diff --git a/poetry.lock b/poetry.lock
index a4a6f862..29878934 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,135 +1,5 @@
 # This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand.
 
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 [[package]]
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 description = "Reusable constraint types to use with typing.Annotated"
 optional = false
 python-versions = ">=3.8"
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 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
-groups = ["main", "dev"]
+groups = ["main"]
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-
 [[package]]
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 version = "1.32.0"
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+markers = {main = "sys_platform == \"win32\" or platform_system == \"Windows\"", dev = "platform_system == \"Windows\" or sys_platform == \"win32\" or os_name == \"nt\""}
 
 [[package]]
 name = "coloredlogs"
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@@ -929,7 +767,7 @@ version = "3.17.0"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.9"
-groups = ["main", "dev"]
+groups = ["main"]
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 version = "3.1.1"
@@ -1187,7 +883,7 @@ version = "0.14.0"
 description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
 optional = false
 python-versions = ">=3.7"
-groups = ["main", "dev"]
+groups = ["main"]
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 optional = false
 python-versions = ">=3.8"
-groups = ["main", "dev"]
+groups = ["main"]
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 optional = false
 python-versions = ">=3.8"
-groups = ["main", "dev"]
+groups = ["main"]
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 socks = ["socksio (==1.*)"]
 zstd = ["zstandard (>=0.18.0)"]
 
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-
 [[package]]
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 version = "2.0.0"
@@ -1359,92 +996,6 @@ MarkupSafe = ">=2.0"
 [package.extras]
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-
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 version = "5.29.3"
@@ -2509,7 +1774,7 @@ version = "2.10.6"
 description = "Data validation using Python type hints"
 optional = false
 python-versions = ">=3.8"
-groups = ["main", "dev"]
+groups = ["main"]
 files = [
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 description = "Core functionality for Pydantic validation and serialization"
 optional = false
 python-versions = ">=3.8"
-groups = ["main", "dev"]
+groups = ["main"]
 files = [
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-python-versions = ">=3.9"
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-
 [[package]]
 name = "regex"
 version = "2024.11.6"
 description = "Alternative regular expression module, to replace re."
 optional = false
 python-versions = ">=3.8"
-groups = ["main", "dev"]
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 [package.extras]
 jupyter = ["ipywidgets (>=7.5.1,<9)"]
 
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 python-versions = ">=3.7"
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-]
-
-[package.dependencies]
-idna = ">=2.0"
-multidict = ">=4.0"
-propcache = ">=0.2.0"
-
-[[package]]
-name = "zipp"
-version = "3.21.0"
-description = "Backport of pathlib-compatible object wrapper for zip files"
-optional = false
-python-versions = ">=3.9"
-groups = ["main", "dev"]
-files = [
-    {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"},
-    {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"},
-]
-
-[package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
-cover = ["pytest-cov"]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-enabler = ["pytest-enabler (>=2.2)"]
-test = ["big-O", "importlib-resources ; python_version < \"3.9\"", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
-type = ["pytest-mypy"]
-
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.12,<3.13"
-content-hash = "11d90797bbc8dee54226f9c44d922333558c96820801cbe2cf677e313ff58fd0"
+content-hash = "53fde8cfa1247c862ebad688612221c48727b30e68dc63f18669f3348e6ce55b"
diff --git a/pyproject.toml b/pyproject.toml
index fdfd9b05..541df6d1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,7 +14,6 @@ PyYAML = "==6.0.2"
 fastapi = "==0.115.11"
 uvicorn = "==0.34.0"
 structlog = "==25.2.0"
-litellm = "==1.63.0"
 llama_cpp_python = "==0.3.5"
 cryptography = "==44.0.2"
 sqlalchemy = "==2.0.39"
@@ -50,7 +49,6 @@ ruff = "==0.11.0"
 bandit = "==1.8.3"
 build = "==1.2.2.post1"
 wheel = "==0.45.1"
-litellm = "==1.63.0"
 pytest-asyncio = "==0.25.3"
 llama_cpp_python = "==0.3.5"
 scikit-learn = "==1.6.1"
diff --git a/src/codegate/muxing/adapter.py b/src/codegate/muxing/adapter.py
index df5a1ab1..5da7543f 100644
--- a/src/codegate/muxing/adapter.py
+++ b/src/codegate/muxing/adapter.py
@@ -1,23 +1,10 @@
-import json
-import uuid
-from abc import ABC, abstractmethod
-from typing import Callable, Dict, Union
 from urllib.parse import urljoin
 
