feat(responses)!: implement support for OpenAI compatible prompts in Responses API (#3965)

# What does this PR do?  This PR is responsible for providing actual implementation of OpenAI compatible prompts in Responses API. This is the follow up PR with actual implementation after introducing #3942 The need of this functionality was initiated in #3514. > Note, https://github.com/llamastack/llama-stack/pull/3514 is divided on three separate PRs. Current PR is the third of three.   Closes #3321 ## Test Plan  Manual testing, CI workflow with added unit tests Comprehensive manual testing with new implementation: **Test Prompts with Images with text on them in Responses API:** I used this image for testing purposes: [iphone 17 image](https://github.com/user-attachments/assets/9e2ee821-e394-4bbd-b1c8-d48a3fa315de) 1. Upload an image: ``` curl -X POST http://localhost:8321/v1/files \ -H "Content-Type: multipart/form-data" \ -F "file=@/Users/ianmiller/iphone.jpeg" \ -F "purpose=assistants" ``` `{"object":"file","id":"file-d6d375f238e14f21952cc40246bc8504","bytes":556241,"created_at":1761750049,"expires_at":1793286049,"filename":"iphone.jpeg","purpose":"assistants"}%` 2. Create prompt: ``` curl -X POST http://localhost:8321/v1/prompts \ -H "Content-Type: application/json" \ -d '{ "prompt": "You are a product analysis expert. Analyze the following product:\n\nProduct Name: {{product_name}}\nDescription: {{description}}\n\nImage: {{product_photo}}\n\nProvide a detailed analysis including quality assessment, target audience, and pricing recommendations.", "variables": ["product_name", "description", "product_photo"] }' ``` `{"prompt":"You are a product analysis expert. Analyze the following product:\n\nProduct Name: {{product_name}}\nDescription: {{description}}\n\nImage: {{product_photo}}\n\nProvide a detailed analysis including quality assessment, target audience, and pricing recommendations.","version":1,"prompt_id":"pmpt_7be2208cb82cdbc35356354dae1f335d1e9b7baeca21ea62","variables":["product_name","description","product_photo"],"is_default":false}%` 3. Create response: ``` curl -X POST http://localhost:8321/v1/responses \ -H "Accept: application/json, text/event-stream" \ -H "Content-Type: application/json" \ -d '{ "input": "Please analyze this product", "model": "openai/gpt-4o", "store": true, "prompt": { "id": "pmpt_7be2208cb82cdbc35356354dae1f335d1e9b7baeca21ea62", "version": "1", "variables": { "product_name": { "type": "input_text", "text": "iPhone 17 Pro Max" }, "product_photo": { "type": "input_image", "file_id": "file-d6d375f238e14f21952cc40246bc8504", "detail": "high" } } } }' ``` `{"created_at":1761750427,"error":null,"id":"resp_f897f914-e3b8-4783-8223-3ed0d32fcbc6","model":"openai/gpt-4o","object":"response","output":[{"content":[{"text":"### Product Analysis: iPhone 17 Pro Max\n\n**Quality Assessment:**\n\n- **Display & Design:**\n - The 6.9-inch display is large, ideal for streaming and productivity.\n - Anti-reflective technology and 120Hz refresh rate enhance viewing experience, providing smoother visuals and reducing glare.\n - Titanium frame suggests a premium build, offering durability and a sleek appearance.\n\n- **Performance:**\n - The Apple A19 Pro chip promises significant performance improvements, likely leading to faster processing and efficient multitasking.\n - 12GB RAM is substantial for a smartphone, ensuring smooth operation for demanding apps and games.\n\n- **Camera System:**\n - The triple 48MP camera setup (wide, ultra-wide, telephoto) is designed for versatile photography needs, capturing high-resolution photos and videos.\n - The 24MP front camera will appeal to selfie enthusiasts and content creators needing quality front-facing shots.\n\n- **Connectivity:**\n - Wi-Fi 7 support indicates future-proof wireless capabilities, providing faster and more reliable internet connectivity.\n\n**Target Audience:**\n\n- **Tech Enthusiasts:** Individuals interested in cutting-edge technology and performance.\n- **Content Creators:** Users who need a robust camera system for photo and video production.\n- **Luxury Consumers:** Those who prefer premium materials and top-of-the-line specs.\n- **Professionals:** Users who require efficient multitasking and productivity features.\n\n**Pricing Recommendations:**\n\n- Given the premium specifications, a higher price point is expected. Consider pricing competitively within the high-end smartphone market while justifying cost through unique features like the titanium frame and advanced connectivity options.\n- Positioning around the $1,200 to $1,500 range would align with expectations for top-tier devices, catering to its target audience while ensuring profitability.