feat(openai): add support for image generation tracking

Adds automatic tracing for OpenAI image generation API calls:
- images.generate()
- images.edit()
- images.create_variation()

For both sync and async clients.

Tracks:
- Prompt/input
- Model (dall-e-2, dall-e-3, gpt-image-1)
- Model parameters (size, quality, style, response_format, n)
- Output (URLs or base64 images via LangfuseMedia)
- Image count in usage_details

Closes #8252

Author: coletebou

langfuse/openai.py

@@ -90,6 +90,48 @@ class OpenAiDefinition:
         type="chat",
         sync=True,
     ),
+    OpenAiDefinition(
+        module="openai.resources.images",
+        object="Images",
+        method="generate",
+        type="image",
+        sync=True,
+    ),
+    OpenAiDefinition(
+        module="openai.resources.images",
+        object="AsyncImages",
+        method="generate",
+        type="image",
+        sync=False,
+    ),
+    OpenAiDefinition(
+        module="openai.resources.images",
+        object="Images",
+        method="edit",
+        type="image",
+        sync=True,
+    ),
+    OpenAiDefinition(
+        module="openai.resources.images",
+        object="AsyncImages",
+        method="edit",
+        type="image",
+        sync=False,
+    ),
+    OpenAiDefinition(
+        module="openai.resources.images",
+        object="Images",
+        method="create_variation",
+        type="image",
+        sync=True,
+    ),
+    OpenAiDefinition(
+        module="openai.resources.images",
+        object="AsyncImages",
+        method="create_variation",
+        type="image",
+        sync=False,
+    ),
     OpenAiDefinition(
         module="openai.resources.completions",
         object="Completions",
@@ -354,9 +396,12 @@ def _extract_chat_response(kwargs: Any) -> Any:
 
 
 def _get_langfuse_data_from_kwargs(resource: OpenAiDefinition, kwargs: Any) -> Any:
-    default_name = (
-        "OpenAI-embedding" if resource.type == "embedding" else "OpenAI-generation"
-    )
+    if resource.type == "embedding":
+        default_name = "OpenAI-embedding"
+    elif resource.type == "image":
+        default_name = "OpenAI-image"
+    else:
+        default_name = "OpenAI-generation"
     name = kwargs.get("name", default_name)
 
     if name is None:
@@ -417,6 +462,8 @@ def _get_langfuse_data_from_kwargs(resource: OpenAiDefinition, kwargs: Any) -> A
         prompt = _extract_chat_prompt(kwargs)
     elif resource.type == "embedding":
         prompt = kwargs.get("input", None)
+    elif resource.type == "image":
+        prompt = kwargs.get("prompt", None)
 
     parsed_temperature = (
         kwargs.get("temperature", 1)
@@ -479,6 +526,44 @@ def _get_langfuse_data_from_kwargs(resource: OpenAiDefinition, kwargs: Any) -> A
             modelParameters["dimensions"] = parsed_dimensions
         if parsed_encoding_format != "float":
             modelParameters["encoding_format"] = parsed_encoding_format
+    elif resource.type == "image":
+        # Image generation parameters
+        modelParameters = {}
+
+        parsed_size = (
+            kwargs.get("size", None)
+            if not isinstance(kwargs.get("size", None), NotGiven)
+            else None
+        )
+        if parsed_size is not None:
+            modelParameters["size"] = parsed_size
+
+        parsed_quality = (
+            kwargs.get("quality", None)
+            if not isinstance(kwargs.get("quality", None), NotGiven)
+            else None
+        )
+        if parsed_quality is not None:
+            modelParameters["quality"] = parsed_quality
+
+        parsed_style = (
+            kwargs.get("style", None)
+            if not isinstance(kwargs.get("style", None), NotGiven)
+            else None
+        )
+        if parsed_style is not None:
+            modelParameters["style"] = parsed_style
+
+        parsed_response_format = (
+            kwargs.get("response_format", None)
+            if not isinstance(kwargs.get("response_format", None), NotGiven)
+            else None
+        )
+        if parsed_response_format is not None:
+            modelParameters["response_format"] = parsed_response_format
+
+        if parsed_n is not None and isinstance(parsed_n, int) and parsed_n > 1:
+            modelParameters["n"] = parsed_n
     else:
         modelParameters = {
             "temperature": parsed_temperature,
@@ -791,6 +876,33 @@ def _get_langfuse_data_from_default_response(
                 "count": len(data),
             }
 