 import structlog
-from fastapi.responses import JSONResponse, StreamingResponse
-from litellm import ModelResponse
-from litellm.types.utils import Delta, StreamingChoices
 
 from codegate.config import Config
 from codegate.db import models as db_models
 from codegate.muxing import rulematcher
-from codegate.muxing.ollama_mappers import (
-    openai_chunk_from_ollama_chat,
-    openai_chunk_from_ollama_generate,
-)
-from codegate.types.ollama import StreamingChatCompletion as OllamaStreamingChatCompletion
-from codegate.types.ollama import StreamingGenerateCompletion as OllamaStreamingGenerateCompletion
 
 logger = structlog.get_logger("codegate")
 
@@ -35,260 +22,20 @@ def get_llamacpp_models_folder():
     return override if override else "./codegate_volume/models"
 
 
-class BodyAdapter:
-    """
-    Format the body to the destination provider format.
+def get_provider_formatted_url(model_route: rulematcher.ModelRoute) -> str:
+    """Get the provider formatted URL to use in base_url. Note this value comes from DB"""
+    if model_route.endpoint.provider_type in [
+        db_models.ProviderType.openai,
+        db_models.ProviderType.vllm,
+    ]:
+        return urljoin(model_route.endpoint.endpoint, "/v1")
+    if model_route.endpoint.provider_type == db_models.ProviderType.openrouter:
+        return urljoin(model_route.endpoint.endpoint, "/api/v1")
+    if model_route.endpoint.provider_type == db_models.ProviderType.llamacpp:
+        return get_llamacpp_models_folder()
+    return model_route.endpoint.endpoint
 
-    We expect the body to always be in OpenAI format. We need to configure the client
-    to send and expect OpenAI format. Here we just need to set the destination provider info.
-    """
 
