diff --git a/poetry.lock b/poetry.lock
index dbc32254..de8e13fa 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -154,19 +154,20 @@ files = [
 
 [[package]]
 name = "anthropic"
-version = "0.26.0"
+version = "0.26.1"
 description = "The official Python library for the anthropic API"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "anthropic-0.26.0-py3-none-any.whl", hash = "sha256:38fc415561d71dcf263b89da0cc6ecec498379b56256fc4242e9128bc707b283"},
-    {file = "anthropic-0.26.0.tar.gz", hash = "sha256:6aaffeb05d515cf9788eef57150a5f827f3786883628ccac71dbe5671ab6f44e"},
+    {file = "anthropic-0.26.1-py3-none-any.whl", hash = "sha256:2812b9b250b551ed8a1f0a7e6ae3f005654098994f45ebca5b5808bd154c9628"},
+    {file = "anthropic-0.26.1.tar.gz", hash = "sha256:26680ff781a6f678a30a1dccd0743631e602b23a47719439ffdef5335fa167d8"},
 ]
 
 [package.dependencies]
 anyio = ">=3.5.0,<5"
 distro = ">=1.7.0,<2"
 httpx = ">=0.23.0,<1"
+jiter = ">=0.1.0,<1"
 pydantic = ">=1.9.0,<3"
 sniffio = "*"
 tokenizers = ">=0.13.0"
@@ -178,13 +179,13 @@ vertex = ["google-auth (>=2,<3)"]
 
 [[package]]
 name = "anyio"
-version = "4.3.0"
+version = "4.4.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"},
-    {file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"},
+    {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
+    {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"},
 ]
 
 [package.dependencies]
@@ -405,17 +406,17 @@ files = [
 
 [[package]]
 name = "boto3"
-version = "1.34.109"
+version = "1.34.115"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "boto3-1.34.109-py3-none-any.whl", hash = "sha256:50a0f24dd737529ae489a3586f260b9220c6aede1ae7851fa4f33878c8805ef8"},
-    {file = "boto3-1.34.109.tar.gz", hash = "sha256:98d389562e03a46fd79fea5f988e9e6032674a0c3e9e42c06941ec588b7e1070"},
+    {file = "boto3-1.34.115-py3-none-any.whl", hash = "sha256:0a580de3d25364da5db26ecc7dde9438ee1be1e529a7c04cc96972b6e2258378"},
+    {file = "boto3-1.34.115.tar.gz", hash = "sha256:67f5a6d6e6eff9c15711c265173b53eb4ad8d05b756b76ef33ac792cea7958f6"},
 ]
 
 [package.dependencies]
-botocore = ">=1.34.109,<1.35.0"
+botocore = ">=1.34.115,<1.35.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.10.0,<0.11.0"
 
@@ -424,13 +425,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.34.109"
+version = "1.34.115"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "botocore-1.34.109-py3-none-any.whl", hash = "sha256:647059a81acbfab85c694b9b57b0ef200dde071449fb8837f10aef9c6472730d"},
-    {file = "botocore-1.34.109.tar.gz", hash = "sha256:804821252597821f7223cb3bfca2a2a513ae0bb9a71e8e22605aff6866e13e71"},
+    {file = "botocore-1.34.115-py3-none-any.whl", hash = "sha256:15b8ad1ee0e9cd57884fb0bcaf3a9551d2552e44a02c2ffb55ec583eebdb888e"},
+    {file = "botocore-1.34.115.tar.gz", hash = "sha256:a5d5e28b9c847b17a1ecb7660b46b83d9512b125f671e03e93d14bf6f0b274c2"},
 ]
 
 [package.dependencies]
@@ -875,13 +876,13 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [[package]]
 name = "cohere"
-version = "5.5.0"
+version = "5.5.3"
 description = ""
 optional = false
 python-versions = "<4.0,>=3.8"
 files = [
-    {file = "cohere-5.5.0-py3-none-any.whl", hash = "sha256:7792e8898c95f2cb955b2d9f23b8602f73f3b698d59f1a1b4896c53809671da0"},
-    {file = "cohere-5.5.0.tar.gz", hash = "sha256:00b492ebf8921e83cb2371f2ee36ddf301422daae3024343a87d4316f02b711b"},
+    {file = "cohere-5.5.3-py3-none-any.whl", hash = "sha256:99d20129713a6dae052368b4839773a214592a76bee345b94a4846d00f702da3"},
+    {file = "cohere-5.5.3.tar.gz", hash = "sha256:8c7ebe2f5bf83fee8e55a24a0acdd4b0e94de274fd0ef32b285978289a03e930"},
 ]
 
 [package.dependencies]
@@ -1366,13 +1367,13 @@ requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
 
 [[package]]
 name = "google-cloud-aiplatform"
-version = "1.51.0"
+version = "1.52.0"
 description = "Vertex AI API client library"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "google-cloud-aiplatform-1.51.0.tar.gz", hash = "sha256:901993b4d14392452699c14cf23d72c01c5488ee36a7e00b23195e64d7d2f5ec"},
-    {file = "google_cloud_aiplatform-1.51.0-py2.py3-none-any.whl", hash = "sha256:f2d3ff231454fe397f02fce1358680dea76ed7c74fc42e6c7e1aa1acb1648df3"},
+    {file = "google-cloud-aiplatform-1.52.0.tar.gz", hash = "sha256:932a56e3050b4bc9a2c0630e6af3c0bd52f0bcf72b5dc01c059874231099edd3"},
+    {file = "google_cloud_aiplatform-1.52.0-py2.py3-none-any.whl", hash = "sha256:8c62f5d0ec39e008737ebba4875105ed7563dd0958f591f95dc7816e4b30f92a"},
 ]
 
 [package.dependencies]
@@ -1395,7 +1396,7 @@ datasets = ["pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0de
 endpoint = ["requests (>=2.28.1)"]
 full = ["cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.109.1)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "nest-asyncio (>=1.0.0,<1.6.0)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "starlette (>=0.17.1)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)"]
 langchain = ["langchain (>=0.1.16,<0.2)", "langchain-core (<0.2)", "langchain-google-vertexai (<2)"]
-langchain-testing = ["absl-py", "cloudpickle (>=2.2.1,<3.0)", "langchain (>=0.1.16,<0.2)", "langchain-core (<0.2)", "langchain-google-vertexai (<2)", "pydantic (>=2.6.3,<3)", "pytest-xdist"]
+langchain-testing = ["absl-py", "cloudpickle (>=2.2.1,<4.0)", "langchain (>=0.1.16,<0.2)", "langchain-core (<0.2)", "langchain-google-vertexai (<2)", "pydantic (>=2.6.3,<3)", "pytest-xdist"]
 lit = ["explainable-ai-sdk (>=1.0.0)", "lit-nlp (==0.4.0)", "pandas (>=1.0.0)", "tensorflow (>=2.3.0,<3.0.0dev)"]
 metadata = ["numpy (>=1.15.0)", "pandas (>=1.0.0)"]
 pipelines = ["pyyaml (>=5.3.1,<7)"]
@@ -1405,7 +1406,7 @@ private-endpoints = ["requests (>=2.28.1)", "urllib3 (>=1.21.1,<1.27)"]
 rapid-evaluation = ["nest-asyncio (>=1.0.0,<1.6.0)", "pandas (>=1.0.0,<2.2.0)"]
 ray = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)"]
 ray-testing = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pytest-xdist", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "ray[train] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "scikit-learn", "tensorflow", "torch (>=2.0.0,<2.1.0)", "xgboost", "xgboost-ray"]
-reasoningengine = ["cloudpickle (>=2.2.1,<3.0)", "pydantic (>=2.6.3,<3)"]
+reasoningengine = ["cloudpickle (>=2.2.1,<4.0)", "pydantic (>=2.6.3,<3)"]
 tensorboard = ["tensorflow (>=2.3.0,<3.0.0dev)"]
 testing = ["bigframes", "cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.109.1)", "google-api-core (>=2.11,<3.0.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "grpcio-testing", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "ipython", "kfp (>=2.6.0,<3.0.0)", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "nest-asyncio (>=1.0.0,<1.6.0)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyfakefs", "pytest-asyncio", "pytest-xdist", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "requests-toolbelt (<1.0.0)", "scikit-learn", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (==2.13.0)", "tensorflow (==2.16.1)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "torch (>=2.0.0,<2.1.0)", "torch (>=2.2.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)", "xgboost"]
 vizier = ["google-vizier (>=0.1.6)"]
@@ -1413,13 +1414,13 @@ xai = ["tensorflow (>=2.3.0,<3.0.0dev)"]
 
 [[package]]
 name = "google-cloud-bigquery"
-version = "3.23.0"
+version = "3.23.1"
 description = "Google BigQuery API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-bigquery-3.23.0.tar.gz", hash = "sha256:7ecdb207727d513b1bce1f213dbb926ed2e1d4f0122778de00f0e56d19d47a01"},
-    {file = "google_cloud_bigquery-3.23.0-py2.py3-none-any.whl", hash = "sha256:dc0a4a47ab541a34aa1dc1f48539d88c091adc0637da7744d7fab6f3bc8886d5"},
+    {file = "google-cloud-bigquery-3.23.1.tar.gz", hash = "sha256:4b4597f9291b42102c9667d3b4528f801d4c8f24ef2b12dd1ecb881273330955"},
+    {file = "google_cloud_bigquery-3.23.1-py2.py3-none-any.whl", hash = "sha256:9fb72884fdbec9c4643cea6b7f21e1ecf3eb61d5305f87493d271dc801647a9e"},
 ]
 
 [package.dependencies]
@@ -1618,13 +1619,13 @@ grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"]
 
 [[package]]
 name = "groq"
-version = "0.6.0"
+version = "0.8.0"
 description = "The official Python library for the groq API"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "groq-0.6.0-py3-none-any.whl", hash = "sha256:99e2e5ea48df074c09bffcc349b049d3573d9cb35da872d4acbbe50a4b266414"},
-    {file = "groq-0.6.0.tar.gz", hash = "sha256:a96f3a49a0d4119a1bec7f6352af0a87733a2865d464da34a4eb27bfe8068c7e"},
+    {file = "groq-0.8.0-py3-none-any.whl", hash = "sha256:f5e4e892d45001241a930db451e633ca1f0007e3f749deaa5d7360062fcd61e3"},
+    {file = "groq-0.8.0.tar.gz", hash = "sha256:37ceb2f706bd516d0bfcac8e89048a24b375172987a0d6bd9efb521c54f6deff"},
 ]
 
