aport-agent-guardrails-crewai

Create a Python-based mini-application that leverages the 'aport-agent-guardrails-crewai' package to manage and monitor interactions between multiple AI agents in a controlled environment. This application will serve as a guardrail system to ensure that all AI agents adhere to specified guidelines and constraints while performing tasks or communicating with each other. Here’s a detailed breakdown of what your application should accomplish:

1. **Setup Environment**: Begin by setting up a Python virtual environment and installing necessary packages including 'aport-agent-guardrails-crewai'. Ensure you have a clear understanding of the package documentation to utilize its 'before_tool_call' hook effectively.

2. **Define Agents**: Create a few different types of AI agents (e.g., a research agent, a communication agent, a data analysis agent). Each agent should have specific roles and capabilities, defined by the tools they use and the tasks they perform.

3. **Guardrail System Implementation**: Implement a guardrail system using 'aport-agent-guardrails-crewai' that monitors every tool call made by these agents. The guardrail should check if the action is compliant with predefined rules and policies before allowing the execution of any tool call. For example, it could prevent data leakage, enforce ethical guidelines, or restrict certain types of queries.

4. **Interaction Management**: Develop a mechanism within your application where these agents can interact with each other and share information under the supervision of the guardrail system. This interaction should be logged and reviewed to ensure compliance and transparency.

5. **User Interface**: Optionally, develop a simple user interface (using frameworks like Flask or Django) that allows users to initiate tasks for these agents, view their status, and monitor the guardrail's actions.

6. **Testing and Validation**: Conduct thorough testing to ensure that the guardrail system works as expected. Test various scenarios where the guardrails should intervene and scenarios where they shouldn’t, to validate the robustness of your implementation.

7. **Documentation**: Provide comprehensive documentation explaining how to set up the environment, run the application, and understand the guardrail mechanisms. Include examples of how to customize the guardrails for different types of AI agent interactions.

By completing this project, you'll not only gain practical experience with the 'aport-agent-guardrails-crewai' package but also contribute to the development of more secure and ethical AI practices.