-    def _get_provider_formatted_url(self, model_route: rulematcher.ModelRoute) -> str:
-        """Get the provider formatted URL to use in base_url. Note this value comes from DB"""
-        if model_route.endpoint.provider_type in [
-            db_models.ProviderType.openai,
-            db_models.ProviderType.vllm,
-        ]:
-            return urljoin(model_route.endpoint.endpoint, "/v1")
-        if model_route.endpoint.provider_type == db_models.ProviderType.openrouter:
-            return urljoin(model_route.endpoint.endpoint, "/api/v1")
-        if model_route.endpoint.provider_type == db_models.ProviderType.llamacpp:
-            return get_llamacpp_models_folder()
-        return model_route.endpoint.endpoint
-
-    def get_destination_info(self, model_route: rulematcher.ModelRoute) -> dict:
-        """Set the destination provider info."""
-        return model_route.model.name, self._get_provider_formatted_url(model_route)
-
-
-class OutputFormatter(ABC):
-
-    @property
-    @abstractmethod
-    def provider_format_funcs(self) -> Dict[str, Callable]:
-        """
-        Return the provider specific format functions. All providers format functions should
-        return the chunk in OpenAI format.
-        """
-        pass
-
-    @abstractmethod
-    def format(
-        self, response: Union[StreamingResponse, JSONResponse], dest_prov: db_models.ProviderType
-    ) -> Union[StreamingResponse, JSONResponse]:
-        """Format the response to the client."""
-        pass
-
-
-class StreamChunkFormatter(OutputFormatter):
-    """
-    Format a single chunk from a stream to OpenAI format.
-    We need to configure the client to expect the OpenAI format.
-    In Continue this means setting "provider": "openai" in the config json file.
-    """
-
-    @property
-    @abstractmethod
-    def provider_format_funcs(self) -> Dict[str, Callable]:
-        """
-        Return the provider specific format functions. All providers format functions should
-        return the chunk in OpenAI format.
-        """
-        pass
-
-    def _clean_chunk(self, chunk: str) -> str:
-        """Clean the chunk from the "data:" and any extra characters."""
-        # Find the first position of 'data:' and add 5 characters to skip 'data:'
-        start_pos = chunk.find("data:") + 5
-        cleaned_chunk = chunk[start_pos:].strip()
-        return cleaned_chunk
-
-    def _format_openai(self, chunk: str) -> str:
-        """
-        The chunk is already in OpenAI format. To standarize remove the "data:" prefix.
-
-        This function is used by both chat and FIM formatters
-        """
-        return self._clean_chunk(chunk)
-
-    def _format_antropic(self, chunk: str) -> str:
-        """
-        Format the Anthropic chunk to OpenAI format.
-
-        This function is used by both chat and FIM formatters
-        """
-        cleaned_chunk = self._clean_chunk(chunk)
-        try:
-            # Use `strict=False` to allow the JSON payload to contain
-            # newlines, tabs and other valid characters that might
-            # come from Anthropic returning code.
-            chunk_dict = json.loads(cleaned_chunk, strict=False)
-        except Exception as e:
-            logger.warning(f"Error parsing Anthropic chunk: {chunk}. Error: {e}")
-            return cleaned_chunk.strip()
-
-        msg_type = chunk_dict.get("type", "")
-
-        finish_reason = None
-        if msg_type == "message_stop":
-            finish_reason = "stop"
-
-        # In type == "content_block_start" the content comes in "content_block"
-        # In type == "content_block_delta" the content comes in "delta"
-        msg_content_dict = chunk_dict.get("delta", {}) or chunk_dict.get("content_block", {})
-        # We couldn't obtain the content from the chunk. Skip it.
-        if not msg_content_dict:
-            return ""
-        msg_content = msg_content_dict.get("text", "")
-
-        open_ai_chunk = ModelResponse(
-            id=f"anthropic-chat-{str(uuid.uuid4())}",
-            model="anthropic-muxed-model",
-            object="chat.completion.chunk",
-            choices=[
-                StreamingChoices(
-                    finish_reason=finish_reason,
-                    index=0,
-                    delta=Delta(content=msg_content, role="assistant"),
-                    logprobs=None,
-                )
-            ],
-        )
-
-        try:
-            return open_ai_chunk.model_dump_json(exclude_none=True, exclude_unset=True)
-        except Exception as e:
-            logger.warning(f"Error serializing Anthropic chunk: {chunk}. Error: {e}")
-            return cleaned_chunk.strip()
-
-    def _format_as_openai_chunk(self, formatted_chunk: str) -> str:
-        """Format the chunk as OpenAI chunk. This is the format how the clients expect the data."""
-        chunk_to_send = f"data: {formatted_chunk}\n\n"
-        return chunk_to_send
-
-    async def _format_streaming_response(
-        self, response: StreamingResponse, dest_prov: db_models.ProviderType
-    ):
-        """Format the streaming response to OpenAI format."""
-        format_func = self.provider_format_funcs.get(dest_prov)
-        openai_chunk = None
-        try:
-            async for chunk in response.body_iterator:
-                openai_chunk = format_func(chunk)
-                # Sometimes for Anthropic we couldn't get content from the chunk. Skip it.
-                if not openai_chunk:
-                    continue
-                yield self._format_as_openai_chunk(openai_chunk)
-        except Exception as e:
-            logger.error(f"Error sending chunk in muxing: {e}")
