PyBoomi Platform

A Python SDK for interacting with the Boomi Platform API.

Overview

PyBoomi Platform provides a comprehensive Python interface for Boomi's integration platform, enabling developers to programmatically manage processes, connections, and other platform resources.

Features

• Authentication and session management
• Process execution and monitoring
• Connection management
• Error handling and logging utilities
• Comprehensive test coverage

Installation

pip install pyboomi-platform

Quick Start

Using Direct Parameters

from pyboomi_platform import BoomiPlatformClient

# Initialize with direct parameters
client = BoomiPlatformClient(
    account_id="your-account-id",
    username="your-username@company.com",
    api_token="your-api-token"
)

# Query for API services using modular client
api_services = client.components.query_component_metadata({
    "QueryFilter": {
        "expression": {
            "operator": "and",
            "nestedExpression": [
                {
                    "argument": ["webservice"],
                    "operator": "EQUALS",
                    "property": "type"
                },
                {
                    "argument": ["true"],
                    "operator": "EQUALS",
                    "property": "currentVersion"
                },
                {
                    "argument": ["false"],
                    "operator": "EQUALS",
                    "property": "deleted"
                }
            ]
        }
    }
})

Using Configuration File

Create a config.yaml file:

boomi:
  username: "your-username@company.com"
  api_token: "your-api-token"
  account_id: "your-account-id"
  timeout: 30
  max_retries: 3
  backoff_factor: 1.5

from pyboomi_platform import BoomiPlatformClient

# Initialize using config file (looks for config.yaml in current directory)
client = BoomiPlatformClient()

# Or specify config file path
client = BoomiPlatformClient(config_path="path/to/your/config.yaml")

Using Environment Variables

Set environment variables:

export BOOMI_USERNAME="your-username@company.com"
export BOOMI_API_TOKEN="your-api-token"
export BOOMI_ACCOUNT_ID="your-account-id"

from pyboomi_platform import BoomiPlatformClient

# Initialize using environment variables
client = BoomiPlatformClient()

Configuration Priority

Configuration values are resolved in this order (highest to lowest priority):

1. Direct parameters passed to constructor
2. Environment variables
3. Configuration file values
4. Default values

Modular Client Architecture

The BoomiPlatformClient now provides a modular client architecture organized by Boomi's official API categories. This improves code organization and discoverability while maintaining 100% backward compatibility.

Using Modular Clients (Recommended)

from pyboomi_platform import BoomiPlatformClient

client = BoomiPlatformClient(
    account_id="your-account-id",
    username="your-username@company.com",
    api_token="your-api-token"
)

# Account Administration
account = client.account_admin.get_account("account-123")
account_group = client.account_admin.create_account_group("MyGroup")

# Component Management
folder = client.components.create_folder("MyFolder")
component = client.components.get_component("component-123")

# Environment Management
env = client.environment.get_environment("env-123")
envs = client.environment.query_environments()

# Process Execution
records = client.execution_statistics.query_execution_record({"executionId": "exec-123"})
artifacts = client.process_execution.create_execution_artifacts_request("exec-123")

Available Client Modules

The client exposes the following specialized modules aligned with Boomi's official API categories:

• client.account_admin - Account Administration (Account, AccountGroup, SSO Config, User Federation, User Roles)
• client.components - Component Management (Component, ComponentMetadata, ComponentReference, Folder, PackagedComponent, Branch)
• client.deployed_process - Deployed Process Management (DeployedPackage)
• client.deployment - Deployment Operations
• client.environment - Environment Management (Environment)
• client.execution_statistics - Execution Statistics (ExecutionRecord, ExecutionSummaryRecord, ExecutionConnector, GenericConnectorRecord, ApiUsageCount)
• client.process_execution - Process Execution (ExecutionArtifacts, ProcessLog, ConnectorDocument, cancel_execution, download_to_path, Process)
• client.runtime_management - Runtime Management (Atom)
• client.security - Security & Access (Role, AssignableRole)
• client.reporting - Reporting & Analytics (AuditLog, ConnectionLicensingReport, Event, CustomTrackedField)

Backward Compatibility

All existing methods continue to work with deprecation warnings. Your existing code will function without modification:

# This still works (with deprecation warning)
folder = client.create_folder("MyFolder")
account = client.get_account("account-123")

For migration guidance, see MIGRATION_GUIDE.md.

