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Overview

The list method allows you to retrieve experiments with support for filtering, sorting, and pagination. This is useful for managing and reviewing your experiment portfolio.

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Method Signature

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Synchronous

def list(
    limit: Optional[int] = 50,
    offset: Optional[int] = 0,
    status: Optional[str] = None,
    name: Optional[str] = None,
    created_by: Optional[str] = None,
    sort_by: Optional[str] = "created_at",
    sort_order: Optional[str] = "desc"
) -> Dict[str, Any]

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Asynchronous

async def list(
    limit: Optional[int] = 50,
    offset: Optional[int] = 0,
    status: Optional[str] = None,
    name: Optional[str] = None,
    created_by: Optional[str] = None,
    sort_by: Optional[str] = "created_at",
    sort_order: Optional[str] = "desc"
) -> Dict[str, Any]

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Parameters

ParameterTypeRequiredDescription
limitintNoMaximum number of experiments to return (default: 50)
offsetintNoNumber of experiments to skip (default: 0)
statusstrNoFilter by experiment status (draft, running, completed, paused)
namestrNoFilter by experiment name (partial match)
created_bystrNoFilter by creator user ID
sort_bystrNoField to sort by (created_at, name, status)
sort_orderstrNoSort order (asc, desc)

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Returns

Returns a dictionary containing:
  • experiments: List of experiment objects
  • total: Total number of experiments matching filters
  • limit: Applied limit
  • offset: Applied offset
  • has_more: Whether more experiments are available

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Examples

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Basic Usage

from keywordsai import KeywordsAI

client = KeywordsAI(api_key="your-api-key")

# Get all experiments
response = client.experiments.list()

print(f"Total experiments: {response['total']}")
print(f"Retrieved: {len(response['experiments'])}")

for experiment in response['experiments']:
    print(f"- {experiment['name']} ({experiment['status']})")

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Pagination

# Get experiments with pagination
response = client.experiments.list(limit=10, offset=0)

print(f"Page 1: {len(response['experiments'])} experiments")
print(f"Has more: {response['has_more']}")

# Get next page
if response['has_more']:
    next_page = client.experiments.list(limit=10, offset=10)
    print(f"Page 2: {len(next_page['experiments'])} experiments")

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Filter by Status

# Get only running experiments
running_experiments = client.experiments.list(status="running")
print(f"Running experiments: {len(running_experiments['experiments'])}")

# Get completed experiments
completed_experiments = client.experiments.list(status="completed")
print(f"Completed experiments: {len(completed_experiments['experiments'])}")

# Get draft experiments
draft_experiments = client.experiments.list(status="draft")
print(f"Draft experiments: {len(draft_experiments['experiments'])}")

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Filter by Name

# Search experiments by name
support_experiments = client.experiments.list(name="support")
print(f"Support-related experiments: {len(support_experiments['experiments'])}")

for experiment in support_experiments['experiments']:
    print(f"- {experiment['name']}")
    print(f"  Status: {experiment['status']}")
    print(f"  Created: {experiment['created_at']}")

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Sorting

# Sort by name alphabetically
response = client.experiments.list(
    sort_by="name",
    sort_order="asc"
)

print("Experiments sorted by name:")
for experiment in response['experiments']:
    print(f"- {experiment['name']}")

# Sort by creation date (newest first)
response = client.experiments.list(
    sort_by="created_at",
    sort_order="desc"
)

print("\nNewest experiments:")
for experiment in response['experiments'][:5]:
    print(f"- {experiment['name']} ({experiment['created_at']})")

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Combined Filtering

# Get recent running experiments
response = client.experiments.list(
    status="running",
    sort_by="created_at",
    sort_order="desc",
    limit=5
)

print("Recent running experiments:")
for experiment in response['experiments']:
    print(f"- {experiment['name']}")
    print(f"  Started: {experiment['started_at']}")
    print(f"  Variants: {len(experiment['variants'])}")

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Filter by Creator

# Get experiments created by specific user
user_experiments = client.experiments.list(created_by="user_123")

print(f"Experiments by user_123: {len(user_experiments['experiments'])}")
for experiment in user_experiments['experiments']:
    print(f"- {experiment['name']} ({experiment['status']})")

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Asynchronous Usage

import asyncio
from keywordsai import AsyncKeywordsAI

async def list_experiments_example():
    client = AsyncKeywordsAI(api_key="your-api-key")
    
    # Get experiments asynchronously
    response = await client.experiments.list(limit=20)
    
    print(f"Retrieved {len(response['experiments'])} experiments")
    
    for experiment in response['experiments']:
        print(f"- {experiment['name']} ({experiment['status']})")
    
    return response

asyncio.run(list_experiments_example())