-            yield self._format_as_openai_chunk(str(e))
-        finally:
-            # Make sure the last chunk is always [DONE]
-            if openai_chunk and "[DONE]" not in openai_chunk:
-                yield self._format_as_openai_chunk("[DONE]")
-
-    def format(
-        self, response: StreamingResponse, dest_prov: db_models.ProviderType
-    ) -> StreamingResponse:
-        """Format the response to the client."""
-        return StreamingResponse(
-            self._format_streaming_response(response, dest_prov),
-            status_code=response.status_code,
-            headers=response.headers,
-            background=response.background,
-            media_type=response.media_type,
-        )
-
-
-class ChatStreamChunkFormatter(StreamChunkFormatter):
-    """
-    Format a single chunk from a stream to OpenAI format given that the request was a chat.
-    """
-
-    @property
-    def provider_format_funcs(self) -> Dict[str, Callable]:
-        """
-        Return the provider specific format functions. All providers format functions should
-        return the chunk in OpenAI format.
-        """
-        return {
-            db_models.ProviderType.ollama: self._format_ollama,
-            db_models.ProviderType.openai: self._format_openai,
-            db_models.ProviderType.anthropic: self._format_antropic,
-            # Our Lllamacpp provider emits OpenAI chunks
-            db_models.ProviderType.llamacpp: self._format_openai,
-            # OpenRouter is a dialect of OpenAI
-            db_models.ProviderType.openrouter: self._format_openai,
-            # VLLM is a dialect of OpenAI
-            db_models.ProviderType.vllm: self._format_openai,
-        }
-
-    def _format_ollama(self, chunk: str) -> str:
-        """Format the Ollama chunk to OpenAI format."""
-        try:
-            chunk_dict = json.loads(chunk)
-            ollama_chunk = OllamaStreamingChatCompletion.model_validate(chunk_dict)
-            open_ai_chunk = openai_chunk_from_ollama_chat(ollama_chunk)
-            return open_ai_chunk.model_dump_json(exclude_none=True, exclude_unset=True)
-        except Exception as e:
-            # Sometimes we receive an OpenAI formatted chunk from ollama. Specifically when
-            # talking to Cline or Kodu. If that's the case we use the format_openai function.
-            if "data:" in chunk:
-                return self._format_openai(chunk)
-            logger.warning(f"Error formatting Ollama chunk: {chunk}. Error: {e}")
-            return chunk
-
-
-class FimStreamChunkFormatter(StreamChunkFormatter):
-
-    @property
-    def provider_format_funcs(self) -> Dict[str, Callable]:
-        """
-        Return the provider specific format functions. All providers format functions should
-        return the chunk in OpenAI format.
-        """
-        return {
-            db_models.ProviderType.ollama: self._format_ollama,
-            db_models.ProviderType.openai: self._format_openai,
-            # Our Lllamacpp provider emits OpenAI chunks
-            db_models.ProviderType.llamacpp: self._format_openai,
-            # OpenRouter is a dialect of OpenAI
-            db_models.ProviderType.openrouter: self._format_openai,
-            # VLLM is a dialect of OpenAI
-            db_models.ProviderType.vllm: self._format_openai,
-            db_models.ProviderType.anthropic: self._format_antropic,
-        }
-
-    def _format_ollama(self, chunk: str) -> str:
-        """Format the Ollama chunk to OpenAI format."""
-        try:
-            chunk_dict = json.loads(chunk)
-            ollama_chunk = OllamaStreamingGenerateCompletion.model_validate(chunk_dict)
-            open_ai_chunk = openai_chunk_from_ollama_generate(ollama_chunk)
-            return open_ai_chunk.model_dump_json(exclude_none=True, exclude_unset=True)
-        except Exception as e:
-            print("Error formatting Ollama chunk: ", chunk, e)
-            return chunk
-
-
-class ResponseAdapter:
-
-    def _get_formatter(
-        self, response: Union[StreamingResponse, JSONResponse], is_fim_request: bool
-    ) -> OutputFormatter:
-        """Get the formatter based on the request type."""
-        if isinstance(response, StreamingResponse):
-            if is_fim_request:
-                return FimStreamChunkFormatter()
-            return ChatStreamChunkFormatter()
-        raise MuxingAdapterError("Only streaming responses are supported.")
-
-    def format_response_to_client(
-        self,
-        response: Union[StreamingResponse, JSONResponse],
-        dest_prov: db_models.ProviderType,
-        is_fim_request: bool,
-    ) -> Union[StreamingResponse, JSONResponse]:
-        """Format the response to the client."""
-        stream_formatter = self._get_formatter(response, is_fim_request)
-        return stream_formatter.format(response, dest_prov)
+def get_destination_info(model_route: rulematcher.ModelRoute) -> dict:
+    """Set the destination provider info."""
+    return model_route.model.name, get_provider_formatted_url(model_route)
diff --git a/src/codegate/muxing/router.py b/src/codegate/muxing/router.py
index 39ec8cea..8e1c2045 100644
--- a/src/codegate/muxing/router.py
+++ b/src/codegate/muxing/router.py
@@ -9,7 +9,7 @@
 from codegate.db.models import ProviderType
 from codegate.muxing import models as mux_models
 from codegate.muxing import rulematcher
-from codegate.muxing.adapter import BodyAdapter, ResponseAdapter
+from codegate.muxing.adapter import get_destination_info
 from codegate.providers.fim_analyzer import FIMAnalyzer
 from codegate.providers.registry import ProviderRegistry
 from codegate.types import anthropic, ollama, openai
@@ -39,11 +39,9 @@ class MuxRouter:
 
