testzeus-sdk 0.0.17

Python SDK for TestZeus testing platform

TestZeus SDK

Python SDK for the TestZeus testing platform.

Installation

Install the package using pip:

pip install testzeus-sdk

Or use Poetry:

poetry add testzeus-sdk

Components

Python SDK

The TestZeus SDK provides programmatic access to the TestZeus testing platform through a Python interface.

Command Line Interface (CLI)

The TestZeus CLI provides a command-line interface for interacting with the TestZeus platform. For detailed CLI documentation, see the CLI README.

# Install CLI
pip install testzeus-cli

# Login to TestZeus
testzeus login

# List tests
testzeus tests list

Getting Started with SDK

import asyncio
from testzeus_sdk import TestZeusClient

async def main():
    # Create a client with email/password
    client = TestZeusClient(email="your-email", password="your-password")
    
    # Use as a context manager
    async with client:
        # Your code here
        pass

# Run the example
asyncio.run(main())

Authentication

The SDK supports three authentication methods:

Email/Password

client = TestZeusClient(email="your-email", password="your-password")

Environment Variables

export TESTZEUS_EMAIL="your-email"
export TESTZEUS_PASSWORD="your-password"

Then create the client without parameters:

client = TestZeusClient()

Core Functionality

Tests Management

List Tests

# Get list of tests with filters and sorting
tests = await client.tests.get_list(
    expand='tags',  # Expand related entities
    sort='id',      # Sort by field
    filters={       # Filter results
        'id': 'y9b88f17vabx476'
    }
)
print(tests)
print(tests['items'][0].data)  # Access test data

Advanced Filtering

The SDK supports powerful filtering capabilities using PocketBase filter syntax. All managers that extend BaseManager support these filtering options:

Simple Filters (Backward Compatible)

# Basic field matching
filters = {
    "name": "Test Name",
    "status": "active",
    "priority": 5
}

# List values (OR condition)
filters = {
    "status": ["active", "pending", "draft"]  # status = "active" OR status = "pending" OR status = "draft"
}

Advanced Operators

# Use complex operators with value objects
filters = {
    "created": {"operator": ">", "value": "2023-01-01"},           # created > "2023-01-01"
    "priority": {"operator": ">=", "value": 3},                   # priority >= 3
    "name": {"operator": "~", "value": "test"},                   # name LIKE "%test%"
    "description": {"operator": "!~", "value": "old"}            # description NOT LIKE "%old%"
}

Supported Operators

Comparison Operators:

• = - Equal (default when no operator specified)
• != - Not equal
• > - Greater than
• >= - Greater than or equal
• < - Less than
• <= - Less than or equal

String Operators:

• ~ - Like/Contains (auto-wraps with % for wildcard matching)
• !~ - Not Like/Contains

Array Operators (for multi-value fields):

• ?= - Any/At least one equal
• ?!= - Any/At least one not equal
• ?> - Any/At least one greater than
• ?>= - Any/At least one greater than or equal
• ?< - Any/At least one less than
• ?<= - Any/At least one less than or equal
• ?~ - Any/At least one like/contains
• ?!~ - Any/At least one not like/contains

Array Operators with Lists

# Check if any tag matches the values
filters = {
    "tags": {"operator": "?=", "value": ["urgent", "important"]}
}
# Result: (tags ?= "urgent" || tags ?= "important")

Logical Grouping

AND Conditions:

filters = {
    "$and": [
        {"status": "active"},
        {"priority": {"operator": ">", "value": 3}},
        {"created": {"operator": ">", "value": "2023-01-01"}}
    ]
}
# Result: (status = "active" && priority > 3 && created > "2023-01-01")

OR Conditions:

filters = {
    "$or": [
        {"status": "urgent"},
        {"priority": {"operator": ">=", "value": 8}}
    ]
}
# Result: (status = "urgent" || priority >= 8)

Complex Combinations:

filters = {
    "tenant": "abc123",  # Always filter by current tenant
    "$or": [
        {"status": "active"},
        {
            "$and": [
                {"status": "draft"},
                {"modified": {"operator": ">", "value": "2023-01-01"}},
                {"tags": {"operator": "?=", "value": ["review", "pending"]}}
            ]
        }
    ]
}
# Result: tenant = "abc123" && (status = "active" || (status = "draft" && modified > "2023-01-01" && (tags ?= "review" || tags ?= "pending")))

