diff --git a/.gitignore b/.gitignore
index 7b004e5..644b09e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -98,7 +98,7 @@ ipython_config.py
 #   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
 #   This is especially recommended for binary packages to ensure reproducibility, and is more
 #   commonly ignored for libraries.
-#uv.lock
+uv.lock
 
 # poetry
 #   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
@@ -165,7 +165,7 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
+.idea/
 
 # Abstra
 # Abstra is an AI-powered process automation framework.
@@ -191,4 +191,4 @@ cython_debug/
 #  exclude from AI features like autocomplete and code analysis. Recommended for sensitive data
 #  refer to https://docs.cursor.com/context/ignore-files
 .cursorignore
-.cursorindexingignore
\ No newline at end of file
+.cursorindexingignore
diff --git a/.python-version b/.python-version
new file mode 100644
index 0000000..c8cfe39
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.10
diff --git a/README.md b/README.md
index caecfec..964c9d5 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,29 @@
 # adk-extras-python
 Open-source extensions to the python Google Agent Development Kit
+
+This adds patches and extensions to [google-adk](https://github.com/google/adk-python). These will usually also be 
+contributed upstream to the core library at the same time - and when adopted there this library will drop it's 
+implementation and pass through to the upstream version (meaning users of this lib will end up importing the core 
+versions instead) whenever doing so would be non-breaking.
+
+## Patches
+
+### LiteLLM Streaming
+
+A drop in replacement for LiteLLM, but with streaming fixed:
+
+```python
+from google.adk.agents import Agent
+from adk_extras.models.lite_llm import LiteLlmWithStreaming as LiteLlm
+
+agent = Agent(
+    name="weather_agent_gpt",
+    model=LiteLlm(model="openai/gpt-4o"),
+    description="Provides weather information using OpenAI's GPT.",
+    instruction="You are a helpful weather assistant powered by GPT-4o. "
+                "Use the 'get_weather' tool for city weather requests. "
+                "Present information clearly.",
+    tools=[get_weather],
+)
+...
+```
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..e0708b6
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,10 @@
+[project]
+name = "adk-extras-python"
+version = "0.1.0"
+description = "Open-source extensions to the python Google Agent Development Kit"
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "google-adk>=1.3.0",
+    "litellm>=1.72.6",
+]
diff --git a/src/adk_extras/__init__.py b/src/adk_extras/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/adk_extras/models/__init__.py b/src/adk_extras/models/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/adk_extras/models/lite_llm.py b/src/adk_extras/models/lite_llm.py
new file mode 100644
index 0000000..ea8f0cc
--- /dev/null
+++ b/src/adk_extras/models/lite_llm.py
@@ -0,0 +1,171 @@
+"""
+LiteLlmWithStreaming - A custom wrapper for LiteLlm that fixes streaming issues.
+
+This module provides a drop-in replacement for the LiteLlm class that properly
+implements async streaming using acompletion instead of the synchronous completion method.
+"""
+
+from typing import AsyncGenerator
+
+from google.adk.models.lite_llm import (
+    LiteLlm,
+    _model_response_to_chunk,
+    _message_to_generate_content_response,
+    _get_completion_inputs,
+    _model_response_to_generate_content_response,
+)
+from google.adk.models.llm_request import LlmRequest
+from google.adk.models.llm_response import LlmResponse
+from google.genai import types
+from litellm import (
+    ChatCompletionAssistantMessage,
+    ChatCompletionMessageToolCall,
+    Function,
+)
+
+
+class LiteLlmWithStreaming(LiteLlm):
+    """
+    Custom wrapper that fixes the streaming issue in LiteLlm.
+
+    This class inherits from LiteLlm and overrides the generate_content_async
+    method to use proper async streaming instead of blocking synchronous calls.
+    """
+
+    async def generate_content_async(
+        self, llm_request: LlmRequest, stream: bool = False
+    ) -> AsyncGenerator[LlmResponse, None]:
+        """Generates content asynchronously with proper streaming support.
+
+        This method overrides the original implementation to use async iteration
+        with acompletion instead of synchronous completion for streaming.
+
+        Args:
+            llm_request: LlmRequest, the request to send to the LiteLlm model.
+            stream: bool = False, whether to do streaming call.
