diff --git a/docs/extras/modules/chains/how_to/learned_prompt_optimization.ipynb b/docs/extras/modules/chains/how_to/learned_prompt_optimization.ipynb
index 45a02af45c..3e0702b4f1 100644
--- a/docs/extras/modules/chains/how_to/learned_prompt_optimization.ipynb
+++ b/docs/extras/modules/chains/how_to/learned_prompt_optimization.ipynb
@@ -17,7 +17,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install necessary packages\n",
+    "# ! pip install langchain langchain-experimental matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,14 +43,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# pick and configure the LLM of your choice\n",
     "\n",
     "from langchain.llms import OpenAI\n",
-    "llm = OpenAI(engine=\"text-davinci-003\")\n"
+    "llm = OpenAI(engine=\"text-davinci-003\")"
    ]
   },
   {
@@ -93,7 +103,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import langchain.chains.rl_chain as rl_chain\n",
+    "import langchain_experimental.rl_chain as rl_chain\n",
     "\n",
     "chain = rl_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)\n"
    ]
@@ -466,12 +476,10 @@
     }
    ],
    "source": [
-    "# note matplotlib is not a dependency of langchain so you need to install to plot\n",
-    "\n",
-    "# from matplotlib import pyplot as plt\n",
-    "# chain.metrics.to_pandas()['score'].plot(label=\"default learning policy\")\n",
-    "# random_chain.metrics.to_pandas()['score'].plot(label=\"random selection policy\")\n",
-    "# plt.legend()\n",
+    "from matplotlib import pyplot as plt\n",
+    "chain.metrics.to_pandas()['score'].plot(label=\"default learning policy\")\n",
+    "random_chain.metrics.to_pandas()['score'].plot(label=\"random selection policy\")\n",
+    "plt.legend()\n",
     "\n",
     "print(f\"The final average score for the default policy, calculated over a rolling window, is: {chain.metrics.to_pandas()['score'].iloc[-1]}\")\n",
     "print(f\"The final average score for the random policy, calculated over a rolling window, is: {random_chain.metrics.to_pandas()['score'].iloc[-1]}\")"
@@ -816,7 +824,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.9.17"
   },
   "orig_nbformat": 4
  },
diff --git a/libs/langchain/langchain/chains/rl_chain/__init__.py b/libs/experimental/langchain_experimental/rl_chain/__init__.py
similarity index 89%
rename from libs/langchain/langchain/chains/rl_chain/__init__.py
rename to libs/experimental/langchain_experimental/rl_chain/__init__.py
index f112dcea09..ca558dd6f3 100644
--- a/libs/langchain/langchain/chains/rl_chain/__init__.py
+++ b/libs/experimental/langchain_experimental/rl_chain/__init__.py
@@ -1,6 +1,6 @@
 import logging
 
-from langchain.chains.rl_chain.base import (
+from langchain_experimental.rl_chain.base import (
     AutoSelectionScorer,
     BasedOn,
     Embed,
@@ -12,7 +12,7 @@ from langchain.chains.rl_chain.base import (
     embed,
     stringify_embedding,
 )
-from langchain.chains.rl_chain.pick_best_chain import (
+from langchain_experimental.rl_chain.pick_best_chain import (
     PickBest,
     PickBestEvent,
     PickBestFeatureEmbedder,
diff --git a/libs/langchain/langchain/chains/rl_chain/base.py b/libs/experimental/langchain_experimental/rl_chain/base.py
similarity index 98%
rename from libs/langchain/langchain/chains/rl_chain/base.py
rename to libs/experimental/langchain_experimental/rl_chain/base.py
index 26ac9a43e1..facf977450 100644
--- a/libs/langchain/langchain/chains/rl_chain/base.py
+++ b/libs/experimental/langchain_experimental/rl_chain/base.py
@@ -19,19 +19,20 @@ from typing import (
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
-from langchain.chains.rl_chain.metrics import (
-    MetricsTrackerAverage,
-    MetricsTrackerRollingWindow,
-)
-from langchain.chains.rl_chain.model_repository import ModelRepository
-from langchain.chains.rl_chain.vw_logger import VwLogger
 from langchain.prompts import (
     BasePromptTemplate,
     ChatPromptTemplate,
     HumanMessagePromptTemplate,
     SystemMessagePromptTemplate,
 )
-from langchain.pydantic_v1 import BaseModel, Extra, root_validator
+
+from langchain_experimental.pydantic_v1 import BaseModel, Extra, root_validator
+from langchain_experimental.rl_chain.metrics import (
+    MetricsTrackerAverage,
+    MetricsTrackerRollingWindow,
+)
+from langchain_experimental.rl_chain.model_repository import ModelRepository
+from langchain_experimental.rl_chain.vw_logger import VwLogger
 
 if TYPE_CHECKING:
     import vowpal_wabbit_next as vw
diff --git a/libs/langchain/langchain/chains/rl_chain/metrics.py b/libs/experimental/langchain_experimental/rl_chain/metrics.py
similarity index 100%
rename from libs/langchain/langchain/chains/rl_chain/metrics.py
rename to libs/experimental/langchain_experimental/rl_chain/metrics.py
diff --git a/libs/langchain/langchain/chains/rl_chain/model_repository.py b/libs/experimental/langchain_experimental/rl_chain/model_repository.py
similarity index 100%
rename from libs/langchain/langchain/chains/rl_chain/model_repository.py
rename to libs/experimental/langchain_experimental/rl_chain/model_repository.py
diff --git a/libs/langchain/langchain/chains/rl_chain/pick_best_chain.py b/libs/experimental/langchain_experimental/rl_chain/pick_best_chain.py
similarity index 99%
rename from libs/langchain/langchain/chains/rl_chain/pick_best_chain.py
rename to libs/experimental/langchain_experimental/rl_chain/pick_best_chain.py
index 0da0780313..f9075dc565 100644
--- a/libs/langchain/langchain/chains/rl_chain/pick_best_chain.py
+++ b/libs/experimental/langchain_experimental/rl_chain/pick_best_chain.py
@@ -3,12 +3,13 @@ from __future__ import annotations
 import logging
 from typing import Any, Dict, List, Optional, Tuple, Type, Union
 