 [package.dependencies]
@@ -1807,13 +1808,13 @@ test = ["Cython (>=0.29.24,<0.30.0)"]
 
 [[package]]
 name = "httpx"
-version = "0.25.2"
+version = "0.27.0"
 description = "The next generation HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "httpx-0.25.2-py3-none-any.whl", hash = "sha256:a05d3d052d9b2dfce0e3896636467f8a5342fb2b902c819428e1ac65413ca118"},
-    {file = "httpx-0.25.2.tar.gz", hash = "sha256:8b8fcaa0c8ea7b05edd69a094e63a2094c4efcb48129fb757361bc423c0ad9e8"},
+    {file = "httpx-0.27.0-py3-none-any.whl", hash = "sha256:71d5465162c13681bff01ad59b2cc68dd838ea1f10e51574bac27103f00c91a5"},
+    {file = "httpx-0.27.0.tar.gz", hash = "sha256:a0cb88a46f32dc874e04ee956e4c2764aba2aa228f650b06788ba6bda2962ab5"},
 ]
 
 [package.dependencies]
@@ -1842,13 +1843,13 @@ files = [
 
 [[package]]
 name = "huggingface-hub"
-version = "0.23.0"
+version = "0.23.2"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "huggingface_hub-0.23.0-py3-none-any.whl", hash = "sha256:075c30d48ee7db2bba779190dc526d2c11d422aed6f9044c5e2fdc2c432fdb91"},
-    {file = "huggingface_hub-0.23.0.tar.gz", hash = "sha256:7126dedd10a4c6fac796ced4d87a8cf004efc722a5125c2c09299017fa366fa9"},
+    {file = "huggingface_hub-0.23.2-py3-none-any.whl", hash = "sha256:48727a16e704d409c4bb5913613308499664f22a99743435dc3a13b23c485827"},
+    {file = "huggingface_hub-0.23.2.tar.gz", hash = "sha256:f6829b62d5fdecb452a76fdbec620cba4c1573655a8d710c1df71735fd9edbd2"},
 ]
 
 [package.dependencies]
@@ -2062,6 +2063,76 @@ MarkupSafe = ">=2.0"
 [package.extras]
 i18n = ["Babel (>=2.7)"]
 
+[[package]]
+name = "jiter"
+version = "0.4.0"
+description = "Fast iterable JSON parser."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jiter-0.4.0-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:4aa6226d82a4a4505078c0bd5947bad65399635fc5cd4b226512e41753624edf"},
+    {file = "jiter-0.4.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:947111ac906740a948e7b63799481acd3d5ef666ccb178d146e25718640b7408"},
+    {file = "jiter-0.4.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:69572ffb4e84ae289a7422b9af4ea123cae2ce0772228859b37d4b26b4bc92ea"},
+    {file = "jiter-0.4.0-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:ba6046cbb5d1baa5a781b846f7e5438596a332f249a857d63f86ef5d1d9563b0"},
+    {file = "jiter-0.4.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d4f346e54602782e66d07df0d1c7389384fd93680052ed6170da2c6dc758409e"},
+    {file = "jiter-0.4.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:49110ce693f07e97d61089d894cea05a0b9894d5ccc6ac6fc583028726c8c8af"},
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+    {file = "jiter-0.4.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:413adb15372ac63db04373240f40925788e4282c997eeafc2040530049a0a599"},
+    {file = "jiter-0.4.0-cp310-none-win32.whl", hash = "sha256:0b48ea71673a97b897e4b94bbc871e62495a5a85f836c9f90712a4c70aa3ef7e"},
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 ]
 
 [package.dependencies]
@@ -4507,19 +4581,18 @@ files = [
 
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 python-versions = ">=3.8"
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 [[package]]
 name = "shapely"
@@ -4658,17 +4731,17 @@ typing = ["mypy (>=1.4)", "rich", "twisted"]
 
 [[package]]
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 description = "Computer algebra system (CAS) in Python"
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 [package.dependencies]
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 [[package]]
 name = "tbb"
@@ -5084,13 +5157,13 @@ tqdm = ">4.64"
 
 [[package]]
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+version = "4.41.1"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 [package.dependencies]
@@ -5190,13 +5263,13 @@ typing-extensions = ">=3.7.4.3"
 
 [[package]]
 name = "types-requests"
-version = "2.32.0.20240521"
+version = "2.32.0.20240523"
 description = "Typing stubs for requests"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -5204,13 +5277,13 @@ urllib3 = ">=2"
 
 [[package]]
 name = "typing-extensions"
-version = "4.11.0"
+version = "4.12.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [[package]]
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 [[package]]
 name = "watchdog"
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 description = "Filesystem events monitoring"
 optional = false
 python-versions = ">=3.8"
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 [package.extras]
@@ -5445,86 +5521,86 @@ watchmedo = ["PyYAML (>=3.10)"]
 
 [[package]]
 name = "watchfiles"
-version = "0.21.0"
+version = "0.22.0"
 description = "Simple, modern and high performance file watching and code reload in python."
 optional = false
 python-versions = ">=3.8"
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+    {file = "watchfiles-0.22.0-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:2f350cbaa4bb812314af5dab0eb8d538481e2e2279472890864547f3fe2281ed"},
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+    {file = "watchfiles-0.22.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:00ad0bcd399503a84cc688590cdffbe7a991691314dde5b57b3ed50a41319a31"},
+    {file = "watchfiles-0.22.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:72a44e9481afc7a5ee3291b09c419abab93b7e9c306c9ef9108cb76728ca58d2"},
+    {file = "watchfiles-0.22.0.tar.gz", hash = "sha256:988e981aaab4f3955209e7e28c7794acdb690be1efa7f16f8ea5aba7ffdadacb"},
 ]
 
 [package.dependencies]
@@ -5820,13 +5896,13 @@ multidict = ">=4.0"
 
 [[package]]
 name = "zipp"
-version = "3.18.2"
+version = "3.19.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.18.2-py3-none-any.whl", hash = "sha256:dce197b859eb796242b0622af1b8beb0a722d52aa2f57133ead08edd5bf5374e"},
-    {file = "zipp-3.18.2.tar.gz", hash = "sha256:6278d9ddbcfb1f1089a88fde84481528b07b0e10474e09dcfe53dad4069fa059"},
+    {file = "zipp-3.19.0-py3-none-any.whl", hash = "sha256:96dc6ad62f1441bcaccef23b274ec471518daf4fbbc580341204936a5a3dddec"},
+    {file = "zipp-3.19.0.tar.gz", hash = "sha256:952df858fb3164426c976d9338d3961e8e8b3758e2e059e0f754b8c4262625ee"},
 ]
 
 [package.extras]
diff --git a/src/talemate/agents/conversation.py b/src/talemate/agents/conversation.py
index 6a7177f8..c8780b53 100644
--- a/src/talemate/agents/conversation.py
+++ b/src/talemate/agents/conversation.py
@@ -21,6 +21,7 @@ from talemate.emit import emit
 from talemate.events import GameLoopEvent
 from talemate.prompts import Prompt
 from talemate.scene_message import CharacterMessage, DirectorMessage
+from talemate.exceptions import LLMAccuracyError
 
 from .base import (
     Agent,
@@ -661,6 +662,11 @@ class ConversationAgent(Agent):
                 empty_result_count += 1
                 if empty_result_count >= 2:
                     break
+                
+                
+        # if result is empty, raise an error
+        if not total_result:
+            raise LLMAccuracyError("Received empty response from AI")
 
         result = result.replace(" :", ":")
 
diff --git a/src/talemate/agents/editor.py b/src/talemate/agents/editor.py
index 01e7aeaf..06dfce18 100644
--- a/src/talemate/agents/editor.py
+++ b/src/talemate/agents/editor.py
@@ -134,6 +134,10 @@ class EditorAgent(Agent):
 
         if not self.actions["fix_exposition"].enabled:
             return content
+        
+        # if not content was generated, return it as is
+        if not content:
+            return content
 
         if not character.is_player:
             if '"' not in content and "*" not in content:
diff --git a/src/talemate/client/__init__.py b/src/talemate/client/__init__.py
index 4cadb99b..8b6c2ed1 100644
--- a/src/talemate/client/__init__.py
+++ b/src/talemate/client/__init__.py
@@ -10,5 +10,6 @@ from talemate.client.lmstudio import LMStudioClient
 from talemate.client.mistral import MistralAIClient
 from talemate.client.openai import OpenAIClient
 from talemate.client.openai_compat import OpenAICompatibleClient
+from talemate.client.tabbyapi import TabbyAPIClient
 from talemate.client.registry import CLIENT_CLASSES, get_client_class, register
 from talemate.client.textgenwebui import TextGeneratorWebuiClient
\ No newline at end of file
diff --git a/src/talemate/client/anthropic.py b/src/talemate/client/anthropic.py
index 02a5da7d..a41e1806 100644
--- a/src/talemate/client/anthropic.py
+++ b/src/talemate/client/anthropic.py
@@ -58,6 +58,15 @@ class AnthropicClient(ClientBase):
     def anthropic_api_key(self):
         return self.config.get("anthropic", {}).get("api_key")
 
+    @property
+    def supported_parameters(self):
+        return [
+            "temperature", 
+            "top_p",
+            "top_k",
+            "max_tokens",
+        ]
+
     def emit_status(self, processing: bool = None):
         error_action = None
         if processing is not None:
@@ -160,14 +169,6 @@ class AnthropicClient(ClientBase):
 
         return prompt
 
-    def tune_prompt_parameters(self, parameters: dict, kind: str):
-        super().tune_prompt_parameters(parameters, kind)
-        keys = list(parameters.keys())
-        valid_keys = ["temperature", "top_p", "max_tokens"]
-        for key in keys:
-            if key not in valid_keys:
-                del parameters[key]
-
     async def generate(self, prompt: str, parameters: dict, kind: str):
         """
         Generates text from the given prompt and parameters.
diff --git a/src/talemate/client/base.py b/src/talemate/client/base.py
index c3c08ccf..27d1a187 100644
--- a/src/talemate/client/base.py
+++ b/src/talemate/client/base.py
@@ -41,6 +41,7 @@ class PromptData(pydantic.BaseModel):
     time: Union[float, int]
     agent_stack: list[str] = pydantic.Field(default_factory=list)
     generation_parameters: dict = pydantic.Field(default_factory=dict)
+    inference_preset: str = None
 
 
 class ErrorAction(pydantic.BaseModel):
@@ -63,6 +64,20 @@ class ExtraField(pydantic.BaseModel):
     required: bool
     description: str
 
