diff --git a/llama_toolchain/inference/api/config.py b/llama_toolchain/inference/api/config.py
index 5a10c0360..6bac2d09d 100644
--- a/llama_toolchain/inference/api/config.py
+++ b/llama_toolchain/inference/api/config.py
@@ -23,6 +23,7 @@ from .datatypes import QuantizationConfig
 class ImplType(Enum):
     inline = "inline"
     remote = "remote"
+    ollama = "ollama"
 
 
 @json_schema_type
@@ -80,10 +81,17 @@ class RemoteImplConfig(BaseModel):
     url: str = Field(..., description="The URL of the remote module")
 
 
+@json_schema_type
+class OllamaImplConfig(BaseModel):
+    impl_type: Literal[ImplType.ollama.value] = ImplType.ollama.value
+    model: str = Field(..., description="The name of the model in ollama catalog")
+    url: str = Field(..., description="The URL for the ollama server")
+
+
 @json_schema_type
 class InferenceConfig(BaseModel):
     impl_config: Annotated[
-        Union[InlineImplConfig, RemoteImplConfig],
+        Union[InlineImplConfig, RemoteImplConfig, OllamaImplConfig],
         Field(discriminator="impl_type"),
     ]
 
diff --git a/llama_toolchain/inference/api_instance.py b/llama_toolchain/inference/api_instance.py
index 366e46fa1..25b5ecf4b 100644
--- a/llama_toolchain/inference/api_instance.py
+++ b/llama_toolchain/inference/api_instance.py
@@ -12,6 +12,10 @@ async def get_inference_api_instance(config: InferenceConfig):
         from .inference import InferenceImpl
 
         return InferenceImpl(config.impl_config)
+    elif config.impl_config.impl_type == ImplType.ollama.value:
+        from .inference import OllamaInference
+
+        return OllamaInference(config.impl_config)
 
