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20
tests/integration/README.md
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@ -211,3 +211,23 @@ def test_asymmetric_embeddings(llama_stack_client, embedding_model_id):
assert query_response.embeddings is not None
```
## TypeScript Client Replays
TypeScript SDK tests can run alongside Python tests when testing against `server:<config>` stacks. Set `TS_CLIENT_PATH` to the path or version of `llama-stack-client-typescript` to enable:
```bash
# Use published npm package (responses suite)
TS_CLIENT_PATH=^0.3.2 scripts/integration-tests.sh --stack-config server:ci-tests --suite responses --setup gpt
# Use local checkout from ~/.cache (recommended for development)
git clone https://github.com/llamastack/llama-stack-client-typescript.git ~/.cache/llama-stack-client-typescript
TS_CLIENT_PATH=~/.cache/llama-stack-client-typescript scripts/integration-tests.sh --stack-config server:ci-tests --suite responses --setup gpt
# Run base suite with TypeScript tests
TS_CLIENT_PATH=~/.cache/llama-stack-client-typescript scripts/integration-tests.sh --stack-config server:ci-tests --suite base --setup ollama
```
TypeScript tests run immediately after Python tests pass, using the same replay fixtures. The mapping between Python suites/setups and TypeScript test files is defined in `tests/integration/client-typescript/suites.json`.
If `TS_CLIENT_PATH` is unset, TypeScript tests are skipped entirely.

166
tests/integration/agents/test_openai_responses.py
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@ -516,169 +516,3 @@ def test_response_with_instructions(openai_client, client_with_models, text_mode
# Verify instructions from previous response was not carried over to the next response
assert response_with_instructions2.instructions == instructions2
@pytest.mark.skip(reason="Tool calling is not reliable.")
def test_max_tool_calls_with_function_tools(openai_client, client_with_models, text_model_id):
"""Test handling of max_tool_calls with function tools in responses."""
if isinstance(client_with_models, LlamaStackAsLibraryClient):
pytest.skip("OpenAI responses are not supported when testing with library client yet.")
client = openai_client
max_tool_calls = 1
tools = [
{
"type": "function",
"name": "get_weather",
"description": "Get weather information for a specified location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city name (e.g., 'New York', 'London')",
},
},
},
},
{
"type": "function",
"name": "get_time",
"description": "Get current time for a specified location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city name (e.g., 'New York', 'London')",
},
},
},
},
]
# First create a response that triggers function tools
response = client.responses.create(
model=text_model_id,
input="Can you tell me the weather in Paris and the current time?",
tools=tools,
stream=False,
max_tool_calls=max_tool_calls,
)
# Verify we got two function calls and that the max_tool_calls do not affect function tools
assert len(response.output) == 2
assert response.output[0].type == "function_call"
assert response.output[0].name == "get_weather"
assert response.output[0].status == "completed"
assert response.output[1].type == "function_call"
assert response.output[1].name == "get_time"
assert response.output[0].status == "completed"
# Verify we have a valid max_tool_calls field
assert response.max_tool_calls == max_tool_calls
def test_max_tool_calls_invalid(openai_client, client_with_models, text_model_id):
"""Test handling of invalid max_tool_calls in responses."""
if isinstance(client_with_models, LlamaStackAsLibraryClient):
pytest.skip("OpenAI responses are not supported when testing with library client yet.")
client = openai_client
input = "Search for today's top technology news."
invalid_max_tool_calls = 0
tools = [
{"type": "web_search"},
]
# Create a response with an invalid max_tool_calls value i.e. 0
# Handle ValueError from LLS and BadRequestError from OpenAI client
with pytest.raises((ValueError, BadRequestError)) as excinfo:
client.responses.create(
model=text_model_id,
input=input,
tools=tools,
stream=False,
max_tool_calls=invalid_max_tool_calls,
)
error_message = str(excinfo.value)
assert f"Invalid max_tool_calls={invalid_max_tool_calls}; should be >= 1" in error_message, (
f"Expected error message about invalid max_tool_calls, got: {error_message}"
)
def test_max_tool_calls_with_builtin_tools(openai_client, client_with_models, text_model_id):
"""Test handling of max_tool_calls with built-in tools in responses."""