+class ParameterReroute(pydantic.BaseModel):
+    talemate_parameter: str
+    client_parameter: str
+    
+    def reroute(self, parameters: dict):
+        if self.talemate_parameter in parameters:
+            parameters[self.client_parameter] = parameters[self.talemate_parameter]
+            del parameters[self.talemate_parameter]
+
+    def __str__(self):
+        return self.client_parameter
+
+    def __eq__(self, other):
+        return str(self) == str(other)
 
 class ClientBase:
     api_url: str
@@ -81,6 +96,7 @@ class ClientBase:
     finalizers: list[str] = []
     double_coercion: Union[str, None] = None
     client_type = "base"
+    
 
     class Meta(pydantic.BaseModel):
         experimental: Union[None, str] = None
@@ -126,6 +142,15 @@ class ClientBase:
     def max_tokens_param_name(self):
         return "max_tokens"
 
+    @property
+    def supported_parameters(self):
+        # each client should override this with the parameters it supports
+        return [
+            "temperature",
+            "max_tokens",
+        ]
+
+
     def set_client(self, **kwargs):
         self.client = AsyncOpenAI(base_url=self.api_url, api_key="sk-1111")
 
@@ -236,8 +261,6 @@ class ClientBase:
 
             if "narrate" in kind:
                 return system_prompts.NARRATOR
-            if "story" in kind:
-                return system_prompts.NARRATOR
             if "director" in kind:
                 return system_prompts.DIRECTOR
             if "create" in kind:
@@ -269,8 +292,6 @@ class ClientBase:
 
             if "narrate" in kind:
                 return system_prompts.NARRATOR_NO_DECENSOR
-            if "story" in kind:
-                return system_prompts.NARRATOR_NO_DECENSOR
             if "director" in kind:
                 return system_prompts.DIRECTOR_NO_DECENSOR
             if "create" in kind:
@@ -428,7 +449,7 @@ class ClientBase:
     def generate_prompt_parameters(self, kind: str):
         parameters = {}
         self.tune_prompt_parameters(
-            presets.configure(parameters, kind, self.max_token_length), kind
+            presets.configure(parameters, kind, self.max_token_length, self), kind
         )
         return parameters
 
@@ -469,6 +490,21 @@ class ClientBase:
         else:
             parameters["extra_stopping_strings"] = dialog_stopping_strings
 
+    def clean_prompt_parameters(self, parameters: dict):
+        """
+        Does some final adjustments to the prompt parameters before sending
+        """
+        
+        # apply any parameter reroutes
+        for param in self.supported_parameters:
+            if isinstance(param, ParameterReroute):
+                param.reroute(parameters)
+        
+        # drop any parameters that are not supported by the client
+        for key in list(parameters.keys()):
+            if key not in self.supported_parameters:
+                del parameters[key]
+
     def finalize(self, parameters: dict, prompt: str):
 
         prompt = util.replace_special_tokens(prompt)
@@ -478,6 +514,7 @@ class ClientBase:
             prompt, applied = fn(parameters, prompt)
             if applied:
                 return prompt
+    
         return prompt
 
     async def generate(self, prompt: str, parameters: dict, kind: str):
@@ -537,6 +574,8 @@ class ClientBase:
 
             time_start = time.time()
             extra_stopping_strings = prompt_param.pop("extra_stopping_strings", [])
+            
+            self.clean_prompt_parameters(prompt_param)        
 
             self.log.debug(
                 "send_prompt",
@@ -577,6 +616,7 @@ class ClientBase:
                     client_type=self.client_type,
                     time=time_end - time_start,
                     generation_parameters=prompt_param,
+                    inference_preset=client_context_attribute("inference_preset"),
                 ).model_dump(),
             )
 
diff --git a/src/talemate/client/cohere.py b/src/talemate/client/cohere.py
index a90600ab..8fb7f40f 100644
--- a/src/talemate/client/cohere.py
+++ b/src/talemate/client/cohere.py
@@ -2,7 +2,7 @@ import pydantic
 import structlog
 from cohere import AsyncClient
 
-from talemate.client.base import ClientBase, ErrorAction
+from talemate.client.base import ClientBase, ErrorAction, ParameterReroute
 from talemate.client.registry import register
 from talemate.config import load_config
 from talemate.emit import emit
@@ -58,6 +58,18 @@ class CohereClient(ClientBase):
     def cohere_api_key(self):
         return self.config.get("cohere", {}).get("api_key")
 
+    @property
+    def supported_parameters(self):
+        return [
+            "temperature", 
+            ParameterReroute(talemate_parameter="top_p", client_parameter="p"),
+            ParameterReroute(talemate_parameter="top_k", client_parameter="k"),
+            ParameterReroute(talemate_parameter="stopping_strings",  client_parameter="stop_sequences"),
+            "frequency_penalty",
+            "presence_penalty",
+            "max_tokens",
+        ]
+
     def emit_status(self, processing: bool = None):
         error_action = None
         if processing is not None:
@@ -160,18 +172,22 @@ class CohereClient(ClientBase):
 
         return prompt
 
-    def tune_prompt_parameters(self, parameters: dict, kind: str):
-        super().tune_prompt_parameters(parameters, kind)
-        keys = list(parameters.keys())
-        valid_keys = ["temperature", "max_tokens"]
-        for key in keys:
-            if key not in valid_keys:
-                del parameters[key]
-
+    def clean_prompt_parameters(self, parameters: dict):
+        
+        super().clean_prompt_parameters(parameters)
+        
         # if temperature is set, it needs to be clamped between 0 and 1.0
         if "temperature" in parameters:
             parameters["temperature"] = max(0.0, min(1.0, parameters["temperature"]))
-
+            
+        # if stop_sequences is set, max 5 items
+        if "stop_sequences" in parameters:
+            parameters["stop_sequences"] = parameters["stop_sequences"][:5]
+            
+        # if both frequency_penalty and presence_penalty are set, drop frequency_penalty
+        if "presence_penalty" in parameters and "frequency_penalty" in parameters:
+            del parameters["frequency_penalty"]
+            
     async def generate(self, prompt: str, parameters: dict, kind: str):
         """
         Generates text from the given prompt and parameters.
diff --git a/src/talemate/client/context.py b/src/talemate/client/context.py
index 7759a579..d1f842f9 100644
--- a/src/talemate/client/context.py
+++ b/src/talemate/client/context.py
@@ -38,6 +38,7 @@ class ContextModel(BaseModel):
     nuke_repetition: float = Field(0.0, ge=0.0, le=3.0)
     conversation: ConversationContext = Field(default_factory=ConversationContext)
     length: int = 96
+    inference_preset: str = None
 
 
 # Define the context variable as an empty dictionary
diff --git a/src/talemate/client/google.py b/src/talemate/client/google.py
index 0addc2b5..78ddaeff 100644
--- a/src/talemate/client/google.py
+++ b/src/talemate/client/google.py
@@ -10,9 +10,10 @@ from vertexai.generative_models import (
     GenerativeModel,
     ResponseValidationError,
     SafetySetting,
+    GenerationConfig,
 )
 
-from talemate.client.base import ClientBase, ErrorAction, ExtraField
+from talemate.client.base import ClientBase, ErrorAction, ExtraField, ParameterReroute
 from talemate.client.registry import register
 from talemate.client.remote import RemoteServiceMixin
 from talemate.config import Client as BaseClientConfig
@@ -54,7 +55,7 @@ class GoogleClient(RemoteServiceMixin, ClientBase):
     auto_break_repetition_enabled = False
     decensor_enabled = True
     config_cls = ClientConfig
-
+    
     class Meta(ClientBase.Meta):
         name_prefix: str = "Google"
         title: str = "Google"
@@ -140,6 +141,16 @@ class GoogleClient(RemoteServiceMixin, ClientBase):
 
         return safety_settings
 
+    @property
+    def supported_parameters(self):
+        return [
+            "temperature", 
+            "top_p",
+            "top_k",
+            ParameterReroute(talemate_parameter="max_tokens", client_parameter="max_output_tokens"),
+            ParameterReroute(talemate_parameter="stopping_strings",  client_parameter="stop_sequences"),
+        ]
+
     def emit_status(self, processing: bool = None):
         error_action = None
         if processing is not None:
@@ -237,11 +248,19 @@ class GoogleClient(RemoteServiceMixin, ClientBase):
         if "disable_safety_settings" in kwargs:
             self.disable_safety_settings = kwargs["disable_safety_settings"]
 
+    def clean_prompt_parameters(self, parameters: dict):
+        super().clean_prompt_parameters(parameters)
+        
+        log.warning("clean_prompt_parameters", parameters=parameters)
+        # if top_k is 0, remove it
+        if "top_k" in parameters and parameters["top_k"] == 0:
+            del parameters["top_k"]
+
     async def generate(self, prompt: str, parameters: dict, kind: str):
         """
         Generates text from the given prompt and parameters.
         """
-
+        
         if not self.ready:
             raise Exception("Google cloud setup incomplete")
 
@@ -268,10 +287,11 @@ class GoogleClient(RemoteServiceMixin, ClientBase):
         try:
 
             chat = self.model_instance.start_chat()
-
+            
             response = await chat.send_message_async(
                 human_message,
                 safety_settings=self.safety_settings,
+                generation_config=parameters,
             )
 
             self._returned_prompt_tokens = self.prompt_tokens(prompt)
diff --git a/src/talemate/client/groq.py b/src/talemate/client/groq.py
index 7e8bf93d..3068c5c7 100644
--- a/src/talemate/client/groq.py
+++ b/src/talemate/client/groq.py
@@ -2,7 +2,7 @@ import pydantic
 import structlog
 from groq import AsyncGroq, PermissionDeniedError
 
-from talemate.client.base import ClientBase, ErrorAction
+from talemate.client.base import ClientBase, ErrorAction, ParameterReroute
 from talemate.client.registry import register
 from talemate.config import load_config
 from talemate.emit import emit
@@ -60,6 +60,17 @@ class GroqClient(ClientBase):
     def groq_api_key(self):
         return self.config.get("groq", {}).get("api_key")
 