+
+        Yields:
+            LlmResponse: The model response.
+        """
+
+        self._maybe_append_user_content(llm_request)
+
+        messages, tools, response_format = _get_completion_inputs(llm_request)
+
+        completion_args = {
+            "model": self.model,
+            "messages": messages,
+            "tools": tools,
+            "response_format": response_format,
+        }
+        completion_args.update(self._additional_args)
+
+        if stream:
+            text = ""
+            # Track function calls by index
+            function_calls = {}  # index -> {name, args, id}
+            completion_args["stream"] = True
+            aggregated_llm_response = None
+            aggregated_llm_response_with_tool_call = None
+            usage_metadata = None
+            fallback_index = 0
+
+            # THE KEY FIX: Use async iteration with acompletion instead of synchronous completion
+            async for part in await self.llm_client.acompletion(**completion_args):
+                for chunk, finish_reason in _model_response_to_chunk(part):
+                    if (
+                        hasattr(chunk, "id")
+                        and hasattr(chunk, "name")
+                        and hasattr(chunk, "args")
+                    ):  # FunctionChunk
+                        index = getattr(chunk, "index", fallback_index)
+                        if index not in function_calls:
+                            function_calls[index] = {"name": "", "args": "", "id": None}
+
+                        if chunk.name:
+                            function_calls[index]["name"] += chunk.name
+                        if chunk.args:
+                            function_calls[index]["args"] += chunk.args
+
+                            # check if args is completed (workaround for improper chunk indexing)
+                            try:
+                                import json
+
+                                json.loads(function_calls[index]["args"])
+                                fallback_index += 1
+                            except json.JSONDecodeError:
+                                pass
+
+                        function_calls[index]["id"] = (
+                            chunk.id or function_calls[index]["id"] or str(index)
+                        )
+                    elif hasattr(chunk, "text"):  # TextChunk
+                        text += chunk.text
+                        yield _message_to_generate_content_response(
+                            ChatCompletionAssistantMessage(
+                                role="assistant",
+                                content=chunk.text,
+                            ),
+                            is_partial=True,
+                        )
+                    elif hasattr(chunk, "prompt_tokens"):  # UsageMetadataChunk
+                        usage_metadata = types.GenerateContentResponseUsageMetadata(
+                            prompt_token_count=chunk.prompt_tokens,
+                            candidates_token_count=chunk.completion_tokens,
+                            total_token_count=chunk.total_tokens,
+                        )
+
+                    if (
+                        finish_reason == "tool_calls" or finish_reason == "stop"
+                    ) and function_calls:
+                        tool_calls = []
+                        for index, func_data in function_calls.items():
+                            if func_data["id"]:
+                                tool_calls.append(
+                                    ChatCompletionMessageToolCall(
+                                        type="function",
+                                        id=func_data["id"],
+                                        function=Function(
+                                            name=func_data["name"],
+                                            arguments=func_data["args"],
+                                            index=index,
+                                        ),
+                                    )
+                                )
+                        aggregated_llm_response_with_tool_call = (
+                            _message_to_generate_content_response(
+                                ChatCompletionAssistantMessage(
+                                    role="assistant",
+                                    content="",
+                                    tool_calls=tool_calls,
+                                )
+                            )
+                        )
+                        function_calls.clear()
+                    elif finish_reason == "stop" and text:
+                        aggregated_llm_response = _message_to_generate_content_response(
+                            ChatCompletionAssistantMessage(
+                                role="assistant", content=text
+                            )
+                        )
+                        text = ""
+
+            # waiting until streaming ends to yield the llm_response as litellm tends
+            # to send chunk that contains usage_metadata after the chunk with
+            # finish_reason set to tool_calls or stop.
+            if aggregated_llm_response:
+                if usage_metadata:
+                    aggregated_llm_response.usage_metadata = usage_metadata
+                    usage_metadata = None
+                yield aggregated_llm_response
+
+            if aggregated_llm_response_with_tool_call:
+                if usage_metadata:
+                    aggregated_llm_response_with_tool_call.usage_metadata = (
+                        usage_metadata
+                    )
+                yield aggregated_llm_response_with_tool_call
+
+        else:
+            response = await self.llm_client.acompletion(**completion_args)
+            yield _model_response_to_generate_content_response(response)