if isinstance(client_with_models, LlamaStackAsLibraryClient):
pytest.skip("OpenAI responses are not supported when testing with library client yet.")
client = openai_client
input = "Search for today's top technology and a positive news story. You MUST make exactly two separate web search calls."
max_tool_calls = [1, 5]
tools = [
{"type": "web_search"},
]
# First create a response that triggers web_search tools without max_tool_calls
response = client.responses.create(
model=text_model_id,
input=input,
tools=tools,
stream=False,
)
# Verify we got two web search calls followed by a message
assert len(response.output) == 3
assert response.output[0].type == "web_search_call"
assert response.output[0].status == "completed"
assert response.output[1].type == "web_search_call"
assert response.output[1].status == "completed"
assert response.output[2].type == "message"
assert response.output[2].status == "completed"
assert response.output[2].role == "assistant"
# Next create a response that triggers web_search tools with max_tool_calls set to 1
response_2 = client.responses.create(
model=text_model_id,
input=input,
tools=tools,
stream=False,
max_tool_calls=max_tool_calls[0],
)
# Verify we got one web search tool call followed by a message
assert len(response_2.output) == 2
assert response_2.output[0].type == "web_search_call"
assert response_2.output[0].status == "completed"
assert response_2.output[1].type == "message"
assert response_2.output[1].status == "completed"
assert response_2.output[1].role == "assistant"
# Verify we have a valid max_tool_calls field
assert response_2.max_tool_calls == max_tool_calls[0]
# Finally create a response that triggers web_search tools with max_tool_calls set to 5
response_3 = client.responses.create(
model=text_model_id,
input=input,
tools=tools,
stream=False,
max_tool_calls=max_tool_calls[1],
)
# Verify we got two web search calls followed by a message
assert len(response_3.output) == 3
assert response_3.output[0].type == "web_search_call"
assert response_3.output[0].status == "completed"
assert response_3.output[1].type == "web_search_call"
assert response_3.output[1].status == "completed"
assert response_3.output[2].type == "message"
assert response_3.output[2].status == "completed"
assert response_3.output[2].role == "assistant"
# Verify we have a valid max_tool_calls field
assert response_3.max_tool_calls == max_tool_calls[1]

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tests/integration/client-typescript/__tests__/inference.test.ts Normal file
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@ -0,0 +1,104 @@
// Copyright (c) Meta Platforms, Inc. and affiliates.
// All rights reserved.
//
// This source code is licensed under the terms described in the LICENSE file in
// the root directory of this source tree.
/**
* Integration tests for Inference API (Chat Completions).
* Ported from: llama-stack/tests/integration/inference/test_openai_completion.py
*
* IMPORTANT: Test cases must match EXACTLY with Python tests to use recorded API responses.
*/
import { createTestClient, requireTextModel } from '../setup';
describe('Inference API - Chat Completions', () => {
// Test cases matching llama-stack/tests/integration/test_cases/inference/chat_completion.json
const chatCompletionTestCases = [
{
id: 'non_streaming_01',
question: 'Which planet do humans live on?',
expected: 'earth',
testId:
'tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_non_streaming[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-inference:chat_completion:non_streaming_01]',
},
{
id: 'non_streaming_02',
question: 'Which planet has rings around it with a name starting with letter S?',
expected: 'saturn',
testId:
'tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_non_streaming[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-inference:chat_completion:non_streaming_02]',
},
];
const streamingTestCases = [
{
id: 'streaming_01',
question: "What's the name of the Sun in latin?",
expected: 'sol',
testId:
'tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_streaming[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-inference:chat_completion:streaming_01]',
},
{
id: 'streaming_02',
question: 'What is the name of the US captial?',
expected: 'washington',
testId:
'tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_streaming[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-inference:chat_completion:streaming_02]',
},
];
test.each(chatCompletionTestCases)(
'chat completion non-streaming: $id',
async ({ question, expected, testId }) => {
const client = createTestClient(testId);
const textModel = requireTextModel();
const response = await client.chat.completions.create({
model: textModel,
messages: [
{
role: 'user',
content: question,
},
],
stream: false,
});
// Non-streaming responses have choices with message property
const choice = response.choices[0];
expect(choice).toBeDefined();
if (!choice || !('message' in choice)) {
throw new Error('Expected non-streaming response with message');
}
const content = choice.message.content;
expect(content).toBeDefined();
const messageContent = typeof content === 'string' ? content.toLowerCase().trim() : '';
expect(messageContent.length).toBeGreaterThan(0);
expect(messageContent).toContain(expected.toLowerCase());
},
);
test.each(streamingTestCases)('chat completion streaming: $id', async ({ question, expected, testId }) => {
const client = createTestClient(testId);
const textModel = requireTextModel();
const stream = await client.chat.completions.create({
model: textModel,
messages: [{ role: 'user', content: question }],
stream: true,
});
const streamedContent: string[] = [];
for await (const chunk of stream) {
if (chunk.choices && chunk.choices.length > 0 && chunk.choices[0]?.delta?.content) {
streamedContent.push(chunk.choices[0].delta.content);
}
}
expect(streamedContent.length).toBeGreaterThan(0);
const fullContent = streamedContent.join('').toLowerCase().trim();
expect(fullContent).toContain(expected.toLowerCase());
});
});

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tests/integration/client-typescript/__tests__/responses.test.ts Normal file
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@ -0,0 +1,132 @@
// Copyright (c) Meta Platforms, Inc. and affiliates.