+    @property
+    def supported_parameters(self):
+        return [
+            "temperature",
+            "top_p",
+            "presence_penalty",
+            "frequency_penalty",
+            ParameterReroute(talemate_parameter="stopping_strings",  client_parameter="stop"),
+            "max_tokens",
+        ]
+
     def emit_status(self, processing: bool = None):
         error_action = None
         if processing is not None:
@@ -162,14 +173,6 @@ class GroqClient(ClientBase):
 
         return prompt
 
-    def tune_prompt_parameters(self, parameters: dict, kind: str):
-        super().tune_prompt_parameters(parameters, kind)
-        keys = list(parameters.keys())
-        valid_keys = ["temperature", "top_p", "max_tokens"]
-        for key in keys:
-            if key not in valid_keys:
-                del parameters[key]
-
     async def generate(self, prompt: str, parameters: dict, kind: str):
         """
         Generates text from the given prompt and parameters.
diff --git a/src/talemate/client/koboldcpp.py b/src/talemate/client/koboldcpp.py
index d7e35f88..2753ecfd 100644
--- a/src/talemate/client/koboldcpp.py
+++ b/src/talemate/client/koboldcpp.py
@@ -7,7 +7,7 @@ from urllib.parse import urljoin, urlparse
 import httpx
 import structlog
 
-from talemate.client.base import STOPPING_STRINGS, ClientBase, Defaults, ExtraField
+from talemate.client.base import STOPPING_STRINGS, ClientBase, Defaults, ParameterReroute
 from talemate.client.registry import register
 import talemate.util as util
 
@@ -81,6 +81,33 @@ class KoboldCppClient(ClientBase):
         else:
             return "max_length"
 
+        
+    @property
+    def supported_parameters(self):
+        if not self.is_openai:
+            # koboldcpp united api
+
+            return [
+                ParameterReroute(talemate_parameter="max_tokens", client_parameter="max_length"),
+                "max_context_length",
+                ParameterReroute(talemate_parameter="repetition_penalty", client_parameter="rep_pen"),
+                ParameterReroute(talemate_parameter="repetition_penalty_range", client_parameter="rep_pen_range"),
+                "top_p",
+                "top_k",
+                ParameterReroute(talemate_parameter="stopping_strings", client_parameter="stop_sequence"),
+                "temperature",
+            ]
+
+        else:
+            # openai api
+            
+            return [
+                "max_tokens",
+                "presence_penalty",
+                "top_p",
+                "temperature",
+            ]
+
     def api_endpoint_specified(self, url: str) -> bool:
         return "/v1" in self.api_url
 
@@ -97,51 +124,11 @@ class KoboldCppClient(ClientBase):
         super().__init__(**kwargs)
         self.ensure_api_endpoint_specified()
 
-    def tune_prompt_parameters(self, parameters: dict, kind: str):
-        super().tune_prompt_parameters(parameters, kind)
-        if not self.is_openai:
-            # adjustments for united api
-            parameters["max_length"] = parameters.pop("max_tokens")
-            parameters["max_context_length"] = self.max_token_length
-            if "repetition_penalty_range" in parameters:
-                parameters["rep_pen_range"] = parameters.pop("repetition_penalty_range")
-            if "repetition_penalty" in parameters:
-                parameters["rep_pen"] = parameters.pop("repetition_penalty")
-            if parameters.get("stop_sequence"):
-                parameters["stop_sequence"] = parameters.pop("stopping_strings")
-                
-            if parameters.get("extra_stopping_strings"):
-                if "stop_sequence" in parameters:
-                    parameters["stop_sequence"] += parameters.pop("extra_stopping_strings")
-                else:
-                    parameters["stop_sequence"] = parameters.pop("extra_stopping_strings")
-                
-                
-            allowed_params = [
-                "max_length",
-                "max_context_length",
-                "rep_pen",
-                "rep_pen_range",
-                "top_p",
-                "top_k",
-                "temperature",
-                "stop_sequence",
-            ]
-        else:
-            allowed_params = ["max_tokens", "presence_penalty", "top_p", "temperature"]
-
-        # drop unsupported params
-        for param in list(parameters.keys()):
-            if param not in allowed_params:
-                del parameters[param]
 
     def set_client(self, **kwargs):
         self.api_key = kwargs.get("api_key", self.api_key)
         self.ensure_api_endpoint_specified()
         
-        
-        
-
     async def get_model_name(self):
         self.ensure_api_endpoint_specified()
         async with httpx.AsyncClient() as client:
diff --git a/src/talemate/client/lmstudio.py b/src/talemate/client/lmstudio.py
index ba4c21f2..bef193f0 100644
--- a/src/talemate/client/lmstudio.py
+++ b/src/talemate/client/lmstudio.py
@@ -1,7 +1,7 @@
 import pydantic
 from openai import AsyncOpenAI
 
-from talemate.client.base import ClientBase
+from talemate.client.base import ClientBase, ParameterReroute
 from talemate.client.registry import register
 
 
@@ -14,26 +14,25 @@ class Defaults(pydantic.BaseModel):
 class LMStudioClient(ClientBase):
     auto_determine_prompt_template: bool = True
     client_type = "lmstudio"
-
+    
     class Meta(ClientBase.Meta):
         name_prefix: str = "LMStudio"
         title: str = "LMStudio"
         defaults: Defaults = Defaults()
 
+    @property
+    def supported_parameters(self):
+        return [
+            "temperature",
+            "top_p",
+            "frequency_penalty",
+            "presence_penalty",
+            ParameterReroute(talemate_parameter="stopping_strings", client_parameter="stop"),
+        ]
+
     def set_client(self, **kwargs):
         self.client = AsyncOpenAI(base_url=self.api_url + "/v1", api_key="sk-1111")
 
-    def tune_prompt_parameters(self, parameters: dict, kind: str):
-        super().tune_prompt_parameters(parameters, kind)
-
-        keys = list(parameters.keys())
-
-        valid_keys = ["temperature", "top_p"]
-
-        for key in keys:
-            if key not in valid_keys:
-                del parameters[key]
-
     async def get_model_name(self):
         model_name = await super().get_model_name()
 
diff --git a/src/talemate/client/mistral.py b/src/talemate/client/mistral.py
index 1585b058..edc3e89b 100644
--- a/src/talemate/client/mistral.py
+++ b/src/talemate/client/mistral.py
@@ -4,7 +4,7 @@ from mistralai.async_client import MistralAsyncClient
 from mistralai.exceptions import MistralAPIStatusException
 from mistralai.models.chat_completion import ChatMessage
 
-from talemate.client.base import ClientBase, ErrorAction
+from talemate.client.base import ClientBase, ErrorAction, ParameterReroute
 from talemate.client.registry import register
 from talemate.config import load_config
 from talemate.emit import emit
@@ -69,6 +69,14 @@ class MistralAIClient(ClientBase):
     def mistralai_api_key(self):
         return self.config.get("mistralai", {}).get("api_key")
 
+    @property
+    def supported_parameters(self):
+        return [
+            "temperature", 
+            "top_p",
+            "max_tokens",
+        ]
+
     def emit_status(self, processing: bool = None):
         error_action = None
         if processing is not None:
@@ -171,17 +179,10 @@ class MistralAIClient(ClientBase):
 
         return prompt
 
-    def tune_prompt_parameters(self, parameters: dict, kind: str):
-        super().tune_prompt_parameters(parameters, kind)
-        keys = list(parameters.keys())
-        valid_keys = ["temperature", "top_p", "max_tokens"]
-        for key in keys:
-            if key not in valid_keys:
-                del parameters[key]
-
+    def clean_prompt_parameters(self, parameters: dict):
+        super().clean_prompt_parameters(parameters)
         # clamp temperature to 0.1 and 1.0
         # Unhandled Error: Status: 422. Message: {"object":"error","message":{"detail":[{"type":"less_than_equal","loc":["body","temperature"],"msg":"Input should be less than or equal to 1","input":1.31,"ctx":{"le":1.0},"url":"https://errors.pydantic.dev/2.6/v/less_than_equal"}]},"type":"invalid_request_error","param":null,"code":null}
-
         if "temperature" in parameters:
             parameters["temperature"] = min(1.0, max(0.1, parameters["temperature"]))
 
diff --git a/src/talemate/client/openai.py b/src/talemate/client/openai.py
index b3a84f1a..4913cd5e 100644
--- a/src/talemate/client/openai.py
+++ b/src/talemate/client/openai.py
@@ -94,7 +94,7 @@ def num_tokens_from_messages(messages: list[dict], model: str = "gpt-3.5-turbo-0
 
 class Defaults(pydantic.BaseModel):
     max_token_length: int = 16384
-    model: str = "gpt-4-turbo"
+    model: str = "gpt-4o"
 
 
 @register()
@@ -117,7 +117,7 @@ class OpenAIClient(ClientBase):
         requires_prompt_template: bool = False
         defaults: Defaults = Defaults()
 
-    def __init__(self, model="gpt-4-turbo", **kwargs):
+    def __init__(self, model="gpt-4o", **kwargs):
         self.model_name = model
         self.api_key_status = None
         self.config = load_config()
@@ -129,6 +129,15 @@ class OpenAIClient(ClientBase):
     def openai_api_key(self):
         return self.config.get("openai", {}).get("api_key")
 
+    @property
+    def supported_parameters(self):
+        return [
+            "temperature", 
+            "top_p", 
+            "presence_penalty",
+            "max_tokens",
+        ]
+
     def emit_status(self, processing: bool = None):
         error_action = None
         if processing is not None:
@@ -241,26 +250,6 @@ class OpenAIClient(ClientBase):
 
         return prompt
 
-    def tune_prompt_parameters(self, parameters: dict, kind: str):
-        super().tune_prompt_parameters(parameters, kind)
-
-        keys = list(parameters.keys())
-
-        valid_keys = ["temperature", "top_p"]
-
-        # GPT-3.5 models tend to run away with the generated
-        # response size so we allow talemate to set the max_tokens
-        #
-        # GPT-4 on the other hand seems to benefit from letting it
-        # decide the generation length naturally and it will generally
-        # produce reasonably sized responses
-        if self.model_name.startswith("gpt-3.5-"):
-            valid_keys.append("max_tokens")
-
-        for key in keys:
-            if key not in valid_keys:
-                del parameters[key]
-
     async def generate(self, prompt: str, parameters: dict, kind: str):
         """
         Generates text from the given prompt and parameters.
diff --git a/src/talemate/client/openai_compat.py b/src/talemate/client/openai_compat.py
index bfe3a53d..b11fcc6a 100644
--- a/src/talemate/client/openai_compat.py
+++ b/src/talemate/client/openai_compat.py
@@ -32,7 +32,7 @@ class OpenAICompatibleClient(ClientBase):
     client_type = "openai_compat"
     conversation_retries = 0
     config_cls = ClientConfig
-
+    
     class Meta(ClientBase.Meta):
         title: str = "OpenAI Compatible API"
         name_prefix: str = "OpenAI Compatible API"
@@ -70,6 +70,15 @@ class OpenAICompatibleClient(ClientBase):
         """
         return not self.api_handles_prompt_template
 