Reference Documentation

• Boomi Official API Documentation: https://developer.boomi.com/docs/category/platform-rest-api-reference
• Migration Guide: MIGRATION_GUIDE.md - Complete guide for migrating to modular clients
• API Reference: API_REFERENCE.md - Detailed method documentation organized by API categories

API Usage Examples

Enhanced Retry Behavior

The BoomiPlatformClient includes robust retry logic with Retry-After header support for rate limiting scenarios:

from pyboomi_platform import BoomiPlatformClient

client = BoomiPlatformClient(
    account_id="your-account-id",
    username="your-username@company.com",
    api_token="your-api-token",
    max_retries=3,  # Maximum retry attempts (default: 3)
    backoff_factor=1.5  # Exponential backoff factor (default: 1.5)
)

# The client automatically handles:
# - Rate limiting (429, 503) with Retry-After header support
# - Server errors (500, 502, 504) with automatic retries
# - Exponential backoff when Retry-After header is not present
# - Connection errors and timeouts

Retry Behavior:

• Rate Limiting (429, 503): The client respects the Retry-After header when present, otherwise uses exponential backoff
• Server Errors (500, 502, 504): Automatically retried using urllib3's retry mechanism
• Connection Errors: Retried with exponential backoff
• Timeouts: Raised immediately as BoomiAPIError

Folder Management

Create a Folder

Recommended (Modular Client):

# Create a folder in the root
folder = client.components.create_folder("MyNewFolder")
print(f"Created folder: {folder['id']}")

# Create a folder in a specific parent folder
subfolder = client.components.create_folder("SubFolder", parent_id="parent-folder-id")
print(f"Created subfolder: {subfolder['id']}")

Legacy (Deprecated):

# Still works but shows deprecation warning
folder = client.create_folder("MyNewFolder")

Get Folder Information

Recommended (Modular Client):

# Get a folder by ID
folder = client.components.get_folder("folder-id-123")
print(f"Folder name: {folder['name']}")
print(f"Parent ID: {folder.get('parentId')}")

# Get multiple folders by IDs
folders = client.components.get_folder_bulk(["folder-id-1", "folder-id-2", "folder-id-3"])
for folder in folders["result"]:
    print(f"Folder: {folder['name']}")

Query Folders

Recommended (Modular Client):

# Query folders by name
results = client.components.query_folder({"name": "MyFolder"})
for folder in results.get("result", []):
    print(f"Found folder: {folder['name']} (ID: {folder['id']})")

# Query all folders (no filters)
all_folders = client.components.query_folder()

# Get next page of results
if "queryToken" in results:
    next_page = client.components.query_more_folders(results["queryToken"])

Update a Folder

Recommended (Modular Client):

# Update folder name
updated = client.components.update_folder("folder-id-123", name="RenamedFolder")

# Update folder parent
moved = client.components.update_folder("folder-id-123", parent_id="new-parent-id")

# Update both name and parent
updated = client.components.update_folder(
    "folder-id-123",
    name="NewName",
    parent_id="new-parent-id"
)

Delete a Folder

Recommended (Modular Client):

# Delete a folder
deleted = client.components.delete_folder("folder-id-123")
print(f"Deleted folder: {deleted['id']}")

Component Management

Create a Component

Components are created using XML content. The client handles the XML formatting and headers automatically:

Recommended (Modular Client):

# Create a Process component
process_xml = """<?xml version="1.0" encoding="UTF-8"?>
<Process>
    <name>MyNewProcess</name>
    <type>process</type>
    <version>1.0.0</version>
</Process>"""

# Create in default location
component = client.components.create_component(process_xml)
print(f"Created component: {component}")

# Create in a specific folder
component = client.components.create_component(process_xml, folder_id="folder-id-123")

Example: Create a Connection Component

connection_xml = """<?xml version="1.0" encoding="UTF-8"?>
<Connection>
    <name>DatabaseConnection</name>
    <type>connection</type>
    <connectionType>database</connectionType>
</Connection>"""

connection = client.components.create_component(connection_xml, folder_id="connections-folder-id")

Note: The create_component method requires valid Boomi component XML. The XML structure must match Boomi's component schema for the specific component type (Process, Connection, etc.).