-import langchain.chains.rl_chain.base as base
 from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.llm import LLMChain
 from langchain.prompts import BasePromptTemplate
 
+import langchain_experimental.rl_chain.base as base
+
 logger = logging.getLogger(__name__)
 
 # sentinel object used to distinguish between
diff --git a/libs/langchain/langchain/chains/rl_chain/vw_logger.py b/libs/experimental/langchain_experimental/rl_chain/vw_logger.py
similarity index 100%
rename from libs/langchain/langchain/chains/rl_chain/vw_logger.py
rename to libs/experimental/langchain_experimental/rl_chain/vw_logger.py
diff --git a/libs/experimental/poetry.lock b/libs/experimental/poetry.lock
index 9e8cf9f1af..a336617ff5 100644
--- a/libs/experimental/poetry.lock
+++ b/libs/experimental/poetry.lock
@@ -937,6 +937,41 @@ files = [
     {file = "frozenlist-1.4.0.tar.gz", hash = "sha256:09163bdf0b2907454042edb19f887c6d33806adc71fbd54afc14908bfdc22251"},
 ]
 
+[[package]]
+name = "fsspec"
+version = "2023.9.0"
+description = "File-system specification"
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "fsspec-2023.9.0-py3-none-any.whl", hash = "sha256:d55b9ab2a4c1f2b759888ae9f93e40c2aa72c0808132e87e282b549f9e6c4254"},
+    {file = "fsspec-2023.9.0.tar.gz", hash = "sha256:4dbf0fefee035b7c6d3bbbe6bc99b2f201f40d4dca95b67c2b719be77bcd917f"},
+]
+
+[package.extras]
+abfs = ["adlfs"]
+adl = ["adlfs"]
+arrow = ["pyarrow (>=1)"]
+dask = ["dask", "distributed"]
+devel = ["pytest", "pytest-cov"]
+dropbox = ["dropbox", "dropboxdrivefs", "requests"]
+full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
+fuse = ["fusepy"]
+gcs = ["gcsfs"]
+git = ["pygit2"]
+github = ["requests"]
+gs = ["gcsfs"]
+gui = ["panel"]
+hdfs = ["pyarrow (>=1)"]
+http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"]
+libarchive = ["libarchive-c"]
+oci = ["ocifs"]
+s3 = ["s3fs"]
+sftp = ["paramiko"]
+smb = ["smbprotocol"]
+ssh = ["paramiko"]
+tqdm = ["tqdm"]
+
 [[package]]
 name = "greenlet"
 version = "2.0.2"
@@ -1010,6 +1045,39 @@ files = [
 docs = ["Sphinx", "docutils (<0.18)"]
 test = ["objgraph", "psutil"]
 
+[[package]]
+name = "huggingface-hub"
+version = "0.17.1"
+description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
+optional = true
+python-versions = ">=3.8.0"
+files = [
+    {file = "huggingface_hub-0.17.1-py3-none-any.whl", hash = "sha256:7a9dc262a2e0ecf8c1749c8b9a7510a7a22981849f561af4345942d421822451"},
+    {file = "huggingface_hub-0.17.1.tar.gz", hash = "sha256:dd828d2a24ee6af86392042cc1052c482c053eb574864669f0cae4d29620e62c"},
+]
+
+[package.dependencies]
+filelock = "*"
+fsspec = "*"
+packaging = ">=20.9"
+pyyaml = ">=5.1"
+requests = "*"
+tqdm = ">=4.42.1"
+typing-extensions = ">=3.7.4.3"
+
+[package.extras]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "black (==23.7)", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (<2.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "urllib3 (<2.0)"]
+cli = ["InquirerPy (==0.3.4)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "black (==23.7)", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (<2.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "urllib3 (<2.0)"]
+docs = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "black (==23.7)", "gradio", "hf-doc-builder", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (<2.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "urllib3 (<2.0)", "watchdog"]
+fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
+inference = ["aiohttp", "pydantic (<2.0)"]
+quality = ["black (==23.7)", "mypy (==1.5.1)", "ruff (>=0.0.241)"]
+tensorflow = ["graphviz", "pydot", "tensorflow"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "numpy", "pydantic (<2.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+torch = ["torch"]
+typing = ["pydantic (<2.0)", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
+
 [[package]]
 name = "idna"
 version = "3.4"
@@ -1223,6 +1291,17 @@ MarkupSafe = ">=2.0"
 [package.extras]
 i18n = ["Babel (>=2.7)"]
 
+[[package]]
+name = "joblib"
+version = "1.3.2"
+description = "Lightweight pipelining with Python functions"
+optional = true
+python-versions = ">=3.7"
+files = [
+    {file = "joblib-1.3.2-py3-none-any.whl", hash = "sha256:ef4331c65f239985f3f2220ecc87db222f08fd22097a3dd5698f693875f8cbb9"},
+    {file = "joblib-1.3.2.tar.gz", hash = "sha256:92f865e621e17784e7955080b6d042489e3b8e294949cc44c6eac304f59772b1"},
+]
+
 [[package]]
 name = "json5"
 version = "0.9.14"
@@ -1743,6 +1822,23 @@ files = [
     {file = "mistune-3.0.1.tar.gz", hash = "sha256:e912116c13aa0944f9dc530db38eb88f6a77087ab128f49f84a48f4c05ea163c"},
 ]
 