// All rights reserved.
//
// This source code is licensed under the terms described in the LICENSE file in
// the root directory of this source tree.
/**
* Integration tests for Responses API.
* Ported from: llama-stack/tests/integration/responses/test_basic_responses.py
*
* IMPORTANT: Test cases and IDs must match EXACTLY with Python tests to use recorded API responses.
*/
import { createTestClient, requireTextModel, getResponseOutputText } from '../setup';
describe('Responses API - Basic', () => {
// Test cases matching llama-stack/tests/integration/responses/fixtures/test_cases.py
const basicTestCases = [
{
id: 'earth',
input: 'Which planet do humans live on?',
expected: 'earth',
// Use client_with_models fixture to match non-streaming recordings
testId:
'tests/integration/responses/test_basic_responses.py::test_response_non_streaming_basic[client_with_models-txt=openai/gpt-4o-earth]',
},
{
id: 'saturn',
input: 'Which planet has rings around it with a name starting with letter S?',
expected: 'saturn',
testId:
'tests/integration/responses/test_basic_responses.py::test_response_non_streaming_basic[client_with_models-txt=openai/gpt-4o-saturn]',
},
];
test.each(basicTestCases)('non-streaming basic response: $id', async ({ input, expected, testId }) => {
// Create client with test_id for all requests
const client = createTestClient(testId);
const textModel = requireTextModel();
// Create a response
const response = await client.responses.create({
model: textModel,
input,
stream: false,
});
// Verify response has content
const outputText = getResponseOutputText(response).toLowerCase().trim();
expect(outputText.length).toBeGreaterThan(0);
expect(outputText).toContain(expected.toLowerCase());
// Verify usage is reported
expect(response.usage).toBeDefined();
expect(response.usage!.input_tokens).toBeGreaterThan(0);
expect(response.usage!.output_tokens).toBeGreaterThan(0);
expect(response.usage!.total_tokens).toBe(response.usage!.input_tokens + response.usage!.output_tokens);
// Verify stored response matches
const retrievedResponse = await client.responses.retrieve(response.id);
expect(getResponseOutputText(retrievedResponse)).toBe(getResponseOutputText(response));
// Test follow-up with previous_response_id
const nextResponse = await client.responses.create({
model: textModel,
input: 'Repeat your previous response in all caps.',
previous_response_id: response.id,
});
const nextOutputText = getResponseOutputText(nextResponse).trim();
expect(nextOutputText).toContain(expected.toUpperCase());
});
test.each(basicTestCases)('streaming basic response: $id', async ({ input, expected, testId }) => {
// Modify test_id for streaming variant
const streamingTestId = testId.replace(
'test_response_non_streaming_basic',
'test_response_streaming_basic',
);
const client = createTestClient(streamingTestId);
const textModel = requireTextModel();
// Create a streaming response
const stream = await client.responses.create({
model: textModel,
input,
stream: true,
});
const events: any[] = [];
let responseId = '';
for await (const chunk of stream) {
events.push(chunk);
if (chunk.type === 'response.created') {
// Verify response.created is the first event
expect(events.length).toBe(1);
expect(chunk.response.status).toBe('in_progress');
responseId = chunk.response.id;
} else if (chunk.type === 'response.completed') {
// Verify response.completed comes after response.created
expect(events.length).toBeGreaterThanOrEqual(2);
expect(chunk.response.status).toBe('completed');
expect(chunk.response.id).toBe(responseId);
// Verify content quality
const outputText = getResponseOutputText(chunk.response).toLowerCase().trim();
expect(outputText.length).toBeGreaterThan(0);
expect(outputText).toContain(expected.toLowerCase());
// Verify usage is reported
expect(chunk.response.usage).toBeDefined();
expect(chunk.response.usage!.input_tokens).toBeGreaterThan(0);
expect(chunk.response.usage!.output_tokens).toBeGreaterThan(0);
expect(chunk.response.usage!.total_tokens).toBe(
