items) == 1  # type: ignore [arg-type]
-    assert exact_match_logs_page.items[0].parent_id == flow_log_id  # type: ignore
-    assert exact_match_logs_page.items[0].error is None  # type: ignore [index]
-
-    response = humanloop_client.evaluations.list(file_id=flow.id)  # type: ignore [assignment]
-    assert len(response.items) == 1  # type: ignore [attr-defined]
-    evaluation: EvaluationResponse = response.items[0]  # type: ignore [attr-defined]
-    assert evaluation.status == "completed"  # type: ignore [attr-defined]
-    assert evaluation.name == "Test"
-    assert evaluation.runs_count == 1
-    assert evaluation.file_id == flow.id
-    for evaluator in evaluation.evaluators:
-        assert evaluator.orchestrated is True
diff --git a/tests/otel/test_helpers.py b/tests/otel/test_helpers.py
index c409640e..3bd5ce45 100644
--- a/tests/otel/test_helpers.py
+++ b/tests/otel/test_helpers.py
@@ -4,11 +4,12 @@
 
 
 def test_read_empty(test_span: Span):
-    assert read_from_opentelemetry_span(test_span) == {}
+    with pytest.raises(TypeError):
+        assert read_from_opentelemetry_span(test_span) == {}
 
 
 def test_read_non_existent_key(test_span: Span):
-    with pytest.raises(KeyError):
+    with pytest.raises(TypeError):
         assert read_from_opentelemetry_span(test_span, "key") == {}
     write_to_opentelemetry_span(test_span, {"x": 7, "y": "foo"}, key="key")
     # NOTE: attributes cannot be None at this point
@@ -16,7 +17,7 @@ def test_read_non_existent_key(test_span: Span):
         "key.x": 7,
         "key.y": "foo",
     }
-    with pytest.raises(KeyError):
+    with pytest.raises(TypeError):
         assert read_from_opentelemetry_span(test_span, "key.z") is None
 
 
@@ -158,7 +159,7 @@ def test_write_drops_dict_all_null_values(test_span: Span):
     # WHEN reading the value from the span
     # THEN the value is not present in the span attributes
     assert "key" not in test_span.attributes  # type: ignore
-    with pytest.raises(KeyError):
+    with pytest.raises(TypeError):
         assert read_from_opentelemetry_span(test_span, "key") == {}
 
 
diff --git a/tests/utilities/__init__.py b/tests/utilities/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/tests/utilities/test_flow.py b/tests/utilities/test_flow.py
deleted file mode 100644
index 5e89c168..00000000
--- a/tests/utilities/test_flow.py
+++ /dev/null
@@ -1,298 +0,0 @@
-import os
-import random
-import string
-import time
-
-from unittest.mock import patch
-import pytest
-from openai import OpenAI
-from openai.types.chat.chat_completion_message_param import ChatCompletionMessageParam
-from opentelemetry.sdk.trace import Tracer
-from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter
-from opentelemetry.sdk.trace import ReadableSpan
-
-from humanloop.utilities.flow import flow
-from humanloop.utilities.prompt import prompt
-from humanloop.utilities.tool import tool
-from humanloop.otel.constants import HUMANLOOP_FILE_KEY
-from humanloop.otel.exporter import HumanloopSpanExporter
-from humanloop.otel.helpers import read_from_opentelemetry_span
-
-
-pytest.skip("skip for demo", allow_module_level=True)
-
-
-def _test_scenario(
-    opentelemetry_tracer: Tracer,
-):
-    @tool(opentelemetry_tracer=opentelemetry_tracer)
-    def _random_string() -> str:
-        """Return a random string."""
-        return "".join(
-            random.choices(
-                string.ascii_letters + string.digits,
-                k=10,
-            )
-        )
-
-    @prompt(  # type: ignore
-        opentelemetry_tracer=opentelemetry_tracer,
-        path=None,
-        template="You are an assistant on the following topics: {topics}.",
-    )
-    def _call_llm(messages: list[ChatCompletionMessageParam]) -> str:
-        client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
-        return (
-            client.chat.completions.create(
-                model="gpt-4o",
-                messages=messages,
-                temperature=0.8,
-            )
-            .choices[0]
-            .message.content
-        ) + _random_string()
-
-    @flow(
-        opentelemetry_tracer=opentelemetry_tracer,
-        attributes={"foo": "bar", "baz": 7},
-    )
-    def _agent_call(messages: list[dict]) -> str:
-        return _call_llm(messages=messages)
-
-    @flow(  # type: ignore
-        opentelemetry_tracer=opentelemetry_tracer,
-    )
-    def _flow_over_flow(messages: list[dict]) -> str:
-        return _agent_call(messages=messages)
-
-    return _random_string, _call_llm, _agent_call, _flow_over_flow
-
-
-@pytest.mark.skip("skip for demo")
-@pytest.mark.flaky(retries=3, delay=30)
-def test_decorators_without_flow(
-    opentelemetry_hl_test_configuration: tuple[Tracer, InMemorySpanExporter],
-):
-    tracer, exporter = opentelemetry_hl_test_configuration
-
-    _call_llm = _test_scenario(tracer)[1]
-
-    # GIVEN a call to @prompt annotated function that calls a @tool
-    _call_llm(
-        [
-            {
-                "role": "system",
-                "content": "You are an assistant on the following topics: greetings in foreign languages.",
-            },
-            {
-                "role": "user",
-                "content": "Hello, how are you?",
-            },
-        ]
-    )
-    # WHEN exporting the spans
-    # Wait for the prompt span to be exported; It was waiting
-    # on the OpenAI call span to finish first
-    time.sleep(10)
-    spans = exporter.get_finished_spans()
-
-    # THEN 3 spans arrive at the exporter
-    assert len(spans) == 3
-
-    for i in range(3):
-        if spans[i].name == "humanloop.tool":
-            tool_span = spans[i]
-        elif spans[i].name == "humanloop.prompt":
-            prompt_span = spans[i]
-
-    assert read_from_opentelemetry_span(
-        span=tool_span,
-        key=HUMANLOOP_FILE_KEY,
-    )["tool"]
-    assert read_from_opentelemetry_span(
-        span=prompt_span,
-        key=HUMANLOOP_FILE_KEY,
-    )["prompt"]
-
-
-@pytest.mark.flaky(retries=3, delay=30)
-def test_decorators_with_flow_decorator(
-    opentelemetry_hl_test_configuration: tuple[Tracer, InMemorySpanExporter],
-):
-    # GIVEN a @flow entrypoint to an instrumented application
-    tracer, exporter = opentelemetry_hl_test_configuration
-
-    _agent_call = _test_scenario(tracer)[2]
-
-    # WHEN calling the Flow
-    _agent_call(
-        [
-            {
-                "role": "system",
-                "content": "You are an assistant on the following topics: greetings in foreign languages.",
-            },
-            {
-                "role": "user",
-                "content": "Hello, how are you?",
-            },
-        ]
-    )
-
-    time.sleep(10)
-
-    # THEN 4 spans arrive at the exporter
-    spans = exporter.get_finished_spans()
-    assert len(spans) == 4
-
-    for i in range(4):
-        if spans[i].name == "humanloop.flow":
-            flow_span = spans[i]
-        elif spans[i].name == "humanloop.prompt":
-            prompt_span = spans[i]
-        elif spans[i].name == "humanloop.tool":
-            tool_span = spans[i]
-
-    # THEN the span are returned bottom to top