+[[package]]
+name = "mpmath"
+version = "1.3.0"
+description = "Python library for arbitrary-precision floating-point arithmetic"
+optional = true
+python-versions = "*"
+files = [
+    {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"},
+    {file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"},
+]
+
+[package.extras]
+develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"]
+docs = ["sphinx"]
+gmpy = ["gmpy2 (>=2.1.0a4)"]
+tests = ["pytest (>=4.6)"]
+
 [[package]]
 name = "multidict"
 version = "6.0.4"
@@ -2016,6 +2112,49 @@ files = [
     {file = "nest_asyncio-1.5.7.tar.gz", hash = "sha256:6a80f7b98f24d9083ed24608977c09dd608d83f91cccc24c9d2cba6d10e01c10"},
 ]
 
+[[package]]
+name = "networkx"
+version = "3.1"
+description = "Python package for creating and manipulating graphs and networks"
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "networkx-3.1-py3-none-any.whl", hash = "sha256:4f33f68cb2afcf86f28a45f43efc27a9386b535d567d2127f8f61d51dec58d36"},
+    {file = "networkx-3.1.tar.gz", hash = "sha256:de346335408f84de0eada6ff9fafafff9bcda11f0a0dfaa931133debb146ab61"},
+]
+
+[package.extras]
+default = ["matplotlib (>=3.4)", "numpy (>=1.20)", "pandas (>=1.3)", "scipy (>=1.8)"]
+developer = ["mypy (>=1.1)", "pre-commit (>=3.2)"]
+doc = ["nb2plots (>=0.6)", "numpydoc (>=1.5)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.13)", "sphinx (>=6.1)", "sphinx-gallery (>=0.12)", "texext (>=0.6.7)"]
+extra = ["lxml (>=4.6)", "pydot (>=1.4.2)", "pygraphviz (>=1.10)", "sympy (>=1.10)"]
+test = ["codecov (>=2.1)", "pytest (>=7.2)", "pytest-cov (>=4.0)"]
+
+[[package]]
+name = "nltk"
+version = "3.8.1"
+description = "Natural Language Toolkit"
+optional = true
+python-versions = ">=3.7"
+files = [
+    {file = "nltk-3.8.1-py3-none-any.whl", hash = "sha256:fd5c9109f976fa86bcadba8f91e47f5e9293bd034474752e92a520f81c93dda5"},
+    {file = "nltk-3.8.1.zip", hash = "sha256:1834da3d0682cba4f2cede2f9aad6b0fafb6461ba451db0efb6f9c39798d64d3"},
+]
+
+[package.dependencies]
+click = "*"
+joblib = "*"
+regex = ">=2021.8.3"
+tqdm = "*"
+
+[package.extras]
+all = ["matplotlib", "numpy", "pyparsing", "python-crfsuite", "requests", "scikit-learn", "scipy", "twython"]
+corenlp = ["requests"]
+machine-learning = ["numpy", "python-crfsuite", "scikit-learn", "scipy"]
+plot = ["matplotlib"]
+tgrep = ["pyparsing"]
+twitter = ["twython"]
+
 [[package]]
 name = "notebook"
 version = "7.0.2"
@@ -2172,6 +2311,73 @@ files = [
     {file = "packaging-23.1.tar.gz", hash = "sha256:a392980d2b6cffa644431898be54b0045151319d1e7ec34f0cfed48767dd334f"},
 ]
 
+[[package]]
+name = "pandas"
+version = "2.0.3"
+description = "Powerful data structures for data analysis, time series, and statistics"
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "pandas-2.0.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e4c7c9f27a4185304c7caf96dc7d91bc60bc162221152de697c98eb0b2648dd8"},
+    {file = "pandas-2.0.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f167beed68918d62bffb6ec64f2e1d8a7d297a038f86d4aed056b9493fca407f"},
+    {file = "pandas-2.0.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ce0c6f76a0f1ba361551f3e6dceaff06bde7514a374aa43e33b588ec10420183"},
+    {file = "pandas-2.0.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba619e410a21d8c387a1ea6e8a0e49bb42216474436245718d7f2e88a2f8d7c0"},
+    {file = "pandas-2.0.3-cp310-cp310-win32.whl", hash = "sha256:3ef285093b4fe5058eefd756100a367f27029913760773c8bf1d2d8bebe5d210"},
+    {file = "pandas-2.0.3-cp310-cp310-win_amd64.whl", hash = "sha256:9ee1a69328d5c36c98d8e74db06f4ad518a1840e8ccb94a4ba86920986bb617e"},
+    {file = "pandas-2.0.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:b084b91d8d66ab19f5bb3256cbd5ea661848338301940e17f4492b2ce0801fe8"},
+    {file = "pandas-2.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:37673e3bdf1551b95bf5d4ce372b37770f9529743d2498032439371fc7b7eb26"},
+    {file = "pandas-2.0.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b9cb1e14fdb546396b7e1b923ffaeeac24e4cedd14266c3497216dd4448e4f2d"},
+    {file = "pandas-2.0.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d9cd88488cceb7635aebb84809d087468eb33551097d600c6dad13602029c2df"},
+    {file = "pandas-2.0.3-cp311-cp311-win32.whl", hash = "sha256:694888a81198786f0e164ee3a581df7d505024fbb1f15202fc7db88a71d84ebd"},
+    {file = "pandas-2.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:6a21ab5c89dcbd57f78d0ae16630b090eec626360085a4148693def5452d8a6b"},
+    {file = "pandas-2.0.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:9e4da0d45e7f34c069fe4d522359df7d23badf83abc1d1cef398895822d11061"},
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+    {file = "pandas-2.0.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:258d3624b3ae734490e4d63c430256e716f488c4fcb7c8e9bde2d3aa46c29089"},
+    {file = "pandas-2.0.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9eae3dc34fa1aa7772dd3fc60270d13ced7346fcbcfee017d3132ec625e23bb0"},
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+    {file = "pandas-2.0.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:5247fb1ba347c1261cbbf0fcfba4a3121fbb4029d95d9ef4dc45406620b25c8b"},
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+[package.extras]
+dev = ["flake8", "mypy", "pycodestyle", "typing_extensions"]
+doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-panels (>=0.5.2)", "sphinx-tabs"]
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+
 [[package]]
 name = "send2trash"
 version = "1.8.2"
@@ -3192,6 +3626,82 @@ nativelib = ["pyobjc-framework-Cocoa", "pywin32"]
 objc = ["pyobjc-framework-Cocoa"]
 win32 = ["pywin32"]
 