     def __init__(self, provider_registry: ProviderRegistry):
         self._ws_crud = WorkspaceCrud()
-        self._body_adapter = BodyAdapter()
         self.router = APIRouter()
         self._setup_routes()
         self._provider_registry = provider_registry
-        self._response_adapter = ResponseAdapter()
 
     @property
     def route_name(self) -> str:
@@ -128,7 +126,7 @@ async def route_to_dest_provider(
 
             # 2. Map the request body to the destination provider format.
             rest_of_path = self._ensure_path_starts_with_slash(rest_of_path)
-            model, base_url = self._body_adapter.get_destination_info(model_route)
+            model, base_url = get_destination_info(model_route)
 
             # 3. Run pipeline. Selecting the correct destination provider.
             provider = self._provider_registry.get_provider(model_route.endpoint.provider_type)
diff --git a/src/codegate/providers/ollama/adapter.py b/src/codegate/providers/ollama/adapter.py
deleted file mode 100644
index f513528e..00000000
--- a/src/codegate/providers/ollama/adapter.py
+++ /dev/null
@@ -1,105 +0,0 @@
-from datetime import datetime
-from typing import AsyncIterator, Dict, Optional, Tuple
-
-from ollama import ChatResponse
-
-from codegate.types.common import (
-    Delta,
-    ModelResponse,
-    StreamingChoices,
-)
-
-
-class OLlamaToModel(AsyncIterator[ModelResponse]):
-    def __init__(self, ollama_response: AsyncIterator[ChatResponse]):
-        self.ollama_response = ollama_response
-        self._aiter = ollama_response.__aiter__()
-
-    @classmethod
-    def _transform_to_int_secs(cls, chunk_created_at: str) -> int:
-        """
-        Convert the datetime to a timestamp in seconds.
-        """
-        datetime_obj = datetime.fromisoformat(chunk_created_at)
-        return int(datetime_obj.timestamp())
-
-    @classmethod
-    def _get_finish_reason_assistant(cls, is_chunk_done: bool) -> Tuple[str, Optional[str]]:
-        """
-        Get the role and finish reason for the assistant based on the chunk done status.
-        """
-        finish_reason = None
-        role = "assistant"
-        if is_chunk_done:
-            finish_reason = "stop"
-            role = None
-        return role, finish_reason
-
-    @classmethod
-    def _get_chat_id_from_timestamp(cls, timestamp_seconds: int) -> str:
-        """
-        Getting a string representation of the timestamp in seconds used as the chat id.
-
-        This needs to be done so that all chunks of a chat have the same id.
-        """
-        timestamp_str = str(timestamp_seconds)
-        return timestamp_str[:9]
-
-    @classmethod
-    def normalize_chat_chunk(cls, chunk: ChatResponse) -> ModelResponse:
-        """
-        Transform an ollama chat chunk to an OpenAI one
-        """
-        timestamp_seconds = cls._transform_to_int_secs(chunk.created_at)
-        role, finish_reason = cls._get_finish_reason_assistant(chunk.done)
-        chat_id = cls._get_chat_id_from_timestamp(timestamp_seconds)
-
-        model_response = ModelResponse(
-            id=f"ollama-chat-{chat_id}",
-            created=timestamp_seconds,
-            model=chunk.model,
-            object="chat.completion.chunk",
-            choices=[
-                StreamingChoices(
-                    finish_reason=finish_reason,
-                    index=0,
-                    delta=Delta(content=chunk.message.content, role=role),
-                    logprobs=None,
-                )
-            ],
-        )
-        return model_response
-
-    @classmethod
-    def normalize_fim_chunk(cls, chunk) -> Dict:
-        """
-        Transform an ollama generation chunk to an OpenAI one
-        """
-        timestamp_seconds = cls._transform_to_int_secs(chunk.created_at)
-        _, finish_reason = cls._get_finish_reason_assistant(chunk.done)
-        chat_id = cls._get_chat_id_from_timestamp(timestamp_seconds)
-
-        model_response = {
-            "id": f"chatcmpl-{chat_id}",
-            "object": "text_completion",
-            "created": timestamp_seconds,
-            "model": chunk.model,
-            "choices": [{"index": 0, "text": chunk.response}],
-            "usage": {"completion_tokens": 0, "prompt_tokens": 0, "total_tokens": 0},
-        }
-        if finish_reason:
-            model_response["choices"][0]["finish_reason"] = finish_reason
-            del model_response["choices"][0]["text"]
-        return model_response
-
-    def __aiter__(self):
-        return self
-
-    async def __anext__(self):
-        try:
-            chunk = await self._aiter.__anext__()
-            if isinstance(chunk, ChatResponse):
-                return self.normalize_chat_chunk(chunk)
-            return chunk
-        except StopAsyncIteration:
-            raise StopAsyncIteration
diff --git a/src/codegate/providers/ollama/completion_handler.py b/src/codegate/providers/ollama/completion_handler.py
index 8d55736d..b1782a9a 100644
--- a/src/codegate/providers/ollama/completion_handler.py
+++ b/src/codegate/providers/ollama/completion_handler.py
@@ -20,6 +20,9 @@
 from codegate.types.ollama import (
     stream_generator as ollama_stream_generator,
 )
+from codegate.types.openai import (
+    ChatCompletion as OpenAIChatCompletion,
+)
 from codegate.types.openai import (
     ChatCompletionRequest,
     completions_streaming,
@@ -27,6 +30,9 @@
 from codegate.types.openai import (
     StreamingChatCompletion as OpenAIStreamingChatCompletion,
 )
+from codegate.types.openai import (
+    single_response_generator as openai_single_response_generator,
+)
 from codegate.types.openai import (
     stream_generator as openai_stream_generator,
 )
@@ -38,6 +44,7 @@
     StreamingChatCompletion,
     StreamingGenerateCompletion,
     OpenAIStreamingChatCompletion,
+    OpenAIChatCompletion,
 ]
 