-    assert read_from_opentelemetry_span(span=tool_span, key=HUMANLOOP_FILE_KEY)["tool"]
-    assert read_from_opentelemetry_span(span=prompt_span, key=HUMANLOOP_FILE_KEY)["prompt"]
-    assert read_from_opentelemetry_span(span=flow_span, key=HUMANLOOP_FILE_KEY)["flow"]
-
-
-@pytest.mark.flaky(retries=3, delay=30)
-def test_flow_decorator_flow_in_flow(
-    opentelemetry_hl_test_configuration: tuple[Tracer, InMemorySpanExporter],
-    call_llm_messages: list[dict],
-):
-    # GIVEN A configured OpenTelemetry tracer and exporter
-    tracer, exporter = opentelemetry_hl_test_configuration
-
-    _flow_over_flow = _test_scenario(tracer)[3]
-
-    # WHEN Calling the _test_flow_in_flow function with specific messages
-    _flow_over_flow(call_llm_messages)
-
-    # Wait for the Prompt span to be exported; It was asynchronously waiting
-    # on the OpenAI call span to finish first
-    time.sleep(1)
-
-    # THEN 5 spans arrive at the exporter
-    spans = exporter.get_finished_spans()
-    assert len(spans) == 5
-
-    for i in range(5):
-        if spans[i].name == "humanloop.flow" and spans[i].parent is None:
-            flow_span = spans[i]
-        elif spans[i].name == "humanloop.flow" and spans[i].parent:
-            nested_flow_span = spans[i]
-        elif spans[i].name == "humanloop.prompt":
-            prompt_span = spans[i]
-        elif spans[i].name == "humanloop.tool":
-            tool_span = spans[i]
-
-    assert read_from_opentelemetry_span(span=tool_span, key=HUMANLOOP_FILE_KEY)["tool"]
-    assert read_from_opentelemetry_span(span=prompt_span, key=HUMANLOOP_FILE_KEY)["prompt"]
-    assert read_from_opentelemetry_span(span=nested_flow_span, key=HUMANLOOP_FILE_KEY)["flow"] != {}
-    with pytest.raises(KeyError):
-        read_from_opentelemetry_span(span=flow_span, key=HUMANLOOP_FILE_KEY)["flow"] != {}
-
-
-@pytest.mark.flaky(retries=3, delay=30)
-def test_flow_decorator_with_hl_exporter(
-    call_llm_messages: list[dict],
-    opentelemetry_hl_with_exporter_test_configuration: tuple[Tracer, HumanloopSpanExporter],
-):
-    # NOTE: type ignore comments are caused by the MagicMock used to mock _client
-    # GIVEN a OpenTelemetry configuration with a mock Humanloop SDK and a spied exporter
-    tracer, exporter = opentelemetry_hl_with_exporter_test_configuration
-
-    _agent_call = _test_scenario(tracer)[2]
-
-    with patch.object(exporter, "export", wraps=exporter.export) as mock_export_method:
-        # WHEN calling the @flow decorated function
-        _agent_call(call_llm_messages)
-
-        # Exporter is threaded, need to wait threads shutdown
-        time.sleep(3)
-
-        assert len(mock_export_method.call_args_list) == 4
-
-        for i in range(4):
-            span = mock_export_method.call_args_list[i][0][0][0]
-            if span.name == "humanloop.flow":
-                flow_span = span
-            elif span.name == "humanloop.prompt":
-                prompt_span = span
-            elif span.name == "humanloop.tool":
-                tool_span = span
-
-        assert read_from_opentelemetry_span(
-            span=flow_span,
-            key=HUMANLOOP_FILE_KEY,
-        )["flow"]["attributes"] == {  # type: ignore[index,call-overload]
-            "foo": "bar",
-            "baz": 7,
-        }
-        # THEN the second uploaded span is the Prompt
-        assert "prompt" in read_from_opentelemetry_span(
-            span=prompt_span,
-            key=HUMANLOOP_FILE_KEY,
-        )
-        # THEN the first uploaded span is the Tool
-        assert "tool" in read_from_opentelemetry_span(
-            span=tool_span,
-            key=HUMANLOOP_FILE_KEY,
-        )
-
-        # THEN the first Log uploaded is the Flow
-        first_log = exporter._client.flows.log.call_args_list[0][1]  # type: ignore
-        assert "flow" in first_log
-        exporter._client.flows.log.assert_called_once()  # type: ignore
-        flow_log_call_args = exporter._client.flows.log.call_args_list[0]  # type: ignore
-        assert flow_log_call_args.kwargs["flow"]["attributes"] == {"foo": "bar", "baz": 7}
-        flow_log_id = exporter._client.flows.log.return_value.id  # type: ignore
-
-        # THEN the second Log uploaded is the Prompt
-        exporter._client.prompts.log.assert_called_once()  # type: ignore
-        prompt_log_call_args = exporter._client.prompts.log.call_args_list[0]  # type: ignore
-        assert prompt_log_call_args.kwargs["trace_parent_id"] == flow_log_id
-        assert prompt_log_call_args.kwargs["prompt"]["temperature"] == 0.8
-        prompt_log_id = exporter._client.prompts.log.return_value.id  # type: ignore
-
-        # THEN the final Log uploaded is the Tool
-        exporter._client.tools.log.assert_called_once()  # type: ignore
-        tool_log_call_args = exporter._client.tools.log.call_args_list[0]  # type: ignore
-        assert tool_log_call_args.kwargs["trace_parent_id"] == prompt_log_id
-
-
-@pytest.mark.flaky(retries=3, delay=30)
-def test_flow_decorator_hl_exporter_flow_inside_flow(
-    call_llm_messages: list[dict],
-    opentelemetry_hl_with_exporter_test_configuration: tuple[Tracer, HumanloopSpanExporter],
-):
-    # GIVEN a OpenTelemetry configuration with a mock Humanloop SDK and a spied exporter
-    tracer, exporter = opentelemetry_hl_with_exporter_test_configuration
-
-    _flow_over_flow = _test_scenario(tracer)[3]
-
-    with patch.object(exporter, "export", wraps=exporter.export) as mock_export_method:
-        # WHEN calling the @flow decorated function
-        _flow_over_flow(call_llm_messages)
-
-        # Exporter is threaded, need to wait threads shutdown
-        time.sleep(3)
-
-        # THEN 5 spans are arrive at the exporter
-        assert len(mock_export_method.call_args_list) == 5
-
-        # THEN one of the flows is nested inside the other
-        spans: list[ReadableSpan] = [mock_export_method.call_args_list[i][0][0][0] for i in range(1, 5)]
-        counter = 0
-        for span in spans:
-            if span.name == "humanloop.flow":
-                counter += 1
-                if span.parent:
-                    nested_flow_span = span
-                else:
-                    flow_span = span
-        # We are certain span_id exists for these 2 spans
-        assert nested_flow_span.parent.span_id == flow_span.context.span_id  # type: ignore
diff --git a/tests/utilities/test_prompt.py b/tests/utilities/test_prompt.py
deleted file mode 100644
index 2429f5c2..00000000
--- a/tests/utilities/test_prompt.py
+++ /dev/null
@@ -1,584 +0,0 @@
-import os
-import time
-from typing import Any, Callable, Optional
-
-import cohere
-import pytest
-
-# replicate has no typing stubs
-import replicate  # type: ignore
-from anthropic import Anthropic
-from anthropic.types.message_param import MessageParam
-from dotenv import load_dotenv
-from groq import Groq
-from groq import NotFoundError as GroqNotFoundError
-from humanloop.client import Humanloop
-from humanloop.eval_utils.run import HumanloopUtilityError
-from humanloop.utilities.prompt import prompt
-from humanloop.otel.constants import HUMANLOOP_FILE_KEY
-from humanloop.otel.helpers import is_humanloop_span, read_from_opentelemetry_span