\n\nOverall, the iPhone 17 Pro Max showcases a blend of innovative features and premium design, aimed at users seeking high performance and superior aesthetics.","type":"output_text","annotations":[]}],"role":"assistant","type":"message","id":"msg_66f4d844-4d9e-4102-80fc-eb75b34b6dbd","status":"completed"}],"parallel_tool_calls":false,"previous_response_id":null,"prompt":{"id":"pmpt_7be2208cb82cdbc35356354dae1f335d1e9b7baeca21ea62","variables":{"product_name":{"text":"iPhone 17 Pro Max","type":"input_text"},"product_photo":{"detail":"high","type":"input_image","file_id":"file-d6d375f238e14f21952cc40246bc8504","image_url":null}},"version":"1"},"status":"completed","temperature":null,"text":{"format":{"type":"text"}},"top_p":null,"tools":[],"truncation":null,"usage":{"input_tokens":830,"output_tokens":394,"total_tokens":1224,"input_tokens_details":{"cached_tokens":0},"output_tokens_details":{"reasoning_tokens":0}},"instructions":null}%` **Test Prompts with PDF files in Responses API:** I used this PDF file for testing purposes: [invoicesample.pdf](https://github.com/user-attachments/files/22958943/invoicesample.pdf) 1. Upload PDF: ``` curl -X POST http://localhost:8321/v1/files \ -H "Content-Type: multipart/form-data" \ -F "file=@/Users/ianmiller/invoicesample.pdf" \ -F "purpose=assistants" ``` `{"object":"file","id":"file-7fbb1043a4bb468cab60ffe4b8631d8e","bytes":149568,"created_at":1761750730,"expires_at":1793286730,"filename":"invoicesample.pdf","purpose":"assistants"}%` 2. Create prompt: ``` curl -X POST http://localhost:8321/v1/prompts \ -H "Content-Type: application/json" \ -d '{ "prompt": "You are an accounting and financial analysis expert. Analyze the following invoice document:\n\nInvoice Document: {{invoice_doc}}\n\nProvide a comprehensive analysis", "variables": ["invoice_doc"] }' ``` `{"prompt":"You are an accounting and financial analysis expert. Analyze the following invoice document:\n\nInvoice Document: {{invoice_doc}}\n\nProvide a comprehensive analysis","version":1,"prompt_id":"pmpt_72e2a184a86f32a568b6afb5455dca5c16bf3cc3f80092dc","variables":["invoice_doc"],"is_default":false}%` 3. Create response: ``` curl -X POST http://localhost:8321/v1/responses \ -H "Content-Type: application/json" \ -d '{ "input": "Please provide a detailed analysis of this invoice", "model": "openai/gpt-4o", "store": true, "prompt": { "id": "pmpt_72e2a184a86f32a568b6afb5455dca5c16bf3cc3f80092dc", "version": "1", "variables": { "invoice_doc": { "type": "input_file", "file_id": "file-7fbb1043a4bb468cab60ffe4b8631d8e", "filename": "invoicesample.pdf" } } } }' ``` `{"created_at":1761750881,"error":null,"id":"resp_da866913-db06-4702-8000-174daed9dbbb","model":"openai/gpt-4o","object":"response","output":[{"content":[{"text":"Here's a detailed analysis of the invoice provided:\n\n### Seller Information\n- **Business Name:** The invoice features a logo with \"Sunny Farm\" indicating the business identity.\n- **Address:** 123 Somewhere St, Melbourne VIC 3000\n- **Contact Information:** Phone number (03) 1234 5678\n\n### Buyer Information\n- **Name:** Denny Gunawan\n- **Address:** 221 Queen St, Melbourne VIC 3000\n\n### Transaction Details\n- **Invoice Number:** #20130304\n- **Date of Transaction:** Not explicitly mentioned, likely inferred from the invoice number or needs clarification.\n\n### Items Purchased\n1. **Apple**\n - Price: $5.00/kg\n - Quantity: 1 kg\n - Subtotal: $5.00\n\n2. **Orange**\n - Price: $1.99/kg\n - Quantity: 2 kg\n - Subtotal: $3.98\n\n3. **Watermelon**\n - Price: $1.69/kg\n - Quantity: 3 kg\n - Subtotal: $5.07\n\n4. **Mango**\n - Price: $9.56/kg\n - Quantity: 2 kg\n - Subtotal: $19.12\n\n5. **Peach**\n - Price: $2.99/kg\n - Quantity: 1 kg\n - Subtotal: $2.99\n\n### Financial Summary\n- **Subtotal for Items:** $36.00\n- **GST (Goods and Services Tax):** 10% of $36.00, which amounts to $3.60\n- **Total Amount Due:** $39.60\n\n### Notes\n- The invoice includes a placeholder text: \"Lorem ipsum dolor sit amet...\" which is typically used as filler text. This might indicate a section intended for terms, conditions, or additional notes that haven’t been completed.\n\n### Visual and Design Elements\n- The invoice uses a simple and clear layout, featuring the business logo prominently and stating essential information such as contact and transaction details in a structured manner.\n- There is a \"Thank You\" note at the bottom, which adds a professional and courteous touch.\n\n### Considerations\n- Ensure the date of the transaction is clear if there are any future references needed.\n- Replace filler text with relevant terms and conditions or any special instructions pertaining to the transaction.