diff --git a/vision_agent/llm/__init__.py b/vision_agent/llm/__init__.py
new file mode 100644
index 00000000..dd5f5c54
--- /dev/null
+++ b/vision_agent/llm/__init__.py
@@ -0,0 +1 @@
+from .llm import LLM, OpenAILLM
diff --git a/vision_agent/llm/llm.py b/vision_agent/llm/llm.py
new file mode 100644
index 00000000..587d4b28
--- /dev/null
+++ b/vision_agent/llm/llm.py
@@ -0,0 +1,86 @@
+import json
+from typing import cast
+from abc import ABC, abstractmethod
+
+from vision_agent.tools import (
+    CHOOSE_PARAMS,
+    CLIP,
+    SYSTEM_PROMPT,
+    GroundingDINO,
+    GroundingSAM,
+    ImageTool,
+)
+
+
+class LLM(ABC):
+    @abstractmethod
+    def generate(self, prompt: str) -> str:
+        pass
+
+
+class OpenAILLM(LLM):
+    r"""An LLM class for any OpenAI LLM model."""
+
+    def __init__(self, model_name: str = "gpt-4-turbo-preview"):
+        from openai import OpenAI
+
+        self.model_name = model_name
+        self.client = OpenAI()
+
+    def generate(self, prompt: str) -> str:
+        response = self.client.chat.completions.create(
+            model=self.model_name,
+            messages=[
+                {"role": "user", "content": prompt},
+            ],
+        )
+
+        return cast(str, response.choices[0].message.content)
+
+    def generate_classifier(self, prompt: str) -> ImageTool:
+        prompt = CHOOSE_PARAMS.format(api_doc=CLIP.doc, question=prompt)
+        response = self.client.chat.completions.create(
+            model=self.model_name,
+            response_format={"type": "json_object"},
+            messages=[
+                {"role": "system", "content": SYSTEM_PROMPT},
+                {"role": "user", "content": prompt},
+            ],
+        )
+
+        params = json.loads(cast(str, response.choices[0].message.content))[
+            "Parameters"
+        ]
+        return CLIP(**params)
+
+    def generate_detector(self, params: str) -> ImageTool:
+        params = CHOOSE_PARAMS.format(api_doc=GroundingDINO.doc, question=params)
+        response = self.client.chat.completions.create(
+            model=self.model_name,
+            response_format={"type": "json_object"},
+            messages=[
+                {"role": "system", "content": SYSTEM_PROMPT},
+                {"role": "user", "content": params},
+            ],
+        )
+
+        params = json.loads(cast(str, response.choices[0].message.content))[
+            "Parameters"
+        ]
+        return GroundingDINO(**params)
+
+    def generate_segmentor(self, params: str) -> ImageTool:
+        params = CHOOSE_PARAMS.format(api_doc=GroundingSAM.doc, question=params)
+        response = self.client.chat.completions.create(
+            model=self.model_name,
+            response_format={"type": "json_object"},
+            messages=[
+                {"role": "system", "content": SYSTEM_PROMPT},
+                {"role": "user", "content": params},
+            ],
+        )
+
+        params = json.loads(cast(str, response.choices[0].message.content))[
+            "Parameters"
+        ]
+        return GroundingSAM(**params)