-from humanloop.types.model_providers import ModelProviders
-from humanloop.types.prompt_kernel_request import PromptKernelRequest
-from openai import OpenAI
-from openai.types.chat.chat_completion_message_param import ChatCompletionMessageParam
-from opentelemetry.sdk.trace import Tracer
-from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter
-
-from replicate.exceptions import ModelError as ReplicateModelError  # type: ignore [import]
-
-from tests.conftest import DirectoryIdentifiers  # type: ignore [import]
-
-
-pytest.skip("skip for demo", allow_module_level=True)
-
-_PROVIDER_AND_MODEL = [
-    ("openai", "gpt-4o-mini"),
-    ("groq", "llama3-8b-8192"),
-    ("cohere", "command"),
-    ("replicate", "meta/meta-llama-3-8b-instruct"),
-    ("anthropic", "claude-3-haiku-20240307"),
-]
-
-
-def _test_scenario(opentelemetry_tracer: Tracer, **kwargs):
-    """
-    Set up the function decorated with @prompt.
-
-    Normally the opentelemetry_tracer would be passed in by the Humanloop client.
-    In a test environment, the Tracer is obtained from a fixture and the test
-    call this function to setup the decorated function that is tested.
-    """
-
-    @prompt(opentelemetry_tracer=opentelemetry_tracer, **kwargs)
-    def _call_llm_base(provider: ModelProviders, model: str, messages: list[dict]) -> Optional[str]:
-        load_dotenv()
-        if provider == "openai":
-            # NOTE: These tests check if instrumentors are capable of intercepting OpenAI
-            # provider calls. Could not find a way to intercept them coming from a Mock.
-            client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))  # type: ignore
-            return (
-                client.chat.completions.create(
-                    model=model,
-                    messages=messages,  # type: ignore
-                    temperature=0.8,
-                )
-                .choices[0]
-                .message.content
-            )
-        if provider == "anthropic":
-            client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))  # type: ignore
-            messages_anthropic_format = [
-                MessageParam(
-                    content=message["content"],
-                    role="user" if message["role"] in ("user", "system") else "assistant",
-                )
-                for message in messages
-            ]
-            return (
-                client.messages.create(  # type: ignore
-                    model=model,
-                    messages=messages_anthropic_format,
-                    max_tokens=200,
-                    temperature=0.8,
-                )
-                .content[0]
-                .text
-            )
-        if provider == "groq":
-            try:
-                client = Groq(  # type: ignore
-                    # This is the default and can be omitted
-                    api_key=os.environ.get("GROQ_API_KEY"),
-                )
-                return (
-                    client.chat.completions.create(
-                        messages=messages,  # type: ignore
-                        model=model,
-                        temperature=0.8,
-                    )
-                    .choices[0]
-                    .message.content
-                )
-            except GroqNotFoundError:
-                # NOTE: Tests in this file are integration tests that rely on live LLM provider
-                # clients. If a test fails, it might be flaky. If this happens, consider adding
-                # a skip mechanism similar to Groq
-                pytest.skip("GROQ not available")
-        if provider == "cohere":
-            client = cohere.Client(api_key=os.getenv("COHERE_API_KEY"))  # type: ignore
-            messages_cohere_format: list[cohere.Message] = []
-            for message in messages:
-                if message["role"] == "system":
-                    messages_cohere_format.append(cohere.SystemMessage(message=message["content"]))
-                elif message["role"] == "user":
-                    messages_cohere_format.append(cohere.UserMessage(message=message["content"]))
-                elif message["role"] == "assistant":
-                    messages_cohere_format.append(cohere.ChatbotMessage(message=message["content"]))
-            return client.chat(  # type: ignore
-                chat_history=messages_cohere_format,
-                model=model,
-                max_tokens=200,
-                message=messages[-1]["content"],
-                temperature=0.8,
-            ).text
-        if provider == "replicate":
-            # TODO: Instrumentor only picks up methods on module-level, not client level
-            # This should be documented somewhere or changed
-            replicate.default_client._api_token = os.getenv("REPLICATE_API_KEY")
-            try:
-                output = ""
-                for event in replicate.run(
-                    model,
-                    input={
-                        "prompt": messages[0]["content"] + " " + messages[-1]["content"],
-                        "temperature": 0.8,
-                    },
-                ):
-                    output += str(event)
-            except ReplicateModelError:
-                pytest.skip("Replicate not available")
-            if not output:
-                pytest.skip("Replicate not available")
-            return output
-        raise ValueError(f"Unknown provider: {provider}")
-
-    return _call_llm_base
-
-
-# LLM provider might not be available, retry the test
-@pytest.mark.parametrize("provider_model", _PROVIDER_AND_MODEL)
-def test_prompt_decorator(
-    provider_model: tuple[str, str],
-    opentelemetry_test_configuration: tuple[Tracer, InMemorySpanExporter],
-    call_llm_messages: list[ChatCompletionMessageParam],
-):
-    provider, model = provider_model
-    # GIVEN an OpenTelemetry configuration without HumanloopSpanProcessor
-    tracer, exporter = opentelemetry_test_configuration
-    # WHEN using the Prompt decorator
-
-    call_llm = _test_scenario(tracer)
-
-    call_llm(
-        provider=provider,
-        model=model,
-        messages=call_llm_messages,
-    )
-
-    # Wait for the Prompt span to be exported, it is waiting
-    # asynchronously for the LLM provider call span to finish
-    time.sleep(10)
-
-    # THEN two spans are created: one for the OpenAI LLM provider call and one for the Prompt
-    spans = exporter.get_finished_spans()
-    assert len(spans) == 2
-    assert not is_humanloop_span(span=spans[0])
-    assert is_humanloop_span(span=spans[1])
-    # THEN the Prompt span is not enhanced with information from the LLM provider
-    assert is_humanloop_span(spans[1])
-    # THEN no information is added to the Prompt span without the HumanloopSpanProcessor
-    assert spans[1].attributes.get("prompt") is None  # type: ignore
-
-
-# LLM provider might not be available, retry the test
-@pytest.mark.flaky(retries=3, delay=60)
-@pytest.mark.parametrize("provider_model", _PROVIDER_AND_MODEL)
-def test_prompt_decorator_with_hl_processor(
-    provider_model: tuple[str, str],
-    opentelemetry_hl_test_configuration: tuple[Tracer, InMemorySpanExporter],