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Pagination Loop

# Get all experiments using pagination
all_experiments = []
offset = 0
limit = 50

while True:
    response = client.experiments.list(limit=limit, offset=offset)
    experiments = response['experiments']
    
    if not experiments:
        break
    
    all_experiments.extend(experiments)
    print(f"Retrieved {len(experiments)} experiments (total: {len(all_experiments)})")
    
    if not response['has_more']:
        break
    
    offset += limit

print(f"Total experiments retrieved: {len(all_experiments)}")

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Detailed Analysis

# Analyze experiment portfolio
response = client.experiments.list(limit=100)
experiments = response['experiments']

# Group by status
status_counts = {}
for experiment in experiments:
    status = experiment['status']
    status_counts[status] = status_counts.get(status, 0) + 1

print("Experiment Status Summary:")
for status, count in status_counts.items():
    print(f"- {status}: {count}")

# Find long-running experiments
from datetime import datetime, timedelta

long_running = []
for experiment in experiments:
    if experiment['status'] == 'running':
        started_at = datetime.fromisoformat(experiment['started_at'])
        if datetime.now() - started_at > timedelta(days=30):
            long_running.append(experiment)

print(f"\nLong-running experiments (>30 days): {len(long_running)}")
for experiment in long_running:
    print(f"- {experiment['name']} (started: {experiment['started_at']})")

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Export Experiment List

import csv
from datetime import datetime

# Export experiments to CSV
response = client.experiments.list(limit=1000)
experiments = response['experiments']

filename = f"experiments_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv"

with open(filename, 'w', newline='', encoding='utf-8') as csvfile:
    fieldnames = ['id', 'name', 'status', 'variants_count', 'created_at', 'created_by']
    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
    
    writer.writeheader()
    for experiment in experiments:
        writer.writerow({
            'id': experiment['id'],
            'name': experiment['name'],
            'status': experiment['status'],
            'variants_count': len(experiment.get('variants', [])),
            'created_at': experiment['created_at'],
            'created_by': experiment.get('created_by', '')
        })

print(f"Exported {len(experiments)} experiments to {filename}")

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Asynchronous Batch Processing

import asyncio
from keywordsai import AsyncKeywordsAI

async def process_experiments_batch():
    client = AsyncKeywordsAI(api_key="your-api-key")
    
    # Get experiments in batches
    batch_size = 25
    offset = 0
    all_experiments = []
    
    while True:
        response = await client.experiments.list(
            limit=batch_size,
            offset=offset
        )
        
        experiments = response['experiments']
        if not experiments:
            break
        
        all_experiments.extend(experiments)
        print(f"Processed batch: {len(experiments)} experiments")
        
        if not response['has_more']:
            break
        
        offset += batch_size
        
        # Small delay to avoid rate limiting
        await asyncio.sleep(0.1)
    
    print(f"Total experiments processed: {len(all_experiments)}")
    return all_experiments

asyncio.run(process_experiments_batch())

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Error Handling

try:
    response = client.experiments.list(
        status="running",
        limit=50
    )
    
    experiments = response['experiments']
    print(f"Retrieved {len(experiments)} experiments")
    
except Exception as e:
    if "invalid status" in str(e).lower():
        print("Invalid status filter provided")
    elif "limit" in str(e).lower():
        print("Invalid limit parameter")
    else:
        print(f"Error listing experiments: {e}")

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Response Structure

The response contains: 
{
    "experiments": [
        {
            "id": "exp_123",
            "name": "Greeting Style Test",
            "description": "Testing formal vs casual approaches",
            "status": "running",
            "variants": [...],
            "metrics": [...],
            "traffic_split": {...},
            "created_at": "2024-01-15T10:30:00Z",
            "created_by": "user_123",
            "started_at": "2024-01-15T11:00:00Z",
            "metadata": {...}
        }
    ],
    "total": 25,
    "limit": 50,
    "offset": 0,
    "has_more": false
}

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Best Practices

  • Use appropriate page sizes (10-50) for UI display
  • Implement pagination for large experiment lists
  • Filter by status to focus on relevant experiments
  • Sort by creation date to see recent activity
  • Cache results when appropriate to reduce API calls
  • Use async methods for better performance in concurrent applications

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Common Use Cases

  • Dashboard displaying experiment overview
  • Finding experiments by name or creator
  • Monitoring running experiments
  • Analyzing experiment portfolio
  • Exporting experiment data
  • Cleaning up old or completed experiments