+[[package]]
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+torch = ">=1.6.0"
+torchvision = "*"
+tqdm = "*"
+transformers = ">=4.6.0,<5.0.0"
+
+[[package]]
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+description = "SentencePiece python wrapper"
+optional = true
+python-versions = "*"
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+]
+
 [[package]]
 name = "setuptools"
 version = "67.8.0"
@@ -3508,6 +4018,20 @@ pure-eval = "*"
 [package.extras]
 tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
+[[package]]
+name = "sympy"
+version = "1.12"
+description = "Computer algebra system (CAS) in Python"
+optional = true
+python-versions = ">=3.8"
+files = [
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+
+[package.dependencies]
+mpmath = ">=0.19"
+
 [[package]]
 name = "tenacity"
 version = "8.2.3"
@@ -3620,6 +4144,17 @@ mxnet = ["mxnet (>=1.5.1,<1.6.0)"]
 tensorflow = ["tensorflow (>=2.0.0,<2.6.0)"]
 torch = ["torch (>=1.6.0)"]
 
+[[package]]
+name = "threadpoolctl"
+version = "3.2.0"
+description = "threadpoolctl"
+optional = true
+python-versions = ">=3.8"
+files = [
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+
 [[package]]
 name = "tinycss2"
 version = "1.2.1"
@@ -3655,6 +4190,60 @@ idna = "*"
 requests = ">=2.1.0"
 requests-file = ">=1.4"
 
+[[package]]
+name = "tokenizers"
+version = "0.13.3"
+description = "Fast and Customizable Tokenizers"
+optional = true
+python-versions = "*"
+files = [
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+dev-torch = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "accelerate (>=0.20.3)", "beautifulsoup4", "black (>=23.1,<24.0)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune]", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "timeout-decorator", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+docs = ["Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision"]
+docs-specific = ["hf-doc-builder"]
+fairscale = ["fairscale (>0.3)"]
+flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
+flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+ftfy = ["ftfy"]
+integrations = ["optuna", "ray[tune]", "sigopt"]
+ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
+modelcreation = ["cookiecutter (==1.7.3)"]
+natten = ["natten (>=0.14.6)"]
+onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
+onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
+optuna = ["optuna"]
+quality = ["GitPython (<3.1.19)", "black (>=23.1,<24.0)", "datasets (!=2.5.0)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "ruff (>=0.0.241,<=0.0.259)", "urllib3 (<2.0.0)"]
+ray = ["ray[tune]"]
+retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
+sagemaker = ["sagemaker (>=2.31.0)"]
+sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
+serving = ["fastapi", "pydantic (<2)", "starlette", "uvicorn"]
+sigopt = ["sigopt"]
+sklearn = ["scikit-learn"]
+speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "timeout-decorator"]
+tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx"]
+tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx"]
+tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+timm = ["timm"]
+tokenizers = ["tokenizers (>=0.11.1,!=0.11.3,<0.14)"]
+torch = ["accelerate (>=0.20.3)", "torch (>=1.10,!=1.12.0)"]
+torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+torch-vision = ["Pillow (<10.0.0)", "torchvision"]
+torchhub = ["filelock", "huggingface-hub (>=0.15.1,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.10,!=1.12.0)", "tqdm (>=4.27)"]
+video = ["av (==9.2.0)", "decord (==0.6.0)"]
+vision = ["Pillow (<10.0.0)"]
+
 [[package]]
 name = "typer"
 version = "0.9.0"
@@ -3804,6 +4539,17 @@ files = [
 mypy-extensions = ">=0.3.0"
 typing-extensions = ">=3.7.4"
 