chunk.response.usage!.input_tokens + chunk.response.usage!.output_tokens,
);
}
}
// Verify we got both events
expect(events.length).toBeGreaterThanOrEqual(2);
const firstEvent = events[0];
const lastEvent = events[events.length - 1];
expect(firstEvent.type).toBe('response.created');
expect(lastEvent.type).toBe('response.completed');
// Verify stored response matches streamed response
const retrievedResponse = await client.responses.retrieve(responseId);
expect(getResponseOutputText(retrievedResponse)).toBe(getResponseOutputText(lastEvent.response));
});
});

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// Copyright (c) Meta Platforms, Inc. and affiliates.
// All rights reserved.
//
// This source code is licensed under the terms described in the LICENSE file in
// the root directory of this source tree.
/** @type {import('ts-jest').JestConfigWithTsJest} */
module.exports = {
preset: 'ts-jest/presets/default-esm',
testEnvironment: 'node',
extensionsToTreatAsEsm: ['.ts'],
moduleNameMapper: {
'^(\\.{1,2}/.*)\\.js$': '$1',
},
transform: {
'^.+\\.tsx?$': [
'ts-jest',
{
useESM: true,
tsconfig: {
module: 'ES2022',
moduleResolution: 'bundler',
},
},
],
},
testMatch: ['<rootDir>/__tests__/**/*.test.ts'],
setupFilesAfterEnv: ['<rootDir>/setup.ts'],
testTimeout: 60000, // 60 seconds (integration tests can be slow)
watchman: false, // Disable watchman to avoid permission issues
};

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tests/integration/client-typescript/package.json Normal file
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@ -0,0 +1,18 @@
{
"name": "llama-stack-typescript-integration-tests",
"version": "0.0.1",
"private": true,
"description": "TypeScript client integration tests for Llama Stack",
"scripts": {
"test": "node run-tests.js"
},
"devDependencies": {
"@swc/core": "^1.3.102",
"@swc/jest": "^0.2.29",
"@types/jest": "^29.4.0",
"@types/node": "^20.0.0",
"jest": "^29.4.0",
"ts-jest": "^29.1.0",
"typescript": "^5.0.0"
}
}

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tests/integration/client-typescript/run-tests.js Executable file
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#!/usr/bin/env node
// Copyright (c) Meta Platforms, Inc. and affiliates.
// All rights reserved.
//
// This source code is licensed under the terms described in the LICENSE file in
// the root directory of this source tree.
/**
* Test runner that finds and executes TypeScript tests based on suite/setup mapping.
* Called by integration-tests.sh via npm test.
*/
const fs = require('fs');
const path = require('path');
const { execSync } = require('child_process');
const suite = process.env.LLAMA_STACK_TEST_SUITE;
const setup = process.env.LLAMA_STACK_TEST_SETUP || '';
if (!suite) {
console.error('Error: LLAMA_STACK_TEST_SUITE environment variable is required');
process.exit(1);
}
// Read suites.json to find matching test files
const suitesPath = path.join(__dirname, 'suites.json');
if (!fs.existsSync(suitesPath)) {
console.log(`No TypeScript tests configured (${suitesPath} not found)`);
process.exit(0);
}
const suites = JSON.parse(fs.readFileSync(suitesPath, 'utf-8'));
// Find matching entry
let testFiles = [];
for (const entry of suites) {
if (entry.suite !== suite) {
continue;
}
const entrySetup = entry.setup || '';
if (entrySetup && entrySetup !== setup) {
continue;
}
testFiles = entry.files || [];
break;
}
if (testFiles.length === 0) {
console.log(`No TypeScript integration tests mapped for suite ${suite} (setup ${setup})`);
process.exit(0);
}
console.log(`Running TypeScript tests for suite ${suite} (setup ${setup}): ${testFiles.join(', ')}`);
// Run Jest with the mapped test files
try {
execSync(`npx jest --config jest.integration.config.js ${testFiles.join(' ')}`, {
stdio: 'inherit',
cwd: __dirname,
});
} catch (error) {
process.exit(error.status || 1);
}

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@ -0,0 +1,162 @@
// Copyright (c) Meta Platforms, Inc. and affiliates.
