## With Ollie

The fastest way to turn a production failure into a test case. Open Ollie from any trace view and describe what went wrong:

_“Add this trace to my customer-support-qa suite with the assertion: the response must cite a specific step from the provided context”_

Ollie creates the test item directly — no copy-pasting required. You can also ask Ollie to run the suite after making changes:

_“Run the customer-support-qa suite against the updated prompt”_

See [Debugging agents](/content/docs/opik/tracing/debug-agents/index.html) for the full workflow.

## With the UI

In the Opik dashboard, navigate to the Test Suites section to create and manage suites visually. You can add test items, define assertions, configure execution policies, and review results — all without writing code.

## With the SDK

### Create a suite

Define the quality bars you care about as suite-level assertions:

```python
import opik

opik_client = opik.Opik()

suite = opik_client.get_or_create_test_suite(
    name="customer-support-qa",
    project_name="test-suites-demo",
    global_assertions=[\
        "The response is grounded in the provided documentation context",\
        "The response directly addresses the user's question",\
        "The response is concise (3 sentences or fewer)",\
    ],
    global_execution_policy={"runs_per_item": 2, "pass_threshold": 2},
)
```

### Add test items

Add individual items or batches. Items can include item-level assertions that are checked in addition to the suite-level assertions:

```python
suite.insert([\
    {\
        "data": {\
            "question": "How do I create a new project?",\
            "context": "To create a new project, go to the Dashboard and click 'New Project'.",\
        },\
    },\
    {\
        "data": {\
            "question": "Can I use this with Kubernetes?",\
            "context": "We support Docker containers and serverless functions.",\
        },\
        "assertions": [\
            "The response does NOT claim Kubernetes is supported",\
            "The response acknowledges that the information is not available",\
        ],\
        "execution_policy": {"runs_per_item": 3, "pass_threshold": 2},\
    },\
])
```

### Define the task and run

The task function receives each item’s `data` and must return an object with `input` and `output` keys:

```python
from openai import OpenAI
from opik.integrations.openai import track_openai

openai_client = track_openai(OpenAI())

def make_task(system_prompt):
    def task(item):
        response = openai_client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[\
                {"role": "system", "content": system_prompt},\
                {"role": "user", "content": f"Question: {item['question']}\n\nContext:\n{item['context']}"},\
            ],
        )
        return {"input": item, "output": response.choices[0].message.content}
    return task

PROMPT_V1 = "You are a helpful assistant. Be as detailed as possible."
PROMPT_V2 = "You are a concise assistant. Answer based ONLY on the provided context."

result_v1 = opik.run_tests(test_suite=suite, task=make_task(PROMPT_V1))
result_v2 = opik.run_tests(test_suite=suite, task=make_task(PROMPT_V2))

print(f"v1 pass rate: {result_v1.pass_rate:.0%}")
print(f"v2 pass rate: {result_v2.pass_rate:.0%}")
```

Each run creates a separate experiment in Opik, making it easy to compare results in the dashboard.

The `input` should contain only the data your agent actually received when generating its response.
The LLM judge uses `input` and `output` to evaluate assertions — if you accidentally include fields like `expected_answer` in `input`, the judge may use them to pass assertions that should fail.

### Update assertions and execution policy

```python
suite.update_test_settings(
    global_assertions=[\
        "The response is grounded in the provided context",\
        "The response is concise",\
    ],
    global_execution_policy={"runs_per_item": 5, "pass_threshold": 3},
)
```

### Inspect suite contents

```python
items = suite.get_items()
assertions = suite.get_global_assertions()
policy = suite.get_global_execution_policy()

print(f"Items: {len(items)}")
print(f"Assertions: {assertions}")
print(f"Policy: {policy}")
```

### Delete test items

```python
items = suite.get_items()
suite.delete([items[0]["id"]])
```

## Execution policies

Execution policies control how many times each item is run and how many must pass. This is useful for handling non-deterministic LLM outputs.

```python
suite = opik_client.get_or_create_test_suite(
    name="flaky-output-tests",
    global_assertions=["Response follows the expected format"],
    global_execution_policy={"runs_per_item": 3, "pass_threshold": 2},
)
```

**Pass/fail logic:**

- A **run** passes if all its assertions pass
- An **item** passes if `runs_passed >= pass_threshold`
- The **pass rate** is the ratio of passed items to total items. A pass rate of `1.0` means every item passed; `0.0` means none did

You can also override the policy for individual items:

```python
suite.insert([{\
    "data": {"question": "Is my account compromised?", "context": "..."},\
    "assertions": ["Response treats the concern with urgency"],\
    "execution_policy": {"runs_per_item": 5, "pass_threshold": 4},\
}])
```