llama-stack-mirror/tests
Ian Miller 0757d5a917
feat(responses)!: implement support for OpenAI compatible prompts in Responses API (#3965)
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
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.

<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
Closes #3321

## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->
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}%`
2025-11-19 11:48:11 -08:00
..
backward_compat feat: add backward compatibility tests for run.yaml (#3952) 2025-10-28 21:51:56 -07:00
common feat(tests): enable MCP tests in server mode (#4146) 2025-11-13 07:23:23 -08:00
containers refactor: replace default all-MiniLM-L6-v2 embedding model by nomic-embed-text-v1.5 in Llama Stack (#3183) 2025-10-14 10:44:20 -04:00
external feat: split API and provider specs into separate llama-stack-api pkg (#3895) 2025-11-13 11:51:17 -08:00
integration fix: Fix max_tool_calls for openai provider and add integration tests for the max_tool_calls feat (#4190) 2025-11-19 10:27:56 -08:00
unit feat(responses)!: implement support for OpenAI compatible prompts in Responses API (#3965) 2025-11-19 11:48:11 -08:00
__init__.py refactor(test): introduce --stack-config and simplify options (#1404) 2025-03-05 17:02:02 -08:00
README.md feat(tests): introduce a test "suite" concept to encompass dirs, options (#3339) 2025-09-05 13:58:49 -07:00

There are two obvious types of tests:

Type Location Purpose
Unit tests/unit/ Fast, isolated component testing
Integration tests/integration/ End-to-end workflows with record-replay

Both have their place. For unit tests, it is important to create minimal mocks and instead rely more on "fakes". Mocks are too brittle. In either case, tests must be very fast and reliable.

Record-replay for integration tests

Testing AI applications end-to-end creates some challenges:

  • API costs accumulate quickly during development and CI
  • Non-deterministic responses make tests unreliable
  • Multiple providers require testing the same logic across different APIs

Our solution: Record real API responses once, replay them for fast, deterministic tests. This is better than mocking because AI APIs have complex response structures and streaming behavior. Mocks can miss edge cases that real APIs exhibit. A single test can exercise underlying APIs in multiple complex ways making it really hard to mock.

This gives you:

  • Cost control - No repeated API calls during development
  • Speed - Instant test execution with cached responses
  • Reliability - Consistent results regardless of external service state
  • Provider coverage - Same tests work across OpenAI, Anthropic, local models, etc.

Testing Quick Start

You can run the unit tests with:

uv run --group unit pytest -sv tests/unit/

For running integration tests, you must provide a few things:

  • A stack config. This is a pointer to a stack. You have a few ways to point to a stack:

    • server:<config> - automatically start a server with the given config (e.g., server:starter). This provides one-step testing by auto-starting the server if the port is available, or reusing an existing server if already running.
    • server:<config>:<port> - same as above but with a custom port (e.g., server:starter:8322)
    • a URL which points to a Llama Stack distribution server
    • a distribution name (e.g., starter) or a path to a run.yaml file
    • a comma-separated list of api=provider pairs, e.g. inference=fireworks,safety=llama-guard,agents=meta-reference. This is most useful for testing a single API surface.
  • Any API keys you need to use should be set in the environment, or can be passed in with the --env option.

You can run the integration tests in replay mode with:

# Run all tests with existing recordings
  uv run --group test \
  pytest -sv tests/integration/ --stack-config=starter

Re-recording tests

Local Re-recording (Manual Setup Required)

If you want to re-record tests locally, you can do so with:

LLAMA_STACK_TEST_INFERENCE_MODE=record \
  uv run --group test \
  pytest -sv tests/integration/ --stack-config=starter -k "<appropriate test name>"

This will record new API responses and overwrite the existing recordings.


You must be careful when re-recording. CI workflows assume a specific setup for running the replay-mode tests. You must re-record the tests in the same way as the CI workflows. This means
- you need Ollama running and serving some specific models.
- you are using the `starter` distribution.

For easier re-recording without local setup, use the automated recording workflow:

# Record tests for specific test subdirectories
./scripts/github/schedule-record-workflow.sh --test-subdirs "agents,inference"

# Record with vision tests enabled
./scripts/github/schedule-record-workflow.sh --test-suite vision

# Record with specific provider
./scripts/github/schedule-record-workflow.sh --test-subdirs "agents" --test-provider vllm

This script:

  • 🚀 Runs in GitHub Actions - no local Ollama setup required
  • 🔍 Auto-detects your branch and associated PR
  • 🍴 Works from forks - handles repository context automatically
  • Commits recordings back to your branch

Prerequisites:

  • GitHub CLI: brew install gh && gh auth login
  • jq: brew install jq
  • Your branch pushed to a remote

Supported providers: vllm, ollama

Next Steps