+    @property
+    def supported_parameters(self):
+        return [
+            "temperature", 
+            "top_p", 
+            "presence_penalty",
+            "max_tokens",
+        ]
+
     def set_client(self, **kwargs):
         self.api_key = kwargs.get("api_key", self.api_key)
         self.api_handles_prompt_template = kwargs.get(
@@ -81,16 +90,6 @@ class OpenAICompatibleClient(ClientBase):
             kwargs.get("model") or kwargs.get("model_name") or self.model_name
         )
 
-    def tune_prompt_parameters(self, parameters: dict, kind: str):
-        super().tune_prompt_parameters(parameters, kind)
-
-        allowed_params = ["max_tokens", "presence_penalty", "top_p", "temperature"]
-
-        # drop unsupported params
-        for param in list(parameters.keys()):
-            if param not in allowed_params:
-                del parameters[param]
-
     def prompt_template(self, system_message: str, prompt: str):
 
         log.debug(
diff --git a/src/talemate/client/presets.py b/src/talemate/client/presets.py
index c878822c..d77eaeae 100644
--- a/src/talemate/client/presets.py
+++ b/src/talemate/client/presets.py
@@ -1,3 +1,12 @@
+from typing import TYPE_CHECKING
+from talemate.config import load_config, InferencePresets
+from talemate.emit.signals import handlers
+from talemate.client.context import set_client_context_attribute
+import structlog
+
+if TYPE_CHECKING:
+    from talemate.client.base import ClientBase
+    
 __all__ = [
     "configure",
     "set_max_tokens",
@@ -11,228 +20,152 @@ __all__ = [
     "PRESET_SIMPLE_1",
 ]
 
-# TODO: refactor abstraction and make configurable
+log = structlog.get_logger("talemate.client.presets")
 
-PRESENCE_PENALTY_BASE = 0.2
+config = load_config(as_model=True)
 
-PRESET_TALEMATE_CONVERSATION = {
-    "temperature": 0.65,
-    "top_p": 0.47,
-    "top_k": 42,
-    "presence_penalty": PRESENCE_PENALTY_BASE,
-    "repetition_penalty": 1.18,
-    "repetition_penalty_range": 2048,
-}
-
-PRESET_TALEMATE_CREATOR = {
-    "temperature": 0.7,
-    "top_p": 0.9,
-    "top_k": 20,
-    "presence_penalty": PRESENCE_PENALTY_BASE,
-    "repetition_penalty": 1.15,
-    "repetition_penalty_range": 512,
-}
-
-PRESET_LLAMA_PRECISE = {
-    "temperature": 0.7,
-    "top_p": 0.1,
-    "top_k": 40,
-    "presence_penalty": PRESENCE_PENALTY_BASE,
-    "repetition_penalty": 1.18,
-}
-
-PRESET_DETERMINISTIC = {
-    "temperature": 0.1,
-    "top_p": 1,
-    "top_k": 0,
-    "repetition_penalty": 1.0,
-}
-
-PRESET_DIVINE_INTELLECT = {
-    "temperature": 1.31,
-    "top_p": 0.14,
-    "top_k": 49,
-    "presence_penalty": PRESENCE_PENALTY_BASE,
-    "repetition_penalty_range": 1024,
-    "repetition_penalty": 1.17,
-}
-
-PRESET_SIMPLE_1 = {
-    "temperature": 0.7,
-    "top_p": 0.9,
-    "top_k": 20,
-    "presence_penalty": PRESENCE_PENALTY_BASE,
-    "repetition_penalty": 1.15,
-}
-
-PRESET_ANALYTICAL = {
-    "temperature": 0.1,
-    "top_p": 0.9,
-    "top_k": 20,
+# Load the config
+CONFIG = {
+    "inference": config.presets.inference,
 }
 
 
-def configure(config: dict, kind: str, total_budget: int):
+# Sync the config when it is saved
+def sync_config(event):
+    CONFIG["inference"] = InferencePresets(
+        **event.data.get("presets", {}).get("inference", {})
+    )
+    
+handlers["config_saved"].connect(sync_config)
+
+
+def get_inference_parameters(preset_name: str) -> dict:
+    """
+    Returns the inference parameters for the given preset name.
+    """
+    presets = CONFIG["inference"].model_dump()
+    if preset_name in presets:
+        return presets[preset_name]
+    
+    raise ValueError(f"Preset name {preset_name} not found in presets.inference")
+
+
+def configure(parameters: dict, kind: str, total_budget: int, client: "ClientBase"):
     """
     Sets the config based on the kind of text to generate.
     """
-    set_preset(config, kind)
-    set_max_tokens(config, kind, total_budget)
-    return config
+    set_preset(parameters, kind, client)
+    set_max_tokens(parameters, kind, total_budget)
+    return parameters
 
 
-def set_max_tokens(config: dict, kind: str, total_budget: int):
+def set_max_tokens(parameters: dict, kind: str, total_budget: int):
     """
     Sets the max_tokens in the config based on the kind of text to generate.
     """
-    config["max_tokens"] = max_tokens_for_kind(kind, total_budget)
-    return config
+    parameters["max_tokens"] = max_tokens_for_kind(kind, total_budget)
+    return parameters
 
 
-def set_preset(config: dict, kind: str):
+def set_preset(parameters: dict, kind: str, client: "ClientBase"):
     """
     Sets the preset in the config based on the kind of text to generate.
     """
-    config.update(preset_for_kind(kind))
+    parameters.update(preset_for_kind(kind, client))
 
 
-def preset_for_kind(kind: str):
+# TODO: can this just be checking all keys in inference.presets.inference?
+PRESET_SUBSTRING_MAPPINGS = {
+    "deterministic": "deterministic",
+    "creative": "creative",
+    "analytical": "analytical",
+    "analyze": "analytical",
+}
 
-    # tag based
-    if "deterministic" in kind:
-        return PRESET_DETERMINISTIC
-    elif "creative" in kind:
-        return PRESET_DIVINE_INTELLECT
-    elif "simple" in kind:
-        return PRESET_SIMPLE_1
-    elif "analytical" in kind:
-        return PRESET_ANALYTICAL
-    elif kind == "conversation":
-        return PRESET_TALEMATE_CONVERSATION
-    elif kind == "conversation_old":
-        return PRESET_TALEMATE_CONVERSATION  # Assuming old conversation uses the same preset
-    elif kind == "conversation_long":
-        return PRESET_TALEMATE_CONVERSATION  # Assuming long conversation uses the same preset
-    elif kind == "conversation_select_talking_actor":
-        return PRESET_TALEMATE_CONVERSATION  # Assuming select talking actor uses the same preset
-    elif kind == "summarize":
-        return PRESET_LLAMA_PRECISE
-    elif kind == "analyze":
-        return PRESET_SIMPLE_1
-    elif kind == "analyze_creative":
-        return PRESET_DIVINE_INTELLECT
-    elif kind == "analyze_long":
-        return PRESET_SIMPLE_1  # Assuming long analysis uses the same preset as simple
-    elif kind == "analyze_freeform":
-        return PRESET_LLAMA_PRECISE
-    elif kind == "analyze_freeform_short":
-        return PRESET_LLAMA_PRECISE  # Assuming short freeform analysis uses the same preset as precise
-    elif kind == "narrate":
-        return PRESET_LLAMA_PRECISE
-    elif kind == "story":
-        return PRESET_DIVINE_INTELLECT
-    elif kind == "create":
-        return PRESET_TALEMATE_CREATOR
-    elif kind == "create_concise":
-        return PRESET_TALEMATE_CREATOR  # Assuming concise creation uses the same preset as creator
-    elif kind == "create_precise":
-        return PRESET_LLAMA_PRECISE
-    elif kind == "director":
-        return PRESET_SIMPLE_1
-    elif kind == "director_short":
-        return (
-            PRESET_SIMPLE_1  # Assuming short direction uses the same preset as simple
-        )
-    elif kind == "director_yesno":
-        return (
-            PRESET_SIMPLE_1  # Assuming yes/no direction uses the same preset as simple
-        )
-    elif kind == "edit_dialogue":
-        return PRESET_DIVINE_INTELLECT
-    elif kind == "edit_add_detail":
-        return PRESET_DIVINE_INTELLECT  # Assuming adding detail uses the same preset as divine intellect
-    elif kind == "edit_fix_exposition":
-        return PRESET_DETERMINISTIC  # Assuming fixing exposition uses the same preset as divine intellect
-    elif kind == "edit_fix_continuity":
-        return PRESET_DETERMINISTIC
-    elif kind == "visualize":
-        return PRESET_SIMPLE_1
-
-    else:
-        return PRESET_SIMPLE_1  # Default preset if none of the kinds match
+PRESET_MAPPING = {
+    "conversation": "conversation",
+    "conversation_select_talking_actor": "analytical",
+    "summarize": "summarization",
+    "analyze": "analytical",
+    "analyze_long": "analytical",
+    "analyze_freeform": "analytical",
+    "analyze_freeform_short": "analytical",
+    "narrate": "creative",
+    "create": "creative_instruction",
+    "create_short": "creative_instruction",
+    "create_concise": "creative_instruction",
+    "director": "scene_direction",
+    "edit_add_detail": "creative",
+    "edit_fix_exposition": "deterministic",
+    "edit_fix_continuity": "deterministic",
+    "visualize": "creative_instruction",
+}
 