// All rights reserved.
//
// This source code is licensed under the terms described in the LICENSE file in
// the root directory of this source tree.
/**
* Global setup for integration tests.
* This file mimics pytest's fixture system by providing shared test configuration.
*/
import LlamaStackClient from 'llama-stack-client';
/**
* Load test configuration from the Python setup system.
* This reads setup definitions from tests/integration/suites.py via get_setup_env.py.
*/
function loadTestConfig() {
const baseURL = process.env['TEST_API_BASE_URL'];
const setupName = process.env['LLAMA_STACK_TEST_SETUP'];
const textModel = process.env['LLAMA_STACK_TEST_TEXT_MODEL'];
const embeddingModel = process.env['LLAMA_STACK_TEST_EMBEDDING_MODEL'];
if (!baseURL) {
throw new Error(
'TEST_API_BASE_URL is required for integration tests. ' +
'Run tests using: ./scripts/integration-test.sh',
);
}
return {
baseURL,
textModel,
embeddingModel,
setupName,
};
}
// Read configuration from environment variables (set by scripts/integration-test.sh)
export const TEST_CONFIG = loadTestConfig();
// Validate required configuration
beforeAll(() => {
console.log('\n=== Integration Test Configuration ===');
console.log(`Base URL: ${TEST_CONFIG.baseURL}`);
console.log(`Setup: ${TEST_CONFIG.setupName || 'NOT SET'}`);
console.log(
`Text Model: ${TEST_CONFIG.textModel || 'NOT SET - tests requiring text model will be skipped'}`,
);
console.log(
`Embedding Model: ${
TEST_CONFIG.embeddingModel || 'NOT SET - tests requiring embedding model will be skipped'
}`,
);
console.log('=====================================\n');
});
/**
* Create a client instance for integration tests.
* Mimics pytest's `llama_stack_client` fixture.
*
* @param testId - Test ID to send in X-LlamaStack-Provider-Data header for replay mode.
* Format: "tests/integration/responses/test_basic_responses.py::test_name[params]"
*/
export function createTestClient(testId?: string): LlamaStackClient {
const headers: Record<string, string> = {};
// In server mode with replay, send test ID for recording isolation
if (process.env['LLAMA_STACK_TEST_STACK_CONFIG_TYPE'] === 'server' && testId) {
headers['X-LlamaStack-Provider-Data'] = JSON.stringify({
__test_id: testId,
});
}
return new LlamaStackClient({
baseURL: TEST_CONFIG.baseURL,
timeout: 60000, // 60 seconds
defaultHeaders: headers,
});
}
/**
* Skip test if required model is not configured.
* Mimics pytest's `skip_if_no_model` autouse fixture.
*/
export function skipIfNoModel(modelType: 'text' | 'embedding'): typeof test {
const model = modelType === 'text' ? TEST_CONFIG.textModel : TEST_CONFIG.embeddingModel;
if (!model) {
const envVar = modelType === 'text' ? 'LLAMA_STACK_TEST_TEXT_MODEL' : 'LLAMA_STACK_TEST_EMBEDDING_MODEL';
const message = `Skipping: ${modelType} model not configured (set ${envVar})`;
return test.skip.bind(test) as typeof test;
}
return test;
}
/**
* Get the configured text model, throwing if not set.
* Use this in tests that absolutely require a text model.