\n\nThis invoice appears standard, representing a small business transaction with clearly itemized products and applicable taxes.","type":"output_text","annotations":[]}],"role":"assistant","type":"message","id":"msg_39f3b39e-4684-4444-8e4d-e7395f88c9dc","status":"completed"}],"parallel_tool_calls":false,"previous_response_id":null,"prompt":{"id":"pmpt_72e2a184a86f32a568b6afb5455dca5c16bf3cc3f80092dc","variables":{"invoice_doc":{"type":"input_file","file_data":null,"file_id":"file-7fbb1043a4bb468cab60ffe4b8631d8e","file_url":null,"filename":"invoicesample.pdf"}},"version":"1"},"status":"completed","temperature":null,"text":{"format":{"type":"text"}},"top_p":null,"tools":[],"truncation":null,"usage":{"input_tokens":529,"output_tokens":513,"total_tokens":1042,"input_tokens_details":{"cached_tokens":0},"output_tokens_details":{"reasoning_tokens":0}},"instructions":null}%` **Test simple text Prompt in Responses API:** 1. Create prompt: ``` curl -X POST http://localhost:8321/v1/prompts \ -H "Content-Type: application/json" \ -d '{ "prompt": "Hello {{name}}! You are working at {{company}}. Your role is {{role}} at {{company}}. Remember, {{name}}, to be {{tone}}.", "variables": ["name", "company", "role", "tone"] }' ``` `{"prompt":"Hello {{name}}! You are working at {{company}}. Your role is {{role}} at {{company}}. Remember, {{name}}, to be {{tone}}.","version":1,"prompt_id":"pmpt_f340a3164a4f65d975c774ffe38ea42d15e7ce4a835919ef","variables":["name","company","role","tone"],"is_default":false}%` 2. Create response: ``` curl -X POST http://localhost:8321/v1/responses \ -H "Accept: application/json, text/event-stream" \ -H "Content-Type: application/json" \ -d '{ "input": "What is the capital of Ireland?", "model": "openai/gpt-4o", "store": true, "prompt": { "id": "pmpt_f340a3164a4f65d975c774ffe38ea42d15e7ce4a835919ef", "version": "1", "variables": { "name": { "type": "input_text", "text": "Alice" }, "company": { "type": "input_text", "text": "Dummy Company" }, "role": { "type": "input_text", "text": "Geography expert" }, "tone": { "type": "input_text", "text": "professional and helpful" } } } }' ``` `{"created_at":1761751097,"error":null,"id":"resp_1b037b95-d9ae-4ad0-8e76-d953897ecaef","model":"openai/gpt-4o","object":"response","output":[{"content":[{"text":"The capital of Ireland is Dublin.","type":"output_text","annotations":[]}],"role":"assistant","type":"message","id":"msg_8e7c72b6-2aa2-4da6-8e57-da4e12fa3ce2","status":"completed"}],"parallel_tool_calls":false,"previous_response_id":null,"prompt":{"id":"pmpt_f340a3164a4f65d975c774ffe38ea42d15e7ce4a835919ef","variables":{"name":{"text":"Alice","type":"input_text"},"company":{"text":"Dummy Company","type":"input_text"},"role":{"text":"Geography expert","type":"input_text"},"tone":{"text":"professional and helpful","type":"input_text"}},"version":"1"},"status":"completed","temperature":null,"text":{"format":{"type":"text"}},"top_p":null,"tools":[],"truncation":null,"usage":{"input_tokens":47,"output_tokens":7,"total_tokens":54,"input_tokens_details":{"cached_tokens":0},"output_tokens_details":{"reasoning_tokens":0}},"instructions":null}%`
2025-12-03 09:53:45 +00:00 · 2025-11-19 19:48:11 +00:00 · 2025-11-19 19:48:11 +00:00 · 0757d5a917
commit 0757d5a917
parent 8852666982
10 changed files with 770 additions and 17 deletions
--- a/tests/unit/providers/agents/meta_reference/test_openai_responses.py
+++ b/tests/unit/providers/agents/meta_reference/test_openai_responses.py
@ -25,6 +25,13 @@ from llama_stack.providers.utils.responses.responses_store import (
    ResponsesStore,
    _OpenAIResponseObjectWithInputAndMessages,
 )
+from llama_stack_api import (
+    OpenAIChatCompletionContentPartImageParam,
+    OpenAIFile,
+    OpenAIFileObject,
+    OpenAISystemMessageParam,
+    Prompt,
+)
 from llama_stack_api.agents import Order
 from llama_stack_api.inference import (
    OpenAIAssistantMessageParam,
@ -38,6 +45,8 @@ from llama_stack_api.inference import (
 )
 from llama_stack_api.openai_responses import (
    ListOpenAIResponseInputItem,
+    OpenAIResponseInputMessageContentFile,
+    OpenAIResponseInputMessageContentImage,
    OpenAIResponseInputMessageContentText,
    OpenAIResponseInputToolFunction,
    OpenAIResponseInputToolMCP,
@ -47,6 +56,7 @@ from llama_stack_api.openai_responses import (
    OpenAIResponseOutputMessageFunctionToolCall,
    OpenAIResponseOutputMessageMCPCall,
    OpenAIResponseOutputMessageWebSearchToolCall,
+    OpenAIResponsePrompt,
    OpenAIResponseText,
    OpenAIResponseTextFormat,
    WebSearchToolTypes,
@ -98,6 +108,19 @@ def mock_safety_api():
    return safety_api