Practical Examples

Filter tests by date range:

tests = await client.tests.get_list(
    filters={
        "$and": [
            {"created": {"operator": ">=", "value": "2023-01-01"}},
            {"created": {"operator": "<", "value": "2023-12-31"}}
        ]
    }
)

Filter by multiple statuses and search in name:

tests = await client.tests.get_list(
    filters={
        "status": ["active", "pending"],
        "name": {"operator": "~", "value": "integration"}
    }
)

Filter test runs by status and date:

test_runs = await client.test_runs.get_list(
    filters={
        "$and": [
            {"status": {"operator": "!=", "value": "draft"}},
            {"start_time": {"operator": ">", "value": "2023-01-01T00:00:00Z"}}
        ]
    },
    sort="-created"  # Sort by created date descending
)

Filter environments by tags:

environments = await client.environments.get_list(
    filters={
        "tags": {"operator": "?=", "value": ["production", "staging"]}
    }
)

Get Single Test

# Get test by ID
test = await client.tests.get_one('311137kown88nd6')
print(test.data)

Create Test

# Create a new test
new_test = await client.tests.create(
    name="New Test",
    test_feature="Example feature",
    status="draft",  # Optional: 'draft', 'ready', 'deleted'
    test_data=["data_id1", "data_id2"],  # Optional: List of test data IDs
    tags=["tag1", "tag2"],  # Optional: List of tag IDs
    environment="env_id"  # Optional: Environment ID
)

Update Test

# Update test properties
updated_test = await client.tests.update(
    'test_id',
    name='Updated Test Name'
)
print(updated_test.data)

Delete Test

# Delete a test
await client.tests.delete('test_id')

Test Runs Management

Track Test Run Status

# Get test run by ID
test_run = await client.test_runs.get_one('test_run_id')

# Check test run status
if test_run.is_running():
    print("Test is currently running")
elif test_run.is_completed():
    print("Test has completed successfully")
elif test_run.is_failed():
    print("Test has failed")
elif test_run.is_crashed():
    print("Test has crashed")
elif test_run.is_cancelled():
    print("Test was cancelled")
elif test_run.is_pending():
    print("Test is pending")

# Get test run duration
duration = test_run.get_duration()
if duration:
    print(f"Test run took {duration} seconds")

Get Detailed Test Run Information

# Get expanded test run details including all outputs, steps, and attachments
expanded_test_run = await client.test_runs.get_expanded('test_run_id')

# Access different components of the test run
test_run_data = expanded_test_run['test_run']
test_run_dashs = expanded_test_run['test_run_dashs']
test_run_dash_outputs = expanded_test_run['test_run_dash_outputs']
test_run_dash_output_steps = expanded_test_run['test_run_dash_output_steps']
test_run_dash_outputs_attachments = expanded_test_run['test_run_dash_outputs_attachments']

# Print test run details
print(f"Test Run Name: {test_run_data['name']}")
print(f"Status: {test_run_data['status']}")
print(f"Start Time: {test_run_data['start_time']}")
print(f"End Time: {test_run_data['end_time']}")

# Print test run steps
for step in test_run_dash_output_steps:
    print(f"Step: {step['name']}")
    print(f"Status: {step['status']}")
    print(f"Is Passed: {step['is_passed']}")
    print(f"Assert Summary: {step['assert_summary']}")

Cancel Test Run

# Cancel a running test
try:
    cancelled_test = await client.test_runs.cancel('test_run_id')
    print(f"Test run cancelled: {cancelled_test.status}")
except ValueError as e:
    print(f"Cannot cancel test: {str(e)}")

# Cancel test run using user email instead of user ID
try:
    cancelled_test = await client.test_runs.cancel_with_email(
        'test_run_id',
        '[e-mail]'
    )
    print(f"Test run cancelled by {cancelled_test.modified_by}")
except ValueError as e:
    print(f"Cannot cancel test: {str(e)}")

Download Test Run Attachments

# Download all attachments for a test run
download_attachment = await client.test_runs.download_all_attachments(
    'test_run_id',
    'local/path/to/save'
)

# Download specific attachment
attachment = await client.test_run_dash_outputs_attachments.download_attachment(
    'attachment_id',
    'local/path/to/save'
)

Test Data Management

# Create test data
test_data = await client.test_data.create({
    "name": "Test Data",
    "type": "test",  # Optional: defaults to "test"
    "status": "draft"  # Optional: defaults to "draft"
})

Environment Management

# List environments
environments = await client.environments.get_list()

# Get environment by ID
environment = await client.environments.get_one('env_id')

Connected Environment Management

Connected Environments allow you to define connections between environments and external systems/integrations.