+[[package]]
+name = "tzdata"
+version = "2023.3"
+description = "Provider of IANA time zone data"
+optional = true
+python-versions = ">=2"
+files = [
+    {file = "tzdata-2023.3-py2.py3-none-any.whl", hash = "sha256:7e65763eef3120314099b6939b5546db7adce1e7d6f2e179e3df563c70511eda"},
+    {file = "tzdata-2023.3.tar.gz", hash = "sha256:11ef1e08e54acb0d4f95bdb1be05da659673de4acbd21bf9c69e94cc5e907a3a"},
+]
+
 [[package]]
 name = "uri-template"
 version = "1.3.0"
@@ -3835,6 +4581,33 @@ secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17.
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
+[[package]]
+name = "vowpal-wabbit-next"
+version = "0.6.0"
+description = "Experimental python bindings for VowpalWabbit"
+optional = true
+python-versions = ">=3.7"
+files = [
+    {file = "vowpal-wabbit-next-0.6.0.tar.gz", hash = "sha256:f0381614d99fac6a0f52e995ee0bfc7b681054f397bea7ff08b8a523d5315a54"},
+    {file = "vowpal_wabbit_next-0.6.0-cp310-cp310-macosx_10_13_universal2.whl", hash = "sha256:cfbb831cfe9eb81185aff7cdca437ae17c6d9aca8d74e26c326e3ef4ee8e81e7"},
+    {file = "vowpal_wabbit_next-0.6.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9d31829778f9c600f5c121f614516ca1bc9ede5d1bc77b1eb3b59b32d9138db9"},
+    {file = "vowpal_wabbit_next-0.6.0-cp310-cp310-win_amd64.whl", hash = "sha256:714347606ab302a2f72870b6ae6dce58de4bec1b489f4bd65d80a8e326e1db8a"},
+    {file = "vowpal_wabbit_next-0.6.0-cp311-cp311-macosx_10_13_universal2.whl", hash = "sha256:3a8482d5c0b9357fdb36b62d659e6b74e93aeab165b910292572a98e91d7a014"},
+    {file = "vowpal_wabbit_next-0.6.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1e4349099b938102f51fb6fedf035bc1deacb2971cd2a48641ca7d45186efda0"},
+    {file = "vowpal_wabbit_next-0.6.0-cp311-cp311-win_amd64.whl", hash = "sha256:c8f58cdc49f270b1bed6f0fdd7520c8ba1b328de5cd8a2760c0ec70a630de92e"},
+    {file = "vowpal_wabbit_next-0.6.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c8b7052ce7212fd1cae8ffd966e240c814f3c1df08fd612437d48f0f23e7694c"},
+    {file = "vowpal_wabbit_next-0.6.0-cp37-cp37m-win_amd64.whl", hash = "sha256:d24d9c380d0e9b41151337c7f9e2a33ec5bfd738fdee9f65c1a40e486234aca3"},
+    {file = "vowpal_wabbit_next-0.6.0-cp38-cp38-macosx_10_13_universal2.whl", hash = "sha256:0d77a8c55249ec9a7f404939ecc6948db0527e522e8a7ae149ec7cd29b3ade04"},
+    {file = "vowpal_wabbit_next-0.6.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:baa2f52f1267fbc26c7757335f9c76a0f00b112971e04c85b8a9bc9e82300597"},
+    {file = "vowpal_wabbit_next-0.6.0-cp38-cp38-win_amd64.whl", hash = "sha256:5d04f91200ecae73196d9f5601853d63afce8c1c8a0d310a608e8ddfa3b190cb"},
+    {file = "vowpal_wabbit_next-0.6.0-cp39-cp39-macosx_10_13_universal2.whl", hash = "sha256:2df4a652729c0db34afd8fb4fc49b0090d6f061e2d49899e5f092fd4c3d23253"},
+    {file = "vowpal_wabbit_next-0.6.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c289a260ab759f04903b441701cff66ea74d6c061d966caaba0c65ac12d05528"},
+    {file = "vowpal_wabbit_next-0.6.0-cp39-cp39-win_amd64.whl", hash = "sha256:8d022cab07274f227df159a81bccf034def7dd54ad70392ee98743ffa4953072"},
+]
+
+[package.dependencies]
+numpy = "*"
+
 [[package]]
 name = "wasabi"
 version = "1.1.2"
@@ -4016,9 +4789,9 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.link
 testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
 
 [extras]
-extended-testing = ["faker", "presidio-analyzer", "presidio-anonymizer"]
+extended-testing = ["faker", "presidio-analyzer", "presidio-anonymizer", "sentence-transformers", "vowpal-wabbit-next"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "443e88f690572715cf58671e4480a006574c7141a1258dff0a0818b954184901"
+content-hash = "0e25f0b8760e893644f6d28e5f2bd6f66a010b3084e82d7b711c90ef34b3b9fa"
diff --git a/libs/experimental/pyproject.toml b/libs/experimental/pyproject.toml
index 272e5b6ad2..e0fed35bc9 100644
--- a/libs/experimental/pyproject.toml
+++ b/libs/experimental/pyproject.toml
@@ -14,6 +14,9 @@ langchain = ">=0.0.239"
 presidio-anonymizer = {version = "^2.2.33", optional = true}
 presidio-analyzer = {version = "^2.2.33", optional = true}
 faker = {version = "^19.3.1", optional = true}
+vowpal-wabbit-next = {version = "0.6.0", optional = true}
+sentence-transformers = {version = "^2", optional = true}
+pandas = {version = "^2.0.1", optional = true}
 
 
 [tool.poetry.group.lint.dependencies]
@@ -43,6 +46,8 @@ extended_testing = [
  "presidio-anonymizer",
  "presidio-analyzer",
  "faker",
+ "vowpal-wabbit-next",
+ "sentence-transformers",
 ]
 