+@pytest.fixture
+def mock_prompts_api():
+    prompts_api = AsyncMock()
+    return prompts_api
+
+
+@pytest.fixture
+def mock_files_api():
+    """Mock files API for testing."""
+    files_api = AsyncMock()
+    return files_api
+
+
@pytest.fixture
 def openai_responses_impl(
    mock_inference_api,
@ -107,6 +130,8 @@ def openai_responses_impl(
    mock_vector_io_api,
    mock_safety_api,
    mock_conversations_api,
+    mock_prompts_api,
+    mock_files_api,
 ):
    return OpenAIResponsesImpl(
        inference_api=mock_inference_api,
@ -116,6 +141,8 @@ def openai_responses_impl(
        vector_io_api=mock_vector_io_api,
        safety_api=mock_safety_api,
        conversations_api=mock_conversations_api,
+        prompts_api=mock_prompts_api,
+        files_api=mock_files_api,
    )


@ -499,7 +526,7 @@ async def test_create_openai_response_with_tool_call_function_arguments_none(ope
    mock_inference_api.openai_chat_completion.return_value = fake_stream_toolcall()


-async def test_create_openai_response_with_multiple_messages(openai_responses_impl, mock_inference_api):
+async def test_create_openai_response_with_multiple_messages(openai_responses_impl, mock_inference_api, mock_files_api):
    """Test creating an OpenAI response with multiple messages."""
    # Setup
    input_messages = [
@ -710,7 +737,7 @@ async def test_create_openai_response_with_instructions(openai_responses_impl, m


 async def test_create_openai_response_with_instructions_and_multiple_messages(
-    openai_responses_impl, mock_inference_api
+    openai_responses_impl, mock_inference_api, mock_files_api
 ):
    # Setup
    input_messages = [
@ -1242,3 +1269,489 @@ async def test_create_openai_response_with_output_types_as_input(