 
-def max_tokens_for_kind(kind: str, total_budget: int):
-    if kind == "conversation":
-        return 75
-    elif kind == "conversation_old":
-        return 75
-    elif kind == "conversation_long":
-        return 300
-    elif kind == "conversation_select_talking_actor":
-        return 30
-    elif kind == "summarize":
-        return 500
-    elif kind == "analyze":
-        return 500
-    elif kind == "analyze_creative":
-        return 1024
-    elif kind == "analyze_long":
-        return 2048
-    elif kind == "analyze_freeform":
-        return 500
-    elif kind == "analyze_freeform_medium":
-        return 192
-    elif kind == "analyze_freeform_medium_short":
-        return 128
-    elif kind == "analyze_freeform_short":
-        return 10
-    elif kind == "narrate":
-        return 500
-    elif kind == "story":
-        return 300
-    elif kind == "create":
-        return min(1024, int(total_budget * 0.35))
-    elif kind == "create_concise":
-        return min(400, int(total_budget * 0.25))
-    elif kind == "create_precise":
-        return min(400, int(total_budget * 0.25))
-    elif kind == "create_short":
-        return 25
-    elif kind == "director":
-        return min(192, int(total_budget * 0.25))
-    elif kind == "director_short":
-        return 25
-    elif kind == "director_yesno":
-        return 2
-    elif kind == "edit_dialogue":
-        return 100
-    elif kind == "edit_add_detail":
-        return 200
-    elif kind == "edit_fix_exposition":
-        return 1024
-    elif kind == "edit_fix_continuity":
-        return 512
-    elif kind == "visualize":
-        return 150
-    # tag based
-    elif "extensive" in kind:
-        return 2048
-    elif "long" in kind:
-        return 1024
-    elif "medium2" in kind:
-        return 512
-    elif "medium" in kind:
-        return 192
-    elif "short2" in kind:
-        return 128
-    elif "short" in kind:
-        return 75
-    elif "tiny2" in kind:
-        return 25
-    elif "tiny" in kind:
-        return 10
-    elif "yesno" in kind:
-        return 2
-    else:
-        return 150  # Default value if none of the kinds match
+def preset_for_kind(kind: str, client: "ClientBase") -> dict:
+    # Check the substrings first(based on order of the original elifs)
+    
+    preset_name = None
+    
+    preset_name = PRESET_MAPPING.get(kind)
+    
+    if not preset_name:
+        for substring, value in PRESET_SUBSTRING_MAPPINGS.items():
+            if substring in kind:
+                preset_name = value
+        
+    if not preset_name:
+        log.warning(f"No preset found for kind {kind}, defaulting to 'scene_direction'", presets=CONFIG["inference"])
+        preset_name = "scene_direction"
+    
+    set_client_context_attribute("inference_preset", preset_name)
+    
+    return get_inference_parameters(preset_name)
+
+
+TOKEN_MAPPING = {
+    "conversation": 75,
+    "conversation_select_talking_actor": 30,
+    "summarize": 500,
+    "analyze": 500,
+    "analyze_long": 2048,
+    "analyze_freeform": 500,
+    "analyze_freeform_medium": 192,
+    "analyze_freeform_medium_short": 128,
+    "analyze_freeform_short": 10,
+    "narrate": 500,
+    "story": 300,
+    "create": lambda total_budget: min(1024, int(total_budget * 0.35)),
+    "create_concise": lambda total_budget: min(400, int(total_budget * 0.25)),
+    "create_short": 25,
+    "director": lambda total_budget: min(192, int(total_budget * 0.25)),
+    "edit_add_detail": 200,
+    "edit_fix_exposition": 1024,
+    "edit_fix_continuity": 512,
+    "visualize": 150,
+}
+
+TOKEN_SUBSTRING_MAPPINGS = {
+    "extensive": 2048,
+    "long": 1024,
+    "medium2": 512,
+    "medium": 192,
+    "short2": 128,
+    "short": 75,
+    "tiny2": 25,
+    "tiny": 10,
+    "yesno": 2,
+}
+
+
+def max_tokens_for_kind(kind: str, total_budget: int) -> int:
+    token_value = TOKEN_MAPPING.get(kind)
+    if callable(token_value):
+        return token_value(total_budget)
+    # If no exact match, check for substrings (order of original elifs)
+    for substring, value in TOKEN_SUBSTRING_MAPPINGS.items():
+        if substring in kind:
+            return value
+    if token_value is not None:
+        return token_value
+    return 150  # Default value if none of the kinds match
\ No newline at end of file
diff --git a/src/talemate/client/remote.py b/src/talemate/client/remote.py
index a2da5e7a..a56209ac 100644
--- a/src/talemate/client/remote.py
+++ b/src/talemate/client/remote.py
@@ -23,13 +23,5 @@ class RemoteServiceMixin:
         self.config = config
         self.set_client(max_token_length=self.max_token_length)
 
-    def tune_prompt_parameters(self, parameters: dict, kind: str):
-        super().tune_prompt_parameters(parameters, kind)
-        keys = list(parameters.keys())
-        valid_keys = ["temperature", "max_tokens"]
-        for key in keys:
-            if key not in valid_keys:
-                del parameters[key]
-
     async def status(self):
         self.emit_status()
diff --git a/src/talemate/client/tabbyapi.py b/src/talemate/client/tabbyapi.py
new file mode 100644
index 00000000..bf8d0bb5
--- /dev/null
+++ b/src/talemate/client/tabbyapi.py
@@ -0,0 +1,236 @@
+import urllib
+import aiohttp
+import random
+import pydantic
+import structlog
+from openai import AsyncOpenAI, NotFoundError, PermissionDeniedError
+
+from talemate.client.base import ClientBase, ExtraField
+from talemate.client.registry import register
+from talemate.config import Client as BaseClientConfig
+from talemate.emit import emit
+from talemate.client.utils import urljoin
+
+log = structlog.get_logger("talemate.client.tabbyapi")
+
+EXPERIMENTAL_DESCRIPTION = """Use this client to use all of TabbyAPI's features"""
+
+class CustomAPIClient:
+    def __init__(self, base_url, api_key):
+        self.base_url = base_url
+        self.api_key = api_key
+        
+    async def get_model_name(self):
+        url = urljoin(self.base_url, "model")
+        headers = {
+            "x-api-key": self.api_key,
+        }
+        async with aiohttp.ClientSession() as session:
+            async with session.get(url, headers=headers) as response:
+                if response.status != 200:
+                    raise Exception(f"Request failed: {response.status}")
+                response_data = await response.json()
+                model_name = response_data.get("id")
+                # split by "/" and take last
+                if model_name:
+                    model_name = model_name.split("/")[-1]
+                return model_name
+
+    async def create_chat_completion(self, model, messages, **parameters):
+        url = urljoin(self.base_url, "chat/completions")
+        headers = {
+            "x-api-key": self.api_key,
+            "Content-Type": "application/json",
+        }
+        data = {
+            "model": model,
+            "messages": messages,
+            **parameters,
+        }
+        async with aiohttp.ClientSession() as session:
+            async with session.post(url, headers=headers, json=data) as response:
+                if response.status != 200:
+                    raise Exception(f"Request failed: {response.status}")
+                return await response.json()
+
+    async def create_completion(self, model, **parameters):
+        url = urljoin(self.base_url, "completions")
+        headers = {
+            "x-api-key": self.api_key,
+            "Content-Type": "application/json",
+        }
+        data = {
+            "model": model,
+            **parameters,
+        }
+        async with aiohttp.ClientSession() as session:
+            async with session.post(url, headers=headers, json=data) as response:
+                if response.status != 200:
+                    raise Exception(f"Request failed: {response.status}")
+                return await response.json()
+
+
+
+class Defaults(pydantic.BaseModel):
+    api_url: str = "http://localhost:5000/v1"
+    api_key: str = ""
+    max_token_length: int = 8192
+    model: str = ""
+    api_handles_prompt_template: bool = False
+    double_coercion: str = None
+
+class ClientConfig(BaseClientConfig):
+    api_handles_prompt_template: bool = False
+
+@register()
+class TabbyAPIClient(ClientBase):
+    client_type = "tabbyapi"
+    conversation_retries = 0
+    config_cls = ClientConfig
+
+    class Meta(ClientBase.Meta):
+        title: str = "TabbyAPI"
+        name_prefix: str = "TabbyAPI"
+        experimental: str = EXPERIMENTAL_DESCRIPTION
+        enable_api_auth: bool = True
+        manual_model: bool = False
+        defaults: Defaults = Defaults()
+        extra_fields: dict[str, ExtraField] = {
+            "api_handles_prompt_template": ExtraField(
+                name="api_handles_prompt_template",
+                type="bool",
+                label="API handles prompt template (chat/completions)",
+                required=False,
+                description="The API handles the prompt template, meaning your choice in the UI for the prompt template below will be ignored. This is not recommended and should only be used if the API does not support the `completions` endpoint or you don't know which prompt template to use.",
+            )
+        }
+
+    def __init__(self, model=None, api_key=None, api_handles_prompt_template=False, **kwargs):
+        self.model_name = model
+        self.api_key = api_key
+        self.api_handles_prompt_template = api_handles_prompt_template
+        super().__init__(**kwargs)
+
+    @property
+    def experimental(self):
+        return EXPERIMENTAL_DESCRIPTION
+
+    @property
+    def can_be_coerced(self):
+        """
+        Determines whether or not this client can pass LLM coercion. (e.g., is able to predefine partial LLM output in the prompt)
+        """
+        return not self.api_handles_prompt_template
+
+    @property
+    def supported_parameters(self):
+        return [
+            "max_tokens", 
+            "presence_penalty", 
+            "frequency_penalty", 
+            "repetition_penalty_range", 
+            "min_p",
+            "top_p",
+            "temperature_last",
+            "temperature"
+        ]
+
+    def set_client(self, **kwargs):
+        self.api_key = kwargs.get("api_key", self.api_key)
+        self.api_handles_prompt_template = kwargs.get("api_handles_prompt_template", self.api_handles_prompt_template)
+        self.client = CustomAPIClient(base_url=self.api_url, api_key=self.api_key)
+        self.model_name = kwargs.get("model") or kwargs.get("model_name") or self.model_name
+
+    def prompt_template(self, system_message: str, prompt: str):
+
+        log.debug(
+            "IS API HANDLING PROMPT TEMPLATE",
+            api_handles_prompt_template=self.api_handles_prompt_template,
+        )
+
+        if not self.api_handles_prompt_template:
+            return super().prompt_template(system_message, prompt)
+
+        if "<|BOT|>" in prompt:
+            _, right = prompt.split("<|BOT|>", 1)
+            if right:
+                prompt = prompt.replace("<|BOT|>", "\nStart your response with: ")
+            else:
+                prompt = prompt.replace("<|BOT|>", "")
+
+        return prompt
+
+    async def get_model_name(self):
+        return await self.client.get_model_name()
+
+    async def generate(self, prompt: str, parameters: dict, kind: str):
+        """
+        Generates text from the given prompt and parameters.
+        """
+        try:
+            if self.api_handles_prompt_template:
+                # Custom API handles prompt template
+                # Use the chat completions endpoint
+                self.log.debug("generate (chat/completions)", prompt=prompt[:128] + " ...", parameters=parameters)
+                human_message = {"role": "user", "content": prompt.strip()}
+                response = await self.client.create_chat_completion(self.model_name, [human_message], **parameters)
+                response = response['choices'][0]['message']['content']
+                return self.process_response_for_indirect_coercion(prompt, response)
+            else:
+                # Talemate handles prompt template
+                # Use the completions endpoint
+                self.log.debug("generate (completions)", prompt=prompt[:128] + " ...", parameters=parameters)
+                parameters["prompt"] = prompt
+                response = await self.client.create_completion(self.model_name, **parameters)
+                return response['choices'][0]['text']
+        except PermissionDeniedError as e:
+            self.log.error("generate error", e=e)
+            emit("status", message="Client API: Permission Denied", status="error")
+            return ""
+        except Exception as e:
+            self.log.error("generate error", e=e)
+            emit("status", message="Error during generation (check logs)", status="error")
+            return ""
+
+    def reconfigure(self, **kwargs):
+        if kwargs.get("model"):
+            self.model_name = kwargs["model"]
+        if "api_url" in kwargs:
+            self.api_url = kwargs["api_url"]
+        if "max_token_length" in kwargs:
+            self.max_token_length = int(kwargs["max_token_length"]) if kwargs["max_token_length"] else 8192
+        if "api_key" in kwargs:
+            self.api_key = kwargs["api_key"]
+        if "api_handles_prompt_template" in kwargs:
+            self.api_handles_prompt_template = kwargs["api_handles_prompt_template"]
+        if "enabled" in kwargs:
+            self.enabled = bool(kwargs["enabled"])
+        if "double_coercion" in kwargs:
+            self.double_coercion = kwargs["double_coercion"]
+
+        log.warning("reconfigure", kwargs=kwargs)
+        self.set_client(**kwargs)
+
+    def jiggle_randomness(self, prompt_config: dict, offset: float = 0.3) -> dict:
+        """
+        adjusts temperature and presence penalty by random values using the base value as a center
+        """
+
+        temp = prompt_config["temperature"]
+
+        min_offset = offset * 0.3
+
+        prompt_config["temperature"] = random.uniform(temp + min_offset, temp + offset)
+
+        # keep min_p in a tight range to avoid unwanted tokens
+        prompt_config["min_p"] = random.uniform(0.05, 0.15)
+
+        try:
+            presence_penalty = prompt_config["presence_penalty"]
+            adjusted_presence_penalty = round(random.uniform(
+                presence_penalty + 0.1, presence_penalty + offset
+            ),1)
+            # Ensure presence_penalty does not exceed 0.5 and does not fall below 0.1
+            prompt_config["presence_penalty"] = min(0.5, max(0.1, adjusted_presence_penalty))
+        except KeyError:
+            pass
\ No newline at end of file
diff --git a/src/talemate/client/textgenwebui.py b/src/talemate/client/textgenwebui.py
index 0b7a7538..7e530089 100644
--- a/src/talemate/client/textgenwebui.py
+++ b/src/talemate/client/textgenwebui.py
@@ -39,6 +39,36 @@ class TextGeneratorWebuiClient(ClientBase):
             headers["Authorization"] = f"Bearer {self.api_key}"
         return headers
 