 [tool.ruff]
diff --git a/libs/langchain/tests/unit_tests/chains/rl_chain/test_pick_best_chain_call.py b/libs/experimental/tests/integration_tests/chains/rl_chain/test_pick_best_chain_call.py
similarity index 79%
rename from libs/langchain/tests/unit_tests/chains/rl_chain/test_pick_best_chain_call.py
rename to libs/experimental/tests/integration_tests/chains/rl_chain/test_pick_best_chain_call.py
index 7eb7ca2aea..765e52e05e 100644
--- a/libs/langchain/tests/unit_tests/chains/rl_chain/test_pick_best_chain_call.py
+++ b/libs/experimental/tests/integration_tests/chains/rl_chain/test_pick_best_chain_call.py
@@ -1,12 +1,12 @@
 from typing import Any, Dict
 
 import pytest
-from test_utils import MockEncoder, MockEncoderReturnsList
-
-import langchain.chains.rl_chain.base as rl_chain
-import langchain.chains.rl_chain.pick_best_chain as pick_best_chain
 from langchain.chat_models import FakeListChatModel
 from langchain.prompts.prompt import PromptTemplate
+from test_utils import MockEncoder, MockEncoderReturnsList
+
+import langchain_experimental.rl_chain.base as rl_chain
+import langchain_experimental.rl_chain.pick_best_chain as pick_best_chain
 
 encoded_keyword = "[encoded]"
 
@@ -90,11 +90,13 @@ def test_update_with_delayed_score_with_auto_validator_throws() -> None:
         User=rl_chain.BasedOn("Context"),
         action=rl_chain.ToSelectFrom(actions),
     )
-    assert response["response"] == "hey"
-    selection_metadata = response["selection_metadata"]
-    assert selection_metadata.selected.score == 3.0
+    assert response["response"] == "hey"  # type: ignore
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    assert selection_metadata.selected.score == 3.0  # type: ignore
     with pytest.raises(RuntimeError):
-        chain.update_with_delayed_score(chain_response=response, score=100)
+        chain.update_with_delayed_score(
+            chain_response=response, score=100  # type: ignore
+        )
 
 
 @pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
@@ -115,13 +117,13 @@ def test_update_with_delayed_score_force() -> None:
         User=rl_chain.BasedOn("Context"),
         action=rl_chain.ToSelectFrom(actions),
     )
-    assert response["response"] == "hey"
-    selection_metadata = response["selection_metadata"]
-    assert selection_metadata.selected.score == 3.0
+    assert response["response"] == "hey"  # type: ignore
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    assert selection_metadata.selected.score == 3.0  # type: ignore
     chain.update_with_delayed_score(
-        chain_response=response, score=100, force_score=True
+        chain_response=response, score=100, force_score=True  # type: ignore
     )
-    assert selection_metadata.selected.score == 100.0
+    assert selection_metadata.selected.score == 100.0  # type: ignore
 
 
 @pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
@@ -140,11 +142,11 @@ def test_update_with_delayed_score() -> None:
         User=rl_chain.BasedOn("Context"),
         action=rl_chain.ToSelectFrom(actions),
     )
-    assert response["response"] == "hey"
-    selection_metadata = response["selection_metadata"]
-    assert selection_metadata.selected.score is None
-    chain.update_with_delayed_score(chain_response=response, score=100)
-    assert selection_metadata.selected.score == 100.0
+    assert response["response"] == "hey"  # type: ignore
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    assert selection_metadata.selected.score is None  # type: ignore
+    chain.update_with_delayed_score(chain_response=response, score=100)  # type: ignore
+    assert selection_metadata.selected.score == 100.0  # type: ignore
 
 
 @pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
@@ -174,9 +176,9 @@ def test_user_defined_scorer() -> None:
         User=rl_chain.BasedOn("Context"),
         action=rl_chain.ToSelectFrom(actions),
     )
-    assert response["response"] == "hey"
-    selection_metadata = response["selection_metadata"]
-    assert selection_metadata.selected.score == 200.0
+    assert response["response"] == "hey"  # type: ignore
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    assert selection_metadata.selected.score == 200.0  # type: ignore
 
 
 @pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
@@ -208,8 +210,8 @@ def test_everything_embedded() -> None:
         User=rl_chain.EmbedAndKeep(rl_chain.BasedOn(ctx_str_1)),
         action=rl_chain.EmbedAndKeep(rl_chain.ToSelectFrom(actions)),
     )
-    selection_metadata = response["selection_metadata"]
-    vw_str = feature_embedder.format(selection_metadata)
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    vw_str = feature_embedder.format(selection_metadata)  # type: ignore
     assert vw_str == expected
 
 
@@ -236,8 +238,8 @@ def test_default_auto_embedder_is_off() -> None:
         User=pick_best_chain.base.BasedOn(ctx_str_1),
         action=pick_best_chain.base.ToSelectFrom(actions),
     )
-    selection_metadata = response["selection_metadata"]
-    vw_str = feature_embedder.format(selection_metadata)
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    vw_str = feature_embedder.format(selection_metadata)  # type: ignore
     assert vw_str == expected
 
 
@@ -264,8 +266,8 @@ def test_default_w_embeddings_off() -> None:
         User=rl_chain.BasedOn(ctx_str_1),
         action=rl_chain.ToSelectFrom(actions),
     )
-    selection_metadata = response["selection_metadata"]
-    vw_str = feature_embedder.format(selection_metadata)
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    vw_str = feature_embedder.format(selection_metadata)  # type: ignore
     assert vw_str == expected
 
 
@@ -292,8 +294,8 @@ def test_default_w_embeddings_on() -> None:
         User=rl_chain.BasedOn(ctx_str_1),
         action=rl_chain.ToSelectFrom(actions),
     )
-    selection_metadata = response["selection_metadata"]
-    vw_str = feature_embedder.format(selection_metadata)
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    vw_str = feature_embedder.format(selection_metadata)  # type: ignore
     assert vw_str == expected
 