    assert stored_with_outputs.input == input_with_output_types
    assert len(stored_with_outputs.input) == 3
+
+
+async def test_create_openai_response_with_prompt(openai_responses_impl, mock_inference_api, mock_prompts_api):
+    """Test creating an OpenAI response with a prompt."""
+    input_text = "What is the capital of Ireland?"
+    model = "meta-llama/Llama-3.1-8B-Instruct"
+    prompt_id = "pmpt_1234567890abcdef1234567890abcdef1234567890abcdef"
+    prompt = Prompt(
+        prompt="You are a helpful {{ area_name }} assistant at {{ company_name }}. Always provide accurate information.",
+        prompt_id=prompt_id,
+        version=1,
+        variables=["area_name", "company_name"],
+        is_default=True,
+    )
+
+    openai_response_prompt = OpenAIResponsePrompt(
+        id=prompt_id,
+        version="1",
+        variables={
+            "area_name": OpenAIResponseInputMessageContentText(text="geography"),
+            "company_name": OpenAIResponseInputMessageContentText(text="Dummy Company"),
+        },
+    )
+
+    mock_prompts_api.get_prompt.return_value = prompt
+    mock_inference_api.openai_chat_completion.return_value = fake_stream()
+
+    result = await openai_responses_impl.create_openai_response(
+        input=input_text,
+        model=model,
+        prompt=openai_response_prompt,
+    )
+
+    mock_prompts_api.get_prompt.assert_called_with(prompt_id, 1)
+    mock_inference_api.openai_chat_completion.assert_called()
+    call_args = mock_inference_api.openai_chat_completion.call_args
+    sent_messages = call_args.args[0].messages
+    assert len(sent_messages) == 2
+
+    system_messages = [msg for msg in sent_messages if msg.role == "system"]
+    assert len(system_messages) == 1
+    assert (
+        system_messages[0].content
+        == "You are a helpful geography assistant at Dummy Company. Always provide accurate information."
+    )
+
+    user_messages = [msg for msg in sent_messages if msg.role == "user"]
+    assert len(user_messages) == 1
+    assert user_messages[0].content == input_text
+
+    assert result.model == model
+    assert result.status == "completed"
+    assert isinstance(result.prompt, OpenAIResponsePrompt)
+    assert result.prompt.id == prompt_id
+    assert result.prompt.variables == openai_response_prompt.variables
+    assert result.prompt.version == "1"
+
+
+async def test_prepend_prompt_successful_without_variables(openai_responses_impl, mock_prompts_api, mock_inference_api):
+    """Test prepend_prompt function without variables."""
+    input_text = "What is the capital of Ireland?"
+    model = "meta-llama/Llama-3.1-8B-Instruct"
+    prompt_id = "pmpt_1234567890abcdef1234567890abcdef1234567890abcdef"
+    prompt = Prompt(
+        prompt="You are a helpful assistant. Always provide accurate information.",
+        prompt_id=prompt_id,
+        version=1,
+        variables=[],
+        is_default=True,
+    )
+
+    openai_response_prompt = OpenAIResponsePrompt(id=prompt_id, version="1")
+
+    mock_prompts_api.get_prompt.return_value = prompt
+    mock_inference_api.openai_chat_completion.return_value = fake_stream()
+
+    await openai_responses_impl.create_openai_response(
+        input=input_text,
+        model=model,
+        prompt=openai_response_prompt,
+    )
+
+    mock_prompts_api.get_prompt.assert_called_with(prompt_id, 1)
+    mock_inference_api.openai_chat_completion.assert_called()
+    call_args = mock_inference_api.openai_chat_completion.call_args
+    sent_messages = call_args.args[0].messages
+    assert len(sent_messages) == 2
+    system_messages = [msg for msg in sent_messages if msg.role == "system"]
+    assert system_messages[0].content == "You are a helpful assistant. Always provide accurate information."
+
+
+async def test_prepend_prompt_invalid_variable(openai_responses_impl, mock_prompts_api):
+    """Test error handling in prepend_prompt function when prompt parameters contain invalid variables."""
+    prompt_id = "pmpt_1234567890abcdef1234567890abcdef1234567890abcdef"
+    prompt = Prompt(
+        prompt="You are a {{ role }} assistant.",
+        prompt_id=prompt_id,
+        version=1,
+        variables=["role"],  # Only "role" is valid
+        is_default=True,
+    )
+
+    openai_response_prompt = OpenAIResponsePrompt(
+        id=prompt_id,
+        version="1",
+        variables={
+            "role": OpenAIResponseInputMessageContentText(text="helpful"),
+            "company": OpenAIResponseInputMessageContentText(
+                text="Dummy Company"
+            ),  # company is not in prompt.variables
+        },
+    )
+
+    mock_prompts_api.get_prompt.return_value = prompt
+
+    # Initial messages
+    messages = [OpenAIUserMessageParam(content="Test prompt")]
+
+    # Execute - should raise ValueError for invalid variable
+    with pytest.raises(ValueError, match="Variable company not found in prompt"):
+        await openai_responses_impl._prepend_prompt(messages, openai_response_prompt)
+
+    # Verify
+    mock_prompts_api.get_prompt.assert_called_once_with(prompt_id, 1)
+
+
+async def test_prepend_prompt_not_found(openai_responses_impl, mock_prompts_api):
+    """Test prepend_prompt function when prompt is not found."""
+    prompt_id = "pmpt_nonexistent"
+    openai_response_prompt = OpenAIResponsePrompt(id=prompt_id, version="1")
+
+    mock_prompts_api.get_prompt.return_value = None  # Prompt not found
+
+    # Initial messages
+    messages = [OpenAIUserMessageParam(content="Test prompt")]
+    initial_length = len(messages)
+
+    # Execute
+    result = await openai_responses_impl._prepend_prompt(messages, openai_response_prompt)
+
+    # Verify
+    mock_prompts_api.get_prompt.assert_called_once_with(prompt_id, 1)
+
+    # Should return None when prompt not found
+    assert result is None
+
+    # Messages should not be modified
+    assert len(messages) == initial_length
+    assert messages[0].content == "Test prompt"
+
+
+async def test_prepend_prompt_variable_substitution(openai_responses_impl, mock_prompts_api):
+    """Test complex variable substitution with multiple occurrences and special characters in prepend_prompt function."""
+    prompt_id = "pmpt_1234567890abcdef1234567890abcdef1234567890abcdef"
+
+    # Support all whitespace variations: {{name}}, {{ name }}, {{ name}}, {{name }}, etc.
+    prompt = Prompt(
+        prompt="Hello {{name}}! You are working at {{ company}}. Your role is {{role}} at {{company}}. Remember, {{ name }}, to be {{ tone }}.",
+        prompt_id=prompt_id,
+        version=1,
+        variables=["name", "company", "role", "tone"],
+        is_default=True,
+    )
+
+    openai_response_prompt = OpenAIResponsePrompt(
+        id=prompt_id,
+        version="1",