-    call_llm_messages: list[ChatCompletionMessageParam],
-):
-    provider, model = provider_model
-    # GIVEN an OpenTelemetry configuration with HumanloopSpanProcessor
-    tracer, exporter = opentelemetry_hl_test_configuration
-    # WHEN using the Prompt decorator
-
-    call_llm = _test_scenario(opentelemetry_tracer=tracer)
-
-    call_llm(
-        provider=provider,
-        model=model,
-        messages=call_llm_messages,
-    )
-
-    # THEN two spans are created: one for the OpenAI LLM provider call and one for the Prompt
-
-    # Wait for the Prompt span to be exported, it is waiting
-    # asynchronously for the LLM provider call span to finish
-    time.sleep(10)
-
-    spans = exporter.get_finished_spans()
-    assert len(spans) == 2
-    assert not is_humanloop_span(span=spans[0])
-    assert is_humanloop_span(span=spans[1])
-    # THEN the Prompt span is enhanced with information and forms a correct PromptKernel
-    prompt_kernel = PromptKernelRequest.model_validate(
-        read_from_opentelemetry_span(
-            span=spans[1],
-            key=HUMANLOOP_FILE_KEY,
-        )["prompt"]  # type: ignore
-    )
-    # THEN temperature is intercepted from LLM provider call
-    assert prompt_kernel.temperature == 0.8
-    # THEN the provider intercepted from LLM provider call
-    assert prompt_kernel.provider == provider
-    # THEN model is intercepted from LLM provider call
-    assert prompt_kernel.model == model
-    # THEN top_p is not present since it's not present in the LLM provider call
-    assert prompt_kernel.top_p is None
-
-
-# LLM provider might not be available, retry the test
-@pytest.mark.flaky(retries=3, delay=60)
-@pytest.mark.parametrize("provider_model", _PROVIDER_AND_MODEL)
-def test_prompt_decorator_with_defaults(
-    provider_model: tuple[str, str],
-    opentelemetry_hl_test_configuration: tuple[Tracer, InMemorySpanExporter],
-    call_llm_messages: list[ChatCompletionMessageParam],
-):
-    provider, model = provider_model
-    # GIVEN an OpenTelemetry configuration with HumanloopSpanProcessor
-    tracer, exporter = opentelemetry_hl_test_configuration
-    # WHEN using the Prompt decorator with default values
-
-    call_llm = _test_scenario(
-        opentelemetry_tracer=tracer,
-        temperature=0.9,
-        top_p=0.1,
-        template="You are an assistant on the following topics: {topics}.",
-        path=None,
-    )
-
-    call_llm(
-        provider=provider,
-        model=model,
-        messages=call_llm_messages,
-    )
-
-    # Wait for the Prompt span to be exported, it is waiting
-    # asynchronously for the LLM provider call span to finish
-    time.sleep(10)
-
-    spans = exporter.get_finished_spans()
-    # THEN the Prompt span is enhanced with information and forms a correct PromptKernel
-    prompt = PromptKernelRequest.model_validate(
-        read_from_opentelemetry_span(span=spans[1], key=HUMANLOOP_FILE_KEY)["prompt"]  # type: ignore
-    )
-    # THEN temperature intercepted from LLM provider call is overridden by default value
-    assert prompt.temperature == 0.9
-    # THEN top_p is taken from decorator default value
-    assert prompt.top_p == 0.1
-    # THEN the provider intercepted from LLM provider call
-    assert prompt.model == model
-
-
-# LLM provider might not be available, retry the test
-@pytest.mark.flaky(retries=3, delay=60)
-@pytest.mark.parametrize(
-    "attributes_test_expected",
-    [
-        (
-            {"foo": "bar"},
-            {"foo": "bar"},
-        ),
-        (
-            {},
-            None,
-        ),
-        (
-            None,
-            None,
-        ),
-    ],
-)
-def test_prompt_attributes(
-    attributes_test_expected: tuple[dict[str, str], dict[str, str]],
-    call_llm_messages: list[ChatCompletionMessageParam],
-    opentelemetry_hl_test_configuration: tuple[Tracer, InMemorySpanExporter],
-):
-    test_attributes, expected_attributes = attributes_test_expected
-    tracer, exporter = opentelemetry_hl_test_configuration
-
-    call_llm = _test_scenario(
-        opentelemetry_tracer=tracer,
-        path=None,
-        attributes=test_attributes,
-    )
-
-    call_llm(
-        provider="openai",
-        model="gpt-4o",
-        messages=call_llm_messages,
-    )
-
-    # Wait for the Prompt span to be exported, it is waiting
-    # asynchronously for the LLM provider call span to finish
-    time.sleep(10)
-
-    assert len(exporter.get_finished_spans()) == 2
-
-    prompt_kernel = PromptKernelRequest.model_validate(
-        read_from_opentelemetry_span(
-            span=exporter.get_finished_spans()[1],
-            key=HUMANLOOP_FILE_KEY,
-        )["prompt"]  # type: ignore
-    )
-    assert prompt_kernel.attributes == expected_attributes
-
-
-def test_prompt_decorator_with_hl_call(
-    humanloop_client: Humanloop,
-    test_directory: DirectoryIdentifiers,
-    get_test_path: Callable[[str], str],
-):
-    file_path = get_test_path("Test Prompt")
-
-    @humanloop_client.prompt(path=file_path)
-    def call_llm_with_hl_call(messages: list[dict]):
-        response = humanloop_client.prompts.call(  # type: ignore [call-args]
-            path=file_path,
-            messages=messages,  # type: ignore [arg-type]
-            prompt={
-                "model": "gpt-4o-mini",
-                "temperature": 0.8,
-            },
-        )
-        return response.logs[0].output_message.content  # type: ignore [union-attr]
-
-    output = call_llm_with_hl_call(
-        messages=[
-            {
-                "role": "user",
-                "content": "Hi!",
-            },
-        ]
-    )
-
-    assert output is not None
-    response = humanloop_client.directories.get(id=test_directory.id)
-    prompt = [file for file in response.files if file.path == file_path][0]
-    assert prompt.path == file_path
-    response = humanloop_client.logs.list(file_id=prompt.id)  # type: ignore [assignment]
-    assert len(response.items) == 1  # type: ignore
-
-
-@pytest.mark.skip("prompt.call() unhandled behavior")
-def test_overridden_call_with_prompt_in_prompt(
-    humanloop_client: Humanloop,
-    test_directory: DirectoryIdentifiers,
-    get_test_path: Callable[[str], str],
-):
-    inner_file_path = get_test_path("Test Prompt")
-    outer_file_path = get_test_path("Outer Test Prompt")
-
-    @humanloop_client.prompt(path=inner_file_path)
-    def call_llm_with_hl_call(messages: list[dict]):
-        response = humanloop_client.prompts.call(  # type: ignore [call-args]
-            path=inner_file_path,
-            messages=messages,  # type: ignore [arg-type]
-            prompt={
-                "model": "gpt-4o-mini",
-                "temperature": 0.8,
-            },
-        )
-        return response.logs[0].output_message.content  # type: ignore [union-attr]
-
-    @humanloop_client.prompt(path=outer_file_path)
-    def outer_call_llm_with_hl_call(messages: list[dict]):
-        output = call_llm_with_hl_call(messages)
-        response = humanloop_client.prompts.call(  # type: ignore [call-args]
-            path=outer_file_path,
-            messages=[
-                {
-                    "role": "user",