Branch Management

Create a Branch

Branches can be created from a parent branch or from a packaged component/deployment:

Recommended (Modular Client):

# Create a branch from another branch (default)
branch = client.components.create_branch(
    parent_branch_id="main-branch-id",
    branch_name="feature-branch"
)
print(f"Created branch: {branch['id']} (ready: {branch.get('ready', False)})")

# Create a branch from a packaged component
branch = client.components.create_branch(
    parent_branch_id="main-branch-id",
    branch_name="release-branch",
    package_id="package-id-12345"
)

Important Notes:

• New branches have ready set to false until creation completes
• When creating from a package, the package_id can reference either a packaged component or a deployment
• The branch creation process is asynchronous; check the ready flag to determine when the branch is ready for use

Update a Branch

Update branch metadata and readiness status:

Recommended (Modular Client):

# Update branch name
updated = client.components.update_branch("branch-id-123", name="UpdatedBranchName")

# Update branch description
updated = client.components.update_branch("branch-id-123", description="New description")

# Mark branch as ready (after creation completes)
updated = client.components.update_branch("branch-id-123", ready=True)

# Update multiple fields at once
updated = client.components.update_branch(
    "branch-id-123",
    name="NewName",
    description="New description",
    ready=True
)

Ready Flag Semantics:

• When a branch is first created, ready is false
• The branch creation process runs asynchronously
• Once creation completes, set ready=True to mark the branch as ready for use
• Only update ready to True after verifying the branch creation has completed successfully

Query Branches

Recommended (Modular Client):

# Query branches by name
results = client.components.query_branches({"name": "feature-branch"})

# Get next page of results
if "queryToken" in results:
    next_page = client.components.query_more_branches(results["queryToken"])

# Get branch details
branch = client.components.get_branch("branch-id-123")
print(f"Branch: {branch['name']}, Ready: {branch.get('ready', False)}")

Delete a Branch

Recommended (Modular Client):

deleted = client.components.delete_branch("branch-id-123")
print(f"Deleted branch: {deleted['id']}")

Execution Artifacts and Logs

Query Execution Records

Recommended (Modular Client):

# Query execution records for a specific execution ID
execution_id = "execution-12345"
records = client.execution_statistics.query_execution_record(execution_id)

for record in records.get("result", []):
    print(f"Record ID: {record['id']}")
    print(f"Status: {record.get('status')}")

# Get next page of execution records
if "queryToken" in records:
    next_page = client.execution_statistics.query_more_execution_record(records["queryToken"])

Request Execution Artifacts

Recommended (Modular Client):

# Create an execution artifacts request
execution_id = "execution-12345"
artifacts_response = client.process_execution.create_execution_artifacts_request(execution_id)

# The response contains a download URL
download_url = artifacts_response["url"]
print(f"Download URL: {download_url}")

# Download the artifacts using the download helper
output_path = client.process_execution.download_to_path(download_url, "/tmp/artifacts.zip")
print(f"Artifacts saved to: {output_path}")

Request Process Logs

Recommended (Modular Client):

# Request process log with default log level (INFO)
execution_id = "execution-12345"
log_response = client.process_execution.create_process_log_request(execution_id)

# Request process log with specific log level
log_response = client.process_execution.create_process_log_request(execution_id, log_level="ALL")

# Download the log file
download_url = log_response["url"]
output_path = client.process_execution.download_to_path(download_url, "/tmp/process-log.zip")

Query Execution Connectors

Recommended (Modular Client):