+        variables={
+            "name": OpenAIResponseInputMessageContentText(text="Alice"),
+            "company": OpenAIResponseInputMessageContentText(text="Dummy Company"),
+            "role": OpenAIResponseInputMessageContentText(text="AI Assistant"),
+            "tone": OpenAIResponseInputMessageContentText(text="professional"),
+        },
+    )
+
+    mock_prompts_api.get_prompt.return_value = prompt
+
+    # Initial messages
+    messages = [OpenAIUserMessageParam(content="Test")]
+
+    # Execute
+    await openai_responses_impl._prepend_prompt(messages, openai_response_prompt)
+
+    # Verify
+    assert len(messages) == 2
+    assert isinstance(messages[0], OpenAISystemMessageParam)
+    expected_content = "Hello Alice! You are working at Dummy Company. Your role is AI Assistant at Dummy Company. Remember, Alice, to be professional."
+    assert messages[0].content == expected_content
+
+
+async def test_prepend_prompt_with_image_variable(openai_responses_impl, mock_prompts_api, mock_files_api):
+    """Test prepend_prompt with image variable - should create placeholder in system message and append image as separate user message."""
+    prompt_id = "pmpt_1234567890abcdef1234567890abcdef1234567890abcdef"
+    prompt = Prompt(
+        prompt="Analyze this {{product_image}} and describe what you see.",
+        prompt_id=prompt_id,
+        version=1,
+        variables=["product_image"],
+        is_default=True,
+    )
+
+    # Mock file content and file metadata
+    mock_file_content = b"fake_image_data"
+    mock_files_api.openai_retrieve_file_content.return_value = type("obj", (object,), {"body": mock_file_content})()
+    mock_files_api.openai_retrieve_file.return_value = OpenAIFileObject(
+        object="file",
+        id="file-abc123",
+        bytes=len(mock_file_content),
+        created_at=1234567890,
+        expires_at=1234567890,
+        filename="product.jpg",
+        purpose="assistants",
+    )
+
+    openai_response_prompt = OpenAIResponsePrompt(
+        id=prompt_id,
+        version="1",
+        variables={
+            "product_image": OpenAIResponseInputMessageContentImage(
+                file_id="file-abc123",
+                detail="high",
+            )
+        },
+    )
+
+    mock_prompts_api.get_prompt.return_value = prompt
+
+    # Initial messages
+    messages = [OpenAIUserMessageParam(content="What do you think?")]
+
+    # Execute
+    await openai_responses_impl._prepend_prompt(messages, openai_response_prompt)
+
+    assert len(messages) == 3
+
+    # Check system message has placeholder
+    assert isinstance(messages[0], OpenAISystemMessageParam)
+    assert messages[0].content == "Analyze this [Image: product_image] and describe what you see."
+
+    # Check original user message is still there
+    assert isinstance(messages[1], OpenAIUserMessageParam)
+    assert messages[1].content == "What do you think?"
+
+    # Check new user message with image is appended
+    assert isinstance(messages[2], OpenAIUserMessageParam)
+    assert isinstance(messages[2].content, list)
+    assert len(messages[2].content) == 1
+
+    # Should be image with data URL
+    assert isinstance(messages[2].content[0], OpenAIChatCompletionContentPartImageParam)
+    assert messages[2].content[0].image_url.url.startswith("data:image/")
+    assert messages[2].content[0].image_url.detail == "high"
+
+
+async def test_prepend_prompt_with_file_variable(openai_responses_impl, mock_prompts_api, mock_files_api):
+    """Test prepend_prompt with file variable - should create placeholder in system message and append file as separate user message."""
+    prompt_id = "pmpt_1234567890abcdef1234567890abcdef1234567890abcdef"
+    prompt = Prompt(
+        prompt="Review the document {{contract_file}} and summarize key points.",
+        prompt_id=prompt_id,
+        version=1,
+        variables=["contract_file"],
+        is_default=True,
+    )
+
+    # Mock file retrieval
+    mock_file_content = b"fake_pdf_content"
+    mock_files_api.openai_retrieve_file_content.return_value = type("obj", (object,), {"body": mock_file_content})()
+    mock_files_api.openai_retrieve_file.return_value = OpenAIFileObject(
+        object="file",
+        id="file-contract-789",
+        bytes=len(mock_file_content),
+        created_at=1234567890,
+        expires_at=1234567890,
+        filename="contract.pdf",
+        purpose="assistants",
+    )
+
+    openai_response_prompt = OpenAIResponsePrompt(
+        id=prompt_id,
+        version="1",
+        variables={
+            "contract_file": OpenAIResponseInputMessageContentFile(
+                file_id="file-contract-789",
+                filename="contract.pdf",
+            )
+        },
+    )
+
+    mock_prompts_api.get_prompt.return_value = prompt
+
+    # Initial messages
+    messages = [OpenAIUserMessageParam(content="Please review this.")]
+
+    # Execute
+    await openai_responses_impl._prepend_prompt(messages, openai_response_prompt)
+
+    assert len(messages) == 3
+
+    # Check system message has placeholder
+    assert isinstance(messages[0], OpenAISystemMessageParam)
+    assert messages[0].content == "Review the document [File: contract_file] and summarize key points."
+
+    # Check original user message is still there
+    assert isinstance(messages[1], OpenAIUserMessageParam)
+    assert messages[1].content == "Please review this."
+
+    # Check new user message with file is appended
+    assert isinstance(messages[2], OpenAIUserMessageParam)
+    assert isinstance(messages[2].content, list)
+    assert len(messages[2].content) == 1
+
+    # First part should be file with data URL
+    assert isinstance(messages[2].content[0], OpenAIFile)
+    assert messages[2].content[0].file.file_data.startswith("data:application/pdf;base64,")
+    assert messages[2].content[0].file.filename == "contract.pdf"
+    assert messages[2].content[0].file.file_id is None
+
+
+async def test_prepend_prompt_with_mixed_variables(openai_responses_impl, mock_prompts_api, mock_files_api):
+    """Test prepend_prompt with text, image, and file variables mixed together."""
+    prompt_id = "pmpt_1234567890abcdef1234567890abcdef1234567890abcdef"
+    prompt = Prompt(
+        prompt="Hello {{name}}! Analyze {{photo}} and review {{document}}. Provide insights for {{company}}.",
+        prompt_id=prompt_id,
+        version=1,
+        variables=["name", "photo", "document", "company"],
+        is_default=True,
+    )
+
+    # Mock file retrieval for image and file
+    mock_image_content = b"fake_image_data"
+    mock_file_content = b"fake_doc_content"
+
+    async def mock_retrieve_file_content(file_id):
+        if file_id == "file-photo-123":