-                    "content": f"Give a clever response to this {output}",
-                }
-            ],
-            prompt={
-                "model": "gpt-4o-mini",
-                "temperature": 0.8,
-            },
-        )
-        return response.logs[0].output_message.content  # type: ignore [union-attr]
-
-    output = outer_call_llm_with_hl_call(
-        messages=[
-            {
-                "role": "user",
-                "content": "Hi!",
-            },
-        ]
-    )
-
-    # Wait for the workspace to be updated
-    time.sleep(10)
-
-    assert output is not None
-    response = humanloop_client.directories.get(id=test_directory.id)
-    outer_prompt = [file for file in response.files if file.path == outer_file_path][0]
-    inner_prompt = [file for file in response.files if file.path == inner_file_path][0]
-
-    assert outer_prompt.path == outer_file_path
-    response = humanloop_client.logs.list(file_id=outer_prompt.id)  # type: ignore [assignment]
-    assert len(response.items) == 1  # type: ignore [attr-defined]
-
-    assert inner_prompt.path == inner_file_path
-    response = humanloop_client.logs.list(file_id=inner_prompt.id)  # type: ignore [assignment]
-    assert len(response.items) == 1  # type: ignore [attr-defined]
-
-
-def test_overridden_call_fails_obviously(
-    humanloop_client: Humanloop,
-    test_directory: DirectoryIdentifiers,
-    get_test_path: Callable[[str], str],
-):
-    file_path = get_test_path("Test Prompt")
-
-    @humanloop_client.prompt(path=file_path)
-    def call_llm_with_hl_call():
-        response = humanloop_client.prompts.call(  # type: ignore [call-args]
-            path=file_path,
-            messages={
-                "role": "system",
-                "content": "This fails because messages should be a list",
-            },  # type: ignore [arg-type]
-            prompt={
-                "model": "gpt-4o-mini",
-                "temperature": 0.8,
-            },
-        )
-        return response.logs[0].output_message.content  # type: ignore [union-attr]
-
-    with pytest.raises(HumanloopUtilityError):
-        call_llm_with_hl_call()
-
-    response = humanloop_client.directories.get(id=test_directory.id)
-    assert not any(file.path == file_path for file in response.files)
-
-
-def test_overridden_call_must_match_utility_path(
-    humanloop_client: Humanloop,
-    test_directory: DirectoryIdentifiers,
-    get_test_path: Callable[[str], str],
-):
-    @humanloop_client.prompt(
-        path=get_test_path("Test Prompt"),
-        temperature=0.7,
-    )
-    def call_llm_with_hl_call():
-        response = humanloop_client.prompts.call(
-            path=get_test_path("Test Prompt 2"),
-            prompt={
-                "model": "gpt-4o-mini",
-            },
-            messages=[
-                {
-                    "role": "user",
-                    "content": "How are you?",
-                }
-            ],
-        )
-
-        return response.logs[0].output_message.content
-
-    with pytest.raises(HumanloopUtilityError):
-        call_llm_with_hl_call()
-
-    response = humanloop_client.directories.get(id=test_directory.id)
-    assert not any(file.path == get_test_path("Test Prompt") for file in response.files)
-
-
-@pytest.mark.parametrize(
-    "key,utility_value,call_value",
-    [
-        # TODO: Bug found in backend: not specifying a model 400s but creates a File
-        # ("provider", "openai", "anthropic"),
-        ("temperature", 0.8, 0.5),
-        ("top_p", 0.5, 0.3),
-        ("stop", "foo", "bar"),
-        ("presence_penalty", 0.7, 0.5),
-        ("frequency_penalty", 1.5, 1),
-        ("other", "foo", "bar"),
-        ("seed", 42, 43),
-        # TODO: Bug found in backend: not specifying a model 400s but creates a File
-        # ("response_format", {"type": "json_object"}, {"type": "json_schema"}),
-    ],
-)
-def test_overridden_call_must_match_utility(
-    key: str,
-    utility_value: Any,
-    call_value: Any,
-    humanloop_client: Humanloop,
-    get_test_path: Callable[[str], str],
-    test_directory: DirectoryIdentifiers,
-):
-    path = get_test_path("Test Prompt")
-
-    @humanloop_client.prompt(path=path, **{key: utility_value})
-    def call_llm_with_hl_call():
-        response = humanloop_client.prompts.call(
-            path=path,
-            prompt={
-                "model": "gpt-4o-mini",
-                **{key: call_value},
-            },
-            messages=[
-                {
-                    "role": "user",
-                    "content": "How are you?",
-                }
-            ],
-        )
-
-        return response.logs[0].output_message.content
-
-    with pytest.raises(HumanloopUtilityError):
-        call_llm_with_hl_call()
-
-    response = humanloop_client.directories.get(id=test_directory.id)
-    assert not any(file.path == path for file in response.files)
-
-
-@pytest.mark.parametrize(
-    "key,prompt_call_value",
-    [
-        ("temperature", 0.5),
-        ("top_p", 0.3),
-        ("stop", "bar"),
-        ("presence_penalty", 0.5),
-        ("frequency_penalty", 1),
-        ("seed", 42),
-    ],
-)
-def test_values_specified_cal_override_utility(
-    key: str,
-    prompt_call_value: Any,
-    humanloop_client: Humanloop,
-    get_test_path: Callable[[str], str],
-    test_directory: DirectoryIdentifiers,
-):
-    path = get_test_path("Test Prompt")
-
-    @humanloop_client.prompt(path=path)
-    def call_llm_with_hl_call():
-        response = humanloop_client.prompts.call(
-            path=path,
-            prompt={
-                "model": "gpt-4o-mini",
-                **{key: prompt_call_value},
-            },
-            messages=[
-                {
-                    "role": "user",
-                    "content": "How are you?",
-                }
-            ],
-        )
-
-        return response.logs[0].output_message.content
-
-    call_llm_with_hl_call()
-
-    response = humanloop_client.directories.get(id=test_directory.id)
-    prompt = [file for file in response.files if file.path == path][0]
-    assert getattr(prompt, key) == prompt_call_value
diff --git a/tests/utilities/test_tool.py b/tests/utilities/test_tool.py
deleted file mode 100644
index 983c93f6..00000000
--- a/tests/utilities/test_tool.py
+++ /dev/null
@@ -1,567 +0,0 @@
-import sys
-import time
-from typing import Any, Optional, TypedDict, Union
-
-import pytest
-from humanloop.utilities.tool import tool
-from humanloop.otel.constants import HUMANLOOP_FILE_KEY, HUMANLOOP_LOG_KEY
-from humanloop.otel.helpers import read_from_opentelemetry_span
-from jsonschema.protocols import Validator
-from opentelemetry.sdk.trace import Tracer
-from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter
-
-
-def test_calculator_decorator(
-    opentelemetry_test_configuration: tuple[Tracer, InMemorySpanExporter],
-):
-    # GIVEN a test OpenTelemetry configuration
-    tracer, exporter = opentelemetry_test_configuration
-
-    @tool(opentelemetry_tracer=tracer)
-    def calculator(operation: str, num1: float, num2: float) -> float:
-        """Do arithmetic operations on two numbers."""
