Mastering pyARS: A Complete Guide to BMC Remedy Automation BMC Remedy remains a cornerstone for enterprise IT Service Management (ITSM). However, interacting with its complex architecture through the standard user interface can be slow and repetitive. For developers and system administrators looking to automate workflows, migrate data, or integrate external systems, pyARS is a powerful solution. This Python library wraps the BMC Remedy Action Request System (ARS) C API, allowing you to control Remedy using clean, object-oriented Python code.
This guide provides a comprehensive introduction to mastering pyARS, covering installation, core concepts, and practical automation examples. What is pyARS?
pyARS is an open-source Python module that acts as a bridge between Python and the BMC Remedy ARS API. Instead of writing verbose C code or relying on slow command-line tools like driver.exe, pyARS lets you perform standard CRUD (Create, Read, Update, Delete) operations directly on Remedy schemas (forms) using Python scripts. Key Benefits
High Performance: It interacts directly with the Remedy C API, making it significantly faster than REST or SOAP web services for bulk operations.
Pythonic Syntax: It translates complex Remedy data structures into native Python dictionaries, lists, and objects.
Comprehensive Coverage: It supports data manipulation, entry creation, attachments, and schema metadata retrieval. Setting Up Your Environment
Before you can use pyARS, you must ensure that your environment meets specific architecture requirements. Because pyARS binds to the native Remedy C API libraries, your Python installation architecture must match your Remedy API binaries (usually 64-bit for modern environments). Prerequisites
BMC Remedy ARS API Libraries: You need the C API DLLs (Windows) or shared objects (Linux) provided with your Remedy installation (e.g., arapiXX.dll). These files must be accessible in your system’s PATH.
Python: A standard Python installation matching the architecture of your ARS API files. Installation
You can install pyARS via pip. Open your terminal or command prompt and run: pip install pyars Use code with caution. Core Concepts and Architecture
To write effective pyARS scripts, you need to understand how it maps Python objects to Remedy components.
The ARS Object: This represents your active session and connection to the BMC Remedy server.
Schemas: In pyARS terminology, a Remedy “Form” is referred to by its database name, the “Schema”.
Entries: These are the actual records or tickets within a form, uniquely identified by an Entry ID (Request ID / Field ID 1).
Field IDs: Remedy relies heavily on internal IDs rather than field names. While pyARS allows using field names, knowing your Field IDs (like 1 for Request ID, 2 for Submitter, 7 for Status) ensures bulletproof scripts. Practical Guide: Step-by-Step Automation
Let’s explore the essential operations needed to automate tasks in BMC Remedy. 1. Establishing a Connection
Every script begins by initializing the ARS object and logging into your server.
from pyars import erp # Initialize the ARS session ars = erp.ARS() # Connect to the Remedy Server server = “://yourcompany.com” username = “automation_user” password = “SecurePassword123” try: ars.Login(server, username, password) print(“Successfully connected to BMC Remedy!”) except Exception as e: print(f”Connection failed: {e}“) Use code with caution. 2. Creating a Ticket (Entry)
To create a record, you pass a dictionary mapping Field IDs or field names to their respective values using the CreateEntry method.
form_name = “HPD:Help Desk” # The Remedy Incident form # Define the ticket fields incident_data = { “Description”: “Automated alert: Disk space running low on Server01.”, “Detailed Description”: “Drive C: has less than 5% free space remaining.”, “Urgency”: “3-Medium”, “Impact”: “3-Moderate”, “Status”: “New” } # Create the entry try: entry_id = ars.CreateEntry(form_name, incident_data) print(f”Incident created successfully. Entry ID: {entry_id}“) except Exception as e: print(f”Failed to create incident: {e}“) Use code with caution. 3. Querying and Retrieving Data
Retrieving data requires defining a query string (qualification) using standard Remedy syntax. The GetListWithOptions or GetListEntryWithFields methods are typically used to fetch matches.
# Query for all ‘New’ incidents created by the automation user qualification = “‘Status’ = “New” AND ‘Submitter’ = “automation_user”” try: # Retrieve entry IDs and matching field values entries = ars.GetListEntryWithFields(form_name, qualification=qualification) for entry_id, field_data in entries.items(): print(f”Found Ticket ID: {entry_id}“) print(f”Summary: {field_data.get(‘Description’)}“) except Exception as e: print(f”Query failed: {e}“) Use code with caution. 4. Updating an Existing Record
Modifying a record requires the form name, the specific Entry ID, and a dictionary containing the fields you want to update.
target_entry_id = “INC000001234567” update_data = { “Status”: “Assigned”, “Work Log”: “Ticket automatically assigned to the Windows Infrastructure Team.” } try: ars.SetEntry(form_name, target_entry_id, update_data) print(f”Ticket {target_entry_id} updated successfully.“) except Exception as e: print(f”Update failed: {e}“) Use code with caution. 5. Terminating the Session
Always clean up and close your network connections when your script finishes execution. ars.Logoff() print(“Session closed cleanly.”) Use code with caution. Best Practices for pyARS Automation
To ensure your automation scripts are robust, secure, and maintainable in a production environment, follow these industry best practices:
Leverage Field IDs over Names: Field labels can change if an administrator modifies the Remedy form presentation layer. Field IDs are static database identifiers and will never change, making your code highly resilient.
Implement Strict Error Handling: Remedy operations frequently hit validation errors, workflow blocking, or network timeouts. Wrap your API calls in try-except blocks to handle exceptions gracefully without crashing your automation pipeline.
Secure Your Credentials: Never hardcode administrative passwords into your Python scripts. Use environment variables, external configuration files, or enterprise credential vaults (like CyberArk or AWS Secrets Manager) to pass credentials securely at runtime.
Optimize Bulk Operations: If you are processing thousands of records, minimize API roundtrips. Use qualifications that return only the specific fields you need, rather than downloading entire records. Conclusion
Mastering pyARS unlocks unprecedented efficiency for managing BMC Remedy systems. By migrating your workflows from manual UI navigation to automated Python scripts, you eliminate human error, drastically reduce execution times, and seamlessly bridge Remedy with modern cloud tooling, monitoring systems, and DevOps pipelines. With the fundamentals covered in this guide, you are fully equipped to build your first enterprise-grade Remedy automation tool.
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