+            return type("obj", (object,), {"body": mock_image_content})()
+        elif file_id == "file-doc-456":
+            return type("obj", (object,), {"body": mock_file_content})()
+
+    mock_files_api.openai_retrieve_file_content.side_effect = mock_retrieve_file_content
+
+    def mock_retrieve_file(file_id):
+        if file_id == "file-photo-123":
+            return OpenAIFileObject(
+                object="file",
+                id="file-photo-123",
+                bytes=len(mock_image_content),
+                created_at=1234567890,
+                expires_at=1234567890,
+                filename="photo.jpg",
+                purpose="assistants",
+            )
+        elif file_id == "file-doc-456":
+            return OpenAIFileObject(
+                object="file",
+                id="file-doc-456",
+                bytes=len(mock_file_content),
+                created_at=1234567890,
+                expires_at=1234567890,
+                filename="doc.pdf",
+                purpose="assistants",
+            )
+
+    mock_files_api.openai_retrieve_file.side_effect = mock_retrieve_file
+
+    openai_response_prompt = OpenAIResponsePrompt(
+        id=prompt_id,
+        version="1",
+        variables={
+            "name": OpenAIResponseInputMessageContentText(text="Alice"),
+            "photo": OpenAIResponseInputMessageContentImage(file_id="file-photo-123", detail="auto"),
+            "document": OpenAIResponseInputMessageContentFile(file_id="file-doc-456", filename="doc.pdf"),
+            "company": OpenAIResponseInputMessageContentText(text="Acme Corp"),
+        },
+    )
+
+    mock_prompts_api.get_prompt.return_value = prompt
+
+    # Initial messages
+    messages = [OpenAIUserMessageParam(content="Here's my question.")]
+
+    # Execute
+    await openai_responses_impl._prepend_prompt(messages, openai_response_prompt)
+
+    assert len(messages) == 3
+
+    # Check system message has text and placeholders
+    assert isinstance(messages[0], OpenAISystemMessageParam)
+    expected_system = "Hello Alice! Analyze [Image: photo] and review [File: document]. Provide insights for Acme Corp."
+    assert messages[0].content == expected_system
+
+    # Check original user message is still there
+    assert isinstance(messages[1], OpenAIUserMessageParam)
+    assert messages[1].content == "Here's my question."
+
+    # Check new user message with media is appended (2 media items)
+    assert isinstance(messages[2], OpenAIUserMessageParam)
+    assert isinstance(messages[2].content, list)
+    assert len(messages[2].content) == 2
+
+    # First part should be image with data URL
+    assert isinstance(messages[2].content[0], OpenAIChatCompletionContentPartImageParam)
+    assert messages[2].content[0].image_url.url.startswith("data:image/")
+
+    # Second part should be file with data URL
+    assert isinstance(messages[2].content[1], OpenAIFile)
+    assert messages[2].content[1].file.file_data.startswith("data:application/pdf;base64,")
+    assert messages[2].content[1].file.filename == "doc.pdf"
+    assert messages[2].content[1].file.file_id is None
+
+
+async def test_prepend_prompt_with_image_using_image_url(openai_responses_impl, mock_prompts_api):
+    """Test prepend_prompt with image variable using image_url instead of file_id."""
+    prompt_id = "pmpt_1234567890abcdef1234567890abcdef1234567890abcdef"
+    prompt = Prompt(
+        prompt="Describe {{screenshot}}.",
+        prompt_id=prompt_id,
+        version=1,
+        variables=["screenshot"],
+        is_default=True,
+    )
+
+    openai_response_prompt = OpenAIResponsePrompt(
+        id=prompt_id,
+        version="1",
+        variables={
+            "screenshot": OpenAIResponseInputMessageContentImage(
+                image_url="https://example.com/screenshot.png",
+                detail="low",
+            )
+        },
+    )
+
+    mock_prompts_api.get_prompt.return_value = prompt
+
+    # Initial messages
+    messages = [OpenAIUserMessageParam(content="What is this?")]
+
+    # Execute
+    await openai_responses_impl._prepend_prompt(messages, openai_response_prompt)
+
+    assert len(messages) == 3
+
+    # Check system message has placeholder
+    assert isinstance(messages[0], OpenAISystemMessageParam)
+    assert messages[0].content == "Describe [Image: screenshot]."
+
+    # Check original user message is still there
+    assert isinstance(messages[1], OpenAIUserMessageParam)
+    assert messages[1].content == "What is this?"
+
+    # Check new user message with image is appended
+    assert isinstance(messages[2], OpenAIUserMessageParam)
+    assert isinstance(messages[2].content, list)
+
+    # Image should use the provided URL
+    assert isinstance(messages[2].content[0], OpenAIChatCompletionContentPartImageParam)
+    assert messages[2].content[0].image_url.url == "https://example.com/screenshot.png"
+    assert messages[2].content[0].image_url.detail == "low"
+
+
+async def test_prepend_prompt_image_variable_missing_required_fields(openai_responses_impl, mock_prompts_api):
+    """Test prepend_prompt with image variable that has neither file_id nor image_url - should raise error."""
+    prompt_id = "pmpt_1234567890abcdef1234567890abcdef1234567890abcdef"
+    prompt = Prompt(
+        prompt="Analyze {{bad_image}}.",
+        prompt_id=prompt_id,
+        version=1,
+        variables=["bad_image"],
+        is_default=True,
+    )
+
+    # Create image content with neither file_id nor image_url
+    openai_response_prompt = OpenAIResponsePrompt(
+        id=prompt_id,
+        version="1",
+        variables={"bad_image": OpenAIResponseInputMessageContentImage()},  # No file_id or image_url
+    )
+
+    mock_prompts_api.get_prompt.return_value = prompt
+    messages = [OpenAIUserMessageParam(content="Test")]
+
+    # Execute - should raise ValueError
+    with pytest.raises(ValueError, match="Image content must have either 'image_url' or 'file_id'"):
+        await openai_responses_impl._prepend_prompt(messages, openai_response_prompt)
--- a/tests/unit/providers/agents/meta_reference/test_openai_responses_conversations.py
+++ b/tests/unit/providers/agents/meta_reference/test_openai_responses_conversations.py
@ -39,6 +39,8 @@ def responses_impl_with_conversations(
    mock_vector_io_api,
    mock_conversations_api,
    mock_safety_api,
+    mock_prompts_api,
+    mock_files_api,
 ):
    """Create OpenAIResponsesImpl instance with conversations API."""
    return OpenAIResponsesImpl(
@ -49,6 +51,8 @@ def responses_impl_with_conversations(
        vector_io_api=mock_vector_io_api,
        conversations_api=mock_conversations_api,
        safety_api=mock_safety_api,
+        prompts_api=mock_prompts_api,
+        files_api=mock_files_api,
    )