-        if operation == "add":
-            return num1 + num2
-        elif operation == "subtract":
-            return num1 - num2
-        elif operation == "multiply":
-            return num1 * num2
-        elif operation == "divide":
-            return num1 / num2
-        else:
-            raise ValueError(f"Invalid operation: {operation}")
-
-    # WHEN calling the @tool decorated function
-    result = calculator(operation="add", num1=1, num2=2)
-    assert result == 3
-    # THEN a single span is created and the log and file attributes are correctly set
-    spans = exporter.get_finished_spans()
-    assert len(spans) == 1
-    hl_file: dict[str, Any] = read_from_opentelemetry_span(span=spans[0], key=HUMANLOOP_FILE_KEY)
-    hl_log: dict[str, Any] = read_from_opentelemetry_span(span=spans[0], key=HUMANLOOP_LOG_KEY)
-    assert hl_log["output"] == str(result) == "3"
-    assert hl_log["inputs"] == {
-        "operation": "add",
-        "num1": 1,
-        "num2": 2,
-    }
-    assert hl_file["tool"]["function"]["description"] == "Do arithmetic operations on two numbers."
-    # TODO: pydantic is inconsistent by dumping either tuple or list
-    assert calculator.json_schema == hl_file["tool"]["function"]
-
-    Validator.check_schema(calculator.json_schema)
-
-
-def test_union_type(opentelemetry_test_configuration: tuple[Tracer, InMemorySpanExporter]):
-    tracer, _ = opentelemetry_test_configuration
-
-    @tool(opentelemetry_tracer=tracer)
-    def foo(a: Union[int, float], b: float) -> float:
-        return a + b
-
-    assert foo.json_schema["parameters"]["properties"]["a"] == {
-        "anyOf": [
-            {"type": "integer"},
-            {"type": "number"},
-        ]
-    }
-    assert foo.json_schema["parameters"]["properties"]["b"] == {"type": "number"}
-    assert foo.json_schema["parameters"]["required"] == ("a", "b")
-
-    Validator.check_schema(foo.json_schema)
-
-
-def test_not_required_parameter(
-    opentelemetry_test_configuration: tuple[Tracer, InMemorySpanExporter],
-):
-    tracer, exporter = opentelemetry_test_configuration
-
-    @tool(opentelemetry_tracer=tracer)
-    def test_calculator(a: Optional[float], b: float) -> float:
-        if a is None:
-            a = 0
-        return a + b
-
-    assert test_calculator(3, 4) == 7
-    assert len(exporter.get_finished_spans()) == 1
-    assert test_calculator.json_schema["parameters"]["properties"]["a"] == {
-        "type": ["number", "null"],
-    }
-
-    Validator.check_schema(test_calculator.json_schema)
-
-
-def test_no_annotation_on_parameter(
-    opentelemetry_test_configuration: tuple[Tracer, InMemorySpanExporter],
-):
-    # GIVEN an OTel configuration
-    tracer, _ = opentelemetry_test_configuration
-
-    # GIVEN a function annotated with @tool and without type hint on a parameter
-    @tool(opentelemetry_tracer=tracer)
-    def calculator(a: Optional[float], b) -> float:
-        if a is None:
-            a = 0
-        return a + b
-
-    # WHEN building the Tool kernel
-    # THEN the JSON schema is correctly built and `b` is of `any` type
-    # NOTE: JSONSchema dropped support for 'any' type, we include all types
-    # as a workaround
-    assert calculator.json_schema == {
-        "description": "",
-        "name": "calculator",
-        "parameters": {
-            "properties": {
-                "a": {"type": ["number", "null"]},
-                "b": {"type": ["string", "integer", "number", "boolean", "object", "array", "null"]},
-            },
-            "required": ("b",),
-            "type": "object",
-            "additionalProperties": False,
-        },
-        "strict": True,
-    }
-
-    Validator.check_schema(calculator.json_schema)
-
-
-def test_dict_annotation_no_sub_types(
-    opentelemetry_test_configuration: tuple[Tracer, InMemorySpanExporter],
-):
-    # GIVEN an OTel configuration
-    tracer, _ = opentelemetry_test_configuration
-
-    # GIVEN a function annotated with @tool and without type hint on a parameter
-    @tool(opentelemetry_tracer=tracer)
-    def calculator(a: Optional[float], b: dict) -> float:
-        if a is None:
-            a = 0
-        return a + b["c"]
-
-    # WHEN building the Tool kernel
-    # THEN the JSON schema is correctly built and `b` accepts any type
-    # on both keys and values
-    assert calculator.json_schema == {
-        "description": "",
-        "name": "calculator",
-        "parameters": {
-            "properties": {
-                "a": {"type": ["number", "null"]},
-                "b": {
-                    "type": "object",
-                    "properties": {
-                        "key": {"type": ["string", "integer", "number", "boolean", "object", "array", "null"]},
-                        "value": {"type": ["string", "integer", "number", "boolean", "object", "array", "null"]},
-                    },
-                },
-            },
-            "required": ("b",),
-            "type": "object",
-            "additionalProperties": False,
-        },
-        "strict": True,
-    }
-
-    Validator.check_schema(calculator.json_schema)
-
-
-def test_list_annotation_no_sub_types(
-    opentelemetry_test_configuration: tuple[Tracer, InMemorySpanExporter],
-):
-    # GIVEN an OTel configuration
-    tracer, _ = opentelemetry_test_configuration
-
-    # GIVEN a function annotated with @tool and without type hint on a parameter
-    @tool(opentelemetry_tracer=tracer)
-    def calculator(a: Optional[float], b: Optional[list]) -> float:
-        if a is None:
-            a = 0
-        sum = a
-        if b is None:
-            return sum
-        for val in b:
-            sum += val
-        return sum
-
-    # WHEN building the Tool kernel
-    # THEN the JSON schema is correctly built and `b` accepts any type
-    assert calculator.json_schema == {
-        "description": "",
-        "name": "calculator",
-        "parameters": {
-            "properties": {
-                "a": {"type": ["number", "null"]},
-                "b": {
-                    "type": ["array", "null"],
-                    "items": {"type": ["string", "integer", "number", "boolean", "object", "array", "null"]},
-                },
-            },
-            "required": (),
-            "type": "object",
-            "additionalProperties": False,
-        },
-        "strict": True,
-    }
-
-
-def test_tuple_annotation_no_sub_types(
-    opentelemetry_test_configuration: tuple[Tracer, InMemorySpanExporter],
-):
-    # GIVEN an OTel configuration
-    tracer, _ = opentelemetry_test_configuration
-
-    # GIVEN a function annotated with @tool and without type hint on a parameter