 
@@ -65,6 +72,9 @@ async def _ollama_dispatcher(  # noqa: C901
     if isinstance(first, OpenAIStreamingChatCompletion):
         stream = openai_stream_generator(prepend(first, stream))
 
+    if isinstance(first, OpenAIChatCompletion):
+        stream = openai_single_response_generator(first, stream)
+
     async for item in stream:
         yield item
 
diff --git a/src/codegate/types/openai/__init__.py b/src/codegate/types/openai/__init__.py
index 1f5bb7c0..ca97e268 100644
--- a/src/codegate/types/openai/__init__.py
+++ b/src/codegate/types/openai/__init__.py
@@ -2,6 +2,7 @@
 from ._generators import (
     completions_streaming,
     message_wrapper,
+    single_response_generator,
     stream_generator,
     streaming,
 )
@@ -72,6 +73,7 @@
     "CopilotCompletionRequest",
     "completions_streaming",
     "message_wrapper",
+    "single_response_generator",
     "stream_generator",
     "streaming",
     "LegacyCompletion",
diff --git a/src/codegate/types/openai/_generators.py b/src/codegate/types/openai/_generators.py
index 7f551aaf..2a36229c 100644
--- a/src/codegate/types/openai/_generators.py
+++ b/src/codegate/types/openai/_generators.py
@@ -29,8 +29,6 @@ async def stream_generator(stream: AsyncIterator[StreamingChatCompletion]) -> As
             # the stream
             chunk = chunk.model_dump_json(exclude_none=True, exclude_unset=True)
             try:
-                if os.getenv("CODEGATE_DEBUG_OPENAI") is not None:
-                    print(chunk)
                 yield f"data: {chunk}\n\n"
             except Exception as e:
                 logger.error("failed generating output payloads", exc_info=e)
@@ -50,6 +48,29 @@ async def stream_generator(stream: AsyncIterator[StreamingChatCompletion]) -> As
         yield "data: [DONE]\n\n"
 
 
+async def single_response_generator(
+    first: ChatCompletion,
+    stream: AsyncIterator[ChatCompletion],
+) -> AsyncIterator[ChatCompletion]:
+    """Wraps a single response object in an AsyncIterator. This is
+    meant to be used for non-streaming responses.
+
+    """
+    yield first.model_dump_json(exclude_none=True, exclude_unset=True)
+
+    # Note: this async for loop is necessary to force Python to return
+    # an AsyncIterator. This is necessary because of the wiring at the
+    # Provider level expecting an AsyncIterator rather than a single
+    # response payload.
+    #
+    # Refactoring this means adding a code path specific for when we
+    # expect single response payloads rather than an SSE stream.
+    async for item in stream:
+        if item:
+            logger.error("no further items were expected", item=item)
+        yield item.model_dump_json(exclude_none=True, exclude_unset=True)
+
+
 async def completions_streaming(request, api_key, base_url):
     if base_url is None:
         base_url = "https://api.openai.com"
@@ -93,6 +114,8 @@ async def streaming(request, api_key, url, cls=StreamingChatCompletion):
             case 200:
                 if not request.stream:
                     body = await resp.aread()
+                    if os.getenv("CODEGATE_DEBUG_OPENAI") is not None:
+                        print(body.decode("utf-8"))
                     yield ChatCompletion.model_validate_json(body)
                     return
 
