LangChain Integration with Python Automation Framework

This project demonstrates how to integrate LangChain into a Python-based automation test framework using OpenRouter as the LLM backend. The goal is to empower your framework with AI-based decision-making and test healing when failures occur.

When a test fails—such as due to a broken locator—the framework captures the error and passes it to a LangChain agent, which analyzes the failure and suggests a possible fix. This enhances your test framework with intelligent, context-aware debugging support.

Requirements

• Python 3.9+
• Existing Python automation framework (custom, Behave, or Pytest-based)
• Browser setup (e.g., Chrome + ChromeDriver)
• OpenRouter account with a valid API key

Dependencies

Required libraries include:

• langchain
• openai
• langchain-community

OpenRouter Setup

To use LangChain with OpenRouter:

1. Go to https://openrouter.ai
2. Create an account or log in
3. Generate your API key at https://openrouter.ai/keys
4. Use this key securely within your environment or configuration file

Project Highlights

• AI-powered test healing triggered on failure
• Integration of LangChain agent using OpenRouter API
• Agent consumes error messages and test context
• Smart suggestions for debugging locator and logic errors
• Easily extensible for other automation scenarios

Execution

Once setup is complete:

1. Run your automation test suite normally.
2. If a failure is encountered, the LangChain agent will automatically be triggered.
3. The agent analyzes the error message and responds with a healing suggestion in your test output logs.

This makes the debugging process more intuitive and dramatically reduces manual effort in diagnosing test failures.

Repository Contents

• langchain_openrouter_agent.py: Initializes and configures the LangChain agent
• healing_agent.py: Passes error messages and context to the agent
• login_page.py or other test files: Sample tests that demonstrate failure handling
• README.md: Project documentation

Future Improvements

• Add automatic retries based on LLM suggestions
• Log healing recommendations in a dashboard or structured report
• Support for multiple LLM models (Meta, Cohere, Mistral, etc.)
• Integrate healing logic into a self-repairing test flow