+    elif resource.type == "image":
+        data = response.get("data", [])
+        completion = []
+        for item in data:
+            image_data = item.__dict__ if hasattr(item, "__dict__") else item
+            image_result = {}
+
+            # Handle URL response
+            if image_data.get("url"):
+                image_result["url"] = image_data["url"]
+
+            # Handle base64 response
+            if image_data.get("b64_json"):
+                # Wrap in LangfuseMedia for proper handling
+                base64_data_uri = f"data:image/png;base64,{image_data['b64_json']}"
+                image_result["image"] = LangfuseMedia(base64_data_uri=base64_data_uri)
+
+            # Include revised_prompt if present (DALL-E 3)
+            if image_data.get("revised_prompt"):
+                image_result["revised_prompt"] = image_data["revised_prompt"]
+
+            completion.append(image_result)
+
+        # If only one image, unwrap from list
+        if len(completion) == 1:
+            completion = completion[0]
+
     usage = _parse_usage(response.get("usage", None))
 
     return (model, completion, usage)
@@ -842,6 +954,28 @@ def _wrap(
     try:
         openai_response = wrapped(**arg_extractor.get_openai_args())
 
+        # Handle image generation (non-streaming)
+        if open_ai_resource.type == "image":
+            model, completion, usage = _get_langfuse_data_from_default_response(
+                open_ai_resource,
+                (openai_response and openai_response.__dict__)
+                if _is_openai_v1()
+                else openai_response,
+            )
+
+            # Calculate image count for usage tracking
+            image_count = 1
+            if isinstance(completion, list):
+                image_count = len(completion)
+
+            generation.update(
+                model=model,
+                output=completion,
+                usage_details={"output": image_count, "total": image_count, "unit": "IMAGES"},
+            ).end()
+
+            return openai_response
+
         if _is_streaming_response(openai_response):
             return LangfuseResponseGeneratorSync(
                 resource=open_ai_resource,
@@ -913,6 +1047,28 @@ async def _wrap_async(
     try:
         openai_response = await wrapped(**arg_extractor.get_openai_args())
 
+        # Handle image generation (non-streaming)
+        if open_ai_resource.type == "image":
+            model, completion, usage = _get_langfuse_data_from_default_response(
+                open_ai_resource,
+                (openai_response and openai_response.__dict__)
+                if _is_openai_v1()
+                else openai_response,
+            )
+
+            # Calculate image count for usage tracking
+            image_count = 1
+            if isinstance(completion, list):
+                image_count = len(completion)
+
+            generation.update(
+                model=model,
+                output=completion,
+                usage_details={"output": image_count, "total": image_count, "unit": "IMAGES"},
+            ).end()
+
+            return openai_response
+
         if _is_streaming_response(openai_response):
             return LangfuseResponseGeneratorAsync(
                 resource=open_ai_resource,

tests/test_openai.py

@@ -1594,3 +1594,68 @@ async def test_async_openai_embeddings(openai):
     assert embedding_data.metadata["async"] is True
     assert embedding_data.usage.input is not None
     assert embedding_data.usage.total is not None
+
+
+def test_openai_image_generation(openai):
+    """Test that image generation calls are tracked in Langfuse."""
+    generation_name = "test_image_generation_" + create_uuid()[:8]
+
+    response = openai.OpenAI().images.generate(
+        name=generation_name,
+        model="dall-e-3",
+        prompt="A white siamese cat",
+        size="1024x1024",
+        quality="standard",
+        n=1,
+        metadata={"test_key": "test_value"},
+    )
+
+    langfuse.flush()
+    sleep(1)
+
+    generation = get_api().observations.get_many(name=generation_name, type="GENERATION")
+
+    assert len(generation.data) != 0
+    generation_data = generation.data[0]
+    assert generation_data.name == generation_name
+    assert generation_data.metadata["test_key"] == "test_value"
+    assert generation_data.input == "A white siamese cat"
+    assert generation_data.type == "GENERATION"
+    assert "dall-e-3" in generation_data.model
+    assert generation_data.start_time is not None
+    assert generation_data.end_time is not None
+    assert generation_data.start_time < generation_data.end_time
+    assert generation_data.output is not None
+    # Check model parameters
+    assert generation_data.model_parameters is not None
+    assert generation_data.model_parameters.get("size") == "1024x1024"
+    assert generation_data.model_parameters.get("quality") == "standard"
+
+
+@pytest.mark.asyncio
+async def test_openai_image_generation_async(openai):
+    """Test that async image generation calls are tracked in Langfuse."""
+    generation_name = "test_image_generation_async_" + create_uuid()[:8]
+
+    response = await openai.AsyncOpenAI().images.generate(
+        name=generation_name,
+        model="dall-e-3",
+        prompt="A sunset over mountains",
+        size="1024x1024",
+        quality="standard",
+        n=1,
+        metadata={"async": True},
+    )
+
+    langfuse.flush()
+    sleep(1)
+
+    generation = get_api().observations.get_many(name=generation_name, type="GENERATION")
+
+    assert len(generation.data) != 0
+    generation_data = generation.data[0]
+    assert generation_data.name == generation_name
+    assert generation_data.metadata["async"] is True
+    assert generation_data.input == "A sunset over mountains"
+    assert generation_data.type == "GENERATION"
+    assert "dall-e-3" in generation_data.model