     from .client import InferenceClient
 
diff --git a/llama_toolchain/inference/ollama.py b/llama_toolchain/inference/ollama.py
new file mode 100644
index 000000000..485e5d558
--- /dev/null
+++ b/llama_toolchain/inference/ollama.py
@@ -0,0 +1,143 @@
+import httpx
+import uuid
+
+from typing import AsyncGenerator
+
+from ollama import AsyncClient
+
+from llama_models.llama3_1.api.datatypes import (
+    BuiltinTool,
+    CompletionMessage, 
+    Message, 
+    StopReason,
+    ToolCall,
+)
+from llama_models.llama3_1.api.tool_utils import ToolUtils
+
+from .api.config import OllamaImplConfig
+from .api.endpoints import (
+    ChatCompletionResponse,
+    ChatCompletionRequest,
+    ChatCompletionResponseStreamChunk,
+    CompletionRequest,
+    Inference,
+)
+
+
+
+class OllamaInference(Inference):
+
+    def __init__(self, config: OllamaImplConfig) -> None:
+        self.config = config
+        self.model = config.model
+
+    async def initialize(self) -> None:
+        self.client = AsyncClient(host=self.config.url)
+        try:
+            status = await self.client.pull(self.model)
+            assert status['status'] == 'success', f"Failed to pull model {self.model} in ollama"
+        except httpx.ConnectError:
+            print("Ollama Server is not running, start it using `ollama serve` in a separate terminal")
+            raise
+
+    async def shutdown(self) -> None:
+        pass
+
+    async def completion(self, request: CompletionRequest) -> AsyncGenerator:
+        raise NotImplementedError()
+
+    def _messages_to_ollama_messages(self, messages: list[Message]) -> list:
+        ollama_messages = []
+        for message in messages:
+            ollama_messages.append(
+                {"role": message.role, "content": message.content}
+            )
+
+        return ollama_messages
+    
+    async def chat_completion(self, request: ChatCompletionRequest) -> AsyncGenerator:
+        if not request.stream:
+            r = await self.client.chat(
+                model=self.model,
+                messages=self._messages_to_ollama_messages(request.messages),
+                stream=False
+            )
+            completion_message = decode_assistant_message_from_content(
+                r['message']['content']
+            )
+            
+            yield ChatCompletionResponse(
+                completion_message=completion_message,
+                logprobs=None,
+            )
+        else:
+            raise NotImplementedError()
+
+
+#TODO: Consolidate this with impl in llama-models
+def decode_assistant_message_from_content(content: str) -> CompletionMessage:
+    ipython = content.startswith("<|python_tag|>")
+    if ipython:
+        content = content[len("<|python_tag|>") :]
+
+    if content.endswith("<|eot_id|>"):
+        content = content[: -len("<|eot_id|>")]
+        stop_reason = StopReason.end_of_turn
+    elif content.endswith("<|eom_id|>"):
+        content = content[: -len("<|eom_id|>")]
+        stop_reason = StopReason.end_of_message
+    else:
+        # Ollama does not return <|eot_id|>
+        # and hence we explicitly set it as the default.
+        #TODO: Check for StopReason.out_of_tokens
+        stop_reason = StopReason.end_of_turn
+
+    tool_name = None
+    tool_arguments = {}
+
+    custom_tool_info = ToolUtils.maybe_extract_custom_tool_call(content)
+    if custom_tool_info is not None:
+        tool_name, tool_arguments = custom_tool_info
+        # Sometimes when agent has custom tools alongside builin tools
+        # Agent responds for builtin tool calls in the format of the custom tools
+        # This code tries to handle that case
+        if tool_name in BuiltinTool.__members__:
+            tool_name = BuiltinTool[tool_name]
+            tool_arguments = {
+                "query": list(tool_arguments.values())[0],
+            }
+    else:
+        builtin_tool_info = ToolUtils.maybe_extract_builtin_tool_call(content)
+        if builtin_tool_info is not None:
+            tool_name, query = builtin_tool_info
+            tool_arguments = {
+                "query": query,
+            }
+            if tool_name in BuiltinTool.__members__:
+                tool_name = BuiltinTool[tool_name]
+        elif ipython:
+            tool_name = BuiltinTool.code_interpreter
+            tool_arguments = {
+                "code": content,
+            }
+
+    tool_calls = []
+    if tool_name is not None and tool_arguments is not None:
+        call_id = str(uuid.uuid4())
+        tool_calls.append(
+            ToolCall(
+                call_id=call_id,
+                tool_name=tool_name,
+                arguments=tool_arguments,
+            )
+        )
+        content = ""
+
+    if stop_reason is None:
+        stop_reason = StopReason.out_of_tokens
+
+    return CompletionMessage(
+        content=content,
+        stop_reason=stop_reason,
+        tool_calls=tool_calls,
+    )
diff --git a/tests/test_ollama_inference.py b/tests/test_ollama_inference.py
new file mode 100644
index 000000000..d37ff26c3
--- /dev/null
+++ b/tests/test_ollama_inference.py
@@ -0,0 +1,176 @@
+import textwrap
+import unittest 
+from datetime import datetime 
+
+from llama_models.llama3_1.api.datatypes import (
+    BuiltinTool,
+    InstructModel,
+    UserMessage,
+    StopReason,
+    SystemMessage,
+)
+
+from llama_toolchain.inference.api.endpoints import (
+    ChatCompletionRequest
+)
+from llama_toolchain.inference.api.config import (
+    OllamaImplConfig
+)
+from llama_toolchain.inference.ollama import (
+    OllamaInference
+)
+
+
+class OllamaInferenceTests(unittest.IsolatedAsyncioTestCase):
+
+    async def asyncSetUp(self):
+        ollama_config = OllamaImplConfig(
+            model="llama3.1",
+            url="http://localhost:11434",
+        )
+
+        # setup ollama 
+        self.inference = OllamaInference(ollama_config)