+    @property
+    def supported_parameters(self):
+        # textgenwebui does not error on unsupported parameters
+        # but we should still drop them so they don't get passed to the API
+        # and show up in our prompt debugging tool.
+
+        # note that this is not the full list of their supported parameters
+        # but only those we send.
+        return [
+            "temperature",
+            "top_p",
+            "top_k",
+            "min_p",
+            "frequency_penalty",
+            "presence_penalty",
+            "repetition_penalty",
+            "repetition_penalty_range",
+            "stopping_strings",
+            "skip_special_tokens",
+            "max_tokens",
+            "stream",
+            # arethese needed?
+            "max_new_tokens",
+            "stop",
+            # talemate internal
+            # These will be removed before sending to the API
+            # but we keep them here since they are used during the prompt finalization
+            "extra_stopping_strings",
+        ]
+
     def __init__(self, **kwargs):
         self.api_key = kwargs.pop("api_key", "")
         super().__init__(**kwargs)
@@ -51,39 +81,6 @@ class TextGeneratorWebuiClient(ClientBase):
         # is this needed?
         parameters["max_new_tokens"] = parameters["max_tokens"]
         parameters["stop"] = parameters["stopping_strings"]
-        
-        
-        # textgenwebui does not error on unsupported parameters
-        # but we should still drop them so they don't get passed to the API
-        # and show up in our prompt debugging tool.
-        
-        # note that this is not the full list of their supported parameters
-        # but only those we send.
-        
-        allowed_params = [
-            "temperature",
-            "top_p",
-            "top_k",
-            "max_tokens",
-            "repetition_penalty",
-            "repetition_penalty_range",
-            "max_tokens",
-            "stopping_strings",
-            "skip_special_tokens",
-            "stream",
-            # is this needed?
-            "max_new_tokens",
-            "stop",
-            # talemate internal
-            # These will be removed before sending to the API
-            # but we keep them here since they are used during the prompt finalization
-            "extra_stopping_strings",
-        ]
-
-        # drop unsupported params
-        for param in list(parameters.keys()):
-            if param not in allowed_params:
-                del parameters[param]
 
     def set_client(self, **kwargs):
         self.api_key = kwargs.get("api_key", self.api_key)
diff --git a/src/talemate/client/utils.py b/src/talemate/client/utils.py
index 91def044..e8f513a1 100644
--- a/src/talemate/client/utils.py
+++ b/src/talemate/client/utils.py
@@ -1,33 +1,9 @@
 import copy
 import random
+from urllib.parse import urljoin as _urljoin
 
+__all__ = ["urljoin"]
 
-def jiggle_randomness(prompt_config: dict, offset: float = 0.3) -> dict:
-    """
-    adjusts temperature and repetition_penalty
-    by random values using the base value as a center
-    """
-
-    temp = prompt_config["temperature"]
-    rep_pen = prompt_config["repetition_penalty"]
-
-    copied_config = copy.deepcopy(prompt_config)
-
-    min_offset = offset * 0.3
-
-    copied_config["temperature"] = random.uniform(temp + min_offset, temp + offset)
-    copied_config["repetition_penalty"] = random.uniform(
-        rep_pen + min_offset * 0.3, rep_pen + offset * 0.3
-    )
-
-    return copied_config
-
-
-def jiggle_enabled_for(kind: str):
-    if kind in ["conversation", "story"]:
-        return True
-
-    if kind.startswith("narrate"):
-        return True
-
-    return False
+def urljoin(base, *args):
+    base = f"{base.rstrip('/')}/"
+    return _urljoin(base, *args)
\ No newline at end of file
diff --git a/src/talemate/config.py b/src/talemate/config.py
index 27099387..6a1677f9 100644
--- a/src/talemate/config.py
+++ b/src/talemate/config.py
@@ -192,6 +192,58 @@ class RecentScene(BaseModel):
     date: str
     cover_image: Union[Asset, None] = None
 
+class InferenceParameters(BaseModel):
+    temperature: float = 1.0
+    temperature_last: bool = True
+    top_p: float | None = 1.0
+    top_k: int | None = 0
+    min_p: float | None = 0.1
+    presence_penalty: float | None = 0.2
+    frequency_penalty: float | None = 0.05
+    repetition_penalty: float | None= 1.0
+    repetition_penalty_range: int | None = 1024
+    # this determines whether or not it should be persisted
+    # to the config file
+    changed: bool = False
+
+
+class InferencePresets(BaseModel):
+
+    analytical: InferenceParameters = InferenceParameters(
+        temperature=0.7, 
+        presence_penalty=0,
+        frequency_penalty=0,
+        repetition_penalty=1.0,
+        min_p=0,
+    )
+    conversation: InferenceParameters = InferenceParameters(
+        temperature=0.85,
+        repetition_penalty_range=2048
+    )
+    creative: InferenceParameters = InferenceParameters()
+    creative_instruction: InferenceParameters = InferenceParameters(
+        temperature=0.85, 
+        repetition_penalty_range=512
+    )
+    deterministic: InferenceParameters = InferenceParameters(
+        temperature=0.1, 
+        top_p=1, 
+        top_k=0, 
+        repetition_penalty=1.0,
+        min_p=0,
+    )
+    scene_direction: InferenceParameters = InferenceParameters(
+        temperature=0.85, 
+    )
+    summarization: InferenceParameters = InferenceParameters(
+        temperature=0.7, 
+    )
+
+
+class Presets(BaseModel):
+    inference_defaults: InferencePresets = InferencePresets()
+    inference: InferencePresets = InferencePresets()
+
 
 def gnerate_intro_scenes():
     """
@@ -353,6 +405,8 @@ class Config(BaseModel):
     tts: TTSConfig = TTSConfig()
 
     recent_scenes: RecentScenes = RecentScenes()
+    
+    presets: Presets = Presets()
 
     class Config:
         extra = "ignore"
@@ -414,6 +468,22 @@ def save_config(config, file_path: str = "./config.yaml"):
             log.error("config validation", error=e)
             return None
 
+    # we dont want to persist the following, so we drop them:
+    # - presets.inference_defaults
+    try:
+        config["presets"].pop("inference_defaults")
+    except KeyError:
+        pass
+    
+    # for normal presets we only want to persist if they have changed
+    for preset_name, preset in list(config["presets"]["inference"].items()):
+        if not preset.get("changed"):
+            config["presets"]["inference"].pop(preset_name)
+            
+    # if presets is empty, remove it
+    if not config["presets"]["inference"]:
+        config.pop("presets")
+
     with open(file_path, "w") as file:
         yaml.dump(config, file)
 
diff --git a/talemate_frontend/src/components/AppConfig.vue b/talemate_frontend/src/components/AppConfig.vue
index 8ebe3408..65ab7d92 100644
--- a/talemate_frontend/src/components/AppConfig.vue
+++ b/talemate_frontend/src/components/AppConfig.vue
@@ -11,6 +11,10 @@
                     <v-icon start>mdi-application</v-icon>
                     Application
                 </v-tab>
+                <v-tab value="presets">
+                    <v-icon start>mdi-tune</v-icon>
+                    Presets
+                </v-tab>
                 <v-tab value="creator">
                     <v-icon start>mdi-palette-outline</v-icon>
                     Creator
@@ -263,6 +267,12 @@
                     </v-card>
                 </v-window-item>
 