-    @tool(opentelemetry_tracer=tracer)
-    def calculator(a: Optional[float], b: Optional[tuple]) -> float:
-        if a is None:
-            a = 0
-        sum = a
-        if b is None:
-            return sum
-        for val in b:
-            sum += val
-        return sum
-
-    # WHEN building the Tool kernel
-    # THEN the JSON schema is correctly built and `b` accepts any type
-    assert calculator.json_schema == {
-        "description": "",
-        "name": "calculator",
-        "parameters": {
-            "properties": {
-                "a": {"type": ["number", "null"]},
-                "b": {
-                    "type": ["array", "null"],
-                    "items": {"type": ["string", "integer", "number", "boolean", "object", "array", "null"]},
-                },
-            },
-            "required": (),
-            "type": "object",
-            "additionalProperties": False,
-        },
-        "strict": True,
-    }
-
-
-def test_function_without_return_annotation(
-    opentelemetry_test_configuration: tuple[Tracer, InMemorySpanExporter],
-):
-    # GIVEN an OTel configuration
-    tracer, _ = opentelemetry_test_configuration
-
-    # GIVEN a function annotated with @tool and without type hint on the return value
-    # WHEN building the Tool kernel
-    @tool(opentelemetry_tracer=tracer)
-    def foo(a: Optional[float], b: float) -> float:
-        """Add two numbers."""
-        if a is None:
-            a = 0
-        return a + b
-
-    # THEN the JSONSchema is valid
-    Validator.check_schema(foo.json_schema)
-
-
-def test_list_annotation_parameter(
-    opentelemetry_test_configuration: tuple[Tracer, InMemorySpanExporter],
-):
-    # GIVEN an OTel configuration
-    tracer, exporter = opentelemetry_test_configuration
-
-    # WHEN defining a tool with a list parameter
-    @tool(opentelemetry_tracer=tracer)
-    def foo(to_join: list[str]) -> str:
-        return " ".join(to_join)
-
-    assert "a b c" == foo(to_join=["a", "b", "c"])
-
-    # THEN the function call results in a Span
-    assert len(exporter.get_finished_spans()) == 1
-    # THEN the argument is correctly described in the JSON schema
-    assert foo.json_schema["parameters"]["properties"]["to_join"] == {  # type: ignore
-        "type": "array",
-        "items": {"type": "string"},
-    }
-    # THEN the JSONSchema is valid
-    Validator.check_schema(foo.json_schema)
-
-
-def test_list_in_list_parameter_annotation(
-    opentelemetry_test_configuration: tuple[Tracer, InMemorySpanExporter],
-):
-    # GIVEN an OTel configuration
-    tracer, _ = opentelemetry_test_configuration
-
-    # GIVEN a tool definition with a list of lists parameter
-    # WHEN building the Tool Kernel
-    @tool(opentelemetry_tracer=tracer)
-    def nested_plain_join(to_join: list[list[str]]):
-        return " ".join([val for sub_list in to_join for val in sub_list])
-
-    # THEN the JSON schema is correctly built and parameter is correctly described
-    assert nested_plain_join.json_schema["parameters"]["properties"]["to_join"] == {
-        "type": "array",
-        "items": {
-            "type": "array",
-            "items": {"type": "string"},
-        },
-    }
-
-    # THEN the JSONSchema is valid
-    Validator.check_schema(nested_plain_join.json_schema)
-
-
-def test_complex_dict_annotation(
-    opentelemetry_test_configuration: tuple[Tracer, InMemorySpanExporter],
-):
-    # GIVEN an OTel configuration
-    tracer, _ = opentelemetry_test_configuration
-
-    # GIVEN a tool definition with a dictionary parameter
-    # WHEN building the Tool Kernel
-    @tool(opentelemetry_tracer=tracer)
-    def foo(a: dict[Union[int, str], list[str]]):
-        return a
-
-    # THEN the parameter is correctly described
-    assert foo.json_schema["parameters"]["properties"]["a"] == {
-        "type": "object",
-        "properties": {
-            "key": {"anyOf": [{"type": "integer"}, {"type": "string"}]},
-            "value": {"type": "array", "items": {"type": "string"}},
-        },
-    }
-
-    # THEN the JSONSchema is valid
-    Validator.check_schema(foo.json_schema)
-
-
-def test_tuple_annotation(
-    opentelemetry_test_configuration: tuple[Tracer, InMemorySpanExporter],
-):
-    # GIVEN an OTel configuration
-    tracer, _ = opentelemetry_test_configuration
-
-    # GIVEN a tool definition with a tuple parameter
-    # WHEN building the Tool Kernel
-    @tool(opentelemetry_tracer=tracer)
-    def foo(a: Optional[tuple[int, Optional[str], float]]):
-        return a
-
-    # THEN the parameter is correctly described
-    assert foo.json_schema["parameters"]["properties"]["a"] == {
-        "type": ["array", "null"],
-        "items": [
-            {"type": "integer"},
-            {"type": ["string", "null"]},
-            {"type": "number"},
-        ],
-    }
-
-    # THEN the JSONSchema is valid
-    Validator.check_schema(foo.json_schema)
-
-
-def test_tool_no_args(
-    opentelemetry_test_configuration: tuple[Tracer, InMemorySpanExporter],
-):
-    # GIVEN an OTel configuration
-    tracer, _ = opentelemetry_test_configuration
-
-    # GIVEN a tool definition without arguments
-    # WHEN building the Tool Kernel
-    @tool(opentelemetry_tracer=tracer)
-    def foo():
-        return 42
-
-    # THEN the JSON schema is correctly built
-    assert foo.json_schema == {
-        "description": "",
-        "name": "foo",
-        "parameters": {
-            "properties": {},
-            "required": [],
-            "type": "object",
-            "additionalProperties": False,
-        },
-        "strict": True,
-    }
-
-    # THEN the JSONSchema is valid
-    Validator.check_schema(foo.json_schema)
-
-
-def test_custom_types_throws(
-    opentelemetry_test_configuration: tuple[Tracer, InMemorySpanExporter],
-):
-    # GIVEN an OTel configuration
-    tracer, _ = opentelemetry_test_configuration
-
-    # GIVEN a user-defined type
-    class Foo(TypedDict):
-        a: int  # type: ignore
-        b: int  # type: ignore
-
-    # WHEN defining a tool with a parameter of that type
-    with pytest.raises(ValueError) as exc:
-
-        @tool(opentelemetry_tracer=tracer)
-        def foo_bar(foo: Foo):
-            return foo.a + foo.b  # type: ignore
-
-    # THEN a ValueError is raised
-    assert exc.value.args[0].startswith("Error parsing signature of @tool annotated function foo_bar")
-
-
-def test_tool_as_higher_order_function(
-    opentelemetry_hl_test_configuration: tuple[Tracer, InMemorySpanExporter],
-):
-    tracer, exporter = opentelemetry_hl_test_configuration
-
-    def calculator(operation: str, num1: float, num2: float) -> float:
-        """Do arithmetic operations on two numbers."""