# Query connectors for an execution
execution_id = "execution-12345"
connectors = client.execution_statistics.query_execution_connector(execution_id)

for connector in connectors.get("result", []):
    print(f"Connector: {connector.get('name')}")

# Get next page
if "queryToken" in connectors:
    next_page = client.execution_statistics.query_more_execution_connector(connectors["queryToken"])

Query Generic Connector Records

Recommended (Modular Client):

# Query generic connector records
execution_id = "execution-12345"
connector_id = "connector-123"
records = client.execution_statistics.query_generic_connector_record(execution_id, connector_id)

for record in records.get("result", []):
    print(f"Record ID: {record['id']}")

# Get next page
if "queryToken" in records:
    next_page = client.execution_statistics.query_more_generic_connector_record(records["queryToken"])

Get Connector Document URL

Recommended (Modular Client):

# Request a connector document download URL
record_id = "generic-connector-record-123"
doc_response = client.process_execution.get_connector_document_url(record_id)

# Download the document
download_url = doc_response["url"]
output_path = client.process_execution.download_to_path(download_url, "/tmp/connector-doc.zip")

Complete Example: Gathering Execution Artifacts

Here's a complete example of gathering all execution artifacts for a given execution:

from pyboomi_platform import BoomiPlatformClient
import os

client = BoomiPlatformClient(
    account_id="your-account-id",
    username="your-username@company.com",
    api_token="your-api-token"
)

execution_id = "execution-12345"
output_dir = "/tmp/execution-artifacts"

# Create output directory
os.makedirs(output_dir, exist_ok=True)

# 1. Query execution records using modular clients
records = client.execution_statistics.query_execution_record(execution_id)
print(f"Found {len(records.get('result', []))} execution records")

# 2. Request execution artifacts
artifacts_response = client.process_execution.create_execution_artifacts_request(execution_id)
artifacts_path = client.process_execution.download_to_path(
    artifacts_response["url"],
    os.path.join(output_dir, "artifacts.zip")
)
print(f"Downloaded artifacts to: {artifacts_path}")

# 3. Request process log
log_response = client.process_execution.create_process_log_request(execution_id, log_level="ALL")
log_path = client.process_execution.download_to_path(
    log_response["url"],
    os.path.join(output_dir, "process-log.zip")
)
print(f"Downloaded process log to: {log_path}")

# 4. Query and download connector documents
connectors = client.execution_statistics.query_execution_connector(execution_id)
for connector in connectors.get("result", []):
    connector_id = connector["id"]

    # Query generic connector records
    records = client.execution_statistics.query_generic_connector_record(execution_id, connector_id)
    for record in records.get("result", []):
        record_id = record["id"]

        # Get connector document URL and download
        doc_response = client.process_execution.get_connector_document_url(record_id)
        doc_path = client.process_execution.download_to_path(
            doc_response["url"],
            os.path.join(output_dir, f"connector-{record_id}.zip")
        )
        print(f"Downloaded connector document: {doc_path}")

print(f"All artifacts saved to: {output_dir}")

Development

Setup Development Environment

# Clone the repository
git clone https://github.com/iesoftwaredeveloper/pyboomi-platform.git
cd pyboomi-platform

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install development dependencies
pip install -r requirements-dev.txt
pip install -e .

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=pyboomi_platform --cov-report=html

Quality Assurance

PyBoomi Platform uses a comprehensive quality assurance process with automated checks and manual validation tools. The QA process follows a two-tier approach:

1. Pre-commit Hooks: Automatic checks that run before each commit to catch issues early
2. Comprehensive Quality Check: Full validation script for thorough testing before pull requests or releases

Pre-commit Hooks

Pre-commit hooks automatically run code quality checks before each commit. This ensures code meets quality standards before it enters the repository.

Setup:

# Install pre-commit hooks (one-time setup)
pre-commit install

What's Checked:

• Code Formatting (black): Ensures consistent code style
• Import Sorting (isort): Organizes imports according to project standards
• Linting (flake8): Detects code quality issues and style violations
• Type Checking (mypy): Validates type hints on the main package
• Security Scanning (bandit): Identifies common security vulnerabilities
• General Checks: Trailing whitespace, end-of-file formatting, YAML validation, large files, merge conflicts, debug statements, and docstring placement

Pre-commit hooks run automatically on git commit. If any check fails, the commit is blocked until issues are resolved.