+        await self.inference.initialize()
+
+        current_date = datetime.now()
+        formatted_date = current_date.strftime("%d %B %Y")
+        self.system_prompt = SystemMessage(
+            content=textwrap.dedent(f"""
+                Environment: ipython
+                Tools: brave_search
+
+                Cutting Knowledge Date: December 2023
+                Today Date:{formatted_date}
+
+            """),
+        )
+
+        self.system_prompt_with_custom_tool = SystemMessage(
+            content=textwrap.dedent("""
+                Environment: ipython
+                Tools: brave_search, wolfram_alpha, photogen
+
+                Cutting Knowledge Date: December 2023
+                Today Date: 30 July 2024
+
+
+                You have access to the following functions:
+
+                Use the function 'get_boiling_point' to 'Get the boiling point of a imaginary liquids (eg. polyjuice)'
+                {"name": "get_boiling_point", "description": "Get the boiling point of a imaginary liquids (eg. polyjuice)", "parameters": {"liquid_name": {"param_type": "string", "description": "The name of the liquid", "required": true}, "celcius": {"param_type": "boolean", "description": "Whether to return the boiling point in Celcius", "required": false}}}
+
+
+                Think very carefully before calling functions.
+                If you choose to call a function ONLY reply in the following format with no prefix or suffix:
+
+                <function=example_function_name>{"example_name": "example_value"}</function>
+
+                Reminder:
+                - If looking for real time information use relevant functions before falling back to brave_search
+                - Function calls MUST follow the specified format, start with <function= and end with </function>
+                - Required parameters MUST be specified
+                - Only call one function at a time
+                - Put the entire function call reply on one line
+
+                """
+            ),
+        )
+
+    async def asyncTearDown(self):
+        await self.inference.shutdown()
+
+    async def test_text(self):
+        request = ChatCompletionRequest(
+            model=InstructModel.llama3_8b_chat,
+            messages=[
+                UserMessage(
+                    content="What is the capital of France?",
+                ),
+            ],
+            stream=False,
+        )
+        iterator = self.inference.chat_completion(request)
+        async for r in iterator:
+            response = r
+
+        self.assertTrue("Paris" in response.completion_message.content)
+        self.assertEquals(response.completion_message.stop_reason, StopReason.end_of_turn)
+
+    async def test_tool_call(self):
+        request = ChatCompletionRequest(
+            model=InstructModel.llama3_8b_chat,
+            messages=[
+                self.system_prompt,
+                UserMessage(
+                    content="Who is the current US President?",
+                ),
+            ],
+            stream=False,
+        )
+        iterator = self.inference.chat_completion(request)
+        async for r in iterator:
+            response = r
+
+        completion_message = response.completion_message
+        
+        self.assertEquals(completion_message.content, "")
+        self.assertEquals(completion_message.stop_reason, StopReason.end_of_message)
+        
+        self.assertEquals(len(completion_message.tool_calls), 1, completion_message.tool_calls)
+        self.assertEquals(completion_message.tool_calls[0].tool_name, BuiltinTool.brave_search)
+        self.assertTrue(
+            "president" in completion_message.tool_calls[0].arguments["query"].lower()
+        )
+        
+    async def test_code_execution(self):
+        request = ChatCompletionRequest(
+            model=InstructModel.llama3_8b_chat,
+            messages=[
+                self.system_prompt,
+                UserMessage(
+                    content="Write code to compute the 5th prime number",
+                ),
+            ],
+            stream=False,
+        )
+        iterator = self.inference.chat_completion(request)
+        async for r in iterator:
+            response = r
+
+        completion_message = response.completion_message
+
+        self.assertEquals(completion_message.content, "")
+        self.assertEquals(completion_message.stop_reason, StopReason.end_of_message)
+        
+        self.assertEquals(len(completion_message.tool_calls), 1, completion_message.tool_calls)
+        self.assertEquals(completion_message.tool_calls[0].tool_name, BuiltinTool.code_interpreter)
+        code = completion_message.tool_calls[0].arguments["code"]
+        self.assertTrue("def " in code.lower(), code)
+
+    async def test_custom_tool(self):
+        request = ChatCompletionRequest(
+            model=InstructModel.llama3_8b_chat,
+            messages=[
+                self.system_prompt_with_custom_tool,
+                UserMessage(
+                    content="Use provided function to find the boiling point of polyjuice in fahrenheit?",
+                ),
+            ],
+            stream=False,
+        )
+        iterator = self.inference.chat_completion(request)
+        async for r in iterator:
+            response = r
+
+        completion_message = response.completion_message
+        
+        self.assertEqual(completion_message.content, "")
+        self.assertEquals(completion_message.stop_reason, StopReason.end_of_turn)
+        
+        self.assertEquals(len(completion_message.tool_calls), 1, completion_message.tool_calls)
+        self.assertEquals(completion_message.tool_calls[0].tool_name, "get_boiling_point")
+
+        args = completion_message.tool_calls[0].arguments
+        self.assertTrue(isinstance(args, dict))
+        self.assertTrue(args["liquid_name"], "polyjuice")