Building safer and smarter LLM agents with enhanced moderation pipline
In this blog, you'll explore how to design a robust and secure agent framework for interacting with LLMs and users. The code demonstrates integrating tools like Google Search, Wikipedia, and a calculator with advanced safety layers for moderation and compliance. This approach ensures the agent can reason, respond accurately, and adhere to ethical guidelines while preventing unsafe or malicious inputs and outputs. It integrates safeguards like content filtering, jailbreak detection, and contextual input moderation while maintaining the functionality of a dynamic ReAct agent.
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