Connected Environment Status Methods

# Get a connected environment
connected_env = await client.connected_environments.get_one("connected_env_id")

# Check if it has a connection
if connected_env.has_connection():
    print(f"Connected to: {connected_env.connection}")


### Tags Management

```python
# List tags
tags = await client.tags.get_list()

# Create tag
tag = await client.tags.create({
    "name": "New Tag"
})

Schedule Management

Create Test Report Schedule

# Create a schedule with cron expression
schedule = await client.test_report_schedules.create_test_report_schedule(
    name="Daily Test Report",
    is_active=True,
    cron_expression="0 9 * * *",  # Run at 9 AM daily
    filter_name_pattern="test-*",  # Filter test runs by name pattern
    filter_tags=["production", "critical"],  # Filter by tags
    notification_channels=["email-channel", "slack-channel"]  # Send to these channels
)

Create Schedule with Time Intervals

from testzeus_sdk.managers.test_report_schedule_manager import FilterTimeIntervals

# Create a schedule with specific time intervals
time_intervals: FilterTimeIntervals = {
    "start_time": "2025-01-01 00:00:00",
    "end_time": "2025-01-01 23:59:59"
}

schedule = await client.test_report_schedules.create_test_report_schedule(
    name="Hourly Report",
    is_active=True,
    filter_time_intervals=time_intervals,
    filter_env=["staging", "production"],  # Filter by environments
    filter_test_data=["dataset1", "dataset2"]  # Filter by test data
)

Update Schedule

# Update an existing schedule
updated_schedule = await client.test_report_schedules.update_test_report_schedule(
    id_or_name="schedule_id_or_name",
    name="Updated Schedule Name",
    is_active=False,
    cron_expression="0 12 * * *"  # Change to run at noon
)

Schedule Validation Rules

The schedule manager enforces several validation rules:

• Name is required for all schedules
• At least one filter must be provided (name pattern, time intervals, cron, tags, environments, or test data)
• Mutually exclusive filters:
  • Cannot use both filter_time_intervals and cron_expression
  • Cannot use both filter_tags and filter_tag_pattern
  • Cannot use both filter_env and filter_env_pattern
  • Cannot use both filter_test_data and filter_test_data_pattern

Notification Channel Management

Create Email Notification Channel

# Create a basic email notification channel
channel = await client.notification_channels.create_notification_channel(
    name="Email Alerts",
    display_name="Email Alert Channel",
    emails=["[e-mail]", "[e-mail]"],
    is_active=True,
    is_default=False
)

Create Webhook Notification Channel

# Create a notification channel with webhooks
channel = await client.notification_channels.create_notification_channel(
    name="Webhook Alerts",
    emails=["[e-mail]"],
    webhooks=["https://webhook.example.com/alerts", "https://backup-webhook.example.com"],
    is_active=True
)

Update Notification Channel

# Update notification channel settings
updated_channel = await client.notification_channels.update_notification_channel(
    id_or_name="channel_id_or_name",
    name="Updated Channel Name",
    emails=["[e-mail]"],
    is_active=False
)

Remove Channel Configurations

# Remove specific configuration types from a channel
await client.notification_channels.remove_config(
    id_or_name="channel_id",
    config_type="webhook"  # Options: "webhook", "slack", "jira"
)

Channel Configuration Methods

# Get a notification channel
channel = await client.notification_channels.get_one("channel_id")

# Check what configurations are enabled
if channel.has_email_config():
    print("Email notifications enabled")
    
if channel.has_webhook_config():
    print("Webhook notifications enabled")

# Get list of enabled channel types
enabled_channels = channel.get_enabled_channels()
print(f"Enabled channels: {enabled_channels}")

Test Run Groups Management

Test Run Groups allow you to execute multiple tests together as a coordinated batch with shared configuration and monitoring.

Create and Execute Test Run Group

# Create and execute a test run group with specific test IDs
test_run_group = await client.test_run_groups.create_and_execute(
    name="Regression Test Suite",
    test_ids=["test_id_1", "test_id_2", "test_id_3"],
    execution_mode="strict",  # or "lenient"
    environment="production",
    notification_channels=["email-channel", "slack-channel"]
)
print(f"Test run group created: {test_run_group.id}")

Create Test Run Group by Tags

# Create and execute a test run group using tags to filter tests
test_run_group = await client.test_run_groups.create_and_execute(
    name="Critical Tests",
    tags=["critical", "smoke"],  # Run all tests with these tags
    execution_mode="strict",
    environment="staging",
    notification_channels=["webhook-alerts"]
)

Execute and Monitor Test Run Group

# Execute a test run group and monitor until completion with automatic report download
test_run_group = await client.test_run_groups.execute_and_monitor(