--- a/tests/unit/providers/agents/meta_reference/test_response_conversion_utils.py
+++ b/tests/unit/providers/agents/meta_reference/test_response_conversion_utils.py
@ -5,6 +5,8 @@
 # the root directory of this source tree.


+from unittest.mock import AsyncMock
+
 import pytest

 from llama_stack.providers.inline.agents.meta_reference.responses.utils import (
@ -46,6 +48,12 @@ from llama_stack_api.openai_responses import (
 )


+@pytest.fixture
+def mock_files_api():
+    """Mock files API for testing."""
+    return AsyncMock()
+
+
 class TestConvertChatChoiceToResponseMessage:
    async def test_convert_string_content(self):
        choice = OpenAIChoice(
@ -78,17 +86,17 @@ class TestConvertChatChoiceToResponseMessage:


 class TestConvertResponseContentToChatContent:
-    async def test_convert_string_content(self):
-        result = await convert_response_content_to_chat_content("Simple string")
+    async def test_convert_string_content(self, mock_files_api):
+        result = await convert_response_content_to_chat_content("Simple string", mock_files_api)
        assert result == "Simple string"

-    async def test_convert_text_content_parts(self):
+    async def test_convert_text_content_parts(self, mock_files_api):
        content = [
            OpenAIResponseInputMessageContentText(text="First part"),
            OpenAIResponseOutputMessageContentOutputText(text="Second part"),
        ]

-        result = await convert_response_content_to_chat_content(content)
+        result = await convert_response_content_to_chat_content(content, mock_files_api)

        assert len(result) == 2
        assert isinstance(result[0], OpenAIChatCompletionContentPartTextParam)
@ -96,10 +104,10 @@ class TestConvertResponseContentToChatContent:
        assert isinstance(result[1], OpenAIChatCompletionContentPartTextParam)
        assert result[1].text == "Second part"

-    async def test_convert_image_content(self):
+    async def test_convert_image_content(self, mock_files_api):
        content = [OpenAIResponseInputMessageContentImage(image_url="https://example.com/image.jpg", detail="high")]

-        result = await convert_response_content_to_chat_content(content)
+        result = await convert_response_content_to_chat_content(content, mock_files_api)

        assert len(result) == 1
        assert isinstance(result[0], OpenAIChatCompletionContentPartImageParam)
--- a/tests/unit/providers/agents/meta_reference/test_responses_safety_utils.py
+++ b/tests/unit/providers/agents/meta_reference/test_responses_safety_utils.py
@ -30,6 +30,8 @@ def mock_apis():
        "vector_io_api": AsyncMock(),
        "conversations_api": AsyncMock(),
        "safety_api": AsyncMock(),
+        "prompts_api": AsyncMock(),
+        "files_api": AsyncMock(),
    }


--- a/tests/unit/providers/agents/meta_reference/test_safety_optional.py
+++ b/tests/unit/providers/agents/meta_reference/test_safety_optional.py
@ -52,6 +52,8 @@ def mock_deps():
    tool_runtime_api = AsyncMock()
    tool_groups_api = AsyncMock()
    conversations_api = AsyncMock()
+    prompts_api = AsyncMock()
+    files_api = AsyncMock()

    return {
        Api.inference: inference_api,
@ -59,6 +61,8 @@ def mock_deps():
        Api.tool_runtime: tool_runtime_api,
        Api.tool_groups: tool_groups_api,
        Api.conversations: conversations_api,
+        Api.prompts: prompts_api,
+        Api.files: files_api,
    }


@ -144,6 +148,8 @@ class TestGuardrailsFunctionality:
                vector_io_api=mock_deps[Api.vector_io],
                safety_api=None,  # No Safety API
                conversations_api=mock_deps[Api.conversations],
+                prompts_api=mock_deps[Api.prompts],
+                files_api=mock_deps[Api.files],
            )

            # Test with string guardrail
@ -191,6 +197,8 @@ class TestGuardrailsFunctionality:
                vector_io_api=mock_deps[Api.vector_io],
                safety_api=None,  # No Safety API
                conversations_api=mock_deps[Api.conversations],
+                prompts_api=mock_deps[Api.prompts],
+                files_api=mock_deps[Api.files],
            )

            # Should not raise when no guardrails requested