*/
export function requireTextModel(): string {
if (!TEST_CONFIG.textModel) {
throw new Error(
'LLAMA_STACK_TEST_TEXT_MODEL environment variable is required. ' +
'Run tests using: ./scripts/integration-test.sh',
);
}
return TEST_CONFIG.textModel;
}
/**
* Get the configured embedding model, throwing if not set.
* Use this in tests that absolutely require an embedding model.
*/
export function requireEmbeddingModel(): string {
if (!TEST_CONFIG.embeddingModel) {
throw new Error(
'LLAMA_STACK_TEST_EMBEDDING_MODEL environment variable is required. ' +
'Run tests using: ./scripts/integration-test.sh',
);
}
return TEST_CONFIG.embeddingModel;
}
/**
* Extracts aggregated text output from a ResponseObject.
* This concatenates all text content from the response's output array.
*
* Copied from llama-stack-client's response-helpers until it's available in published version.
*/
export function getResponseOutputText(response: any): string {
const pieces: string[] = [];
for (const output of response.output ?? []) {
if (!output || output.type !== 'message') {
continue;
}
const content = output.content;
if (typeof content === 'string') {
pieces.push(content);
continue;
}
if (!Array.isArray(content)) {
continue;
}
for (const item of content) {
if (typeof item === 'string') {
pieces.push(item);
continue;
}
if (item && item.type === 'output_text' && 'text' in item && typeof item.text === 'string') {
pieces.push(item.text);
}
}
}
return pieces.join('');
}

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[
{
"suite": "responses",
"setup": "gpt",
"files": ["__tests__/responses.test.ts"]
},
{
"suite": "base",
"setup": "ollama",
"files": ["__tests__/inference.test.ts"]
}
]

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{
"compilerOptions": {
"target": "ES2022",
"module": "ES2022",
"lib": ["ES2022"],
"moduleResolution": "bundler",
"esModuleInterop": true,
"allowSyntheticDefaultImports": true,
"strict": true,
"skipLibCheck": true,
"resolveJsonModule": true,
"types": ["jest", "node"]
},
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tests/integration/responses/test_tool_responses.py
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@ -600,3 +600,155 @@ def test_response_streaming_multi_turn_tool_execution(responses_client, text_mod
assert expected_output.lower() in final_response.output_text.lower(), (
f"Expected '{expected_output}' to appear in response: {final_response.output_text}"
)
def test_max_tool_calls_with_function_tools(responses_client, text_model_id):
"""Test handling of max_tool_calls with function tools in responses."""
max_tool_calls = 1
tools = [
{
"type": "function",
"name": "get_weather",
"description": "Get weather information for a specified location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city name (e.g., 'New York', 'London')",
},
},
},
},
{
"type": "function",
"name": "get_time",
"description": "Get current time for a specified location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city name (e.g., 'New York', 'London')",
},
},
},
},
]
response = responses_client.responses.create(
model=text_model_id,
input="Can you tell me the weather in Paris and the current time?",
tools=tools,
stream=False,
max_tool_calls=max_tool_calls,
)
# Verify we got two function calls and that the max_tool_calls does not affect function tools
assert len(response.output) == 2
assert response.output[0].type == "function_call"
assert response.output[0].name == "get_weather"
assert response.output[0].status == "completed"
assert response.output[1].type == "function_call"
assert response.output[1].name == "get_time"
assert response.output[1].status == "completed"
# Verify we have a valid max_tool_calls field
assert response.max_tool_calls == max_tool_calls
def test_max_tool_calls_invalid(responses_client, text_model_id):
"""Test handling of invalid max_tool_calls in responses."""
input = "Search for today's top technology news."
invalid_max_tool_calls = 0
tools = [
{"type": "web_search"},
]
# Create a response with an invalid max_tool_calls value i.e. 0
# Handle ValueError from LLS and BadRequestError from OpenAI client
with pytest.raises((ValueError, llama_stack_client.BadRequestError, openai.BadRequestError)) as excinfo:
responses_client.responses.create(
model=text_model_id,
input=input,
tools=tools,
stream=False,
max_tool_calls=invalid_max_tool_calls,
)
error_message = str(excinfo.value)
assert f"Invalid max_tool_calls={invalid_max_tool_calls}; should be >= 1" in error_message, (
f"Expected error message about invalid max_tool_calls, got: {error_message}"
)
def test_max_tool_calls_with_mcp_tools(responses_client, text_model_id):
"""Test handling of max_tool_calls with mcp tools in responses."""