    name="Full Integration Suite",
    test_ids=["test1", "test2", "test3"],
    execution_mode="lenient",
    environment="staging",
    sleep_interval=30,  # Check status every 30 seconds
    output_dir="reports",
    format="ctrf",  # Options: "ctrf", "pdf", "csv", "zip"
    filename="integration-report.json"
)

Execute and Monitor Status Only

# Execute a test run group and monitor only the status (without CTRF report handling)
# Uses "lenient" execution mode by default
test_run_group = await client.test_run_groups.execute_and_monitor_status(
    name="Quick Smoke Test",
    test_ids=["test1", "test2", "test3"],
    environment="staging",
    sleep_interval=30  # Check status every 30 seconds
)

# Or use tags instead of test_ids
test_run_group = await client.test_run_groups.execute_and_monitor_status(
    name="Tagged Tests",
    tags=["smoke", "critical"],
    environment="production",
    notification_channels=["email-alerts"]
)

if test_run_group:
    print(f"Test run group completed: {test_run_group.id}")
else:
    print("Test run group failed, crashed, or was cancelled")

This method prints progress updates during monitoring:

Test runs progress: 2/5 completed, 1 pending, 1 running, 0 failed, 0 crashed, 1 cancelled

Cancel Test Run Group

# Cancel all running test runs in a group
try:
    cancelled_group = await client.test_run_groups.cancel_group("group_id")
    print(f"Test run group cancelled: {cancelled_group.status}")
except ValueError as e:
    print(f"Cannot cancel group: {str(e)}")

Download Test Run Group Reports

# Download the consolidated report for a test run group
from pathlib import Path

report_path = await client.test_run_groups.download_report(
    id_or_name="group_id",
    output_dir="reports",
    format="pdf"  # Options: "ctrf", "pdf", "csv", "zip"
)

if report_path:
    print(f"Report downloaded to: {report_path}")

Download All Test Run Group Attachments

# Download all attachments from all test runs in the group
downloaded_attachments = await client.test_run_groups.download_all_attachments(
    id_or_name="group_id",
    output_dir="attachments"
)

# Structure: attachments/<group-name>/<test-run-name>/files...
for test_run_name, files in downloaded_attachments.items():
    print(f"Test run '{test_run_name}': {len(files)} files downloaded")

Test Run Group Validation Rules

• Name is required for all test run groups
• Must specify either test_ids OR tags (mutually exclusive)
• Cannot specify both test_ids and tags
• Only groups in "pending" or "running" status can be cancelled

Test Suites Management

Test Suites orchestrate multiple tests through a DAG-style workflow_definition. Tests are selected per node using test_id (or suite_id for nested suites).

Workflow Definition Shape

• workflow_definition.version: Version string, e.g. "1.0".
• workflow_definition.inputs: Optional suite-level input definitions.
• workflow_definition.nodes: Execution nodes.
• workflow_definition.edges: Dependencies between nodes.
• workflow_definition.outputs: Optional suite outputs exposed to parent suites.
• workflow_definition.settings: Optional workflow settings.

Node fields commonly used:

• id: Stable node identifier.
• name: Human-readable node name.
• test_id: Test to execute for a normal test node.
• suite_id: Nested suite to execute (instead of test_id).
• inputs: Variable mappings for node parameters.
• settings: Node-level behavior (timeout/retry/failure policy fields).

Edge fields:

• id: Stable edge identifier.
• from_node: Upstream node ID.
• to_node: Downstream node ID.
• dependency_type: "temporal" or "hard".

Create a Test Suite (Test Nodes)

suite = await client.test_suites.create(
    {
        "name": "checkout-suite",
        "status": "draft",
        "execution_mode": "lenient",  # Required by gateway schema ("lenient"|"strict"); runtime fail-fast behavior is driven by node required/optional policy in Theseus.
        "workflow_definition": {
            "version": "1.0",
            "inputs": [
                {"name": "base_url", "type": "string", "required": True},
                {"name": "locale", "type": "string", "required": False, "default": "en-US"},
            ],
            "nodes": [
                {"id": "n1", "name": "Login", "test_id": "test_login_id"},
                {"id": "n2", "name": "Checkout", "test_id": "test_checkout_id"},
            ],
            "edges": [
                {
                    "id": "e1",
                    "from_node": "n1",
                    "to_node": "n2",
                    "dependency_type": "temporal",
                }
            ],
            "outputs": [],
            "settings": {"max_parallel_nodes": 2},
        },
        "input_schema": [
            {"name": "base_url", "type": "string", "required": True},
            {"name": "locale", "type": "string", "required": False},
        ],
        "default_inputs": {"locale": "en-US"},
        "environment": "staging_env_id",
