diff --git a/backend/requirements.txt b/backend/requirements.txt
index d9edbb5..d47cc7b 100644
--- a/backend/requirements.txt
+++ b/backend/requirements.txt
@@ -4,4 +4,5 @@ quart-cors
 sqlalchemy
 nest_asyncio
 replicate
-qdrant-client[fastembed]
\ No newline at end of file
+qdrant-client[fastembed]
+ollama
\ No newline at end of file
diff --git a/backend/src/agents/expert.py b/backend/src/agents/expert.py
index f01271e..18cb2eb 100644
--- a/backend/src/agents/expert.py
+++ b/backend/src/agents/expert.py
@@ -11,7 +11,7 @@ CLASS_DESCRIPTION = """
 Expert AI in any field, powered by Llama 3 70b.
 Will get guidelines, documents, and other advanced features in the future.
 
-Available models are: [llama/qwen]-[big/small]
+Available models are: [llama/qwen]-[big/small] (replicate), ollama:MODEL_NAME
 """.strip()
 
 class ExpertAgent(Agent):
@@ -51,7 +51,9 @@ class ExpertAgent(Agent):
 
         model = self.config['model']
 
-        if model == "qwen-big":
+        if model.startswith("ollama:"):
+            response = await inference.ollama_chat_completion(self.app, messages, model=model[7:])
+        elif model == "qwen-big":
             response = await inference.replicate_qwen_completion(self.app, messages, model=inference.Models.qwen3_235b)
         elif model == "qwen-small":
             response = await inference.replicate_qwen_completion(self.app, messages, model=inference.Models.qwen3_8b)
diff --git a/backend/src/inference.py b/backend/src/inference.py
index 512fcf0..08df92b 100644
--- a/backend/src/inference.py
+++ b/backend/src/inference.py
@@ -1,6 +1,7 @@
 import threading
 from stream import Stream
 
+
 VALID_ROLES = ['user', 'assistant', 'system']
 
 class LlamaMessage:
@@ -17,6 +18,28 @@ class Models:
     qwen3_8b = "lucataco/qwen3-vl-8b-instruct:39e893666996acf464cff75688ad49ac95ef54e9f1c688fbc677330acc478e11"
     qwen3_235b = "qwen/qwen3-235b-a22b-instruct-2507"
 
+async def ollama_chat_completion(app, messages, model, opts={}):
+    from ollama import AsyncClient
+
+    content_stream = Stream(app)
+
+    print("START OLLAMA INFERENCE")
+    stream_response = await AsyncClient().chat(
+        model=model,
+        messages=[{'role': msg.role, 'content': msg.content} for msg in messages],
+        stream=True,
+    )
+
+    async for part in stream_response:
+        content_stream.feed(part.message.content)
+        print(part['message']['content'], end='', flush=True)
+
+    content_stream.close()
+
+    print("END OLLAMA INFERENCE")
+    return await content_stream.content_wait()
+
+
 async def replicate_llama_completion(app, messages, model=None, opts={}):
     if model is None:
         model = Models.llama_8b