with make_mcp_server(tools=dependency_tools()) as mcp_server_info:
input = "Get the experiment ID for 'boiling_point' and get the user ID for 'charlie'"
max_tool_calls = [1, 5]
tools = [
{"type": "mcp", "server_label": "localmcp", "server_url": mcp_server_info["server_url"]},
]
# First create a response that triggers mcp tools without max_tool_calls
response = responses_client.responses.create(
model=text_model_id,
input=input,
tools=tools,
stream=False,
)
# Verify we got two mcp tool calls followed by a message
assert len(response.output) == 4
mcp_list_tools = [output for output in response.output if output.type == "mcp_list_tools"]
mcp_calls = [output for output in response.output if output.type == "mcp_call"]
message_outputs = [output for output in response.output if output.type == "message"]
assert len(mcp_list_tools) == 1
assert len(mcp_calls) == 2, f"Expected two mcp calls, got {len(mcp_calls)}"
assert len(message_outputs) == 1, f"Expected one message output, got {len(message_outputs)}"
# Next create a response that triggers mcp tools with max_tool_calls set to 1
response_2 = responses_client.responses.create(
model=text_model_id,
input=input,
tools=tools,
stream=False,
max_tool_calls=max_tool_calls[0],
)
# Verify we got one mcp tool call followed by a message
assert len(response_2.output) == 3
mcp_list_tools = [output for output in response_2.output if output.type == "mcp_list_tools"]
mcp_calls = [output for output in response_2.output if output.type == "mcp_call"]
message_outputs = [output for output in response_2.output if output.type == "message"]
assert len(mcp_list_tools) == 1
assert len(mcp_calls) == 1, f"Expected one mcp call, got {len(mcp_calls)}"
assert len(message_outputs) == 1, f"Expected one message output, got {len(message_outputs)}"
# Verify we have a valid max_tool_calls field
assert response_2.max_tool_calls == max_tool_calls[0]
# Finally create a response that triggers mcp tools with max_tool_calls set to 5
response_3 = responses_client.responses.create(
model=text_model_id,
input=input,
tools=tools,
stream=False,
max_tool_calls=max_tool_calls[1],
)
# Verify we got two mcp tool calls followed by a message
assert len(response_3.output) == 4
mcp_list_tools = [output for output in response_3.output if output.type == "mcp_list_tools"]
mcp_calls = [output for output in response_3.output if output.type == "mcp_call"]
message_outputs = [output for output in response_3.output if output.type == "message"]
assert len(mcp_list_tools) == 1
assert len(mcp_calls) == 2, f"Expected two mcp calls, got {len(mcp_calls)}"
assert len(message_outputs) == 1, f"Expected one message output, got {len(message_outputs)}"
# Verify we have a valid max_tool_calls field
assert response_3.max_tool_calls == max_tool_calls[1]

6
tests/integration/suites.py
View file

@ -50,7 +50,7 @@ SETUP_DEFINITIONS: dict[str, Setup] = {
name="ollama",
description="Local Ollama provider with text + safety models",
env={
"OLLAMA_URL": "http://0.0.0.0:11434",
"OLLAMA_URL": "http://0.0.0.0:11434/v1",
"SAFETY_MODEL": "ollama/llama-guard3:1b",
},
defaults={
@ -64,7 +64,7 @@ SETUP_DEFINITIONS: dict[str, Setup] = {
name="ollama",
description="Local Ollama provider with a vision model",
env={
"OLLAMA_URL": "http://0.0.0.0:11434",
"OLLAMA_URL": "http://0.0.0.0:11434/v1",
},
defaults={
"vision_model": "ollama/llama3.2-vision:11b",
@ -75,7 +75,7 @@ SETUP_DEFINITIONS: dict[str, Setup] = {
name="ollama-postgres",
description="Server-mode tests with Postgres-backed persistence",
env={
"OLLAMA_URL": "http://0.0.0.0:11434",
"OLLAMA_URL": "http://0.0.0.0:11434/v1",
"SAFETY_MODEL": "ollama/llama-guard3:1b",
"POSTGRES_HOST": "127.0.0.1",
"POSTGRES_PORT": "5432",