        "tags": ["smoke_tag_id"],
        "notification_channels": ["channel_id"],
    }
)
print(suite.id)

Create a Test Suite (Nested Suite Node)

nested = await client.test_suites.create(
    {
        "name": "parent-suite",
        "status": "draft",
        "execution_mode": "lenient",
        "workflow_definition": {
            "version": "1.0",
            "nodes": [
                {"id": "n1", "name": "Auth Seed", "test_id": "seed_auth_test_id"},
                {"id": "n2", "name": "Child Flow", "suite_id": "child_suite_id"},
            ],
            "edges": [
                {
                    "id": "e1",
                    "from_node": "n1",
                    "to_node": "n2",
                    "dependency_type": "hard",
                }
            ],
        },
    }
)
print(nested.id)

Update and Fetch a Suite

updated = await client.test_suites.update(
    "suite_id_or_name",
    {
        "status": "ready",
        "default_inputs": {"locale": "en-US", "timezone": "UTC"},
        "notification_channels": ["ops_channel_id"],
    },
)

suite = await client.test_suites.get_one("suite_id_or_name")
print(suite.name, suite.status, suite.default_inputs)

Start and Control a Suite Run

Using the run() Method (Recommended)

The run() method provides a simplified way to create and start a test suite run:

# Simple usage - automatically generates name with timestamp
run = await client.test_suite_runs.run(
    display_name="Checkout Regression Run",
    test_suite="checkout-suite",  # Can use ID or name
    input_values={
        "base_url": "https://staging.example.com",
        "locale": "en-US",
    },
    environment="staging_env_id",  # Optional: can use ID or name
    notification_channels=["email-alerts", "slack-channel"],  # Optional: can use IDs or names
)

print(f"Run created: {run.id}")
print(f"Name: {run.name}")  # Auto-generated: "Checkout Regression Run_101026_143022"
print(f"Status: {run.status}")  # Always "pending"
print(f"Execution Mode: {run.execution_mode}")  # Always "lenient"

Key features of run() method:

• Auto-generates name: Creates display_name_<timestamp> in ddyymm_hhmmss format
• Auto-fetches workflow: Retrieves workflow_definition from the test suite automatically
• Simplified parameters: Only requires display_name and test_suite
• Name-to-ID conversion: Automatically converts names to IDs for environment and notification_channels
• Always sets: status="pending", execution_mode="lenient", is_deleted=False

Using the create() Method (Advanced)

For more control, use the create() method directly:

run = await client.test_suite_runs.create(
    {
        "name": "checkout-suite-run",
        "display_name": "Checkout Suite Run",
        "test_suite": "suite_id",
        "status": "pending",
        "created_by": "user_id",  # Required
        "workflow_snapshot": workflow_definition,  # Snapshot from test suite
        "input_values": {
            "base_url": "https://staging.example.com",
            "locale": "en-US",
        },
        "environment": "staging_env_id",
        "execution_mode": "strict",  # Optional override
        "notification_channels": ["channel_id"],
    }
)

print(run.id, run.status)

Control Suite Runs

# Pause a running suite
await client.test_suite_runs.pause(run.id, mode="graceful", reason="maintenance window")

# Resume a paused suite
await client.test_suite_runs.resume(run.id)

# Cancel a running suite
await client.test_suite_runs.cancel(run.id)

Inspect Runs, Node Runs, and Schedules

suite_runs = await client.test_suite_runs.get_list(page=1, per_page=20)
node_runs = await client.test_suite_node_runs.get_list(
    page=1,
    per_page=20,
    filters={"test_suite_run": "suite_run_id"},
)
schedules = await client.test_suite_schedules.get_list(page=1, per_page=20)

print(len(suite_runs["items"]), len(node_runs["items"]), len(schedules["items"]))

Create a Suite Schedule

schedule = await client.test_suite_schedules.create(
    {
        "name": "daily-checkout",
        "test_suite": "suite_id",
        "cron_expression": "0 9 * * *",
        "is_active": True,
        "input_values": {"locale": "en-US"},
        "notification_channels": ["channel_id"],
    }
)
print(schedule.id)

Test-Level Helpers Used by Suites

# Merged input schema + detected variables + defaults for a test
params = await client.tests.get_input_params("test_id")

# Test suites whose workflow references a given test
dependent = await client.tests.get_dependent_test_suites("test_id")

Extensions Management

Extensions provide additional functionality and integrations for TestZeus.

# List all available extensions
extensions = await client.extensions.get_list()
for extension in extensions["items"]:
    print(f"Extension: {extension.name}")

# Get specific extension
extension = await client.extensions.get_one("extension_id")
print(f"Extension details: {extension.data}")


### AI Test Generator

The AI Test Generator helps create test cases automatically using artificial intelligence.