 
@@ -324,15 +326,15 @@ def test_default_embeddings_mixed_w_explicit_user_embeddings() -> None:
         User2=rl_chain.BasedOn(ctx_str_2),
         action=rl_chain.ToSelectFrom(actions),
     )
-    selection_metadata = response["selection_metadata"]
-    vw_str = feature_embedder.format(selection_metadata)
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    vw_str = feature_embedder.format(selection_metadata)  # type: ignore
     assert vw_str == expected
 
 
 @pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
 def test_default_no_scorer_specified() -> None:
     _, PROMPT = setup()
-    chain_llm = FakeListChatModel(responses=[100])
+    chain_llm = FakeListChatModel(responses=["hey", "100"])
     chain = pick_best_chain.PickBest.from_llm(
         llm=chain_llm,
         prompt=PROMPT,
@@ -345,9 +347,9 @@ def test_default_no_scorer_specified() -> None:
         action=rl_chain.ToSelectFrom(["0", "1", "2"]),
     )
     # chain llm used for both basic prompt and for scoring
-    assert response["response"] == "100"
-    selection_metadata = response["selection_metadata"]
-    assert selection_metadata.selected.score == 100.0
+    assert response["response"] == "hey"  # type: ignore
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    assert selection_metadata.selected.score == 100.0  # type: ignore
 
 
 @pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
@@ -366,15 +368,15 @@ def test_explicitly_no_scorer() -> None:
         action=rl_chain.ToSelectFrom(["0", "1", "2"]),
     )
     # chain llm used for both basic prompt and for scoring
-    assert response["response"] == "hey"
-    selection_metadata = response["selection_metadata"]
-    assert selection_metadata.selected.score is None
+    assert response["response"] == "hey"  # type: ignore
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    assert selection_metadata.selected.score is None  # type: ignore
 
 
 @pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
 def test_auto_scorer_with_user_defined_llm() -> None:
     llm, PROMPT = setup()
-    scorer_llm = FakeListChatModel(responses=[300])
+    scorer_llm = FakeListChatModel(responses=["300"])
     chain = pick_best_chain.PickBest.from_llm(
         llm=llm,
         prompt=PROMPT,
@@ -388,9 +390,9 @@ def test_auto_scorer_with_user_defined_llm() -> None:
         action=rl_chain.ToSelectFrom(["0", "1", "2"]),
     )
     # chain llm used for both basic prompt and for scoring
-    assert response["response"] == "hey"
-    selection_metadata = response["selection_metadata"]
-    assert selection_metadata.selected.score == 300.0
+    assert response["response"] == "hey"  # type: ignore
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    assert selection_metadata.selected.score == 300.0  # type: ignore
 
 
 @pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
@@ -418,8 +420,9 @@ def test_calling_chain_w_reserved_inputs_throws() -> None:
 
 @pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
 def test_activate_and_deactivate_scorer() -> None:
-    llm, PROMPT = setup()
-    scorer_llm = FakeListChatModel(responses=[300])
+    _, PROMPT = setup()
+    llm = FakeListChatModel(responses=["hey1", "hey2", "hey3"])
+    scorer_llm = FakeListChatModel(responses=["300", "400"])
     chain = pick_best_chain.PickBest.from_llm(
         llm=llm,
         prompt=PROMPT,
@@ -433,24 +436,24 @@ def test_activate_and_deactivate_scorer() -> None:
         action=pick_best_chain.base.ToSelectFrom(["0", "1", "2"]),
     )
     # chain llm used for both basic prompt and for scoring
-    assert response["response"] == "hey"
-    selection_metadata = response["selection_metadata"]
-    assert selection_metadata.selected.score == 300.0
+    assert response["response"] == "hey1"  # type: ignore
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    assert selection_metadata.selected.score == 300.0  # type: ignore
 
     chain.deactivate_selection_scorer()
     response = chain.run(
         User=pick_best_chain.base.BasedOn("Context"),
         action=pick_best_chain.base.ToSelectFrom(["0", "1", "2"]),
     )
-    assert response["response"] == "hey"
-    selection_metadata = response["selection_metadata"]
-    assert selection_metadata.selected.score is None
+    assert response["response"] == "hey2"  # type: ignore
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    assert selection_metadata.selected.score is None  # type: ignore
 
     chain.activate_selection_scorer()
     response = chain.run(
         User=pick_best_chain.base.BasedOn("Context"),
         action=pick_best_chain.base.ToSelectFrom(["0", "1", "2"]),
     )
-    assert response["response"] == "hey"
-    selection_metadata = response["selection_metadata"]
-    assert selection_metadata.selected.score == 300.0
+    assert response["response"] == "hey3"  # type: ignore
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    assert selection_metadata.selected.score == 400.0  # type: ignore
diff --git a/libs/langchain/tests/unit_tests/chains/rl_chain/test_pick_best_text_embedder.py b/libs/experimental/tests/integration_tests/chains/rl_chain/test_pick_best_text_embedder.py
similarity index 99%
rename from libs/langchain/tests/unit_tests/chains/rl_chain/test_pick_best_text_embedder.py
rename to libs/experimental/tests/integration_tests/chains/rl_chain/test_pick_best_text_embedder.py
index 1fdbdff644..fafa77e9f4 100644
--- a/libs/langchain/tests/unit_tests/chains/rl_chain/test_pick_best_text_embedder.py
+++ b/libs/experimental/tests/integration_tests/chains/rl_chain/test_pick_best_text_embedder.py
@@ -1,8 +1,8 @@
 import pytest
 from test_utils import MockEncoder
 