@@ -145,6 +168,8 @@ async def message_wrapper(lines, cls=StreamingChatCompletion):
     messages = get_data_lines(lines)
     async for payload in messages:
         try:
+            if os.getenv("CODEGATE_DEBUG_OPENAI") is not None:
+                print(payload)
             item = cls.model_validate_json(payload)
             yield item
         except Exception as e:
diff --git a/src/codegate/types/openai/_response_models.py b/src/codegate/types/openai/_response_models.py
index c6f62b26..aef3f47f 100644
--- a/src/codegate/types/openai/_response_models.py
+++ b/src/codegate/types/openai/_response_models.py
@@ -88,11 +88,11 @@ class AudioMessage(pydantic.BaseModel):
 
 class Message(pydantic.BaseModel):
     content: str | None
-    refusal: str | None
+    refusal: str | None = None
     tool_calls: List[ToolCall] | None = None
     role: str
     function_call: FunctionCall | None = None  # deprecated
-    audio: AudioMessage | None
+    audio: AudioMessage | None = None
 
 
 class Choice(pydantic.BaseModel):
diff --git a/tests/muxing/test_adapter.py b/tests/muxing/test_adapter.py
deleted file mode 100644
index 802439c1..00000000
--- a/tests/muxing/test_adapter.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import pytest
-
-from codegate.db.models import ProviderType
-from codegate.muxing.adapter import BodyAdapter, ChatStreamChunkFormatter
-
-
-class MockedEndpoint:
-    def __init__(self, provider_type: ProviderType, endpoint_route: str):
-        self.provider_type = provider_type
-        self.endpoint = endpoint_route
-
-
-class MockedModelRoute:
-    def __init__(self, provider_type: ProviderType, endpoint_route: str):
-        self.endpoint = MockedEndpoint(provider_type, endpoint_route)
-
-
-@pytest.mark.parametrize(
-    "provider_type, endpoint_route, expected_route",
-    [
-        (ProviderType.openai, "https://api.openai.com/", "https://api.openai.com/v1"),
-        (ProviderType.openrouter, "https://openrouter.ai/api", "https://openrouter.ai/api/v1"),
-        (ProviderType.openrouter, "https://openrouter.ai/", "https://openrouter.ai/api/v1"),
-        (ProviderType.ollama, "http://localhost:11434", "http://localhost:11434"),
-        (ProviderType.vllm, "http://localhost:8000", "http://localhost:8000/v1"),
-    ],
-)
-def test_catch_all(provider_type, endpoint_route, expected_route):
-    body_adapter = BodyAdapter()
-    model_route = MockedModelRoute(provider_type, endpoint_route)
-    actual_route = body_adapter._get_provider_formatted_url(model_route)
-    assert actual_route == expected_route
-
-
-@pytest.mark.parametrize(
-    "chunk, expected_cleaned_chunk",
-    [
-        (
-            (
-                'event: content_block_delta\ndata:{"type": "content_block_delta", "index": 0, '
-                '"delta": {"type": "text_delta", "text": "\n  metadata:\n    name: trusty"}}'
-            ),
-            (
-                '{"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", '
-                '"text": "\n  metadata:\n    name: trusty"}}'
-            ),
-        ),
-        (
-            (
-                "event: content_block_delta\n"
-                'data:{"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", '
-                '"text": "v1\nkind: NetworkPolicy\nmetadata:"}}'
-            ),
-            (
-                '{"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text"'
-                ': "v1\nkind: NetworkPolicy\nmetadata:"}}'
-            ),
-        ),
-    ],
-)
-def test_clean_chunk(chunk, expected_cleaned_chunk):
-    formatter = ChatStreamChunkFormatter()
-    gotten_chunk = formatter._clean_chunk(chunk)
-    assert gotten_chunk == expected_cleaned_chunk
diff --git a/tests/test_server.py b/tests/test_server.py
index 0bdbb965..dc8bb11d 100644
--- a/tests/test_server.py
+++ b/tests/test_server.py
@@ -13,11 +13,11 @@
 from uvicorn.config import Config as UvicornConfig
 