-        if operation == "add":
-            return num1 + num2
-        elif operation == "subtract":
-            return num1 - num2
-        elif operation == "multiply":
-            return num1 * num2
-        elif operation == "divide":
-            return num1 / num2
-        else:
-            raise ValueError(f"Invalid operation: {operation}")
-
-    higher_order_fn_tool = tool(opentelemetry_tracer=tracer)(calculator)
-
-    @tool(opentelemetry_tracer=tracer)  # type: ignore
-    def calculator(operation: str, num1: float, num2: float) -> float:
-        """Do arithmetic operations on two numbers."""
-        if operation == "add":
-            return num1 + num2
-        elif operation == "subtract":
-            return num1 - num2
-        elif operation == "multiply":
-            return num1 * num2
-        elif operation == "divide":
-            return num1 / num2
-        else:
-            raise ValueError(f"Invalid operation: {operation}")
-
-    higher_order_fn_tool(operation="add", num1=1, num2=2)
-    calculator(operation="add", num1=1, num2=2)
-
-    # Processor handles HL spans asynchronously, wait for them
-    time.sleep(1)
-
-    assert len(spans := exporter.get_finished_spans()) == 2
-
-    hl_file_higher_order_fn = read_from_opentelemetry_span(
-        span=spans[0],
-        key=HUMANLOOP_FILE_KEY,
-    )
-    hl_file_decorated_fn = read_from_opentelemetry_span(
-        span=spans[1],
-        key=HUMANLOOP_FILE_KEY,
-    )
-    assert hl_file_higher_order_fn["tool"]["source_code"] == hl_file_decorated_fn["tool"]["source_code"]  # type: ignore
-
-
-if sys.version_info >= (3, 10):
-    # Testing that function parsing for Tool decorator
-    # works with Python 3.10 and above syntax
-
-    def test_python310_syntax(
-        opentelemetry_test_configuration: tuple[Tracer, InMemorySpanExporter],
-    ):
-        # GIVEN an OTel configuration
-        tracer, _ = opentelemetry_test_configuration
-
-        # GIVEN a function annotated with @tool where a parameter uses `|` for Optional
-        @tool(opentelemetry_tracer=tracer)
-        def calculator(a: float, b: float | None = None) -> float:
-            # NOTE: dummy function, only testing its signature not correctness
-            if a is None:
-                a = 0
-            return a + b  # type: ignore
-
-        # WHEN building the Tool kernel
-        # THEN the JSON schema is correct
-        assert calculator.json_schema == {
-            "description": "",
-            "name": "calculator",
-            "parameters": {
-                "properties": {
-                    "a": {"type": "number"},
-                    "b": {"type": ["number", "null"]},
-                },
-                "required": ("a",),
-                "type": "object",
-                "additionalProperties": False,
-            },
-            "strict": True,
-        }
-
-        Validator.check_schema(calculator.json_schema)
-
-    def test_python310_union_syntax(
-        opentelemetry_test_configuration: tuple[Tracer, InMemorySpanExporter],
-    ):
-        # GIVEN an OTel configuration
-        tracer, _ = opentelemetry_test_configuration
-
-        # GIVEN a function annotated with @tool where a parameter uses `|` for Union
-        @tool(opentelemetry_tracer=tracer)
-        def calculator(a: float, b: float | int | str) -> float:
-            # NOTE: dummy function, only testing its signature not correctness
-            return a + b  # type: ignore
-
-        # WHEN building the Tool kernel
-        # THEN the JSON schema is correct
-        assert calculator.json_schema == {
-            "description": "",
-            "name": "calculator",
-            "parameters": {
-                "properties": {
-                    "a": {"type": "number"},
-                    "b": {"anyOf": [{"type": "number"}, {"type": "integer"}, {"type": "string"}]},
-                },
-                "required": ("a", "b"),
-                "type": "object",
-                "additionalProperties": False,
-            },
-            "strict": True,
-        }
-
-        Validator.check_schema(calculator.json_schema)
-
-    def test_python_list_ellipsis(
-        opentelemetry_test_configuration: tuple[Tracer, InMemorySpanExporter],
-    ):
-        # GIVEN an OTel configuration
-        tracer, _ = opentelemetry_test_configuration
-
-        # GIVEN a function annotated with @tool where a parameter uses `...`
-        @tool(opentelemetry_tracer=tracer)
-        def calculator(b: ...) -> float | None:  # type: ignore
-            # NOTE: dummy function, only testing its signature not correctness
-            if isinstance(b, list):
-                return sum(b)
-            return None
-
-        # WHEN building the Tool kernel
-        # THEN the JSON schema is correct
-        assert calculator.json_schema == {
-            "description": "",
-            "name": "calculator",
-            "parameters": {
-                "properties": {
-                    # THEN b is of any type
-                    "b": {"type": ["string", "integer", "number", "boolean", "object", "array", "null"]},
-                },
-                "required": ("b",),
-                "type": "object",
-                "additionalProperties": False,
-            },
-            "strict": True,
-        }