-import langchain.chains.rl_chain.base as rl_chain
-import langchain.chains.rl_chain.pick_best_chain as pick_best_chain
+import langchain_experimental.rl_chain.base as rl_chain
+import langchain_experimental.rl_chain.pick_best_chain as pick_best_chain
 
 encoded_keyword = "[encoded]"
 
diff --git a/libs/langchain/tests/unit_tests/chains/rl_chain/test_rl_chain_base_embedder.py b/libs/experimental/tests/integration_tests/chains/rl_chain/test_rl_chain_base_embedder.py
similarity index 99%
rename from libs/langchain/tests/unit_tests/chains/rl_chain/test_rl_chain_base_embedder.py
rename to libs/experimental/tests/integration_tests/chains/rl_chain/test_rl_chain_base_embedder.py
index 1928eb26c6..7e8b23857f 100644
--- a/libs/langchain/tests/unit_tests/chains/rl_chain/test_rl_chain_base_embedder.py
+++ b/libs/experimental/tests/integration_tests/chains/rl_chain/test_rl_chain_base_embedder.py
@@ -3,7 +3,7 @@ from typing import List, Union
 import pytest
 from test_utils import MockEncoder
 
-import langchain.chains.rl_chain.base as base
+import langchain_experimental.rl_chain.base as base
 
 encoded_keyword = "[encoded]"
 
diff --git a/libs/langchain/tests/unit_tests/chains/rl_chain/test_utils.py b/libs/experimental/tests/integration_tests/chains/rl_chain/test_utils.py
similarity index 100%
rename from libs/langchain/tests/unit_tests/chains/rl_chain/test_utils.py
rename to libs/experimental/tests/integration_tests/chains/rl_chain/test_utils.py
diff --git a/libs/experimental/tests/unit_tests/__init__.py b/libs/experimental/tests/unit_tests/__init__.py
index e69de29bb2..f177990421 100644
--- a/libs/experimental/tests/unit_tests/__init__.py
+++ b/libs/experimental/tests/unit_tests/__init__.py
@@ -0,0 +1,9 @@
+import ctypes
+
+
+def is_libcublas_available() -> bool:
+    try:
+        ctypes.CDLL("libcublas.so")
+        return True
+    except OSError:
+        return False
diff --git a/libs/experimental/tests/unit_tests/test_data_anonymizer.py b/libs/experimental/tests/unit_tests/test_data_anonymizer.py
index 138b60eca8..6abd42d754 100644
--- a/libs/experimental/tests/unit_tests/test_data_anonymizer.py
+++ b/libs/experimental/tests/unit_tests/test_data_anonymizer.py
@@ -2,6 +2,8 @@ from typing import Iterator, List
 
 import pytest
 
+from . import is_libcublas_available
+
 
 @pytest.fixture(scope="module", autouse=True)
 def check_spacy_model() -> Iterator[None]:
@@ -12,6 +14,13 @@ def check_spacy_model() -> Iterator[None]:
     yield
 
 
+@pytest.fixture(scope="module", autouse=True)
+def check_libcublas() -> Iterator[None]:
+    if not is_libcublas_available():
+        pytest.skip(reason="libcublas.so is not available")
+    yield
+
+
 @pytest.mark.requires("presidio_analyzer", "presidio_anonymizer", "faker")
 @pytest.mark.parametrize(
     "analyzed_fields,should_contain",
diff --git a/libs/experimental/tests/unit_tests/test_reversible_data_anonymizer.py b/libs/experimental/tests/unit_tests/test_reversible_data_anonymizer.py
index 9484a0e9dc..b3634d7c45 100644
--- a/libs/experimental/tests/unit_tests/test_reversible_data_anonymizer.py
+++ b/libs/experimental/tests/unit_tests/test_reversible_data_anonymizer.py
@@ -3,6 +3,8 @@ from typing import Iterator, List
 
 import pytest
 
+from . import is_libcublas_available
+
 
 @pytest.fixture(scope="module", autouse=True)
 def check_spacy_model() -> Iterator[None]:
@@ -13,6 +15,13 @@ def check_spacy_model() -> Iterator[None]:
     yield
 
 
+@pytest.fixture(scope="module", autouse=True)
+def check_libcublas() -> Iterator[None]:
+    if not is_libcublas_available():
+        pytest.skip(reason="libcublas.so is not available")
+    yield
+
+
 @pytest.mark.requires("presidio_analyzer", "presidio_anonymizer", "faker")
 @pytest.mark.parametrize(
     "analyzed_fields,should_contain",
diff --git a/libs/langchain/pyproject.toml b/libs/langchain/pyproject.toml
index 3a11d658e4..ac8f5c45df 100644
--- a/libs/langchain/pyproject.toml
+++ b/libs/langchain/pyproject.toml
@@ -129,7 +129,6 @@ markdownify = {version = "^0.11.6", optional = true}
 assemblyai = {version = "^0.17.0", optional = true}
 dashvector = {version = "^1.0.1", optional = true}
 sqlite-vss = {version = "^0.1.2", optional = true}
-vowpal-wabbit-next = {version = "0.6.0", optional = true}
 
 
 [tool.poetry.group.test.dependencies]
@@ -343,11 +342,9 @@ extended_testing = [
  "xmltodict",
  "faiss-cpu",
  "openapi-schema-pydantic",
- "sentence-transformers",
  "markdownify",
  "dashvector",
  "sqlite-vss",
- "vowpal-wabbit-next",
 ]
 
 [tool.ruff]