 from codegate import __version__
+from codegate.cli import UvicornServer, cli
+from codegate.codegate_logging import LogFormat, LogLevel
 from codegate.pipeline.factory import PipelineFactory
 from codegate.providers.registry import ProviderRegistry
 from codegate.server import init_app
-from src.codegate.cli import UvicornServer, cli
-from src.codegate.codegate_logging import LogFormat, LogLevel
 
 
 @pytest.fixture
@@ -183,7 +183,7 @@ def uvicorn_config(mock_app):
 
 @pytest.fixture
 def server_instance(uvicorn_config):
-    with patch("src.codegate.cli.Server", autospec=True) as mock_server_class:
+    with patch("codegate.cli.Server", autospec=True) as mock_server_class:
         mock_server_instance = mock_server_class.return_value
         mock_server_instance.serve = AsyncMock()
         yield UvicornServer(uvicorn_config, mock_server_instance)
@@ -204,8 +204,8 @@ def test_serve_default_options(cli_runner):
     """Test serve command with default options."""
     # Use patches for run_servers and logging setup
     with (
-        patch("src.codegate.cli.run_servers") as mock_run,
-        patch("src.codegate.cli.setup_logging") as mock_setup_logging,
+        patch("codegate.cli.run_servers") as mock_run,
+        patch("codegate.cli.setup_logging") as mock_setup_logging,
     ):
         # Invoke the CLI command
         result = cli_runner.invoke(cli, ["serve"])
@@ -223,8 +223,8 @@ def test_serve_default_options(cli_runner):
 def test_serve_custom_options(cli_runner):
     """Test serve command with custom options."""
     with (
-        patch("src.codegate.cli.run_servers") as mock_run,
-        patch("src.codegate.cli.setup_logging") as mock_setup_logging,
+        patch("codegate.cli.run_servers") as mock_run,
+        patch("codegate.cli.setup_logging") as mock_setup_logging,
     ):
         # Invoke the CLI command with custom options
         result = cli_runner.invoke(
@@ -315,8 +315,8 @@ def temp_config_file(tmp_path):
 def test_serve_with_config_file(cli_runner, temp_config_file):
     """Test serve command with config file."""
     with (
-        patch("src.codegate.cli.run_servers") as mock_run,
-        patch("src.codegate.cli.setup_logging") as mock_setup_logging,
+        patch("codegate.cli.run_servers") as mock_run,
+        patch("codegate.cli.setup_logging") as mock_setup_logging,
     ):
         # Invoke the CLI command with the configuration file
         result = cli_runner.invoke(cli, ["serve", "--config", str(temp_config_file)])
@@ -357,8 +357,8 @@ def test_serve_priority_resolution(cli_runner: CliRunner, temp_config_file: Path
     # Set up environment variables and ensure they get cleaned up after the test
     with (
         patch.dict(os.environ, {"LOG_LEVEL": "INFO", "PORT": "9999"}, clear=True),
-        patch("src.codegate.cli.run_servers") as mock_run,
-        patch("src.codegate.cli.setup_logging") as mock_setup_logging,
+        patch("codegate.cli.run_servers") as mock_run,
+        patch("codegate.cli.setup_logging") as mock_setup_logging,
     ):
         # Execute CLI command with specific options overriding environment and config file settings
         result = cli_runner.invoke(
@@ -419,8 +419,8 @@ def test_serve_priority_resolution(cli_runner: CliRunner, temp_config_file: Path
 def test_serve_certificate_options(cli_runner: CliRunner) -> None:
     """Test serve command with certificate options."""
     with (
-        patch("src.codegate.cli.run_servers") as mock_run,
-        patch("src.codegate.cli.setup_logging") as mock_setup_logging,
+        patch("codegate.cli.run_servers") as mock_run,
+        patch("codegate.cli.setup_logging") as mock_setup_logging,
     ):
         # Execute CLI command with certificate options
         result = cli_runner.invoke(