```python
# List AI test generator configurations
ai_generators = await client.tests_ai_generator.get_list()
for generator in ai_generators["items"]:
    print(f"AI Generator: {generator.name}")

# Get specific AI generator
ai_generator = await client.tests_ai_generator.get_one("generator_id")
print(f"Generator config: {ai_generator.data}")

# Generate tests using AI
generated_tests = await client.tests_ai_generator.generate_tests(
    generator_id="generator_id",
    specification={
        "api_endpoint": "https://api.example.com",
        "methods": ["GET", "POST", "PUT", "DELETE"],
        "authentication": "bearer_token"
    }
)

Bulk Test Operations

Run multiple tests efficiently using the test manager's bulk operations:

# Run multiple tests as a coordinated group
test_run_result = await client.tests.run_tests(
    ids_or_names=["test1", "test2", "test3"],  # Can use names or IDs
    name="Nightly Regression",
    execution_mode="strict",
    environment="production",
    tags=["regression", "critical"],
    notification_channels=["email-alerts", "slack-notifications"]
)

print(f"Test run group created: {test_run_result}")

Available Managers

The SDK provides managers for all TestZeus collections:

• client.tests - Tests
• client.test_runs - Test Runs
• client.test_run_groups - Test Run Groups
• client.test_run_dashs - Test Run Dashboards
• client.test_data - Test Data
• client.environments - Environments
• client.connected_environments - Connected Environments
• client.tags - Tags
• client.test_report_schedules - Test Report Schedules
• client.notification_channels - Notification Channels
• client.extensions - Extensions
• client.tests_ai_generator - AI Test Generator
• client.agent_configs - Agent Configurations
• client.test_devices - Test Devices
• client.test_designs - Test Designs
• client.test_run_dash_outputs - Test Run Dashboard Outputs
• client.test_run_dash_output_steps - Test Run Dashboard Output Steps
• client.tenant_consumption - Tenant Consumption
• client.tenant_consumption_logs - Tenant Consumption Logs

Contributing

1. Clone the repository
2. Install dependencies with Poetry: poetry install
3. Run tests: poetry run pytest

License

MIT

Release Process

This project uses automated releases through GitHub Actions. The process is:

1. To create a new release, use one of the following make commands:

   # Interactive version bump (will prompt for version type)
   make release
   
   # Specific version bumps
   make release-patch   # Increments the patch version (e.g., 1.0.0 -> 1.0.1)
   make release-minor   # Increments the minor version (e.g., 1.0.0 -> 1.1.0)
   make release-major   # Increments the major version (e.g., 1.0.0 -> 2.0.0)
   make release-custom  # Set a custom version (will prompt for version)
   

2. The command will:

   • Update the version in pyproject.toml
   • Commit the change with a release message
   • Create a git tag for the version (e.g., v1.0.1)
   • Push the commit and tag to GitHub

3. The GitHub Actions workflow will automatically:

   • Detect the new tag
   • Build the package
   • Run tests
   • Publish the package to PyPI

Note: The release workflow only runs on tagged releases (not on pushes to the main branch).