+                <!-- PRESETS -->
+
+                <v-window-item value="presets">
+                    <AppConfigPresets :immutable-config="app_config" ref="presets"></AppConfigPresets>
+                </v-window-item>
+
                 <!-- CREATOR -->
 
                 <v-window-item value="creator">
@@ -325,8 +335,13 @@
 </template>
 <script>
 
+import AppConfigPresets from './AppConfigPresets.vue';
+
 export default {
     name: 'AppConfig',
+    components: {
+        AppConfigPresets,
+    },
     data() {
         return {
             tab: 'game',
@@ -431,6 +446,13 @@ export default {
         },
 
         saveConfig() {
+
+            // check if presets component is present
+            if(this.$refs.presets) {
+                // update app_config.presets from $refs.presets.config
+                this.app_config.presets = this.$refs.presets.config;
+            }
+
             this.sendRequest({
                 action: 'save',
                 config: this.app_config,
diff --git a/talemate_frontend/src/components/AppConfigPresets.vue b/talemate_frontend/src/components/AppConfigPresets.vue
new file mode 100644
index 00000000..b099c97a
--- /dev/null
+++ b/talemate_frontend/src/components/AppConfigPresets.vue
@@ -0,0 +1,135 @@
+<template>
+
+    <v-alert density="compact" type="warning" variant="text">
+        <p>
+            This interface is a work in progress and right now serves as a very basic way to edit inference parameter presets.
+        </p>
+        <p class="text-caption text-grey">
+            Not all clients support all parameters, and generally it is assumed that the client implementation
+            handles the parameters in a sane way, especially if values are passed for all of them. <span class="text-primary">All presets are used</span> and will be selected depending on the action the agent is performing. If you don't know what these mean, it is recommended to leave them as they are.
+        </p>
+    </v-alert>
+
+    <v-row>
+        <v-col cols="4">
+            <!-- list with all presets by key, read from `config` -->
+            <v-list slim selectable v-model:selected="selected" color="primary">
+                <v-list-item v-for="(preset, preset_key) in config.inference" :key="preset_key" :value="preset_key" prepend-icon="mdi-tune">
+                    <v-list-item-title>{{ toLabel(preset_key) }}</v-list-item-title>
+                </v-list-item>
+            </v-list>
+        </v-col>
+        <v-col cols="8">
+            <!--
+            class InferenceParameters(BaseModel):
+                temperature: float = 1.0
+                temperature_last: bool = True
+                top_p: float | None = 1.0
+                top_k: int | None = 0
+                min_p: float | None = 0.1
+                presence_penalty: float | None = 0.2
+                frequency_penalty: float | None = 0.2
+                repetition_penalty: float | None= 1.1
+                repetition_penalty_range: int | None = 1024
+
+            Display editable form for the selected preset
+
+            Will use sliders for float and int values, and checkboxes for bool values
+            -->
+            <div v-if="selected.length === 1">
+                <v-form>
+                    <v-card>
+                        <v-card-title>
+                            <v-row no-gutters>
+                                <v-col cols="8">
+                                    {{ toLabel(selected[0]) }}
+                                </v-col>
+                                <v-col cols="4" class="text-right">
+                                    <v-btn variant="text" size="small" color="warning" prepend-icon="mdi-refresh" @click="config.inference[selected[0]] = {...immutableConfig.presets.inference_defaults[selected[0]]}">Reset</v-btn>
+                                </v-col>
+                            </v-row>
+                        </v-card-title>
+
+                        <v-card-text>
+                            <v-slider thumb-label="always" density="compact" v-model="config.inference[selected[0]].temperature" min="0.1" max="2.0" step="0.05" label="Temperature" @update:model-value="setPresetChanged(selected[0])"></v-slider>
+
+                            <v-slider thumb-label="always" density="compact" v-model="config.inference[selected[0]].top_p" min="0.1" max="1.0" step="0.05" label="Top P" @update:model-value="setPresetChanged(selected[0])"></v-slider>
+
+                            <v-slider thumb-label="always" density="compact" v-model="config.inference[selected[0]].top_k" min="0" max="1024" step="1" label="Top K" @update:model-value="setPresetChanged(selected[0])"></v-slider>
+
+                            <v-slider thumb-label="always" density="compact" v-model="config.inference[selected[0]].min_p" min="0" max="1.0" step="0.01" label="Min P" @update:model-value="setPresetChanged(selected[0])"></v-slider>
+
+                            <v-slider thumb-label="always" density="compact" v-model="config.inference[selected[0]].presence_penalty" min="0" max="1.0" step="0.01" label="Presence Penalty" @update:model-value="setPresetChanged(selected[0])"></v-slider>
+
+                            <v-slider thumb-label="always" density="compact" v-model="config.inference[selected[0]].frequency_penalty" min="0" max="1.0" step="0.01" label="Frequency Penalty" @update:model-value="setPresetChanged(selected[0])"></v-slider>
+
+                            <v-slider thumb-label="always" density="compact" v-model="config.inference[selected[0]].repetition_penalty" min="1.0" max="1.20" step="0.01" label="Repetition Penalty" @update:model-value="setPresetChanged(selected[0])"></v-slider>
+
+                            <v-slider thumb-label="always" density="compact" v-model="config.inference[selected[0]].repetition_penalty_range" min="0" max="4096" step="256" label="Repetition Penalty Range" @update:model-value="setPresetChanged(selected[0])"></v-slider>
+
+                            <v-checkbox density="compact" v-model="config.inference[selected[0]].temperature_last" label="Sample temperature last" @update:model-value="setPresetChanged(selected[0])"></v-checkbox>
+
+
+                        </v-card-text>
+                    </v-card>
+                </v-form>
+            </div>
+            <div v-else>
+                <v-alert color="grey" variant="text">Select a preset to edit</v-alert>
+            </div>
+        </v-col>
+    </v-row>
+
+</template>
+<script>
+
+export default {
+    name: 'AppConfigPresets',
+    components: {
+    },
+    props: {
+        immutableConfig: Object,
+    },
+    watch: {
+        immutableConfig: {
+            handler: function(newVal) {
+
+                console.log("immutableConfig changed", newVal)
+
+                if(!newVal) {
+                    this.config = {};
+                    return;
+                }
+
+                this.config = {...newVal.presets};
+            },
+            immediate: true,
+            deep: true,
+        },
+    },
+    emits: [
+        'update',
+    ],
+    data() {
+        return {
+            selected: [],
+            config: {
+                inference: {},
+            },
+        }
+    },
+    methods: {
+
+        setPresetChanged(presetName) {
+            // this ensures that the change gets saved
+            console.log("setPresetChanged", presetName)
+            this.config.inference[presetName].changed = true;
+        },
+
+        toLabel(key) {
+            return key.replace(/_/g, ' ').replace(/\b\w/g, l => l.toUpperCase());
+        },
+    },
+}
+
+</script>
\ No newline at end of file
diff --git a/talemate_frontend/src/components/AudioQueue.vue b/talemate_frontend/src/components/AudioQueue.vue
index 6e1aef2b..46d02de4 100644
--- a/talemate_frontend/src/components/AudioQueue.vue
+++ b/talemate_frontend/src/components/AudioQueue.vue
@@ -3,7 +3,7 @@
     <span>{{ queue.length }} sound(s) queued</span>
     <v-icon :color="isPlaying ? 'green' : ''" v-if="!isMuted" @click="toggleMute">mdi-volume-high</v-icon>
     <v-icon :color="isPlaying ? 'red' : ''" v-else @click="toggleMute">mdi-volume-off</v-icon>
-    <v-icon class="ml-1" @click="stopAndClear">mdi-stop-circle-outline</v-icon>
+    <v-icon v-if="isPlaying" class="ml-1" @click="stopAndClear">mdi-stop-circle-outline</v-icon>
   </div>
 </template>
 
diff --git a/talemate_frontend/src/components/DebugToolPromptLog.vue b/talemate_frontend/src/components/DebugToolPromptLog.vue
index b594d7e8..25804db6 100644
--- a/talemate_frontend/src/components/DebugToolPromptLog.vue
+++ b/talemate_frontend/src/components/DebugToolPromptLog.vue
@@ -99,6 +99,7 @@ export default {
                     time: parseInt(data.data.time),
                     num: this.total++,
                     generation_parameters: data.data.generation_parameters,
+                    inference_preset: data.data.inference_preset,
                     // immutable copy of original generation parameters
                     original_generation_parameters: JSON.parse(JSON.stringify(data.data.generation_parameters)),
                     original_prompt: data.data.prompt,
diff --git a/talemate_frontend/src/components/DebugToolPromptView.vue b/talemate_frontend/src/components/DebugToolPromptView.vue
index 7df9c0e6..ad71a421 100644
--- a/talemate_frontend/src/components/DebugToolPromptView.vue
+++ b/talemate_frontend/src/components/DebugToolPromptView.vue
@@ -5,9 +5,9 @@
             <v-card-title>
                 #{{ prompt.num }}
                 <v-chip color="grey-lightne-1" variant="text">{{ prompt.agent_name }}</v-chip>
-                <v-chip color="grey" variant="text">{{ prompt.agent_action }}</v-chip>
-                <v-divider vertical></v-divider>
-                <v-chip color="grey" variant="text">{{ prompt.kind }}</v-chip>
+                <v-chip size="small" label class="mr-1" color="primary" variant="tonal"><strong class="mr-1">action</strong>{{ prompt.agent_action }}</v-chip>
+                <v-chip class="mr-1" size="small" color="grey" label variant="tonal"><strong class="mr-1">task</strong> {{ prompt.kind }}</v-chip>
+                <v-chip size="small" color="grey" label variant="tonal"><strong class="mr-1">preset</strong> {{ prompt.inference_preset }}</v-chip>
                 <v-chip size="small" color="primary" variant="text" label>{{ prompt.prompt_tokens }}<v-icon size="14"
                 class="ml-1">mdi-arrow-down-bold</v-icon></v-chip>
                 <v-chip size="small" color="secondary" variant="text" label>{{ prompt.response_tokens }}<v-icon size="14"