Manual Execution:

# Run pre-commit hooks on all files
pre-commit run --all-files

Comprehensive Quality Check

For thorough validation (recommended before pull requests or releases), use the comprehensive quality check script:

# Run all quality checks
./scripts/quality_check.sh

This script performs a complete quality audit including:

1. Code Formatting Check (black): Verifies code formatting without making changes
2. Import Sorting Check (isort): Validates import organization
3. Linting (flake8): Comprehensive code quality analysis
4. Type Checking (mypy): Full type validation
5. Security Scanning (bandit): Security vulnerability detection
6. Dependency Vulnerability Scanning (pip-audit): Checks for known vulnerabilities in dependencies
7. Dead Code Detection (vulture): Identifies unused code (min confidence 80%)
8. Complexity Analysis (radon): Measures code complexity and maintainability
   • Cyclomatic complexity
   • Maintainability index
   • Raw metrics
9. Test Execution with Coverage: Runs full test suite and generates coverage reports

The script generates:

• Terminal coverage report
• HTML coverage report in htmlcov/index.html

Note: The script uses set -e, so it will stop on the first failure. Fix issues and re-run until all checks pass.

Individual Tool Commands

For quick fixes or targeted checks, you can run individual tools:

Formatting:

# Format code (auto-fix)
black pyboomi_platform tests

# Check formatting without changes
black --check pyboomi_platform tests

Import Sorting:

# Sort imports (auto-fix)
isort pyboomi_platform tests

# Check import sorting without changes
isort --check-only pyboomi_platform tests

Linting:

# Lint code
flake8 pyboomi_platform tests

Type Checking:

# Type check the main package
mypy pyboomi_platform

Security Scanning:

# Security scan
bandit -r pyboomi_platform

Dependency Vulnerability Check:

# Check for vulnerable dependencies
pip-audit -r requirements.txt

Dead Code Detection:

# Find unused code
vulture pyboomi_platform --min-confidence 80

Complexity Analysis:

# Cyclomatic complexity
radon cc pyboomi_platform --min B

# Maintainability index
radon mi pyboomi_platform --min B

# Raw metrics
radon raw pyboomi_platform

QA Workflow

Follow this workflow to ensure code quality:

1. During Development:

   • Pre-commit hooks automatically check code on each commit
   • Fix any issues flagged by hooks before committing

2. Before Creating a Pull Request:

   • Run the comprehensive quality check: ./scripts/quality_check.sh
   • Ensure all checks pass, including test coverage
   • Review the HTML coverage report to identify any gaps

3. Before Release:

   • Run the comprehensive quality check
   • Verify test coverage meets project standards
   • Ensure all security scans pass
   • Review complexity metrics for any concerning areas

4. Quick Fixes:

   • Use individual tool commands for targeted fixes
   • Format code: black pyboomi_platform tests
   • Sort imports: isort pyboomi_platform tests
   • Re-run specific checks as needed

Best Practices:

• Let pre-commit hooks catch issues early - don't skip them
• Run the comprehensive check before significant commits
• Address security vulnerabilities immediately
• Maintain test coverage above project thresholds
• Review complexity metrics periodically to identify refactoring opportunities

For AI Agents

This SDK is designed to be easily understood and used by AI agents. For comprehensive documentation tailored for AI consumption, see:

• AGENTS.md: Structured guide covering project structure, core components, method categories, common patterns, and examples
• API_REFERENCE.md: Complete API reference with all methods, parameters, return types, and examples organized by category

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Ensure all tests pass
6. Submit a pull request

Author

Robert Little

Development Notes

This codebase was developed with AI assistance but has been thoroughly reviewed, tested, and modified by the author. All design decisions, architecture choices, and final implementation remain under the creative control and responsibility of the author.